Robotics Course for Beginners

Lesson 1 • Core Mechanical Concepts

Introduces joints, links, degrees of freedom, and basic kinematics. Connects mechanical structure to robot movement capability.

Lesson 2 • History and Evolution of Robotics

Traces robotics from early automata to modern systems. Provides context for understanding why current designs exist.

Lesson 3 • Robot Classifications and Applications

Categorises robots by mobility, purpose, and environment. Helps students match robot types to real-world use cases.

Lesson 4 • Basic Electronics for Robotics

Covers voltage, current, resistance, and simple circuits relevant to robot power and control. Prepares students for hardware assembly in later chapters.

Lesson 1 • Microcontrollers and Single-Board Computers

Compares microcontrollers and single-board computers as robot brains. Students learn when each platform is appropriate for a given application.

Lesson 2 • Actuators and Motors

Explains DC motors, servo motors, and stepper motors as the primary sources of robot motion. Links actuator choice to torque, speed, and precision requirements.

Lesson 3 • Power Systems and Wiring

Covers battery types, voltage regulators, and safe wiring practices. Ensures students can power a robot reliably and safely.

Lesson 4 • Structural Materials and Chassis Design

Reviews common frame materials and chassis configurations for beginner robots. Connects material choice to weight, strength, and cost trade-offs.

Lesson 1 • Functions and Modular Code

Teaches how to write reusable functions and organise code into logical modules. Promotes clean, maintainable robot programs.

Lesson 2 • Reading Sensor Data in Code

Shows how to read analogue and digital sensor inputs within a program. Bridges hardware knowledge from Chapter 2 with software control.

Lesson 3 • Debugging and Testing Programs

Introduces systematic debugging strategies and testing workflows for embedded robot code. Builds habits that reduce errors in later, more complex projects.

Lesson 4 • Introduction to Programming Concepts

Establishes variables, data types, operators, and control flow as universal programming foundations. Removes the assumption of prior coding experience.

Lesson 1 • Orientation and Motion Sensing

Uses accelerometers and gyroscopes to measure robot tilt, rotation, and acceleration. Provides data needed for balance and navigation control.

Lesson 2 • Line and Colour Detection

Explains reflectance and colour sensors used for line-following and object sorting tasks. Connects perception to task-specific robot behaviour.

Lesson 3 • Sensor Fusion Fundamentals

Combines data from multiple sensors to produce more reliable environmental models. Introduces weighted averaging and complementary filter concepts.

Lesson 4 • Distance and Obstacle Detection

Covers ultrasonic and infrared sensors for measuring distance and detecting obstacles. Directly enables collision-avoidance behaviour built in later chapters.

Lesson 1 • Collision Avoidance Behaviours

Implements reactive obstacle avoidance using sensor data and control logic. Integrates perception from Chapter 4 with motion control from this chapter.

Lesson 2 • Differential Drive Kinematics

Models the motion of two-wheeled differential drive robots mathematically. Enables students to calculate wheel speeds for desired robot trajectories.

Lesson 3 • PID Control Fundamentals

Explains proportional, integral, and derivative terms and their combined effect on system response. Students tune a PID controller for a simulated robot axis.

Lesson 4 • Open-Loop vs. Closed-Loop Control

Contrasts open-loop commands with feedback-driven closed-loop control. Establishes why feedback is essential for accurate robot motion.

Lesson 1 • Software Integration and Testing

Combines sensor reading, control logic, and actuator commands into a single cohesive program. Students run iterative tests to validate each subsystem.

Lesson 2 • Performance Evaluation and Documentation

Defines success metrics, measures robot performance, and records findings in a project report. Builds professional documentation habits for future projects.

Lesson 3 • Electronics Integration and Wiring

Connects microcontroller, motor drivers, sensors, and power supply into a unified system. Reinforces safe wiring practices and power budgeting.

Lesson 4 • Project Planning and Design

Guides students through defining robot goals, constraints, and component selection before building. Establishes an engineering design process habit.

Lesson 1 • Navigation in Dynamic Environments

Addresses how robots handle moving obstacles and changing environments during navigation. Extends static path planning to real-world unpredictability.

Lesson 2 • Localisation Concepts

Explains how robots estimate their position using odometry and landmark detection. Establishes the localisation problem as a prerequisite for mapping.

Lesson 3 • Occupancy Grid Mapping

Introduces grid-based maps where cells represent free, occupied, or unknown space. Students implement a simple occupancy grid from sensor data.

Lesson 4 • Introduction to SLAM

Presents simultaneous localisation and mapping as the integration of position estimation and map building. Provides conceptual grounding without requiring advanced maths.

Lesson 1 • Introduction to Machine Learning for Robots

Explains supervised, unsupervised, and reinforcement learning concepts in a robotics context. Prepares students to apply pre-trained models to robot perception tasks.

Lesson 2 • Reinforcement Learning for Motion Control

Applies basic reinforcement learning to teach a robot to navigate or balance through trial and reward. Demonstrates adaptive control beyond hand-coded rules.

Lesson 3 • Human-Robot Interaction Basics

Covers voice commands, gesture recognition, and LED feedback as interaction modalities. Enables students to build robots that communicate meaningfully with users.

Lesson 4 • Object Recognition on a Robot

Deploys a pre-trained image classification model on a robot platform for real-time object recognition. Connects vision hardware from Chapter 4 to intelligent decision-making.