Research on Collaborative Reconnaissance Mission Planning Algorithms for Multiple Unmanned Aerial Vehicles

This study addresses the collaborative reconnaissance mission planning problem for multiple unmanned aerial vehicles (UAVs) by proposing a method that integrates clustering and an improved ant colony algorithm to enhance the real-time performance and adaptability of mission planning. Traditional methods have limitations in dynamic environments. Therefore, this paper divides the mission area based on the detection radius of UAVs' payloads and introduces clustering algorithms to effectively cluster targets, thereby reducing the task magnitude and enhancing the robustness of clustering results. Subsequently, an optimization model is constructed, incorporating key factors such as UAV flight range and survival probability as constraints to ensure the comprehensiveness of mission planning. By improving the ant colony algorithm framework, the performance of the model is optimized, and the adaptability to complex environments is enhanced. Simulation results and comparative analysis with existing intelligent algorithms demonstrate that the proposed algorithm significantly improves key indicators such as mission completion time, flight distance, and total distance, verifying its effectiveness in enhancing collaborative reconnaissance efficiency and reducing UAV risks.