Robot Exploration by Subjectively Maximizing Objective Information Gain

Bailu Si, K. Pawelzik, J. M. Herrmann

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Localization, mapping and action selection are three main aspects in robot exploration. This paper proposes an autonomous exploration method for robot localization and mapping in unknown environments. First an ideal global-probabilistic measure, which we call objective objective function, is introduced to evaluate the objective exploration performance. By minimizing a local approximation of this measure (which we call subjective objective function) the robot learns the internal models, and achieves a consistent correlation between the internal representation and the reality. Furthermore, an action policy search method is used to learn the optimal action selection strategy by maximizing the information gain obtained in exploration. Simulation results demonstrate that the proposed framework provides an integrated solution for localization and mapping task in unstructured environment
Original languageEnglish
Title of host publicationRobotics and Biomimetics, 2004. ROBIO 2004. IEEE International Conference on
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages930-935
Number of pages6
ISBN (Print)0-7803-8614-8
DOIs
Publication statusPublished - 1 Aug 2004

Keywords

  • learning (artificial intelligence)
  • mobile robots
  • path planning
  • probability
  • action policy search
  • autonomous exploration
  • ideal global-probabilistic measure
  • objective information gain
  • objective objective function
  • optimal action selection
  • robot exploration
  • robot localization
  • subjective objective function
  • Artificial intelligence
  • Hidden Markov models
  • Learning
  • Mobile robots
  • Navigation
  • Robot localization
  • Robot sensing systems
  • Robustness
  • Search methods
  • Working environment noise

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