Journal
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
Volume 15, Issue 2, Pages 1202-1210Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2018.2879426
Keywords
Manipulability; mobile manipulation; visual guidance
Categories
Funding
- European Union's Seventh Framework Programme [608849 EuRoC]
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Industry 4.0 demands the heavy usage of robotic mobile manipulators with high autonomy and intelligence. The goal is to accomplish dexterous manipulation tasks without prior knowledge of the object status in unstructured environments. It is important for the mobile manipulator to recognize and detect the objects, determine manipulation pose, and adjust its pose in the workspace fast and accurately. In this research, we developed a stereo vision algorithm for the object pose estimation using point cloud data from multiple stereo vision systems. An improved iterative closest point algorithm method is developed for the pose estimation. With the pose input, algorithms and several criteria are studied for the robot to select and adjust its pose by maximizing its manipulability on a given manipulation task. The performance of each technical module and the complete robotic system is finally shown by the virtual robot in the simulator and real robot in experiments. This study demonstrates a setup of autonomous mobile manipulator for various flexible manufacturing and logistical scenarios.
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