{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,3,30]],"date-time":"2022-03-30T04:29:16Z","timestamp":1648614556317},"reference-count":44,"publisher":"Cambridge University Press (CUP)","issue":"1","license":[{"start":{"date-parts":[[2009,3,9]],"date-time":"2009-03-09T00:00:00Z","timestamp":1236556800000},"content-version":"unspecified","delay-in-days":7007,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Robotica"],"published-print":{"date-parts":[[1990,1]]},"abstract":"<jats:title>SUMMARY<\/jats:title><jats:p>A prototype robot system for automated handling of flexible electrical wires of variable length is described. The handling process involves the selection of a single wire from a tray of many, grasping the wire close to its end with a robot manipulator, and either placing the end in a crimping press or, for tinning applications, dipping the end in a bath of molten solder. This system relies exclusively on the use of vision to identify the position and orientation of the wires prior to their being grasped by the robot end-effector. Two distinct vision algorithms are presented. The first approach utilises binary imaging techniques and involves object segmentation by thresholding followed by thinning and image analysis. An alternative general-purpose approach, based on more robust grey-scale processing techniques, is also described. This approach relies in the analysis of object boundaries generated using a dynamic contour-following algorithm. A simple Robot Control Language (<jats:italic>RCL<\/jats:italic>) is described which facilitates robot control in a Cartesian frame of reference and object description using frames (homogeneous transformations). The integration of this language with the robot vision system is detailed, and, in particular, a camera model which compensates for both photometric distortion and manipulator inaccuracies is presented. The system has been implemented using conventional computer architectures; average sensing cycle times of two and six seconds have been achieved for the grey-scale and binary vision algorithms, respectively.<\/jats:p>","DOI":"10.1017\/s0263574700007311","type":"journal-article","created":{"date-parts":[[2009,3,10]],"date-time":"2009-03-10T09:20:09Z","timestamp":1236676809000},"page":"47-60","source":"Crossref","is-referenced-by-count":2,"title":["A system for robot manipulation of electrical wires using vision"],"prefix":"10.1017","volume":"8","author":[{"given":"David","family":"Vernon","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"56","published-online":{"date-parts":[[2009,3,9]]},"reference":[{"key":"S0263574700007311_ref042","volume-title":"Computer Image Processing and Recognition","author":"Hall","year":"1979"},{"key":"S0263574700007311_ref013","first-page":"1","article-title":"Recognising Partially Visible Objects Using Feature Indexed Hypotheses","volume":"RA-2","author":"Knoll","year":"1986","journal-title":"IEEE J. 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