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:

The traditional dung beetle optimization algorithm (DBO) exhibits the poor stability and insufficient optimization ability in the trajectory planning of unmanned aerial vehicles (UAVs) in complex environments,DBO Optimization Algorithm with Group-based Optimization and Adaptive t-Distribution (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.The smoothness and safety of the trajectory are further optimized 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,When the GOTDBO algorithm is applied to route planning in scenarios with different complex environments,it can plan compact and efficient routes,performs excellently in terms of maximum range,and effectively improves the economy of endurance.In terms of threat avoidance,the trajectory planned by the GOTDBO algorithm has the least number of approaches to threat areas,thus ensuring higher flight safety.In terms of altitude control,the degree of altitude deviation is low,enabling stable and accurate altitude control.Although the GOTDBO algorithm is comparable to other algorithms in the trajectory smoothness,it has significant advantages in multiple core indicators.It is energy-saving and efficient,safe,and reliable in UAV trajectory planning,and has high application value and broad prospects.

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