Path Planning of a Mobile Robot Based on the Improved RRT Algorithm
Source abstract
Abstract
RRT (rapidly exploring random tree) is a sampling-based planning algorithm that has been widely used due to its simple structure and fast speed. However, the RRT algorithm has several issues such as low planning efficiency, high randomness, and poor path quality. To address these issues, this paper proposes a novel method, the adjustable probability and sampling area and the Dijkstra optimization-based RRT algorithm (APSD-RRT), which consists of the following two modules: an APS-RRT planner and an optimizer. The APS-RRT planner can reach a feasible path quickly using the proposed adjustable probability and sampling area strategies, while the optimizer applies the Dijkstra algorithm to prune and improve the initial path generated by the APS-RRT planner and smooths-out sharp nodes based on the interpolation method. A series of experiments are conducted to demonstrate that our method can perform much better in terms of the balance between the computing cost and performance.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Robotic Path Planning Algorithms · Vehicle License Plate Recognition · Software Testing and Debugging Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Science, Hubei University of Technology, Wuhan 430068, China
References (26)
Show source-provided references
1. Methodology for path planning and optimization of mobile robots: A review10.1016/j.procs.2018.07.018 ↗
2. Sánchez-Ibáñez, J.R., Pérez-del-Pulgar, C.J., and García-Cerezo, A. (2021). Path planning for autonomous mobile robots: A review. Sensors, 21.10.3390/s21237898 ↗
3. Gul, F., Mir, I., Abualigah, L., Sumari, P., and Forestiero, A. (2021). A Consolidated Review of Path Planning and Optimization Techniques: Technical Perspectives and Future Directions. Electronics, 10.10.3390/electronics10182250 ↗
4. A formal basis for the heuristic determination of minimum cost paths10.1109/TSSC.1968.300136 ↗
5. Real-time obstacle avoidance for manipulators and mobile robots10.1177/027836498600500106 ↗
6. Genetic algorithms for the traveling salesman problem10.1007/BF02125403 ↗
7. A mobile robot path planning using genetic algorithm in static environment10.3844/jcssp.2008.341.344 ↗
8. Probabilistic roadmaps for path planning in high-dimensional configuration spaces10.1109/70.508439 ↗
9. LaValle, S.M. (1998). Rapidly-Exploring Random Trees: A New Tool for Path Planning, TR 98-11, Department of Computer Science, Iowa State University.
10. Sampling-based algorithms for optimal motion planning10.1177/0278364911406761 ↗
11. Gammell, J.D., Srinivasa, S.S., and Barfoot, T.D. (2014, January 14–18). Informed RRT: Optimal sampling-based path planning focused via direct sampling of an admissible ellipsoidal heuristic. Proceedings of the 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, Chicago, IL, USA.10.1109/IROS.2014.6942976 ↗
12. RRT*-SMART: A rapid convergence implementation of RRT10.5772/56718 ↗
13. Wu, Z., Meng, Z., Zhao, W., and Wu, Z. (2021). Fast-RRT: A RRT-based optimal path finding method. Appl. Sci., 11.10.3390/app112411777 ↗
14. APSO: An A*-PSO hybrid algorithm for mobile robot path planning10.1109/ACCESS.2023.3272223 ↗
15. RRT-based path planning for an intelligent litchi-picking manipulator10.1016/j.compag.2018.10.031 ↗
16. Kuffner, J.J., and LaValle, S.M. (2000, January 24–28). RRT-connect: An efficient approach to single-query path planning. Proceedings of the 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No. 00CH37065), San Francisco, CA, USA.
17. Urmson, C., and Simmons, R. (2003, January 27–31). Approaches for heuristically biasing RRT growth. Proceedings of the 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No. 03CH37453), Las Vegas, NV, USA.
18. Research on robot motion planning based on RRT algorithm with nonholonomic constraints10.1007/s11063-021-10536-4 ↗
19. Chai, Q., and Wang, Y. (2022). RJ-RRT: Improved RRT for Path Planning in Narrow Passages. Appl. Sci., 12.10.3390/app122312033 ↗
20. Huang, S. (2021, January 18–20). Path planning based on mixed algorithm of RRT and artificial potential field method. Proceedings of the 2021 4th International Conference on Intelligent Robotics and Control Engineering (IRCE), Lanzhou, China.10.1109/IRCE53649.2021.9570910 ↗
21. Optimal path planning using generalized voronoi graph and multiple potential functions10.1109/TIE.2019.2962425 ↗
22. Chen, Y., Cheng, C., Zhang, Y., Li, X., and Sun, L. (2022). A neural network-based navigation approach for autonomous mobile robot systems. Appl. Sci., 12.10.3390/app12157796 ↗
23. Ichter, B., Harrison, J., and Pavone, M. (2018, January 21–25). Learning sampling distributions for robot motion planning. Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia.10.1109/ICRA.2018.8460730 ↗
24. Neural network approximation based near-optimal motion planning with kinodynamic constraints using RRT10.1109/TIE.2018.2816000 ↗
25. Neural RRT*: Learning-based optimal path planning10.1109/TASE.2020.2976560 ↗
26. EGNN: Graph structure learning based on evolutionary computation helps more in graph neural networks10.1016/j.asoc.2023.110040 ↗