Multi-UAV Cooperative Strike Task Assignment and Path Planning Algorithm for Urban Environments

To address the challenges of target allocation efficiency and path planning safety in the collaborative strike tasks of multi-rotor unmanned aerial vehicles (UAVs) in urban environments, this paper proposes a two-phase decision-making framework that integrates game theory and intelligent optimization. A Stackelberg contract model for the task allocation phase is constructed to design a strike contract that incorporates risk-benefit considerations. The UAV dynamically selects the contract based on its state and realizes the balance between efficiency and fairness through reputation incentives. An improved multi-objective Ivy algorithm is used in the path planning phase. The algorithm incorporates Levy flight to enhance the global search and establish a multi-objective optimization function of path length-energy consumption-safety. The two phases form a closed-loop optimization through path cost feedback. Simulated results show that the framework improves the total effectiveness of task assignment by 4% and reduces the length of generated paths by 13. 4% compared to other methods. The framework provides an efficient and robust cooperative scheme for multi-UAV swarms in urban combat.