To enable a mobile manipulator to perform human tasks from a single teaching demonstration is vital to flexible manufacturing. We call our proposed method MMPA (Mobile Manipulator Process Automation with One-shot Teaching). Currently, there is no effective and robust MMPA framework which is not influenced by harsh industrial environments and the mobile base's parking precision. The proposed MMPA framework consists of two stages: collecting data (mobile base's location, environment information, end-effector's path) in the teaching stage for robot learning; letting the end-effector repeat the nearly same path as the reference path in the world frame to reproduce the work in the automation stage. More specifically, in the automation stage, the robot navigates to the specified location without the need of a precise parking. Then, based on colored point cloud registration, the proposed IPE (Iterative Pose Estimation by Eye & Hand) algorithm could estimate the accurate 6D relative parking pose of the robot arm base without the need of any marker. Finally, the robot could learn the error compensation from the parking pose's bias to modify the end-effector's path to make it repeat a nearly same path in the world coordinate system as recorded in the teaching stage. Hundreds of trials have been conducted with a real mobile manipulator to show the superior robustness of the system and the accuracy of the process automation regardless of the harsh industrial conditions and parking precision. For the released code, please contact [email protected]

翻译：为实现移动机械臂通过单次示教演示执行人类任务，对柔性制造至关重要。我们提出的方法称为MMPA（单次示教移动机械臂过程自动化）。目前，尚未存在能抵御恶劣工业环境及移动基座停车精度影响的鲁棒MMPA框架。所提MMPA框架包含两个阶段：在示教阶段采集数据（移动基座位置、环境信息、末端执行器路径）以供机器人学习；在自动化阶段使末端执行器在世界坐标系中重复与参考路径近乎一致的路径以复现作业。具体而言，在自动化阶段，机器人无需精确停车即可导航至指定位置；随后，基于彩色点云配准，所提IPE（眼手协同迭代位姿估计）算法无需任何标记即可估算机械臂基座的精确6D相对停车位姿；最后，机器人可从停车位姿偏差中学习误差补偿，修正末端执行器路径，使其在世界坐标系中重复与示教阶段记录的近乎相同的路径。通过实际移动机械臂进行了数百次试验，结果表明该系统在恶劣工业条件与停车精度下均展现出卓越鲁棒性与过程自动化精度。如需获取已发布代码，请联系[email protected]