IEICE TRANSACTIONS on Information
Vision System for Depalletizing Robot Using Genetic Labeling
Summary :
In this paper, we present a vision system for a depalletizing robot which recognizes carton objects. The algorithm consists of the extraction of object candidates and a labeling process to determine whether or not they actually exist. We consider this labeling a combinatorial optimization of labels, we propose a new labeling method applying Genetic Algorithm (GA). GA is an effective optimization method, but it has been inapplicable to real industrial systems because of its processing time and difficulty of finding the global optimum solution. We have solved these problems by using the following guidelines for designing GA: (1) encoding high-level information to chromosomes, such as the existence of object candidates; (2) proposing effective coding method and genetic operations based on the building block hypothesis; and (3) preparing a support procedure in the vision system for compensating for the mis-recognition caused by the pseudo optimum solution in labeling. Here, the hypothesis says that a better solution can be generated by combining parts of good solutions. In our problem, it is expected that a global desirable image interpretation can be obtained by combining subimages interpreted consistently. Through real image experiments, we have proven that the reliability of the vision system we have proposed is more than 98% and the recognition speed is 5 seconds/image, which is practical enough for the real-time robot task.
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- IEICE TRANSACTIONS on Information Vol.E78-D No.12 pp.1552-1558
- Publication Date
- 1995/12/25
- Publicized
- Online ISSN
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- Type of Manuscript
- Special Section PAPER (Special Issue on Machine Vision Applications)
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Manabu HASHIMOTO, Kazuhiko SUMI, Shin'ichi KURODA, "Vision System for Depalletizing Robot Using Genetic Labeling" in IEICE TRANSACTIONS on Information,
vol. E78-D, no. 12, pp. 1552-1558, December 1995, doi: .
Abstract: In this paper, we present a vision system for a depalletizing robot which recognizes carton objects. The algorithm consists of the extraction of object candidates and a labeling process to determine whether or not they actually exist. We consider this labeling a combinatorial optimization of labels, we propose a new labeling method applying Genetic Algorithm (GA). GA is an effective optimization method, but it has been inapplicable to real industrial systems because of its processing time and difficulty of finding the global optimum solution. We have solved these problems by using the following guidelines for designing GA: (1) encoding high-level information to chromosomes, such as the existence of object candidates; (2) proposing effective coding method and genetic operations based on the building block hypothesis; and (3) preparing a support procedure in the vision system for compensating for the mis-recognition caused by the pseudo optimum solution in labeling. Here, the hypothesis says that a better solution can be generated by combining parts of good solutions. In our problem, it is expected that a global desirable image interpretation can be obtained by combining subimages interpreted consistently. Through real image experiments, we have proven that the reliability of the vision system we have proposed is more than 98% and the recognition speed is 5 seconds/image, which is practical enough for the real-time robot task.
URL: https://global.ieice.org/en_transactions/information/10.1587/e78-d_12_1552/_p
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@ARTICLE{e78-d_12_1552,
author={Manabu HASHIMOTO, Kazuhiko SUMI, Shin'ichi KURODA, },
journal={IEICE TRANSACTIONS on Information},
title={Vision System for Depalletizing Robot Using Genetic Labeling},
year={1995},
volume={E78-D},
number={12},
pages={1552-1558},
abstract={In this paper, we present a vision system for a depalletizing robot which recognizes carton objects. The algorithm consists of the extraction of object candidates and a labeling process to determine whether or not they actually exist. We consider this labeling a combinatorial optimization of labels, we propose a new labeling method applying Genetic Algorithm (GA). GA is an effective optimization method, but it has been inapplicable to real industrial systems because of its processing time and difficulty of finding the global optimum solution. We have solved these problems by using the following guidelines for designing GA: (1) encoding high-level information to chromosomes, such as the existence of object candidates; (2) proposing effective coding method and genetic operations based on the building block hypothesis; and (3) preparing a support procedure in the vision system for compensating for the mis-recognition caused by the pseudo optimum solution in labeling. Here, the hypothesis says that a better solution can be generated by combining parts of good solutions. In our problem, it is expected that a global desirable image interpretation can be obtained by combining subimages interpreted consistently. Through real image experiments, we have proven that the reliability of the vision system we have proposed is more than 98% and the recognition speed is 5 seconds/image, which is practical enough for the real-time robot task.},
keywords={},
doi={},
ISSN={},
month={December},}
Copy
TY - JOUR
TI - Vision System for Depalletizing Robot Using Genetic Labeling
T2 - IEICE TRANSACTIONS on Information
SP - 1552
EP - 1558
AU - Manabu HASHIMOTO
AU - Kazuhiko SUMI
AU - Shin'ichi KURODA
PY - 1995
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E78-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 1995
AB - In this paper, we present a vision system for a depalletizing robot which recognizes carton objects. The algorithm consists of the extraction of object candidates and a labeling process to determine whether or not they actually exist. We consider this labeling a combinatorial optimization of labels, we propose a new labeling method applying Genetic Algorithm (GA). GA is an effective optimization method, but it has been inapplicable to real industrial systems because of its processing time and difficulty of finding the global optimum solution. We have solved these problems by using the following guidelines for designing GA: (1) encoding high-level information to chromosomes, such as the existence of object candidates; (2) proposing effective coding method and genetic operations based on the building block hypothesis; and (3) preparing a support procedure in the vision system for compensating for the mis-recognition caused by the pseudo optimum solution in labeling. Here, the hypothesis says that a better solution can be generated by combining parts of good solutions. In our problem, it is expected that a global desirable image interpretation can be obtained by combining subimages interpreted consistently. Through real image experiments, we have proven that the reliability of the vision system we have proposed is more than 98% and the recognition speed is 5 seconds/image, which is practical enough for the real-time robot task.
ER -
English
