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Robust Navigation using Markov Models

Julien Burlet 1 Olivier Aycard 1 Thierry Fraichard 1
1 E-MOTION - Geometry and Probability for Motion and Action
GRAVIR - IMAG - Graphisme, Vision et Robotique, Inria Grenoble - Rhône-Alpes
Abstract : To reach a given goal, a mobile robot first computes a motion plan (ie a sequence of actions that will take it to its goal), and then executes it. Markov Decision Processes (MDPs) have been successfully used to solve these two problems. Their main advantage is that they provide a theoretical framework to deal with the uncertainties related to the robot's motor and perceptive actions during both planning and execution stages. While a previous paper addressed the motion planning stage, this paper deals with execution stage. It describes an approach based on Markov localization and focuses on experimental aspects, in particular the learning of the transition function (that encodes the uncertainties related to the robot actions) and the sensor model. Experimental results carry out with a real robot demonstrate the robustness of the whole navigation approach.
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Submitted on : Wednesday, October 24, 2007 - 6:34:53 PM
Last modification on : Monday, August 19, 2019 - 4:42:05 PM
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  • HAL Id : inria-00182045, version 1




Julien Burlet, Olivier Aycard, Thierry Fraichard. Robust Navigation using Markov Models. Proc. of the IEEE-RSJ Int. Conf. on Intelligent Robots and Systems, Aug 2005, Edmonton, AB (CA), France. ⟨inria-00182045⟩



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