Pierre Laroche

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In this paper, we present a new tool for automatically solving Markov Decision Processes. Using a predefined partition of the MDP, a directed graph is built to decompose the global MDP into small local MDPs which are independently solved. An approximate solution for the global MDP is obtained using local solutions. Our approach has been tested on a mobile(More)
In this paper, we present a new method to localize a mobile robot in dynamic environments. This method is based on places recognition, and a match between places recognized and the sequence of places that the mobile robot is able to see during a run from an initial place to an ending place. Our method gives a coarse idea of the robot's position and(More)
Concurrent measurements from the CSU-CHILL multiparameter Doppler radar, the Office National d'Etudes et de Recherches Aérospatiales VHF lightning interferometer, and the National Lightning Detection Network, obtained during phase A of the Stratosphere–Troposphere Experiments: Radiation, Aerosols, Ozone (STERAO-A) field project, provided a unique dataset(More)
In this paper, we present two state aggregation methods, used to build stochastic plans, modelling our environment with Markov Decision Processes. Classical methods used to compute stochastic plans are highly untractable for problems necessiting a large number of states, like our robotics application. The use of aggregation techniques allows to reduce the(More)
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