IcLQG: Combining local and global optimization for control in information space
When a mobile robot does not have perfect knowledge of its position, conventional controllers can experience failures such as collisions because the uncertainty of the position is not considered in choosing control actions. In this paper, we show how global planning and local feedback control can be...
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Institute of Electrical and Electronics Engineers
2010
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| Online Access: | http://hdl.handle.net/1721.1/59401 https://orcid.org/0000-0002-8293-0492 |
| _version_ | 1811073345375961088 |
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| author | Roy, Nicholas Huynh, Vu Anh |
| author2 | Massachusetts Institute of Technology. Department of Aeronautics and Astronautics |
| author_facet | Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Roy, Nicholas Huynh, Vu Anh |
| author_sort | Roy, Nicholas |
| collection | MIT |
| description | When a mobile robot does not have perfect knowledge of its position, conventional controllers can experience failures such as collisions because the uncertainty of the position is not considered in choosing control actions. In this paper, we show how global planning and local feedback control can be combined to generate control laws in the space of distributions over position, that is, in information space. We give a novel algorithm for computing ldquoinformation-constrainedrdquo linear quadratic Gaussian (icLQG) policies for controlling a robot with imperfect state information. The icLQG algorithm uses the belief roadmap algorithm to efficiently search for a trajectory that approximates the globally-optimal motion plan in information space, and then iteratively computes a feedback control law to locally optimize the global approximation. The icLQG algorithm is not only robust to imperfect state information but also scalable to high-dimensional systems and environments. In addition, icLQG is capable of answering multiple queries efficiently. We demonstrate performance results for controlling a vehicle on the plane and a helicopter in three dimensions. |
| first_indexed | 2024-09-23T09:31:36Z |
| format | Article |
| id | mit-1721.1/59401 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T09:31:36Z |
| publishDate | 2010 |
| publisher | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | mit-1721.1/594012022-09-26T12:01:33Z IcLQG: Combining local and global optimization for control in information space Roy, Nicholas Huynh, Vu Anh Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Roy, Nicholas Roy, Nicholas Huynh, Vu Anh When a mobile robot does not have perfect knowledge of its position, conventional controllers can experience failures such as collisions because the uncertainty of the position is not considered in choosing control actions. In this paper, we show how global planning and local feedback control can be combined to generate control laws in the space of distributions over position, that is, in information space. We give a novel algorithm for computing ldquoinformation-constrainedrdquo linear quadratic Gaussian (icLQG) policies for controlling a robot with imperfect state information. The icLQG algorithm uses the belief roadmap algorithm to efficiently search for a trajectory that approximates the globally-optimal motion plan in information space, and then iteratively computes a feedback control law to locally optimize the global approximation. The icLQG algorithm is not only robust to imperfect state information but also scalable to high-dimensional systems and environments. In addition, icLQG is capable of answering multiple queries efficiently. We demonstrate performance results for controlling a vehicle on the plane and a helicopter in three dimensions. National Science Foundation (U.S.). Division of Information, Robotics, and Intelligent Systems (grant # 0546467) Singapore-MIT Alliance 2010-10-19T15:29:22Z 2010-10-19T15:29:22Z 2009-07 Article http://purl.org/eprint/type/JournalArticle 978-1-4244-3803-7 INSPEC Accession Number: 11010176 http://hdl.handle.net/1721.1/59401 Vu Anh Huynh, and N. Roy. “icLQG: Combining local and global optimization for control in information space.” Robotics and Automation, 2009. ICRA '09. IEEE International Conference on. 2009. 2851-2858. © 2009 IEEE https://orcid.org/0000-0002-8293-0492 en_US http://dx.doi.org/10.1109/ROBOT.2009.5152607 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2009. IROS 2009. Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf Institute of Electrical and Electronics Engineers IEEE |
| spellingShingle | Roy, Nicholas Huynh, Vu Anh IcLQG: Combining local and global optimization for control in information space |
| title | IcLQG: Combining local and global optimization for control in information space |
| title_full | IcLQG: Combining local and global optimization for control in information space |
| title_fullStr | IcLQG: Combining local and global optimization for control in information space |
| title_full_unstemmed | IcLQG: Combining local and global optimization for control in information space |
| title_short | IcLQG: Combining local and global optimization for control in information space |
| title_sort | iclqg combining local and global optimization for control in information space |
| url | http://hdl.handle.net/1721.1/59401 https://orcid.org/0000-0002-8293-0492 |
| work_keys_str_mv | AT roynicholas iclqgcombininglocalandglobaloptimizationforcontrolininformationspace AT huynhvuanh iclqgcombininglocalandglobaloptimizationforcontrolininformationspace |