Behavior-Based Switch-Time MPC for Mobile Robots

dc.contributor.author Droge, Greg en_US
dc.contributor.author Kingston, Peter en_US
dc.contributor.author Egerstedt, Magnus B. en_US
dc.contributor.corporatename Georgia Institute of Technology. School of Electrical and Computer Engineering en_US
dc.contributor.corporatename Georgia Institute of Technology. Center for Robotics and Intelligent Machines en_US
dc.date.accessioned 2013-02-13T16:03:45Z
dc.date.available 2013-02-13T16:03:45Z
dc.date.issued 2012-10
dc.description © 2012 IEEE. 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 distribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. en_US
dc.description Presented at the 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), October 7-12, 2012, Vilamoura, Algarve, Portugal. en_US
dc.description DOI: 10.1109/IROS.2012.6385676 en_US
dc.description.abstract Model predictive control can be computationally intensive as it has to compute an optimal control trajectory at each time instant. As such, we present a method in which parametrized behaviors are introduced as a level of abstraction to give a finite representation to the control trajectory optimization. As these control laws can be designed to accomplish different tasks, the robot is able to use the presented framework to tune the parameters online to achieve desirable results. Moreover, we build on switch-time optimization techniques to allow the model predictive control framework to optimize over a series of given behaviors, allowing for an added level of adaptability. We illustrate the utility of the framework through the control of a nonholonomic mobile robot. en_US
dc.identifier.citation G. Droge, P. Kingston, and M. Egerstedt, “Behavior-Based Switch-Time MPC for Mobile Robots,” IEEE/RSJ International Conference on Intelligent Robots and Systems , Algarve, Portugal, Oct. 2012. en_US
dc.identifier.doi 10.1109/IROS.2012.6385676
dc.identifier.isbn 978-1-4673-1737-5
dc.identifier.issn 2153-0858
dc.identifier.uri http://hdl.handle.net/1853/46179
dc.language.iso en_US en_US
dc.publisher Georgia Institute of Technology en_US
dc.publisher.original Institute of Electrical and Electronics Engineers en_US
dc.subject Mobile robots en_US
dc.subject Switch time optimization techniques en_US
dc.subject Model predictive control en_US
dc.title Behavior-Based Switch-Time MPC for Mobile Robots en_US
dc.type Text
dc.type.genre Proceedings
dc.type.genre Post-print
dspace.entity.type Publication
local.contributor.author Egerstedt, Magnus B.
local.contributor.corporatename School of Electrical and Computer Engineering
local.contributor.corporatename College of Engineering
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