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    <titleInfo>
        <title>Hand-eye coordination for grasping moving objects</title>
    </titleInfo>
    <name type="personal" ID="pka1">
        <namePart type="family">Allen</namePart>
        <namePart type="given">Peter K.</namePart>
        <role>
            <roleTerm type="text">author</roleTerm>
        </role>
        <affiliation>Columbia University. Computer Science</affiliation>
    </name>
    <name type="personal">
        <namePart type="family">Timcenko</namePart>
        <namePart type="given">Aleksandar</namePart>
        <role>
            <roleTerm type="text">author</roleTerm>
        </role>
        <affiliation>Columbia University. Computer Science</affiliation>
    </name>
    <name type="personal">
        <namePart type="family">Yoshimi</namePart>
        <namePart type="given">Billibon</namePart>
        <role>
            <roleTerm type="text">author</roleTerm>
        </role>
        <affiliation>Columbia University. Computer Science</affiliation>
    </name>
    <name type="personal">
        <namePart type="family">Michelman</namePart>
        <namePart type="given">Paul</namePart>
        <role>
            <roleTerm type="text">author</roleTerm>
        </role>
        <affiliation>Columbia University. Computer Science</affiliation>
    </name>
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        <namePart>Columbia University. Computer Science</namePart>
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        <dateIssued encoding="w3cdtf" keyDate="yes">1991</dateIssued>
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    <abstract>Most robotic grasping tasks assume a stationary or fixed object. In this paper, we explore the requirements for grasping a moving object. This task requires proper coordination between at least 3 separate subsystems: dynamic vision sensing, real-time arm control, and grasp control. As with humans, our system first visually tracks the object&apos;s 3-D position. Because the object is in motion, this must be done in a dynamic manner to coordinate the motion of the robotic arm as it tracks the object. The dynamic vision system is used to feed a real-time arm control algorithm that plans a trajectory. The arm control algorithm is implemented in two steps: 1) filtering and prediction, and 2) kinematic transformation computation. Once the trajectory of the object is tracked, the hand must intercept the object to actually grasp it. We present 3 different strategies for intercepting the object and results from the tracking algorithm.</abstract>
    <subject>
        <topic>Robotics</topic>
    </subject>
    <relatedItem type="host">
        <titleInfo>
            <title>Sensor Fusion III: 3D Perception and Recognition</title>
        </titleInfo>
        <name type="personal">
            <namePart type="family">Schenker</namePart>
            <namePart type="given">Paul S.</namePart>
            <role>
                <roleTerm type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <place>
               <placeTerm type="text">Bellingham, Wash.</placeTerm>
            </place>
            <publisher>Society of Photo-optical Instrumentation Engineers</publisher>
            <dateIssued encoding="w3cdtf">1991</dateIssued>
        </originInfo>
        <part>
            <extent unit="page">
                <start>176</start>
                <end>188</end>
            </extent>
        </part>
        <identifier type="doi">http://dx.doi.org/10.1117/12.25255</identifier>
        <relatedItem type="series">
            <titleInfo>
                <title>Proceedings of SPIE--The International Society for Optical Engineering</title>
                <partNumber>1383</partNumber>
            </titleInfo>
            <identifier type="issn">0277-786X</identifier>
        </relatedItem>
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    <identifier type="hdl">http://hdl.handle.net/10022/AC:P:15264</identifier>
    
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        <recordIdentifier>9265</recordIdentifier>
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