Vision based obstacle avoidance and target tracking for autonomous mobile robots

Masahiro Yamada, Chi Hsien Lin, Ming Yang Cheng

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

3 Citations (Scopus)

Abstract

This paper proposes a hierarchical switching strategy for mobile robots to detect obstacles and to track a given target using vision information only. In the proposed approach, the disparity image obtained from a stereo camera is used to build an obstacle map based on V-disparity. Color information and a self windowing technique are employed to track the target. A switching strategy based on a hierarchical architecture is employed to determine whether the mobile robot should perform obstacle avoidance or target tracking. To verify the effectiveness of the proposed approach, several experiments were conducted. The results indicate that the proposed approach is feasible.

Original languageEnglish
Title of host publicationAMC2010 - The 11th IEEE International Workshop on Advanced Motion Control, Proceedings
Pages153-158
Number of pages6
DOIs
Publication statusPublished - 2010 Jun 25
Event2010 11th IEEE International Workshop on Advanced Motion Control, AMC2010 - Nagaoka, Niigata, Japan
Duration: 2010 Mar 212010 Mar 24

Publication series

NameInternational Workshop on Advanced Motion Control, AMC

Other

Other2010 11th IEEE International Workshop on Advanced Motion Control, AMC2010
CountryJapan
CityNagaoka, Niigata
Period10-03-2110-03-24

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Modelling and Simulation
  • Computer Science Applications
  • Electrical and Electronic Engineering

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  • Cite this

    Yamada, M., Lin, C. H., & Cheng, M. Y. (2010). Vision based obstacle avoidance and target tracking for autonomous mobile robots. In AMC2010 - The 11th IEEE International Workshop on Advanced Motion Control, Proceedings (pp. 153-158). [5464007] (International Workshop on Advanced Motion Control, AMC). https://doi.org/10.1109/AMC.2010.5464007