Enhanced Artificial Potential Field-based Moving Obstacle Avoidance for UAV in Three-Dimensional Environment
Yuanlin Luo, Xiaoyan Huang, Chengfang Wu, Supeng Leng · 2020
The obstacle avoidance problem plays an essential role in the issues of unmanned aerial vehicle(UAV). An enhanced artificial potential field(EAPF) model is proposed in this paper, considering the three-dimensional vector field and focusing on avoiding the moving obstacle. In the proposed EAPF model, a steering force is designed to change the orientation of UAV's velocity, which can ensure that UAV gets rid of the local minimum situation. And a restoring force considering a slowing down strategy is designed to drag UAV back on the given trajectory. Moreover, an EAPF-based obstacle avoidance(EAPF-OA) algorithm is also proposed to help UAV avoid moving obstacle in flight. Simulation results demonstrate that the proposed EAPFOA algorithm significantly outperforms the traditional APF-based algorithm on the performance in terms of smoothness, safety and energy consumption.