Achieving safe and precise landings for a swarm of drones poses a significant challenge, primarily attributed to conventional control and planning methods. This paper presents the implementation of multi-agent deep reinforcement learning (MADRL) techniques for the precise landing of a drone swarm at relocated target locations. The system is trained in a realistic simulated environment with a maximum velocity of 3 m/s in training spaces of 4 x 4 x 4 m and deployed utilizing Crazyflie drones with a Vicon indoor localization system. The experimental results revealed that the proposed approach achieved a landing accuracy of 2.26 cm on stationary and 3.93 cm on moving platforms surpassing a baseline method used with a Proportional-integral-derivative (PID) controller with an Artificial Potential Field (APF). This research highlights drone landing technologies that eliminate the need for analytical centralized systems, potentially offering scalability and revolutionizing applications in logistics, safety, and rescue missions.

翻译：实现无人机集群的安全精准降落是一项重大挑战，这主要归因于传统的控制与规划方法。本文提出了一种多智能体深度强化学习（MADRL）技术的实现，用于无人机集群在重新定位的目标位置进行精准降落。该系统在一个最大速度为3 m/s、训练空间为4 x 4 x 4 m的真实感模拟环境中进行训练，并部署在使用Vicon室内定位系统的Crazyflie无人机上。实验结果表明，所提方法在静止平台上的降落精度达到2.26厘米，在移动平台上的精度达到3.93厘米，超越了使用比例-积分-微分（PID）控制器结合人工势场（APF）的基线方法。这项研究强调了无需解析式集中系统的无人机降落技术，有望提供可扩展性，并革新物流、安全与救援任务中的应用。