AHRRT: An Enhanced Rapidly-Exploring Random Tree Algorithm with Heuristic Search for UAV Urban Path Planning

Aim: ing at improving convergence rate and path feasibility of traditional Rapidly-exploring Random Tree algorithm (RRT), this paper proposed an enhanced RRT with heuristic search (AHRRT). The AHRRT cooperated four strategies: Adaptive step size, Target Bias, Attraction-repulsion strategy and a pruning operation. First, Adaptive step size strategy helps reduce collisions caused by large step sizes and improve the feasibility and safety of the path. Second, Target Bias strategy enhances tree expansion efficiency by directing the search tree toward the target, reducing computational overhead. Third, Attraction-repulsion strategy helps improve obstacle avoidance ability, making the path smoother and avoiding oscillations or invalid sampling caused by large step sizes. Finally, the pruning strategy can further optimize the initial path by removing redundant nodes. Simulation experiments in 2D and 3D validate the effectiveness of AHRRT, demonstrating significant improvements over traditional RRT, RRT variants, A* and ACO algorithm in terms of path quality, convergence speed, and computational efficiency, especially in complex urban environments, enhancing its practical applicability and feasibility in urban UAV path planning.