UAV Trajectory Planning Algorithms in Uncertain Environments

UAV trajectory planning has attracted significant attention in recent years as one of the important prerequisites for ensuring the safe completion of UAV missions. However, in the event of a complex and uncertain mission environment, the real-time collision-free trajectory planning for UAVs will undoubtedly present a significant challenge. In this paper, for the problem of real-time UAV trajectory planning in uncertain environments, we propose a UAV trajectory planning method based on an improved artificial potential field(IAPF) algorithm, which solves problem of goals non-reachable with obstacles nearby (GNRON) and the local-minima problem, and is able to avoid complex obstacles in real time, including moving, sudden and concave obstacles. Firstly, to address the GNRON problem, we propose the concept of distance repulsion, which is designed to minimize the repulsive potential field at the location of goal point. Besides, with regard to the local-minima problem, we propose the concept of rotational repulsion, which enables the UAV to circumvent concave obstacles. In addition, compared to the conventional artificial potential field(APF) method which only considers the distance between the UAV and the obstacle, the IAPF algorithm takes the velocity difference between the two into account and proposes a velocity potential field for more effective avoidance of moving obstacles. Finally, the simulation experiment verifies that, compared with the traditional APF method, the algorithm proposed in this paper is capable of addressing the problem of GNRON and local-minima, and has the feasibility and effectiveness for the UAV trajectory planning problem in uncertain environments.