Course coordinator
Associate Professor Jen Jen Chung
Course overview
- Study period
- Semester 2, 2024 (22/07/2024 - 18/11/2024)
- Study level
- Undergraduate
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Elec Engineering & Comp Science School
Modern robotic algorithms and techniques for use in practical applications. Coverage of advanced navigation, motion planning and control methodologies for intelligent robotic systems.
This course teaches the fundamentals of robotic systems covering navigation, motion planning, perception and control of mobile robots and robot arms.ﾠIts emphasis is a principled, algorithmic approach to robotics and deals specifically with challenges arising from noise and uncertainty in the sense-think-act cycle of an autonomous robot. Students will gain a systematic understanding of the design of robotic systemsﾠthrough a team project assignment developing algorithms in ROS2 to be deployed and tested in simulation and in hardware.
Course requirements
Assumed background
Fluid knowledge of statistics, probability, linear algebra and differential equations (MATH2001 and STAT2201 or equivalent) are essential. Knowledge of control system modelling and design (METR4201 and ELEC3004) is assumed. Python/C++ and MATLAB programming skills are assumed.
Prerequisites
You'll need to complete the following courses before enrolling in this one:
ELEC3004 or METR3200 or METR4201
Incompatible
You can't enrol in this course if you've already completed the following:
METR4200 or METR7201 or METR7202 or ELEC3700 or ELEC7700
Course contact
Course staff
Lecturer
Associate Professor Jen Jen Chung
Timetable
The timetable for this course is available on the UQ Public Timetable.
Additional timetable information
Workshops start in week 2.
Aims and outcomes
The course covers the modelling, analysis, design and control of mobile robotic systems. At the end of this course, students should acquire the skills to apply the concepts of
- Probability theory for mobile robot navigation and planning
- Forward and inverse kinematics for robot control
- Computer vision for robot perception
Students should also a foundational understanding of how modernﾠrobotic technologies influence and are influenced by contemporaryﾠsocietal needs and concerns.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Explain the see-think-act cycle in robotic systems
LO2.
Analyse and model sensing and actuation noise in robotic systems
LO3.
Derive and implement recursive state estimation and filters for robot navigation
LO4.
Design and implement global, local and reactive motion planning algorithms
LO5.
Apply forward and inverse kinematics to control multi-linked robots
LO6.
Evaluate and explain different sensing modalities used in robotic perception
LO7.
Design and implement sensing, planning and control algorithms on a practical robotic system
LO8.
Apply a systematic approach to the design process for robotic systems
LO9.
Discuss the practical application of robotic systems to intelligent mechatronics applications
LO10.
Work as an effective member of a team, demonstrating respect for others' ideas, work styles and backgrounds
LO11.
Think creatively, independently and in teams, about engineering problems of complex systems
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Presentation, Project |
Team Project Proposal Presentations
|
pass/fail |
27/08/2024 - 28/08/2024
All team members must be in attendance and contribute to the presentation. |
| Computer Code, Paper/ Report/ Annotation, Practical/ Demonstration, Presentation, Project |
Team Project Interim Assessment
|
pass/fail |
10/09/2024 - 23/10/2024
Team members must be in attendance and contribute to the presentation in order to attempt the individual oral assessment component. Teams that attempt their Interim Assessment after Week 12 will be subject to the code freeze applied to all teams at 4PM on Monday Week 13. |
| Computer Code, Paper/ Report/ Annotation, Practical/ Demonstration, Presentation, Project |
Team Project Final Demo
|
50% |
21/10/2024 - 25/10/2024
All team members must be in attendance and contribute to the presentation. Demonstrations will be scheduled during Week 13's class sessions. A code freeze is applied at 4PM on Monday Week 13. A Peer Assessment Factor must be submitted for the Team Project Final Demo. |
| Examination |
Final Exam (Individual)
|
50% |
End of Semester Exam Period 2/11/2024 - 16/11/2024 |
A hurdle is an assessment requirement that must be satisfied in order to receive a specific grade for the course. Check the assessment details for more information about hurdle requirements.
Assessment details
Team Project Proposal Presentations
- Hurdle
- Identity Verified
- Team or group-based
- In-person
- Mode
- Oral
- Category
- Presentation, Project
- Weight
- pass/fail
- Due date
27/08/2024 - 28/08/2024
All team members must be in attendance and contribute to the presentation.
Task description
The team project will consist of a robotics challenge to be solved by a group of four students (or three or five depending on number of enrolments). The project brief will be released in Week 1 so you can start getting familiar with the requirements. The objective of the project is for students to learn practical robotics skills, practice applying robotics concepts to solve problems, and gain experience working in teams. Teams will be assessed on the performance of their solution. Specific details of the task requirements and guidelines are provided in the project brief document.
The Team Project Proposal Presentations will be held in class during the workshop (WKS1) sessions in Week 6. Teams must prepare and deliver a 10-minute presentation detailing and justifying their proposed approach to the project brief, including a planned schedule and breakdown of tasks for each team member. A 5-minute Q&A will follow each presentation.
Hurdle requirements
Teams must demonstrate an understanding of the project requirements and present a clear and feasible plan to address the project brief, including an effective breakdown of tasks for each team member.Submission guidelines
Team presentations will occur in class during the workshop (WKS1) sessions of Week 6.
Deferral or extension
You cannot defer or apply for an extension for this assessment.
Teams that fail the project presentation must reattempt the assessment.
Team Project Interim Assessment
- Hurdle
- Identity Verified
- Team or group-based
- In-person
- Mode
- Oral, Product/ Artefact/ Multimedia, Written
- Category
- Computer Code, Paper/ Report/ Annotation, Practical/ Demonstration, Presentation, Project
- Weight
- pass/fail
- Due date
10/09/2024 - 23/10/2024
Team members must be in attendance and contribute to the presentation in order to attempt the individual oral assessment component. Teams that attempt their Interim Assessment after Week 12 will be subject to the code freeze applied to all teams at 4PM on Monday Week 13.
Task description
The team project will consist of a robotics challenge to be solved by a group of four students (or three or five depending on number of enrolments). The project brief will be released in Week 1 so you can start getting familiar with the requirements. The objective of the project is for students to learn practical robotics skills, practice applying robotics concepts to solve problems, and gain experience working in teams. Teams will be assessed on the performance of their solution. Specific details of the task requirements and guidelines are provided in the project brief document.
From week 8, teams can undertake the Team Project Interim Assessment. This assessment is a pass/fail hurdle, students and teams may make multiple attempts to pass the interim assessment until the end of semester. The purpose of the Interim Assessment is for teams to receive detailed feedback and support on the progress of their team project in preparation for the Final Demo.
The interim assessment involves the following elements:
- System Demonstration (team): Live demonstration by the team of their robot in simulation accomplishing the first stage of the project brief during the in-class workshop (WKS1) session.
- Oral Assessment (individual): Students will be asked technical questions about their individual technical contributions, the system implementation, and design choices during the in-class workshop (WKS1) session.
- Report Submission (team): Written documentation describing all modules within the system and how they interact to achieve the target task. The report should clearly state which components were custom designed by the team and which were integrated from existing libraries.
- Code Submission (team): Complete git repository required to run the demo including relevant code comments and README instructions for installation and operation.
Hurdle requirements
Teams will be provided an Interim Demo checklist of requirements that their solution must meet. The submitted code and documentation in the report must be sufficient for the teaching team to run and evaluate the solution against the checklist. The in-class system demonstration must match the submitted solution and individual students must demonstrate a satisfactory technical contribution to the team solution.Submission guidelines
Teams that wish to conduct their in-class system demonstration and oral assessment must submit their code and report 24 hours prior to their workshop (WKS1) session.
Deferral or extension
You cannot defer or apply for an extension for this assessment.
Teams that fail the project interim assessment must reattempt the assessment. As the Team Project Interim Assessment is a team-based assessment, no extensions or deferrals are possible.
Team Project Final Demo
- Identity Verified
- Team or group-based
- In-person
- Mode
- Activity/ Performance, Oral, Product/ Artefact/ Multimedia, Written
- Category
- Computer Code, Paper/ Report/ Annotation, Practical/ Demonstration, Presentation, Project
- Weight
- 50%
- Due date
21/10/2024 - 25/10/2024
All team members must be in attendance and contribute to the presentation. Demonstrations will be scheduled during Week 13's class sessions. A code freeze is applied at 4PM on Monday Week 13.
A Peer Assessment Factor must be submitted for the Team Project Final Demo.
- Other conditions
- Time limited, Peer assessment factor.
Task description
The team project will consist of a robotics challenge to be solved by a group of four students (or three or five depending on number of enrolments). The project brief will be released in Week 1 so you can start getting familiar with the requirements. The objective of the project is for students to learn practical robotics skills, practice applying robotics concepts to solve problems, and gain experience working in teams. Teams will be assessed on the performance of their solution. Specific details of the task requirements and guidelines are provided in the project brief document.
The Final Project Demo will be in Week 13, during scheduled class sessions.
To be fair with all teams, a code freeze will be applied at 4PM on Monday Week 13. All teams must submit their git repository containing all necessary code to run the demo at this time.
While marks will be given for working subsystems, the assessment scheme is biased towards completeness; marking criteria will reflect minimum expected functionality. If the hardware system does not completely function at testing, a demonstration in simulation, videos of working prototypes and incremental progress will be assessed on their merits according to the marking criteria. Anything not submitted or shown at the demonstration session will not be assessed. Late projects will not be assessed. Penalties will be applied for failing to meet specific requirements or exceeding design restrictions or limitations. Penalties will also apply for missing or inaccurate files/documentation submitted with the robot system.
At the Team Project Final Demo, you will be asked to evaluate each member of your team. The peer assessment factors (PAFs) will modify your project mark to reflect your relative contribution to the assessment, as judged by your teammates.
The evaluations will be performed by having each team member assessed by the other team members using a peer assessment form. Team members are to use the following criteria:
- Team Player: Did this person work as a member of a team? Did they wait to be told what to do? Alternatively, did they attempt to control the whole project?
- Creative Input: The extent to which this team member contributed to generating new ideas in the project, and general problem-solving ability.
- Technical Contribution: The technical skills that this team member brought to the project in areas such as navigation, planning, perception and control, as well as the overall software architecture design.
- Reliability: Did this person perform their assignments effectively and on time? Did this person respond to emails, etc.?
- Hard Work: The extent to which this person slogged away at their appointed tasks, regardless of their success in achieving good outcomes.
- Communication: Was this person easy to communicate with? Did you have difficulties understanding their issues? Did they have difficulties understanding yours?
- Meeting Attendance: Did this person attend meetings on time and every week?
The peer assessment factor will be calculated by: (4 * Your Peer Mark / Total of Peer Marks Assigned in Your Team) ^0.6.
This formula also normalises for team sizes other than 4. A three-member team will typically have peer assessment factors greater than 1 (meaning they don't have to implement as much functionality to get the same overall product mark as a four-member team). A five-member team will typically have peer assessment factors less than 1 (meaning they will have to implement more functionality to get the same mark as a four-member team).
As an example, if all members of a team receive equal peer marks, your peer assessment factor will be 1. If you are in a three-person team and all members of the team receive equal peer marks, your peer assessment factor will be 1.188. If you are in a five-person team and all members of the team receive equal peer marks, your peer assessment factor will be 0.875.
If a student does not submit a PAF form, their contribution to the PAF will be accounted as a score of zero awarded to each student, making the resultant scores determined entirely by the team members who did submit a PAF form. The course coordinator and teaching staff will moderate the peer assessments to ensure that marks are indicative of a student's achievement in the course; PAFs which mischaracterise contributions or unfairly penalize or reward team members will be set to null.
The PAF will be accompanied by a Tutor Assessment Factor (TAF); the two will be multiplied together for the final scaling factor of the final project demo. The TAF will be based on in-class observations of the student interactions and contributions by casual academics and staff.
Submission guidelines
The PAF will be conducted via Blackboard (Buddycheck) and must be completed by Week 13 Friday 25/10/2024 16:00.
Deferral or extension
You cannot defer or apply for an extension for this assessment.
The Team Project Final Demo is a scheduled class-wide exhibit, you cannot defer these, and no extensions will be available.
Late submission
For the PAF, a penalty of 0.1 (10% of the nominal peer assessment factor of 1) will apply to your PAF per 24 hours from the time submission is due for up to 7 days. After 7 days, you will receive a PAF of zero.
Final Exam (Individual)
- Hurdle
- Mode
- Written
- Category
- Examination
- Weight
- 50%
- Due date
End of Semester Exam Period
2/11/2024 - 16/11/2024
- Other conditions
- Time limited.
Hurdle requirements
Students must achieve at least 40% on the final exam to pass the course.Exam details
| Planning time | 10 minutes |
|---|---|
| Duration | 120 minutes |
| Calculator options | (In person) Casio FX82 series or UQ approved , labelled calculator only |
| Open/closed book | Closed Book examination - specified written materials permitted |
| Materials | One A4 sheet of handwritten notes, double sided, is permitted |
| Exam platform | Paper based |
| Invigilation | Invigilated in person |
Submission guidelines
Deferral or extension
You may be able to defer this exam.
Course grading
Full criteria for each grade is available in the Assessment Procedure.
| Grade | Description |
|---|---|
| 1 (Low Fail) |
Absence of evidence of achievement of course learning outcomes. Course grade description: Overall percentage less than 20%. |
| 2 (Fail) |
Minimal evidence of achievement of course learning outcomes. Course grade description: Overall percentage 20% to less than 45%. |
| 3 (Marginal Fail) |
Demonstrated evidence of developing achievement of course learning outcomes Course grade description: Overall percentage 45% to less than 50% OR an overall percentage of 50% or higher with either (or both) a failure to pass the team project interim assessment or a final exam mark less than 40%. |
| 4 (Pass) |
Demonstrated evidence of functional achievement of course learning outcomes. Course grade description: Overall percentage 50% to less than 65% AND team project interim assessment passed AND a final exam mark of at least 40%. |
| 5 (Credit) |
Demonstrated evidence of proficient achievement of course learning outcomes. Course grade description: Overall percentage 65% to less than 75% AND team project interim assessment passed AND a final exam mark of at least 40%. |
| 6 (Distinction) |
Demonstrated evidence of advanced achievement of course learning outcomes. Course grade description: Overall percentage 75% to less than 85% AND team project interim assessment passed AND a final exam mark of at least 40%. |
| 7 (High Distinction) |
Demonstrated evidence of exceptional achievement of course learning outcomes. Course grade description: Overall percentage 85% or higher AND team project interim assessment passed AND a final exam mark of at least 40%. |
Additional course grading information
Specific grading criteria will be provided for each assessment item.
Students must pass the team project interim assessment and receive at least 40% in the final exam to receive a passing grade for the course.
Supplementary assessment
Supplementary assessment is not available for some items in this course.
Supplementary assessment on a final grade of 3 is not available in this course, except where a student fails because of a final exam mark less than 40%, where a mark of 40% would have resulted in a grade of 4 or higher.
Additional assessment information
Artificial Intelligence (AI) and Machine Translation (MT) are emerging tools that may support students in completing the Team Project assessment tasks. Students may appropriately use AI and/or MT in completing Team Project assessment tasks. Students must clearly reference any use of AI or MT in each instance.
A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
Having Troubles?
If you are having difficulties with any aspect of the course material, you should seek help. Speak to the course teaching staff.
If external circumstances are affecting your ability to work on the course, you should seek help as soon as possible. The University and UQ Union have organisations and staff who are able to help, for example, UQ Student Services are able to help with study and exam skills, tertiary learning skills, writing skills, financial assistance, personal issues, and disability services (among other things).
Complaints and criticisms should be directed in the first instance to the course coordinator. If you are not satisfied with the outcome, you may bring the matter to the attention of the School of EECS Director of Teaching and Learning.
Learning resources
You'll need the following resources to successfully complete the course. We've indicated below if you need a personal copy of the reading materials or your own item.
Library resources
Find the required and recommended resources for this course on the UQ Library website.
Learning activities
The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.
Filter activity type by
Please select
| Learning period | Activity type | Topic |
|---|---|---|
Multiple weeks From Week 1 To Week 12 |
Lecture |
Live Lectures Lectures develop a scaffold to help you understand the course content by providing a continuous development of the material. Key concepts will be introduced and discussed. There will be one lecture per week. Students are encouraged to attend the lecture live, on-campus, to be able to ask direct questions to the lecturer. Students will be also be pointed to recommended book chapters, exercises and online material that are relevant to the course's learning objectives. It is expected that students dedicate time to studying the recommended material. |
Multiple weeks From Week 1 To Week 13 |
Not Timetabled |
Self-study This is a demanding course in terms of time, effort and self-discipline. Since most things we learn come from insight built through personal investigation and exploration, self study is a crucial part of this course. The expected total time commitment for the course is approximately 10 hours per week of productive effort. You should be looking to invest five hours per week in self or group study outside of the tutorials, lectures and practicals. Experience suggests that the students who make this investment achieve superior results. Students will be pointed to recommended book chapters, exercises and online material that are relevant to the course's learning objectives. This material will be published progressively throughout the semester on the course Blackboard page. Having efficient and effective independent learning processes is the key to doing well in this course. This includes working steadily throughout the semester; it is not possible to leave it all to the end of semester. |
Multiple weeks From Week 2 To Week 13 |
Practical |
Workshop (Practical) A two hour workshop (WKS1) session will be held each week. During these sessions, students will learn practical robotics skills using ROS2 and robot simulators. Students will be given the opportunity to deploy their work on robot hardware and work on team projects. Engagement with these activities, combined with student's self-review of workshop exercises, will be a significant contributor to the student learning process. |
Tutorial |
Workshop (Tutorial) A one hour workshop (WKS2) session will be held each week. During these sessions, students will apply the concepts and techniques presented in lectures to solve problems/exercises under the guidance of tutors. This provides students with the opportunity to discuss any theoretical questions which arise from the lectures. On select weeks, guest lecturers will be invited to present during the workshop sessions to provide students with perspectives from industry and research on state-of-the-art developments in robotics. |
Policies and procedures
University policies and procedures apply to all aspects of student life. As a UQ student, you must comply with University-wide and program-specific requirements, including the:
- Student Code of Conduct Policy
- Student Integrity and Misconduct Policy and Procedure
- Assessment Procedure
- Examinations Procedure
- Reasonable Adjustments - Students Policy and Procedure
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.
You'll also need to be aware of the following policies and procedures while completing this course:
School guidelines
Your school has additional guidelines you'll need to follow for this course:
Course guidelines
Students will have the opportunity to deploy their algorithmic solutions on robot hardware. Students must be inducted with the relevant occupational health and safety knowledge to understand the risks and safe handling procedures prior to using any robot equipment.