Re-Planning of Reconnaissance Missions for Multi-UAV Systems Under Potential Faults

In distributed multiple unmanned aerial vehicle (UAV) systems, health status deteriorates with flight distance, increasing faults risk and reducing missions success. A health assessment-based task re-planning method is designed to ensure efficient missions completion and minimize faults risk through proactive task redistribution. The task re-planning model considers dynamic health, flight paths, and mission constraints. A health assessment framework guides task reassignment to maintain operational efficiency, while a scoring mechanism balances health and benefits, and a load balancing mechanism prevents overloading. A task clustering-based auction algorithm and a collision feedback-based path planning (CFPP) algorithm are also designed to quickly generate feasible task plans during reassignment. These algorithms optimize UAV health and meet real-time re-planning requirements. Simulation results demonstrate the method's effectiveness in maintaining UAV efficiency, even under potential faults.