Online task recognition and real-time adaptive assistance for computer-aided machine control
2006 (English)In: IEEE Transactions on robotics, ISSN 1552-3098, Vol. 22, no 5, 1029-1033 p.Article in journal (Refereed) Published
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.
Place, publisher, year, edition, pages
2006. Vol. 22, no 5, 1029-1033 p.
Hidden Markov models (HMMs), human-machine collaborative systems (HMCSs), support vector machines (SVMs), virtual fixtures
Computer Vision and Robotics (Autonomous Systems)
IdentifiersURN: urn:nbn:se:kth:diva-16036DOI: 10.1109/TRO.2006.878976ISI: 000241080500015ScopusID: 2-s2.0-33750212019OAI: oai:DiVA.org:kth-16036DiVA: diva2:334078
QC 20100525 QC 20110927. Conference: 2nd International Conference on Informatics in Control, Automation and Robotics (ICINCO 2005). Barcelona, SPAIN. SEP 14-17, 2005 2010-08-052010-08-052012-01-23Bibliographically approved