Trap Space Trajectory Planning for Unmanned Aerial Vehicle Based on Human-RRT Algorithm

Abstract

Aiming at the problem of UAV trajectory planning, the aircraft can fall into the trap space easily and fall into an infinite loop or spend a lot of time planning the route. In this paper, Trap space trajectory planning based on Human-RRT algorithm is proposed. First introduced the concept of trap space and its impact on the trajectory planning of UAV, and briefly introduced the existing RRT (Rapidly exploring Random Tree) algorithm and the lack of response trap space. On the basis of the standard RRT, this paper avoids the need of repeated searching for the RRT algorithm by setting the virtual target points manually, and at the same time sets the fast convergence strategy to improve the efficiency of the algorithm. Finally, the optimal trajectory planning is achieved by extending and optimizing the nodes and deleting the redundant nodes. Compared with the existing methods, the H-RRT algorithm can verify that the H-RRT algorithm can effectively solve the trap space planning problem, improve the efficiency of the trajectory planning and optimize the trajectory performance.