An embedded implementation of Bayesian network robot programming methods

Mark Post

Research output: Contribution to conferencePaperpeer-review

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A wide variety of approaches exist for dealing with uncertainty in robotic reasoning, but relatively few consider the propagation of statistical information throughout an entire robotic system. The concept of Bayesian Robot Programming (BRP) involves making decisions based on inference into probability distributions, but can be complex and difficult to implement due to the number of priors and random variables involved. In this work, we apply Bayesian network structures to a modified BRP paradigm to provide intuitive structure and simplify the programming process. The use of discrete random variables in the network can allow high inference speeds, and an efficient programming toolkit suitable for use on embedded platforms has been developed for use on mobile robots. A simple example of navigational reasoning for a small mobile robot is provided as an example of how such a network can be used for probabilistic decisional programming.
Original languageEnglish
Number of pages9
Publication statusPublished - 9 Sep 2015
EventIMA Conference on Mathematics of Robotics - University of Oxford, St Anne's College, Oxford, United Kingdom
Duration: 9 Sep 201511 Sep 2015


ConferenceIMA Conference on Mathematics of Robotics
CountryUnited Kingdom


  • Bayesian robot programming
  • Bayesian approach
  • Bayesian networks
  • robotic reasoning
  • probabilistic decisional programming
  • mobile robotics
  • robotics

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