EGB339 Introduction to Robotics

Unit Outline: Semester 2 2025, Gardens Point, Internal

Overview

• This unit introduces the fundamental concepts and algorithms of robotics and computer vision.
• You will learn how to solve typical fundamental real-world computer vision and robotics problems.
• You will build from this unit in EGB439 (Advanced Robotics).

Learning Outcomes

On successful completion of this unit you will be able to:

1. Recognise and evaluate realistic problems in robotics and computer vision at an introductory level.
2. Implement fundamental computer vision and robotics algorithms to solve realistic engineering problems, at an introductory level.
3. Develop and implement mathematical models and algorithms to describe and control the kinematic structure of a multi-link robot arm at an introductory level.
4. Use recognised project planning and management techniques to complete practical projects, at a developed level.

Content

Learning Approaches

You will have access to short and focused video lectures that deliver the content asynchronously. You can
access these videos flexibly to complete your learning in this unit.
In addition, you can expect the following timetabled activities:

• Each week you can engage with the lecturer in an interactive consultation session to discuss and deepen the content delivered via the video lectures.
• Tutorials let you practice and apply the core concepts of every week by solving focussed problems.
• Computer Labs allow you to solve practical robotics and computer vision problems which will be assessed.
• A visit to the QUT Centre for Robotics lets you experience a wide range of robotic technology in action and gets you into contact with professional engineers working on a variety of real-world robotics applications.

You are expected to self-guide your learning, engage with the video lectures, take notes, ensure you understand the delivered content, check your understanding with the provided quizzes (not assessed),
undertake background reading, research, and problem-solving during additional hours of study. We expect
you to actively participate in the tutorials and computer labs, and engage in the weekly timetabled
consultation session with the lecturer.

Feedback on Learning and Assessment

• Throughout the semester, you are given problems in the tutorials which allow self-assessment of performance and formative assessment by the teaching team.
• Regular programming tasks allow self-assessment of performance against timelines and benchmarks given in the description of each task. 
• Problem-solving assessment submissions will be marked by an automated online grading system.
• You will receive regular feedback on your progress on the practical projects and assessments during the Computer Labs from the teaching team.
• Your knowledge will be assessed in an end-of-semester exam.

Assessment

Overview

Assessment will be based on practical and theory performance in the form of Applied Projects (45%) and Problem Solving Tasks (20%) and an Exam (35%). The practical performance will be assessed throughout the semester based on demonstrated robot performance using an automated marking system at specific milestones, and a viva voce component where students show and explain their project in an interactive manner. Theory performance is assessed in problem-solving tasks throughout the semester and assessed via an online grading tool, and in an end-of-semester exam.

Unit Grading Scheme

7- point scale

Assessment Tasks

Assessment: Problem Solving Task

You will implement fundamental robotics and kinematics algorithms to solve real-world inspired problems. You will also implement fundamental computer vision algorithms to solve real-world inspired problems. You will be required to understand the problem scenarios, choose suitable algorithmic approaches, and implement them.

You will build upon your knowledge gained in this assessment in the applied project.

Your implementations will be assessed automatically online.

This assignment is eligible for the 48-hour late submission period and assignment extensions.

You may use generative artificial intelligence (GenAI) tools to prepare for, generate and refine content for this assessment task.

AI-generated content may be inaccurate, unreliable, or biased. It is your responsibility to critically evaluate any information you use.

You must clearly acknowledge and appropriately reference any AI-generated content following the guidance in Cite | Write (APA, Harvard, AGLC or Vancouver). You may be asked to demonstrate authorship of your assessment. Find out more on keeping good records to authenticate authorship.

Assessment: Applied Project

You will apply what you learned in the robotics and computer vision problem-solving tasks, and implement robotics, kinematics, and computer vision algorithms to perceive the environment and control a real robot arm. This is a single cohesive assessment with two interconnected deliverable parts:

Part 1: You will develop and implement foundational robotics and kinematics algorithms for the robot arm, establishing core functionality.

Part 2: You will extend your Part 1 implementation by integrating computer vision algorithms, allowing the robot to perceive and interact with its environment.

You will submit code that runs on a robotic system and is automatically assessed for each part. You will also undergo a viva voce for both parts, where you explain your solution to the teaching team to assess your understanding of the implementation.

You may use generative artificial intelligence (GenAI) tools to prepare for, generate and refine content for this assessment task.

AI-generated content may be inaccurate, unreliable, or biased. It is your responsibility to critically evaluate any information you use.

You must clearly acknowledge and appropriately reference any AI-generated content following the guidance in Cite | Write (APA, Harvard, AGLC or Vancouver). You may be asked to demonstrate authorship of your assessment. Find out more on keeping good records to authenticate authorship.

Assessment: Exam

The knowledge you have gained in this unit will be assessed in an exam. The exam will cover concepts from both robotics and computer vision. It will be a mix of short response questions and multiple-choice questions.

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

Resource Materials

Reference book(s)

M.W. Spong, S. Hutchinson and M. Vidyasagar, Robot Modeling and Control, 2nd
edition, Wiley, 2020

Robotics, Vision & Control: Fundamental Algorithms in Python, P. Corke, Springer 2023.

Software

Python, Robotics Toolbox for Python, Spatial Maths package for Python

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 for safe and responsible behaviour and will be required to wear appropriate protective items (e.g. closed shoes).

You will undergo a health and safety induction before the commencement of the practical sessions and will be issued with a safety induction card. If you do not have a safety induction card you will be denied access to laboratories.

Standards/Competencies

This unit is designed to support your development of the following standards\competencies.

Engineers Australia Stage 1 Competency Standard for Professional Engineer

1: Knowledge and Skill Base

1. 
   Relates to: Problem Solving Task
2. 
   Relates to: Problem Solving Task

2: Engineering Application Ability

1. 
   Relates to: Applied Project, Exam
2. 
   Relates to: Problem Solving Task, Applied Project, Exam
3. 
   Relates to: Applied Project, Exam
4. 
   Relates to: Applied Project

3: Professional and Personal Attributes

1. 
   Relates to: Problem Solving Task
2. 
   Relates to: Applied Project, Exam

Course Learning Outcomes

EN01 Bachelor of Engineering (Honours)

1. Manage projects to solve complex engineering problems, using appropriate information, engineering methods, and technologies.
   Relates to: Applied Project
2. Deploy appropriate approaches to engineering design and quality.
   Relates to: Applied Project, Exam
3. Demonstrate coherent knowledge and skills of physical, mathematical, statistical, computer, and information sciences that are fundamental to professional engineering practice.
   Relates to: Problem Solving Task, Exam
4. Demonstrate a thorough understanding of one engineering discipline, its research directions, and its application in contemporary professional engineering practice.
   Relates to: Problem Solving Task, Exam