PINGU: Extending Air-Bearing Spacecraft Emulators with Open-Source Actuators and Learned Control for Contact-Rich Proximity Operations

2026-09-20

AI Summary

研究通过增加反应轮和力/扭矩感知机械臂，并结合强化学习控制方法，扩展了低成本平面气浮航天器模拟器的功能，以处理接触丰富的近距离操作任务。

Abstract

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.

Problem

• air-bearing testbeds
• spacecraft GNC
• contact-rich operations
• inertia-coupled manipulation

Innovation

• reaction wheel
• force/torque-sensed robotic arms
• reinforcement learning
• digital twin
• unified ROS 2 abstraction layer