基於多智能體強化學習的纜繩懸掛負載分散式空中操控

摘要

本文首次提出了一种去中心化方法，使微型飞行器（MAV）团队能够实现对缆绳悬挂负载的六自由度（6-DoF）实时操控。我们的方法利用多智能体强化学习（MARL）为每个MAV训练一个外环控制策略。与采用集中式方案的最先进控制器不同，我们的策略无需全局状态、MAV间通信或邻近MAV信息。相反，智能体仅通过负载姿态观测进行隐式通信，这赋予了系统高度的可扩展性和灵活性。同时，该方法显著降低了推理时的计算成本，使得策略能够在机载设备上部署。此外，我们为MAV引入了一种新的动作空间设计，采用线性加速度和机体角速率。这一选择，结合鲁棒的低级控制器，确保了在动态三维运动中尽管存在由缆绳张力引起的显著不确定性，仍能实现可靠的仿真到现实迁移。我们通过一系列真实世界实验验证了该方法，包括在负载模型不确定性下的全姿态控制，展示了与最先进集中式方法相当的设定点跟踪性能。我们还展示了具有异构控制策略的智能体之间的协作，以及对单个MAV完全失联的鲁棒性。实验视频请访问：https://autonomousrobots.nl/paper_websites/aerial-manipulation-marl

English

This paper presents the first decentralized method to enable real-world 6-DoF manipulation of a cable-suspended load using a team of Micro-Aerial Vehicles (MAVs). Our method leverages multi-agent reinforcement learning (MARL) to train an outer-loop control policy for each MAV. Unlike state-of-the-art controllers that utilize a centralized scheme, our policy does not require global states, inter-MAV communications, nor neighboring MAV information. Instead, agents communicate implicitly through load pose observations alone, which enables high scalability and flexibility. It also significantly reduces computing costs during inference time, enabling onboard deployment of the policy. In addition, we introduce a new action space design for the MAVs using linear acceleration and body rates. This choice, combined with a robust low-level controller, enables reliable sim-to-real transfer despite significant uncertainties caused by cable tension during dynamic 3D motion. We validate our method in various real-world experiments, including full-pose control under load model uncertainties, showing setpoint tracking performance comparable to the state-of-the-art centralized method. We also demonstrate cooperation amongst agents with heterogeneous control policies, and robustness to the complete in-flight loss of one MAV. Videos of experiments: https://autonomousrobots.nl/paper_websites/aerial-manipulation-marl

基於多智能體強化學習的纜繩懸掛負載分散式空中操控

Decentralized Aerial Manipulation of a Cable-Suspended Load using Multi-Agent Reinforcement Learning

摘要

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