A Novel Approach for Robot-Student Interaction (RSI) in Laboratory Teaching

Through the assistance of teachers and increased student participation, this study introduced a teaching assistant robot to improve laboratory education. The robot used cutting-edge technology like real-time navigation, sensor and actuator detection, voice command recognition, and pneumatic symbol identification. Omnidirectional mobility was made possible using Mecanum wheels, and obstacle avoidance and path planning were optimized by the A* algorithm. Detailed maps were created using LiDAR and a SLAM technique to enable seamless navigation in challenging situations. The robot's CNN models and YOLO enabled intuitive interaction by recognizing voice commands and symbols. It accomplished 90.4 % overall accuracy, 90.1 % path efficiency, 84.1 % performance in pneumatic symbol, sensors, and actuator recognition, and 91.2 % speech recognition accuracy. These findings demonstrated how the robot may use state-of-the-art navigation and interface technologies in lab environments to improve student engagement and educator support.