Piecewise-potential-field-based path planning method for fixed-wing UAV formation

Author:

Fang Yuxuan,Yao Yiping,Zhu Feng,Chen Kai

Abstract

AbstractThe multi-UAV path planning method based on artificial potential field (APF) has the advantage of rapid processing speed and the ability to deal with dynamic obstacles, though some problems remain—such as a lack of consideration of the initial heading constraint of the UAVs, making it easy to fall into a local minimum trap, and the path not being sufficiently smooth. Consequently, a fixed-wing UAV formation path planning method based on piecewise potential field (PPF) is proposed, where the problem of UAV formation flight path planning in different states can be solved by suitable design of the PPF function. Firstly, the potential field vector can be used to represent the potential field functions of obstacles and target points to meet the kinematic constraints of the UAV. Secondly, the local minimum region can be detected, the additional potential field vector being set to break away from this region. Finally, the change rules of the potential field vector of a UAV in the formation reconstruction scene can be designed, a smooth formation flight track being assured by adjusting the corresponding speed of each UAV track point. Considering the path planning of a five-UAV formation as an example, we conducted simulation experiments. The results showed that—compared with the existing methods based on APF—the results obtained using the PPF-based method considered the initial heading limits of the UAVs, the planned path being considerably smoother. Moreover, the proposed method could plan multiple UAV tracks, satisfying the known constraints without conflict in complex scenarios.

Funder

National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Keeping Safe Distance from Obstacles for Autonomous Vehicles by Genetic Algorithms;18th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2023);2023

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