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Proceedings Paper

Robot Guidance Using A Morphological Vision Algorithm
Author(s): Robert M. Lougheed; Leonard M. Tomko
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Paper Abstract

An algorithm has been developed to guide a robot by identifying the orientation of a randomly-acquired part held in the robot's gripper. A program implementing this algorithm is being used to demonstrate the feasibility of part-independent robotic bin picking*. The project task was to extract unmodified industrial parts from a compartmentalized tray and position them on a fixture. The parts are singulated in the compartments but are positionally and rotationally unconstrained. The part is acquired based upon three-dimensional image data which is processed by a 3D morphological algorithm described in [1]. The vision algorithm discussed here inspects the parts, determines their orientation and calculates the robot trajectory to a keyed housing with which the part must be mated. When parts are extracted during a bin picking operation their position and orientation are affected by many factors, such as gripper insertion-induced motion, interference with container side walls during extraction, slippage due to gravity and vibration during robot motions. The loss of the known position and orientation of the part in the robot gripper makes accurate fixturing impossible. Our solution to this problem was to redetermine the orientation of the part after acquisition. This paper describes the application in detail and discusses the problems encountered in robot acquisition of unconstrained parts. Next, the physical setup and image acquisition system, including lighting and optical components, are discussed. The principles of morphological (shape-based) image processing are presented, followed by a description of the interactive algorithm development process which was used for this project. The algorithm is illustrated step by step with a series of diagrams showing the effects of the transformations applied to the data. The algorithms were run on ERIM' s new fourth generation hybrid image processing architecture, the Cyto-HSS, which is described in detail in [2], and the performance is compared to the same programs executed on a general-purpose mid-sized computer.

Paper Details

Date Published: 11 December 1985
PDF: 10 pages
Proc. SPIE 0579, Intelligent Robots and Computer Vision IV, (11 December 1985); doi: 10.1117/12.950823
Show Author Affiliations
Robert M. Lougheed, Environmental Research Institute of Michigan (United States)
Leonard M. Tomko, Environmental Research Institute of Michigan (United States)

Published in SPIE Proceedings Vol. 0579:
Intelligent Robots and Computer Vision IV
David P. Casasent, Editor(s)

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