双任务无人机编队的协同绕行规划
Cooperative Detour Planning for Dual-Task Drone Fleets
AI总结：
本文提出一种去中心化框架，使无人机编队同时执行送货任务和观察交通状况。通过动态信息值模型，无人机在路径规划中考虑交通不确定性，并通过局部聚类优化实现高效任务执行。
摘要：
随着城市空中交通的发展，商业无人机编队提供了一种有吸引力但尚未被充分探索的机会，作为移动传感器网络实时监测城市交通。本文提出了一种去中心化框架，使无人机编队同时执行送货任务和观测网络交通状况。我们用动态信息值建模城市环境，这些值与道路段相关，并随时间累积交通状况的不确定性，在无人机访问时重置。该问题被建模为混合整数线性规划问题，无人机在尊重每个送货的最大绕行和每个无人机的电池预算下最大化交通信息奖励。不同于计算负担大的集中方法，我们的方法专注于动态局部聚类。当无人机进入通信范围时，它们交换对交通状况的信心，并从孤立路径规划转为局部联合优化模式，解决耦合约束以获得各自无人机的重新规划路径。基于西班牙巴塞罗那真实城市网络的仿真结果表明，与忽略交通监测任务的最短路径策略相比，所提方法更有效地利用电池和绕行预算探索城市区域并获得足够的交通信息；并且，得益于其去中心化方式，这种“相遇-合并”策略在网络覆盖方面实现了接近全局最优，计算开销显著低于集中基线。
英文摘要：
As Urban air mobility scales, commercial drone fleets offer a compelling, yet underexplored opportunity to function as mobile sensor networks for real-time urban traffic monitoring. In this paper, we propose a decentralized framework that enables drone fleets to simultaneously execute delivery tasks and observe network traffic conditions. We model the urban environment with dynamic information values associated with road segments, which accumulate traffic condition uncertainty over time and are reset upon drone visitation. This problem is formulated as a mixed-integer linear programming problem where drones maximize the traffic information reward while respecting the maximum detour for each delivery and the battery budget of each drone. Unlike centralized approaches that are computationally heavy for large fleets, our method focuses on dynamic local clustering. When drones enter communication range, they exchange their belief in traffic status and transition from isolated path planning to a local joint optimization mode, resolving coupled constraints to obtain replanned paths for each drone, respectively. Simulation results built on the real city network of Barcelona, Spain, demonstrate that, compared to a shortest-path policy that ignores the traffic monitoring task, our proposed method better utilizes the battery and detour budget to explore the city area and obtain adequate traffic information; and, thanks to its decentralized manner, this ``meet-and-merge" strategy achieves near-global optimality in network coverage with significantly reduced computation overhead compared to the centralized baseline.
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。
