Courses / Module

On successful completion of the module, students should be able to:

• 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 apply the principle methods for mobile robot planning and navigation.

Delivery methods	Hours
Lectures	24
Labs / Practicals	0
Tutorials	0
Planned learning activities	0
Independent student activities	101
Total	125

• Module Graded (numeric value) or Ungraded (Pass/Not Passed): Graded
• Continuous Assessment detail(s): The course will include a number of 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 50%.

Assessment type	Options	Weighting	Duration
Continuous Assessment		50%
University scheduled written examination	On-campus	50%	120 minutes
Other		0%
Total		100%	120 minutes

• 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, Roland Siegwart. Probabilistic Robotics by Thrun, Burgard and Fox

For a full timetable search, go to NUIM Timetable. Venue locations can be found here.