Improved Potential Field-Based Collision Avoidance Control for Autonomous Vehicles

Deng, Weishun; Zhang, Kaijiong; Zhang, Xi; Yu, Fan; Lin, Mengqi; Shang, Shiliang; Guo, Mengge

doi:10.4271/2020-01-0123

Cited by 1 publication

(1 citation statement)

References 26 publications

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“…By adding the angle function and fuzzy control idea to the traditional artificial potential field method to control the stability of the path, the path planned by this method can quickly jump out of the local minima trap and remain stable [30]. By discretizing the obstacle boundary in the road, establishing the road boundary potential field and adding relevant constraints to improve the safety of obstacle avoidance, and at the same time add random escape force, so as to jump out of the local minimum [31,32]. In the established safety potential field model, a unified function of vehicle potential field based on Lennard-Jones potential is established.…”

Section: Introductionmentioning

confidence: 99%

Research on Path Planning Algorithm for Driverless Vehicles

Pei

Zhang

2022

Mathematics

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In a complex environment, although the artificial potential field (APF) method of improving the repulsion function solves the defect of local minimum, the planned path has an oscillation phenomenon which cannot meet the vehicle motion. In order to improve the efficiency of path planning and solve the oscillation phenomenon existing in the improved artificial potential field method planning path. This paper proposes to combine the improved artificial potential field method with the rapidly exploring random tree (RRT) algorithm to plan the path. First, the improved artificial potential field method is combined with the RRT algorithm, and the obstacle avoidance method of the RRT algorithm is used to solve the path oscillation; The vehicle kinematics model is then established, and under the premise of ensuring the safety of the vehicle, a model predictive control (MPC) trajectory tracking controller with constraints is designed to verify whether the path planned by the combination of the two algorithms is optimal and conforms to the vehicle motion. Finally, the feasibility of the method is verified in simulation. The simulation results show that the method can effectively solve the problem of path oscillation and can plan the optimal path according to different environments and vehicle motion.

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Section: Introductionmentioning

confidence: 99%