电池驱动自主无人机的自主充电与飞行任务规划
Autonomous Recharging and Flight Mission Planning for Battery-operated Autonomous Drones
AI总结：
本文针对电池驱动的自主无人机，构建了能耗估算机器学习模型，将飞行任务规划与充电优化建模为多准则ATSP问题，设计了具有性能保证的近似算法，并通过仿真和真实实验验证了其有效性与实用性。
摘要：
无人机（UAVs），通常被称为drones，正越来越多地部署于全球各地，作为简化监控、巡检、测绘和物流日常工作的手段。在执行自主任务时，drones需要一个智能决策系统来进行轨迹规划和巡回优化。鉴于其机载电池容量有限，一个关键的设计挑战是确保底层算法能够在长途飞行中高效优化任务目标以及充电操作。鉴于这一点，本工作对能量受限drone的自动巡回管理系统进行了全面研究：（1）我们构建了一个机器学习模型，该模型在考虑现实世界情况和外部气象因素的同时，估算典型多旋翼drones的能量消耗。（2）利用该模型，飞行任务规划和充电优化的联合规划被表述为多准则非对称旅行商问题（ATSP），其中drone寻求访问所有目标站点并在必要时加油的时间最优且能量可行的巡回路线。（3）我们设计了一种具有可证明的最坏情况性能保证的高效近似算法，并将其在drone管理系统中实现，该系统支持动态环境中的实时飞行路径跟踪和重新计算。（4）所提方法的有效性和实用性通过广泛的数值模拟以及真实世界实验得到了验证。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
