Efficient UAV Motion Planning Strategy in Dynamic Environments
Shuai Liu, Hao Chen, Xiaoming Mai, Na Dong · 2024
As Unmanned Aerial Vehicles(UAVs) continue to be increasingly utilized in surveillance, search and rescue, and other areas, motion planning in complex environments becomes exceptionally crucial. This paper proposes an efficient UAV motion planning framework for safe flight in complex environments. Firstly, the Enhanced A* path planning algorithm is used to find a safe and feasible initial path in a discrete grid space. Then, the Optimal Boundary Value Problem(OBVP) model is utilized to optimize the trajectory to generate a safe and smooth trajectory. During the UAV's operation, the trajectory is adjusted by selecting velocity or replanning strategies based on observed dynamic obstacles to ensure flight safety. Experimental results demonstrate that the proposed motion planning framework can generate high-quality trajectories quickly, ensuring safe flight in dynamic and complex environments. The motion planning framework exhibits great potential and applicability for practical applications.