EGH432 Foundations of Kinematics and Algorithms in Robotics
To view more information for this unit, select Unit Outline from the list below. Please note the teaching period for which the Unit Outline is relevant.
| Unit code: | EGH432 |
|---|---|
| Prerequisite(s): | (Admission to EN52 or EN56 or EN62 or EN72) or 192 credit points of completed study |
| Credit points: | 6 |
| Timetable | Details in HiQ, if available |
| Availabilities |
|
Unit Outline: Semester 1 - 6 Week A 2027, Gardens Point, Internal
| Unit code: | EGH432 |
|---|---|
| Credit points: | 6 |
| Pre-requisite: | Admission to (EN52 or EN56 or EN62 or EN72) or 192 credit points of completed study |
| Coordinator: | Krishna Manaswi Digumarti | krishnamanaswi.digumarti@qut.edu.au |
Overview
This unit introduces you to important and foundational concepts in robotics, namely, how to represent the position and orientation of things (robots, cameras, widgets) in two or three dimensions. These representations form the foundation of professional practice in robotics and AI. This unit also introduces mathematical graphs which, combined with these representations, can be used to describe relative positions of places and objects. Search algorithms applied to such graphs are foundational to most robot navigation tasks. You will acquire key knowledge needed for later units in Decision and Control, Robot motion, control and planning, SLAM and Mobile Robotics.
Learning Outcomes
On successful completion of this unit you will be able to:
- Apply key approaches in coordinate representation to solving engineering problems
- Develop and implement algorithmic-based strategies for contextualised real world engineering problems
- Analyse, interpret and report on your solutions and the adopted methodology used to solve engineering problems
Content
Interactive lectures will concentrate on the following content:
- Representing robot information in 2D and 3D space.
- Positioning and modelling of robot motion in 3D spaces
- Introduction to graphs and search algorithms in robotic applications
Learning Approaches
The teaching of robotics and AI requires a careful blend of theory and practice. The unit needs to introduce several new principles and theories that may be unfamiliar to you. Each principle requires repeated engagement from different perspectives for you to gain sufficient understanding to apply the theory in an appropriate practice. The unit uses a three-pronged approach to engaging you with the foundation of robotics and AI, through the following timetabled activities.
Lectures (2 hours each week): are used to provide an introduction to the material, and immediate application of the material with small, focused problems to be completed in the lecture. Solutions are discussed and resolved in class and compared to a benchmark solution. Principles are introduced, discussed and dissected in the lecture, treating each principle deeply.
Computer lab tutorials (2 hours each week): Tutorials focus on a single integrated problem that brings together material from multiple units. The early material is reinforced as necessary and used as a foundation for learning the new material. Principles are integrated with material from previous modules and grounded in application scenarios. Computer labs are used to refine and verify hand-calculated answers.
Python Experience: Programming-based problem-solving and simulation tasks are conducted in structured theoretical and numerical experiments to provide exposure to unit's concepts. These experiments link the theoretical elements of the lectures and tutorials to practice. You will be assessed on your ability to translate the theory to a computer-based problem and solving it.
You are expected to
- Engage with timetabled learning activities on campus and ask questions.
- Engage with online resources outside of timetabled learning activities. They will be available on the unit
Canvas site. - Prepare for learning activities according to the unit schedule and follow up on any work not completed.
- Complete assessment tasks by working consistently throughout the semester and meeting the due dates that are published via the unit Canvas site.
Feedback on Learning and Assessment
Feedback will be given regularly throughout the semester by tutors and lecturers. You will receive formative feedback (to help you understand how you are progressing in this unit) on your approach to solving problems in the lectures and tutorials. You will receive summative feedback (to grade your work against expected learning outcomes) on the programming assessments.
Tutors and lecturers are available for feedback and advice in the lab sessions. The discussions forum on Canvas will have sections to discuss each week's content.
Assessment
Overview
You will be individually assessed through multiple problem-solving tasks and examinations.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Problem solving tasks
You will work on problem solving tasks with the Python programming language. You will individually solve specific real-world inspired problems by implementing algorithms. You will be required to prepare written and illustrated reports on your solution to the problem.
This assignment is eligible for the 48-hour late submission period and assignment extensions
The ethical and responsible use of generative artificial intelligence (GenAI) tools is authorised in this assessment. See the relevant assessment details in Canvas for specific guidelines.
Assessment: Examination
The examination is a theory-based exam with a combination of short/long answers. It involves problem-solving using some of the techniques learnt in the class and the application of these techniques under given circumstances.
This assignment is not eligible for the 48-hour late submission period and assignment extensions. You must be present in-person to take this exam.
The use of generative artificial intelligence (GenAI) tools is prohibited during this assessment.
Academic Integrity
Academic integrity is a commitment to undertaking academic work and assessment in a manner that is ethical, fair, honest, respectful and accountable.
The Academic Integrity Policy sets out the range of conduct that can be a failure to maintain the standards of academic integrity. This includes, cheating in exams, plagiarism, self-plagiarism, collusion and contract cheating. It also includes providing fraudulent or altered documentation in support of an academic concession application, for example an assignment extension or a deferred exam.
You are encouraged to make use of QUT’s learning support services, resources and tools to assure the academic integrity of your assessment. This includes the use of text matching software that may be available to assist with self-assessing your academic integrity as part of the assessment submission process.
Breaching QUT’s Academic Integrity Policy or engaging in conduct that may defeat or compromise the purpose of assessment can lead to a finding of student misconduct (Code of Conduct – Student) and result in the imposition of penalties under the Management of Student Misconduct Policy, ranging from a grade reduction to exclusion from QUT.
Resources
Learning material in this unit will be managed from QUT's Learning Management System (LMS).
Risk Assessment Statement
You will undertake lectures and tutorials in the traditional classrooms, lecture theatres and computer labs of QUT. You will follow all legitimate instructions of staff in accordance with QUT workplace health and safety requirements.
There are no unusual health or safety risks associated with this unit. You will be made aware of evacuation procedures and assembly areas in the first few weeks. In the event of a fire alarm sounding, or on a lecturer's or tutor's instruction, you should leave the room and assemble in the designated area which will be indicated to you. You should be conscious of your health and safety at all times whilst on campus.
Course Learning Outcomes
This unit is designed to support your development of the following course/study area learning outcomes.EN52 Master of Robotics and Artificial Intelligence
- Demonstrate and apply advanced and specialist discipline knowledge, concepts and practices in Robotics and AI
Relates to: Problem solving tasks, Examination - Critically analyse, evaluate and apply appropriate methods to Robotics and AI problems to achieve research-informed solutions
Relates to: Problem solving tasks, Examination - Communicate complex information effectively and succinctly in oral and written form for diverse purposes and audiences
Relates to: Problem solving tasks
EN62 Graduate Certificate in Robotics
- Demonstrate and apply advanced discipline knowledge, concepts and practices as they relate to contemporary practice in Robotics
Relates to: Problem solving tasks, Examination - Analyse and evaluate Robotics problems using technical approaches informed by contemporary practice to achieve innovative, critically informed solutions
Relates to: Problem solving tasks, Examination - Effectively communicate Robotics problems, related complex data and information, and solutions in contemporary professional formats for diverse purposes and audiences
Relates to: Problem solving tasks
EN72 Master of Advanced Robotics and Artificial Intelligence
- Demonstrate and apply advanced and specialist discipline knowledge, concepts and practices in Advanced Robotics and AI and Data Analytics domains
Relates to: Problem solving tasks, Examination - Critically analyse, evaluate and apply appropriate methods to problems to achieve research-informed solutions in Advanced Robotics and AI and Data Analytics domains
Relates to: Problem solving tasks, Examination - Communicate complex information effectively and succinctly in oral and written form for diverse purposes and audiences
Relates to: Problem solving tasks
EN79 Graduate Diploma in Engineering Studies
- Demonstrate and apply advanced discipline knowledge, concepts and practices as they relate to contemporary Engineering practice
Relates to: Problem solving tasks, Examination - Analyse and evaluate Engineering problems using technical approaches informed by contemporary practice and leading edge research to achieve innovative, critically informed solutions
Relates to: Problem solving tasks, Examination - Effectively communicate Engineering problems, related complex data and information, and solutions in contemporary professional formats for diverse purposes and audiences
Relates to: Problem solving tasks
EV01 Bachelor of Engineering (Honours)
- Demonstrate coherent knowledge and skills of physical, mathematical, statistical, computer, and information sciences that are fundamental to professional engineering practice.
Relates to: Problem solving tasks, Examination - Demonstrate a thorough understanding of one engineering discipline, its research directions, and its application in contemporary professional engineering practice.
Relates to: Problem solving tasks, Examination