Level Sets and Stable Manifold Approximations for Perceptually Driven Nonholonomically Constrained Navigation

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dc.contributor.authorLopes, Gabriel A. D.
dc.contributor.authorKoditschek, Daniel E
dc.date2023-05-16T22:30:16.000
dc.date.accessioned2023-05-22T19:06:12Z
dc.date.available2023-05-22T19:06:12Z
dc.date.issued2004-09-28
dc.date.submitted2005-06-01T14:14:13-07:00
dc.description.abstractThis paper addresses problems of robot navigation with nonholonomic motion constraints and perceptual cues arising from onboard visual servoing in partially engineered environments. We focus on a unicycle motion model and a variety of artificial beacon constellations motivated by relevance to the autonomous hexapod, RHex. We propose a general hybrid procedure that adapts to the constrained motion setting the standard feedback controller arising from a navigation function in the fully actuated case by switching back and forth between moving "down" and "across" the associated gradient field toward the stable manifold it induces in the constrained dynamics. Guaranteed to avoid obstacles in all cases, we provide some reasonably general sufficient conditions under which the new procedure guarantees convergence to the goal. Simulations are provided for perceptual models previously introduced by other authors.
dc.description.commentsCopyright 2004 IEEE. Reprinted from Proceedings of the 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2004), Volume 2, pages 1481-1486. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it. NOTE: At the time of publication, author Daniel Koditschek was affiliated with the University of Michigan. Currently (August 2005), he is a faculty member in the Department of Electrical and Systems Engineering at the University of Pennsylvania.
dc.description.commentsCopyright 2004 IEEE. Reprinted from Proceedings of the 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2004), pages 1481-1486. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it. NOTE: At the time of publication, author Daniel Koditschek was affiliated with the University of Michigan. Currently (June 2005), he is a faculty member in the Department of Electrical and Systems Engineering at the University of Pennsylvania.
dc.identifier.urihttps://repository.upenn.edu/handle/20.500.14332/33312
dc.legacy.articleid1127
dc.legacy.fulltexturlhttps://repository.upenn.edu/cgi/viewcontent.cgi?article=1127&context=ese_papers&unstamped=1
dc.source.issue131
dc.source.journalDepartmental Papers (ESE)
dc.source.statuspublished
dc.subject.otherGRASP
dc.subject.otherKodlab
dc.titleLevel Sets and Stable Manifold Approximations for Perceptually Driven Nonholonomically Constrained Navigation
dc.typePresentation
digcom.identifierese_papers/131
digcom.identifier.contextkey74142
digcom.identifier.submissionpathese_papers/131
digcom.typeconference
dspace.entity.typePublication
relation.isAuthorOfPublicationb6e8657c-2331-43df-97c3-92e79f74a7be
relation.isAuthorOfPublication.latestForDiscoveryb6e8657c-2331-43df-97c3-92e79f74a7be
upenn.schoolDepartmentCenterDepartmental Papers (ESE)
upenn.schoolDepartmentCenterGeneral Robotics, Automation, Sensing and Perception Laboratory
upenn.schoolDepartmentCenterKod*lab
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