发表机构
University of Luxembourg; Tohoku University; Georgia Institute of Technology（卢森堡大学; 东北大学; 佐治亚理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
PINGU在开源ATMOS气浮试验台上集成反作用轮与力传感机械臂，通过统一ROS 2抽象层和强化学习环境，实现经典控制与学习策略互换，验证了接触丰富操作能力。
AI 中文摘要
低成本平面气浮试验台已成熟为自由飞行航天器制导、导航与控制的标准替代平台，但此类平台大多仅配备推力器，很少具备用于富含接触、惯性耦合操作的能力。基于开源ATMOS试验台，我们贡献了一个反作用轮和两个带力/力矩传感的机械臂（LEVION），配备可互换末端执行器，并通过统一的ROS 2抽象层将其集成作为一等控制执行器。在软件栈之上，我们构建了强化学习训练环境和数字孪生，以及一个利用这些新增自由度的控制器，使得经典最优控制器和学习策略可以在同一硬件上无需修改即可互换。我们在四个基准任务上验证了集成系统PINGU：点对位姿导航（经典LQR对比仿真到现实PPO）、机械臂引起的质心偏移下的动态扰动抑制、反作用轮动量稳定以及力控制对接。结果表明，这些新增功能将ATMOS级仿真器扩展到富含接触的领域，并在一个可复现平台上桥接了经典最优控制与强化学习。
英文摘要
Low-cost planar air-bearing testbeds have matured into a standard proxy for free-flying spacecraft GNC, but they remain largely thruster-only and are rarely equipped for contact-rich, inertia-coupled manipulation. Building on the open-source ATMOS testbed, we contribute a reaction wheel and two force/torque-sensed robotic arms (LEVION) with interchangeable end-effectors, integrated as first-class control actuators through a unified ROS 2 abstraction layer. On top of the software stack we build a reinforcement-learning training environment and digital twin, and a controller that exploits these added degrees of freedom, letting classical optimal controllers and learned policies be swapped on the same hardware without modification. We validate the integrated system, PINGU, across four benchmark tasks: point-to-pose navigation (classical LQR vs. sim-to-real PPO), dynamic disturbance rejection under arm-induced center-of-mass shifts, reaction-wheel momentum stabilization, and force-controlled docking. The results show that these additions extend an ATMOS-class emulator into the contact-rich regime and bridge classical optimal control and reinforcement learning on one reproducible platform.
