NDLI: Autonomous optimization of fine motions for robotic assembly

Content Provider	IEEE Xplore Digital Library
Author	Krabbe, E. Kristiansen, E. Hansen, L. Bourne, D.
Copyright Year	2014
Description	Author affiliation: Robot. Inst., Carnegie Mellon Univ., Pittsburgh, PA, USA (Bourne, D.) \|\| Dept. of Mech. & Manuf. Eng., Aalborg Univ., Aalborg, Denmark (Krabbe, E.; Kristiansen, E.; Hansen, L.)
Abstract	In the past, robotic assembly has required rigid fixturing and special purpose robotic tools for every assembly component. Unfortunately, rigid fixtures and special purpose robotic tools often have to be customized for varying geometries. Alternatively, it is possible to operate in a semi-structured environment, defined by the use of softer fixtures (e.g. pickup bin) and softer robotic tools (e.g. suction cups or compliant pads) that can be used for many assembly applications without modification, but they demand specific motion plans that can tolerate greater positional uncertainty. We have developed a system that supports autonomous generation of parameterized fine motion plans for assembly that are robust under positional uncertainty and compliance introduced by the use of a suction cup instead of a gripper. To accomplish this a classifier is trained, implemented and tested for performance in the semi-structured environment for distinguishing between a failed or successful assembly. The trained classifier is then integrated with the entire system and many robot-attended experiments are performed that vary the fine motion parameters, and optimize them for successful outcomes using an Interval Estimation optimization algorithm. An approach to machine learning based on Support Vector Machines and Principal Component Analysis is used to make the optimization autonomous. We achieved a 99.7% classification accuracy with the trained classifier and by running repeated robot-attended experiments with artificial positional uncertainty and optimizing fine motion parameters, we were able to achieve a 38% improvement compared to fine motion plans with initial best guess parameters.
Starting Page	4168
Ending Page	4175
File Size	4100754
Page Count	8
File Format	PDF
ISBN	9781479936854
DOI	10.1109/ICRA.2014.6907465
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-05-31
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Assembly Force Training Batteries Robots Optimization Force sensors
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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