论文标题
Fairfly：一个公平的运动规划师，用于Urban Priade的自治无人机舰队
FairFly: A Fair Motion Planner for Fleets of Autonomous UAVs in Urban Airspace
论文作者
论文摘要
我们提供了一个解决方案，即公平地计划了一个无人驾驶汽车（UAV）的舰队，这些飞机（UAV）具有不同的任务和操作员，因此没有人不公平地以牺牲他人为代价来完成任务 - 除非明确协商。当数百个无人机共享一个城市领空时，相关当局应将走廊分配给他们，以便他们完成任务，但是没有意外地给出了一个非常快的道路，以牺牲另一个车辆的牺牲，因此被迫等待和浪费能量。我们的解决方案Fairfly解决了一般自治系统（包括无人机车队）的公平计划问题，但要受到城市应用程序典型的复杂任务。 Fairfly以时间逻辑为每个任务正式正式。离线搜索找到了满足任务的最公平的路径，可以通过无人机飞行，从而导致更轻的在线控制负载。如果需要，它允许无人机之间的明确谈判能够实现不平衡的路径持续时间。我们提出了三个公平的概念，其中包括减少能耗的观念。我们在模拟中验证了我们的结果，并证明了由于飞行公平轨迹而导致的较轻的计算负载和更少的无人机能源消耗。
We present a solution to the problem of fairly planning a fleet of Unmanned Aerial Vehicles (UAVs) that have different missions and operators, such that no one operator unfairly gets to finish its missions early at the expense of others - unless this was explicitly negotiated. When hundreds of UAVs share an urban airspace, the relevant authorities should allocate corridors to them such that they complete their missions, but no one vehicle is accidentally given an exceptionally fast path at the expense of another, which is thus forced to wait and waste energy. Our solution, FairFly, addresses the fair planning question for general autonomous systems, including UAV fleets, subject to complex missions typical of urban applications. FairFly formalizes each mission in temporal logic. An offline search finds the fairest paths that satisfy the missions and can be flown by the UAVs, leading to lighter online control load. It allows explicit negotiation between UAVs to enable imbalanced path durations if desired. We present three fairness notions, including one that reduces energy consumption. We validate our results in simulation, and demonstrate a lighter computational load and less UAV energy consumption as a result of flying fair trajectories.
