Local Dynamic Obstacle Avoidance Path Planning Algorithm for Unmanned Vehicles Based on Potential Field Method

被引：0
|
作者：
Zhai L. [1 ]
Zhang X. [1 ]
Zhang X. [1 ]
Wang C. [2 ]
机构：
[1] School of Machinery Engineering, Beijing Institute of Technology, Beijing
[2] National Engineering Research Center of Electric Vehicles, Beijing
来源：
Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology | 2022年 / 42卷 / 07期
关键词：
artificial potential field method; dynamic obstacle avoidance; local path planning; unmanned vehicles;
D O I：
10.15918/j.tbit1001-0645.2021.333
中图分类号：
学科分类号：
摘要：
To achieve dynamic real-time obstacle avoidance of unmanned vehicles, a local obstacle avoidance path planning algorithm was proposed based on artificial potential field method. Firstly, improving the potential field environment and the potential field force were arranged in the new method to solve the local minimum value and target unreachable problem of the traditional potential field method. And then, considering the safety of vehicle collisions, the working conditions of lateral dynamic obstacles and the same direction dynamic obstacles were analyzed, and a dynamic window method was used for real-time dynamic obstacle avoidance planning. To ensure path flatness and traceability, a BSL curve was used to smoothing the planned path. Finally, the proposed control algorithm was verified under the co-simulation platform of CarSim and Matlab/Simulink. The simulation results show the effectiveness, safety and traceability of the planning algorithm for obstacle avoidance. © 2022 Beijing Institute of Technology. All rights reserved.
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页码：696 / 705
页数：9
相关论文
共 10 条
  • [1] GARROTE L, PREMEBIDA C, SILVA M, Et al., An RRT-based navigation approach for mobile robot sand automated vehicles[C], Proceedings of IEEE International Conference on Industrial Informatics, pp. 326-331, (2014)
  • [2] ZHANG Jing, HE You, PENG Yingning, Et al., Collaborative game path planning based on neural network and artificial potential field[J], Aeronautical Journal, 40, 3, pp. 228-238, (2019)
  • [3] Bing LU, Guofa LI, YU Huilong, Et al., A daptive potential field-based path planning for complex autonomous driving scenarios[J], IEEE Access, 8, (2020)
  • [4] YADOLLAH R, AMIR K, BAKHTIAR L, Et al., A potential field-based model predictive path-planning controller for autonomous road vehicles[J], IEEE Transactions on Intelligent Transportation Systems, 18, 5, pp. 1255-1267, (2017)
  • [5] GUO Xiaopeng, Research on path planning algorithm based on improved artificial potential field method, (2017)
  • [6] CHEN Xiangru, Research on vehicle travel path modeling based on artificial potential field theory, (2021)
  • [7] ZHAO Ming, ZHENG Zeyu, PAN Yijun, Et al., Path planning method of mobile robot based on improved artificial potential field method[J], Application Research of Computers, 37, pp. 66-72, (2020)
  • [8] HAN Yuhong, Research on obstacle avoidance path planning for unmanned vehicles based on improved artificial potential field method, (2020)
  • [9] CHEN Guanxing, ZHANG Anhua, HUA Hong, Et al., Research on path planning of improved artificial potential field method in unknown environment[J], Mechanics and Electronics, 39, 5, (2021)
  • [10] ZHANG Yong, HUANG Linxiong, ZHANG Yu, Et al., Research on intelligent vehicle dynamic obstacle avoidance based on artificial potential field method[J], Transportation, 5, pp. 87-92, (2020)
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