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Sensor-Guided Assembly of Segmented Structures with Industrial Robots

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This paper presents a robotic assembly methodology for the manufacturing of large segmented composite structures. The approach addresses three key steps in the assembly process: panel localization and pick-up, panel transport, and panel placement. Multiple stationary and robot-mounted cameras provide information for localization and alignment. A robot wrist-mounted force/torque sensor enables gentle but secure panel pick-up and placement. Human-assisted path planning ensures reliable collision-free motion of the robot with a large load in a tight space. A finite state machine governs the process flow and user interface. It allows process interruption and return to the previous known state in case of error condition or when secondary operations are needed. For performance verification, a high resolution motion capture system provides the ground truth reference. An experimental testbed integrating an industrial robot, vision and force sensors, and representative laminated composite panels demonstrates the feasibility of the proposed assembly process. Experimental results show sub-millimeter placement accuracy with shorter cycle times, lower contact force, and reduced panel oscillation than manual operations. This work demonstrates the versatility of sensor guided robotic assembly operation in a complex end-to-end tasks using the open source Robot Operating System (ROS) software framework.

Contributor(s)
Publisher
MDPI AG
Date Issued
2021-03-17
Language
English
Type
Genre
Form
electronic document
Media type
Creator role
Faculty
Identifier
2076-3417
Has this item been published elsewhere?
Volume
11
Volume
6
Peng, . Y.-C., Chen, . S., Jivani, . D., Wason, . J., Lawler, . W., Saunders, . G., J. Radke, . R., Trinkle, . J., Nath, . S., & T. Wen, . J. (2021). (Vols. 6). https://doi.org/10.3390/app11062669
Peng, Yuan-Chih, Shuyang Chen, Devavrat Jivani, John Wason, William Lawler, Glenn Saunders, Richard J. Radke, Jeff Trinkle, Shridhar Nath, and John T. Wen. 2021. https://doi.org/10.3390/app11062669.
Peng, Yuan-Chih, et al. 17 Mar. 2021, https://doi.org/10.3390/app11062669.