Active obstacle avoidance method of autonomous vehicle based on improved artificial potential field

被引：23

作者：

Duan, Yijian ^{[1
]}

Yang, Changbo ^{[2
]}

Zhu, Jihong ^{[3
]}

Meng, Yanmei ^{[1
]}

Liu, Xin ^{[1
]}

机构：

[1] Guangxi Univ, Sch Mech Engn, Nanning 530004, Guangxi, Peoples R China

[2] Dongfeng Liuzhou Motor Co Ltd, Liuzhou, Guangxi, Peoples R China

[3] Tsinghua Univ, Dept Precis Instrument, Beijing, Peoples R China

来源：

INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS | 2022年 / 19卷 / 04期

关键词：

Autonomous vehicles; active obstacle avoidance; path planning; second virtual target potential field; local minimum point;

D O I：

10.1177/17298806221115984

中图分类号：

TP24 [机器人技术];

学科分类号：

080202 ; 1405 ;

摘要：

Aiming at the local minimum point problem in an artificial potential field based on a safe distance model, this article proposes an algorithm for active obstacle avoidance path planning and tracking of autonomous vehicles using an improved artificial potential field. First, a possible road operating condition in which the artificial potential field based on the safety-distance model falls into a local minimum point is studied. Subsequently, an improved artificial potential field method is proposed by introducing the second virtual target attraction potential field, which successfully overcomes the local minimum point problem. Second, a model for autonomous vehicle active obstacle avoidance path planning and tracking based on the improved artificial potential field is established. Finally, MATLAB/CarSim co-simulations were performed under the road conditions of constant- and variable-velocity obstacle vehicles. The simulation results demonstrate that the improved artificial potential field method can effectively solve the local minimum point problem of the artificial potential field based on the safe distance model. Additionally, the safety and stability of autonomous vehicle active obstacle avoidance are improved.

引用

页数：10