Potential-Field-RRT: A Path-Planning Algorithm for UAVs Based on Potential-Field-Oriented Greedy Strategy to Extend Random Tree

This paper proposes a random tree algorithm based on potential field oriented greedy strategy for the path planning of unmanned aerial vehicles (UAVs). Potential-field-RRT (PF-RRT) discards defect traditional artificial (APF) algorithms that are prone to fall into local errors, and introduces fields as an aid expansion process trees. It reasonably triggers principle strength descending gradient optimization, accelerating better region reducing search time. Compared with other optimization improve sampling method reduce time tree, PF-RRT takes full advantage without limiting arbitrariness expansion. Secondly, construction is triangle inequality root node new quality in one iteration. Simulation experiments comparison show has advantages fast acquisition high-quality initial solutions optimal convergence process. original algorithm, obtaining solution using can loss by 20% 70% about 25%. In addition, feasibility UAV demonstrated actual flight test at end experiment.