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

In this research, the problems of how to teach a robot to execute skilled operations are studied. Human workers usually accumulate his experience after executing the same task repetitively. In the process of training, the worker must find ways of adjusting his/her execution. In our system, the parameters for the impedance control scheme are used as the targets for adjustment. After mass amount of training, the worker is supposed to be able to execute deburring tasks successfully. This is because the worker might have gotten some knowledge about tuning the parameters required in the impedance control scheme. Thus, the rules for adjusting the parameters in impedance control are the operational skills to be identified. In this research, a training scheme, called the evolution-based virtual training scheme, is proposed in extracting knowledge for robotic deburring tasks. In this approach, an evolution strategy is employed for searching for the best set of fuzzy rules. This learning scheme has been successfully applied in adjusting the parameters of impedance controllers required in deburring operations. In general, the results of deburring are much more satisfactory when compared with those in previous research. When executing a deburring task, the robot simulator can find its optimal adjusting rules for parameters after several generations of evolution.