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Simple Near-Realtime Crane Workspace Mapping Using Machine Vision

[+] Author Affiliations
M. Sazzad Rahman, Joshua Vaughan

University of Louisiana at Lafayette, Lafayette, LA

Paper No. DSCC2014-6342, pp. V003T28A005; 8 pages
doi:10.1115/DSCC2014-6342
From:
  • ASME 2014 Dynamic Systems and Control Conference
  • Volume 3: Industrial Applications; Modeling for Oil and Gas, Control and Validation, Estimation, and Control of Automotive Systems; Multi-Agent and Networked Systems; Control System Design; Physical Human-Robot Interaction; Rehabilitation Robotics; Sensing and Actuation for Control; Biomedical Systems; Time Delay Systems and Stability; Unmanned Ground and Surface Robotics; Vehicle Motion Controls; Vibration Analysis and Isolation; Vibration and Control for Energy Harvesting; Wind Energy
  • San Antonio, Texas, USA, October 22–24, 2014
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-4620-9
  • Copyright © 2014 by ASME

abstract

Overhead cranes are widely used in industries all over the world. It is not easy to move crane payloads without oscillation, increasing the likelihood of obstacle collisions and other accidents. Even experienced crane operators make mistakes that cause loss of money and time. Some reasons for these incidents are limitations of the operator’s field of view, depth perception, knowledge of the workspace, and the dynamic environment of the workspace. One possible solution to these problems could be aiding the operator with a dynamic map of the workspace that shows the current position of obstacles. The probable areas of finding obstacles based on the previous positions of obstacles could also be shown. This paper describes a simple method of generating such a map of the crane workspace using machine vision.

Copyright © 2014 by ASME
Topics: Machinery , Cranes

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