DC Field | Value | Language |
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dc.contributor.author | Nam, SH | - |
dc.contributor.author | Oh, SY | - |
dc.date.accessioned | 2016-03-31T13:43:59Z | - |
dc.date.available | 2016-03-31T13:43:59Z | - |
dc.date.created | 2009-08-10 | - |
dc.date.issued | 1999-01 | - |
dc.identifier.issn | 0924-669X | - |
dc.identifier.other | 1999-OAK-0000000640 | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/20480 | - |
dc.description.abstract | A real-time visual servo tracking system for an industrial robot has been implemented using PSD (Position Sensitive Detector) cameras, neural networks, and an extended trapezoidal motion planning method. PSD and directly transduces the light's projected position on its sensor plane into an analog current and lends itself to fast real-time tracking. A neural network, after proper training, transforms the PSD sensor reading into a 3D position of the target, which is then input to an extended trapezoidal motion planning algorithm. This algorithm implements a continuous motion update strategy in response to an ever-changing sensor information from the moving target, while greatly reducing the tracking delay. This planning method is found to be very useful for sensor-based control such as moving target tracking or weld-seam tracking in which the robot needs to change its motion in real time in response to incoming sensor information. Further, for real-time usage of the neural net, a new architecture called LANN (Locally Activated Neural Network) has been developed based on the concept of CMAC input partitioning and local learning. Experimental evidence shows that an industrial robot can smoothly track a moving target of unknown motion with speeds of up to 1 m/s and with oscillation frequency up to 5 Hz. | - |
dc.description.statementofresponsibility | X | - |
dc.language | English | - |
dc.publisher | KLUWER ACADEMIC PUBL | - |
dc.relation.isPartOf | APPLIED INTELLIGENCE | - |
dc.subject | visual servoing | - |
dc.subject | target tracking | - |
dc.subject | position sensitive detectors | - |
dc.subject | extended trapezoidal motion planning | - |
dc.subject | locally activated neural network | - |
dc.subject | MOTOR-COORDINATION | - |
dc.subject | NEURAL CONTROLLER | - |
dc.subject | NETWORK | - |
dc.subject | SYSTEMS | - |
dc.subject | ROBOT | - |
dc.title | Real-time dynamic visual tracking using PSD sensors and extended trapezoidal motion planning | - |
dc.type | Article | - |
dc.contributor.college | 전자전기공학과 | - |
dc.identifier.doi | 10.1023/A:1008385515068 | - |
dc.author.google | Nam, SH | - |
dc.author.google | Oh, SY | - |
dc.relation.volume | 10 | - |
dc.relation.issue | 1 | - |
dc.relation.startpage | 53 | - |
dc.relation.lastpage | 70 | - |
dc.contributor.id | 10071831 | - |
dc.relation.journal | APPLIED INTELLIGENCE | - |
dc.relation.index | SCI급, SCOPUS 등재논문 | - |
dc.relation.sci | SCI | - |
dc.collections.name | Journal Papers | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | APPLIED INTELLIGENCE, v.10, no.1, pp.53 - 70 | - |
dc.identifier.wosid | 000078943200004 | - |
dc.date.tcdate | 2019-01-01 | - |
dc.citation.endPage | 70 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 53 | - |
dc.citation.title | APPLIED INTELLIGENCE | - |
dc.citation.volume | 10 | - |
dc.contributor.affiliatedAuthor | Oh, SY | - |
dc.identifier.scopusid | 2-s2.0-0032665155 | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
dc.description.wostc | 13 | - |
dc.type.docType | Article | - |
dc.subject.keywordPlus | MOTOR-COORDINATION | - |
dc.subject.keywordPlus | NEURAL CONTROLLER | - |
dc.subject.keywordPlus | NETWORK | - |
dc.subject.keywordPlus | SYSTEMS | - |
dc.subject.keywordPlus | ROBOT | - |
dc.subject.keywordAuthor | visual servoing | - |
dc.subject.keywordAuthor | target tracking | - |
dc.subject.keywordAuthor | position sensitive detectors | - |
dc.subject.keywordAuthor | extended trapezoidal motion planning | - |
dc.subject.keywordAuthor | locally activated neural network | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
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