Path Planning for Distributed Multiple Unmanned Aerial Vehicles in Infectious Disease Testing Sample Transfer
To solve the path planning problem for infectious disease testing sample transfer by distributed multiple unmanned aerial vehicles(UAVs),a UAV path optimization model with minimum total cost is developed based on the complete UAV-based infectious disease testing sample transfer process.The fuzzy logic-controlled neighborhood search genetic algorithm is designed to solve the model.The validity of the model and algorithm is verified through case studies,and the results show that UAV tasks are mainly allocated according to the distance and intensity of demand points,and each task satisfies the constraints.Besides,further parameter analysis is conducted to explore effects of key parameters on results:the maximum battery capacity and the minimum remaining power limit are important factors affecting the total cost,and the change of these two factors will cause the change of the number of UAVs required for infectious disease testing sample transfer,resulting in a significant change of the total cost.The results can provide a new idea for solving the infectious disease testing sample transfer problem,and provide a theoretical basis for the decision making of relevant logistics companies.
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