Collision-Free Path Planning Method for Robots Based on an Improved Rapidly-Exploring Random Tree Algorithm
Abstract
:Featured Application
Abstract
1. Introduction
Algorithm 1 Basic-RRT algorithm. |
1: $T\leftarrow $ InitTree(${q}_{start}$); |
2: for $i=$ 1 to n do |
3: ${q}_{rand}\leftarrow $ RandomSample(i); |
4: ${q}_{near}\leftarrow $ NearestNeighbor(${q}_{rand},T$); |
5: ${q}_{new}\leftarrow $ Extend(${q}_{rand},{q}_{near},\epsilon $); |
6: if CollisionFree(${q}_{near},{q}_{new}$) then |
7: AddNewNode($T,{q}_{new}$); |
8: end if |
9: if Distance(${q}_{new},{q}_{goal})<{\rho}_{min}$ then |
10: return T; |
11: end if |
12: end for |
13: return $Failed$; |
Algorithm 2 GoalBiasedSample() |
1: $num\leftarrow $ RandomNumber; RandomNumber $\in [0,1]$ |
2: if $num<$ k then |
3: ${q}_{rand}={q}_{goal}$; |
4: else |
5: ${q}_{rand}=$ RandomSample(); |
6: end if |
7: return ${q}_{rand}$; |
2. The NC-RRT Method
2.1. The Method of Gradually Changing the Sampling Area
Algorithm 3 CSA-RRT algorithm. |
1: $T\leftarrow $ InitTree(${q}_{start}$); |
2: $R\leftarrow $${D}_{farthest}$; |
3: for $i=$ 1 to n do |
4: ${q}_{rand}\leftarrow $ RandomSample(i); |
5: if Distance$({q}_{rand},{q}_{goal})>R$ then |
6: continue; |
7: end if |
8: ${q}_{near}\leftarrow $ NearestNeighbor(${q}_{rand},T$); |
9: ${q}_{new}\leftarrow $ Extend(${q}_{rand},{q}_{near},\epsilon $); |
10: if CollisionFree(${q}_{near},{q}_{new}$) then |
11: AddNewNode($T,{q}_{new}$); |
12: $R=$ Distance$({q}_{new},{q}_{goal})$; |
13: else |
14: $R=R+k\times \epsilon $; |
15: continue; |
16: end if |
17: if Distance(${q}_{new},{q}_{goal})<{\rho}_{min}$ then |
18: return T; |
19: end if |
20: end for |
21: return $Failed$; |
2.2. The Node Control Mechanism
Algorithm 4 NC-RRT algorithm. |
1: $T\leftarrow $ InitTree(${q}_{start}$); |
2: ${\Delta}_{T}\leftarrow $$\left\{\delta \left({q}_{start}\right)=0\right\}$; |
3: $R\leftarrow $${D}_{farthest}$; |
4: $control\leftarrow $ 1; |
5: for $i=$ 1 to n do |
6: ${q}_{rand}\leftarrow $ RandomSample(i); |
7: if Distance$({q}_{rand},{q}_{goal})>R$ then |
8: continue; |
9: end if |
10: ${T}_{ctrl}\leftarrow $ LessThanControl(${\Delta}_{T},control,T$); |
11: ${q}_{near}\leftarrow $ NearestNeighbor(${q}_{rand},{T}_{ctrl}$); |
12: ${q}_{new}\leftarrow $ Extend(${q}_{rand},{q}_{near},\epsilon $); |
13: if CollisionFree(${q}_{near},{q}_{new}$) then |
14: AddNewNode($T,{q}_{new}$); |
15: ${\Delta}_{T}\leftarrow $$\left\{\delta \left({q}_{new}\right)=0\right\}$; |
16: ${\Delta}_{T}\leftarrow $ BranchNodeUpdate(${\Delta}_{T},{q}_{new},T$); |
17: $R=$ Distance$({q}_{new},{q}_{goal})$; |
18: $control=1$; |
19: else |
20: $R=R+k\times \epsilon $; |
21: $control=$ c; (c $=2,3,...)$ |
22: continue; |
23: end if |
24: if Distance(${q}_{new},{q}_{goal})<{\rho}_{min}$ then |
25: return T; |
26: end if |
27: end for |
28: return $Failed$; |
3. Simulation and Analysis
3.1. Simulations in a Two-Dimensional Environment
3.2. Simulation of the 6-DOF Manipulator
4. Conclusions and Future Work
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Abbreviations
C-space | Configuration space |
RRT | Rapidly-exploring Random Tree |
PRM | Probabilistic Roadmap Method |
CSA-RRT | Gradually Changing the Sampling Area-RRT |
NC-RRT | Node Control-RRT |
DOF | Degree-Of-Freedom |
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Algorithm | Average Computational Time (s) | Average Number of Nodes in the Tree | Average Number of Collision Detections | Average Path Length | The Success Rate of the Algorithm |
---|---|---|---|---|---|
Basic-RRT | 0.463 | 844.520 | 38,026 | 954.369 | 96% |
CSA-RRT | 0.058 | 92.900 | 4860.1 | 915.982 | 100% |
NC-RRT | 0.055 | 82.860 | 3941.7 | 977.667 | 100% |
Algorithm | Average Computational Time (s) | Average Number of Nodes in the Tree | Average Number of Collision Detections | Average Path Length | The Success Rate of the Algorithm |
---|---|---|---|---|---|
Basic-RRT | 0.471 | 928.680 | 35,821 | 865.236 | 84% |
CSA-RRT | 0.118 | 122.909 | 9705.3 | 805.751 | 88% |
NC-RRT | 0.159 | 126.817 | 9214.6 | 992.595 | 88% |
Algorithm | Average Computational Time (s) | Average Number of Nodes in the Tree | Average Number of Collision Detections | Average Path Length | The Success Rate of the Algorithm |
---|---|---|---|---|---|
Basic-RRT | 0.550 | 1145.600 | 40,159 | 871.825 | 78% |
CSA-RRT | 0.106 | 172.732 | 8867.2 | 868.609 | 82% |
NC-RRT | 0.117 | 126.245 | 6746.4 | 942.615 | 98% |
Algorithm | Basic-RRT | CSA-RRT | NC-RRT |
---|---|---|---|
Average computational time (s) | 3.658 | 1.541 | 1.469 |
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Share and Cite
Wang, X.; Luo, X.; Han, B.; Chen, Y.; Liang, G.; Zheng, K. Collision-Free Path Planning Method for Robots Based on an Improved Rapidly-Exploring Random Tree Algorithm. Appl. Sci. 2020, 10, 1381. https://doi.org/10.3390/app10041381
Wang X, Luo X, Han B, Chen Y, Liang G, Zheng K. Collision-Free Path Planning Method for Robots Based on an Improved Rapidly-Exploring Random Tree Algorithm. Applied Sciences. 2020; 10(4):1381. https://doi.org/10.3390/app10041381
Chicago/Turabian StyleWang, Xinda, Xiao Luo, Baoling Han, Yuhan Chen, Guanhao Liang, and Kailin Zheng. 2020. "Collision-Free Path Planning Method for Robots Based on an Improved Rapidly-Exploring Random Tree Algorithm" Applied Sciences 10, no. 4: 1381. https://doi.org/10.3390/app10041381