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http://hdl.handle.net/10119/18153
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| Title: | Estimating the Center of Mass of an Unknown Object for Nonprehensile Manipulation |
| Authors: | Gao, Ziyan
Elibol, Armagan
Nak-Young, Chong |
| Keywords: | Planar Pushing
Frictional Interaction
Center of Mass Estimation
Few-Shot Learning
Voting Theorem |
| Issue Date: | 2022-08 |
| Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
| Magazine name: | 2022 IEEE International Conference on Mechatronics and Automation (ICMA) |
| Start page: | 1755 |
| End page: | 1760 |
| DOI: | 10.1109/ICMA54519.2022.9855972 |
| Abstract: | With the increasing prevalence of robot-led automation in many fields such as industry, health-care, agriculture,
etc., robot manipulator arms are often required to handle sophisticated manipulation tasks in which both the object and environmental physical parameters are unknown. In order to deal with these tasks, fast and accurate estimation of the inertial parameters of the object and frictional characteristics of flooring surfaces are of crucial importance toward developing intelligent and efficient object manipulation strategies. In this work, we propose an integrated framework for estimating the center of mass of an unknown planar object using a force sensor-less manipulator arm pushing the object on a horizontal plane. We evaluate two algorithmic solutions through extensive pusher-slider frictional interaction simulations. The result shows that the proposed framework can estimate the center of mass location efficiently and accurately only with a few pushing interactions. |
| Rights: | This is the author's version of the work. Copyright (C)2022 IEEE. 2022 IEEE International Conference on Mechatronics and Automation (ICMA), 2022, pp.1755-1760. DOI:10.1109/ICMA54519.2022.9855972. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
| URI: | http://hdl.handle.net/10119/18153 |
| Material Type: | author |
| Appears in Collections: | b11-1. 会議発表論文・発表資料 (Conference Papers)
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Size | Format |
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| ICMA2022_FinalVersion.pdf | | 2155Kb | Adobe PDF | View/Open |
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