Thesis: Development of a Physical Assistance Robot – Gesture Recognition, Face Tracking, and Basic Conversational Skills
This research will contribute to the development of physical assistance robots that can improve accessibility and support across different environments, including healthcare, customer service, and personal assistance. The project will focus on building an interactive, responsive robot capable of engaging users through gestures, facial recognition, and basic conversational skills.
Description
This thesis aims to design and implement a physical assistance robot that can recognize hand gestures, track human faces, and engage in basic conversational interactions. The objective is to create a user-friendly robotic assistant that enhances accessibility and provides support in various settings, leveraging advanced computer vision and natural language processing technologies.
Key Components
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Gesture Recognition
- The robot will use advanced computer vision techniques to detect and interpret hand gestures.
- By implementing machine learning models, such as those in MediaPipe, the robot will be able to achieve real-time hand tracking and gesture classification, allowing it to respond dynamically to user commands.
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Face Tracking
- Incorporating face detection and tracking algorithms, the robot will be equipped to recognize and follow human faces during interactions.
- Lightweight convolutional neural networks (CNNs) will be used to ensure real-time processing and reliable performance, even in varying conditions.
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Conversational Abilities
- Using natural language processing frameworks, the robot will engage in basic conversations, greeting users and answering simple questions.
- Machine learning will enhance the interaction, allowing the robot to adapt and improve its responses over time.
Challenges
- Integration of Features: Combining gesture recognition, face tracking, and conversational abilities into a cohesive system that works seamlessly.
- Hardware Selection: Choosing an appropriate hardware platform that can support the computational demands of real-time gesture recognition and face tracking while remaining power-efficient.
- Deployment and Testing: Developing the software, deploying it on the selected hardware, and conducting rigorous testing to ensure reliability and accuracy in various environments.