Performing predefined tasks using the human–robot interaction on speech recognition for an industrial robot

Abstract

People who are not experts in robotics can easily implement complex robotic applications by using human–robot interaction (HRI). HRI systems require many complex operations such as robot control, image processing, natural speech recognition, and decision making. In this study, interactive control with an industrial robot was performed by using speech recognition software in the Turkish language. The collected voice data were converted to text data by using automatic speech recognition module based on deep neural networks (DNN). The proposed DNN (p-DNN) was compared to classic classification algorithms. Converted text data was improved in another module to select the process to be applied. According to selected process, position data were defined using image processing. The determined position information was sent to the robot using a fuzzy controller. The developed HRI system was implemented on a KUKA KR Agilus KR6 R900 sixx robot manipulator. The word accuracy rate of the p-DNN model was measured as 90.37%. The developed image processing module and fuzzy controller worked with minimal errors. The contribution of this study is that an industrial robot is easily programming using this software by people who are not experts in robotics and know Turkish.