Projects
This section showcases hands-on projects and applied research focused on solving real-world problems through engineering, computer vision, and robotics. Each project highlights the process from problem definition to prototyping and implementation
Robotic Scarecrow
An automated scarecrow designed to deter animals from crops using sensors and programmed movement, demonstrating practical robotics applications in agriculture
Concept Car based on drone tech
A conceptual vehicle model integrating drone-inspired design and automation principles to explore alternative mobility and surveillance capabilities
3kg Sumo Robot
A competitive robot built to operate within strict weight limits, focusing on mechanical strength, control systems, and strategic movement.
Robotic Hand Assist for Rehabilitation
The objective of this project is to design and develop a robotic hand assist that uses computer vision and robotic actuation to help individuals with impaired hand movement. The system identifies objects using computer vision and assists the human hand in gripping them, supporting rehabilitation for patients recovering from nerve damage.
Purpose
To aid hand movement recovery by combining computer vision, machine learning, and mechanical actuation, enabling patients to hold objects with assisted motion.
Form Factor
The robotic hand assist is a wearable robotic device that is strapped onto the human hand using velcro, allowing assisted movement while remaining lightweight and portable
Key Components
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3D-printed human hand–like structure with velcro straps
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Microcontroller for controlling system logic
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Camera module to enable computer vision
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Servo motors with string mechanisms to facilitate finger movement
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Machine learning / computer vision library for object detection
Technology & Tools Used
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Python
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OpenCV
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Google MediaPipe
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YOLO (You Only Look Once) – selected for its ability to detect common everyday objects
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Arduino (hardware control)
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3D Printing (design sourced from Thingiverse)
Project Workflow
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Defined the project objective.
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Explored computer vision using Python libraries such as OpenCV, MediaPipe, and YOLO, selecting YOLO for its comprehensive everyday object detection capabilities.
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Learned how to run Python code on a computer and interface it with Arduino hardware.
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Identified a suitable 3D hand design from Thingiverse for printing.
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Began the 3D printing process; the initial print was undersized, requiring a reprint (in progress).
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Assembly of components and device testing (to be done)