Research on Risk Avoidance Path Planning for Unmanned Vehicle Based on Genetic Algorithm and Bezier Curve
Gaoyang Xie, Liqing Fang, Xujun Su, Deqing Guo, Ziyuan Qi, Yanan Li, Jinli Che · Drones · 2025
In the process of autonomous driving, the identification and avoidance of risk points is of great significance for the safe and efficient navigation of unmanned vehicles. To solve this problem, a new strategy combining a Bezier curve and the genetic algorithm is proposed in this paper. Firstly, in order to make the curvature of the path continuous, the design uses two symmetric Bezier curves as the path curves. Then, in order to describe the influence range of risk points more accurately, the artificial potential field model is used to describe the risk points, and the integral of the curve path in the potential field is calculated. Finally, an improved genetic algorithm is designed. The limit of the path and the risk value of the path are added to the fitness function, and the selection operator and the mutation operator are improved. It can be seen from the results of simulation and real vehicle experiments that this new strategy can provide an effective path planning method to avoid risk points.