Algorithmic Robotics (12062.1)
| Available teaching periods | Delivery mode | Location |
|---|---|---|
| View teaching periods | On-campus |
Bruce, Canberra |
| EFTSL | Credit points | Faculty |
| 0.125 | 3 | Faculty Of Science And Technology |
| Discipline | Study level | HECS Bands |
| School Of Information Technology & Systems | Level 3 - Undergraduate Advanced Unit | Band 2 2021 (Commenced After 1 Jan 2021) Band 3 2021 (Commenced Before 1 Jan 2021) |
Learning outcomes
Upon successful completion of this unit, students will be able to:1. Understand algorithmic thinking and related robotics topics such as the Sense, Think, Act loop;
2. Understand recursive state estimation techniques, and Gaussian Filters, including linear and nonlinear filters such as the Extended Kalman Filter;
3. Understand key robot motion and perception algorithms used in modern robotics;
4. Apply key algorithmic concepts in the application of robot localisation and mapping, including Simultaneous Localisation and Mapping; and
5. Understand foundational algorithmic concepts in Planning and Control, including Markov Decision Processes and Partially Observable Markov Decision Processes.
Graduate attributes
1. UC graduates are professional - employ up-to-date and relevant knowledge and skills1. UC graduates are professional - communicate effectively
1. UC graduates are professional - use creativity, critical thinking, analysis and research skills to solve theoretical and real-world problems
1. UC graduates are professional - work collaboratively as part of a team, negotiate, and resolve conflict
2. UC graduates are global citizens - communicate effectively in diverse cultural and social settings
2. UC graduates are global citizens - make creative use of technology in their learning and professional lives
3. UC graduates are lifelong learners - reflect on their own practice, updating and adapting their knowledge and skills for continual professional and academic development
Prerequisites
12058 Robot Dynamics AND6698 Discrete Mathematics
Corequisites
None.Incompatible units
None.Equivalent units
None.Assumed knowledge
None.| Year | Location | Teaching period | Teaching start date | Delivery mode | Unit convener |
|---|---|---|---|---|---|
| 2026 | Bruce, Canberra | Semester 1 | 16 February 2026 | On-campus | Dr Maleen Jayasuriya |
| 2027 | Bruce, Canberra | Semester 1 | 15 February 2027 | On-campus | Dr Maleen Jayasuriya |
Required texts
Probabilistic Robotics
Sebastian Thrun, Wolfram Burgard, Dieter Fox
Foundations of Robotics: A Multidisciplinary Approach with Python and ROS
Damith Herath and David St-Onge (Eds). 2022 / 978-9811919824
Submission of assessment items
Extensions & Late submissions
Artificial Intelligence
- Guided - Students will be guided in how GenAI must/may be used in completing the assessment as detailed in the unit outline and assessment instructions. More detailed information can be found at GenAI and Assessment at UC
All use of GenAI must be cited (in-text) and referenced (in the reference list) according to the UC Referencing Guide, with appendices summarising any prompts and output received from GenAI (as this information would otherwise not be available to the marker).
GenAI (and any other sources or reference material, other than your previous assessment submissions) is not permitted during the Interview assessment task.
Special assessment requirements
Information on extensions and special consideration for assessments can be found in the Assessment Policy and Assessment Procedures.
All assessment items will receive a numerical mark. The final grade will be determined as a weighted average of the individual assessment items.
| Grade |
Cumulative Mark |
| Pass |
Minimum 50% of combined weighted marks of all assessment items |
| Credit |
Minimum 65% of combined weighted marks of all assessment items |
| Distinction |
Minimum 75% of combined weighted marks of all assessment items |
| High Distinction |
Minimum 85% of combined weighted marks of all assessment items |
The unit convenor reserves the right to question students on any of their submitted work for moderation and academic integrity purposes.
Supplementary assessment
There will be no supplementary or deferred assessments.
Students must apply academic integrity in their learning and research activities at UC. This includes submitting authentic and original work for assessments and properly acknowledging any sources used.
Academic integrity involves the ethical, honest and responsible use, creation and sharing of information. It is critical to the quality of higher education. Our academic integrity values are honesty, trust, fairness, respect, responsibility and courage.
UC students have to complete the Academic Integrity Module annually to learn about academic integrity and to understand the consequences of academic integrity breaches (or academic misconduct).
UC uses various strategies and systems, including detection software, to identify potential breaches of academic integrity. Suspected breaches may be investigated, and action can be taken when misconduct is found to have occurred.
Information is provided in the Academic Integrity Policy, Academic Integrity Procedure, and University of Canberra (Student Conduct) Rules 2023. For further advice, visit Study Skills.
Learner engagement
Expected Average Student Workload: * denotes an assessable item
| Lectures |
12x 2h |
= 24h |
| Laboratory classes |
12x 2h |
= 24h |
| Preparation (lectures, tutorials, computer labs, reading) |
12x 3h |
= 36h |
| * In Class Test |
= 10h |
|
| * Lab Portfolios |
= 24h |
|
| * Integration Demo |
= 32h |
|
| Total |
= 150 hours |
Participation requirements
Your participation in both class (lecture, laboratory classes) and online activities will enhance your understanding of the unit content and therefore the quality of your assessment responses. Lack of participation may result in your inability to satisfactorily pass assessment items.
Required IT skills
- Familiarity with Python programming language
- Familiarity with ROS2 (Robot Operating System Version 2)
- Basic Linux laptop/computer (optional) is recommended for personal study
In-unit costs
Textbook (available as open access for free), (Optional) PC/Laptop with Linux for self/further study.
Work placement, internships or practicums
None
Additional information
Lab Safety: Students will be required to undergo a formal lab induction prior to starting any lab work.
In all cases of absence, sickness or personal problems, it is the student's responsibility to ensure that the unit convenor is informed. The minimum participation requirement must be met in order to pass the unit (regardless of supporting documentation).
It is important that students refer to the unit website (through UCLearn – UC's online learning environment) on a regular basis for any variations in the schedule and deadlines for the assessment tasks, which will be announced on the Unit Website. It is also the student's responsibility to ensure that they regularly check their UC email account, as electronic messages (whether via the unit's UCLearn site or directly) will be sent to this account.
The online discussion forum on the unit's UCLearn site is a very useful place for posting questions and students are strongly encouraged to make use of it.
