An Improved Bi-RRT* Algorithm for UAV Path Planning

Unmanned Aerial Vehicle (UAV) path planning is a critical task that directly affects the efficiency and safety of UAV operations in various fields. This paper proposes an improved Bi-RRT* algorithm to enhance the efficiency of path planning while ensuring feasible and smooth navigation. The proposed algorithm integrates a goal point switching mechanism and dynamic ellipsoid sampling with a goal-bias strategy to improve the sampling process, an enhanced expansion strategy combining goal-point-oriented growth with an improved artificial potential field (APF) mechanism to accelerate convergence, and a density-aware node rewiring strategy with greedy pruning and B-spline smoothing to generate an optimized path. Extensive simulations in five complex 3D environments demonstrate that the proposed method significantly outperforms five existing RRT* variants, achieving shorter path lengths, fewer iterations, reduced computation time, and a lower number of nodes while maintaining high robustness and adaptability in obstacle-dense environments.