Hostname: page-component-cd9895bd7-jn8rn Total loading time: 0 Render date: 2024-12-29T14:54:50.786Z Has data issue: false hasContentIssue false
  • English
  • Français

Robotic arm self-taught path planning using transportation model heuristics

Published online by Cambridge University Press:  09 March 2009

Earnest W. Fant  and
Sylvia L. Tran
Earnest W. Fant
Affiliation:
University of Arkansas, Department of Industrial Engineering, Fayetteville, Arkansas 72701 (USA)
Sylvia L. Tran
Affiliation:
Sylvia L. Tran, Baldor Electric Company. Engineering Department, Fort Smith, Arkansas 72901 (USA)
Get access Rights & Permissions [Opens in a new window]

Extract

The objective of this experimental research was to determine whether or not an integrated vision and robotic arm system, using grey level detection and a transportation model heuristic, could plan and execute its own point-to-point navigation. The heuristic is the shortest-route algorithm which deals with finding the smallest possible distance to visit multiple locations only once. The only point taught by a human operator was a starting position and all other points within the workspace, to be visited by the robotic arm to perform tasks such as screw-fastening, adhesive application and detailed inspection of assemblies, were learned by the system.

Keywords

Path planningModel heuristicsRobotic armIntegrated visionApplications
Type
Research Article
Information
Robotica , Volume 12 , Issue 1 , January 1994 , pp. 17 - 21
Copyright
Copyright © Cambridge University Press 1994

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Fu, K., Gonzales, R. and Lee, C., ROBOTICS: Control, Sensing, Vision, and Intelligence (McGraw-Hill Book Company, New York, 1987).Google Scholar
2.McDonald, A., Robot Technology Theory, Design and Applications, A Reston Book (Prentice-Hall, Inc., New Jersey, 1986).Google Scholar
3.Groover, M., Weiss, M., Nagel, R. and Odrey, N., INDUSTRIAL ROBOTICS: Technology, Programming, and Applications (McGraw-Hill Book Company, New York, 1986).Google Scholar
4.Hollingum, J., Machine Vision, the Eyes of Automation (IFS Publication, U.K., 1984).Google Scholar
5.krage, D., “Vision Simplifies Disk Drive Assembly” Robotics World 23–27 (03, 1988).Google Scholar
6.Zuech, N. and Miller, R., Machine Vision (The Fairmont Press, Georgia, 1987).Google Scholar
7.Rehkugler, G. and Throop, J., “Apple Sorting with Machine Vision” Transactions of American Society of Agricultural Engineers 29, No. 5, 1388–1396 (09–10, 1986).CrossRefGoogle Scholar
8.Sarkar, N. and Wolfe, R., “Computer Vision Based System for Quality Separation of Fresh Market Tomatoes” Transactions of American Society of Agricultural Engineers 28, No. 5, 1714–1718 (09–10, 1985).CrossRefGoogle Scholar
9.Miller, B. and Delwiche, M., “Color Vision System for Peach Grading” Transactions of American Society of Agricultural Engineers 32, No. 4, 1484–1490 (07–08, 1989).Google Scholar
10.Tran, S., Color Recognition Using Grey Scale for Robotic Guidance (Master's Thesis, University of Arkansas, 08, 1991).Google Scholar