Voice Activated Human Following Robot Using Computer Vision

This paper presents the development of a voice-activated human-following robot that integrates computer vision and sensor-based obstacle avoidance to enable autonomous navigation in dynamic environments. The system employs a Raspberry Pi 4 as the primary processing unit and a Pi Camera to perform real-time human detection using a lightweight YOLOv5 model. Voice commands are captured through a microphone and processed using speech recognition to activate or control the robot. Ultrasonic sensors are used to detect and avoid obstacles, enhancing safety and reliability. The robot successfully combines vision, voice, and proximity sensing into a low-cost, flexible platform suitable for applications such as personal assistance, industrial tool handling, and smart surveillance. Experimental results demonstrate effective human tracking, responsive voice control, and reliable navigation in indoor settings