UAV 3D Path Planning Method Based on Multi-Strategy Improved Dung Beetle Algorithm

The objective of three-dimensional trajectory planning for UAVs is to efficiently navigate through obstacles while satisfying constraint conditions. To address the widespread application and computational complexity of UAV path planning, a multi-strategy improved dung beetle optimizer (MIDBO) algorithm is proposed to solve the three-dimensional path planning problem for UAVs under various scenarios and obstacle conditions. Firstly, addressing the shortcomings of the Dung Beetle Optimization (DBO) algorithm, such as low convergence accuracy and susceptibility to local optima, MIDBO improves the acceptance of both larvae and thief beetles to local and global optima. This allows for dynamic changes in their search capabilities based on their own abilities, thereby improving population quality while maintaining the good search capability of high-fitness individuals. Secondly, by integrating the follower position update mechanism from the sparrow search algorithm to perturb the algorithm and using a greedy strategy to update positions, the convergence accuracy of the algorithm is enhanced. Lastly, when the algorithm stagnates, the Cauchy-Gaussian mutation strategy is introduced to enhance the algorithm's ability to escape local optima. The results demonstrate that MIDBO can plan high-quality paths in complex multi-obstacle scenarios, with higher convergence accuracy and stability compared to control algorithms, showing significant advantages.