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Bayesian Modeling and Reasoning for Real World Robotics: Basics and Examples
Abstract : Cognition and Reasoning with uncertain and partial knowledge is prob- ably the biggest challenge for autonomous mobile robotics. Previous robotics sys- tems based on a purely logical or geometrical paradigm are limited in their ability to deal with partial or uncertain knowledge, adaptation to new environments and noisy sensors. Representing knowledge as a joint probability distribution increases the possibility for robotics systems to increase their quality of perception on their environment and helps them to take the right actions towards a more realistic and robust behavior. Dealing with uncertainty is thus a ma jor challenge for robotics in a real and unconstrained environment. Here, we propose a new formalism and method- ology called Bayesian Programming which aims at the design of efficient robotics systems evolving in a real and uncontrolled environment. This original formalism will be exemplified by two interesting experiments where robots are driven by a Bayesian Program (BP). These examples represents situations where the robot can sense only a small part of its global environment using noisy sensors. The second fact about these environments is they cannot be constrained so that to ease the control of the robot.
Cited literature [24 references]
https://hal.inria.fr/inria-00182055
Contributor : Christian Laugier Connect in order to contact the contributor
Submitted on : Wednesday, October 24, 2007 - 6:43:37 PM
Last modification on : Wednesday, February 2, 2022 - 3:58:20 PM
Long-term archiving on: : Monday, April 12, 2010 - 12:34:12 AM
Contributor : Christian Laugier Connect in order to contact the contributor
Submitted on : Wednesday, October 24, 2007 - 6:43:37 PM
Last modification on : Wednesday, February 2, 2022 - 3:58:20 PM
Long-term archiving on: : Monday, April 12, 2010 - 12:34:12 AM
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