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.

翻译：近年来，空中无人机集群已被考虑用于城市物流或自动化建造中的末端配送。与此同时，多无人机协同运输载荷是另一个重要的研究领域。然而，针对多无人机对大量载荷的高效协同运输算法仍有待探索。本文中，我们将无人机集群协同运输载荷问题建模为一种新颖的、一般化的欠容量车辆路径问题（VRP），该建模本身可能具有独立研究价值。与标准VRP和带容量约束的VRP不同，我们必须额外考虑由多台无人机协同提升载荷时的等待时间及相应的协调问题。在算法层面，我们提供了一种避免死锁的解编码方案，并设计了合适的交替最小化框架来解决该问题。在硬件方面，我们将算法与避碰系统和无人机控制器进行集成。该方法及系统集成的影响已通过真实纳米四旋翼无人机集群仿真实验和大规模集群仿真实验得到成功验证。总体而言，我们为空中无人机集群协同运输提供了一个框架，该框架仅使用运输单个载荷所需的无人机数量。