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Intelligent autonom...
Intelligent autonomous navigation for mobile robots : Spatial concept acquisition and object discrimination
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- Antonelo, Eric Aislan (author)
- Högskolan i Halmstad,Halmstad Embedded and Intelligent Systems Research (EIS)
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- Figueiredo, Maurício Fernandes (author)
- Department of Computer Science, State University of Maringá, 87020-900, Maringá - PR, Brazil
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- Baerveldt, Albert Jan (author)
- Högskolan i Halmstad,Halmstad Embedded and Intelligent Systems Research (EIS)
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- Calvo, Rodrigo A. (author)
- Department of Computer Science and Statistics, University of São Paulo, São Carlos, Brazil
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(creator_code:org_t)
- New York : IEEE Press, 2005
- 2005
- English.
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In: 2005 IEEE International Symposium on Computational Intelligence in Robotics and Automation, Proceedings. - New York : IEEE Press. - 0780393554 ; , s. 553-557
- Related links:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Subject headings
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- An autonomous system able to construct its own navigation strategy for mobile robots is proposed. The navigation strategy is molded from navigation experiences (succeeding as the robot navigates) according to a classical reinforcement learning procedure. The autonomous system is based on modular hierarchical neural networks. Initially the navigation performance is poor (many collisions occur). Computer simulations show that after a period of learning the autonomous system generates efficient obstacle avoidance and target seeking behaviors. Experiments also offer support for concluding that the autonomous system develops a variety of object discrimination capability and of spatial concepts.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Robotteknik och automation (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Robotics (hsv//eng)
Keyword
- intelligent autonomous navigation
- neural networks
- reinforcement learning
- mobile robots
- biologically inspired models
Publication and Content Type
- ref (subject category)
- kon (subject category)
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