Multi-unmanned aerial vehicle task scheduling method and system and storage medium

Abstract

The invention discloses a multi-unmanned aerial vehicle task scheduling method and system and a storage medium, the first stage is a multi-unmanned aerial vehicle task allocation stage, a multi-unmanned aerial vehicle task scheduling problem is divided into a plurality of single-unmanned aerial vehicle scheduling sub-problems, and a simulated annealing algorithm embedded with a tabu table is proposed to realize multi-unmanned aerial vehicle task allocation; and the second stage is a single unmanned aerial vehicle task scheduling stage, and a variable neighborhood search descent algorithm is designed according to the task allocation scheme in the first stage by considering the observation capability of the unmanned aerial vehicle platform and the requirements of the tasks so as to provide an effective and feasible task scheduling scheme. And in the first stage, according to a feedback result in the second stage, combining the tabu factor, the transfer factor and the exchange factor to iteratively adjust and update the task allocation scheme until a stop criterion is met. In conclusion, a two-stage iterative optimization method is provided for solving the multi-unmanned aerial vehicle cooperative task scheduling problem. Simulation experiments verify the superiority and efficiency of the method.

Description

technical field [0001] The invention relates to a UAV task scheduling technology, in particular to a multi-UAV task scheduling method, system and storage medium. Background technique [0002] In recent years, Unmanned Aerial Vehicle (UAV) has become more and more popular and widely used in many fields, such as traffic inspection [1,2] ,Earthquake Relief [3] ,Logistics [4,5] , target reconnaissance [6] Wait. Among them, drone traffic data collection [7] It is a new application and is the focus of the present invention's research. Traffic data collection refers to the acquisition of traffic flow data of various roads in the city in different time periods. Traditional manual data collection will undoubtedly consume a lot of manpower and vehicle resources. To make matters worse, during rush hour, traffic congestion can lead to severe skews in the collected data. In contrast, drones are more flexible and maneuverable, and can reach their destination quickly. At present, ...

AI Technical Summary

Problems solved by technology

However, the current multi-UAV task scheduling framework based on layered thinking mainly emphasizes the idea of ​​​​divide and conquer. The connection between each layer is not strong, and it lacks the idea of ​​layer-by-layer feedback and iterative optimization. This will lead to large-scale task scheduling. When the problem is solved, the solution speed is greatly slowed down, and the solution quality is greatly reduced

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