Dynamical systems such as robots, manipulators and vehicles rely on several building blocks and software layers that enable the them to operate autonomously in complex environments. The collection of building blocks called autonomy stack and consisting of perception, planning and coordination, localisation and mapping, and control, define the brain of autonomous systems allowing it to perform complex tasks without human intervention or in human machine teaming applications.
This course introduces students to the methodology and algorithms involved in the autonomy stack. Perception is discussed on the example of various sensors and in particular LIDAR sensors used for autonomous driving. Localisation and mapping is covered through particle filters and graph based SLAM. Rapidly-exploring random tree plans, spline based Planning and raceline optimisation are discussed to give examples on planning and coordination. Control is covered through reference tracking and path following algorithms building on planning and coordination with explicit examples on 'wall following' and 'follow the gap' for obstacle avoidance.
Students will be challenged with the combination of different concepts in the the autonomy stack based on group based projects. Simulations, student projects and examples are based on the virtual environment Webots (https://cyberbotics.com).
Since modern controller designs and various components of the autonomy stack rely on big data, additionally 'optimization in control' with applications in reinforcement and machine learning, and model predictive control, will equip students with a complete set of tools necessary to work on or to supervise intelligent autonomous systems projects.
Learning Outcomes
Upon successful completion, students will have the knowledge and skills to:
- Explain the mathematical foundations of methods used to design and operate autonomous vehicles.
- Experiment with and implement motion control and guidance algorithms for autonomous vehicles.
- Analyse the performance of localisation and mapping algorithms based on the different perception methods.
- Apply planning and coordination algorithms to autonomous systems applications in simulation environments.
- Combine the four components of the autonomy stack to simulate intelligent autonomous systems.
- Motivate the selection of algorithms in the autonomy stack for different autonomous systems applications.
Indicative Assessment
- Project with individual staged deliverables (65) [LO 1,2,3,4,5,6]
- Final exam (35) [LO 1,2,3,4,5,6]
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Workload
130 hours including lectures, tutorials and self study
- 12 x 2 hr Lectures/week
- 11 x 2 hr supervised directed learning
- Self-directed learning as requested
Requisite and Incompatibility
Prescribed Texts
None
Preliminary Reading
N. Correll, B. Hayes, C. Heckman, A. Roncone; Introduction to Autonomous Robots, MIT Press, 2022
S. Thrun, W. Burgard, 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.
Fees
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:
- 2
- 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.
| Units | EFTSL |
|---|---|
| 6.00 | 0.12500 |
Course fees
- Domestic fee paying students
| Year | Fee |
|---|---|
| 2026 | $5520 |
- International fee paying students
| Year | Fee |
|---|---|
| 2026 | $7020 |
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.
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 |
|---|---|---|---|---|---|---|
| 9016 | 27 Jul 2026 | 03 Aug 2026 | 31 Aug 2026 | 30 Oct 2026 | In Person | N/A |