A Distributed Model for Mobile Robot Environment-Learning and Navigation
A distributed method for mobile robot navigation, spatial learning, and path planning is presented. It is implemented on a sonar-based physical robot, Toto, consisting of three competence layers: 1) Low-level navigation: a collection of reflex-like rules resulting in emergent boundary-tracing....
| Autor principal: | |
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| Idioma: | en_US |
| Publicado em: |
2004
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| Acesso em linha: | http://hdl.handle.net/1721.1/7027 |
| _version_ | 1826192299180163072 |
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| author | Mataric, Maja J. |
| author_facet | Mataric, Maja J. |
| author_sort | Mataric, Maja J. |
| collection | MIT |
| description | A distributed method for mobile robot navigation, spatial learning, and path planning is presented. It is implemented on a sonar-based physical robot, Toto, consisting of three competence layers: 1) Low-level navigation: a collection of reflex-like rules resulting in emergent boundary-tracing. 2) Landmark detection: dynamically extracts landmarks from the robot's motion. 3) Map learning: constructs a distributed map of landmarks. The parallel implementation allows for localization in constant time. Spreading of activation computes both topological and physical shortest paths in linear time. The main issues addressed are: distributed, procedural, and qualitative representation and computation, emergent behaviors, dynamic landmarks, minimized communication. |
| first_indexed | 2024-09-23T09:09:20Z |
| id | mit-1721.1/7027 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T09:09:20Z |
| publishDate | 2004 |
| record_format | dspace |
| spelling | mit-1721.1/70272019-04-10T21:03:07Z A Distributed Model for Mobile Robot Environment-Learning and Navigation Mataric, Maja J. A distributed method for mobile robot navigation, spatial learning, and path planning is presented. It is implemented on a sonar-based physical robot, Toto, consisting of three competence layers: 1) Low-level navigation: a collection of reflex-like rules resulting in emergent boundary-tracing. 2) Landmark detection: dynamically extracts landmarks from the robot's motion. 3) Map learning: constructs a distributed map of landmarks. The parallel implementation allows for localization in constant time. Spreading of activation computes both topological and physical shortest paths in linear time. The main issues addressed are: distributed, procedural, and qualitative representation and computation, emergent behaviors, dynamic landmarks, minimized communication. 2004-10-20T20:22:47Z 2004-10-20T20:22:47Z 1990-05-01 AITR-1228 http://hdl.handle.net/1721.1/7027 en_US AITR-1228 9091597 bytes 7065742 bytes application/postscript application/pdf application/postscript application/pdf |
| spellingShingle | Mataric, Maja J. A Distributed Model for Mobile Robot Environment-Learning and Navigation |
| title | A Distributed Model for Mobile Robot Environment-Learning and Navigation |
| title_full | A Distributed Model for Mobile Robot Environment-Learning and Navigation |
| title_fullStr | A Distributed Model for Mobile Robot Environment-Learning and Navigation |
| title_full_unstemmed | A Distributed Model for Mobile Robot Environment-Learning and Navigation |
| title_short | A Distributed Model for Mobile Robot Environment-Learning and Navigation |
| title_sort | distributed model for mobile robot environment learning and navigation |
| url | http://hdl.handle.net/1721.1/7027 |
| work_keys_str_mv | AT mataricmajaj adistributedmodelformobilerobotenvironmentlearningandnavigation AT mataricmajaj distributedmodelformobilerobotenvironmentlearningandnavigation |