Unmanned Aerial Vehicle (UAV) technology is a promising solution for providing high-quality mobile services to ground users, where a UAV with limited service coverage travels among multiple geographical user locations (e.g., hotspots) for servicing their demands locally. How to dynamically determine a UAV swarm's cooperative path planning to best meet many users' spatio-temporally distributed demands is an important question but is unaddressed in the literature. To our best knowledge, this paper is the first to design and analyze cooperative path planning algorithms of a large UAV swarm for optimally servicing many spatial locations, where ground users' demands are released dynamically in the long time horizon. Regarding a single UAV's path planning design, we manage to substantially simplify the traditional dynamic program and propose an optimal algorithm of low computation complexity, which is only polynomial with respect to both the numbers of spatial locations and user demands. After coordinating a large number $K$ of UAVs, this simplified dynamic optimization problem becomes intractable and we alternatively present a fast iterative cooperation algorithm with provable approximation ratio $1-(1-\frac{1}{K})^{K}$ in the worst case, which is proved to obviously outperform the traditional approach of partitioning UAVs to serve different location clusters separately. To relax UAVs' battery capacity limit for sustainable service provisioning, we further allow UAVs to travel to charging stations in the mean time and thus jointly design UAVs' path planning over users' locations and charging stations. Despite of the problem difficulty, for the optimal solution, we successfully transform the problem to an integer linear program by creating novel directed acyclic graph of the UAV-state transition diagram, and propose an iterative algorithm with constant approximation ratio.
翻译:无人驾驶航空飞行器(UAV)技术是向地面用户提供高质量移动服务的一个很有希望的解决方案,在地面用户中,一个服务覆盖面有限的无人驾驶飞行器在多个地理用户地点(如热点)之间流动,以满足当地的需求。如何动态地确定UAV的暖流合作路径规划,以最好地满足许多用户的瞬间分布需求,这是一个重要问题,但在文献中却得不到解决。根据我们的最佳知识,本文是设计和分析大型UAV平均路程规划算法,以优化服务许多空间地点,地面用户的需求在很长的时间范围内动态地发布。关于单一的UAVL路径规划设计,我们设法大大简化传统的动态程序,提出一种最低计算复杂性的最佳算法,这与空间地点和用户需求的数量相比都是多元的。在对UAVVA的大量美元后,这种简化的动态优化问题变得棘手了,而我们则通过一个快速的代位的合作算法,与最易变近的直线比率比率,在最短的O-BAVI-ILOO 和最短的运行站点上,从而证实地将UAVIL1-KQ-laeval-laeval-lax-lax的运行到最慢路路路段向不同的站。