School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China; Key Laboratory of Biomimetic Robots and Systems of Chinese Ministry of Education, Beijing, 100081, China.
School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China; Key Laboratory of Biomimetic Robots and Systems of Chinese Ministry of Education, Beijing, 100081, China.
ISA Trans. 2024 May;148:397-411. doi: 10.1016/j.isatra.2024.02.034. Epub 2024 Mar 1.
The acceleration and deceleration (AD) motions are the basic motion modes of robot astronauts moving in a space station. Controlling the locomotion of the robot astronaut is very challenging due to the strong nonlinearity of its complex multi-body dynamics in a gravity-free environment. However, after training, humans can move well in space stations by pushing the bulkhead, and the motion mechanism of humans is a good reference for robot astronauts. The contribution of this study is modeling the human AD motion in a microgravity environment and proposing a human-like control method for robot astronauts moving in space stations. Specifically, the movement and contact force data of the human body during AD motion were collected on an air-floating platform. Through human AD modeling analysis, the mechanism of human motion is discovered, and semi-sinusoidal primitives of contact forces are proposed for AD motion. Then, a dynamic guidance model of human AD motion is built to complete motion planning under contact constraints, which is used as the expected model for the AD control of robot astronauts. Benefiting from the force primitives, accurate and safe planning of human-like AD motion can be completed. The characteristics and mechanism of human AD motion have been analyzed from the perspective of optimization. Lastly, based on the proposed dynamic guidance model, the AD motion policy is mapped to the robot astronaut system via a system control method based on the equivalent mapping of dynamic responses (force, velocity and pose). Through comparative analysis with real human motion data and simulation results under different conditions, the proposed AD control method can achieve human-like motion efficiently and stably. Even when confronted with errors in the robot's contact velocities and inertia parameters, the method can significantly reduce the motion errors while ensuring stability.
加速度和减速度（AD）运动是机器人宇航员在空间站中移动的基本运动模式。由于其在无重力环境下复杂多体动力学的强非线性，控制机器人宇航员的运动极具挑战性。然而，经过训练，人类可以通过推舱壁在空间站中很好地移动，人类的运动机制为机器人宇航员提供了很好的参考。本研究的贡献在于对微重力环境下的人类 AD 运动进行建模，并为在空间站中移动的机器人宇航员提出类似人类的控制方法。具体来说，在空气浮动平台上收集了人体在 AD 运动过程中的运动和接触力数据。通过人类 AD 建模分析，发现了人类运动的机制，并提出了用于 AD 运动的半正弦接触力原语。然后，建立了人类 AD 运动的动力学制导模型，以完成接触约束下的运动规划，该模型作为机器人宇航员 AD 控制的期望模型。得益于力原语，可以完成精确且安全的类似人类的 AD 运动规划。从优化的角度分析了人类 AD 运动的特点和机制。最后，基于所提出的动力学制导模型，通过基于动力学响应（力、速度和姿态）等效映射的系统控制方法，将 AD 运动策略映射到机器人宇航员系统。通过与真实人类运动数据和不同条件下的模拟结果进行对比分析，所提出的 AD 控制方法可以高效稳定地实现类似人类的运动。即使面对机器人接触速度和惯性参数的误差，该方法也可以在保证稳定性的同时显著减少运动误差。
