Two-Stage Focused Inference for Resource-Constrained Collision-Free Navigation
Long-term operations of resource-constrained robots typically require hard decisions be made about which data to process and/or retain. The question then arises of how to choose which data is most useful to keep to achieve the task at hand. As spacial scale grows, the size of the map will grow witho...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | en_US |
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2015
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| Online Access: | http://hdl.handle.net/1721.1/97917 https://orcid.org/0000-0001-8438-7668 https://orcid.org/0000-0002-8863-6550 https://orcid.org/0000-0001-8576-1930 https://orcid.org/0000-0003-2492-6660 |
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| author | Mu, Beipeng Agha-mohammadi, Ali-akbar Paull, Liam Graham, Matthew How, Jonathan P. Leonard, John Joseph |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Mu, Beipeng Agha-mohammadi, Ali-akbar Paull, Liam Graham, Matthew How, Jonathan P. Leonard, John Joseph |
| author_sort | Mu, Beipeng |
| collection | MIT |
| description | Long-term operations of resource-constrained robots typically require hard decisions be made about which data to process and/or retain. The question then arises of how to choose which data is most useful to keep to achieve the task at hand. As spacial scale grows, the size of the map will grow without bound, and as temporal scale grows, the number of measurements will grow without bound. In this work, we present the first known approach to tackle both of these issues. The approach has two stages. First, a subset of the variables (focused variables) is selected that are most useful for a particular task. Second, a task-agnostic and principled method (focused inference) is proposed to select a subset of the measurements that maximizes the information over the focused variables. The approach is then applied to the specific task of robot navigation in an obstacle-laden environment. A landmark selection method is proposed to minimize the probability of collision and then select the set of measurements that best localizes those landmarks. It is shown that the two-stage approach outperforms both only selecting measurement and only selecting landmarks in terms of minimizing the probability of collision. The performance improvement is validated through detailed simulation and real experiments on a Pioneer robot. |
| first_indexed | 2024-09-23T09:52:54Z |
| format | Article |
| id | mit-1721.1/97917 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T09:52:54Z |
| publishDate | 2015 |
| record_format | dspace |
| spelling | mit-1721.1/979172022-09-30T17:25:13Z Two-Stage Focused Inference for Resource-Constrained Collision-Free Navigation Mu, Beipeng Agha-mohammadi, Ali-akbar Paull, Liam Graham, Matthew How, Jonathan P. Leonard, John Joseph Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Massachusetts Institute of Technology. Department of Mechanical Engineering Massachusetts Institute of Technology. Laboratory for Information and Decision Systems Mu, Beipeng Agha-mohammadi, Ali-akbar Paull, Liam Graham, Matthew How, Jonathan P. Leonard, John Joseph Long-term operations of resource-constrained robots typically require hard decisions be made about which data to process and/or retain. The question then arises of how to choose which data is most useful to keep to achieve the task at hand. As spacial scale grows, the size of the map will grow without bound, and as temporal scale grows, the number of measurements will grow without bound. In this work, we present the first known approach to tackle both of these issues. The approach has two stages. First, a subset of the variables (focused variables) is selected that are most useful for a particular task. Second, a task-agnostic and principled method (focused inference) is proposed to select a subset of the measurements that maximizes the information over the focused variables. The approach is then applied to the specific task of robot navigation in an obstacle-laden environment. A landmark selection method is proposed to minimize the probability of collision and then select the set of measurements that best localizes those landmarks. It is shown that the two-stage approach outperforms both only selecting measurement and only selecting landmarks in terms of minimizing the probability of collision. The performance improvement is validated through detailed simulation and real experiments on a Pioneer robot. United States. Army Research Office. Multidisciplinary University Research Initiative (Grant W911NF-11-1-0391) United States. Office of Naval Research (Grant N00014-11-1-0688) National Science Foundation (U.S.) (Award IIS-1318392) 2015-07-29T13:15:39Z 2015-07-29T13:15:39Z 2015-07 Article http://purl.org/eprint/type/ConferencePaper 2330-765X http://hdl.handle.net/1721.1/97917 Mu, Beipeng, Ali-akbar Agha-mohammadi, Liam Paull, Matthew Graham, Jonathan How, John Leonard. "Two-Stage Focused Inference for Resource-Constrained Collision-Free Navigation." 2015 Robotics: Science and Systems Conference (July 2015). https://orcid.org/0000-0001-8438-7668 https://orcid.org/0000-0002-8863-6550 https://orcid.org/0000-0001-8576-1930 https://orcid.org/0000-0003-2492-6660 en_US http://www.roboticsproceedings.org/rss11/p04.html Proceedings of the 2015 Robotics: Science and Systems Conference Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Robotics Proceedings |
| spellingShingle | Mu, Beipeng Agha-mohammadi, Ali-akbar Paull, Liam Graham, Matthew How, Jonathan P. Leonard, John Joseph Two-Stage Focused Inference for Resource-Constrained Collision-Free Navigation |
| title | Two-Stage Focused Inference for Resource-Constrained Collision-Free Navigation |
| title_full | Two-Stage Focused Inference for Resource-Constrained Collision-Free Navigation |
| title_fullStr | Two-Stage Focused Inference for Resource-Constrained Collision-Free Navigation |
| title_full_unstemmed | Two-Stage Focused Inference for Resource-Constrained Collision-Free Navigation |
| title_short | Two-Stage Focused Inference for Resource-Constrained Collision-Free Navigation |
| title_sort | two stage focused inference for resource constrained collision free navigation |
| url | http://hdl.handle.net/1721.1/97917 https://orcid.org/0000-0001-8438-7668 https://orcid.org/0000-0002-8863-6550 https://orcid.org/0000-0001-8576-1930 https://orcid.org/0000-0003-2492-6660 |
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