Building Machines
That Can See.

Computer Vision • Robotics • Artificial Intelligence

About

Nick Hilton

My forty-year career in manufacturing spanned R&D, analytical chemistry, and quality management. This background instilled a deep fascination with how things work and a strong belief in how technology can drive improvement.

Today, I apply that industrial mindset to hands-on projects in robotics, computer vision and AI. Currently, I experiment with these technologies in my home lab, using three projects to deepen my practical expertise:

A Star Wars inspired Pit Droid.
A desktop machine vision system.
An animatronic android head.

Each project is a set of experiments to investigate how machines perceive, interpret and respond to their environment.

Projects

Learn By Doing

Pit Droid

The Pit Droid project started as a simple exercise in 3D printing but quickly evolved into an advanced robotics development platform. Initially built to practice post-processing, painting and weathering, the droid soon became a canvas for learning electronics, coding, and Raspberry Pi integration.

The project expanded significantly when I utilized the droid’s large eye to house a camera, unlocking key AI workflows:

Computer Vision: Enabling environmental awareness and real-time tracking.
Natural Language UI: Integrating voice recognition and text-to-speech engine workflows.
Hardware Control: Synthesizing software logic with physical actuators.

Animatronic Head

The Animatronic Head is an experimental edge-computing platform that builds on the Pit Droid’s foundation to explore lifelike expression. Powered by a Raspberry Pi, this project blends mechanical design with interactive artificial intelligence to upgrade from simple robotic beeps to a natural voice UI.

The platform relies on synchronized hardware and perception workflows:

Computer Vision: Utilizing embedded eye cameras for real-time object tracking and identification.
Coordinated Actuation: Driving precision servos for fluid motion across the eyes, eyelids, jaw, and neck.
Audio Intelligence: Implementing integrated arrays to process spoken commands and respond dynamically.

Desktop Machine Vision Inspection System

Industrial Inspection

The Industrial Inspection project bridges my career experience in manufacturing with modern desktop automation principles. Inspired by the industrial conveyor and vision systems used to scale factory quality and productivity, this project replicates a miniature production line using a desktop conveyor belt and a Raspberry Pi.

The system acts as a scaled-down manufacturing architecture to execute advanced industrial workflows:

Computer Vision: Deploying a high-resolution camera to analyze moving objects for colors, profiles, and defects.
Tracking & Sortation: Building custom machine learning models to log items in real time as they traverse the belt.
Data Logic: Structuring an automated decision pipeline to trigger physical sortation or rejection.

Far from static display pieces, these three projects serve as active, evolving testbeds that I continuously develop to deepen my hands-on expertise in computer vision, edge AI, and advanced robotics.

Features

Under The Bonnet

Systems Integration

These prototypes unify diverse engineering disciplines that are traditionally developed in isolation. By merging these subsystems, I explore the complex interfaces required to build cohesive, self-contained intelligent hardware.

Mechanical Design

Concepts are engineered using full-scale CAD software before being realized via additive manufacturing (3D printing) and standard hardware components. This enables rapid prototyping, functional verification, and continuous hardware iterations.

Embedded Systems

Raspberry Pi single-board computers serve as the central execution layer, coordinating high-resolution camera feeds, sensory arrays, motor controllers, and software logic loops to form fully integrated physical systems.

Robotics

Robotics provides the precise kinetic execution and environmental interaction across my builds—driving everything from the Pit Droid's joint articulation to the automated conveyor mechanics of the bottle inspection array.

Computer Vision

Digital imaging sensors allow hardware to map visual spaces. Custom computer vision pipelines process data in real time to classify target objects, detect boundary lines, measure physical anomalies, and parse spatial assets.

Artificial Intelligence

Deep learning frameworks complement classical computer vision by analyzing highly complex geometric patterns and environments. AI models are deployed where deterministic, rule-based coding falls short.