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To enable a mobile manipulator to perform human tasks from a single teachingdemonstration is vital to flexible manufacturing. We call our proposed methodMMPA (Mobile Manipulator Process Automation with One-shot Teaching). Currently,there is no effective and robust MMPA framework which is not influenced byharsh industrial environments and the mobile base's parking precision. Theproposed MMPA framework consists of two stages: collecting data (mobile base'slocation, environment information, end-effector's path) in the teaching stagefor robot learning; letting the end-effector repeat the nearly same path as thereference path in the world frame to reproduce the work in the automationstage. More specifically, in the automation stage, the robot navigates to thespecified location without the need of a precise parking. Then, based oncolored point cloud registration, the proposed IPE (Iterative Pose Estimationby Eye & Hand) algorithm could estimate the accurate 6D relative parking poseof the robot arm base without the need of any marker. Finally, the robot couldlearn the error compensation from the parking pose's bias to modify theend-effector's path to make it repeat a nearly same path in the worldcoordinate system as recorded in the teaching stage. Hundreds of trials havebeen conducted with a real mobile manipulator to show the superior robustnessof the system and the accuracy of the process automation regardless of theharsh industrial conditions and parking precision. For the released code,please contact marketing@amigaga.com

A General Mobile Manipulator Automation Framework for Flexible Manufacturing in Hostile Industrial Environments