A Multi-UAV Cooperative Path Planning Method Based on an Improved Dung Beetle Optimizer

Complex terrain imposes highly nonlinear constraints on multi-UAV cooperative path planning. It also leads to path redundancy and inefficient information sharing among UAVs. To address these issues, this paper proposes a cooperative path-planning method based on an improved Dung Beetle Optimizer (DBO) under dynamic constraints. First, a multi-objective path optimization model is constructed. The model considers detection efficiency, path smoothness, obstacle-avoidance cost, and energy consumption. State-space equations are introduced to constrain the path generation process. This ensures that the planned paths satisfy UAV dynamic constraints and remain feasible for control implementation. Second, to reduce the risk of premature convergence in the standard DBO, the Latin hypercube sampling-Orchestrated Dung Beetle Optimizer (LO-DBO) is proposed. Latin hypercube sampling is used to generate a uniformly distributed initial population in the solution space, thereby enhancing the diversity of the initial population. A dynamic convergence factor is then introduced to balance global exploration in the early stage and local exploitation in the later stage. In addition, a cooperative search mechanism is designed to help individuals escape local optima and improve the algorithm’s global search capability. Experiments on CEC2017 benchmark functions and multi-UAV cooperative path-planning scenarios show that LO-DBO achieves higher convergence accuracy, better solution quality, and greater planning efficiency than several representative optimization algorithms. Compared with other improved DBO, LO-DBO can coordinate multi-UAV resources more effectively. It also enables safe obstacle avoidance and efficient full-coverage inspection operations in complex terrain scenarios.