id stringlengths 15 54 | text stringlengths 3 133k | title stringclasses 1
value |
|---|---|---|
rosdep_install/12287_22.txt | [ KenWhitesell ](https://forum.djangoproject.com/u/KenWhitesell) February 22,
2022, 2:12pm 4 | |
rosdep_install/12287_20.txt | [ AK-8420 ](https://forum.djangoproject.com/u/AK-8420) February 22, 2022,
1:43pm 3 | |
rosdep_install/367_177.txt | **[ NikolausDemmel ](/NikolausDemmel) ** commented Apr 4, 2018 | |
rosdep_install/12287_1.txt | # [ 【solved】django-admin causes ModuleNotFoundError of setting files
](/t/solved-django-admin-causes-modulenotfounderror-of-setting-files/12287) | |
rosdep_install/367_21.txt | Search or jump to... | |
rosdep_install/iamgettinganerrorimp_131.txt | To prevent this error, make sure all necessary modules and packages are
installed and correctly spelled, and regularly update your code and
environment to ensure compatibility with any changes in the module or package. | |
rosdep_install/367_163.txt | **[ NikolausDemmel ](/NikolausDemmel) ** commented Apr 4, 2018 | |
rosdep_install/iamgettinganerrorimp_152.txt | 1K | |
rosdep_install/12287_28.txt | hi [ @KenWhitesell ](/u/kenwhitesell) I getting same issue and tried the
solution provided.
Im trying to run daphne for websocket but its failing and throwing same error
like above with my project directory name. im attaching daphne error log for
more info below. | |
rosdep_install/367_66.txt | **[ mitchellwills ](/mitchellwills) ** commented Jan 6, 2015 | |
rosdep_install/12287_33.txt | Hello Sir,
I have tried all possible solutions suggested by you and other guys in
STACKOVERFLOW website. I am still facing the same error. Here is the detailed
error link that i have posted on Stackoverflow [ <frozen importlib._bootstrap>
error in python django - Stack Overflow
](https://stackoverflow.com/questions/7... | |
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rosdep_install/367_42.txt | * [ Code ](/ros-infrastructure/rosdep)
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rosdep_install/12287_9.txt | directory structure: | |
rosdep_install/iamgettinganerrorimp_178.txt | [ __ Back ](javascript:) | |
rosdep_install/367_56.txt | # rosdep fails if pip is not installed #367 | |
rosdep_install/15447_34.txt | mm have you tried emptying ` api.py ` , put ` FOO = 'bar' ` in it (and only
that). | |
rosdep_install/367_168.txt | Copy link | |
rosdep_install/15447_33.txt | [ plopidou ](https://forum.djangoproject.com/u/plopidou) August 16, 2022,
9:07am 8 | |
rosdep_install/15447_7.txt |
import requests
from models import Company
from dateutil import parser
class GetRequest:
# some code...
| |
rosdep_install/12287_12.txt | Runnning the following command, the contents of setting_local.py was output
successfully. | |
rosdep_install/367_27.txt | We read every piece of feedback, and take your input very seriously. | |
rosdep_install/iamgettinganerrorimp_138.txt | 5 | |
path_planning/26453_12.txt | is the earliest time step the agent can arrive at n, that is tl
.
The h-value is independent of the time interval and is defned for graph vertices. It estimates the travel time from the
vertex to the goal (e.g., it equals the straight-line distance
between the vertices divided by the maximum speed of the
agent). As in ... | |
path_planning/PMC10708786_70.txt |
National Library of Medicine
8600 Rockville Pike
Bethesda, MD 20894 | |
path_planning/PMC10708786_1.txt | Search PMC Full-Text Archive
Search PMC Full-Text Archive
Search in PMC
Advanced Search User Guide
Journal List Sensors (Basel) PMC10708786
As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National In... | |
path_planning/1729881418787075icid_33.txt | Crossref
ISI
Google Scholar
32. Wu PW. Grasping the object with collision avoidance of wheeled mobile manipulator in dynamic environments. In: 2013 26th IEEE/RSJ international conference on intelligent robots and systems, Tokyo, Japan, 3–8 November 2013, pp. 5300–5305. IEEE. | |
path_planning/PMC10708786_54.txt | After verifying the feasibility of obstacle avoidance in the simulation environment, experimental verification was conducted. According to the simulation results, the person’s right-hand was moved according to the above positions, and then the simulation speed was sent to the manipulator for path-planning experiments. ... | |
path_planning/26453_14.txt | ′
) that is
less than f(n). According to Lemma 1, n
′
A∗−T S is located
at the bottom of a SIPP-IP state n
′
SIP P −IP in OPEN, i.e.,
n
′
A∗−T S.t = n
′
SIP P −IP .tl
. As in SIPP-IP, the states are ordered by the values f(nSIP P −IP ) = n.tl + h(n.v) that is
equal to the f-value of the bottom A*-TS state f(nA∗−T S),
t... | |
path_planning/documentrepidrep1typ_2.txt | time dimension to the robot's con guration space, assuming bounded velocities and
known trajectories of the obstacles (Reif and Sharir, 1985; Erdmann and Lozano-Perez,
1987; Fujimura and Samet, 1989a). Reif and Sharir (1985) solved the planar problem
for a polygonal robot among many moving polygonal obstacles, by searc... | |
path_planning/p113_7.txt | (1)
In (1), LineTo(xgoal) samples randomly in the line between xgoal
and the node of the tree that is closest to xgoal. Uniform(X ) samples the environment uniformly. Finally, Ellipsis(x0, xgoal) samples
inside an ellipsis so that the path from x0 to xgoal is inside it. Same
as Informed RRT* we need to sample the envir... | |
path_planning/documentrepidrep1typ_5.txt | V
A
-VB
CCA,B
V
A,B
A
^
B
^
V
B
λ
f
λ
r
λ
AB
Figure 2: The Relative Velocity vA;B and the Collision Cone CCA;B.
equivalently, by translating the collision cone CCA;B by vB, as shown in Figure 3. The
Velocity Obstacle V O is then de ned as:
V O = CCA;B vB (2)
B
^
VB
VOB
V
A
VB
A
^
Figure 3: The velocity obstacle V OB.... | |
path_planning/1729881418787075icid_17.txt | Figure 11. Performance of the different AVFs. AVFs: attractive velocity functions.
The robot begins to move toward the target at the same speed. When approaching the goal with AVF1, the robot moves backward and forward again and again, which makes the robot oscillate near the goal. AVF2 and the proposed function make t... | |
path_planning/26453_17.txt | imposed a limit of 100, 000, 000 of generated nodes for all
algorithms. For each instance, we tracked whether the algorithm produced a solution and recorded the algorithm’s
runtime and the solution cost.
Fig. 4 presents the Success Rate (SR) plots, where SR is
the ratio of the successfully solved instances to all of th... | |
path_planning/PMC10708786_22.txt | To begin with, it is necessary to distinguish the oscillation section. In this study, the manipulator receives velocity data with an interval of 0.5 s to avoid any interference with real-time processing speed. To ensure efficient processing, a three-step over-planning method is employed, in which the manipulator calcul... | |
path_planning/p113_9.txt | inside Xnear (lines 2-6). One should note that the computation of ci
is related to the length of the path from x0 to xi. Thus, cost(xi)
needs to compute ci whenever cj for any intermediate node in the
path to xi is changed. Therefore, when cost(xi) is called, it will
recompute ci from xi up to x0 if a new node has been... | |
path_planning/1729881418787075icid_9.txt | Figure 5. Tangential velocity field. (a) Static obstacle. (b) Moving obstacle:
inside of η. (c) Moving obstacle:
outside of η.
For stationary obstacle, if η > 180°, the direction of
is tangent to the circle OP in the opposite side of η as shown in Figure 5(a). If η < 180°,
is in the side of η. If η = 180°, ... | |
path_planning/26453_11.txt | ′
in succ do
15 if n
′
in CLOSED or in OPEN then
16 continue
17 f(n
′
) ← n
′
.tl + h(n
′
.v)
18 Add n
′
to OPEN
19 return ϕ
Function getSuccessors(n, G(V, E), SI):
20 SUCC = ϕ
21 for each e = (n.v, v′
) in available motions do
22 intrvls = projectIntervals(n, e, SI)
23 if v
′
.vel = 0 then
24 for each ti in intrvls do... | |
path_planning/PMC10708786_55.txt | An external file that holds a picture, illustration, etc.
Object name is sensors-23-09617-g016.jpg
Figure 16
Real-time obstacle-avoidance experiment of manipulator. | |
path_planning/PMC10708786_18.txt | 𝑉𝑟𝑒𝑝𝑥⎧⎩⎨⎪⎪⎪⎪0,𝑑𝑜𝑏>𝑑𝑓𝛽⋅𝑠𝑖𝑛[(1−𝑑𝑜𝑏−𝑑𝑠𝑑𝑓−𝑑𝑠).𝜋2].𝜈𝑟𝑒𝑝𝑥𝑑2𝑜𝑏,𝑑𝑠<𝑑𝑜𝑏≤𝑑𝑓𝛽⋅(𝜈𝑟𝑒𝑝𝑥/𝑑2𝑜𝑏),𝑑𝑜𝑏≤𝑑𝑠
(3)
The distance between the obstacle and the closest control point of the manipulator is denoted by 𝑑𝑜𝑏, while 𝛽 represents the intensity factor of the repulsive velocity. The... | |
path_planning/p113_13.txt | and β in (1) are 0.1 and 2, correspondingly. Also, we set ro, rb
and rg of Fig 2 to 10m, 1.5m and 0.5m, respectively. The limited
time for Tree Expansion-and-Rewiring is set to 10 ms and we set
k in Algorithm 1 to 100. In order to have a fair comparison with
117
CL-RRT, we had a disturbance free model and used C-PBP as... | |
path_planning/PMC10708786_71.txt | Web Policies
FOIA
HHS Vulnerability Disclosure | |
path_planning/PMC10708786_2.txt | Keywords: human-robot collaboration, velocity potential field method, real-time obstacle avoidance, trajectory planning
Go to:
1. Introduction
Industrial robots are becoming more and more prevalent. The interaction between robots and humans has gone through four stages: competition, coexistence, collaboration, and co-w... | |
path_planning/PMC10708786_10.txt | 2.2. Improved Velocity Potential Field
The attraction velocity potential field function is defined as a vector that extends from the end point of the robot to the target, and points in the direction of attraction velocity. The magnitude of the attraction velocity is determined by the distance between the target and the... | |
path_planning/PMC10708786_43.txt | In the process of human-machine collaboration, the safety distance between the manipulator and the human body is very important. Setting a reasonable safety distance can protect the safety of the operator very well. In order to ensure the safety of the human-machine collaboration experiment, in this paper, the safety d... | |
path_planning/1729881418787075icid_13.txt | Figure 8. kp's membership function. | |
path_planning/1729881418787075icid_14.txt | Figure 9. kd's membership function.
The following fuzzy control rules shown in Table 1 are designed.
Table 1. Fuzzy control rules.
IF v VS SL M FA VF
THEN
VSL SM ME BG VB
VS: Very Slow; SL: Slow; M: Medium; FA: Fast; VF: Very Fast; BG: Big; VB: Very Big; VSL: Very Small; SM: Small; ME: Medium.
Then RVF can be expresse... | |
path_planning/PMC10708786_50.txt | As shown in Figure 14, the manipulator approaches the target in a straight line when avoiding obstacles correctly, then moves towards the virtual target point in the lateral and posterior direction after approaching the obstacle, and finally moves towards the target in a straight line after leaving the repulsive potent... | |
path_planning/26453_3.txt | constraints.
Next, we mention only the works that to some extent
deal with taking the agent’s kinematic and/or kinodynamic
constraints into account. (Ma et al. 2019) introduced SIPPwRT that allowed for the planning with different velocities. Still, acceleration actions and effects were not considered in this work. Simi... | |
path_planning/26453_2.txt | 12330
instantaneous acceleration, then the constructed plan, shown
in red, will actually lead to the collision (if it is applied with
real kinodynamic constraints) as shown by the dashed red
line. A straightforward modifcation of SIPP that takes accelerating/decelerating actions into account is to apply only
dynamicall... | |
path_planning/documentrepidrep1typ_21.txt | velocity space, that we call the Velocity Obstacle. It represents the colliding velocities
of the robot with a given obstascle, that moves at a given velocity. General trajectories
are approximated by a sequence of piecewise constant segments. Robot dynamics are
considered by restricting the set of potential avoidance ... | |
path_planning/PMC10708786_45.txt | An external file that holds a picture, illustration, etc.
Object name is sensors-23-09617-g012.jpg
Figure 12
The path-planning result of the manipulator. (a) The result of MATLAB simulation; (b) The result of manipulator operation. | |
path_planning/1729881418787075icid_11.txt | Figure 6. Virtual spring damping system.
Based on the spring damping system, the repulsive velocity vr, within the distance of influence dr, is defined as
(10)
where d0 is the closest distance between the robot and the obstacle
, and
is the first derivative of
.
The coefficient
is positively correlated to the v... | |
path_planning/PMC10708786_46.txt | An external file that holds a picture, illustration, etc.
Object name is sensors-23-09617-g013a.jpg
An external file that holds a picture, illustration, etc.
Object name is sensors-23-09617-g013b.jpg
Figure 13
Comparison between simulation and experimental velocity of the manipulator. (a) Simulation velocity of manipul... | |
path_planning/PMC10708786_65.txt | Go to:
Data Availability Statement
Data sharing isn’t applicable to this article as no datasets were generated during the current study. | |
path_planning/p113_15.txt | LUDERS, B. D., KARAMAN, S., FRAZZOLI, E., AND HOW,
J. P. 2010. Bounds on tracking error using closed-loop rapidlyexploring random trees. In American Control Conference, IEEE.
OTTE, M., AND FRAZZOLI, E. 2015. RRTX: Real-time motion
planning/replanning for environments with unpredictable obstacles. In Algorithmic Foundat... | |
path_planning/PMC10708786_37.txt | An external file that holds a picture, illustration, etc.
Object name is sensors-23-09617-g009a.jpg
An external file that holds a picture, illustration, etc.
Object name is sensors-23-09617-g009b.jpg
Figure 9
The results with the virtual target point improved. (a) The path of manipulator obstacle avoidance; (b) Joint a... | |
path_planning/1729881418787075icid_29.txt | Google Scholar
24. Li GH, Yamashita A, Asama H, et al. An efficient improved artificial potential field based regression search method for robot path planning. In: 2012 IEEE international conference on mechatronics and automation, Chengdu, China, 5–8 August 2012, pp. 1227–1232. IEEE.
GO TO REFERENCE
Google Scholar
25. ... | |
path_planning/documentrepidrep1typ_28.txt | reducing the size of some of the V OBj
. | |
path_planning/1729881418787075icid_2.txt | Information for
International Journal of Advanced Robotic Systems
Impact Factor: 2.3 / 5-Year Impact Factor: 2.3 | |
path_planning/PMC10708786_13.txt | By introducing trigonometric functions, the manipulator can accelerate the attraction speed from zero to the maximum speed allowed while moving away from the target point. Once the maximum speed is achieved, the manipulator moves at a uniform speed for a specified distance. | |
path_planning/documentrepidrep1typ_11.txt | r
-VB
P
T
f
Figure 10: Trajectory tangent to B.
11 | |
path_planning/26453_16.txt | SIPP-IP SIPP1 SIPP2 A*
Figure 4: Success Rates of the evaluated algorithms.
room empty warehouse random Sydney 0
1
2
%
45
13 55 125
535
25
10 40 73
420
72
19 108
208
1381 Runtime relative to A*
SIPP-IP SIPP1 SIPP2
Figure 5: Runtimes of SIPP-IP, SIPP1 and SIPP2 compared
to A*. Absolute values (in ms) are shown above the... | |
path_planning/PMC10708786_36.txt | 3.1.3. Simulation after Setting Local Oscillation Processing Strategy
Under the given conditions of the experiment, an additional radius “𝑟𝑠=0.05 m“ beyond the buffer zone was set. With this setting, while keeping the obstacle positions unchanged, Figure 9 illustrates the results of the simulation, using Figure 7a as... | |
path_planning/26453_10.txt | suggested, depending on how the collision detection mechanism is specifed2
.
To demonstrate how projecting helps in solving instances
that were unsolvable for standard SIPP, recall the example
depicted in Fig. 2. When expanding the start node SIPPIP projects, the time interval of A, which is [0, 5] (coincides with the ... | |
path_planning/PMC10708786_64.txt | Go to:
Informed Consent Statement
Not applicable. | |
path_planning/documentrepidrep1typ_24.txt | center of Bb
and Q.
The magnitude of the velocity vA tangent to Bb
in P is found using the velocity
obstacle V O, whose boundary f represents all front tangent velocities. Then, the intersection R of the trajectory line t with f gives the magnitude of vA. The magnitude of
the corresponding relative velocity follows f... | |
path_planning/documentrepidrep1typ_23.txt | proven by contradiction. Then a geometric construction is used to de ne the map between
the tangent velocities and the corresponding points on @Bf .
To prove the necessary condition, let us assume that there exists a velocity vA tangent
to obstacle Bb
such that its corresponding relative velocity, vA;B is not on the bo... | |
path_planning/PMC10708786_67.txt | Go to:
Footnotes
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any id... | |
path_planning/PMC10708786_68.txt | Go to:
References
1. Robla-Gomez S., Becerra V., Llata J., Gonzalez-Sarabia E., Torre-Ferrero C., Perez-Oria J. Working Together: A Review on Safe Human-Robot Collaboration in Industrial Environments. IEEE Access. 2017;5:26754–26773. doi: 10.1109/ACCESS.2017.2773127. [CrossRef] [Google Scholar]
2. Hopko S., Khurana R.,... | |
path_planning/PMC10708786_58.txt | Table 1 compares the experimental results of the traditional VPF with the improved VPF proposed in this paper. From Table 1, it can be seen that the improved VPF proposed in this paper eliminates the local oscillation problem related to the traditional VPF algorithm and accomplishes the obstacle-avoidance task with a g... | |
path_planning/PMC10708786_12.txt | 𝑉𝑎𝑡𝑡=⎧⎩⎨⎪⎪𝑠𝑖𝑔𝑛(𝜈attx)⋅𝛼⋅𝜆𝑥⋅𝑑max⋅𝑠𝑖𝑛(𝜋(𝑑0−𝑑)2(𝑑0−𝑑max)),𝑑∈[𝑑0,𝑑max]𝑠𝑖𝑔𝑛(𝜈𝑎𝑡𝑡𝑥)⋅𝛼⋅𝜆𝑥⋅𝑑∗𝑚𝑎𝑥,𝑑∈[𝑑∗𝑚𝑎𝑥,𝑑1]𝑠𝑖𝑔𝑛(𝜈𝑎𝑡𝑥)⋅𝛼⋅𝜆𝑥⋅𝑑max⋅𝑠𝑖𝑛(𝜋𝑑2𝑑1),𝑑∈[𝑑1,0]
(2)
In the x-direction, 𝑠𝑖𝑔𝑛(𝑣𝑎𝑡𝑡𝑥) represents the positive and negative values of velocity, 𝛼 represe... | |
path_planning/PMC10708786_20.txt | An external file that holds a picture, illustration, etc.
Object name is sensors-23-09617-g004.jpg
Figure 4
Motion path of the manipulator during local oscillation. | |
path_planning/PMC10708786_59.txt | Table 1
Comparison of experimental results between traditional VPF and improved VPF. | |
path_planning/1729881418787075icid_34.txt | Google Scholar
33. Huang JZ. Research on the working environment 3D information detection of the industrial robot based on binocular vision. Thesis, South China University of Technology, Guangzhou, China, 2017.
GO TO REFERENCE
Google Scholar
34. Zhai JM, Lianzhong LI, Peisen GUO, et al. Design and experiment on M2M2A o... | |
path_planning/PMC10708786_15.txt | An external file that holds a picture, illustration, etc.
Object name is sensors-23-09617-g002.jpg
Figure 2
Attraction and repulsive velocity curve model of x direction. (a) Attraction velocity; (b) Repulsive velocity. | |
path_planning/PMC10708786_52.txt | Figure 15b shows that the velocity profile of the manipulator is not smooth enough in the obstacle-avoidance sequence. Due to the setting of the virtual target point, the 2-axis and 3-axis of the manipulator need to steer for a short time, so the velocity change in the relevant axes at the beginning and end nodes of ob... | |
path_planning/documentrepidrep1typ_14.txt | this knowledge about maneuver types can be used by an intelligent vehicle in avoiding a
potentially dangerous obstacle: a car avoiding a big truck may choose a rear avoidance
maneuver, even though a front maneuver might also be feasible, for the sake of safety. A
procedure for computing the sets of avoidance velocities... | |
path_planning/documentrepidrep1typ_16.txt | VO
v
A
S
S
Sf
d
A r
Figure 14: a: TG strategy. b: MV strategy. c: ST strategy.
4.2. Heuristic Search
For on-line applications, the trajectory can be generated incrementally by expanding
only the node corresponding to the current robot position, and generating only one branch
per node, using some heuristics to choose am... | |
path_planning/PMC10708786_57.txt | An external file that holds a picture, illustration, etc.
Object name is sensors-23-09617-g017.jpg
Figure 17
Obstacle avoidance and local oscillation of manipulator. (a) Obstacle avoidance of manipulator; (b) Obstacle-avoidance local oscillation of manipulator. | |
path_planning/documentrepidrep1typ_6.txt | B
B
^
V
B
V
B
VOB
VOB
B
^
1
1
1
1
2
2
2
2
Figure 4: Velocity obstacles for B1 and B2.
where is the Minkowski vector sum operator.
The VO partitions the absolute velocities of A into avoiding and col liding velocities.
Selecting vA outside of V O would avoid collision with B, or:
A(t) \ B(t) = ; if vA(t) 62 V O(t) (3)... | |
path_planning/documentrepidrep1typ_13.txt | V
B
A
^
B
^
VOB
V
B
B
^
λB f
B r
B r
λ
B f λ
λ
Sff
S
fr S
rr
S
rf
V
A
1 1
1 1
2
1
2
2 2
2
Figure 12: Classi cation of the reachable avoidance velocities.
The properties of the RAV generated by multiple obstacles are summarized in the
following Theorem, whose proof is given in Appendix A.
Theorem 1: Given a robot A and ... | |
path_planning/26453_0.txt | Safe Interval Path Planning with Kinodynamic Constraints
Zain Alabedeen Ali1
, Konstatin Yakovlev2, 3
1 Moscow Institute of Physics and Technology, Moscow, Russia
2 Federal Research Center for Computer Science and Control of Russian Academy of Sciences, Moscow, Russia
3 AIRI, Moscow, Russia
ali.za@phystech.edu, yakovle... | |
path_planning/PMC10708786_61.txt | Go to:
Funding Statement
This research was funded by the National Natural Science Foundation of China (51105213) and the Demonstration and Guidance Special Project of Science and Technology for the Benefit of the People in Qingdao (22-3-7-smjk-11-nsh). | |
path_planning/1729881418787075icid_8.txt | Figure 4. The velocity potential fields. (a) Static obstacle. (b) Moving obstacle.
RVF
is formed around obstacles for avoiding collision when the robot approaches the obstacle to a certain distance, which is claimed as a radius of repulsive potential field dr, the smaller the distance is, the greater the repulsive ve... | |
path_planning/documentrepidrep1typ_8.txt | VB
VOB
V
A
VB
A
^
RAV
K
H
L
M
Figure 7: The reachable avoidance velocities RAV.
of feasible accelerations at time t, FA(t), is de ned as:
FA(t) = fx j x = f (x; x_ ; u); u 2 Ug (6)
where x is the position vector, f (x; x_ ; u) represents the robot dynamics, u is the vector
of the actuator e orts, and U is the set of ... | |
path_planning/26453_5.txt | Method
As our method relies on SIPP, which in turn relies on A*
with timesteps, we frst discuss the background and then
delve into the details of the suggested approach. We assume
that a reader is familiar with vanilla A* for static graphs.
Background
A* with time steps The straightforward way to solve the
considered p... |
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