Online task recognition and real-time adaptive assistance for computer-aided machine control

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Fältnamn	Indikatorer	Metadata
000		02822naa a2200361 4500
001		oai:DiVA.org:kth-16036
003		SwePub
008		100805s2006 \| \|\|\|\|\|\|\|\|\|\|\|000 \|\|eng\|
024	7	a https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-160362 URI
024	7	a https://doi.org/10.1109/TRO.2006.8789762 DOI
040		a (SwePub)kth
041		a engb eng
042		9 SwePub
072	7	a ref2 swepub-contenttype
072	7	a art2 swepub-publicationtype
100	1	a Ekvall, Staffanu KTH,Datorseende och robotik, CVAP4 aut0 (Swepub:kth)u1xtxu9i
245	1 0	a Online task recognition and real-time adaptive assistance for computer-aided machine control
264	1	c 2006
338		a print2 rdacarrier
500		a QC 20100525 QC 20110927. Conference: 2nd International Conference on Informatics in Control, Automation and Robotics (ICINCO 2005). Barcelona, SPAIN. SEP 14-17, 2005
520		a Segmentation and recognition of operator-generated motions are commonly facilitated to provide appropriate assistance during task execution in teleoperative and human-machine collaborative settings. The assistance is usually provided in a virtual fixture framework where the level of compliance can be altered online, thus improving the performance in terms of execution time and overall precision. However, the fixtures are typically inflexible, resulting in a degraded performance in cases of unexpected obstacles or incorrect fixture models. In this paper, we present a method for online task tracking and propose the use of adaptive virtual fixtures that can cope with the above problems. Here, rather than executing a predefined plan, the operator has the ability to avoid unforeseen obstacles and deviate from the model. To allow this, the probability of following a certain trajectory (subtask) is estimated and used to automatically adjusts the compliance, thus providing the online decision of how to fixture the movement.
650	7	a NATURVETENSKAPx Data- och informationsvetenskapx Datorseende och robotik0 (SwePub)102072 hsv//swe
650	7	a NATURAL SCIENCESx Computer and Information Sciencesx Computer Vision and Robotics0 (SwePub)102072 hsv//eng
653		a Hidden Markov models (HMMs)
653		a human-machine collaborative systems (HMCSs)
653		a support vector machines (SVMs)
653		a virtual fixtures
700	1	a Aarno, Danielu KTH,Numerisk Analys och Datalogi, NADA4 aut0 (Swepub:kth)u16in80f
700	1	a Kragic, Danicau KTH,Centrum för Autonoma System, CAS4 aut0 (Swepub:kth)u1ydsyln
710	2	a KTHb Datorseende och robotik, CVAP4 org
773	0	t IEEE Transactions on roboticsg 22:5, s. 1029-1033q 22:5<1029-1033x 1552-3098x 1941-0468
856	4 8	u https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-16036
856	4 8	u https://doi.org/10.1109/TRO.2006.878976

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Av författaren/redakt...: Ekvall, Staffan; Aarno, Daniel; Kragic, Danica

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