Development of a Physical Assistance Robot: Gesture Recognition, Face Tracking, and Basic Conversational Skills
Overview: This thesis focuses on the design and implementation of a physical assistance robot capable of recognizing hand gestures, tracking human faces, and engaging in basic conversational interactions. The goal is to create a user-friendly robotic assistant that can effectively communicate and interact with individuals in various settings, enhancing accessibility and support.
Description
Key Components:
1. Gesture Recognition:
- The robot will utilize advanced computer vision techniques to detect and interpret hand gestures. By implementing machine learning models like those in MediaPipe, the robot can achieve real-time hand tracking and gesture classification, allowing it to respond dynamically to user commands.
2. Face Tracking:
- Incorporating face detection and tracking algorithms, the robot will be equipped to recognize and follow human faces during interactions. Lightweight CNNs will enable real-time processing, ensuring reliable performance even in varying conditions.
3. Conversational Abilities:
- Using natural language processing frameworks, the robot will engage in basic conversations, capable of greeting users and answering simple questions. This interaction will be enhanced through machine learning, allowing the robot to adapt and improve its responses over time.
Challenges:
Several challenges will need to be addressed in this project:
- Integration of Features: Combining gesture recognition, face tracking, and conversational abilities into a cohesive system that works seamlessly.
- Hardware Selection: Choosing the appropriate hardware platform that can support the computational demands of real-time gesture recognition and face tracking while ensuring power efficiency.
- Deployment and Testing: Developing the software and deploying it on the chosen hardware, followed by rigorous testing to ensure reliability and accuracy in various environments