Evolutionary trajectory learning for autonomous robots by means of geometric approximations of polygonal obstacles

M. M.A. Hashem, Keigo Watanabe, Kiyotaka Izumi

Research output: Contribution to journalConference article


This paper addresses the issue of flexible geometric approximations of polygonal obstacles for Intelligent Autonomous Robot (IAR) navigation which is the extension of our previous work. The trajectory learning problem for IAR navigation is formulated as a constrained discrete-time-optimal control problem where the polygonal obstacles are the constraints. From the visibility and sensor modeling concepts, polygonal obstacles within the environment are approximated as either by circles or by ellipses depending on the shape and size of them. Furthermore, some practical issues are identified and resolved through these type of approximations. The effectiveness of these methods is illustrated by some simulations of the robot within a heavily obstacle environment.

Original languageEnglish
Pages (from-to)II-734 - II-739
JournalProceedings of the IEEE International Conference on Systems, Man and Cybernetics
Publication statusPublished - Dec 1 1999
Event1999 IEEE International Conference on Systems, Man, and Cybernetics 'Human Communication and Cybernetics' - Tokyo, Jpn
Duration: Oct 12 1999Oct 15 1999

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Hardware and Architecture

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