关键词: 无人机, 航迹规划, 蜣螂优化算法, 佳点集, 自适应t分布

Abstract: Aiming at the problems of poor stability and insufficient optimization ability of the traditional dung beetle optimization algorithm (DBO) in unmanned aerial vehicle (UAV) trajectory planning in complex environments, a dung beetle optimization algorithm with composite population strategy and adaptive t-distribution disturbance (GOTDBO) is proposed. Based on the DBO algorithm, the GOTDBO algorithm combines the composite population initialization strategy, the adaptive disturbance global exploration strategy and the adaptive t-distribution disturbance strategy, effectively enhancing the global exploration and local exploitation capabilities of the algorithm and improving the convergence speed of the algorithm. By constructing an objective function that comprehensively considers the total flight length, corner curvature and maximum flight direction change, and introducing the penalty function method to handle no-fly zones and other constraints in the path, the smoothness and safety of the trajectory are further optimized. Experimental results show that in terms of the flight range, the GOTDBO algorithm performs excellently in different scenarios of complex environments, with remarkable maximum flight range. It can plan compact and efficient trajectories, improving the endurance economy. In terms of threat avoidance, it approaches the threat area the least number of times, providing higher flight safety. In terms of altitude control, the degree of altitude deviation is low, enabling stable and accurate altitude control. Although the path smoothness is comparable to that of other algorithms, the GOTDBO algorithm has significant advantages in multiple core indicators. It is energy - efficient, safe, and reliable in UAV trajectory planning, and has high application value and broad prospects.

Key words: UAV, trajectory planning, dung beetle optimization algorithm, good point set, adaptive t-distribution