使用空中无人机集群求解欠容量车辆路径问题的最优协同运输
Optimal Collaborative Transportation for Under-Capacitated Vehicle Routing Problems using Aerial Drone Swarms
AI总结：
本文将多无人机协同运输建模为欠容量VRP，提出避免死锁的解编码与交替最小化算法，并结合避障和控制器，在真实纳米四旋翼集群及大规模仿真中验证了框架有效性。
摘要：
空中无人机集群近来已被考虑用于城市物流中的最后一英里配送或自动化施工。与此同时，由多架无人机协同运输有效载荷也是近期研究的另一个重要领域。然而，用于由许多无人机协同运输许多有效载荷的高效协调算法仍有待研究。在本工作中，我们将无人机集群对有效载荷的协同运输表述为车辆路径问题（VRP）的一种新颖的欠容量推广，该推广本身也可能具有独立的研究价值。与标准VRP和带容量约束的VRP不同，我们还必须考虑由多架无人机协同吊起有效载荷时的等待时间，以及相应的协调。在算法方面，我们给出了一种避免死锁的解编码，并构建了适当的交替最小化方案来求解该问题。在硬件方面，我们将算法与避障和无人机控制器相集成。该方法及系统集成的影响在真实纳米四旋翼无人机集群以及大规模集群仿真中均得到了成功的实证验证。总体而言，我们提供了一个用于空中无人机集群协同运输的框架，该框架仅使用运输任一单个有效载荷所必需数量的无人机。
英文摘要：
Swarms of aerial drones have recently been considered for last-mile deliveries in urban logistics or automated construction. At the same time, collaborative transportation of payloads by multiple drones is another important area of recent research. However, efficient coordination algorithms for collaborative transportation of many payloads by many drones remain to be considered. In this work, we formulate the collaborative transportation of payloads by a swarm of drones as a novel, under-capacitated generalization of vehicle routing problems (VRP), which may also be of separate interest. In contrast to standard VRP and capacitated VRP, we must additionally consider waiting times for payloads lifted cooperatively by multiple drones, and the corresponding coordination. Algorithmically, we provide a solution encoding that avoids deadlocks and formulate an appropriate alternating minimization scheme to solve the problem. On the hardware side, we integrate our algorithms with collision avoidance and drone controllers. The approach and the impact of the system integration are successfully verified empirically, both on a swarm of real nano-quadcopters and for large swarms in simulation. Overall, we provide a framework for collaborative transportation with aerial drone swarms, that uses only as many drones as necessary for the transportation of any single payload.
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。
