Multi-UAV Path Planning Under Radar Threats

Collaborative execution of data collection tasks by multiple unmanned aerial vehicle (Multi-UAV) can lead to reduced energy consumption, minimized interference, and extended network lifespan. To overcome the limitation of limited onboard energy and long search time for path planning by a single UAV, a multi-UAV path planning scheme is designed specifically for radar-threat environments. This scheme ensures that UAVs, while satisfying certain energy constraints, establish a Multi-UAV Multi-node Path Cost (MMPC) model. Each UAV needs to access different nodes with varying data volumes to upload while avoiding radar threats as much as possible. The objective is to find the optimal flight path for each UAV for minimizing the total path costs. Based on the basic Ant Colony Optimization (ACO) framework, this approach incorporates virtual nodes, modifies node selection strategies, adjusts path visibility factors and introduces the 2-opt method for node swapping. These enhancements improve the search efficiency to a certain extent, enhance the path diversity within the population and prevent the premature convergence to the local optima. Simulation results demonstrate the improved ACO's good convergence and the minimized path cost, thus validating its effectiveness.