A Joint Routing and Charging Management for Drones

The global greenhouse effect and air pollution problems have been getting deteriorated in recent years. The power generation in the future is expected to shift from fossil fuels to renewables, and many countries have also announced the ban on the sale of vehicles powered by fossil fuels in the next few decades, so as to effectively alleviate the global greenhouse effect and air pollution problems. In addition to electric vehicles that will replace traditional fuel vehicles as the main ground transportation vehicles in the future, drones have also gradually been widely used for military and civilian purposes recently. The recent literature estimated that drones will become the major means of transport for goods delivery services before 2040, and the development of passenger drones will also extend the traditional human ground transportation to low-altitude airspace transportation. In recent years, the literature has proposed the use of renewable power supply, battery swapping and charging stations to refill the battery of drones. However, the uncertainty of renewable power generation cannot guarantee the stable power supply of drones. It may even be very possible that a large number of drones need to be charged during the same period, causing congestion in charging stations or battery swapping facilities and delaying the arranged schedules of drones. Although studies have proposed to employ moving electric vehicles along with wireless charging technology to provide electricity to drones with urgent needs, the charging schemes are still oversimplified and have many restrictions. In addition, different charging options, such as charging stations and battery exchange services, as well as wireless charging for drones, should be provided to fit the individual need of each drone. In view of this, this work presented a joint solution of routing and charging management to meet the mission characteristics of various drones by providing adaptive routing and charging plans to individual drones, and reduce the power load of the renewable power generation during the peak period as well. We ran a series of simulations for the proposed mechanism to evaluate its performance. The simulation results exhibited that the proposed algorithm can be used by drone operators to fit the need of operation of each individual drone.