Optimization of Multitask Scheduling for Swarm UAV System with Charging Platform

Abstract

Swarm UAV technology have potential application in a wide range because of its ability to utilize large number, low cost and unified scheduled UAVs. Unified scheduling is tasks and resources allocation through UAV scheduling optimization, which is the key problem for swarm UAV research. Existing scheduling research mainly focuses on scheduling analysis for small-scale, short-time tasks of swarm UAVs, and rarely consider the application of charging platform. Scheduling optimization must consider the influence of charging and other factors for future UAV multi-task and long-time applications . An improved swarm UAV task scheduling method based on a unified scheduling model and improved genetic algorithms was proposed. First, the wireless charging platform resources are incorporated into the UAV working environment, and the working scenario is modeled systematically. Then, the genetic algorithm is used to optimize the task and charging platform resource assignment. Finally, the proposed method is tested and validated using simulated scenarios. Test results show that the proposed method can better adapt to changes in tasks, environment and resources, and still shows good results with relatively large swarms.