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:

1. Explain the mathematical foundations of methods used to design and operate autonomous vehicles.
2. Experiment with and implement motion control and guidance algorithms for autonomous vehicles.
3. Analyse the performance of localisation and mapping algorithms based on the different perception methods.
4. Apply planning and coordination algorithms to autonomous systems applications in simulation environments.
5. Combine the four components of the autonomy stack to simulate intelligent autonomous systems.
6. Motivate the selection of algorithms in the autonomy stack for different autonomous systems applications.

Field Trips

Not applicable.

Additional Course Costs

Not applicable.

Recommended Resources

There is no single book we are going to follow for the course.

• The course is inspired by the F1Tenth course out of UPenn. Corresponding material can be found on the following homepage: https://roboracer.ai/course
• Several topics discussed in the course can be found in the book "Introduction to Autonomous Robots" (https://mitpress.mit.edu/9780262047555/introduction-to-autonomous-robots/)
• Several topics discussed in the course can be found in the book "Optimization: A Bootcamp for Machine Learning, Inverse Problems, and Control" (https://www.cambridge.org/core/books/optimization/D030795E11729431A9381DC612EE7602)

Staff Feedback

Students will be given feedback in the following forms in this course:

• Written comments on assignments/assessments through Canvas.
• Students can ask for clarification by emailing the course convenor if the information on wattle is not precise enough.

Student Feedback

ANU is committed to the demonstration of educational excellence and regularly seeks feedback from students. Students are encouraged to offer feedback directly to their Course Convener or through their College and Course representatives (if applicable). Feedback can also be provided to Course Conveners and teachers via the Student Experience of Learning & Teaching (SELT) feedback program. SELT surveys are confidential and also provide the Colleges and ANU Executive with opportunities to recognise excellent teaching, and opportunities for improvement.

Other Information

This course introduces fundamental concepts that could potentially be addressed by certain Generative AI tools (e.g., ChatGPT). Hence, the use of any Generative AI tools is not permitted in graded assessments within this course.

Class Schedule

Week/Session	Summary of Activities	Assessment
1	Introduction to Autonomous Driving: Perception, Planning & Control
2	Rigid Body Transformations & (Vehicle) Dynamics
3	Path Planning: Dijkstra Algorithm, RRT and Spline Based Planners	Problem Set/Lab Assignment (5%)
4	Raceline Optimisation and Pure Pursuit
5	Reactive methods for navigation: PID for wall following, reference tracking, path following and, follow-the-gap for obstacle avoidance
6	Localisation and Mapping I: EKF and Particle Filter	Problem Set/Lab Assignment (20%)
7	Localisation and Mapping II: EKF & Graph Based SLAM
8	Optimisation (in Control)
9	Neural Networks, Reinforcement Learning & Machine Learning	Problem Set/Lab Assignment (20%)
10	Model Predictive Control
11	Learning Based Model Predictive Control
12	Wrap up content	Problem Set/Lab Assignment (20%)

Tutorial Registration

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.

Assessment Summary

Assessment task	Value	Learning Outcomes
Project with individual staged deliverables	65 %	L1,L2,L3,L4,L5,L6
Exam	35 %	L1,L2,L3,L4,L5,L6

* If the Due Date and Return of Assessment date are blank, see the Assessment Tab for specific Assessment Task details

Policies

ANU has educational policies, procedures and guidelines , which are designed to ensure that staff and students are aware of the University’s academic standards, and implement them. Students are expected to have read the Academic Integrity Rule before the commencement of their course. Other key policies and guidelines include:

• Academic Integrity Policy and Procedure
• Student Assessment (Coursework) Policy and Procedure
• Extenuating Circumstances Application
• Student Surveys and Evaluations
• Deferred Examinations
• Student Complaint Resolution Policy and Procedure
• Code of practice for teaching and learning

Assessment Requirements

The ANU is using 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. For additional information regarding Turnitin please visit the Academic Skills website. In rare cases where online submission using Turnitin software is not technically possible; or where not using Turnitin software has been justified by the Course Convener and approved by the Associate Dean (Education) on the basis of the teaching model being employed; students shall submit assessment online via ‘Canvas’ outside of Turnitin, or failing that in hard copy, or through a combination of submission methods as approved by the Associate Dean (Education). The submission method is detailed below.

Moderation of Assessment

Marks that are allocated during Semester are to be considered provisional until formalised by the College examiners meeting at the end of each Semester. If appropriate, some moderation of marks might be applied prior to final results being released.

Participation

Tutorials are compulsory. Attendance will be recorded and will impact Assessment Task 1.

Assessment Task 1

Project with individual staged deliverables

Project with individual exercises covering content presented in the lectures and in the tutorials.

• Deadlines: Thursdays 16:59 (in weeks 3, 6, 9 and 12) and during tutorials. (Written assessment items will be marked within two weeks after the deadline and marks will be communicated through Canvas.)
• Attendance of Tutorials is compulsory to hand in a report. Exercises need to be discussed with course convenor/tutor during tutorials. Discussions will be taken into account for the marking.
• Submission through Canvas.
• Solutions need to be written in Word or Latex and submitted as PDF.
• Submission ideally in groups of 3 students.

Assessment Task 2

Exam

Exam at the end of the semester. Format will be communicated in the lectures and through Canvas.

Academic Integrity

Academic integrity is a core part of the ANU culture as a community of scholars. The University’s students are an integral part of that community. The academic integrity principle commits all students to engage in academic work in ways that are consistent with, and actively support, academic integrity, and to uphold this commitment by behaving honestly, responsibly and ethically, and with respect and fairness, in scholarly practice.

The University expects all staff and students to be familiar with the academic integrity principle, the Academic Integrity Rule 2021, the Policy: Student Academic Integrity and Procedure: Student Academic Integrity, and to uphold high standards of academic integrity to ensure the quality and value of our qualifications.

The Academic Integrity Rule 2021 is a legal document that the University uses to promote academic integrity, and manage breaches of the academic integrity principle. The Policy and Procedure support the Rule by outlining overarching principles, responsibilities and processes. The Academic Integrity Rule 2021 commences on 1 December 2021 and applies to courses commencing on or after that date, as well as to research conduct occurring on or after that date. Prior to this, the Academic Misconduct Rule 2015 applies.

 

The University commits to assisting all students to understand how to engage in academic work in ways that are consistent with, and actively support academic integrity. All coursework students must complete the online Academic Integrity Module (Epigeum), and Higher Degree Research (HDR) students are required to complete research integrity training. The Academic Integrity website provides information about services available to assist students with their assignments, examinations and other learning activities, as well as understanding and upholding academic integrity.

Online Submission

Submissions through Canvas

Hardcopy Submission

Hard copy submission will not be accepted.

Late Submission

Policy regarding late submission is detailed below:

• Late submission not permitted. A submission of assessment tasks without an extension after the due date will be marked with 0.
• If an extension for an individual student has been approved, it is the student's responsibility to discuss alternative assessment items with the course convenor.

Referencing Requirements

The Academic Skills website has information to assist you with your writing and assessments. The website includes information about Academic Integrity including referencing requirements for different disciplines. There is also information on Plagiarism and different ways to use source material. Any use of artificial intelligence must be properly referenced. Failure to properly cite use of Generative AI will be considered a breach of academic integrity.

Returning Assignments

Marked reports/assignments are returned back in Canvas.

Extensions and Penalties

Extensions and late submission of assessment pieces are covered by the Student Assessment (Coursework) Policy and Procedure. Extensions may be granted for assessment pieces that are not examinations or take-home examinations. If you need an extension, you must request an extension in writing on or before the due date. If you have documented and appropriate medical evidence that demonstrates you were not able to request an extension on or before the due date, you may be able to request it after the due date.

Resubmission of Assignments

Not applicable.

Privacy Notice

The ANU has made a number of third party, online, databases available for students to use. Use of each online database is conditional on student end users first agreeing to the database licensor’s terms of service and/or privacy policy. Students should read these carefully. In some cases student end users will be required to register an account with the database licensor and submit personal information, including their: first name; last name; ANU email address; and other information.
In cases where student end users are asked to submit ‘content’ to a database, such as an assignment or short answers, the database licensor may only use the student’s ‘content’ in accordance with the terms of service – including any (copyright) licence the student grants to the database licensor. Any personal information or content a student submits may be stored by the licensor, potentially offshore, and will be used to process the database service in accordance with the licensors terms of service and/or privacy policy.
If any student chooses not to agree to the database licensor’s terms of service or privacy policy, the student will not be able to access and use the database. In these circumstances students should contact their lecturer to enquire about alternative arrangements that are available.

Distribution of grades policy

Academic Quality Assurance Committee monitors the performance of students, including attrition, further study and employment rates and grade distribution, and College reports on quality assurance processes for assessment activities, including alignment with national and international disciplinary and interdisciplinary standards, as well as qualification type learning outcomes.

Since first semester 1994, ANU uses a grading scale for all courses. This grading scale is used by all academic areas of the University.

Support for students

The University offers students support through several different services. You may contact the services listed below directly or seek advice from your Course Convener, Student Administrators, or your College and Course representatives (if applicable).

• ANU Health, safety & wellbeing for medical services, counselling, mental health and spiritual support
• ANU Accessibility for students with a disability or ongoing or chronic illness
• ANU Dean of Students for confidential, impartial advice and help to resolve problems between students and the academic or administrative areas of the University
• ANU Academic Skills supports you make your own decisions about how you learn and manage your workload.
• ANU Counselling promotes, supports and enhances mental health and wellbeing within the University student community.
• ANUSA supports and represents all ANU students