Design of an unmanned ground vehicle, bearcat III, theory and practice

  • Authors:

  • Masoud Ghaffari;Souma M. Alhaj Ali;Vidyasagar Murthy;Xiaoqun Liao;Justin Gaylor;Ernest L. Hall

  • Affiliations:

  • Center for Robotics Research, Department of Mechanical, Industrial and Nuclear Engineering, University of Cincinnati, Cincinnati, Ohio 45221-0072;Center for Robotics Research, Department of Mechanical, Industrial and Nuclear Engineering, University of Cincinnati, Cincinnati, Ohio 45221-0072;Center for Robotics Research, Department of Mechanical, Industrial and Nuclear Engineering, University of Cincinnati, Cincinnati, Ohio 45221-0072;Center for Robotics Research, Department of Mechanical, Industrial and Nuclear Engineering, University of Cincinnati, Cincinnati, Ohio 45221-0072;Center for Robotics Research, Department of Mechanical, Industrial and Nuclear Engineering, University of Cincinnati, Cincinnati, Ohio 45221-0072;Center for Robotics Research, Department of Mechanical, Industrial and Nuclear Engineering, University of Cincinnati, Cincinnati, Ohio 45221-0072

  • Venue:
  • Journal of Robotic Systems - Intelligent Ground Vehicle Competition (IGVC) 2003 Special Issue (Part 2)
  • Year:
  • 2004

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Abstract

The purpose of this paper is to describe the design and implementation of an unmanned ground vehicle, called the Bearcat III, named after the University of Cincinnati mascot. The Bearcat III is an electric powered, three-wheeled vehicle that was designed for the Intelligent Ground Vehicle Competition and has been tested in the contest for 5 years. The dynamic model, control system, and design of the sensory systems are described. For the autonomous challenge line following, obstacle detection and pothole avoidance are required. Line following is accomplished with a dual camera system and video tracker. Obstacle detection is accomplished with either a rotating ultrasound or laser scanner. Pothole detection is implemented with a video frame grabber. For the navigation challenge waypoint following and obstacle detection are required. The waypoint navigation is implemented with a global positioning system. The Bearcat III has provided an educational test bed for not only the contest requirements but also other studies in developing artificial intelligence algorithms such as adaptive learning, creative control, automatic calibration, and internet-based control. The significance of this effort is in helping engineering and technology students understand the transition from theory to practice. © 2004 Wiley Periodicals, Inc.