Moving-Horizon Planning Based on Particle Swarm Optimization for Intercepting Moving Targets using Multi-Unmanned Aerial Vehicles

Several swarms of unmanned aerial vehicles have been used in many applications involving the interception of multiple dynamic ground or aerial threats. These swarms can perform multiple tasks simultaneously while adapting to environmental changes and various failures. The complexity of intercepting moving targets necessitates sophisticated path-planning methodologies that enhance operational efficiency and effectiveness. This work presents a collaborative moving-horizon planning approach for intercepting moving targets in dynamic and uncertain environments using multi-vehicles. It explores the use of particle swarm optimization and grey wolf optimization algorithms to generate online optimal trajectories for multi-vehicles. The proposed planning approach predicts future target trajectories and generates optimal control inputs for each vehicle at each time step. The simulation results highlight the effectiveness of the proposed algorithm in improving the coordination and adaptability between vehicles in various scenarios and different environments with multiple targets and obstacles. Finally, Monte-Carlo simulations evaluate the robustness of the proposed path-planning approach in crowded environments and uncertainties with the random movement of targets.