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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Yamaguchi, T. Kanazawa, N. Akita, K. Yoshihara, M. |
| Copyright Year | 1995 |
| Description | Author affiliation: Dept. of Inf. Sci., Utsunomiya Univ., Japan (Yamaguchi, T.; Kanazawa, N.; Akita, K.; Yoshihara, M.) |
| Abstract | This paper proposes a gestural instruction learning algorithm for robots which move in response to video information. Applying the algorithm to an actual moving robot in a trajectory learning experiment confirms that it enables a robot to understand, on the same level that a dog might, both the meaning of a human macro sign (i.e. a figure-eight sign) and the qualitative sense inherent in a human macro qualitative instruction (i.e. a figure-eight trajectory with a large width). The proposed algorithm refines the robot moving trajectory through the use of a fuzzy associative memory system. It is demonstrated that the use of macro qualitative instructions in the proposed algorithm enables trajectory learning to be attained more quickly than with the use of micro instructions in a conventional algorithm.< |
| Starting Page | 87 |
| Ending Page | 90 |
| File Size | 333046 |
| Page Count | 4 |
| File Format | |
| ISBN | 0780324617 |
| DOI | 10.1109/FUZZY.1995.410049 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1995-03-20 |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Humans Robot sensing systems Associative memory Machinery production industries Fuzzy systems Intelligent manufacturing systems Manufacturing industries Intelligent robots Charge coupled devices Charge-coupled image sensors |
| Content Type | Text |
| Resource Type | Article |
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