The Control Method of Autonomous Flight Avoidance Barriers of UAVs in Confined Environments

Abstract

This paper proposes an improved 3D-Vector Field Histogram (3D-VFH) algorithm for autonomous flight and local obstacle avoidance of multi-rotor unmanned aerial vehicles (UAVs) in a confined environment. Firstly, the method employs a target point coordinate system based on polar coordinates to convert the point cloud data, considering that long-range point cloud information has no effect on local obstacle avoidance by UAVs. This enables UAVs to effectively utilize obstacle information for obstacle avoidance and improves the real-time performance of the algorithm. Secondly, a sliding window algorithm is used to estimate the optimal flight path of the UAV and implement obstacle avoidance control, thereby maintaining the attitude stability of the UAV during obstacle avoidance flight. Finally, experimental analysis is conducted, and the results show that the UAV has good attitude stability during obstacle avoidance flight, can autonomously follow the expected trajectory, and can avoid dynamic obstacles, achieving precise obstacle avoidance.

Obstacle Avoidance and Path Planning Methods for Autonomous Navigation of Mobile Robot

“…Initial tests have shown that the mobile robot can use the VHF to traverse very crowded obstacle courses at high average speeds, and can pass through narrow openings (e.g., doorways) and move through narrow corridors without oscillating [ 41 ]. In [ 45 ], an improved 3D-VFH algorithm is proposed for autonomous flight and local obstacle avoidance of multirotor UAVs in confined environments.…”