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Obstacle Identification for Vision Assisted Control Architecture of a Hybrid Mechanism Mobile Robot

[+] Author Affiliations
Anil Kumar, Hailin Ren, Pinhas Ben-Tzvi

Virginia Tech, Blacksburg, VA

Paper No. DSCC2017-5324, pp. V002T21A008; 8 pages
  • ASME 2017 Dynamic Systems and Control Conference
  • Volume 2: Mechatronics; Estimation and Identification; Uncertain Systems and Robustness; Path Planning and Motion Control; Tracking Control Systems; Multi-Agent and Networked Systems; Manufacturing; Intelligent Transportation and Vehicles; Sensors and Actuators; Diagnostics and Detection; Unmanned, Ground and Surface Robotics; Motion and Vibration Control Applications
  • Tysons, Virginia, USA, October 11–13, 2017
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-5828-8
  • Copyright © 2017 by ASME


This paper presents a monocular vision-based, unsupervised floor detection algorithm for semi-autonomous control of a Hybrid Mechanism Mobile Robot (HMMR). The paper primarily focuses on combining monocular vision cues with inertial sensing and ultrasonic ranging for on-line obstacle identification and path planning in the event of limited wireless connectivity. A novel, unsupervised vision algorithm was developed for floor detection and identifying traversable areas, in order to avoid obstacles in semi-autonomous control architecture. The floor detection algorithms were validated and experimentally tested in an indoor environment under various lighting conditions.

Copyright © 2017 by ASME
Topics: Mobile robots



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