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The following paper reviews recent developments in the field of optimization of space robotics. The extent of focus of this paper is on the perception (robotic sense of analyzing surroundings) in space robots in the exploration of extra-terrestrial planets. Robots play a crucial role in exploring extra-terrestrial and planetary bodies. Their advantages are far from being counted on finger tips. With the advent of autonomous robots in the field of robotics, the role for space exploration has further hustled up. Optimization of such autonomous robots has turned into a necessity of the hour. Optimized robots tend to have a superior role in space exploration. With so many considerations to monitor, an optimized solution will nevertheless help a planetary rover perform better under tight circumstances. Keeping in view the above mentioned area, the paper describes recent developments in the optimization of autonomous extra-terrestrial rovers. | Recent Developments in the Optimization of Space Robotics for Perception
in Planetary Exploration | 8,500 |
We present a number of powerful local mechanisms for maintaining a dynamic swarm of robots with limited capabilities and information, in the presence of external forces and permanent node failures. We propose a set of local continuous algorithms that together produce a generalization of a Euclidean Steiner tree. At any stage, the resulting overall shape achieves a good compromise between local thickness, global connectivity, and flexibility to further continuous motion of the terminals. The resulting swarm behavior scales well, is robust against node failures, and performs close to the best known approximation bound for a corresponding centralized static optimization problem. | Distributed Cohesive Control for Robot Swarms: Maintaining Good
Connectivity in the Presence of Exterior Forces | 8,501 |
We consider the problem of organizing a scattered group of $n$ robots in two-dimensional space, with geometric maximum distance $D$ between robots. The communication graph of the swarm is connected, but there is no central authority for organizing it. We want to arrange them into a sorted and equally-spaced array between the robots with lowest and highest label, while maintaining a connected communication network. In this paper, we describe a distributed method to accomplish these goals, without using central control, while also keeping time, travel distance and communication cost at a minimum. We proceed in a number of stages (leader election, initial path construction, subtree contraction, geometric straightening, and distributed sorting), none of which requires a central authority, but still accomplishes best possible parallelization. The overall arraying is performed in $O(n)$ time, $O(n^2)$ individual messages, and $O(nD)$ travel distance. Implementation of the sorting and navigation use communication messages of fixed size, and are a practical solution for large populations of low-cost robots. | A Parallel Distributed Strategy for Arraying a Scattered Robot Swarm | 8,502 |
Pose Graph Optimization (PGO) is the problem of estimating a set of poses from pairwise relative measurements. PGO is a nonconvex problem, and currently no known technique can guarantee the computation of an optimal solution. In this paper, we show that Lagrangian duality allows computing a globally optimal solution, under certain conditions that are satisfied in many practical cases. Our first contribution is to frame the PGO problem in the complex domain. This makes analysis easier and allows drawing connections with the recent literature on unit gain graphs. Exploiting this connection we prove non-trival results about the spectrum of the matrix underlying the problem. The second contribution is to formulate and analyze the dual problem in the complex domain. Our analysis shows that the duality gap is connected to the number of eigenvalues of the penalized pose graph matrix, which arises from the solution of the dual. We prove that if this matrix has a single eigenvalue in zero, then (i) the duality gap is zero, (ii) the primal PGO problem has a unique solution, and (iii) the primal solution can be computed by scaling an eigenvector of the penalized pose graph matrix. The third contribution is algorithmic: we exploit the dual problem and propose an algorithm that computes a guaranteed optimal solution for PGO when the penalized pose graph matrix satisfies the Single Zero Eigenvalue Property (SZEP). We also propose a variant that deals with the case in which the SZEP is not satisfied. The fourth contribution is a numerical analysis. Empirical evidence shows that in the vast majority of cases (100% of the tests under noise regimes of practical robotics applications) the penalized pose graph matrix does satisfy the SZEP, hence our approach allows computing the global optimal solution. Finally, we report simple counterexamples in which the duality gap is nonzero, and discuss open problems. | Pose Graph Optimization in the Complex Domain: Lagrangian Duality,
Conditions For Zero Duality Gap, and Optimal Solutions | 8,503 |
This paper presents an equivalence between feasible kinodynamic planning and optimal kinodynamic planning, in that any optimal planning problem can be transformed into a series of feasible planning problems in a state-cost space whose solutions approach the optimum. This transformation gives rise to a meta-algorithm that produces an asymptotically optimal planner, given any feasible kinodynamic planner as a subroutine. The meta-algorithm is proven to be asymptotically optimal, and a formula is derived relating expected running time and solution suboptimality. It is directly applicable to a wide range of optimal planning problems because it does not resort to the use of steering functions or numerical boundary-value problem solvers. On a set of benchmark problems, it is demonstrated to perform, using the EST and RRT algorithms as subroutines, at a superior or comparable level to related planners. | Asymptotically Optimal Planning by Feasible Kinodynamic Planning in
State-Cost Space | 8,504 |
Workspace and joint space analysis are essential steps in describing the task and designing the control loop of the robot, respectively. This paper presents the descriptive analysis of a family of delta-like parallel robots by using algebraic tools to induce an estimation about the complexity in representing the singularities in the workspace and the joint space. A Gr{\"o}bner based elimination is used to compute the singularities of the manipulator and a Cylindrical Algebraic Decomposition algorithm is used to study the workspace and the joint space. From these algebraic objects, we propose some certified three dimensional plotting describing the the shape of workspace and of the joint space which will help the engineers or researchers to decide the most suited configuration of the manipulator they should use for a given task. Also, the different parameters associated with the complexity of the serial and parallel singularities are tabulated, which further enhance the selection of the different configuration of the manipulator by comparing the complexity of the singularity equations. | Workspace and Singularity analysis of a Delta like family robot | 8,505 |
This paper presents a novel decentralized control strategy for a multi-robot system that enables parallel multi-target exploration while ensuring a time-varying connected topology in cluttered 3D environments. Flexible continuous connectivity is guaranteed by building upon a recent connectivity maintenance method, in which limited range, line-of-sight visibility, and collision avoidance are taken into account at the same time. Completeness of the decentralized multi-target exploration algorithm is guaranteed by dynamically assigning the robots with different motion behaviors during the exploration task. One major group is subject to a suitable downscaling of the main traveling force based on the traveling efficiency of the current leader and the direction alignment between traveling and connectivity force. This supports the leader in always reaching its current target and, on a larger time horizon, that the whole team realizes the overall task in finite time. Extensive Monte~Carlo simulations with a group of several quadrotor UAVs show the scalability and effectiveness of the proposed method and experiments validate its practicability. | Decentralized Simultaneous Multi-target Exploration using a Connected
Network of Multiple Robots | 8,506 |
The emergent global behaviours of robotic swarms are important to achieve their navigation task goals. These emergent behaviours can be verified to assess their correctness, through techniques like model checking. Model checking exhaustively explores all possible behaviours, based on a discrete model of the system, such as a swarm in a grid. A common problem in model checking is the state-space explosion that arises when the states of the model are numerous. We propose a novel implementation of symmetry reduction, in the form of encoding navigation algorithms relatively with respect to a reference, based on the symmetrical properties of swarms in grids. We applied the relative encoding to a swarm navigation algorithm, Alpha, modelled for the NuSMV model checker. A comparison of the state-space and verification results with an absolute (or global) and a relative encoding of the Alpha algorithm highlights the advantages of our approach, allowing model checking larger grid sizes and number of robots, and consequently, verifying more complex emergent behaviours. For example, a property was verified for a grid with 3 robots and a maximum allowed size of 8x8 cells in a global encoding, whereas this size was increased to 16x16 using a relative encoding. Also, the time to verify a property for a swarm of 3 robots in a 6x6 grid was reduced from almost 10 hours to only 7 minutes. Our approach is transferable to other swarm navigation algorithms. | Symmetry Reduction Enables Model Checking of More Complex Emergent
Behaviours of Swarm Navigation Algorithms | 8,507 |
We present a method to solve planning problems involving sequential decision making in unpredictable environments while accomplishing a high level task specification expressed using the formalism of linear temporal logic. Our method improves the state of the art by introducing a pruning step that preserves correctness while significantly reducing the time needed to compute an optimal policy. Our theoretical contribution is coupled with simulations substantiating the value of the proposed method. | Motion Planning with Safety Constraints and High-Level Task
Specifications | 8,508 |
Trajectory planning is a critical step while programming the parallel manipulators in a robotic cell. The main problem arises when there exists a singular configuration between the two poses of the end-effectors while discretizing the path with a classical approach. This paper presents an algebraic method to check the feasibility of any given trajectories in the workspace. The solutions of the polynomial equations associated with the tra-jectories are projected in the joint space using Gr{\"o}bner based elimination methods and the remaining equations are expressed in a parametric form where the articular variables are functions of time t unlike any numerical or discretization method. These formal computations allow to write the Jacobian of the manip-ulator as a function of time and to check if its determinant can vanish between two poses. Another benefit of this approach is to use a largest workspace with a more complex shape than a cube, cylinder or sphere. For the Orthoglide, a three degrees of freedom parallel robot, three different trajectories are used to illustrate this method. | An algebraic method to check the singularity-free paths for parallel
robots | 8,509 |
The subject of this paper is about the kinematic analysis and the trajectory planning of the Orthoglide 5-axis. The Orthoglide 5-axis a five degrees of freedom parallel kinematic machine developed at IRCCyN and is made up of a hybrid architecture, namely, a three degrees of freedom translational parallel manip-ulator mounted in series with a two degrees of freedom parallel spherical wrist. The simpler the kinematic modeling of the Or-thoglide 5-axis, the higher the maximum frequency of its control loop. Indeed, the control loop of a parallel kinematic machine should be computed with a high frequency, i.e., higher than 1.5 MHz, in order the manipulator to be able to reach high speed motions with a good accuracy. Accordingly, the direct and inverse kinematic models of the Orthoglide 5-axis, its inverse kine-matic Jacobian matrix and the first derivative of the latter with respect to time are expressed in this paper. It appears that the kinematic model of the manipulator under study can be written in a quadratic form due to the hybrid architecture of the Orthoglide 5-axis. As illustrative examples, the profiles of the actuated joint angles (lengths), velocities and accelerations that are used in the control loop of the robot are traced for two test trajectories. | Kinematic Analysis and Trajectory Planning of the Orthoglide 5-axis | 8,510 |
We define the nervous system of a robot as the processing unit responsible for controlling the robot body, together with the links between the processing unit and the sensorimotor hardware of the robot - i.e., the equivalent of the central nervous system in biological organisms. We present autonomous robots that can merge their nervous systems when they physically connect to each other, creating a "virtual nervous system" (VNS). We show that robots with a VNS have capabilities beyond those found in any existing robotic system or biological organism: they can merge into larger bodies with a single brain (i.e., processing unit), split into separate bodies with independent brains, and temporarily acquire sensing and actuating capabilities of specialized peer robots. VNS-based robots can also self-heal by removing or replacing malfunctioning body parts, including the brain. | Virtual Nervous Systems for Self-Assembling Robots - A preliminary
report | 8,511 |
Whole Body Operational Space Control (WBOSC) is a pioneering algorithm in the field of human-centered Whole-Body Control (WBC). It enables floating-base highly-redundant robots to achieve unified motion/force control of one or more operational space objectives while adhering to physical constraints. Limited studies exist on the software architecture and APIs that enable WBOSC to perform and be integrated into a larger system. In this paper we address this by presenting ControlIt!, a new open-source software framework for WBOSC. Unlike previous implementations, ControlIt! is multi-threaded to increase servo frequencies on standard PC hardware. A new parameter binding mechanism enables tight integration between ControlIt! and external processes via an extensible set of transport protocols. To support a new robot, only two plugins and a URDF model needs to be provided --- the rest of ControlIt! remains unchanged. New WBC primitives can be added by writing a Task or Constraint plugin. ControlIt!'s capabilities are demonstrated on Dreamer, a 16-DOF torque controlled humanoid upper body robot containing both series elastic and co-actuated joints, and using it to perform a product disassembly task. Using this testbed, we show that ControlIt! can achieve average servo latencies of about 0.5ms when configured with two Cartesian position tasks, two orientation tasks, and a lower priority posture task. This is significantly higher than the 5ms that was achieved using UTA-WBC, the prototype implementation of WBOSC that is both application and platform-specific. Variations in the product's position is handled by updating the goal of the Cartesian position task. ControlIt!'s source code is released under an LGPL license and we hope it will be adopted and maintained by the WBC community for the long term as a platform for WBC development and integration. | ControlIt! - A Software Framework for Whole-Body Operational Space
Control | 8,512 |
In the recent past, several sampling-based algorithms have been proposed to compute trajectories that are collision-free and dynamically-feasible. However, the outputs of such algorithms are notoriously jagged. In this paper, by focusing on robots with car-like dynamics, we present a fast and simple heuristic algorithm, named Convex Elastic Smoothing (CES) algorithm, for trajectory smoothing and speed optimization. The CES algorithm is inspired by earlier work on elastic band planning and iteratively performs shape and speed optimization. The key feature of the algorithm is that both optimization problems can be solved via convex programming, making CES particularly fast. A range of numerical experiments show that the CES algorithm returns high-quality solutions in a matter of a few hundreds of milliseconds and hence appears amenable to a real-time implementation. | A Convex Optimization Approach to Smooth Trajectories for Motion
Planning with Car-Like Robots | 8,513 |
The visual cue of optical flow plays a major role in the navigation of flying insects, and is increasingly studied for use by small flying robots as well. A major problem is that successful optical flow control seems to require distance estimates, while optical flow is known to provide only the ratio of velocity to distance. In this article, a novel, stability-based strategy is proposed to estimate distances with monocular optical flow and knowledge of the control inputs (efference copies). It is shown analytically that given a fixed control gain, the stability of a constant divergence control loop only depends on the distance to the approached surface. At close distances, the control loop first starts to exhibit self-induced oscillations, eventually leading to instability. The proposed stability-based strategy for estimating distances has two major attractive characteristics. First, self-induced oscillations are easy for the robot to detect and are hardly influenced by wind. Second, the distance can be estimated during a zero divergence maneuver, i.e., around hover. The stability-based strategy is implemented and tested both in simulation and with a Parrot AR drone 2.0. It is shown that it can be used to: (1) trigger a final approach response during a constant divergence landing with fixed gain, (2) estimate the distance in hover, and (3) estimate distances during an entire landing if the robot uses adaptive gain control to continuously stay on the 'edge of oscillation'. | Distance estimation with efference copies and optical flow maneuvers: a
stability-based strategy | 8,514 |
Planning under uncertainty is a key requirement for physical systems due to the noisy nature of actuators and sensors. Using a belief space approach, planning solutions tend to generate actions that result in information seeking behavior which reduce state uncertainty. While recent work has dealt with planning for Gaussian beliefs, for many cases, a multi-modal belief is a more accurate representation of the underlying belief. This is particularly true in environments with information symmetry that cause uncertain data associations which naturally lead to a multi-modal hypothesis on the state. Thus, a planner cannot simply base actions on the most-likely state. We propose an algorithm that uses a Receding Horizon Planning approach to plan actions that sequentially disambiguate the multi-modal belief to a uni-modal Gaussian and achieve tight localization on the true state, called a Multi-Modal Motion Planner (M3P). By combining a Gaussian sampling-based belief space planner with M3P, and introducing a switching behavior in the planner and belief representation, we present a holistic end-to-end solution for the belief space planning problem. Simulation results for a 2D ground robot navigation problem are presented that demonstrate our method's performance. | Motion Planning in Non-Gaussian Belief Spaces (M3P): The Case of a
Kidnapped Robot | 8,515 |
Spring Loaded Inverted Pendulum (SLIP) model has a long history in describing running behavior in animals and humans as well as has been used as a design basis for robots capable of dynamic locomotion. Anchoring the SLIP for lossy physical systems resulted in newer models which are extended versions of original SLIP with viscous damping in the leg. However, such lossy models require an additional mechanism for pumping energy to the system to control the locomotion and to reach a limit-cycle. Some studies solved this problem by adding an actively controllable torque actuation at the hip joint and this actuation has been successively used in many robotic platforms, such as the popular RHex robot. However, hip torque actuation produces forces on the COM dominantly at forward direction with respect to ground, making height control challenging especially at slow speeds. The situation becomes more severe when the horizontal speed of the robot reaches zero, i.e. steady hoping without moving in horizontal direction, and the system reaches to singularity in which vertical degrees of freedom is completely lost. To this end, we propose an extension of the lossy SLIP model with a slider-crank mechanism, SLIP- SCM, that can generate a stable limit-cycle when the body is constrained to vertical direction. We propose an approximate analytical solution to the nonlinear system dynamics of SLIP- SCM model to characterize its behavior during the locomotion. Finally, we perform a fixed-point stability analysis on SLIP-SCM model using our approximate analytical solution and show that proposed model exhibits stable behavior in our range of interest. | Extending The Lossy Spring-Loaded Inverted Pendulum Model with a
Slider-Crank Mechanism | 8,516 |
In our prior work, we outlined an approach, named DisCoF, for cooperative pathfinding in distributed systems with limited sensing and communication range. Contrasting to prior works on cooperative pathfinding with completeness guarantees, which often assume the access to global information, DisCoF does not make this assumption. The implication is that at any given time in DisCoF, the robots may not all be aware of each other, which is often the case in distributed systems. As a result, DisCoF represents an inherently online approach since coordination can only be realized in an opportunistic manner between robots that are within each other's sensing and communication range. However, there are a few assumptions made in DisCoF to facilitate a formal analysis, which must be removed to work with distributed multi-robot platforms. In this paper, we present DisCoF$^+$, which extends DisCoF by enabling an asynchronous solution, as well as providing flexible decoupling between robots for performance improvement. We also extend the formal results of DisCoF to DisCoF$^+$. Furthermore, we evaluate our implementation of DisCoF$^+$ and demonstrate a simulation of it running in a distributed multi-robot environment. Finally, we compare DisCoF$^+$ with DisCoF in terms of plan quality and planning performance. | DisCoF$^+$: Asynchronous DisCoF with Flexible Decoupling for Cooperative
Pathfinding in Distributed Systems | 8,517 |
We present an initial examination of a novel approach to accurately position a patient during head and neck intensity modulated radiotherapy (IMRT). Position-based visual-servoing of a radio-transparent soft robot is used to control the flexion/extension cranial motion of a manikin head. A Kinect RGB-D camera is used to measure head position and the error between the sensed and desired position is used to control a pneumatic system which regulates pressure within an inflatable air bladder (IAB). Results show that the system is capable of controlling head motion to within 2mm with respect to a reference trajectory. This establishes proof-of-concept that using multiple IABs and actuators can improve cancer treatment. | A Real-Time Soft Robotic Patient Positioning System for Maskless
Head-and-Neck Cancer Radiotherapy: An Initial Investigation | 8,518 |
In this study we describe the development of a ride assistance application which can be implemented on the widespread smart phones and tablet. The ride assistance application has a signal processing and pattern classification module which yield almost 100% recognition accuracy for real-time signal pattern classification. We introduce a novel framework to build a training dictionary with an overwhelming discriminating capacity which eliminates the need of human intervention spotting the pattern on the training samples. We verify the recognition accuracy of the proposed methodologies by providing the results of another study in which the hand posture and gestures are tracked and recognized for steering a robotic wheelchair. | Paradigm Shift in Continuous Signal Pattern Classification: Mobile Ride
Assistance System for two-wheeled Mobility Robots | 8,519 |
The swing-twist decomposition is a standard routine in motion planning for humanoid limbs. In this paper the decomposition formulas are derived and discussed in terms of Clifford algebra. With the decomposition one can express an arbitrary spinor as a product of a twist-free spinor and a swing-free spinor (or vice-versa) in 3-dimensional Euclidean space. It is shown that in the derived decomposition formula the twist factor is a generalized projection of a spinor onto a vector in Clifford algebra. As a practical application of the introduced theory an optimized decomposition algorithm is proposed. It favourably compares to existing swing-twist decomposition implementations. | Swing-twist decomposition in Clifford algebra | 8,520 |
We explore the probabilistic foundations of shared control in complex dynamic environments. In order to do this, we formulate shared control as a random process and describe the joint distribution that governs its behavior. For tractability, we model the relationships between the operator, autonomy, and crowd as an undirected graphical model. Further, we introduce an interaction function between the operator and the robot, that we call "agreeability"; in combination with the methods developed in~\cite{trautman-ijrr-2015}, we extend a cooperative collision avoidance autonomy to shared control. We therefore quantify the notion of simultaneously optimizing over agreeability (between the operator and autonomy), and safety and efficiency in crowded environments. We show that for a particular form of interaction function between the autonomy and the operator, linear blending is recovered exactly. Additionally, to recover linear blending, unimodal restrictions must be placed on the models describing the operator and the autonomy. In turn, these restrictions raise questions about the flexibility and applicability of the linear blending framework. Additionally, we present an extension of linear blending called "operator biased linear trajectory blending" (which formalizes some recent approaches in linear blending such as~\cite{dragan-ijrr-2013}) and show that not only is this also a restrictive special case of our probabilistic approach, but more importantly, is statistically unsound, and thus, mathematically, unsuitable for implementation. Instead, we suggest a statistically principled approach that guarantees data is used in a consistent manner, and show how this alternative approach converges to the full probabilistic framework. We conclude by proving that, in general, linear blending is suboptimal with respect to the joint metric of agreeability, safety, and efficiency. | Assistive Planning in Complex, Dynamic Environments: a Probabilistic
Approach | 8,521 |
The project which we have performed is based on voice recognition and we desire to create a four legged robot that can acknowledge the given instructions which are given through vocal commands and perform the tasks. The main processing unit of the robot will be Arduino Uno. We are using 8 servos for the movement of its legs while two servos will be required for each leg. The interface between a human and the robot is generated through Python programming and Eclipse software and it is implemented by using Bluetooth module HC 06. | Speech Controlled Quadruped | 8,522 |
One major challenge in implementation of formation control problems stems from the packet loss that occur in these shared communication channel. In the presence of packet loss the coordination information among agents is lost. Moreover, there is a move to use wireless channels in formation control applications. It has been found in practice that packet losses are more pronounced in wireless channels, than their wired counterparts. In our analysis, we first show that packet loss may result in loss of rigidity. In turn this causes the entire formation to fail. Later, we present an estimation based formation control algorithm that is robust to packet loss among agents. The proposed estimation algorithm employs minimal spanning tree algorithm to compute the estimate of the node variables (coordination variables). Consequently, this reduces the communication overhead required for information exchange. Later, using simulation, we verify the data that is to be transmitted for optimal estimation of these variables in the event of a packet loss. Finally, the effectiveness of the proposed algorithm is illustrated using suitable simulation example. | Formation Control in Multi-Agent Systems Over Packet Dropping Links | 8,523 |
We introduce the use of hierarchical clustering for relaxed, deterministic coordination and control of multiple robots. Traditionally an unsupervised learning method, hierarchical clustering offers a formalism for identifying and representing spatially cohesive and segregated robot groups at different resolutions by relating the continuous space of configurations to the combinatorial space of trees. We formalize and exploit this relation, developing computationally effective reactive algorithms for navigating through the combinatorial space in concert with geometric realizations for a particular choice of hierarchical clustering method. These constructions yield computationally effective vector field planners for both hierarchically invariant as well as transitional navigation in the configuration space. We apply these methods to the centralized coordination and control of $n$ perfectly sensed and actuated Euclidean spheres in a $d$-dimensional ambient space (for arbitrary $n$ and $d$). Given a desired configuration supporting a desired hierarchy, we construct a hybrid controller which is quadratic in $n$ and algebraic in $d$ and prove that its execution brings all but a measure zero set of initial configurations to the desired goal with the guarantee of no collisions along the way. | Coordinated Robot Navigation via Hierarchical Clustering | 8,524 |
In this paper we present control strategies for implementing reconfigurable planar microassmbly using multiple stress-engineered MEMS microrobots (MicroStressBots). A MicroStressBot is an electrostatic microrobot that consists of an untethered scratch drive actuator (USDA) that provides forward motion, and a steering-arm actuator that determines whether the robot moves in straight line or turns. The steering-arm is actuated through electrostatic pull-down to the substrate initiated by the applied global power delivery and control signal. Control of multiple MicroStressBots is achieved by varying the geometry of the steering-arm, and hence affecting its electrostatic pull-down and/or release voltages. Independent control of many MicroStressBots is achieved by fabricating the arms of the individual microrobots in such a way that the robots move differently from one another during portions of the global control signal. In this paper we analyze the scalability of control in an obstacle free configuration space. Based on robust control strategies, we derive the control signals that command some of the robots to make progress toward the goal, while the others stay in small orbits, for several classes of steering-arm geometries. We also present a comprehensive analysis and comparison between the numbers of required independent pull-down and release voltages, demonstrating significant improvement in terms of the efficiency as well as the size of the control signal presented in past work. Our analysis presents an important step for developing multi-microrobots control of MicroStressBots. | Scalability of Controlling Heterogenous Stress-Engineered MEMS
Microrobots (MicroStressBots) through Common Control Signal using
Electrostatic Hysteresis | 8,525 |
Bio-inspired vehicles are currently leading the way in the quest to produce a vehicle capable of flight and underwater navigation. However, a fully functional vehicle has not yet been realized. We present the first fully functional vehicle platform operating in air and underwater with seamless transition between both mediums. These unique capabilities combined with the hovering, high maneuverability and reliability of multirotor vehicles, results in a disruptive technology for both civil and military application including air/water search and rescue, inspection, repairs and survey missions among others. The invention was built on a bio-inspired locomotion force analysis that combines flight and swimming. Three main advances in the present work has allowed this invention. The first is the discovery of a seamless transition method between air and underwater. The second is the design of a multi-medium propulsion system capable of efficient operation in air and underwater. The third combines the requirements for lift and thrust for flight (for a given weight) and the requirements for thrust and neutral buoyancy (in water) for swimming. The result is a careful balance between lift, thrust, weight, and neutral buoyancy implemented in the vehicle design. A fully operational prototype demonstrated the flight, and underwater navigation capabilities as well as the rapid air/water and water/air transition. | Demonstration of an Aerial and Submersible Vehicle Capable of Flight and
Underwater Navigation with Seamless Air-Water Transition | 8,526 |
Many robotic tasks rely on the accurate localization of moving objects within a given workspace. This information about the objects' poses and velocities are used for control,motion planning, navigation, interaction with the environment or verification. Often motion capture systems are used to obtain such a state estimate. However, these systems are often costly, limited in workspace size and not suitable for outdoor usage. Therefore, we propose a lightweight and easy to use, visual-inertial Simultaneous Localization and Mapping approach that leverages cost-efficient, paper printable artificial landmarks, socalled fiducials. Results show that by fusing visual and inertial data, the system provides accurate estimates and is robust against fast motions and changing lighting conditions. Tight integration of the estimation of sensor and fiducial pose as well as extrinsics ensures accuracy, map consistency and avoids the requirement for precalibration. By providing an open source implementation and various datasets, partially with ground truth information, we enable community members to run, test, modify and extend the system either using these datasets or directly running the system on their own robotic setups. | An Open Source, Fiducial Based, Visual-Inertial Motion Capture System | 8,527 |
We present a new vision for smart objects and the Internet of Things wherein mobile robots interact with wirelessly-powered, long-range, ultra-high frequency radio frequency identification (UHF RFID) tags outfitted with sensing capabilities. We explore the technology innovations driving this vision by examining recently-commercialized sensor tags that could be affixed-to or embedded-in objects or the environment to yield true embodied intelligence. Using a pair of autonomous mobile robots outfitted with UHF RFID readers, we explore several potential applications where mobile robots interact with sensor tags to perform tasks such as: soil moisture sensing, remote crop monitoring, infrastructure monitoring, water quality monitoring, and remote sensor deployment. | A New Vision for Smart Objects and the Internet of Things: Mobile Robots
and Long-Range UHF RFID Sensor Tags | 8,528 |
In this paper we focus on the challenging problem of place categorization and semantic mapping on a robot without environment-specific training. Motivated by their ongoing success in various visual recognition tasks, we build our system upon a state-of-the-art convolutional network. We overcome its closed-set limitations by complementing the network with a series of one-vs-all classifiers that can learn to recognize new semantic classes online. Prior domain knowledge is incorporated by embedding the classification system into a Bayesian filter framework that also ensures temporal coherence. We evaluate the classification accuracy of the system on a robot that maps a variety of places on our campus in real-time. We show how semantic information can boost robotic object detection performance and how the semantic map can be used to modulate the robot's behaviour during navigation tasks. The system is made available to the community as a ROS module. | Place Categorization and Semantic Mapping on a Mobile Robot | 8,529 |
In this paper, we present an approach for dynamic exploration and mapping of unknown environments using a swarm of biobotic sensing agents, with a stochastic natural motion model and a leading agent (e.g., an unmanned aerial vehicle). The proposed robust mapping technique constructs a topological map of the environment using only encounter information from the swarm. A sliding window strategy is adopted in conjunction with a topological mapping strategy based on local interactions among the swarm in a coordinate-free fashion to obtain local maps of the environment. These maps are then merged into a global topological map which can be visualized using a graphical representation that integrates geometric as well as topological feature of the environment. Localized robust topological features are extracted using tools from topological data analysis. Simulation results have been presented to illustrate and verify the correctness of our dynamic mapping algorithm. | Dynamic Topological Mapping with Biobotic Swarms | 8,530 |
We study the problem of optimal multi-robot path planning on graphs (MPP) over four distinct minimization objectives: the total arrival time, the makespan (last arrival time), the total distance, and the maximum (single-robot traveled) distance. On the structure side, we show that each pair of these four objectives induces a Pareto front and cannot always be optimized simultaneously. Then, through reductions from 3-SAT, we further establish that computation over each objective is an NP-hard task, providing evidence that solving MPP optimally is generally intractable. Nevertheless, in a related paper, we design complete algorithms and efficient heuristics for optimizing all four objectives, capable of solving MPP optimally or near-optimally for hundreds of robots in challenging setups. | Optimal Multi-Robot Path Planning on Graphs: Structure and Computational
Complexity | 8,531 |
We study the problem of optimal multi-robot path planning on graphs MPP over four distinct minimization objectives: the makespan (last arrival time), the maximum (single-robot traveled) distance, the total arrival time, and the total distance. In a related paper, we show that these objectives are distinct and NP-hard to optimize. In this work, we focus on efficiently algorithmic solutions for solving these optimal MPP problems. Toward this goal, we first establish a one-to-one solution mapping between MPP and network-flow. Based on this equivalence and integer linear programming (ILP), we design novel and complete algorithms for optimizing over each of the four objectives. In particular, our exact algorithm for computing optimal makespan solutions is a first such that is capable of solving extremely challenging problems with robot-vertex ratio as high as 100%. Then, we further improve the computational performance of these exact algorithms through the introduction of principled heuristics, at the expense of some optimality loss. The combination of ILP model based algorithms and the heuristics proves to be highly effective, allowing the computation of 1.x-optimal solutions for problems containing hundreds of robots, densely populated in the environment, often in just seconds. | Optimal Multi-Robot Path Planning on Graphs: Complete Algorithms and
Effective Heuristics | 8,532 |
Probabilistic Cell Decomposition (PCD) is a probabilistic path planning method combining the concepts of approximate cell decomposition with probabilistic sampling. It has been shown that the use of lazy evaluation techniques and supervised sampling in important areas result in a high performance path planning method. Even if it was postulated before that PCD is probabilistically complete, we present a detailed proof of probabilistic completeness here for the first time. | On Probabilistic Completeness of Probabilistic Cell Decomposition | 8,533 |
Simplified models of the dynamics such as the linear inverted pendulum model (LIPM) have proven to perform well for biped walking on flat ground. However, for more complex tasks the assumptions of these models can become limiting. For example, the LIPM does not allow for the control of contact forces independently, is limited to co-planar contacts and assumes that the angular momentum is zero. In this paper, we propose to use the full momentum equations of a humanoid robot in a trajectory optimization framework to plan its center of mass, linear and angular momentum trajectories. The model also allows for planning desired contact forces for each end-effector in arbitrary contact locations. We extend our previous results on LQR design for momentum control by computing the (linearized) optimal momentum feedback law in a receding horizon fashion. The resulting desired momentum and the associated feedback law are then used in a hierarchical whole body control approach. Simulation experiments show that the approach is computationally fast and is able to generate plans for locomotion on complex terrains while demonstrating good tracking performance for the full humanoid control. | Trajectory generation for multi-contact momentum-control | 8,534 |
This work presents approaches for the estimation of quantities important for the control of the momentum of a humanoid robot. In contrast to previous approaches which use simplified models such as the Linear Inverted Pendulum Model, we present estimators based on the momentum dynamics of the robot. By using this simple yet dynamically-consistent model, we avoid the issues of using simplified models for estimation. We develop an estimator for the center of mass and full momentum which can be reformulated to estimate center of mass offsets as well as external wrenches applied to the robot. The observability of these estimators is investigated and their performance is evaluated in comparison to previous approaches. | Humanoid Momentum Estimation Using Sensed Contact Wrenches | 8,535 |
Bi-directional search is a widely used strategy to increase the success and convergence rates of sampling-based motion planning algorithms. Yet, few results are available that merge both bi-directional search and asymptotic optimality into existing optimal planners, such as PRM*, RRT*, and FMT*. The objective of this paper is to fill this gap. Specifically, this paper presents a bi-directional, sampling-based, asymptotically-optimal algorithm named Bi-directional FMT* (BFMT*) that extends the Fast Marching Tree (FMT*) algorithm to bi-directional search while preserving its key properties, chiefly lazy search and asymptotic optimality through convergence in probability. BFMT* performs a two-source, lazy dynamic programming recursion over a set of randomly-drawn samples, correspondingly generating two search trees: one in cost-to-come space from the initial configuration and another in cost-to-go space from the goal configuration. Numerical experiments illustrate the advantages of BFMT* over its unidirectional counterpart, as well as a number of other state-of-the-art planners. | An Asymptotically-Optimal Sampling-Based Algorithm for Bi-directional
Motion Planning | 8,536 |
When executing whole-body motions, humans are able to use a large variety of support poses which not only utilize the feet, but also hands, knees and elbows to enhance stability. While there are many works analyzing the transitions involved in walking, very few works analyze human motion where more complex supports occur. In this work, we analyze complex support pose transitions in human motion involving locomotion and manipulation tasks (loco-manipulation). We have applied a method for the detection of human support contacts from motion capture data to a large-scale dataset of loco-manipulation motions involving multi-contact supports, providing a semantic representation of them. Our results provide a statistical analysis of the used support poses, their transitions and the time spent in each of them. In addition, our data partially validates our taxonomy of whole-body support poses presented in our previous work. We believe that this work extends our understanding of human motion for humanoids, with a long-term objective of developing methods for autonomous multi-contact motion planning. | Analyzing Whole-Body Pose Transitions in Multi-Contact Motions | 8,537 |
We discuss some of the challenges facing shared autonomy. In particular, we explore 1) shared autonomy over unreliable networks, 2) how we can model individual human operators (in contrast to the average of a human operator), and 3) how our approach naturally models and integrates sliding autonomy into the joint human-machine system. We include a Background Section for completeness. | A Unified Approach to 3 Basic Challenges in Shared Autonomy | 8,538 |
This paper studies path synthesis for nonholonomic mobile robots moving in two-dimensional space. We first address the problem of interpolating paths expressed as sequences of straight line segments, such as those produced by some planning algorithms, into smooth curves that can be followed without stopping. Our solution has the advantage of being simpler than other existing approaches, and has a low computational cost that allows a real-time implementation. It produces discretized paths on which curvature and variation of curvature are bounded at all points, and preserves obstacle clearance. Then, we consider the problem of computing a time-optimal speed profile for such paths. We introduce an algorithm that solves this problem in linear time, and that is able to take into account a broader class of physical constraints than other solutions. Our contributions have been implemented and evaluated in the framework of the Eurobot contest. | Efficient Path Interpolation and Speed Profile Computation for
Nonholonomic Mobile Robots | 8,539 |
This paper addresses the design and development of an autonomous biped robot using master and worker combination of controllers. In addition, the bot is wirelessly controllable. The work presented here explains the walking pattern, system control and actuator control techniques for 10 Degree of Freedom (DOF) biped humanoid. Bi-pedal robots have better mobility than conventional wheeled robots, but they tend to topple easily. In order to walk stably in various environments, such as on rough terrain, up and down slopes, or in regions containing obstacles, it is necessary, that robot should adapt to the ground conditions with a foot motion, as well as maintain its stability with a torso motion. It is desirable to select a walking pattern that requires small torque and velocity of the joint actuators. The work proposed a low cost solution using open source hardware-software and application. The work extends to develop and implement new algorithms by adding gyroscope and accelerometer to further the research in the field of biped robots. | Modeling and Analysis of Walking Pattern for a Biped Robot | 8,540 |
A mobile robot deployed for remote inspection, surveying or rescue missions can fail due to various possibilities and can be hardware or software related. These failure scenarios necessitate manual recovery (self-rescue) of the robot from the environment. It would bring unforeseen challenges to recover the mobile robot if the environment where it was deployed had hazardous or harmful conditions (e.g. ionizing radiations). While it is not fully possible to predict all the failures in the robot, failures can be reduced by employing certain design/usage considerations. Few example failure cases based on real experiences are presented in this short article along with generic suggestions on overcoming the illustrated failure situations. | Few common failure cases in mobile robots | 8,541 |
This paper introduces a novel motion planning algorithm for stochastic scenarios. We extend the concept of a navigation function to such scenarios. Our main idea is to consider both the Gaussian distribution probabilities of the players' locations and disc (or star sets) geometry of the objects operating in the work space. We do so by formulating a probability density function that encloses both. We use the PDF to define a metric between the robot, the obstacles and the configuration space boundary. In order to define the navigation function we formulate a safe probability value for collision. By analytically investigating the PDF we find a convenient approximation for a safe distance in the sense of that metric. We prove that the resulting map is a navigation function and demonstrate our algorithm for various scenarios. | A Navigation Function For Uncertain Environment | 8,542 |
We study the problem of capturing an Omnidirectional Evader in convex environments using a Differential Drive Robot (DDR). The DDR wins the game if at any time instant it captures (collides with) the evader. The evader wins if it can avoid capture forever. Both players are unit disks with the same maximum (bounded) speed, but the DDR can only change its motion direction at a bounded rate. We show that despite this limitation, the DDR can capture the evader. | Capturing an Omnidirectional Evader in Convex Environments using a
Differential Drive Robot | 8,543 |
In manipulation tasks, a robot interacts with movable object(s). The configuration space in manipulation planning is thus the Cartesian product of the configuration space of the robot with those of the movable objects. It is the complex structure of such a "Composite Configuration Space" that makes manipulation planning particularly challenging. Previous works approximate the connectivity of the Composite Configuration Space by means of discretization or by creating random roadmaps. Such approaches involve an extensive pre-processing phase, which furthermore has to be re-done each time the environment changes. In this paper, we propose a high-level Grasp-Placement Table similar to that proposed by Tournassoud et al. (1987), but which does not require any discretization or heavy pre-processing. The table captures the potential connectivity of the Composite Configuration Space while being specific only to the movable object: in particular, it does not require to be re-computed when the environment changes. During the query phase, the table is used to guide a tree-based planner that explores the space systematically. Our simulations and experiments show that the proposed method enables improvements in both running time and trajectory quality as compared to existing approaches. | A Single-Query Manipulation Planner | 8,544 |
In this paper, we propose a new method for path planning to a point for robot in environment with obstacles. The resulting algorithm is implemented as a simple variation of Dijkstra's algorithm. By adding a constraint to the shortest-path, the algorithm is able to exclude all the paths between two points that violate the constraint.This algorithm provides the robot the possibility to move from the initial position to the final position (target) when we have enough samples in the domain. In this case the robot follows a smooth path that does not fall in to the obstacles. Our method is simpler than the previous proposals in the literature and performs comparably to the best methods, both on simulated and some real datasets. | Motion planning using shortest path | 8,545 |
In this paper we consider the problem of coordinating robotic systems with different kinematics, sensing and vision capabilities to achieve certain mission goals. An approach that makes use of a heterogeneous team of agents has several advantages when cost, integration of capabilities, or large search areas need to be considered. A heterogeneous team allows for the robots to become "specialized", accomplish sub-goals more effectively, and thus increase the overall mission efficiency. Two main scenarios are considered in this work. In the first case study we exploit mobility to implement a power control algorithm that increases the Signal to Interference plus Noise Ratio (SINR) among certain members of the network. We create realistic sensing fields and manipulation by using the geometric properties of the sensor field-of-view and the manipulability metric, respectively. The control strategy for each agent of the heterogeneous system is governed by an artificial physics law that considers the different kinematics of the agents and the environment, in a decentralized fashion. Through simulation results we show that the network is able to stay connected at all times and covers the environment well. The second scenario studied in this paper is the biologically-inspired coordination of heterogeneous physical robotic systems. A team of ground rovers, designed to emulate desert seed-harvester ants, explore an experimental area using behaviors fine-tuned in simulation by a genetic algorithm. Our robots coordinate with a base station and collect clusters of resources scattered within the experimental space. We demonstrate experimentally that through coordination with an aerial vehicle, our ant-like ground robots are able to collect resources two times faster than without the use of heterogeneous coordination. | Exploiting Heterogeneous Robotic Systems in Cooperative Missions | 8,546 |
One of the most fundamental problems when designing controllers for dynamic systems is the tuning of the controller parameters. Typically, a model of the system is used to obtain an initial controller, but ultimately the controller parameters must be tuned manually on the real system to achieve the best performance. To avoid this manual tuning step, methods from machine learning, such as Bayesian optimization, have been used. However, as these methods evaluate different controller parameters on the real system, safety-critical system failures may happen. In this paper, we overcome this problem by applying, for the first time, a recently developed safe optimization algorithm, SafeOpt, to the problem of automatic controller parameter tuning. Given an initial, low-performance controller, SafeOpt automatically optimizes the parameters of a control law while guaranteeing safety. It models the underlying performance measure as a Gaussian process and only explores new controller parameters whose performance lies above a safe performance threshold with high probability. Experimental results on a quadrotor vehicle indicate that the proposed method enables fast, automatic, and safe optimization of controller parameters without human intervention. | Safe Controller Optimization for Quadrotors with Gaussian Processes | 8,547 |
Monocular optical flow has been widely used to detect obstacles in Micro Air Vehicles (MAVs) during visual navigation. However, this approach requires significant movement, which reduces the efficiency of navigation and may even introduce risks in narrow spaces. In this paper, we introduce a novel setup of self-supervised learning (SSL), in which optical flow cues serve as a scaffold to learn the visual appearance of obstacles in the environment. We apply it to a landing task, in which initially 'surface roughness' is estimated from the optical flow field in order to detect obstacles. Subsequently, a linear regression function is learned that maps appearance features represented by texton distributions to the roughness estimate. After learning, the MAV can detect obstacles by just analyzing a still image. This allows the MAV to search for a landing spot without moving. We first demonstrate this principle to work with offline tests involving images captured from an on-board camera, and then demonstrate the principle in flight. Although surface roughness is a property of the entire flow field in the global image, the appearance learning even allows for the pixel-wise segmentation of obstacles. | Optical-Flow based Self-Supervised Learning of Obstacle Appearance
applied to MAV Landing | 8,548 |
The object of the research is the adaptive algorithms that are used by the operator when educating the robotic systems. Operator, being the target-setting subject, is interested in the goal that robotic systems, being the conductor of his targets (criteria), would provide a maximum effectiveness of these targets' (criteria's) achievement. Thus, the adaptive algorithms provide the adequate reflection of the operator's goals, found in the robotic systems' actions. This work considers potential possibilities of such target adaption of the robotic systems used for the class of the allocation problems. | Research of the Robot's Learning Effectiveness in the Changing
Environment | 8,549 |
Herein we suggest a mobile robot-training algorithm that is based on the preference approximation of the decision taker who controls the robot, which in its turn is managed by the Markov chain. Setup of the model parameters is made on the basis of the data referring to the situations and decisions involving the decision taker. The model that adapts to the decision taker's preferences can be set up either a priori, during the process of the robot's normal operation, or during specially planned testing sessions. Basing on the simulation modelling data of the robot's operation process and on the decision taker's robot control we have set up the model parameters thus illustrating both working capacity of all algorithm components and adaptation effectiveness. | Learning Mobile Robot Based on Adaptive Controlled Markov Chains | 8,550 |
Magnetometer has received wide applications in attitude determination and scientific measurements. Calibration is an important step for any practical magnetometer use. The most popular three-axis magnetometer calibration methods are attitude-independent and have been founded on an approximate maximum likelihood (ML) estimation with a quartic subjective function, derived from the fact that the magnitude of the calibrated measurements should be constant in a homogeneous magnetic field. This paper highlights the shortcomings of those popular methods and proposes to use the quadratic optimal ML estimation instead for magnetometer calibration. Simulation and test results show that the optimal ML calibration is superior to the approximate ML methods for magnetometer calibration in both accuracy and stability, especially for those situations without sufficient attitude excitation. The significant benefits deserve the moderately increased computation burden. The main conclusion obtained in the context of magnetometer in this paper is potentially applicable to various kinds of three-axis sensors. | On Calibration of Three-axis Magnetometer | 8,551 |
Research in mobile robotics often demands platforms that have an adequate balance between cost and reliability. In the case of terrestrial robots, one of the available options is the GNBot, an open-hardware project intended for the evaluation of swarm search strategies. The lack of basic odometry sensors such as wheel encoders had so far difficulted the implementation of an accurate high-level controller in this platform. Thus, the aim of this thesis is to improve motion control in the GNBot by incorporating a gyroscope whilst maintaining the requisite of no wheel encoders. Among the problems that have been tackled are: accurate in-place rotations, minimal drift during linear motions, and arc-performing functionality. Additionally, the resulting controller is calibrated autonomously by using both the gyroscope module and the infrared rangefinder on board each robot, greatly simplifying the calibration of large swarms. The report first explains the design decisions that were made in order to implement the self-calibration routine, and then evaluates the performance of the new motion controller by means of off-line video tracking. The motion accuracy of the new controller is also compared with the previously existing solution in an odor search experiment. | Self-calibration of a differential wheeled robot using only a gyroscope
and a distance sensor | 8,552 |
The goal of this paper is to develop efficient regrasp algorithms for single-arm and dual-arm regrasp and compares the performance of single-arm and dual-arm regrasp by running the two algorithms thousands of times. We focus on pick-and-place regrasp which reorients an object from one placement to another by using a sequence of pick-ups and place-downs. After analyzing the simulation results, we find dual-arm regrasp is not necessarily better than single-arm regrasp: Dual-arm regrasp is flexible. When the two hands can grasp the object with good clearance, dual-arm regrasp is better and has higher successful rate than single-arm regrasp. However, dual-arm regrasp suffers from geometric constraints caused by the two arms. When the grasps overlap, dual-arm regrasp is bad. Developers need to sample grasps with high density to reduce overlapping. This leads to exploded combinatorics in previous methods, but is possible with the algorithms presented in this paper. Following the results, practitioners may choose single-arm or dual-arm robots by considering the object shapes and grasps. Meanwhile, they can reduce overlapping and implement practical dual-arm regrasp by using the presented algorithms. | Developing and Comparing Single-arm and Dual-arm Regrasp | 8,553 |
The goal of this paper is to present the design and development of a low cost cargo transit system which can be adapted in developing countries like India where there is abundant and cheap human labour which makes the process of automation in any industry a challenge to innovators. The need of the hour is an automation system that can diligently transfer cargo from one place to another and minimize human intervention in the cargo transit industry. Therefore, a solution is being proposed which could effectively bring down human labour and the resources needed to implement them. The reduction in human labour and resources is achieved by the use of low cost components and very limited modification of the surroundings and the existing vehicles themselves. The operation of the cargo transit system has been verified and the relevant results are presented. An economical and robust cargo transit system is designed and implemented. | Low Cost Swarm Based Diligent Cargo Transit System | 8,554 |
Inverse dynamics is used extensively in robotics and biomechanics applications. In manipulator and legged robots, it can form the basis of an effective nonlinear control strategy by providing a robot with both accurate positional tracking and active compliance. In biomechanics applications, inverse dynamics control can approximately determine the net torques applied at anatomical joints that correspond to an observed motion. In the context of robot control, using inverse dynamics requires knowledge of all contact forces acting on the robot; accurately perceiving external forces applied to the robot requires filtering and thus significant time delay. An alternative approach has been suggested in recent literature: predicting contact and actuator forces simultaneously under the assumptions of rigid body dynamics, rigid contact, and friction. Existing such inverse dynamics approaches have used approximations to the contact models, which permits use of fast numerical linear algebra algorithms. In contrast, we describe inverse dynamics algorithms that are derived only from first principles and use established phenomenological models like Coulomb friction. We assess these inverse dynamics algorithms in a control context using two virtual robots: a locomoting quadrupedal robot and a fixed-based manipulator gripping a box while using perfectly accurate sensor data from simulation. The data collected from these experiments gives an upper bound on the performance of such controllers in situ. For points of comparison, we assess performance on the same tasks with both error feedback control and inverse dynamics control with virtual contact force sensing. | Inverse Dynamics with Rigid Contact and Friction | 8,555 |
This paper describes the synthesis and evaluation of a novel state estimator for a Quadrotor Micro Aerial Vehicle. Dynamic equations which relate acceleration, attitude and the aero-dynamic propeller drag are encapsulated in an extended Kalman filter framework for estimating the velocity and the attitude of the quadrotor. It is demonstrated that exploiting the relationship between the body frame accelerations and velocities, due to blade flapping, enables drift free estimation of lateral and longitudinal components of body frame translational velocity along with improvements to roll and pitch components of body attitude estimations. Real world data sets gathered using a commercial off-the-shelf quadrotor platform, together with ground truth data from a Vicon system, are used to evaluate the effectiveness of the proposed algorithm. | Improved State Estimation in Quadrotor MAVs: A Novel Drift-Free Velocity
Estimator | 8,556 |
In distributed mobile sensing applications, networks of agents that are heterogeneous respecting both actuation as well as body and sensory footprint are often modelled by recourse to power diagrams --- generalized Voronoi diagrams with additive weights. In this paper we adapt the body power diagram to introduce its "free subdiagram," generating a vector field planner that solves the combined sensory coverage and collision avoidance problem via continuous evaluation of an associated constrained optimization problem. We propose practical extensions (a heuristic congestion manager that speeds convergence and a lift of the point particle controller to the more practical differential drive kinematics) that maintain the convergence and collision guarantees. | Voronoi-Based Coverage Control of Heterogeneous Disk-Shaped Robots | 8,557 |
Fine robotic assembly, in which the parts to be assembled are small and fragile and lie in an unstructured environment, is still out of reach of today's industrial robots. The main difficulties arise in the precise localization of the parts in an unstructured environment and the control of contact interactions. Our contribution in this paper is twofold. First, we propose a taxonomy of the manipulation primitives that are specifically involved in fine assembly. Such a taxonomy is crucial for designing a scalable robotic system (both hardware and software) given the complexity of real-world assembly tasks. Second, we present a hardware and software architecture where we have addressed, in an integrated way, a number of issues arising in fine assembly, such as workspace optimization, external wrench compensation, position-based force control, etc. Finally, we show the above taxonomy and architecture in action on a highly dexterous task -- bimanual pin insertion -- which is one of the key steps in our long term project, the autonomous assembly of an IKEA chair. | A Framework for Fine Robotic Assembly | 8,558 |
Collaborative robots could transform several industries, such as manufacturing and healthcare, but they present a significant challenge to verification. The complex nature of their working environment necessitates testing in realistic detail under a broad range of circumstances. We propose the use of Coverage-Driven Verification (CDV) to meet this challenge. By automating the simulation-based testing process as far as possible, CDV provides an efficient route to coverage closure. We discuss the need, practical considerations, and potential benefits of transferring this approach from microelectronic design verification to the field of human-robot interaction. We demonstrate the validity and feasibility of the proposed approach by constructing a custom CDV testbench and applying it to the verification of an object handover task. | Coverage-Driven Verification - An approach to verify code for robots
that directly interact with humans | 8,559 |
One of the goals of computer-aided surgery is to match intraoperative data to preoperative images of the anatomy and add complementary information that can facilitate the task of surgical navigation. In this context, mechanical palpation can reveal critical anatomical features such as arteries and cancerous lumps which are stiffer that the surrounding tissue. This work uses position and force measurements obtained during mechanical palpation for registration and stiffness mapping. Prior approaches, including our own, exhaustively palpated the entire organ to achieve this goal. To overcome the costly palpation of the entire organ, a Bayesian optimization framework is introduced to guide the end effector to palpate stiff regions while simultaneously updating the registration of the end effector to an a priori geometric model of the organ, hence enabling the fusion of ntraoperative data into the a priori model obtained through imaging. This new framework uses Gaussian processes to model the stiffness distribution and Bayesian optimization to direct where to sample next for maximum information gain. The proposed method was evaluated with experimental data obtained using a Cartesian robot interacting with a silicone organ model and an ex vivo porcine liver. | Using Bayesian Optimization to Guide Probing of a Flexible Environment
for Simultaneous Registration and Stiffness Mapping | 8,560 |
The deformable and continuum nature of soft robots promises versatility and adaptability. However, control of modular, multi-limbed soft robots for terrestrial locomotion is challenging due to the complex robot structure, actuator mechanics and robot-environment interaction. Traditionally, soft robot control is performed by modeling kinematics using exact geometric equations and finite element analysis. The research presents an alternative, model-free, data-driven, reinforcement learning inspired approach, for controlling multi-limbed soft material robots. This control approach can be summarized as a four-step process of discretization, visualization, learning and optimization. The first step involves identification and subsequent discretization of key factors that dominate robot-environment, in turn, the robot control. Graph theory is used to visualize relationships and transitions between the discretized states. The graph representation facilitates mathematical definition of periodic control patterns (simple cycles) and locomotion gaits. Rewards corresponding to individual arcs of the graph are weighted displacement and orientation change for robot state-to-state transitions. These rewards are specific to surface of locomotion and are learned. Finally, the control patterns result from optimization of reward dependent locomotion task (e.g. translation) cost function. The optimization problem is an Integer Linear Programming problem which can be quickly solved using standard solvers. The framework is generic and independent of type of actuator, soft material properties or the type of friction mechanism, as the control exists in the robot's task space. Furthermore, the data-driven nature of the framework imparts adaptability to the framework toward different locomotion surfaces by re-learning rewards. | Model-free control framework for multi-limb soft robots | 8,561 |
This paper describes a novel amphibious robot, which adopts a dual-swing-legs propulsion mechanism, proposing a new locomotion mode. The robot is called FroBot, since its structure and locomotion are similar to frogs. Our inspiration comes from the frog scooter and breaststroke. Based on its swing leg mechanism, an unusual universal wheel structure is used to generate propulsion on land, while a pair of flexible caudal fins functions like the foot flippers of a frog to generate similar propulsion underwater. On the basis of the prototype design and the dynamic model of the robot, some locomotion control simulations and experiments were conducted for the purpose of adjusting the parameters that affect the propulsion of the robot. Finally, a series of underwater experiments were performed to verify the design feasibility of FroBot and the rationality of the control algorithm. | Design, Modeling and Control of A Novel Amphibious Robot with
Dual-swing-legs Propulsion Mechanism | 8,562 |
Robots built from soft materials can alter their shape and size in a particular profile. This shape-changing ability could be extremely helpful for rescue robots and those operating in unknown terrains and environments. In changing shape, soft materials also store and release elastic energy, a feature that can be exploited for effective robot movement. However, design and control of these moving soft robots are non-trivial. The research presents design methodology for a 3D-printed, motor-tendon actuated soft robot capable of locomotion. In addition to shape change, the robot uses friction manipulation mechanisms to effect locomotion. The motor-tendon actuators comprise of nylon tendons embedded inside the soft body structure along a given path with one end fixed on the body and the other attached to a motor. These actuators directly control the deformation of the soft body which influences the robot locomotion behavior. Static stress analysis is used as a tool for designing the shape of the paths of these tendons embedded inside the body. The research also presents a novel model-free learning-based control approach for soft robots which interact with the environment at discrete contact points. This approach involves discretization of factors dominating robot-environment interactions as states, learning of the results as robot transitions between these robot states and evaluation of desired periodic state control sequences optimizing a cost function corresponding to a locomotion task (rotation or translation). The clever discretization allows the framework to exist in robot's task space, hence, facilitating calculation of control sequences without modeling the actuator, body material or details of the friction mechanisms. The flexibility of the framework is experimentally explored by applying it to robots with different friction mechanisms and different shapes of tendon paths. | Design and locomotion control of soft robot using friction manipulation
and motor-tendon actuation | 8,563 |
Soft materials have many important roles in animal locomotion and object manipulation. In robotic applications soft materials can store and release energy, absorb impacts, increase compliance and increase the range of possible shape profiles using minimal actuators. The shape changing ability is also a potential tool to manipulate friction forces caused by contact with the environment. These advantages are accompanied by challenges of soft material actuation and the need to exploit frictional interactions to generate locomotion. Accordingly, the design of soft robots involves exploitation of continuum properties of soft materials for manipulating frictional interactions that result in robot locomotion. The research presents design and control of a soft body robot that uses its shape change capability for locomotion. The bioinspired (caterpillar) modular robot design is a soft monolithic body which interacts with the environment at discrete contact points (caterpillar prolegs). The deformable body is actuated by muscle-like shape memory alloy coils and the discrete contact points manipulate friction in a binary manner. This novel virtual grip mechanism combines two materials with different coefficients of frictions (sticky-slippery) to control the robot-environment friction interactions. The research also introduces a novel control concept that discretizes the robot-environment-friction interaction into binary states. This facilitates formulation of a control framework that is independent of the specific actuator or soft material properties and can be applied to multi-limbed soft robots. The transitions between individual robot states are assigned a reward that allow optimized state transition control sequences to be calculated. This conceptual framework is extremely versatile and we show how it can be applied to situations in which the robot loses limb function. | Design and control of a soft, shape-changing, crawling robot | 8,564 |
Active SLAM is the task of actively planning robot paths while simultaneously building a map and localizing within. Existing work has focused on planning paths with occupancy grid maps, which do not scale well and suffer from long term drift. This work proposes a Topological Feature Graph (TFG) representation that scales well and develops an active SLAM algorithm with it. The TFG uses graphical models, which utilize independences between variables, and enables a unified quantification of exploration and exploitation gains with a single entropy metric. Hence, it facilitates a natural and principled balance between map exploration and refinement. A probabilistic roadmap path-planner is used to generate robot paths in real time. Experimental results demonstrate that the proposed approach achieves better accuracy than a standard grid-map based approach while requiring orders of magnitude less computation and memory resources. | Information-based Active SLAM via Topological Feature Graphs | 8,565 |
Tensegrity robots are a class of compliant robots that have many desirable traits when designing mass efficient systems that must interact with uncertain environments. Various promising control approaches have been proposed for tensegrity systems in simulation. Unfortunately, state estimation methods for tensegrity robots have not yet been thoroughly studied. In this paper, we present the design and evaluation of a state estimator for tensegrity robots. This state estimator will enable existing and future control algorithms to transfer from simulation to hardware. Our approach is based on the unscented Kalman filter (UKF) and combines inertial measurements, ultra wideband time-of-flight ranging measurements, and actuator state information. We evaluate the effectiveness of our method on the SUPERball, a tensegrity based planetary exploration robotic prototype. In particular, we conduct tests for evaluating both the robot's success in estimating global position in relation to fixed ranging base stations during rolling maneuvers as well as local behavior due to small-amplitude deformations induced by cable actuation. | State Estimation for Tensegrity Robots | 8,566 |
This paper presents the behaviour control of a service robot for intelligent object search in a domestic environment. A major challenge in service robotics is to enable fetch-and-carry missions that are satisfying for the user in terms of efficiency and human-oriented perception. The proposed behaviour controller provides an informed intelligent search based on a semantic segmentation framework for indoor scenes and integrates it with object recognition and grasping. Instead of manually annotating search positions in the environment, the framework automatically suggests likely locations to search for an object based on contextual information, e.g. next to tables and shelves. In a preliminary set of experiments we demonstrate that this behaviour control is as efficient as using manually annotated locations. Moreover, we argue that our approach will reduce the intensity of labour associated with programming fetch-and-carry tasks for service robots and that it will be perceived as more human-oriented. | Where to look first? Behaviour control for fetch-and-carry missions of
service robots | 8,567 |
In this paper we present a new approach for dynamic motion planning for legged robots. We formulate a trajectory optimization problem based on a compact form of the robot dynamics. Such a form is obtained by projecting the rigid body dynamics onto the null space of the Constraint Jacobian. As consequence of the projection, contact forces are removed from the model but their effects are still taken into account. This approach permits to solve the optimal control problem of a floating base constrained multibody system while avoiding the use of an explicit contact model. We use direct transcription to numerically solve the optimization. As the contact forces are not part of the decision variables the size of the resultant discrete mathematical program is reduced and therefore solutions can be obtained in a tractable time. Using a predefined sequence of contact configurations (phases), our approach solves motions where contact switches occur. Transitions between phases are automatically resolved without using a model for switching dynamics. We present results on a hydraulic quadruped robot (HyQ), including single phase (standing, crouching) as well as multiple phase (rearing, diagonal leg balancing and stepping) dynamic motions. | Projection based whole body motion planning for legged robots | 8,568 |
We propose a method for checking and enforcing multi-contact stability based on the Zero-tilting Moment Point (ZMP). The key to our development is the generalization of ZMP support areas to take into account (a) frictional constraints and (b) multiple non-coplanar contacts. We introduce and investigate two kinds of ZMP support areas. First, we characterize and provide a fast geometric construction for the support area generated by valid contact forces, with no other constraint on the robot motion. We call this set the full support area. Next, we consider the control of humanoid robots using the Linear Pendulum Mode (LPM). We observe that the constraints stemming from the LPM induce a shrinking of the support area, even for walking on horizontal floors. We propose an algorithm to compute the new area, which we call pendular support area. We show that, in the LPM, having the ZMP in the pendular support area is a necessary and sufficient condition for contact stability. Based on these developments, we implement a whole-body controller and generate feasible multi-contact motions where an HRP-4 humanoid locomotes in challenging multi-contact scenarios. | ZMP support areas for multi-contact mobility under frictional
constraints | 8,569 |
Sensor gloves are popular input devices for a large variety of applications including health monitoring, control of music instruments, learning sign language, dexterous computer interfaces, and tele-operating robot hands. Many commercial products as well as low-cost open source projects have been developed. We discuss here how low-cost (approx. 250 EUROs) sensor gloves with force feedback can be build, provide an open source software interface for Matlab and present first results in learning object manipulation skills through imitation learning on the humanoid robot iCub. | Low-cost Sensor Glove with Force Feedback for Learning from
Demonstrations using Probabilistic Trajectory Representations | 8,570 |
We show that all self-motions of pentapods with linear platform of Type 1 and Type 2 can be generated by line-symmetric motions. Thus this paper closes a gap between the more than 100 year old works of Duporcq and Borel and the extensive study of line-symmetric motions done by Krames in the 1930's. As a consequence we also get a new solution set for the Borel Bricard problem. Moreover we discuss the reality of self-motions and give a sufficient condition for the design of linear pentapods of Type 1 and Type 2, which have a self-motion free workspace. | On the line-symmetry of self-motions of linear pentapods | 8,571 |
In this paper, we present a light-weight, multi- axis compliant tenegrity joint that is biologically inspired by the human elbow. This tensegrity elbow actuates by shortening and lengthening cable in a method inspired by muscular actuation in a person. Unlike many series elastic actuators, this joint is structurally compliant not just along each axis of rotation, but along other axes as well. Compliant robotic joints are indispensable in unpredictable environments, including ones where the robot must interface with a person. The joint also addresses the need for functional redundancy and flexibility, traits which are required for many applications that investigate the use of biologically accurate robotic models. | A light-weight, multi-axis compliant tensegrity joint | 8,572 |
The exploration of planetary surfaces is predominately unmanned, calling for a landing vehicle and an autonomous and/or teleoperated rover. Artificial intelligence and machine learning techniques can be leveraged for better mission planning. This paper describes the coordinated use of both global navigation and metaheuristic optimization algorithms to plan the safe, efficient missions. The aim is to determine the least-cost combination of a safe landing zone (LZ) and global path plan, where avoiding terrain hazards for the lander and rover minimizes cost. Computer vision methods were used to identify surface craters, mounds, and rocks as obstacles. Multiple search methods were investigated for the rover global path plan. Several combinatorial optimization algorithms were implemented to select the shortest distance path as the preferred mission plan. Simulations were run for a sample Google Lunar X Prize mission. The result of this study is an optimization scheme that path plans with the A* search method, and uses simulated annealing to select ideal LZ-path- goal combination for the mission. Simulation results show the methods are effective in minimizing the risk of hazards and increasing efficiency. This paper is specific to a lunar mission, but the resulting architecture may be applied to a large variety of planetary missions and rovers. | Optimized Mission Planning for Planetary Exploration Rovers | 8,573 |
Industries such as flexible manufacturing and home care will be transformed by the presence of robotic assistants. Assurance of safety and functional soundness for these robotic systems will require rigorous verification and validation. We propose testing in simulation using Coverage-Driven Verification (CDV) to guide the testing process in an automatic and systematic way. We use a two-tiered test generation approach, where abstract test sequences are computed first and then concretized (e.g., data and variables are instantiated), to reduce the complexity of the test generation problem. To demonstrate the effectiveness of our approach, we developed a testbench for robotic code, running in ROS-Gazebo, that implements an object handover as part of a human-robot interaction (HRI) task. Tests are generated to stimulate the robot's code in a realistic manner, through stimulating the human, environment, sensors, and actuators in simulation. We compare the merits of unconstrained, constrained and model-based test generation in achieving thorough exploration of the code under test, and interesting combinations of human-robot interactions. Our results show that CDV combined with systematic test generation achieves a very high degree of automation in simulation-based verification of control code for robots in HRI. | Systematic and Realistic Testing in Simulation of Control Code for
Robots in Collaborative Human-Robot Interactions | 8,574 |
Recent years have witnessed the prosperity of robots and in order to support consensus and cooperation for multi-robot system, wireless communications and networking among robots and the infrastructure have become indispensable. In this technical note, we first provide an overview of the research contributions on communication-aware motion planning (CAMP) in designing wireless-connected robotic networks (WCRNs), where the degree-of-freedom (DoF) provided by motion and communication capabilities embraced by the robots have not been fully exploited. Therefore, we propose the framework of joint communication-motion planning (JCMP) as well as the architecture for incorporating JCMP in WCRNs. The proposed architecture is motivated by the observe-orient-decision-action (OODA) model commonly adopted in robotic motion control and cognitive radio. Then, we provide an overview of the orient module that quantify the connectivity assessment. Afterwards, we highlight the JCMP module and compare it with the conventional communication-planning, where the necessity of the JCMP is validated via both theoretical analysis and simulation results of an illustrative example. Finally, a series of open problems are discussed, which picture the gap between the state-of-the-art and a practical WCRN. | Joint Communication-Motion Planning in Wireless-Connected Robotic
Networks: Overview and Design Guidelines | 8,575 |
Multimodal tactile sensing could potentially enable robots to improve their performance at manipulation tasks by rapidly discriminating between task-relevant objects. Data-driven approaches to this tactile perception problem show promise, but there is a dearth of suitable training data. In this two-page paper, we present a portable handheld device for the efficient acquisition of multimodal tactile sensing data from objects in their natural settings, such as homes. The multimodal tactile sensor on the device integrates a fabric-based force sensor, a contact microphone, an accelerometer, temperature sensors, and a heating element. We briefly introduce our approach, describe the device, and demonstrate feasibility through an evaluation with a small data set that we captured by making contact with 7 task-relevant objects in a bathroom of a person's home. | A Handheld Device for the In Situ Acquisition of Multimodal Tactile
Sensing Data | 8,576 |
Swarm robotics is a promising approach for the coordination of large numbers of robots. While previous studies have shown that evolutionary robotics techniques can be applied to obtain robust and efficient self-organized behaviors for robot swarms, most studies have been conducted in simulation, and the few that have been conducted on real robots have been confined to laboratory environments. In this paper, we demonstrate for the first time a swarm robotics system with evolved control successfully operating in a real and uncontrolled environment. We evolve neural network-based controllers in simulation for canonical swarm robotics tasks, namely homing, dispersion, clustering, and monitoring. We then assess the performance of the controllers on a real swarm of up to ten aquatic surface robots. Our results show that the evolved controllers transfer successfully to real robots and achieve a performance similar to the performance obtained in simulation. We validate that the evolved controllers display key properties of swarm intelligence-based control, namely scalability, flexibility, and robustness on the real swarm. We conclude with a proof-of-concept experiment in which the swarm performs a complete environmental monitoring task by combining multiple evolved controllers. | Evolution of Collective Behaviors for a Real Swarm of Aquatic Surface
Robots | 8,577 |
We present a novel method to compute the approximate global penetration depth (PD) between two non-convex geometric models. Our approach consists of two phases: offline precomputation and run-time queries. In the first phase, our formulation uses a novel sampling algorithm to precompute an approximation of the high-dimensional contact space between the pair of models. As compared with prior random sampling algorithms for contact space approximation, our propagation sampling considerably speeds up the precomputation and yields a high quality approximation. At run-time, we perform a nearest-neighbor query and local projection to efficiently compute the translational or generalized PD. We demonstrate the performance of our approach on complex 3D benchmarks with tens or hundreds of thousands of triangles, and we observe significant improvement over previous methods in terms of accuracy, with a modest improvement in the run-time performance. | Efficient Penetration Depth Computation between Rigid Models using
Contact Space Propagation Sampling | 8,578 |
The Kilobot is a widely used platform for investigation of swarm robotics. Physical Kilobots are slow moving and require frequent recalibration and charging, which significantly slows down the development cycle. Simulators can speed up the process of testing, exploring and hypothesis generation, but usually require time consuming and error-prone translation of code between simulator and robot. Moreover, code of different nature often obfuscates direct comparison, as well as determination of the cause of deviation, between simulator and actual robot swarm behaviour. To tackle these issues we have developed a C-based simulator that allows those working with Kilobots to use the same programme code in both the simulator and the physical robots. Use of our simulator, coined Kilombo, significantly simplifies and speeds up development, given that a simulation of 1000 robots can be run at a speed 100 times faster than real time on a desktop computer, making high-throughput pre-screening possible of potential algorithms that could lead to desired emergent behaviour. We argue that this strategy, here specifically developed for Kilobots, is of general importance for effective robot swarm research. The source code is freely available under the MIT license. | Kilombo: a Kilobot simulator to enable effective research in swarm
robotics | 8,579 |
Planning under process and measurement uncertainties is a challenging problem. In its most general form it can be modeled as a Partially Observed Markov Decision Process (POMDP) problem. However POMDPs are generally difficult to solve when the underlying spaces are continuous, particularly when beliefs are non-Gaussian, and the difficulty is further exacerbated when there are also non-convex constraints on states. Existing algorithms to address such challenging POMDPs are expensive in terms of computation and memory. In this paper, we provide a feedback policy in non-Gaussian belief space via solving a convex program for common non-linear observation models. The solution involves a Receding Horizon Control strategy using particle filters for the non-Gaussian belief representation. We develop a way of capturing non-convex constraints in the state space and adapt the optimization to incorporate such constraints, as well. A key advantage of this method is that it does not introduce additional variables in the optimization problem and is therefore more scalable than existing constrained problems in belief space. We demonstrate the performance of the method on different scenarios. | Feedback Motion Planning Under Non-Gaussian Uncertainty and Non-Convex
State Constraints | 8,580 |
Probabilistic completeness is an important property in motion planning. Although it has been established with clear assumptions for geometric planners, the panorama of completeness results for kinodynamic planners is still incomplete, as most existing proofs rely on strong assumptions that are difficult, if not impossible, to verify on practical systems. In this paper, we focus on an important class of kinodynamic planners, namely those that interpolate trajectories in the state space. We provide a proof of probabilistic completeness for these planners under assumptions that can be readily verified from the system's equations of motion and the user-defined interpolation function. Our proof relies crucially on a property of interpolated trajectories, termed second-order continuity (SOC), which we show is tightly related to the ability of a planner to benefit from denser sampling. We analyze the impact of this property in simulations on a low-torque pendulum. Our results show that a simple RRT using a second-order continuous interpolation swiftly finds solution, while it is impossible for the same planner using standard Bezier curves (which are not SOC) to find any solution. | Completeness of Randomized Kinodynamic Planners with State-based
Steering | 8,581 |
This paper develops a comparative framework for the design of actuated inertial appendages for planar, aerial reorientation. We define the Inertial Reorientation template, the simplest model of this behavior, and leverage its linear dynamics to reveal the design constraints linking a task with the body designs capable of completing it. As practicable inertial appendage designs lead to morphology that is generally more complex, we advance a notion of "anchoring" whereby a judicious choice of physical design in concert with an appropriate control policy yields a system whose closed loop dynamics are sufficiently captured by the template as to permit all further design to take place in its far simpler parameter space. This approach is effective and accurate over the diverse design spaces afforded by existing platforms, enabling performance comparison through the shared task space. We analyze examples from the literature and find advantages to each body type, but conclude that tails provide the highest potential performance for reasonable designs. Thus motivated, we build a physical example by retrofitting a tail to a RHex robot and present empirical evidence of its efficacy. | Comparative Design, Scaling, and Control of Appendages for Inertial
Reorientation | 8,582 |
This paper focuses on the problem of supplying the workstations of assembly lines with components during the production process. For that specific problem, this paper presents a Mixed Integer Linear Program (MILP) that aims at minimizing the energy consumption of the supplying strategy. More specifically, in contrast of the usual formulations that only consider component flows, this MILP handles the mass flow that are routed from one workstation to the other. | A mass-flow MILP formulation for energy-efficient supplying in assembly
lines | 8,583 |
This paper presents an anthropomorphic robotic bear for the exploration of human-robot interaction including verbal and non-verbal communications. This robot is implemented with a hybrid face composed of a mechanical faceplate with 10 DOFs and an LCD-display-equipped mouth. The facial emotions of the bear are designed based on the description of the Facial Action Coding System as well as some animal-like gestures described by Darwin. The mouth movements are realized by synthesizing emotions with speech. User acceptance investigations have been conducted to evaluate the likability of these facial behaviors exhibited by the eBear. Multiple Kernel Learning is proposed to fuse different features for recognizing user's facial expressions. Our experimental results show that the developed Bear-Like robot can perceive basic facial expressions and provide emotive conveyance towards human beings. | eBear: An Expressive Bear-Like Robot | 8,584 |
This article proposes an emotive lifelike robotic face, called ExpressionBot, that is designed to support verbal and non-verbal communication between the robot and humans, with the goal of closely modeling the dynamics of natural face-to-face communication. The proposed robotic head consists of two major components: 1) a hardware component that contains a small projector, a fish-eye lens, a custom-designed mask and a neck system with 3 degrees of freedom; 2) a facial animation system, projected onto the robotic mask, that is capable of presenting facial expressions, realistic eye movement, and accurate visual speech. We present three studies that compare Human-Robot Interaction with Human-Computer Interaction with a screen-based model of the avatar. The studies indicate that the robotic face is well accepted by users, with some advantages in recognition of facial expression and mutual eye gaze contact. | ExpressionBot: An Emotive Lifelike Robotic Face for Face-to-Face
Communication | 8,585 |
We present a highly functional and cost-effective prosthesis for transfemoral amputees that uses series elastic actuators. These actuators allow for accurate force control, low impedance and large dynamic range. The design involves one active joint at the knee and a passive joint at the ankle. Additionally, the socket was designed using mirroring of compliances to ensure maximum comfort. | Retractable Prosthesis for Transfemoral Amputees Using Series Elastic
Actuators and Force Control | 8,586 |
Purpose. To obtain the interference immunity of the data exchange by spread spectrum signals with variable entropy of the telemetric information data exchange with autonomous mobile robots. Methodology. The results have been obtained by the theoretical investigations and have been confirmed by the modeling experiments. Findings. The interference immunity in form of dependence of bit error probability on normalized signal/noise ratio of the data exchange by spread spectrum signals with variable entropy has been obtained.It has been proved that the interference immunity factor (needed normalized signal/noise ratio) is at least 2 dB better under condition of equal time complexity as compared with correlation processing methods of orthogonal signals. Originality. For the first time the interference immunity in form of dependence of bit error probability on normalized signal/noise ratio of the data exchange by spread spectrum signals with variable entropy has been obtained. Practical value. The obtained results prove the feasibility of using variable entropy spread spectrum signals data exchange method in the distributed telemetric information processing systems in specific circumstances. | The interference immunity of the telemetric information data exchange
with autonomous mobile robots | 8,587 |
Current approaches for visual-inertial odometry (VIO) are able to attain highly accurate state estimation via nonlinear optimization. However, real-time optimization quickly becomes infeasible as the trajectory grows over time, this problem is further emphasized by the fact that inertial measurements come at high rate, hence leading to fast growth of the number of variables in the optimization. In this paper, we address this issue by preintegrating inertial measurements between selected keyframes into single relative motion constraints. Our first contribution is a \emph{preintegration theory} that properly addresses the manifold structure of the rotation group. We formally discuss the generative measurement model as well as the nature of the rotation noise and derive the expression for the \emph{maximum a posteriori} state estimator. Our theoretical development enables the computation of all necessary Jacobians for the optimization and a-posteriori bias correction in analytic form. The second contribution is to show that the preintegrated IMU model can be seamlessly integrated into a visual-inertial pipeline under the unifying framework of factor graphs. This enables the application of incremental-smoothing algorithms and the use of a \emph{structureless} model for visual measurements, which avoids optimizing over the 3D points, further accelerating the computation. We perform an extensive evaluation of our monocular \VIO pipeline on real and simulated datasets. The results confirm that our modelling effort leads to accurate state estimation in real-time, outperforming state-of-the-art approaches. | On-Manifold Preintegration for Real-Time Visual-Inertial Odometry | 8,588 |
Control of systems of automated guided vehicles involves action planning at many levels. For efficient control of these systems, accurate estimation of cost parameters (speed, energy, task completion performance, \textit{et~cetera} is required. These parameters change along time, particularly in battery-operated robots, which are very sensitive to battery level variations. This work addresses the problem of on-line cost parameter identification and estimation for proper control decisions of the individual mobile robots and for the system as a whole. Several filtering and estimation methods have been investigated with respect to travelling times, which are dramatically affected by battery charges and condition of facility's floors, among other factors. Results show that these parameters depend on the robot, the route and the moment, so they are linked to a particular robot, a region of the floor and a time period (or to a battery level). Moreover, differences with static, pre-runtime travelling time computations, either heuristically or by characterization of real robots, are large enough to affect to system's performance and overall productivity and efficiency. | A Study of Time-varying Cost Parameter Estimation Methods in Automated
Transportation Systems based on Mobile Robots | 8,589 |
This research proposes new tools for investigation of behavioral diversity in multi-robot systems and a significant body of results using these tools in simulated and real mobile robot experiments. The experiments specifically describe a framework of defining behavior-based strategies for multi-robot tasks as robot foraging, robot soccer and robot formation. The research focuses specifically on motor schema-based multi-robot systems, which are an important example of behavior-based control. | Diversity and Intelligence in Multi-robot Teams | 8,590 |
This work introduces a novel technique for fabricating functional robots using 3D printers. Simultaneously depositing photopolymers and a non-curing liquid allows complex, pre-filled fluidic channels to be fabricated. This new printing capability enables complex hydraulically actuated robots and robotic components to be automatically built, with no assembly required. The technique is showcased by printing linear bellows actuators, gear pumps, soft grippers and a hexapod robot, using a commercially-available 3D printer. We detail the steps required to modify the printer and describe the design constraints imposed by this new fabrication approach. | Printable Hydraulics: A Method for Fabricating Robots by 3D Co-Printing
Solids and Liquids | 8,591 |
Mobile robots are becoming part of our every day living at home, work or entertainment. Due to their limited power capabilities, the development of new energy consumption models can lead to energy conservation and energy efficient designs. In this paper, we carry out a number of experiments and we focus on the motors power consumption of a specific robot called Wifibot. Based on the experimentation results, we build models for different speed and acceleration levels. We compare the motors power consumption to other robot running modes. We, also, create a simple robot network scenario and we investigate whether forwarding data through a closer node could lead to longer operation times. We assess the effect energy capacity, traveling distance and data rate on the operation time. | Modeling the power consumption of a Wifibot and studying the role of
communication cost in operation time | 8,592 |
Pick-and-place regrasp is an important manipulation skill for a robot. It helps a robot accomplish tasks that cannot be achieved within a single grasp, due to constraints such as kinematics or collisions between the robot and the environment. Previous work on pick-and-place regrasp only leveraged flat surfaces for intermediate placements, and thus is limited in the capability to reorient an object. In this paper, we extend the reorientation capability of a pick-and-place regrasp by adding a vertical pin on the working surface and using it as the intermediate location for regrasping. In particular, our method automatically computes the stable placements of an object leaning against a vertical pin, finds several force-closure grasps, generates a graph of regrasp actions, and searches for the regrasp sequence. To compare the regrasping performance with and without using pins, we evaluate the success rate and the length of regrasp sequences while performing tasks on various models. Experiments on reorientation and assembly tasks validate the benefit of using support pins for regrasping. | Analyzing the Utility of a Support Pin in Sequential Robotic
Manipulation | 8,593 |
Robotic manipulation of deformable objects remains a challenging task. One such task is folding a garment autonomously. Given start and end folding positions, what is an optimal trajectory to move the robotic arm to fold a garment? Certain trajectories will cause the garment to move, creating wrinkles, and gaps, other trajectories will fail altogether. We present a novel solution to find an optimal trajectory that avoids such problematic scenarios. The trajectory is optimized by minimizing a quadratic objective function in an off-line simulator, which includes material properties of the garment and frictional force on the table. The function measures the dissimilarity between a user folded shape and the folded garment in simulation, which is then used as an error measurement to create an optimal trajectory. We demonstrate that our two-arm robot can follow the optimized trajectories, achieving accurate and efficient manipulations of deformable objects. | Folding Deformable Objects using Predictive Simulation and Trajectory
Optimization | 8,594 |
This paper solves the classical problem of simultaneous localization and mapping (SLAM) in a fashion which avoids linearized approximations altogether. Based on creating virtual synthetic measurements, the algorithm uses a linear time- varying (LTV) Kalman observer, bypassing errors and approximations brought by the linearization process in traditional extended Kalman filtering (EKF) SLAM. Convergence rates of the algorithm are established using contraction analysis. Different combinations of sensor information can be exploited, such as bearing measurements, range measurements, optical flow, or time-to-contact. As illustrated in simulations, the proposed algorithm can solve SLAM problems in both 2D and 3D scenarios with guaranteed convergence rates in a full nonlinear context. | Analytical SLAM Without Linearization | 8,595 |
We present a general framework to autonomously achieve a task, where autonomy is acquired by learning sensorimotor patterns of a robot, while it is interacting with its environment. To accomplish the task, using the learned sensorimotor contingencies, our approach predicts a sequence of actions that will lead to the desirable observations. Gaussian processes (GP) with automatic relevance determination is used to learn the sensorimotor mapping. In this way, relevant sensory and motor components can be systematically found in high-dimensional sensory and motor spaces. We propose an incremental GP learning strategy, which discerns between situations, when an update or an adaptation must be implemented. RRT* is exploited to enable long-term planning and generating a sequence of states that lead to a given goal; while a gradient-based search finds the optimum action to steer to a neighbouring state in a single time step. Our experimental results prove the successfulness of the proposed framework to learn a joint space controller with high data dimensions (10$\times$15). It demonstrates short training phase (less than 12 seconds), real-time performance and rapid adaptations capabilities. | Self-learning and adaptation in a sensorimotor framework | 8,596 |
The Sixth International Workshop on Domain-Specific Languages and Models for Robotic Systems (DSLRob'15) was held September 28, 2015 in Hamburg (Germany), as part of the IROS 2015 conference. The main topics of the workshop were Domain-Specific Languages (DSLs) and Model-driven Software Development (MDSD) for robotics. A domain-specific language is a programming language dedicated to a particular problem domain that offers specific notations and abstractions that increase programmer productivity within that domain. Model-driven software development offers a high-level way for domain users to specify the functionality of their system at the right level of abstraction. DSLs and models have historically been used for programming complex systems. However recently they have garnered interest as a separate field of study. Robotic systems blend hardware and software in a holistic way that intrinsically raises many crosscutting concerns (concurrency, uncertainty, time constraints, ...), for which reason, traditional general-purpose languages often lead to a poor fit between the language features and the implementation requirements. DSLs and models offer a powerful, systematic way to overcome this problem, enabling the programmer to quickly and precisely implement novel software solutions to complex problems within the robotics domain. | Proceedings of the Sixth International Workshop on Domain-Specific
Languages and Models for Robotic Systems (DSLRob 2015) | 8,597 |
With 3D sensing becoming cheaper, environment-aware and visually-guided robot arms capable of safely working in collaboration with humans will become common. However, a reliable calibration is needed, both for camera internal calibration, as well as Eye-to-Hand calibration, to make sure the whole system functions correctly. We present a framework, using a novel combination of well proven methods, allowing a quick automatic calibration for the integration of systems consisting of the robot and a varying number of 3D cameras by using a standard checkerboard calibration grid. Our approach allows a quick camera-to-robot recalibration after any changes to the setup, for example when cameras or robot have been repositioned. Modular design of the system ensures flexibility regarding a number of sensors used as well as different hardware choices. The framework has been proven to work by practical experiments to analyze the quality of the calibration versus the number of positions of the checkerboard used for each of the calibration procedures. | Automatic Calibration of a Robot Manipulator and Multi 3D Camera System | 8,598 |
This paper presents an estimation and control algorithm for an aerial manipulator using a hexacopter with a 2-DOF robotic arm. The unknown parameters of a payload are estimated by an on-line estimator based on parametrization of the aerial manipulator dynamics. With the estimated mass information and the augmented passivity-based controller, the aerial manipulator can fly with the unknown object. Simulation for an aerial manipulator is performed to compare estimation performance between the proposed control algorithm and conventional adaptive sliding mode controller. Experimental results show a successful flight of a custom-made aerial manipulator while the unknown parameters related to an additional payload were estimated satisfactorily. | Control of an Aerial Manipulator using On-line Parameter Estimator for
an Unknown Payload | 8,599 |
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