Due to its mobility and agility, unmanned aerial vehicle (UAV) has emerged as a promising technology for various tasks, such as sensing, inspection and delivery. However, a typical UAV has limited energy storage and cannot fly a long distance without being recharged. This motivates several existing proposals to use trucks and other ground vehicles to offer riding to help UAVs save energy and expand the operation radius. We present the first theoretical study regarding how UAVs should optimally hitch on ground vehicles, considering vehicles' different travelling patterns and supporting capabilities. For a single UAV, we derive closed-form optimal vehicle selection and hitching strategy. When vehicles only support hitching, a UAV would prefer the vehicle that can carry it closest to its final destination. When vehicles can offer hitching plus charging, the UAV may hitch on a vehicle that carries it farther away from its destination and hitch a longer distance. The UAV may also prefer to hitch on a slower vehicle for the benefit of battery recharging. For multiple UAVs in need of hitching, we develop the max-saving algorithm (MSA) to optimally match UAV-vehicle collaboration. We prove that the MSA globally optimizes the total hitching benefits for the UAVs.
翻译:由于其机动性和灵活性,无人驾驶飞行器(无人驾驶飞行器)已成为各种任务(如遥感、检查和交付)的有希望的技术,但是,典型的无人驾驶飞行器的能源储存有限,不能在不充电的情况下长途飞行。这促使若干现有提议,即使用卡车和其他地面车辆提供骑车帮助无人驾驶飞行器节省能源和扩大运行半径。我们介绍关于无人驾驶飞行器应如何最佳地搭乘地面飞行器的第一项理论研究,考虑到车辆的不同旅行模式和支助能力。对于单一的无人驾驶飞行器,我们制定了封闭式最佳车辆选择和搭便策略。当车辆只支持搭车时,无人驾驶飞行器会更喜欢能够将车辆运载到最接近最终目的地的车辆。当车辆提供搭乘加充电时,无人驾驶飞行器可能搭乘远距离更远的车辆。无人驾驶飞行器可能更远地搭乘较慢的车辆,以获得电池再充电的好处。对于需要搭车的多个无人驾驶飞行器来说,我们开发了最优化的救程算法(MSA),以便最优化地与无人驾驶飞行器合作。我们证明,全球最优化地将AASS最接近。