Integrating Visual Perception with Conservative Enhanced Bio-Inspired Optimization for Safe UAV Trajectory Planning

Unmanned Aerial Vehicle (UAV) trajectory planning in complex three-dimensional environments with threats remains a challenging optimization problem requiring efficient algorithms and threat detection capabilities. This study proposes the Conservative Enhanced Dwarf Mongoose Optimization Algorithm (CEDMOA), which introduces four key innovations to the original DMOA: hybrid population initialization, adaptive vocalization parameters, elite-guided learning strategy, and intelligent restart mechanisms. This work proposed the integration of CEDMOA with a novel vision-based threat detection system using YOLO object detection technology, enabling the identification and incorporation of threats into the optimization process. CEDMOA was comprehensively evaluated on the CEC2022 benchmark test suite, demonstrating superior performance compared to other state-of-the-art algorithms in solution quality and convergence stability. The results show the approach successfully generates an optimal collision-free flight trajectory in complex environments in UAV trajectory planning with both static and dynamic threats. Combining metaheuristic optimization with computer vision technology provides a robust framework for autonomous navigation that adapts to changing threat conditions. Experimental results validate the effectiveness of both the enhanced algorithm and the vision-based threat integration approach for practical UAV operations.