id stringlengths 15 54 | text stringlengths 3 133k | title stringclasses 1
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path_planning/p113_1.txt | ∗e-mail:kourosh.naderi@aalto.fi
†
e-mail:joose.rajamaki@aalto.fi
‡
e-mail:perttu.hamalainen@aalto.fi
1 Introduction
Path planning in dynamic environments is a demanding problem
encountered in many robotic tasks and computer games [Rastgoo
et al. 2014; Sud et al. 2008]. Real-time path planning algorithms
are used to rea... | |
path_planning/documentrepidrep1typ_19.txt | The initial velocity of the robot and of vehicle 2 is (vx = 30:0 m=s; vy = 0:0 m=s), and
the initial velocity of vehicle 1 is (vx = 23 m=s; vy = 0:0 m=s). The initial velocities are
represented by vectors attached to the center of each vehicle. The grey circles represent
positions of the robot and of the obstacles when... | |
path_planning/documentrepidrep1typ_18.txt | 3
4
5
6
7
8
target 6 5
7 8
1
2
3
4
5
6
7
8
1
2
3
4
5
6
7
8
1
2
3
4
(a) (b)
(c) (d)
Figure 16: Snapshots of the trajectory avoiding static and moving obstacles.
maneuver. Later, due to the change in direction of the obstacle, the front avoidance
maneuver became feasible, which explains the higher speeds at the distal en... | |
path_planning/documentrepidrep1typ_1.txt | Jet Propulsion Laboratory
California Institute of Technology
Pasadena, CA 91109 USA
y Department of Mechanical, Nuclear and Aerospace Engineering
University of California, Los Angeles
Los Angeles, CA 90024 USA
Abstract
This paper presents a method for robot motion planning in dynamic environments.
It consists of select... | |
path_planning/PMC10708786_47.txt | The two images in the upper part of Figure 13 represent the simulation results, while the two images in the lower part represent the experimental results. By comparing them, it can be observed that the speed of the manipulator during the experiment fluctuates slightly but is generally consistent with that in the simula... | |
path_planning/PMC10708786_38.txt | Compared to Figure 7b,c, in Figure 9b, the oscillation of each joint angle of the manipulator in the path section of the shock avoidance region disappears. Likewise, in Figure 9c,d, the velocity and angular velocity are smoother and more continuous, with only transient fluctuations observed at the initial and end posit... | |
path_planning/1729881418787075icid_15.txt | Tangential velocity function
TVF is defined as
(15)
where
is unit directional vector of tangential VPF that has been defined in the “VPFs in 3-D space” section. γ is the strength of the potential field obtained by fuzzy logic approach in the same way as
. Figure 10 shows the membership functions of γ. | |
path_planning/p113_14.txt | patches instead of just one node. We tested our algorithm against
CL-RRT which can be considered the state-of-the-art in RRT-based
real-time path planning. Our simulations show that the combination
of retaining the tree with the two methods of rewiring the large tree
in RT-RRT* enables the algorithm to find shorter pat... | |
path_planning/1729881418787075icid_5.txt | Figure 1. Architecture of robots collaboration with collision avoidance.
The geometry and parameters of robots are built offline. Joint angles of robots are retrieved from their encoders. The location and geometry of obstacles are known here by a binocular vision system that was introduced in the study by Huang.33 All ... | |
path_planning/1729881418787075icid_18.txt | Figure 12. Trajectories with different RVFs. (a) Robot with a speed of 10 mm/s. (b) Robot with a speed of 50 mm/s. RVFs: repulsive velocity functions.
Repulsive velocities in the literature23,28–30 are related to
, and RVF1, RVF2, and RVF3 correspond to r = 0, r = 1, and r = 2, respectively. RVF4 from the study by Wu3... | |
path_planning/PMC10708786_34.txt | An external file that holds a picture, illustration, etc.
Object name is sensors-23-09617-g008a.jpg
An external file that holds a picture, illustration, etc.
Object name is sensors-23-09617-g008b.jpg
Figure 8
Joint velocity under local oscillation. (a) The speed variation of joint 1–3; (b) The speed variation of joint ... | |
path_planning/PMC10708786_19.txt | 𝜃˙=piv(𝐽−1𝑖)⋅𝑉𝑟𝑒𝑝(𝑥,𝑦,𝑧)+piv(𝐽−16)⋅𝑉𝑎𝑡(𝑥,𝑦,𝑧)
(4)
2.3. Local Oscillation and Virtual Target Point
Since the artificial potential field method is based on the idea of the potential field method, whether the force potential field method or the velocity potential field method is utilized, the direction of... | |
path_planning/documentrepidrep1typ_25.txt | a segment of the boundary of the velocity obstacle V OB, @(V OB). Subset Sd, instead,
does not include in its boundary any segment from the boundary of V OB. From Lemma 1,
velocities with tip on these segments generate trajectories grazing B, and therefore only
subsets Sf and Sr include tangent velocities.
Tangent velo... | |
path_planning/PMC10708786_5.txt | Numerous researchers have improved the VPF; however, current methods do not deal with the dynamic obstacle-avoidance problem of manipulators in human-robot collaboration well. These methods cannot match the real-time and safety criteria for manipulator obstacle avoidance in robot collaboration scenarios. Hence, in this... | |
path_planning/1729881418787075icid_19.txt | Figure 13. TVF with different directions.
The robot moves from point S (0, 400, 0) to point G (400, 0, 100). The simulation results are shown in Figure 14 that the path of the robot keeps the shortest by applying
. Compared to all other tangential velocity in the plane L,
lies the smallest angle with
, so the r... | |
path_planning/PMC10708786_14.txt | As the manipulator approaches the target point, the attraction speed gradually decreases to zero. This S-curve-like velocity design scheme adjusts its velocity amplitude to approach the target in almost a straight line when there are no obstacles, avoiding the problem of oversized initial velocity or limited velocity, ... | |
path_planning/1729881418787075icid_6.txt | Figure 2. The simplified model of robot. (a) Layer one. (b) Layer two.
Step 2: Layer two
Layer two will be checked when the potential collision is not eliminated in layer one. In this layer, the manipulator is represented by mixed SSVs, as shown in Figure 2(b).
Distance checking
As shown in Figure 3, there are two SSVs... | |
path_planning/1729881418787075icid_3.txt | Open access
Research article
First published online July 13, 2018
A novel non-collision trajectory planning algorithm based on velocity potential field for robotic manipulator
X Xu, Y Hu, […], and PS Guo+2View all authors and affiliations
All Articles
https://doi.org/10.1177/1729881418787075 | |
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path_planning/26453_15.txt | The agent was modeled as a disk whose diameter equals
the length of the grid cell. The confguration is defned as
(x, y, θ, vel) where x, y are the coordinates of the cell, θ ∈
{0
◦
, 90◦
, 180◦
, 270◦} is the orientation, and vel ∈ {0, 2} is
the velocity of the agent.
The following motion primitives were defned: accele... | |
path_planning/documentrepidrep1typ_9.txt | reachable
velocities
kinematic constraints
reachable velocities due to
minimum
turning
radius
Figure 8: Dynamic versus kinematic constraints.
accelerations prevent the vehicle from moving sideways at non-zero forward speeds, thus
automatically satisfying the non-holonomic constraints (Shiller and Sundar, 1996). For
thi... | |
path_planning/PMC10708786_9.txt | An external file that holds a picture, illustration, etc.
Object name is sensors-23-09617-g001.jpg
Figure 1
Obstacle-avoidance velocity generation of Mico2 manipulator. | |
path_planning/PMC10708786_23.txt | An external file that holds a picture, illustration, etc.
Object name is sensors-23-09617-g005.jpg
Figure 5
Diagram of the virtual target-point setting method. | |
path_planning/1729881418787075icid_12.txt | Figure 7. v’s membership function.
is divided into five fuzzy sets: Very Small (VSL), Small (SM), Medium (ME), Big (BG), and Very Big (VB). According to simulation results and experimental experiences, the membership functions of kp and kd are shown in Figure 8 and Figure 9, respectively. | |
path_planning/PMC10708786_16.txt | An external file that holds a picture, illustration, etc.
Object name is sensors-23-09617-g003.jpg
Figure 3
Multi-obstacle repulsive velocity potential field model. | |
path_planning/PMC10708786_30.txt | 3.1.2. Simulation of Local Oscillation
In the absence of oscillation strategy processing, multiple obstacles are placed in the manipulator workspace until the manipulator experiences localized oscillations during motion. This can verify the ability of the robot to autonomously avoid obstacles and observe oscillations i... | |
path_planning/1729881418787075icid_26.txt | Figure 20. Trajectories of robots and moving obstacle. (a) Trajectories at the projection of XY plane. (b) Magnification of trajectories near obstacles.
Conclusions
In this article, a real-time algorithm based on VPF is introduced to coordinate collision-free trajectories for robotic manipulators in 3-D space. The robo... | |
path_planning/1729881418787075icid_22.txt | Figure 16. Collision avoidance processes (down: front view, up: top view). (a) t = 0 s. (b) t = 2 s. (c) t = 2.5 s. (d) t = 3 s. (e) t = 3.8 s. (f) t = 5 s. (g) t = 6 s. (h) t = 9 s. | |
path_planning/documentrepidrep1typ_26.txt | Sr
Sd
Sf
VOB
A
^
1
2 3
4
5
6
1
2 3
4
5
6
7
8
9
1
2 3
4
5
6
7
8
9
λ
f
r
λ
λ
f
r
λ
Sd
Sd
Sd
Snd
Figure 24: The computation of the Reachable Avoidance Velocities RAV. | |
path_planning/p113_11.txt | contains the path from x0 to xgoal, we made it possible to rewire the
path to xgoal very quickly. Note that, when rewiring is done on the
path from x0 to xgoal, we need to update the path again (see Section 4.2). Using Qs in Algorithm 5, allows us to grow the Rewiring
Circle between iterations. As the Rewiring Circle g... | |
path_planning/1729881418787075icid_7.txt | Figure 3. Distance estimate of SSVs. SSVs: swept sphere volumes. (a) LSS to LSS. (b) LSS to RSS.
Assuming object #p is represented by mp SSVs and they are
, object #q is represented by mq SSVs and they are
. Then the matrix of distance checking is
(3)
where
is the distance of SSV
and
,
... | |
path_planning/PMC10708786_35.txt | The velocity in the oscillation section displays apparent fluctuations in Figure 8, with joint velocities oscillating in the oscillation area, where the amplitude of oscillation increases and then decreases, corresponding to the manipulator traveling through the buffer zone, the repulsion zone, back through the buffer ... | |
path_planning/PMC10708786_28.txt | The obstacle is a sphere with a radius of 0.04 m with spatial location coordinates 𝑜𝑏𝑠=[0.4−0.30.3]. To further examine the variations in joint velocity and arm end velocity in the absence of obstacles, the step planning time for the manipulator is modified to t = 0.01 s. Fine interpolation is conducted to obtain mo... | |
path_planning/1729881418787075icid_31.txt | Crossref
ISI
Google Scholar
30. Hu M, Li CH. Application in real-time planning of UAV based on velocity vector field. In: International conference on electrical and control engineering, Wuhan, China, 26–28 June 2010, pp. 5022–5026. IEEE. | |
path_planning/documentrepidrep1typ_15.txt | o
i+1,k,2
Figure 13: Tree representation for the global search.
generating the avoidance maneuvers, are the velocities computed by discretizing RAV.
The search tree is formally de ned as follows:
nj (ti) = fxj ; RAV (ti))g (9)
oj;l(ti) = fvl(ti) j vl(ti) 2 RAVj (ti)g (10)
ej;k(ti) = f(nj (ti); nk(ti+1)) j nk(ti+1) = nj... | |
path_planning/documentrepidrep1typ_17.txt | 3
4
6 5
7 8
target
1
2
3
4
6 5
7 8
start start
(a) (b)
Figure 15: Trajectories avoiding static obstacles.
5. Examples
This Section presents several examples of navigation in static and dynamic environments using the Velocity Obstacle approach.
5.1. Avoidance of static obstacles
The rst two examples demonstrate the app... | |
path_planning/26453_4.txt | edge, w(e) ∈ N.
Based on the source/target velocity, each motion (edge)
can be classifed as either accelerating, decelerating, or uniform. In the latter case, the agent’s velocity at the target
confguration is the same as in the source one, while in the
12331
frst two cases, it changes (increases and decreases, respect... | |
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path_planning/PMC10708786_8.txt | The spatial velocity transformation for a manipulator typically requires a Jacobi non-square form, which consequently needs a solution via a non-square format Jacobi matrix [26]. Currently, the most popular method used in practical applications to obtain the pseudo-inverse solution is the Singular Value Decomposition (... | |
path_planning/1729881418787075icid_38.txt | International Journal of Advanced Robotic Systems
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path_planning/26453_1.txt | v = 1
t0 = 12
0 1 2 3 4 5 6 7 8 9
Distance (m)
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
..
Time(s)
Figure 1: An example of the challenging path planning instance with dynamic obstacles.
In our work, we adopt an assumption (more realistic from
the practical perspective) that decelerating to a full stop and,
similarly, acceler... | |
path_planning/1729881418787075icid_27.txt | Google Scholar
5. Zidane IM, Ibrahim K. Wavefront and a-star algorithms for mobile robot path planning. In: 2017 3rd international conference on advanced intelligent systems and informatics, Cairo, Egypt, 9–11 September 2017, pp. 69–80. IEEE.
Google Scholar
6. Zhai JM, Liu K, He H, et al. An efficient approach for coll... | |
path_planning/1729881418787075icid_4.txt | Contents
Abstract
Introduction
Simplified model and distance estimating
An improved collision-free algorithm based on VPF
Simulations and experimentation
Conclusions
Declaration of conflicting interests
Funding
ORCID iD
References
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path_planning/PMC10708786_26.txt | 3.1. Simulation of Improved Velocity Potential Field Method and Comparative Analysis
3.1.1. Obstacle-Free Simulation Verification
Assuming that the maximum speed of the manipulator is within its speed limit, the trajectory of the manipulator from the initial position to the target position is shown in Figure 6a. Set th... | |
path_planning/documentrepidrep1typ_12.txt | B
^
VB
VOB
A
^
RVA
V
A
Sf
S
d
S
r
Figure 11: General structure of the reachable avoidance velocities.
absolute velocities generating trajectories tangent to Bb
, since their corresponding relative
velocities lay on f and r . For example, the only tangent velocities in Figure 7 are
represented by the segments KH and L... | |
path_planning/1729881418787075icid_24.txt | Figure 18. Trajectories of the two robots with different priorities.
Experiment
In this experiment, robot ABB (IRB120) and SCARA working in an overlapped working envelope are set up. The collision avoidance algorithm is implemented on two robots with two obstacles of which one is stationary and the other is moving. The... | |
path_planning/PMC10708786_39.txt | 3.2. Manipulator Real-Time Obstacle-Avoidance Planning Experiment
3.2.1. Experiment Initialization Settings
This paper’s experiments were conducted with a KINOVA Mico2 lightweight manipulator (Kinova Inc., Boisbriand QC, Canada) and a Kinect 2.0 vision sensor (Microsoft, Redmond, WA, USA). The primary hardware of the c... | |
path_planning/PMC10708786_40.txt | An external file that holds a picture, illustration, etc.
Object name is sensors-23-09617-g010.jpg
Figure 10
The experimental platform. | |
path_planning/PMC10708786_11.txt | 𝜆(𝑥,𝑦,𝑧)=∣∣𝑣𝑎𝑡𝑡∣∣∣∣𝑣𝑎𝑡𝑡𝑥∣∣+∣∣𝑣𝑎𝑡𝑡𝑦∣∣+∣∣𝑣𝑎𝑡𝑡𝑧∣∣
(1)
The distance separating the end of the manipulator from the target point determines three distinct zones: acceleration section [𝑑0,𝑑𝑚𝑎𝑥], uniform speed section [𝑑𝑚𝑎𝑥,𝑑1], and deceleration section [𝑑1,0]. For the x-direction, the attrac... | |
path_planning/PMC10708786_3.txt | Robot path planning is a crucial research domain in robotics, as robots can perform a plethora of tasks efficiently, via both offline teaching and online autonomous planning, without depending on path-planning technologies. However, as robots move towards collaborating with humans, including in domestic scenarios, the ... | |
path_planning/PMC10708786_72.txt | Help
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path_planning/PMC10708786_53.txt | As the position of link 1 is fixed, the distance between the hand and link 1 remains unchanged after the hand trajectory reaches the target position. According to the curve information in Figure 15c, it can be seen that during the motion process of the manipulator, the distance between the end link 6 of the manipulator... | |
path_planning/documentrepidrep1typ_27.txt | B. Generating the Set of Avoidance Velocities
The procedure used for generating the sets of Reachable Avoidance Velocity RAV,
Si (i=d,f,r), for robot A is illustrated in Figure 24. The Figure shows the set of reachable
velocities RV, the velocity obstacle V OB for a long time horizon Th, and the line laa
passing throug... | |
path_planning/PMC10708786_24.txt | In Figure 5, points 𝑂1−𝑂3 denote the end coordinates of the manipulator upon entering the oscillation region, the center point coordinates of the obstacle and the target point coordinates, respectively. As the three points do not fall on the same straight line, they determine a plane, designated as 𝑄0. This plane se... | |
path_planning/PMC10708786_41.txt | 3.2.2. Collision-Free Scenario Path-Planning Experiments
The path-planning condition of the manipulator without the possibility of the collision was modeled and tested. The human right-hand was utilized as the experimental object, with the activity carried out inside a particular range outside the safety radius of the ... | |
path_planning/PMC10708786_44.txt | In Figure 12a, the blue sample points in the simulation environment represent the movement trajectory of the human right-hand, which does not affect the movement of the manipulator, and allows it to reach the target position accurately. The range of the repulsive potential field is set to be 0.15 m in the simulation ex... | |
path_planning/PMC10708786_51.txt | An external file that holds a picture, illustration, etc.
Object name is sensors-23-09617-g015.jpg
Figure 15
The results of the manipulator running. (a) Obstacle-avoidance angular of the manipulator; (b) Obstacle-avoidance angular velocity of the manipulator; (c) Distance between the manipulator and the obstacle in rea... | |
path_planning/1729881418787075icid_28.txt | Google Scholar
18. Ding XQ. Vision trajectory planning for mobile robots based on hybrid artificial potential field. J Zhejiang Univ Sci B 2016; 50: 1298–1306.
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19. Masoud AA. Decentralized self-organizing potential field-based control for individually motivated mobile agents in a cluttere... | |
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path_planning/1729881418787075icid_10.txt | (7)
where
,
,
; φ, θ, and ψ are the robot’s Euler angles and φG, θG, ψG are the robot’s final Euler angles.
In most cases, the Jacobian matrix is not a square matrix when translating velocity in Cartesian space into velocity in joint space. Therefore, pseudoinverse Jacobian matrix piv(J) needs to be cons... | |
path_planning/26453_6.txt | generates only the successors that correspond to the “wait
and move” actions that land into the neighboring confgurations within their safe intervals. This means that to generate
a successor, SIPP waits the minimal amount of time possible
so that when the move to the new confguration is used, we
arrive at the new safe ... | |
path_planning/PMC10708786_25.txt | Go to:
3. Simulation and Experiments
The velocity potential field method-based obstacle avoidance algorithm for manipulator trajectories, presented in this study, can efficiently plan smooth motion paths that avoid collisions and mitigate potential local oscillations. To ascertain its effectiveness and real-time perfor... | |
path_planning/documentrepidrep1typ_10.txt | Yf
Yr
Y
VB
T
f
CCA,B
λ
f
r
λ
A
^
VA
X
∂Bf
∂Br
B
^
Figure 9: Grazing arcs in an avoidance maneuver.
consist of the shortest segment connecting Tf = f \@B to Yf , and of the shortest segment
connecting Tr = r \ @B to Yr. 2
This Lemma is proved in Appendix A.
As discussed earlier, the boundary of the velocity obstacle V... | |
path_planning/26453_9.txt | about the safe intervals of the graph vertices, we will never
omit “safe” to avoid confusion.
Projecting intervals The role of the projection operation
is to propagate the information on all of the available waitand-move actions from the predecessor to the successor. Formally, the input for the projection procedure is ... | |
path_planning/1729881418787075icid_1.txt | Browse by discipline | |
path_planning/26453_13.txt | contain all possible valid timesteps at which we can get at
the target node of the edge starting from a timestep in the
time interval of the input state (by defnition of projectIntervals). This is equivalent to generating all A*-TS states from
the A*-TS states included in the input state using an edge.
By using all the... | |
path_planning/PMC10708786_27.txt | {𝑞_inti=[−1.6581−0.0873−1.76281.0996−1.8326−1.8326]𝑞_end=[−1.30901.6232−0.97740.2967−1.6754−0.6807]
(5)
An external file that holds a picture, illustration, etc.
Object name is sensors-23-09617-g006.jpg
Figure 6
The results of path planning without obstacles. (a) The results of simulation; (b) The velocity in Cartesi... | |
path_planning/PMC10708786_63.txt | Go to:
Institutional Review Board Statement
The study was conducted following the guidelines of the Declaration of Helsinki and was approved by the Ethics Committee at the College of Electromechanical Engineering, Qingdao University of Science & Technology. | |
path_planning/1729881418787075icid_21.txt | Figure 15. Time diagram of the distance between the two robots. | |
path_planning/PMC10708786_33.txt | The oscillation segment was magnified, revealing that the manipulator is susceptible to trembling during operation due to the short planning duration of each step. Although the fluctuation range of the manipulator’s angle is small, typically around 0.01–0.02 rad, the arm experiences frequent angle jitter during this ti... | |
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path_planning/documentrepidrep1typ_0.txt | 1
Motion Planning in Dynamic Environments using
Velocity Obstacles
Paolo Fiorini
and Zvi Shillery | |
path_planning/p113_4.txt | X is considered to be bounded. Besides spaces such as X , the
calligraphy alphabet is used to refer to sets. X contains obstacles,
which may be dynamic. Xobs ( X denotes the set of all obstacles in
the environment, and we assume it is known. Thus, the free space
is denoted by Xfree = X \ Xobs. The tree is denoted by T ... | |
path_planning/PMC10708786_4.txt | As one of the branches of APF, the VPF offers the advantages of a simple potential field, more efficiency, and easier computation. In the manipulator’s path planning, the VPF is more frequently utilized. It is worth noting that the elastic band methods which are based on curve shortening flows in a potential field also... | |
path_planning/1729881418787075icid_30.txt | Google Scholar
29. Huang L. Velocity planning for a mobile robot to track a moving target—a potential field approach. Robot Auto Syst 2009; 57(1): 55–63. | |
path_planning/PMC10708786_7.txt | 2.1. Implementation of the Velocity Potential Field Method on the Manipulator
The core ideal of path planning using the velocity potential field is to convert the distance between the manipulator, target, or obstacle into a spatial velocity in Cartesian coordinates. This spatial velocity is then mapped to the robot’s j... | |
path_planning/PMC10708786_48.txt | 3.2.3. Manipulator Real-Time Obstacle-Avoidance Experiment
After verifying the feasibility of speed control of the manipulator in collision-free scenarios, this section verifies the real-time obstacle-avoidance path planning of the manipulator. The initial and target postures of the manipulator are set to be the same a... | |
path_planning/1729881418787075icid_23.txt | Figure 17. Time-joint angle curve of robot #1.
Path priority strategies in process of collision avoidance
In this simulation, there are five priority levels for robot #1 and robot #2 in which
are (0, 1), (0.25, 0.75), (0.5, 0.5), (0.75, 0.25), and (1, 0), respectively. Starting point and goal point are the same as th... | |
path_planning/PMC10708786_17.txt | The repulsive velocity function for the x-direction can be defined as: | |
path_planning/PMC10708786_21.txt | The method of velocity potential field is employed to control the manipulator’s velocity directly in this paper. The extremely few time steps between velocities are implemented to attain smooth velocity interpolation. However, an oscillation path may generate a locally unsmooth trajectory, often along the edge of the r... | |
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path_planning/p113_8.txt | node (e.g. xu), we find the grid square gu, where xu is located.
Then, we return all nodes of the tree that are inside gu and in its
adjacent squares. A grid square gj is considered adjacent to other
grid square gi when gj is the closest grid square to gi which has at
least one node of the tree inside it. Fig 2 illustr... | |
path_planning/PMC10708786_6.txt | Go to:
2. Improved Velocity Potential Field for Local Oscillation
The current path-planning algorithm utilized for autonomous obstacle-avoidance research in static obstacle situations is suited more towards two-dimensional objects, such as unmanned aerial vehicles, unmanned vehicles, and mobile robots. As space dimensi... | |
path_planning/PMC10708786_49.txt | An external file that holds a picture, illustration, etc.
Object name is sensors-23-09617-g014.jpg
Figure 14
Real-time obstacle-avoidance planning of manipulator. | |
path_planning/1729881418787075icid_25.txt | Figure 19. Collision avoidance process among robots and obstacles. (a) t = 0. (b) t = 5 s. (c) t = 10 s. (d) t = 20 s. (e) t = 25 s. (f) t = 30 s. (g) t = 35 s. (h) t = 46 s. | |
path_planning/26453_18.txt | the multi-agent path planning solver and conducting experiments on real robots.
12336
Acknowledgments
This work was partially supported by the Analytical Center for the Government of the Russian Federation in accordance with the subsidy agreement (agreement identifer
000000D730321P5Q0002; grant No. 70-2021-00138).
Refe... | |
path_planning/documentrepidrep1typ_7.txt | V Oh = fvA j vA 2 V O ; k vA;B k dm
Th
g (5)
where dm is the shortest relative distance between the robot and the obstacle. The set
V Oh represents velocities that would result in collision, occurring beyond the time horizon.
Figure 5 shows the modi ed V O, where the velocity set V Oh has been removed.
7
B
^
VB
VOB
V
... | |
path_planning/26453_8.txt | Indeed, the agent can wait when vel = 0. The duration
of the wait action is 1 time step. For the sake of brevity, we
ignore the agent’s orientation and motions that change it.
SIPP starts with expanding the initial search node
((A, vel = 0), [0, 5]) in which only the accelerating motion
is applicable. This results in g... | |
path_planning/1729881418787075icid_39.txt |
PDF
Help
| |
path_planning/documentrepidrep1typ_22.txt | Fujimura, K. and Samet, H. (1989a). A hierarchical strategy for path planning among
moving obstacles. IEEE Transaction on Robotics and Automation, 5(1):61{69.
Fujimura, K. and Samet, H. (1989b). Time-minimal paths among moving obstacles.
In IEEE International Conference on Robotics and Automation, pages 1110{1115,
Scot... | |
path_planning/26453_19.txt | 12337 | |
path_planning/1729881418787075icid_20.txt | Figure 14. Trajectories with different TVFs. (a) 3-D map. (b) Top view. TVFs: tangential velocity functions. 3-D: three-dimensional.
Simulations in Robot Studio
Collision avoidance in different velocity
This simulation is carried out in Robot Studio with two ABB robots,
. Point (330, −180, 520) and (320, 210, 590) are... | |
path_planning/p113_12.txt | 9: (x
0
0
, ..., x
0
k) ← (x0, ..., xi)
10: Block xi and Break;
11: Update best path with (x
0
0
, ..., x
0
k) if necessary
12: (x0, ..., xk) ← choose to stay in x0 or follow best path
13: return (x0, ..., xk)
Note that, H(xi) returns infinity if xi is blocked and xgoal has not
been changed, i.e. xi is already visited ... | |
path_planning/PMC10708786_66.txt | Go to:
Conflicts of Interest
The authors declare no conflict of interest. | |
path_planning/PMC10708786_62.txt | Go to:
Author Contributions
Conceptualization, T.W., Y.C. and X.W.; methodology, T.W. and Q.W.; software, Q.W. and T.W.; validation, Q.W., X.W. and T.W.; data curation, Q.W.; writing—original draft preparation, Q.W.; writing—review and editing, Q.W. and Y.C.; project administration, Y.C. and X.W.; funding acquisition, ... | |
path_planning/documentrepidrep1typ_4.txt | Velocity of A A
V
A
Figure 1: The robot and a moving obstacle.
2. The Velocity Obstacle
In this section, we present the concept of Velocity Obstacle (VO) for a single and
multiple obstacles. For simplicity, we restrict our analysis to circular robots and obstacles,
thus considering a planar problem with no rotations. T... | |
path_planning/PMC10708786_31.txt | An external file that holds a picture, illustration, etc.
Object name is sensors-23-09617-g007.jpg
Figure 7
Simulation results when localized oscillations occur. (a) Obstacle-avoidance path for manipulators when localized oscillations occur; (b) The angle change of joints 1–3; (c) The angle change of joints 4–6. | |
path_planning/PMC10708786_42.txt | An external file that holds a picture, illustration, etc.
Object name is sensors-23-09617-g011.jpg
Figure 11
The real-time collected data. (a) The position of right-hand joint; (b) The distance between the manipulator and the obstacle. | |
path_planning/PMC10708786_32.txt | The manipulator moves from the initial position towards the target position. When encountering an obstacle on the way, under the effect of the obstacle repulsion speed, the combined speed-controlled robot smoothly deviates to the left and reaches the target position, which verifies the correctness of the repulsive pote... |
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