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Optimal planning of robot calibration experiments by genetic algorithms
- Date Issued:
- 1995
- Summary:
- In this thesis work, techniques developed in the science of genetic computing is applied to solve the problem of planning a robot calibration experiment. Robot calibration is a process by the robot accuracy is enhanced through modification of its control software. The selection of robot measurement configurations is an important element in successfully completing a robot calibration experiment. A classical genetic algorithm is first customized for a type of robot measurement configuration selection problem in which the robot workspace constraints are defined in terms of robot joint limits. The genetic parameters are tuned in a systematic way to greatly enhance the performance of the algorithm. A recruit-oriented genetic algorithm is then proposed, together with new genetic operators. Examples are also given to illustrate the concepts of this new genetic algorithm. This new algorithm is aimed at solving another type of configuration selection problem, in which not all points in the robot workspace are measurable by an external measuring device. Extensive simulation studies are conducted for both classical and recruit-oriented genetic algorithms, to examine the effectiveness of these algorithms.
Title: | Optimal planning of robot calibration experiments by genetic algorithms. |
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Name(s): |
Huang, Weizhen. Florida Atlantic University, Degree grantor Wu, Jie, Thesis advisor |
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Type of Resource: | text | |
Genre: | Electronic Thesis Or Dissertation | |
Issuance: | monographic | |
Date Issued: | 1995 | |
Publisher: | Florida Atlantic University | |
Place of Publication: | Boca Raton, Fla. | |
Physical Form: | application/pdf | |
Extent: | 150 p. | |
Language(s): | English | |
Summary: | In this thesis work, techniques developed in the science of genetic computing is applied to solve the problem of planning a robot calibration experiment. Robot calibration is a process by the robot accuracy is enhanced through modification of its control software. The selection of robot measurement configurations is an important element in successfully completing a robot calibration experiment. A classical genetic algorithm is first customized for a type of robot measurement configuration selection problem in which the robot workspace constraints are defined in terms of robot joint limits. The genetic parameters are tuned in a systematic way to greatly enhance the performance of the algorithm. A recruit-oriented genetic algorithm is then proposed, together with new genetic operators. Examples are also given to illustrate the concepts of this new genetic algorithm. This new algorithm is aimed at solving another type of configuration selection problem, in which not all points in the robot workspace are measurable by an external measuring device. Extensive simulation studies are conducted for both classical and recruit-oriented genetic algorithms, to examine the effectiveness of these algorithms. | |
Identifier: | 15186 (digitool), FADT15186 (IID), fau:11958 (fedora) | |
Collection: | FAU Electronic Theses and Dissertations Collection | |
Note(s): |
College of Engineering and Computer Science Thesis (M.S.)--Florida Atlantic University, 1995. |
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Subject(s): |
Genetic algorithms Robots--Calibration Combinatorial optimization |
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Held by: | Florida Atlantic University Libraries | |
Persistent Link to This Record: | http://purl.flvc.org/fcla/dt/15186 | |
Sublocation: | Digital Library | |
Use and Reproduction: | Copyright © is held by the author, with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder. | |
Use and Reproduction: | http://rightsstatements.org/vocab/InC/1.0/ | |
Host Institution: | FAU | |
Is Part of Series: | Florida Atlantic University Digital Library Collections. |