Autonomous Recharging and Flight Mission Planning for Battery-operated Autonomous Drones

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 onboard batteries, a key design challenge is ensuring the underlying algorithms can efficiently optimize the mission objectives along with recharging operations during long-haul flights. Against this backdrop, the present work undertakes a comprehensive study on automated management systems for battery-constrained drones: (1) We construct a machine learning model to estimate the energy expenditure of drones, considering diverse real-world factors and flight scenarios. (2) Leveraging this model, the joint problem of flight mission planning and recharging optimization is formulated as a multi-criteria combinatorial program aimed at completing a tour mission for a set of target sites in the shortest time while minimizing recharging duration. (3) We devise an efficient approximation algorithm, with provable near-optimal performance guarantees, and implement it in a drone management system, which supports real-time flight path tracking and re-computation in dynamic environments. (4) We validate the effectiveness and practicality of the proposed approach through extensive numerical simulations as well as real-world experiments.