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Proscriptive Bayesian Programming Application for Collision Avoidance

By C Koike, C Pradalier, P Bessiere and E Mazer

Abstract

Evolve safely in an unchanged environment and possibly following an optimal trajectory is one big challenge presented by situated robotics research field. Collision avoidance is a basic security requirement and this paper proposes a solution based on a probabilistic approach called Bayesian Programming. This approach aims to deal with the uncertainty, imprecision and incompleteness of the information handled. Some examples illustrate the process of embodying the programmer preliminary knowledge into a Bayesian program and experimental results of these examples implementation in an electrical vehicle are described and commented. Some videos illustrating these experiments can be found at http://www-laplace.imag.fr

Topics: Robotics
Year: 2003
OAI identifier: oai:cogprints.org:3757

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Citations

  1. (1998). A hybrid collision avoidance method for mobile robots.
  2. (2000). A robotic CAD system using a Bayesian framework.
  3. (2003). Bayesian robots progamming. Autonomous Robots,
  4. (2002). Motion planning through policy search.
  5. (1985). Real-time obstacle avoidance for manipulators and mobile robots.
  6. (1994). Robust obstacle avoidance in unknown and cramped environment.
  7. (2003). Survey: Probabilistic methodology and techniques for artefact conception and development.
  8. (1997). The dynamic window approach to collision avoidance.
  9. (2002). Towards real-time global motion planning in a dynamic environment using the NLVO concept.
  10. (2002). Using bayesian programming for multi-sensor data fusion in automotive applications.

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