基于改进人工势场法的无人车路径规划与跟踪控制

doi:10.16182/j.issn1004731x.joss.23-0768

摘要/Abstract

摘要：

针对无人车在换道超车复杂场景下躲避动态障碍物的工况，提出一种基于改进人工势场法的路径规划算法和基于模型预测控制器的跟踪控制策略。引入安全椭圆理论及预测距离概念调整势场影响区域，通过加入速度势场改变势场函数，解决车辆躲避动态障碍物的问题。以线性三自由度车辆动力学模型为基础，建立包含势场环境的模型预测控制器。通过CarSim/Simulink联合仿真验证算法的有效性。结果表明：该算法可有效解决传统势场法缺陷，提出的换道超车避障控制器对不同车速下的避障车辆跟踪效果良好，最大质心侧偏角均小于1°，前轮转角均在［-10°~10°］合理范围内，车辆能较好完成换道超车操作并且保持稳定性和安全性。

关键词: 无人车, 改进人工势场法, 路径规划, 跟踪控制, 模型预测控制器

Abstract:

A path planning algorithm based on improved artificial potential field method and a tracking control strategy based on model predictive controller are proposed for the unmanned vehicle avoiding dynamic obstacles in the complex scene of lane changing and overtaking. The theory of safety ellipse and the concept of prediction distance are introduced to adjust the influence region of potential field. By adding velocity potential field to change potential field function, the problem of vehicle avoiding dynamic obstacles is solved. Based on the linear three-degree-of-freedom vehicle dynamics model, a model prediction controller including potential field environment is established. The effectiveness of the algorithm is verified by CarSim/Simulink co-simulation. The results show that the proposed algorithm can effectively solve the defects of traditional potential field method and the proposed lane change overtaking obstacle avoidance controller has good tracking effect on the obstacle avoidance vehicles at different speeds,in which the maximum side deflection angle of the center of mass is less than 1°, and the front wheel angles are within the reasonable range [-10°~10°], The vehicle can better complete the lane change overtaking operation and maintain stability and safety.

Key words: unmanned vehicle, improved artificial potential field method, path planning, tracking control, model predictive control

中图分类号:

TP391.9

郭明皓,姬鹏,黄海威 . 基于改进人工势场法的无人车路径规划与跟踪控制[J]. 系统仿真学报, 2024, 36(10): 2423-2434.

Guo Minghao,Ji Peng,Huang Haiwei . Unmanned Vehicle Path Planning and Tracking Control Based on Improved Artificial Potential Field Method[J]. Journal of System Simulation, 2024, 36(10): 2423-2434.

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参考文献 20

1	李骏, 万文星, 郝三强, 等. 复杂路况下无人驾驶路径跟踪模型预测控制研究[J]. 汽车工程, 2022, 44(5): 664-674.
	Li Jun, Wan Wenxing, Hao Sanqiang, et al. Research on Model Predictive Control of Autonomous Vehicle Path Tracking Under Complex Road Condition[J]. Automotive Engineering, 2022, 44(5): 664-674.
2	牛秦玉, 李博. 基于模拟退火遗传算法的全向AGV路径规划[J/OL]. 计算机集成制造系统. (2022-05-17) [2023-04-16]. .
	Niu Qinyu, Li Bo. Omnidirectional AGV Path Planning Based on Simulated Annealing Genetic Algorithm[J/OL]. Computer Integrated Manufacturing Systems. (2022-05-17) [2023-04-16]. .
3	辛鹏, 王艳辉, 刘晓立, 等. 优化改进RRT和人工势场法的路径规划算法[J]. 计算机集成制造系统, 2023, 29(9): 2899-2907.
	Xin Peng, Wang Yanhui, Liu Xiaoli, et al. Path Planning Algorithm Based on Optimize and Improve RRT and Artificial Potential Field[J]. Computer Integrated Manufacturing Systems, 2023, 29(9): 2899-2907.
4	余翔, 姜陈, 段思睿, 等. 改进A^*算法和人工势场法的路径规划[J]. 系统仿真学报, 2024, 36(3): 782-794.
	Yu Xiang, Jiang Chen, Duan Sirui, et al. Path Planning for Improvement of A^* Algorithm and Artificial Potential Field Method[J]. Journal of System Simulation, 2024, 36(3): 782-794.
5	李二超, 王玉华. 改进人工势场法的移动机器人避障轨迹研究[J]. 计算机工程与应用, 2022, 58(6): 296-304.
	Li Erchao, Wang Yuhua. Research on Obstacle Avoidance Trajectory of Mobile Robot Based on Improved Artificial Potential Field[J]. Computer Engineering and Applications, 2022, 58(6): 296-304.
6	余政. 基于改进人工势场法的智能汽车超车轨迹规划策略[J]. 农业装备与车辆工程, 2021, 59(9): 64-68.
	Yu Zheng. Intelligent Vehicle Overtaking Trajectory Planning Strategy Based on Improved Artificial Potential Field Method[J]. Agricultural Equipment & Vehicle Engineering, 2021, 59(9): 64-68.
7	程志, 张志安, 李金芝, 等. 改进人工势场法的移动机器人路径规划[J]. 计算机工程与应用, 2019, 55(23): 29-34.
	Cheng Zhi, Zhang Zhian, Li Jinzhi, et al. Mobile Robots Path Planning Based on Improved Artificial Potential Field[J]. Computer Engineering and Applications, 2019, 55(23): 29-34.
8	宋洁, 张小俊, 张云龙. 智能汽车换道避障路径规划与跟踪控制[J]. 现代制造工程, 2022(10): 43-50, 20.
	Song Jie, Zhang Xiaojun, Zhang Yunlong. Path Planning and Tracking Control for Intelligent Vehicles Changing Lanes to Avoid Obstacles[J]. Modern Manufacturing Engineering, 2022(10): 43-50, 20.
9	Wang Ruijie, Yin Guodong, Jin Xianjian. Robust Adaptive Sliding Mode Control for Nonlinear Four-wheel Steering Autonomous Vehicles Path Tracking Systems[C]//2016 IEEE 8th International Power Electronics and Motion Control Conference (IPEMC-ECCE Asia). Piscataway: IEEE, 2016: 2999-3006.
10	邓海鹏, 麻斌, 赵海光, 等. 自主驾驶车辆紧急避障的路径规划与轨迹跟踪控制[J]. 兵工学报, 2020, 41(3): 585-594.
	Deng Haipeng, Ma Bin, Zhao Haiguang, et al. Path Planning and Tracking Control of Autonomous Vehicle for Obstacle Avoidance[J]. Acta Armamentarii, 2020, 41(3): 585-594.
11	Rasekhipour Y, Khajepour A, Chen S K, et al. A Potential Field-based Model Predictive Path-planning Controller for Autonomous Road Vehicles[J]. IEEE Transactions on Intelligent Transportation Systems, 2017, 18(5): 1255-1267.
12	陈天德, 黄炎焱, 沈炜. 基于虚拟障碍物法的无震荡航路规划[J]. 兵工学报, 2019, 40(3): 651-658.
	Chen Tiande, Huang Yanyan, Shen Wei. Non-oscillation Path Planning Based on Virtual Obstacle Method[J]. Acta Armamentarii, 2019, 40(3): 651-658.
13	朱伟达. 基于改进型人工势场法的车辆避障路径规划研究[D]. 镇江: 江苏大学, 2017.
	Zhu Weida. Research on Path Planning and Obstacle Avoidance Method for Vehicle Based on Improved Artificial Potential Field[D]. Zhenjiang: Jiangsu University, 2017.
14	肖婷. 智能车在复杂环境下的路径规划与跟随控制算法研究[D]. 长春: 吉林大学, 2019.
	Xiao Ting. Research on the Path Planning and the Tracking Control Algorithm of the Intelligent Vehicle in Complex Environment[D]. Changchun: Jilin University, 2019.
15	安林芳. 智能车辆自动驾驶路径规划研究[D]. 长沙: 湖南大学, 2017.
	An Linfang. Research on the Path Planning for Automatic Driving of Intelligent Vehicle[D]. Changsha: Hunan University, 2017.
16	Pacejka H B, Bert C W. Tyre Models for Vehicle Dynamics Analysis[J]. Journal of Applied Mechanics, 1995, 62(1): 268.
17	余志生. 汽车理论[M]. 6版. 北京: 机械工业出版社, 2019.
	Yu Zhisheng. Automobile Theory[M]. 6th ed. Beijing: China Machine Press, 2019.
18	唐志荣, 冀杰, 吴明阳, 等. 基于改进人工势场法的车辆路径规划与跟踪[J]. 西南大学学报(自然科学版), 2018, 40(6): 174-182.
	Tang Zhirong, Ji Jie, Wu Mingyang, et al. Vehicles Path Planning and Tracking Based on an Improved Artificial Potential Field Method[J]. Journal of Southwest University(Natural Science Edition), 2018, 40(6): 174-182.
19	Guo Lie, Ge Pingshu, Sun Dachuan, et al. Adaptive Cruise Control Based on Model Predictive Control with Constraints Softening[J]. Applied Sciences, 2020, 10(5): 1635.
20	Thrun S, Montemerlo M, Dahlkamp H, et al. Stanley: The Robot that Won the DARPA Grand Challenge[J]. Journal of Robotic Systems, 2006, 23(9): 661-692.

参数	传统人工势场法	改进人工势场法
路径长度/m	102.49	99.37
迭代次数	196	174
运行时间/s	12.23	10.64

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