Enhancing Path Planning in UAVs: an Improved Goal-Biased RRT Algorithm for Complex 3D Environments

This paper addresses the issues of high randomness and low search efficiency in the traditional Rapidly-exploring Random Tree (RRT) algorith <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$m$</tex> when applied to high-dimensional spaces and complex environments. An improved algorithm is proposed, which incorporates a goal-biased strategy to direct sampling points towards the goal, enhancing the efficiency of path generation. Additionally, fixed sample points that point towards the goal are generated within the exploration space, reducing search randomness. The Euclidean heuristic function is used to calculate the cost of sample points within the exploration space, selecting the point with the minimum cost to form the path, thereby optimizing path quality. Experimental results demonstrate that the improved algorithm outperforms the traditional RRT algorithm in terms of path length, generation time, and the number of explored nodes. This study provides an efficient and reliable solution for path planning in complex three-dimensional environments for unmanned aerial vehicles (UAVs), improving the overall efficiency and reliability of UAV mission execution.