Dynamic Path Planning for Space-Time Optimization Cooperative Tasks of Multiple Unmanned Aerial Vehicles in Uncertain Environment
Huimin Zhao, M. Gu, Shaopeng Qiu, Ang Zhao, Wu Deng · IEEE Transactions on Consumer Electronics · 2025
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 multi-depot 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.