Dynamic Path Planning for Space-Time Optimization Cooperative Tasks of Multiple Unmanned Aerial Vehicles in Uncertain Environment

Traditional path planning methods face significant challenges in addressing the high task density and complex airspace requirements of multi-UAV systems in uncertain environments, particularly in mitigating collision risks. This paper proposes a novel dynamic space-time optimization method that integrates an enhanced multi-ant colony system for vehicle routing problems with time windows with a proactive collision avoidance strategy. The approach begins by formulating a multi-UAV cooperative path optimization model that simultaneously maximizes node coverage and minimizes path conflicts for multidepot routing scenarios with time constraints. The core methodology combines a space-time optimization algorithm with node weight quantification to detect and resolve path conflicts, along with an innovative node selection strategy that constructs a probabilistic conflict resolution model. Experimental validation using real-world task data demonstrates the method’s effectiveness, showing a 95.23% conflict resolution rate while significantly reducing isolated nodes and improving path planning efficiency compared to conventional approaches. The proposed solution provides a robust framework for safe and efficient multi-UAV operations in dense uncertain deployment scenarios.