Research on Improvement of Rapidly Exploring Random Tree Algorithm in Robot Path Planning

被引：1
|
作者：
Wang S. [1 ,2 ]
Duan R. [1 ,2 ]
Liao Y. [1 ,2 ]
机构：
[1] Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, Xi'an Jiaotong University, Xi'an
[2] Shaanxi Key Laboratory of Mechanical Product Quality Assurance and Diagnostics, Xi'an Jiaotong University, Xi'an
来源：
Hsi-An Chiao Tung Ta Hsueh/Journal of Xi'an Jiaotong University | 2022年 / 56卷 / 07期
关键词：
adaptive expansion step; dynamic weight coefficient; pruning; RRT algorithm; smoothing processing;
D O I：
10.7652/xjtuxb202207001
中图分类号：
学科分类号：
摘要：
In order to solve the problems of the basic rapidly exploring random tree (RRT) algorithm in path planning, such as large randomness, excessive redundant nodes, oscillation around the target point and long planned paths, an improved RRT algorithm was proposed. Firstly, the algorithm applies the target bias strategy and introduces dynamic weight coefficient to make the tree expand to the target point as much as possible while avoiding obstacles instantly. Next, the oscillation of the tree near the target point is reduced by using the adaptive expansion step. Finally, the path is pruned and smoothed by cubic B-spline curves. Simulation results show that compared with the basic RRT algorithm, the improved RRT algorithm effectively reduces the number of redundant nodes, the planned path is shorter by 19.56%, and the planning time is greatly reduced by 54.08%, which effectively improves the efficiency of path planning. © 2022 Xi'an Jiaotong University. All rights reserved.
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页码：1 / 8
页数：7
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  • [1] CHEN Lerui, CAO Jianfu, HU Heyu, Fault mechanism analysis for heavy-load industrial robot with nonlinear spectrum, Journal of Xi'an Jiaotong University, 55, 5, pp. 92-101, (2021)
  • [2] JIANG Yichen, CHEN Gang, Particle swarm optimization sliding mode steering control of driving robot integrated with adaptive curve preview [J], Journal of Xi'an Jiaotong University, 55, 3, pp. 175-185, (2021)
  • [3] JU Xingquan, MAO Xintao, ZAI Shougang, Et al., Development and application analysis of cooperative robot in aerospace field [J], Aerospace China, 2, pp. 63-67, (2022)
  • [4] MU Xiaoqi, ZHANG Xiaodong, XU Haipeng, Et al., Dynamic stability analysis models for human-robot systems of elderly-assistant robots helping elderly prevent falling, Journal of Xi'an Jiaotong University, 54, 8, pp. 67-76, (2020)
  • [5] AJEIL F H, IBRAHEEM I K, AZAR A T, Et al., Grid-based mobile robot path planning using aging-based ant colony optimization algorithm in static and dynamic environments [J], Sensors, 20, 7, (2020)
  • [6] ZHONG Xunyu, TIAN Jun, HU Huosheng, Et al., Hybrid path planning based on safe A* algorithm and adaptive window approach for mobile robot in large-scale dynamic environment [J], Journal of Intelligent & Robotic Systems, 99, 1, pp. 65-77, (2020)
  • [7] WEI Tong, LONG Chen, Path planning for mobile robot based on improved genetic algorithm, Journal of Beijing University of Aeronautics and Astronautics, 46, 4, pp. 703-711, (2020)
  • [8] ZHAO Xiao, WANG Zheng, HUANG Chengkan, Et al., Mobile robot path planning based on an improved A* algorithm [J], Robot, 40, 6, pp. 903-910, (2018)
  • [9] LIANG Ye, WANG Lindong, Applying genetic algorithm and ant colony optimization algorithm into marine investigation path planning model, Soft Computing, 24, 11, pp. 8199-8210, (2020)
  • [10] LIU Guijie, LIU Peng, MU Weilei, Et al., A path optimization algorithm for AUV using an improved ant colony algorithm with optimal energy consumption [J], Journal of Xi'an Jiaotong University, 50, 10, pp. 93-98, (2016)
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