Mobile Robot Path Planning based on Improved RRT Algorithm
DOI:
https://doi.org/10.6919/ICJE.202603_12(3).0018Keywords:
Path Planning; RRT; RRT*; Path Optimization; Random Sampling; Path Smoothing.Abstract
To address the limitations of the Rapidly-exploring Random Tree(RRT) algorithm in mobile robot path planning, specifically issues such as a large sampling range, long search times, and insufficient path smoothness, an improved RRT algorithm is proposed. This algorithm introduces a dynamic sampling strategy to reduce random sampling redundancy and accelerate convergence. Furthermore, redundant waypoints are eliminated to enhance path quality while reducing memory consumption. Finally, Bézier curves are employed to optimize path smoothness, replacing traditional linear connections with continuous curves for smoother turns. Simulation experiments demonstrate that the improved RRT algorithm achieves average performance and stability enhancements of 48% and 50%, respectively, with a 16% reduction in path length and superior path quality.
Downloads
References
[1] MA G J,DUAN Y L,LI M Z,et al. A probability smoothing Bi-RRT path planning algorithm for indoor robot[J]. Future generation computer systems,2023,143(5):349-360.
[2] SHI K,WU P,LIU M. Research on path planning method of forging handling robot based on combined strategy[C]//Proceedings of the 2021 IEEE international conference on industrial technology and engineering management. Beijing, China: IEEE, 2021: 123-128.
[3] ZHANG H,TAO Y,ZHU W. Global path planning of unmanned surface vehicle based on improved A-star algorithm[J]. Sensors,2023,23(14):6647.
[4] ALSHAMMREI S,BOUBAKER S. Improved dijkstra algorithm for mobile robot path planning and obstacle avoidance[J]. Computers, materials & continua,2022,72(3):5939-5954.
[5] DIJKSTRA E W. A note on two problems in connexion with graphs[J]. Numerische mathematik,1959, 1(1):269-271.
[6] TAMAS H,BALAZS N,PETER G. Design of a low-complexity graph-based motion-planning algorithm for autonomous vehicles[J]. Applied sciences-basel,2020,10(21):7716.
[7] WANG J K,CHI W Z,LI C M,et al. Neural RRT*:learning-based optimal path planning[J]. IEEE transactions on automation science and engineering,2020,17(4),1-11.
[8] LAVALLE S M. Rapidly-exploring random trees: A new tool for path planning[J]. The annual research report,1998,98(11):1-24.
[9] WEI K,REN B. A met hod on dynamic path planning for robotic manipulator autonomous obstacle avoidance based on an improved RRT algorithm[J]. Sensors,2018,18(2):67-81.
[10] SHI Y Y,LI Q Q,BU S Q,et al. Research on intelligent vehicle path planning based on rapidly-exploring random tree[J]. Mathematical problems in engineering, 2020,20(9):1-10.
[11] HSU D,LATOMBE J C,MOTWANI R. Path planning in expansive configuration spaces[C]//Proceedings of the 1997 IEEE international conference on robotics and automation. Albuquerque, NM, USA:IEEE,1997:2719-2726.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 International Core Journal of Engineering
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
