Unmanned aerial vehicles (UAVs), commonly known as drones, are being increasingly deployed throughout the globe as a means to streamline logistic and monitoring routines. When dispatched on autonomous missions, drones require an intelligent decision-making system for trajectory planning and tour optimization. Given the limited capacity of their on-board batteries, a key design challenge is to ensure the underlying algorithms can efficiently optimize the mission objectives along with recharging operations during long-haul flights. This paper presents a comprehensive study on automated management systems for battery-operated drones: (1) We conduct empirical studies to model the battery performance of drones, considering various flight scenarios. (2) We study a joint problem of flight mission planning and recharging optimization for drones with an objective to complete a tour mission for a set of sites of interest in the shortest time considering the possibilities of recharging. (3) We present algorithms for solving the problem of flight mission planning and recharging optimization. (4) We implemented our algorithms in a drone management system, which supports real-time flight path tracking and re-computation in dynamic environments. We also evaluated the results of our algorithms in a case study using data from empirical studies, which shows significant improvement over a typical benchmark algorithm.
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