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Building Autonomous Robotic Systems

Autonomous robots operate without human intervention — they perceive, plan, decide, and act on their own. This advanced 7-week course takes you deep into the engineering and AI behind self-driving cars, delivery drones, and warehouse robots. You'll learn path planning algorithms, simultaneous localization and mapping (SLAM), sensor fusion, control theory, and real-time decision making. By the end of the course, you'll have built a fully autonomous mobile robot capable of navigating an environment, avoiding obstacles, and completing missions independently.

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  • Flexible learning schedule
  • Certificate of completion
  • Expert instructor support
  • Hands-on projects

What You'll Learn

Understand the architecture of autonomous robotic systems
Implement path planning algorithms (A*, Dijkstra, RRT)
Build and tune PID controllers for precise robot motion
Use SLAM (Simultaneous Localization and Mapping) for environment mapping
Fuse data from multiple sensors (LiDAR, IMU, cameras, encoders)
Implement obstacle avoidance and dynamic re-planning
Program a fully autonomous robot that navigates an unknown environment
Understand ROS (Robot Operating System) fundamentals

Course Syllabus

1

Week 1

Autonomous Systems Architecture
  • What makes a robot autonomous? Levels of autonomy
  • The sense-plan-act paradigm in robotics
  • Hardware overview: LiDAR, IMUs, motor encoders, and GPS
  • Introduction to ROS (Robot Operating System): nodes, topics, and messages
  • Setting up your ROS development environment
  • Building a differential-drive robot base
  • Hands-on: Teleoperate your robot using ROS
2

Week 2

Localisation: Where Am I?
  • The localisation problem: why robots need to know where they are
  • Odometry: estimating position from wheel encoders
  • Introduction to probabilistic robotics: dealing with uncertainty
  • Kalman filters for sensor fusion (IMU + odometry)
  • GPS and compass integration for outdoor robots
  • Visualising robot position in ROS (RViz)
  • Hands-on: Implement odometry-based localisation on your robot
3

Week 3

Mapping: What's Around Me?
  • The mapping problem: building a model of the environment
  • Occupancy grid maps: representing space as a grid
  • Introduction to SLAM: mapping and localising simultaneously
  • LiDAR-based SLAM using existing ROS packages
  • Camera-based visual odometry and feature matching
  • Evaluating map quality and handling dynamic environments
  • Hands-on: Generate a map of a room using your robot and LiDAR
4

Week 4

Path Planning: How Do I Get There?
  • Graph-based planning: Dijkstra's and A* algorithms
  • Sampling-based planning: RRT and PRM
  • Global vs. local planning in robotics
  • Configuring the ROS navigation stack
  • Setting goal positions and autonomous waypoint navigation
  • Handling narrow corridors and tight spaces
  • Hands-on: Program autonomous point-to-point navigation
5

Week 5

Obstacle Avoidance & Dynamic Replanning
  • Local obstacle avoidance: the Dynamic Window Approach (DWA)
  • Reactive vs. deliberative control strategies
  • Costmaps: how robots evaluate traversability
  • Handling moving obstacles and dynamic environments
  • Recovery behaviours: what to do when the robot gets stuck
  • Safety zones and emergency stop mechanisms
  • Hands-on: Navigate a cluttered environment with moving obstacles
6

Week 6

Multi-Sensor Fusion & Advanced Control
  • Why one sensor is never enough: the case for fusion
  • Extended Kalman Filter (EKF) for fusing LiDAR, IMU, and odometry
  • Advanced PID tuning for smooth and responsive motion
  • Introduction to model predictive control (MPC)
  • Behaviour trees: structuring complex robot decision-making
  • Power management and battery optimisation for field robots
  • Hands-on: Integrate all sensors for robust autonomous navigation
7

Week 7

Capstone: Fully Autonomous Mission
  • Designing a complete autonomous mission (delivery, patrol, or exploration)
  • System integration: combining all subsystems
  • Testing in simulated environments (Gazebo)
  • Field testing and iterating on performance
  • Documenting your autonomous system
  • Final demonstration: your robot completes a mission autonomously
Prerequisites
  • Completion of 'Introduction to Robotics' and 'Building Robots With Vision'
  • Comfortable with Python programming (functions, classes, libraries)
  • Basic understanding of linear algebra and trigonometry
  • Experience with Raspberry Pi or similar single-board computers
Who This Is For
  • Students aiming for careers in autonomous systems and self-driving technology
  • Robotics enthusiasts ready to tackle advanced projects
  • Engineering students and graduates seeking practical autonomy skills
  • Professionals transitioning into robotics from software or mechanical engineering
  • Researchers interested in mobile robotics and autonomous navigation
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