Unmanned Aerial Vehicles, often called drones, have rapidly emerged for commercial and personal use in recent years. Drones are a promising and effective transportation mode because they can travel faster than traditional ground-based vehicles, particularly when obstacles limit quick response or in cases of congestion. An important consideration for drones is that travel times are impacted in various ways by real-time local conditions, including weather and terrain. While goods and supplies can be acquired at more traditional outlets (e.g., stores, warehouses, restaurants, hospitals, fire stations, etc.), drones are being increasingly relied upon to extend access, particularly for special services associated with food, drug, and equipment delivery. The reason is that they can reliably access almost anywhere, providing quick response without the need for more expensive (and larger) vehicles that are restricted to congested roadways. How to locate drone base stations and allocate service in order to optimize overall response is a challenging task, especially given spatiotemporal heterogeneity in distributed demand and service response times/costs that can vary over a day. This paper introduces an extension of p-median problem to aid in the deployment of a drone system that accounts for continuous planar travel costs. Results show that drone travel times can be significantly reduced across a region. A key feature in this work is the representation of both demand and flight trajectories across a continuous terrain.

近年来，无人驾驶飞机（通常称为无人机）迅速出现，用于商业和个人用途。无人机是一种有前途且有效的运输方式，因为它们可以比传统的地面车辆行驶得更快，特别是在障碍物限制快速反应或拥堵的情况下。无人机的重要考虑因素是，旅行时间受实时本地条件（包括天气和地形）以各种方式影响。虽然可以在更传统的商店（例如，商店，仓库，饭店，医院，消防局等）获得商品和物资，但越来越多地依赖无人机来扩展访问权限，尤其是与食品，药品和药品相关的特殊服务设备交付。原因是他们几乎可以在任何地方可靠地访问，提供快速响应，而无需使用价格昂贵（和更大）的车辆，这些车辆仅限于拥挤的道路。如何定位无人机基站并分配服务以优化整体响应是一项具有挑战性的任务，尤其是考虑到分布式需求的时空异质性和服务响应时间/成本每天可能变化。本文介绍了p中位数问题的扩展，以帮助部署考虑了连续飞机旅行成本的无人机系统。结果表明，整个地区的无人机旅行时间可以大大减少。这项工作的关键特征是连续地形上的需求和飞行轨迹的表示。如何定位无人机基站并分配服务以优化整体响应是一项具有挑战性的任务，尤其是考虑到分布式需求的时空异质性和服务响应时间/成本每天可能变化。本文介绍了p中位数问题的扩展，以帮助部署考虑了连续飞机旅行成本的无人机系统。结果表明，整个地区的无人机旅行时间可以大大减少。这项工作的关键特征是连续地形上的需求和飞行轨迹的表示。如何定位无人机基站并分配服务以优化整体响应是一项具有挑战性的任务，尤其是考虑到分布式需求的时空异质性和服务响应时间/成本每天可能变化。本文介绍了p中位数问题的扩展，以帮助部署考虑了连续飞机旅行成本的无人机系统。结果表明，整个地区的无人机旅行时间可以大大减少。这项工作的关键特征是连续地形上的需求和飞行轨迹的表示。本文介绍了p中位数问题的扩展，以帮助部署考虑了连续飞机旅行成本的无人机系统。结果表明，整个地区的无人机旅行时间可以大大减少。这项工作的关键特征是连续地形上的需求和飞行轨迹的表示。本文介绍了p中位数问题的扩展，以帮助部署考虑了连续飞机旅行成本的无人机系统。结果表明，整个地区的无人机旅行时间可以大大减少。这项工作的关键特征是连续地形上的需求和飞行轨迹的表示。