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Robot Task Planning Using Semantic Maps
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- Galindo, Cipriano (author)
- Dept. of System Engineering and Automation, University of Malaga, Spain
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- Fernández-Madrigal, Juan-Antonio (author)
- Dept. of System Engineering and Automation, University of Malaga, Spain
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- González, Javier (author)
- Dept. of System Engineering and Automation, University of Malaga, Spain
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- Dept of System Engineering and Automation, University of Malaga, Spain Akademin för naturvetenskap och teknik (creator_code:org_t)
- Amsterdam : Elsevier, 2008
- 2008
- English.
- In: Robotics and Autonomous Systems. - Amsterdam : Elsevier. - 0921-8890 .- 1872-793X. ; 56:11, s. 955-966
- Journal article (peer-reviewed)
Abstract
Subject headings
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- Task planning for mobile robots usually relies solely on spatial information and on shallow domain knowledge, like labels attached to objects and places. Although spatial information is necessary for performing basic robot operations (navigation and localization), the use of deeper domain knowledge is pivotal to endow a robot with higher degrees of autonomy and intelligence. In this paper, we focus on semantic knowledge, and show how this type of knowledge can be profitably used for robot task planning. We start by defining a specific type of semantic maps, which integrate hierarchical spatial information and semantic knowledge. We then proceed to describe how these semantic maps can improve task planning in two ways: extending the capabilities of the planner by reasoning about semantic information, and improving the planning efficiency in large domains. We show several experiments that demonstrate the effectiveness of our solutions in a domain involving robot navigation in a domestic environment.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Keyword
- Computer science
- Datavetenskap
- TECHNOLOGY
- TEKNIKVETENSKAP
- Datalogi
- Computer and Systems Science
Publication and Content Type
- ref (subject category)
- art (subject category)
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