Autonomous robot navigation using optimal control of probabilistic regular languages

Goutham Mallapragada, Ishanu Chattopadhyay, Asok Ray

Research output: Contribution to journalArticle

4 Scopus citations

Abstract

This paper addresses autonomous intelligent navigation of mobile robotic platforms based on the recently reported algorithms of language-measure- theoretic optimal control. Real-time sensor data and model-based information on the robot's motion dynamics are fused to construct a probabilistic finite state automaton model that dynamically computes a time-dependent discrete-event supervisory control policy. The paper also addresses detection and avoidance of livelocks that might occur during execution of the robot navigation algorithm. Performance and robustness of autonomous intelligent navigation under the proposed algorithm have been experimentally validated on Segway RMP robotic platforms in a laboratory environment.

Original languageEnglish (US)
Pages (from-to)13-26
Number of pages14
JournalInternational Journal of Control
Volume82
Issue number1
DOIs
StatePublished - Jan 1 2009

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science Applications

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