An Anti-Disturbance Resilience Enhanced Algorithm for UAV 3D Route Planning

• Published in: Sensors (Basel, Switzerland) Switzerland), 2022-03, Vol.22 (6), p.2151
• Main Authors: Xu, Zhining, Zhang, Long, Ma, Xiaoshan, Liu, Yang, Yang, Lin, Yang, Feng
• Format: Article
• Language: English
• Subjects: A algorithm; Aerospace environments; Algorithms; artificial potential fields algorithm; Controllers; Efficiency; Local optimization; Monte Carlo simulation; Planning; Potential fields; Resilience; Risk assessment; Route planning; three-dimensional route planning; Unmanned aerial vehicles; Wind
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22062151

Considering that the actual operating environment of UAV is complex and easily disturbed by the space environment of urban buildings, the RoutE Planning Algorithm of Resilience Enhancement (REPARE) for UAV 3D route planning based on the A* algorithm and artificial potential fields algorithm is carried out in a targeted manner. First of all, in order to ensure the safety of the UAV design, we focus on the capabilities of the UAV body and build a risk identification, assessment, and modeling method such that the mission control parameters of the UAV can be determined. Then, the three-dimensional route planning algorithm based on the artificial potential fields algorithm is used to ensure the safe operation of the UAV online and in real time. At the same time, by adjusting the discriminant coefficient of potential risks in real time to deal with time-varying random disturbance encountered by the UAV, the resilience of the UAV 3D flight route planning can be improved. Finally, the effectiveness of the algorithm is verified by the simulation. The simulation results show that the REPARE algorithm can effectively solve the traditional route planning algorithm's insufficiency in anti-disturbance. It is safer than a traditional A* route planning algorithm, and its running time is shorter than that of the traditional artificial potential field route planning algorithm. It solves the problems of local optimization, enhances the UAV's ability to tolerate general uncertain disturbances, and eventually improves resilience of the system.