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28 results on '"Emmanuel Mazer"'

1. Autonomous Robot Controller Using Bitwise GIBBS Sampling

Author

Raphaël Laurent, Emmanuel Mazer, Marvin Faix, Dominique Vaufreydaz, Rémi Canillas, PERVASIVE INTERACTION (PERVASIVE INTERACTION), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Informatique de Grenoble (LIG ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Probayes [Montbonnot], Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Institut d'Informatique et de Mathématiques Appliquées de Grenoble (IMAG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), and Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)

Subjects
Computer science, Bayesian programming, 01 natural sciences, Computer Science::Robotics, symbols.namesake, Gibbs sampling, Control theory, 0103 physical sciences, Obstacle avoidance, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], Bitwise operation, Stochastic computing, 010308 nuclear & particles physics, business.industry, Control engineering, Autonomous robot, [MATH.MATH-PR]Mathematics [math]/Probability [math.PR], autonomous robots, symbols, stochastic computing, [INFO.INFO-ES]Computer Science [cs]/Embedded Systems, Artificial intelligence, business, Von Neumann architecture
Abstract

International audience; In the present paper we describe a bio-inspired non von Neumann controller for a simple sensorimotor robotic system. This controller uses a bitwise version of the Gibbs sampling algorithm to select commands so the robot can adapt its course of action and avoid perceived obstacles in the environment. The VHDL specification of the circuit implementation of this controller is based on stochastic computation to perform Bayesian inference at a low energy cost. We show that the proposed unconventional architecture allows to successfully carry out the obstacle avoidance task and to address scalability issues observed in previous works.

Published
2016

2. Bayesian Programming

Author

Pierre Bessière, Emmanuel Mazer, Juan Manuel Ahuactzin-Larios, Kamel Mekhnacha, Geometry and Probability for Motion and Action (E-MOTION), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Physiologie de la Perception et de l'Action (LPPA), Collège de France (CdF (institution))-Centre National de la Recherche Scientifique (CNRS), 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), Institut d'Informatique et de Mathématiques Appliquées de Grenoble (IMAG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), Probayes [Montbonnot], Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Grenoble (INPG)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Grenoble (INPG)-Inria Grenoble - Rhône-Alpes, Laboratoire d'Informatique de Grenoble (LIG), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Inria Grenoble - Rhône-Alpes, and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)

Subjects
[MATH.MATH-PR]Mathematics [math]/Probability [math.PR], 03 medical and health sciences, 0302 clinical medicine, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], 0202 electrical engineering, electronic engineering, information engineering, [SCCO.COMP]Cognitive science/Computer science, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], 020201 artificial intelligence & image processing, 02 engineering and technology, 030217 neurology & neurosurgery, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

International audience; Features Presents a new modeling methodology and inference algorithms for Bayesian programming Explains how to build efficient Bayesian models Addresses controversies, historical notes, epistemological debates, and tricky technical questions in a dedicated chapter separate from the main text Encourages further research on new programming languages and specialized hardware for computing large-scale Bayesian inference problems Offers an online Python package for running and modifying the Python program examples in the book Summary Probability as an Alternative to Boolean Logic While logic is the mathematical foundation of rational reasoning and the fundamental principle of computing, it is restricted to problems where information is both complete and certain. However, many real-world problems, from financial investments to email filtering, are incomplete or uncertain in nature. Probability theory and Bayesian computing together provide an alternative framework to deal with incomplete and uncertain data. Decision-Making Tools and Methods for Incomplete and Uncertain Data Emphasizing probability as an alternative to Boolean logic, Bayesian Programming covers new methods to build probabilistic programs for real-world applications. Written by the team who designed and implemented an efficient probabilistic inference engine to interpret Bayesian programs, the book offers many Python examples that are also available on a supplementary website together with an interpreter that allows readers to experiment with this new approach to programming. Principles and Modeling Only requiring a basic foundation in mathematics, the first two parts of the book present a new methodology for building subjective probabilistic models. The authors introduce the principles of Bayesian programming and discuss good practices for probabilistic modeling. Numerous simple examples highlight the application of Bayesian modeling in different fields. Formalism and Algorithms The third part synthesizes existing work on Bayesian inference algorithms since an efficient Bayesian inference engine is needed to automate the probabilistic calculus in Bayesian programs. Many bibliographic references are included for readers who would like more details on the formalism of Bayesian programming, the main probabilistic models, general purpose algorithms for Bayesian inference, and learning problems. FAQs Along with a glossary, the fourth part contains answers to frequently asked questions. The authors compare Bayesian programming and possibility theories, discuss the computational complexity of Bayesian inference, cover the irreducibility of incompleteness, and address the subjectivist versus objectivist epistemology of probability. The First Steps toward a Bayesian Computer A new modeling methodology, new inference algorithms, new programming languages, and new hardware are all needed to create a complete Bayesian computing framework. Focusing on the methodology and algorithms, this book describes the first steps toward reaching that goal. It encourages readers to explore emerging areas, such as bio-inspired computing, and develop new programming languages and hardware architectures.

Published
2013

3. The Ariadne's Clew Algorithm

Author

Juan-Manuel Ahuactzin, Pierre Bessière, Emmanuel Mazer, Laboratoire d'Informatique Fondamentale et d'Intelligence Artificielle (LIFIA), Institut National Polytechnique de Grenoble (INPG), Automatic Programming and Decisional Systems in Robotics (SHARP), 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)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Grenoble (INPG)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Grenoble (INPG)-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria), Institut d'Informatique et de Mathématiques Appliquées de Grenoble (IMAG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Leibniz (Leibniz - IMAG), Laboratoire de Génie Informatique (LGI - IMAG), Université Joseph Fourier - Grenoble 1 (UJF)-IMAG, Laboratoire de Génie Informatique - LAboratoire de SCiences COgnitives (IMAG-LGI / LASCO3), IMAG, Bessiere, Pierre, 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, Université Joseph Fourier - Grenoble 1 (UJF)-IMAG-Centre National de la Recherche Scientifique (CNRS), Departamento de Ingeniería En Sistemas Computacionales, Universidad de las Américas, and Laugier, Christian

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], FOS: Computer and information sciences, 0209 industrial biotechnology, Optimization problem, Computer science, Computer Science - Artificial Intelligence, [SCCO.COMP]Cognitive science/Computer science, 02 engineering and technology, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 020901 industrial engineering & automation, Artificial Intelligence, [SCCO.COMP] Cognitive science/Computer science, 0202 electrical engineering, electronic engineering, information engineering, Six degrees of freedom, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], Motion planning, Representation (mathematics), ComputingMilieux_MISCELLANEOUS, Degrees of freedom, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], Artificial Intelligence (cs.AI), Obstacle, Path (graph theory), Robot, 020201 artificial intelligence & image processing, Algorithm
Abstract

http://www.jair.org/papers/paper468.html; International audience; We present a new approach to path planning, called the Ariadne's clew algorithm. It is designed to find paths in high-dimensional continuous spaces and applies to robots with many degrees of freedom in static, as well as dynamic environments --- ones where obstacles may move. The Ariadne's clew algorithm comprises two sub-algorithms, called SEARCH and EXPLORE, applied in an interleaved manner. EXPLORE builds a representation of the accessible space while SEARCH looks for the target. Both are posed as optimization problems. We describe a real implementation of the algorithm to plan paths for a six degrees of freedom arm in a dynamic environment where another six degrees of freedom arm is used as a moving obstacle. Experimental results show that a path is found in about one second without any pre-processing.

Published
2011
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4. Bayesian Approach to Action Selection and Attention Focusing

Author

Emmanuel Mazer, Pierre Bessière, Carla Koike, Geometry and Probability for Motion and Action (E-MOTION), Laboratoire d'Informatique de Grenoble (LIG), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF), and Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)

Subjects
0209 industrial biotechnology, Computer science, business.industry, Bayesian probability, [SCCO.COMP]Cognitive science/Computer science, Mobile robot, 02 engineering and technology, Machine learning, computer.software_genre, Action selection, Motor model, 020901 industrial engineering & automation, Joint probability distribution, 0202 electrical engineering, electronic engineering, information engineering, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], 020201 artificial intelligence & image processing, Artificial intelligence, business, computer
Published
2008
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5. Bayesian Robot Programming

Author

Pierre Bessière, Julien Diard, Olivier Lebeltel, Emmanuel Mazer, Geometry and Probability for Motion and Action (E-MOTION), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF), Laboratoire d'Informatique de Grenoble (LIG), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Inria Grenoble - Rhône-Alpes, and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)

Subjects
0209 industrial biotechnology, Iterative and incremental development, business.industry, Computer science, Probabilistic logic, [SCCO.COMP]Cognitive science/Computer science, Robotics, 02 engineering and technology, Bayesian inference, Sensor fusion, Machine learning, computer.software_genre, Robot learning, 020901 industrial engineering & automation, Artificial Intelligence, 0202 electrical engineering, electronic engineering, information engineering, Robot, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], 020201 artificial intelligence & image processing, Artificial intelligence, business, computer, Functional reactive programming
Abstract

International audience; We propose a new method to program robots based on Bayesian inference and learning. It is called BRP for Bayesian Robot Programming. The capacities of this programming method are demonstrated through a succession of increasingly complex experiments. Starting from the learning of simple reactive behaviors, we present instances of behavior combinations, sensor fusion, hierarchical behavior composition, situation recognition and temporal sequencing. This series of experiments comprises the steps in the incremental development of a complex robot program. The advantages and drawbacks of BRP are discussed along with these different experiments and summed up as a conclusion. These different robotics programs may be seen as an illustration of probabilistic programming applicable whenever one must deal with problems based on uncertain or incomplete knowledge. The scope of possible applications is obviously much broader than robotics.

Published
2004
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6. OrthoPilot: Vers une approche Bayésienne de la détermination des centres articulaires

Author

Pierre Bessière, Emmanuel Mazer, Kamel Mekhnacha, Laugier Yann, Franck Leitner, 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), Gestes Medico-chirurgicaux Assistés par Ordinateur (TIMC-IMAG-GMCAO), Techniques de l'Ingénierie Médicale et de la Complexité - Informatique, Mathématiques et Applications, Grenoble - UMR 5525 (TIMC-IMAG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Grenoble (INPG)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Grenoble (INPG)-Inria Grenoble - Rhône-Alpes, Université Joseph Fourier - Grenoble 1 (UJF)-Centre National de la Recherche Scientifique (CNRS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Université Joseph Fourier - Grenoble 1 (UJF)-Centre National de la Recherche Scientifique (CNRS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS), and VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)

Subjects
[SCCO.COMP]Cognitive science/Computer science, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], [SDV.MHEP.CHI]Life Sciences [q-bio]/Human health and pathology/Surgery, ComputingMilieux_MISCELLANEOUS, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

International audience

Published
2002

7. Modélisation bayésienne d'interactions robot/environnement téléopérées

Author

Pierre Bessière, Eric Dedieu, Olivier Lebeltel, Emmanuel Mazer, Bessiere, Pierre, Laboratoire Leibniz (Leibniz - IMAG), and Université Joseph Fourier - Grenoble 1 (UJF)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], [SCCO.COMP] Cognitive science/Computer science, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], [SCCO.COMP]Cognitive science/Computer science, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Published
1997

8. Modeleur probabiliste pour la CAO robotique

Author

Pierre Bessière, Emmanuel Mazer, Kamel Mekhnacha, Bessiere, Pierre, Laboratoire Leibniz (Leibniz - IMAG), and Université Joseph Fourier - Grenoble 1 (UJF)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], [SCCO.COMP] Cognitive science/Computer science, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], [SCCO.COMP]Cognitive science/Computer science, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Published
1997

9. Méthode probabiliste bayésienne pour la calibration en robotique

Author

Pierre Bessière, Emmanuel Mazer, Kamel Mekhnacha, Bessiere, Pierre, Laboratoire Leibniz (Leibniz - IMAG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Informatique Fondamentale et d'Intelligence Artificielle (LIFIA), and Institut National Polytechnique de Grenoble (INPG)

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], [SCCO.COMP] Cognitive science/Computer science, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], [SCCO.COMP]Cognitive science/Computer science, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Published
1996

10. Fast direct and inverse model acquisition by function decomposition

Author

R. Balaniuk, Emmanuel Mazer, Pierre Bessière, Laboratoire d'Informatique Fondamentale et d'Intelligence Artificielle (LIFIA), Institut National Polytechnique de Grenoble (INPG), Automatic Programming and Decisional Systems in Robotics (SHARP), 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, and Institut National de Recherche en Informatique et en Automatique (Inria)

Subjects
Generalized inverse, [SCCO.COMP]Cognitive science/Computer science, 02 engineering and technology, Machine learning, computer.software_genre, Functional decomposition, Electronic mail, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Data acquisition, 0502 economics and business, 0202 electrical engineering, electronic engineering, information engineering, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], ComputingMilieux_MISCELLANEOUS, 050205 econometrics, Mathematics, business.industry, 05 social sciences, Robotics, Function (mathematics), Inverse problem, 020201 artificial intelligence & image processing, Artificial intelligence, Multidimensional systems, business, computer, Algorithm
Abstract

A computational approach to direct and generalized inverse model acquisition is presented. The approach is based on a proposed method to direct model acquisition from partial information. The method decomposes a hyper-space function in one variable functions, simplifying the learning problem. The acquired direct model is then implemented in a tree-like structure that can be used in the inverse sense without additional learning effort. The authors' approach is able to acquire complete models in hyper-spaces requiring only selected data focused in one-dimension sub-spaces, strongly reducing the data acquisition effort. The authors' approach is particularly interesting for applications in robotics. The acquisition of direct models in robotics frequently takes place in high dimension phase spaces. When traditional approximation methods are used, enormous data bases, containing the examples to be interpolated, are required.

Published
1995

11. Le fil d'Ariane : une méthode de planification générale dans les espaces continus ; Application à la planification automatique de trajectoires

Author

Pierre Bessière, Juan-Manuel Ahuactzin, Emmanuel Mazer, Laboratoire d'Informatique Fondamentale et d'Intelligence Artificielle (LIFIA), Institut National Polytechnique de Grenoble (INPG), and Bessiere, Pierre

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], [SCCO.COMP] Cognitive science/Computer science, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], [SCCO.COMP]Cognitive science/Computer science, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Published
1995

12. Function decomposition for acquisition of direct and differential inverse models

Author

Remis Balaniuk, Emmanuel Mazer, Pierre Bessière, Automatic Programming and Decisional Systems in Robotics (SHARP), 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, and Institut National de Recherche en Informatique et en Automatique (Inria)

Subjects
[SCCO.COMP]Cognitive science/Computer science, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], ComputingMilieux_MISCELLANEOUS, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

International audience

Published
1995

13. Cooperating parallel genetic algorithms

Author

Pierre Bessière, El-Ghazali Talbi, Juan-Manuel Ahuactzin, Emmanuel Mazer, Laboratoire d'Informatique Fondamentale et d'Intelligence Artificielle (LIFIA), Institut National Polytechnique de Grenoble (INPG), and Bessiere, Pierre

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], [SCCO.COMP] Cognitive science/Computer science, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], [SCCO.COMP]Cognitive science/Computer science, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Published
1995

14. La modélisation sensorielle : une approche originale de la notion de modélisation dans la programmation des robots

Author

Pierre Bessière, Eric Dedieu, Emmanuel Mazer, Laboratoire d'Informatique Fondamentale et d'Intelligence Artificielle (LIFIA), Institut National Polytechnique de Grenoble (INPG), and Bessiere, Pierre

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], [SCCO.COMP] Cognitive science/Computer science, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], [SCCO.COMP]Cognitive science/Computer science, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Published
1994

15. La poubelle lumineuse : expérience de modélisation quantitative des interactions fonctionnelles sensori-motrices

Author

Olivier Lebeltel, Pierre Bessière, Emmanuel Mazer, Bessiere, Pierre, Laboratoire d'Informatique Fondamentale et d'Intelligence Artificielle (LIFIA), and Institut National Polytechnique de Grenoble (INPG)

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], [SCCO.COMP] Cognitive science/Computer science, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], [SCCO.COMP]Cognitive science/Computer science, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Published
1994

16. The 'Beam in the Bin' experiment

Author

Pierre Bessière, Eric Dedieu, Olivier Lebeltel, Emmanuel Mazer, Laboratoire d'Informatique Fondamentale et d'Intelligence Artificielle (LIFIA), Institut National Polytechnique de Grenoble (INPG), and Bessiere, Pierre

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], [SCCO.COMP] Cognitive science/Computer science, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], [SCCO.COMP]Cognitive science/Computer science, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Published
1994

17. Shape : a method for surface prevision

Author

Pierre Bessière, Remis Balaniuk, Emmanuel Mazer, Laboratoire d'Informatique Fondamentale et d'Intelligence Artificielle (LIFIA), Institut National Polytechnique de Grenoble (INPG), Automatic Programming and Decisional Systems in Robotics (SHARP), 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, and Institut National de Recherche en Informatique et en Automatique (Inria)

Subjects
[SCCO.COMP]Cognitive science/Computer science, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], ComputingMilieux_MISCELLANEOUS, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

International audience

Published
1994

18. The Ariadne's clew algorithm: Global planning with local methods

Author

Juan-Manuel Ahuactzin, Emmanuel Mazer, Pierre Bessière, El-Ghazali Talbi, Bessiere, Pierre, Laboratoire d'Informatique Fondamentale et d'Intelligence Artificielle (LIFIA), Institut National Polytechnique de Grenoble (INPG), EEC ESPRIT projects SUPERNODE II & PAPAGENA - CONACYT (Mexico), Laboratoire de Génie Informatique (LGI - IMAG), Université Joseph Fourier - Grenoble 1 (UJF)-IMAG, Departamento de Ingeniería En Sistemas Computacionales, Universidad de las Américas, A.K. Peters, K. Goldberg, D. Halperin, J.C. Latombe & R. Wilson, and K. Goldberg, D. Halperin, J.C. Latombe & R. Wilson

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], 0209 industrial biotechnology, [INFO.INFO-RO] Computer Science [cs]/Operations Research [cs.RO], Computer science, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], Parallel manipulator, [SCCO.COMP]Cognitive science/Computer science, 02 engineering and technology, [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO], Robot control, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 020901 industrial engineering & automation, Search algorithm, [SCCO.COMP] Cognitive science/Computer science, Genetic algorithm, Path (graph theory), 0202 electrical engineering, electronic engineering, information engineering, Robot, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], 020201 artificial intelligence & image processing, Motion planning, Massively parallel, Algorithm
Abstract

The goal of the work described in this paper is to build a path planner able to drive a robot in a dynamic environment where the obstacles are moving. In order to do so, we propose a method, called "Ariadne's clew algorithm", to build a global path planner based on the combination of two local planning algorithms: an EXPLORE algorithm and a SEARCH algorithm. The purpose of the EXPLORE algorithm is to collect information about the environment with an increasingly fine resolution by placing landmarks in the searched space. The goal of the SEARCH algorithm is to opportunistically check if the target can be easily reached from any given placed landmark. The Ariadne's clew algorithm is shown to be very fast in most cases allowing planning in dynamic environments. Hence, it is shown to be complete, which means that it is sure to find a path when one exists. Finally, we describe a massively parallel implementation of this algorithm.

Published
1993

19. Robot motion planning with the Ariadne's clew algorithm

Author

Emmanuel Mazer, Juan-Manuel Ahuactzin, El-Ghazali Talbi, Pierre Bessière, Laboratoire d'Informatique Fondamentale et d'Intelligence Artificielle (LIFIA), Institut National Polytechnique de Grenoble (INPG), Laboratoire de Génie Informatique (LGI - IMAG), and Université Joseph Fourier - Grenoble 1 (UJF)-IMAG

Subjects
[SCCO.COMP]Cognitive science/Computer science, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Published
1993

20. Parallel robot motion planning

Author

El-Ghazali Talbi, Juan-Manuel Ahuactzin, Emmanuel Mazer, Pierre Bessière, Laboratoire d'Informatique Fondamentale et d'Intelligence Artificielle (LIFIA), Institut National Polytechnique de Grenoble (INPG), Laboratoire de Génie Informatique (LGI - IMAG), and Université Joseph Fourier - Grenoble 1 (UJF)-IMAG

Subjects
[SCCO.COMP]Cognitive science/Computer science, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Published
1992

21. Connexionisme et génération de mouvement en robotique

Author

Pierre Bessière, Emmanuel Mazer, Laboratoire d'Informatique Fondamentale et d'Intelligence Artificielle (LIFIA), Institut National Polytechnique de Grenoble (INPG), Laboratoire de Génie Informatique (LGI - IMAG), and Université Joseph Fourier - Grenoble 1 (UJF)-IMAG

Subjects
[SCCO.COMP]Cognitive science/Computer science, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Published
1992

22. A massively parallel implementation of the Ariadne's clew algorithm

Author

Juan-Manuel Ahuactzin, El-Ghazali Talbi, Chatroux, T., Pierre Bessière, Emmanuel Mazer, Laboratoire d'Informatique Fondamentale et d'Intelligence Artificielle (LIFIA), Institut National Polytechnique de Grenoble (INPG), Laboratoire de Génie Informatique (LGI - IMAG), and Université Joseph Fourier - Grenoble 1 (UJF)-IMAG

Subjects
[SCCO.COMP]Cognitive science/Computer science, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]

23. The 'Beam in the Bin' experiment; an application of Probability as Logic to autonomous robotic

Author

Pierre Bessière, Eric Dedieu, Emmanuel Mazer, Olivier Lebeltel, Laboratoire d'Informatique Fondamentale et d'Intelligence Artificielle (LIFIA), Institut National Polytechnique de Grenoble (INPG), and Bessiere, Pierre

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], [SCCO.COMP] Cognitive science/Computer science, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], [SCCO.COMP]Cognitive science/Computer science, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]

24. The Ariadne's clew algorithm; From Animal to Animats

Author

Emmanuel Mazer, Juan-Manuel Ahuactzin, El-Ghazali Talbi, Pierre Bessière, Laboratoire d'Informatique Fondamentale et d'Intelligence Artificielle (LIFIA), Institut National Polytechnique de Grenoble (INPG), Laboratoire de Génie Informatique (LGI - IMAG), and Université Joseph Fourier - Grenoble 1 (UJF)-IMAG

Subjects
[SCCO.COMP]Cognitive science/Computer science, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]

25. Using genetic algorithms for robot motion planning

Author

Pierre Bessière, Emmanuel Mazer, Juan-Manuel Ahuactzin, El-Ghazali Talbi, Laboratoire d'Informatique Fondamentale et d'Intelligence Artificielle (LIFIA), Institut National Polytechnique de Grenoble (INPG), Laboratoire de Génie Informatique (LGI - IMAG), Université Joseph Fourier - Grenoble 1 (UJF)-IMAG, CNRS, Consejo Nacional de Ciencia y Tecnologia (Mexico), projet ESPRIT 'Supernode 2', and Bessiere, Pierre

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], 0209 industrial biotechnology, Mathematical optimization, Optimization problem, Holonomic, Computer science, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], Parallel algorithm, parallel algorithms, [SCCO.COMP]Cognitive science/Computer science, Mobile robot, robot motion planning, [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO], 02 engineering and technology, Kinematics, genetic algorithms, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 020901 industrial engineering & automation, [SCCO.COMP] Cognitive science/Computer science, Genetic algorithm, 0202 electrical engineering, electronic engineering, information engineering, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], Robot, 020201 artificial intelligence & image processing, Motion planning
Abstract

We present an ongoing research work on robot motion planning using genetic algorithms. Our goal is to use this technique to build fast motion planners for robot with six or more degree of freedom. After a short review of the existing methods, we will introduce the genetic algorithms by showing how they can be used to solve the invers kinematic problem. In the second part of the paper, we show that the path planning problem can be expressed as an optimization problem and thus solved with a genetic algorithm. We illustrate the approach by building a path planner for a planar arm with two degree of freedom, then we demonstrate the validity of the method by planning paths for an holonomic mobile robot. Finally we describe an implementation of the selected genetic algorithm on a massively parallel machine and show that fast planning response is made possible by using this approach.

26. A Bayesian framework for robotic programming

Author

Julien Diard, Olivier Lebeltel, Emmanuel Mazer, Pierre Bessière, Laboratoire Leibniz (Leibniz - IMAG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), Automatic Programming and Decisional Systems in Robotics (SHARP), 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, and Institut National de Recherche en Informatique et en Automatique (Inria)

Subjects
0209 industrial biotechnology, Generic programming, Computer science, business.industry, Principle of maximum entropy, [SCCO.COMP]Cognitive science/Computer science, Mobile robot, 0102 computer and information sciences, 02 engineering and technology, Bayesian inference, Sensor fusion, 01 natural sciences, Inductive programming, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Computer Science::Robotics, 020901 industrial engineering & automation, 010201 computation theory & mathematics, Programming paradigm, Reactive programming, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], Artificial intelligence, business
Abstract

We propose an original method for programming robots based on bayesian inference and learning. This method formally deals with problems of uncertainty and incomplete information that are inherent to the field. Indeed, the principal difficulties of robot programming comes from the unavoidable incompleteness of the models used. We present the formalism for describing a robotic task as well as the resolution methods. This formalism is inspired by the theory of probability, suggested by the physicist E. T. Jaynes: “Probability as Logic” [1]. Learning and maximum entropy principle translate incompleteness into uncertainty. Bayesian inference offers a formal framework for reasoning with this uncertainty. The main contribution of this paper is the definition of a generic system of robotic programming and its experimental application. We illustrate it by programming a surveillance task with a mobile robot: the Khepera. In order to do this, we use generic programming resources called “descriptions”. We show how to define and use these resources in an incremental way (reactive behaviors, sensor fusion, situation recognition and sequences of behaviors) within a systematic and unified framework.

27. An application of genetic algorithm to robotics: the 'Ariadne's Clew' algorithm

Author

Pierre Bessière, Juan-Manuel Ahuactzin, El-Ghazali Talbi, Emmanuel Mazer, Laboratoire d'Informatique Fondamentale et d'Intelligence Artificielle (LIFIA), Institut National Polytechnique de Grenoble (INPG), Laboratoire de Génie Informatique (LGI - IMAG), and Université Joseph Fourier - Grenoble 1 (UJF)-IMAG

Subjects
[SCCO.COMP]Cognitive science/Computer science, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]

28. Méthode probabiliste bayésienne pour la prise en compte des incertitudes géométriques en robotique

Author

Pierre Bessière, Emmanuel Mazer, Kamel Mekhnacha, Bessiere, Pierre, Laboratoire Leibniz (Leibniz - IMAG), and Université Joseph Fourier - Grenoble 1 (UJF)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], [SCCO.COMP] Cognitive science/Computer science, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], [SCCO.COMP]Cognitive science/Computer science, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]

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