Research on Multi UAVS Task Allocation Method for Post Disaster Emergency Goods Distribution

Abstract

Abstract When a natural disaster occurs, ensuring timely and efficient delivery of emergency supplies to every affected location is crucial in mitigate the losses caused by the disaster. This study focuses on the distributing emergency supplies to heterogeneous multi-drone (UAV) swarms in the post-disaster scenario. Given the known distribution stations and disaster locations, a model is proposed that enables multiple drones to distribute various emergency supplies to multiple disaster zones. The model incorporates multiple objective constraints, including UAV range, priority of emergency goods, and UAV losses, resulting in a multi-objective optimization problem. To enhance the solving ability of genetic algorithm, we improve genetic algorithm by introducing adaptive cross operators, and its feasibility is compared with traditional genetic algorithms. Simulation experiments demonstrate that the improved genetic algorithm is significantly superior to traditional genetic algorithms in solving the optimal allocation scheme, thus providing a scientific basis for decision-making in emergency supply distribution.