• Offered by School of Engineering
  • ANU College ANU College of Engineering Computing & Cybernetics
  • Course subject Engineering
  • Areas of interest Engineering, Mechatronics, Robotics
  • Academic career PGRD
  • Course convener
    • Prof Iman Shames
  • Mode of delivery In Person
  • Offered in Second Semester 2024
    See Future Offerings
  • STEM Course

This course is for graduate students interested in the fields of robot perception, motion planning, control theory, and autonomous systems. This course introduces students to the methodology and algorithms involved in the design and control of an autonomous race car. Topics include localisation, scan matching, PID control, mapping, learning based visual navigation, model-predictive control and ethical decision making. Students test and implement their algorithms in a racing sand-box.

Learning Outcomes

Upon successful completion, students will have the knowledge and skills to:

  1. Explain the mathematical foundations of methods used to design and operate autonomous vehicles.
  2. Understand and derive the models describing the motion of an autonomous vehicle as well as its sensors.
  3. Derive and implement motion control and guidance algorithms for an autonomous vehicle.
  4. Develop and implement algorithms governing the perception of an autonomous vehicle as well as localisation and mapping algorithms.
  5. Explain and reflect on the ethical considerations in designing the autonomy stack of an autonomous vehicle.

Indicative Assessment

  1. Project Phase 1 (20) [LO 1,2]
  2. Project Phase 2 (30) [LO 1,2,3]
  3. Project Phase 3 (30) [LO 1,2,4]
  4. Reflection (5) [LO 5]
  5. Final Demonstration (15) [LO 1,2,3,4]

The ANU uses Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. While the use of Turnitin is not mandatory, the ANU highly recommends Turnitin is used by both teaching staff and students. For additional information regarding Turnitin please visit the ANU Online website.


130 hours in total over the semester consisting of:

  • 12 × 2 hr Lectures/week
  • Self-directed learning as required.

Inherent Requirements

Information on inherent requirements for this course is currently not available.

Requisite and Incompatibility

To enrol in this course you must be studying Master of Engineering in Mechatronics, Master of Engineering in Electrical Engineering, or Master of Machine Learning and Computer Vision.

Prescribed Texts


Preliminary Reading

S. Thrun, W. Burgard, and D. Fox, Probabilistic Robotics, MIT Press, 2005.

Assumed Knowledge

Mathematics including differential equations, probability theory, statistics, and matrix analysis. Students are also required to have adequate programming and software skills.


Tuition fees are for the academic year indicated at the top of the page.  

Commonwealth Support (CSP) Students
If you have been offered a Commonwealth supported place, your fees are set by the Australian Government for each course. At ANU 1 EFTSL is 48 units (normally 8 x 6-unit courses). More information about your student contribution amount for each course at Fees

Student Contribution Band:
Unit value:
6 units

If you are a domestic graduate coursework student with a Domestic Tuition Fee (DTF) place or international student you will be required to pay course tuition fees (see below). Course tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.

Where there is a unit range displayed for this course, not all unit options below may be available.

6.00 0.12500
Domestic fee paying students
Year Fee
2024 $4980
International fee paying students
Year Fee
2024 $6360
Note: Please note that fee information is for current year only.

Offerings, Dates and Class Summary Links

ANU utilises MyTimetable to enable students to view the timetable for their enrolled courses, browse, then self-allocate to small teaching activities / tutorials so they can better plan their time. Find out more on the Timetable webpage.

The list of offerings for future years is indicative only.
Class summaries, if available, can be accessed by clicking on the View link for the relevant class number.

Second Semester

Class number Class start date Last day to enrol Census date Class end date Mode Of Delivery Class Summary
7359 22 Jul 2024 29 Jul 2024 31 Aug 2024 25 Oct 2024 In Person View

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