基于势场法的无人车局部动态避障路径规划算法

翟丽; 张雪莹; 张闲; 王承平

doi:10.15918/j.tbit1001-0645.2021.333

基于势场法的无人车局部动态避障路径规划算法

doi: 10.15918/j.tbit1001-0645.2021.333

翟丽^1,*, ,,
张雪莹^1,,
张闲²,
王承平²

1.
北京理工大学机械与车辆学院, 北京　100081
2.
电动车辆国家工程研究中心, 北京　100081

基金项目: 国家重点研发项目（2017YFB0102400）

详细信息

作者简介:
张雪莹：翟丽（1973—），女，教授，E-mail：zhaili26@bit.edu.cn.

通讯作者:
张雪莹（1997—），女，硕士生，E-mail：zhangxueying_jlu@163.com.

中图分类号: TP242.6
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- 被引次数: 0
出版历程
- 收稿日期: 2021-11-30
- 刊出日期: 2022-08-17

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

1.
School of Machinery Engineering, Beijing Institute of Technology, Beijing 100081, China
2.
National Engineering Research Center of Electric Vehicles, Beijing 100081, China

摘要

摘要: 根据无人车动态实时避障的需求，提出一种基于人工势场法的局部避障路径规划算法，通过改进势场环境及势场力来解决传统势场法局部极小值和目标不可达的问题. 考虑车辆碰撞安全性，对侧向动态障碍物和同向动态障碍物工况进行分析，采用动态窗口法进行实时动态避障规划. 同时为保证规划路径的平滑性和可跟踪性，采用贝塞尔曲线对轨迹进行平滑处理. 最后,在CarSim和Matlab/Simulink 联合仿真平台下，对所提出的控制算法进行验证. 仿真结果表明了规划算法的避障有效性、安全性以及可跟踪性.
- 人工势场法 /
- 无人车 /
- 局部路径规划 /
- 动态避障
Abstract: 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.
- artificial potential field method /
- unmanned vehicles /
- local path planning /
- dynamic obstacle avoidance

HTML全文

图 1 局部极小值工况

Figure 1. The local minimum value case

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图 2 Matlab仿真图

Figure 2. Matlab simulation diagram

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图 3 局部极小值工况

Figure 3. local minimum value case when obstacle locates behind target

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图 4 Matlab仿真图

Figure 4. Matlab simulation diagram for obstacle locates behind target

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图 5 局部极小值工况

Figure 5. local minimum value case when 2 obstacles locate between the vehicle and target

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图 6 Matlab仿真图

Figure 6. Matlab simulation diagram for 2 obstacles locate between the vehicle and target

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图 7 目标不可达工况

Figure 7. Target unreachable condition

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图 8 Matlab仿真图

Figure 8. Matlab simulation diagram for target unreachable condition

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图 9 动态规划示意图

Figure 9. Dynamic programming diagram

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图 10 路径平滑示意图

Figure 10. Path smoothing diagram

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图 11 侧向避障过程示意图

Figure 11. Schematic diagram of the lateral obstacle avoidance process

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图 12 同向避障过程示意图

Figure 12. Schematic diagram of the same-direction obstacle avoidance process

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图 13 复杂避障过程示意图

Figure 13. Schematic diagram of complex obstacle avoidance process

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图 14 十字路口虚拟道路环境

Figure 14. Crossroads virtual road environment

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图 15 传统人工势场法仿真结果（发生碰撞）

Figure 15. Simulation results of traditional artificial potential field method (Collision)

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图 16 改进人工势场法仿真结果

Figure 16. Improved artificial potential field method simulation results

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表 1 算法仿真实验数据

Table 1. Algorithm simulation experimental data

算法仿真时间/s 仿真步长/s 规划时间/s 规划步长/ms

传统势场法 10.50 0.05 1.3728 6.537
改进势场法 24.75 3.8844 7.847

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