An Adaptive Memory Model for Long-Term Navigation of Autonomous Mobile Robots
This paper introduces an environmental representation for autonomous mobile robots that continuously adapts over time. The presented approach is inspired by human memory information processing and stores the current as well as past knowledge of the environment. In this paper, the memory model is app...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2011-01-01
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| Series: | Journal of Robotics |
| Online Access: | http://dx.doi.org/10.1155/2011/506245 |
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| _version_ | 1832556703147622400 |
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| author | M. Hentschel B. Wagner |
| author_facet | M. Hentschel B. Wagner |
| author_sort | M. Hentschel |
| collection | DOAJ |
| description | This paper introduces an environmental representation for autonomous mobile robots that continuously adapts over time. The presented approach is inspired by human memory information processing and stores the current as well as past knowledge of the environment. In this paper, the memory model is applied to time-variant information about obstacles and driveable routes in the workspace of the autonomous robot and used for solving the navigation cycle of the robot. This includes localization and path planning as well as vehicle control. The presented approach is evaluated in a real-world experiment within changing indoor environment. The results show that the environmental representation is stable, improves its quality over time, and adapts to changes. |
| format | Article |
| id | doaj-art-b2dbc888ff804738b7b6d8018939cf54 |
| institution | Kabale University |
| issn | 1687-9600 1687-9619 |
| language | English |
| publishDate | 2011-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Robotics |
| spelling | doaj-art-b2dbc888ff804738b7b6d8018939cf542025-02-03T05:44:42ZengWileyJournal of Robotics1687-96001687-96192011-01-01201110.1155/2011/506245506245An Adaptive Memory Model for Long-Term Navigation of Autonomous Mobile RobotsM. Hentschel0B. Wagner1Department of Real Time Systems, Institute for Systems Engineering, Leibniz Universität Hannover, 30167 Hannover, GermanyDepartment of Real Time Systems, Institute for Systems Engineering, Leibniz Universität Hannover, 30167 Hannover, GermanyThis paper introduces an environmental representation for autonomous mobile robots that continuously adapts over time. The presented approach is inspired by human memory information processing and stores the current as well as past knowledge of the environment. In this paper, the memory model is applied to time-variant information about obstacles and driveable routes in the workspace of the autonomous robot and used for solving the navigation cycle of the robot. This includes localization and path planning as well as vehicle control. The presented approach is evaluated in a real-world experiment within changing indoor environment. The results show that the environmental representation is stable, improves its quality over time, and adapts to changes.http://dx.doi.org/10.1155/2011/506245 |
| spellingShingle | M. Hentschel B. Wagner An Adaptive Memory Model for Long-Term Navigation of Autonomous Mobile Robots Journal of Robotics |
| title | An Adaptive Memory Model for Long-Term Navigation of Autonomous Mobile Robots |
| title_full | An Adaptive Memory Model for Long-Term Navigation of Autonomous Mobile Robots |
| title_fullStr | An Adaptive Memory Model for Long-Term Navigation of Autonomous Mobile Robots |
| title_full_unstemmed | An Adaptive Memory Model for Long-Term Navigation of Autonomous Mobile Robots |
| title_short | An Adaptive Memory Model for Long-Term Navigation of Autonomous Mobile Robots |
| title_sort | adaptive memory model for long term navigation of autonomous mobile robots |
| url | http://dx.doi.org/10.1155/2011/506245 |
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