ENN581 Robot Motion, Control and Planning
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: | ENN581 |
|---|---|
| Prerequisite(s): | EGH432 and EGH437 |
| Credit points: | 12 |
| Timetable | Details in HiQ, if available |
| Availabilities |
|
| CSP student contribution | $1,192 |
| Domestic tuition unit fee | $4,368 |
| International unit fee | $6,216 |
Unit Outline: Semester 2 2026, Gardens Point, Internal
| Unit code: | ENN581 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | EGH432 and EGH437 |
| Coordinator: | Krishna Manaswi Digumarti | krishnamanaswi.digumarti@qut.edu.au |
Overview
This unit will provide knowledge on principal aspects of robot motion, control and planning which is crucial in robotics. This will provide an understanding of the kinematics, dynamics, and control of both mobile and manipulator arm robots. Path planning algorithms for both type of robots will be discussed.
Learning Outcomes
On successful completion of this unit you will be able to:
- Critically analyse and reflect on the dynamics of mobile and manipulator arm robots
- Critically analyse and reflect on the kinematics of mobile and manipulator arm robots
- Design and implement programming and simulation knowledge to complex robotics problems
Content
The content of this unit will include:
- Methods to describe and compute forward and inverse kinematics
- Techniques to generate trajectories
- Dynamics and dynamic models
- Approaches to control
- Full and under actuation, notion of task and configuration space
- Path planning algorithms
These topics will be covered in the context of both articulated robot arms and mobile robot platforms.
Learning Approaches
You can expect the following activities in this unit:
- Blended learning and interactive lectures by well-experienced teaching staff.
- Practicums for simulation, coding and live demonstrations on robots.
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.
Practicals (2 hours each week): focus on translating theory into code and working with the python programming language. With a blend of simulated and real problems, and demonstrations on robots, these sessions link the lectures to practice.
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 or 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
The assessments include multiple individual quizzes and software-based design exercises to assess the knowledge gained through this unit.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Mid-semester Examination
The mid-semester examination will test your knowledge in the unit in an iterative way, your understanding of dynamics and kinematics, approaches to control to full and under actuated robots, including notions of task and configuration space.
The use of generative artificial intelligence (GenAI) tools is prohibited during this assessment.
Assessment: Software based design exercises
These authentic software-based design exercises will evaluate your ability to apply programming and simulation techniques related to simulation and control of robots. 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.
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
You have access to lab spaces and workshops at QUT and can use a range of tools after receiving an induction.
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 and lecture theatres. As such, there are no extraordinary workplace health and safety issues associated with these components of the unit.
You will be required to undertake practical sessions in the laboratory under the supervision of the lecturer and technical staff of the School. In any laboratory practicals you will be advised of the requirements of safe and responsible behaviour and will be required to wear appropriate protective items (e.g. closed shoes).
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: Mid-semester Examination, Software based design exercises, Examination - Critically analyse, evaluate and apply appropriate methods to Robotics and AI problems to achieve research-informed solutions
Relates to: Software based design exercises
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: Mid-semester Examination, Software based design exercises, 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: Software based design exercises
EN79 Graduate Diploma in Engineering Studies
- Demonstrate and apply advanced discipline knowledge, concepts and practices as they relate to contemporary Engineering practice
Relates to: Mid-semester Examination, Software based design exercises, 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: Software based design exercises