Autonomous Robots for Space: Trajectory Learning and Adaptation Using Imitation.
Autonomous Robots for Space: Trajectory Learning and Adaptation Using Imitation.
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DOI：
10.3389/frobt.2021.638849
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发表时间：
2021
影响因子：
3.4
通讯作者：
Fallah S
中科院分区：
文献类型：
--
作者：
Ashith Shyam RB;Hao Z;Montanaro U;Dixit S;Rathinam A;Gao Y;Neumann G;Fallah S
本文增加了正在进行的努力，以提供更多的自主空间机器人，并介绍了编程的概念，通过演示或模仿学习的自由浮动航天器上的机械手的轨迹规划。一个冗余的7自由度机械臂安装在小型航天器上，专门用于碎片清除、在轨维修和组装、自主和交会对接。机器人（或机械手）手臂的运动会在航天器上产生反作用力，从而使航天器的姿态发生变化，从而促使姿态确定与控制系统（ADCS）采取大的校正措施。本文介绍的方法能够找到最小化姿态变化的轨迹，从而减少ADCS的负载。在航天器轨迹规划和控制中的关键因素之一是功耗。在这项工作中介绍的方法进行轨迹学习离线收集数据的演示和编码为轨迹的概率分布。通过调节概率分布，学习的轨迹分布可以用于在先前看不见的情况下进行规划。因此，部署后几乎不需要计算功率。从条件分布采样提供了从相同的开始到目标状态的几个可能的轨迹。为了确定使姿态变化最小化的轨迹，定义了成本项，并且将使该成本最小化的轨迹视为最优轨迹。
This paper adds on to the on-going efforts to provide more autonomy to space robots and introduces the concept of programming by demonstration or imitation learning for trajectory planning of manipulators on free-floating spacecraft. A redundant 7-DoF robotic arm is mounted on small spacecraft dedicated for debris removal, on-orbit servicing and assembly, autonomous and rendezvous docking. The motion of robot (or manipulator) arm induces reaction forces on the spacecraft and hence its attitude changes prompting the Attitude Determination and Control System (ADCS) to take large corrective action. The method introduced here is capable of finding the trajectory that minimizes the attitudinal changes thereby reducing the load on ADCS. One of the critical elements in spacecraft trajectory planning and control is the power consumption. The approach introduced in this work carry out trajectory learning offline by collecting data from demonstrations and encoding it as a probabilistic distribution of trajectories. The learned trajectory distribution can be used for planning in previously unseen situations by conditioning the probabilistic distribution. Hence almost no power is required for computations after deployment. Sampling from a conditioned distribution provides several possible trajectories from the same start to goal state. To determine the trajectory that minimizes attitudinal changes, a cost term is defined and the trajectory which minimizes this cost is considered the optimal one.
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