Robotics Hardware Engineering: Motors, Sensors & Python Control

Ideal For

This course is designed for:

• Engineering students and recent graduates in electrical, mechanical, computer, and systems engineering who want to add robotics and embedded systems to their professional skill set

• STEM educators and teachers at secondary and tertiary level who want to build a practical robotics capability that they can bring directly into their classrooms and labs

• Makers, hobbyists, and tinkerers who have worked with Arduino kits and hobby projects and are ready to step up to professional-grade robotics hardware and software

• Software developers and programmers who want to cross over into hardware — writing code that controls physical systems rather than just running on screens

• Electronics and embedded systems engineers looking to add Python-based robotics programming and autonomous system design to their existing hardware skills

• Industrial automation and manufacturing professionals working in Nigerian manufacturing, oil and gas, and logistics sectors who want to understand how robotic automation systems are built and operated

• Technical founders and entrepreneurs building robotics-based products or services for African markets — from security robots to agricultural automation to assistive devices

• RoboQuest Nigeria participants and coaches preparing for competitive robotics events at secondary and tertiary level

• Complete beginners to programming — a dedicated Python foundations track covering two full modules is built into the course, starting from absolute zero

Recommended prior knowledge: Basic computer literacy and comfort with using a laptop. No prior robotics, electronics, or programming experience required.

Summary of Learning  

By the end of this course, participants will be able to:

• Set up, configure, and operate Raspberry Pi 4 and NVIDIA Jetson Nano as professional embedded computing platforms — including OS installation, network configuration, remote access, and Linux terminal operation

• Write clean, object-oriented Python code from scratch — including classes, inheritance, file I/O, and exception handling — applied directly to hardware control scenarios throughout

• Interface Python programmes with physical hardware through GPIO — controlling LEDs, buzzers, and buttons using digital output, digital input, PWM, and interrupt-driven event detection

• Select, wire, and programme DC motors, servo motors, and stepper motors using H-bridge driver circuits — implementing forward, reverse, speed ramping, differential drive, and emergency stop functions via Python

• Integrate and read live data from ultrasonic, infrared, IMU, LiDAR, and camera sensor modules — applying moving average, complementary, and Kalman filter techniques to improve sensor reliability

• Design and implement multi-sensor data fusion systems that combine perception from multiple sensors into unified, reliable environmental awareness for autonomous robot decision-making

• Design robot behaviour using Finite State Machine (FSM) methodology — mapping states, transitions, conditions, and actions for autonomous, rule-based robot control

• Implement and tune a PID (Proportional-Integral-Derivative) controller in Python — systematically adjusting Kp, Ki, and Kd parameters to achieve smooth, stable autonomous robot motion

• Assemble, wire, test, and debug a complete autonomous robot from individual components — including systematic fault isolation across hardware, firmware, and software layers

• Build a professional robotics project portfolio — including wiring diagrams, documented Python codebase, parameter tuning logs, and a video demonstration of autonomous robot operation

Course Description 

Participants will learn to set up and programme Raspberry Pi and NVIDIA Jetson Nano embedded computers, control DC, servo, and stepper motors using Python and H-bridge driver circuits, integrate ultrasonic, infrared, IMU, LiDAR, and camera sensors, and design autonomous robot behaviour using finite state machines and PID control — culminating in a complete autonomous robot built, tuned, and demonstrated by every participant.