Robotics Course

Lesson 1 • Safety and Ethics in Robotics

Covers functional safety standards, risk assessment, and ethical deployment principles. Grounds all subsequent technical work in responsible practice.

Lesson 2 • History and Evolution of Robots

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

Lesson 3 • Robot Classification and Taxonomy

Categorises robots by morphology, mobility, and application domain. Enables precise communication about robot types throughout the course.

Lesson 1 • Inverse Kinematics Methods

Covers analytical and numerical IK solutions including Jacobian-based approaches. Students solve IK for target poses and handle singularities.

Lesson 2 • Joints, Links, and Degrees of Freedom

Defines revolute, prismatic, and spherical joints and their DOF contributions. Connects joint selection to workspace and payload requirements.

Lesson 3 • Forward Kinematics and DH Parameters

Applies Denavit-Hartenberg convention to derive end-effector pose from joint angles. Students build transformation matrices for standard arm configurations.

Lesson 4 • End-Effector and Gripper Design

Examines gripper types, grasping mechanics, and tool-centre-point calibration. Links mechanical design choices to task performance.

Lesson 1 • Proprioceptive Sensors

Covers encoders, IMUs, and force-torque sensors that measure internal robot state. Students integrate sensor data into joint-level feedback loops.

Lesson 2 • Exteroceptive Sensors

Introduces cameras, LiDAR, ultrasonic, and infrared sensors for environment perception. Connects sensor choice to range, resolution, and latency needs.

Lesson 3 • Electric Motors and Drives

Compares DC, brushless, and stepper motors on torque, speed, and efficiency. Students match motor specs to joint load requirements.

Lesson 4 • Hydraulic and Pneumatic Actuators

Explains fluid-power actuators for high-force and compliant applications. Contrasts with electric actuators on bandwidth and control complexity.

Lesson 1 • PID Controller Design and Tuning

Derives PID equations and applies Ziegler-Nichols and manual tuning methods. Students implement and tune PID on a simulated joint.

Lesson 2 • Force and Impedance Control

Covers compliant control strategies for contact-rich tasks such as assembly and polishing. Bridges rigid motion control with physical interaction.

Lesson 3 • Trajectory Generation and Tracking

Generates smooth joint and Cartesian trajectories using polynomial and spline methods. Students evaluate tracking error under different motion profiles.

Lesson 4 • State-Space and Modern Control

Introduces state-space representation, pole placement, and LQR design. Extends control capability beyond single-loop PID for multi-joint systems.

Lesson 1 • Robot Operating System Fundamentals

Introduces ROS nodes, topics, services, and the computation graph. Students build a minimal ROS workspace and run publisher-subscriber examples.

Lesson 2 • Programming Paradigms for Robots

Contrasts teach-pendant, scripting, and task-level programming approaches. Helps students choose the right paradigm for a given application.

Lesson 3 • Testing, Debugging, and Logging

Covers unit testing, bag-file replay, and visualisation tools for diagnosing robot software faults. Builds systematic debugging habits.

Lesson 4 • Simulation Environments

Uses physics-based simulators to develop and validate robot behaviour before hardware deployment. Reduces risk and accelerates iteration cycles.

Lesson 1 • Pose Estimation and Tracking

Estimates 6-DOF object pose using keypoint methods and tracks objects across frames. Feeds accurate pose data into robot manipulation pipelines.

Lesson 2 • Object Detection and Recognition

Applies classical feature matching and deep learning detectors to identify objects. Students benchmark detection accuracy and inference speed.

Lesson 3 • Image Processing Fundamentals

Covers filtering, edge detection, morphology, and colour segmentation on 2D images. Provides the preprocessing skills needed for higher-level vision tasks.

Lesson 4 • Camera Calibration and Geometry

Derives the pinhole camera model and performs intrinsic and extrinsic calibration. Enables accurate mapping between pixel coordinates and 3D space.

Lesson 1 • Localisation and SLAM

Implements particle filter localisation and graph-based SLAM for unknown environments. Students evaluate localisation accuracy with ground-truth comparison.

Lesson 2 • Path Planning Algorithms

Compares Dijkstra, A*, RRT, and PRM planners on completeness and optimality. Students select and configure planners for different environment types.

Lesson 3 • Manipulation Motion Planning

Plans collision-free arm trajectories in configuration space using MoveIt and similar tools. Students solve pick-and-place tasks with obstacle constraints.

Lesson 4 • Obstacle Avoidance and Local Planning

Applies dynamic window approach and potential field methods for real-time avoidance. Integrates local planners with global path outputs.

Lesson 1 • Performance Evaluation and Benchmarking

Defines KPIs for speed, accuracy, reliability, and energy use, then measures them systematically. Enables data-driven optimisation of the complete system.

Lesson 2 • Human-Robot Interaction Design

Applies HRI principles including intent recognition, gesture control, and safety zones. Students design interfaces that enable safe, intuitive human collaboration.

Lesson 3 • System Integration and Testing

Integrates mechanical, electrical, software, and perception subsystems into a unified robot. Students execute structured integration tests and resolve interface conflicts.

Lesson 4 • Multi-Robot Systems and Coordination

Covers task allocation, communication protocols, and formation control for robot teams. Extends single-robot skills to collaborative multi-agent scenarios.