Neuromorphic Event-Based Slip Detection and Suppression in Robotic Grasping and Manipulation
Slip detection is essential for robots to make robust grasping and fine manipulation. In this paper, a novel dynamic vision-based finger system for slip detection and suppression is proposed. We also present a baseline and feature based approach to detect object slips under illumination and vibratio...
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| Veröffentlicht in: | IEEE access 2020, Vol.8, p.153364-153384 |
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| creator | Muthusamy, Rajkumar Huang, Xiaoqian Zweiri, Yahya Seneviratne, Lakmal Gan, Dongming |
| description | Slip detection is essential for robots to make robust grasping and fine manipulation. In this paper, a novel dynamic vision-based finger system for slip detection and suppression is proposed. We also present a baseline and feature based approach to detect object slips under illumination and vibration uncertainty. A threshold method is devised to autonomously sample noise and object feature events in real-time to improve slip detection and suppression. Moreover, a fuzzy based suppression strategy using incipient slip feedback is proposed for regulating the grip force. A comprehensive experimental study of our proposed approaches under uncertainty and system for high-performance precision manipulation are presented. We also propose a slip metric to evaluate such performance quantitatively. For a class of objects, results indicate that the system can effectively detect incipient slip events at a sampling rate of 2kHz (Δt = 500μs) and suppress them before a gross slip occurs. The event-based approach holds promises to high precision manipulation task requirement in industrial manufacturing and household services. |
| doi_str_mv | 10.1109/ACCESS.2020.3017738 |
| format | Article |
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In this paper, a novel dynamic vision-based finger system for slip detection and suppression is proposed. We also present a baseline and feature based approach to detect object slips under illumination and vibration uncertainty. A threshold method is devised to autonomously sample noise and object feature events in real-time to improve slip detection and suppression. Moreover, a fuzzy based suppression strategy using incipient slip feedback is proposed for regulating the grip force. A comprehensive experimental study of our proposed approaches under uncertainty and system for high-performance precision manipulation are presented. We also propose a slip metric to evaluate such performance quantitatively. For a class of objects, results indicate that the system can effectively detect incipient slip events at a sampling rate of 2kHz (Δt = 500μs) and suppress them before a gross slip occurs. The event-based approach holds promises to high precision manipulation task requirement in industrial manufacturing and household services.</description><identifier>ISSN: 2169-3536</identifier><identifier>EISSN: 2169-3536</identifier><identifier>DOI: 10.1109/ACCESS.2020.3017738</identifier><identifier>CODEN: IAECCG</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Dynamic vision sensor ; event camera ; Feature extraction ; fuzzy control ; Grasping ; Grasping (robotics) ; Grip force ; Neuromorphics ; object manipulation ; Object recognition ; Performance evaluation ; Robot sensing systems ; robotic grasping ; Slip ; slip detection ; slip suppression ; Task analysis ; Uncertainty ; vision based tactile sensing</subject><ispartof>IEEE access, 2020, Vol.8, p.153364-153384</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c408t-66577632256afcbfad865244c6d7e99c76c6eb10f1964121963dbed2efee09a13</citedby><cites>FETCH-LOGICAL-c408t-66577632256afcbfad865244c6d7e99c76c6eb10f1964121963dbed2efee09a13</cites><orcidid>0000-0001-5327-1902 ; 0000-0002-5372-0154 ; 0000-0001-6405-8402 ; 0000-0002-2782-9068 ; 0000-0003-4331-7254</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9171323$$EHTML$$P50$$Gieee$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,864,2100,4022,27632,27922,27923,27924,54932</link.rule.ids></links><search><creatorcontrib>Muthusamy, Rajkumar</creatorcontrib><creatorcontrib>Huang, Xiaoqian</creatorcontrib><creatorcontrib>Zweiri, Yahya</creatorcontrib><creatorcontrib>Seneviratne, Lakmal</creatorcontrib><creatorcontrib>Gan, Dongming</creatorcontrib><title>Neuromorphic Event-Based Slip Detection and Suppression in Robotic Grasping and Manipulation</title><title>IEEE access</title><addtitle>Access</addtitle><description>Slip detection is essential for robots to make robust grasping and fine manipulation. In this paper, a novel dynamic vision-based finger system for slip detection and suppression is proposed. We also present a baseline and feature based approach to detect object slips under illumination and vibration uncertainty. A threshold method is devised to autonomously sample noise and object feature events in real-time to improve slip detection and suppression. Moreover, a fuzzy based suppression strategy using incipient slip feedback is proposed for regulating the grip force. A comprehensive experimental study of our proposed approaches under uncertainty and system for high-performance precision manipulation are presented. We also propose a slip metric to evaluate such performance quantitatively. For a class of objects, results indicate that the system can effectively detect incipient slip events at a sampling rate of 2kHz (Δt = 500μs) and suppress them before a gross slip occurs. The event-based approach holds promises to high precision manipulation task requirement in industrial manufacturing and household services.</description><subject>Dynamic vision sensor</subject><subject>event camera</subject><subject>Feature extraction</subject><subject>fuzzy control</subject><subject>Grasping</subject><subject>Grasping (robotics)</subject><subject>Grip force</subject><subject>Neuromorphics</subject><subject>object manipulation</subject><subject>Object recognition</subject><subject>Performance evaluation</subject><subject>Robot sensing systems</subject><subject>robotic grasping</subject><subject>Slip</subject><subject>slip detection</subject><subject>slip suppression</subject><subject>Task analysis</subject><subject>Uncertainty</subject><subject>vision based tactile sensing</subject><issn>2169-3536</issn><issn>2169-3536</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>DOA</sourceid><recordid>eNpNkV1LwzAYhYsoONRf4E3B6858p73UOefAD3B6J4QkfasZW1OTVvDfm60i5iIfh_OcvHCy7ByjKcaouryazear1ZQggqYUYSlpeZBNCBZVQTkVh__ux9lZjGuUVpkkLifZ2yMMwW996D6czedf0PbFtY5Q56uN6_Ib6MH2zre5bpM0dF2AGHdv1-bP3vg-UYugY-fa973nQbeuGzZ6B51mR43eRDj7PU-y19v5y-yuuH9aLGdX94VlqOwLIbiUghLChW6saXRdCk4Ys6KWUFVWCivAYNTgSjBM0k5rAzWBBgBVGtOTbDnm1l6vVRfcVodv5bVTe8GHd6VDmnQDqrQGTImIlpVhUCJjOK8lg1pYIzmlKetizOqC_xwg9mrth9Cm8RVhnAlGGRfJRUeXDT7GAM3frxipXStqbEXtWlG_rSTqfKQcAPwRFZaYEkp_ACI9iNc</recordid><startdate>2020</startdate><enddate>2020</enddate><creator>Muthusamy, Rajkumar</creator><creator>Huang, Xiaoqian</creator><creator>Zweiri, Yahya</creator><creator>Seneviratne, Lakmal</creator><creator>Gan, Dongming</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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| source | IEEE Open Access Journals; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals |
| subjects | Dynamic vision sensor event camera Feature extraction fuzzy control Grasping Grasping (robotics) Grip force Neuromorphics object manipulation Object recognition Performance evaluation Robot sensing systems robotic grasping Slip slip detection slip suppression Task analysis Uncertainty vision based tactile sensing |
| title | Neuromorphic Event-Based Slip Detection and Suppression in Robotic Grasping and Manipulation |
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