Abstract
Uninterrupted power supply has become indispensable during the maintenance task of active electric power lines as a result of today's highly information-oriented society and increasing demand of electric utilities. The maintenance task has the risk of electric shock and the danger of falling from high place. Therefore it is necessary to realize an autonomous robot system using electro-hydraulic manipulator because hydraulic manipulators have the advantage of electric insulation. Meanwhile it is relatively difficult to realize autonomous assembly tasks particularly in the case of manipulating flexible objects such as electric lines. In this report, a discrete event control system is introduced for automatic assembly task of electric lines into sleeves as one of a typical task of active electric power lines. In the implementation of a discrete event control system, LVQNN (learning vector quantization neural network) is applied to the insertion task of electric lines to sleeves. In order to apply these proposed control system to the unknown environment, virtual learning data for LVQNN was generated by fuzzy inference. By the experimental results of two types of electric lines and sleeves, these proposed discrete event control and neural network learning algorithm are confirmed very effective to the insertion tasks of electric lines to sleeves as a typical task of active electric power maintenance tasks.
