Unmanned Vehicle Path Planning and Tracking Control Based on Improved Artificial Potential Field Method

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

Abstract

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

CLC Number:

TP391.9

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.

Figures/Tables 14

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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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