Robotics & Electronics Engineering Portfolio. Embedded systems, autonomous robots, control dynamics, and computer vision — building hardware that delivers measurable results.
Led full system architecture of a featherweight combat robot. Secured €2,000 in independent sponsorship. Applied MATLAB modelling to optimise rotational dynamics, achieving a 35% increase in rotational force and 95% competition reliability.
Ultrasonic sensor-based object detection with PID motor control on Raspberry Pi. Encoder feedback maintains speed stability within 5% across variable loads up to 5 kg, achieving 92% detection accuracy with 38 mm max positional error.
End-to-end YOLOv8 inference pipeline on Raspberry Pi 5. Model quantisation and pruning achieve 15 FPS on embedded hardware with 87% species classification accuracy for population monitoring applications.
Reaction wheel arm balancing system modelled from first principles. Full derivation of rotational dynamics, control strategy design and implementation, interactive mathematical reference materials, and structured video presentation.
Camera-driven animatronic eye system that detects and tracks a person's position in real time, physically moving the eye mechanism to follow them around the room. Computer vision pipeline on Raspberry Pi drives servo-actuated eye movement with smooth, continuous tracking.
I'm a 2nd year BSc Robotics & Intelligent Devices student at Maynooth University, driven by a genuine interest in building intelligent systems from the ground up.
My work spans the full stack: embedded firmware, circuit design, computer vision, and control theory. I build hardware that demonstrates these disciplines with real, measurable outcomes.
Alongside my studies I hold leadership roles across the university — running the Engineering & Robotics Society, representing my year group to faculty, and coaching at the BJJ Club.
Open to internships, collaborations, robotics competitions, and new projects.
peterjohnkernohan@gmail.com