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

## TL;DR

This paper presents an integrated system for autonomous drone mission planning that combines machine learning-based energy estimation with an optimized tour routing algorithm, enabling efficient recharging and real-time navigation in energy-constrained scenarios.

## Contribution

It introduces a novel energy-aware tour planning framework for drones, including a machine learning model and an approximation algorithm with performance guarantees.

## Key findings

- The energy estimation model accurately predicts drone energy consumption.
- The proposed routing algorithm finds near-optimal tours with recharging considerations.
- Real-world experiments validate the system's effectiveness in dynamic environments.

## Abstract

Unmanned aerial vehicles (UAVs), commonly known as drones, are being increasingly deployed throughout the globe as a means to streamline monitoring, inspection, mapping, and logistic 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 to ensure the underlying algorithms can efficiently optimize the mission objectives along with recharging operations during long-haul flights. With this in view, the present work undertakes a comprehensive study on automated tour management systems for an energy-constrained drone: (1) We construct a machine learning model that estimates the energy expenditure of typical multi-rotor drones while accounting for real-world aspects and extrinsic meteorological factors. (2) Leveraging this model, the joint program of flight mission planning and recharging optimization is formulated as a multi-criteria Asymmetric Traveling Salesman Problem (ATSP), wherein a drone seeks for the time-optimal energy-feasible tour that visits all the target sites and refuels whenever necessary. (3) We devise an efficient approximation algorithm with provable worst-case performance guarantees and implement it in a drone management system, which supports real-time flight path tracking and re-computation in dynamic environments. (4) The effectiveness and practicality of the proposed approach are validated through extensive numerical simulations as well as real-world experiments.