Evolutionary-based virtual training in extracting fuzzy knowledge for deburring tasks

Shun Feng Su*, Tar Jyh Horng, Kuu-Young Young


研究成果: Article同行評審

8 引文 斯高帕斯(Scopus)


In this research, the problems of how to teach a robot to execute skilled operations are studied. Human workers usually accumulate their experience after executing the same task repetitively. In the process of training, a worker needs to find ways of adjusting his/her execution. In our system, the parameters for an impedance control scheme are considered as the targets for adjustment in the training process. The way to make adjustments is represented as a set of fuzzy rules in our research. Furthermore, a training scheme, called the evolutionary-based virtual training scheme, is proposed to extract knowledge (a set of fuzzy rules) for robotic deburring tasks. In this approach, an evolutionary algorithm is employed to find the best set of fuzzy rules and a simulation system is built to evaluate the execution performances of candidates. This learning scheme has been applied in finding a set of fuzzy rules that can adjust the parameters of impedance controllers required in deburring operations with satisfactory performance in deburring tasks.


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