Learning to automatically detect features for mobile robots using second-order Hidden Markov Models

Autor: Richard Washington, Jean-François Mari, Olivier Aycard
Přispěvatelé: Geometry and Probability for Motion and Action (E-MOTION), Laboratoire d'informatique GRAphique, VIsion et Robotique de Grenoble (GRAVIR - IMAG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria), Knowledge representation, reasonning (ORPAILLEUR), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS), NASA Ames Research Center (ARC), Automatic Programming and Decisional Systems in Robotics (SHARP), INRIA, Laugier, Christian, Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Lorraine (INPL)-Université Nancy 2-Université Henri Poincaré - Nancy 1 (UHP)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Lorraine (INPL)-Université Nancy 2-Université Henri Poincaré - Nancy 1 (UHP)
Jazyk: angličtina
Rok vydání: 2003
Předmět:
FOS: Computer and information sciences
0209 industrial biotechnology
Computer science
Computer Science - Artificial Intelligence
[INFO.INFO-OH]Computer Science [cs]/Other [cs.OH]
lcsh:TK7800-8360
02 engineering and technology
lcsh:QA75.5-76.95
[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Computer Science::Robotics
020901 industrial engineering & automation
Artificial Intelligence
mobile robots
0202 electrical engineering
electronic engineering
information engineering
Computer Science::Networking and Internet Architecture
sensor data interpretation
Computer vision
robot mobile
Hidden Markov model
interprétation de données
Interpretation (logic)
Artificial neural network
business.industry
Maximum-entropy Markov model
lcsh:Electronics
hidden markov models
Pattern recognition
Mobile robot
Computer Science Applications
[INFO.INFO-OH] Computer Science [cs]/Other [cs.OH]
Artificial Intelligence (cs.AI)
Order (business)
Pattern recognition (psychology)
020201 artificial intelligence & image processing
Artificial intelligence
lcsh:Electronic computers. Computer science
hmm
business
Software
Zdroj: proceedings of the IJCAI'03-Workshop on Reasoning with Uncertainty in Robotics-IJCAI-RUR'03
proceedings of the IJCAI'03-Workshop on Reasoning with Uncertainty in Robotics-IJCAI-RUR'03, Aug 2003, Acapulco, Mexico, France. 8 p
[Research Report] RR-4659, INRIA. 2002, pp.32
International Journal of Advanced Robotic Systems
International Journal of Advanced Robotic Systems, 2004, 1, 1 (4), pp.231--245
International Journal of Advanced Robotic Systems, Vol 1 (2004)
International Journal of Advanced Robotic Systems, InTech, 2004, 1, 1 (4), pp.231--245
International Journal of Advanced Robotic Systems, Vol 1, Iss 4 (2008)
ISSN: 1729-8806
1729-8814
Popis: In this paper, we propose a new method based on Hidden Markov Models to interpret temporal sequences of sensor data from mobile robots to automatically detect features. Hidden Markov Models have been used for a long time in pattern recognition, especially in speech recognition. Their main advantages over other methods (such as neural networks) are their ability to model noisy temporal signals of variable length. We show in this paper that this approach is well suited for interpretation of temporal sequences of mobile-robot sensor data. We present two distinct experiments and results: the first one in an indoor environment where a mobile robot learns to detect features like open doors or T-intersections, the second one in an outdoor environment where a different mobile robot has to identify situations like climbing a hill or crossing a rock.
2004
Databáze: OpenAIRE
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