Abstract: Abstract: UAV trajectory planning is an important challenge for UAVs to perform complex missions， which involves finding optimal paths in dynamic and uncertain environments， and the global search and local development problems in UAV trajectory planning have always been the focus of research， based on which this paper designs a multi-strategy hybrid optimization algorithm combined with the dung beetle optimization algorithm to improve the algorithm. In order to improve the global search and local exploitation ability of the algorithm， this paper adopts Latin hypercubic sampling to generate the initial population， adaptive variable spiral strategy to adjust the search direction， and optimal domain perturbation strategy to improve the overall convergence performance. In order to avoid the algorithm falling into the local optimum， Brownian motion and Levy flight strategy are added to dynamically adjust the algorithm development. Experimental results show that the MSIDBO proposed in this paper significantly improves the efficiency and accuracy of trajectory planning based on the guarantee of path length， smoothness and obstacle avoidance performance. The algorithm demonstrates superior global search capability and local optimization capability in uncertainty and dynamic environments， and is applicable to a variety of complex mission scenarios.

Key words: Key words: UAV, trajectory planning, dung beetle optimization algorithm, optimal domain perturbation, adaptive spiral search, nonlinear control factor