• Model and analyse the mobile kinematics of a variety of wheel robotics platforms.
• Explain the role and importance of probabilistic methods in mobile robotics.
• Understand the sensing and perception systems and algorithms of a mobile robot.
• Describe how state estimation techniques can be used to estimate the state of a robotic system from sensor data.
• Formulate the tasks of robot localisation and SLAM within the recursive state estimation paradigm.
• Understand and explain how the above methods and techniques can be combined into an overall framework for mobile robot planning and navigation.

• Module Graded (numeric value) or Ungraded (Pass/Not Passed): Graded
• Continuous Assessment detail(s): The course will include practical assignments where students will be required to implement/apply the algorithms and techniques covered during the course. The percentage split between continuous assessment and the final exam will be specified at the beginning of the course. Continuous assessment may be worth up to 25%.

• Pass standard: 40%
• Could the result of this module be capped? No

• Are supplemental/resit registrations permitted? Yes
• University scheduled written examination (Autumn): 120 minutes, On-campus

• CS422 Robotics & Automation or equivalent

Introduction to Autonomous Mobile Robots, Siegwart, Roland; Nourbakhsh, Illah Reza; Scaramuzza, Davide, MIT Press; second edition (2011). ISBN¿ 978-0262015356

Probabilistic Robotics,  Sebastian Thrun, Wolfram Burgard, Dieter Fox.¿ MIT Press (2005). ISBN 978-0262201629

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