UAV Obstacle Avoidance Trajectory Planning Based on Improved Zebra Optimization Algorithm

An upgraded Zebra Optimization Algorithm (GSZOA) is presented to solve the issues of slow convergence speed and easy fall into local optimum of Zebra Optimization Algorithm (ZOA) in obstacle avoidance trajectory planning of Unmanned aerial vehicle (UAV). In order to solve the problem of slow convergence, chaotic mapping is invoked for ZOA to enhance the population diversity and accelerate the convergence speed. The golden sine algorithm is integrated to improve the zebra position update formula, which effectively coordinates the global search and local mining ability. Aiming at the problem of easily falling into local optimization, a cycle mutation strategy is introduced to reduce the probability of this problem. Finally, the improved algorithm is compared with the other five algorithms on the classical benchmark function to verify the superiority of the GSZOA algorithm. It is also applied to UAV obstacle avoidance trajectory planning, and the simulation results show that show that the GSZOA algorithm has better optimization seeking ability and faster convergence.

1 Introduction

The advancement of aviation technology and automation technology has led to the widespread use of UAVs in both military and civil sectors, such as agricultural plant protection, logistics and distribution, and fire rescue, by virtue of their maneuverability, low cost, and ease of operation [1–3]. The dynamic and complex low-altitude environment poses a serious threat to the safety of autonomous UAV flight technology, therefore, finding a safe path to reach the target point is crucial for ensuring the safe operation of UAVs and enhancing operational efficiency [4].