An improved ant colony algorithm for multiple unmanned aerial vehicles route planning

The successful application of UAVs on modern battlefields demonstrates their broad prospects in the military field. To ensure the success rate of penetration and strike missions, this paper establishes a multi-UAV cooperative route planning model guided by mission requirements, considering low-altitude obstacles and enemy detection threats. First, we establish an interaction model between UAVs, terrain, and enemy threats to clarify the feasibility of low-altitude penetration missions. Based on this, we introduce an ant colony algorithm enhanced with a fuzzy logic memory mechanism (FLM-IACO) to obtain lower-cost flight routes. Then we implement a pheromone initialization approach factoring in altitude, prioritizing the development of feasible flight routes within radar detection blind spots to enhance stealth capabilities. Moreover, a mixed pheromone structure delineates infeasible regions identified during the path-planning process. Subsequent nodes' reliability is assessed using membership degrees. In the final stage, a layered extension and correction strategy is employed to refine the flight paths. Simulations under different terrain data evaluate our method across various mission requirements. The results demonstrate that our approach consistently achieves shorter, safer, and more energy- efficient flight paths within the same number of iterations, underscoring its significant practical value.