EGB339 Introduction to Robotics

Unit Outline: Semester 2 2024, 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, working individually and in a team.
• 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 individual and team-based projects, at a developed level.
5. Collaborate in a team to solve challenging robotics and computer vision problems 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 work in teams 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

• You are given weekly 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. 
• Individual assessment submissions will be marked by an automated online grading system. You will receive regular feedback for your progress on the team-based projects and assessments during the Computer Labs by the teaching team.

Assessment

Unit Grading Scheme

7- point scale

Assessment Tasks

Assessment: Robotics Team Project

In a group of two (or potentially three), you will apply what you learned and implement robotics and kinematics algorithms to control a simulated robot arm. You will demonstrate the performance of your implementation and receive feedback.

The late submission period does not apply and no assignment extensions are available.

Assessment: Computer Vision Team Project

In a group of two (or potentially three), you will apply what you learned and implement computer vision algorithms for a robotics application. You will practically demonstrate the performance of your implementation and receive feedback.

The late submission period does not apply and no assignment extensions are available.

Assessment: Robotics Problem Solving Task

You will implement fundamental robotics and kinematics algorithms to solve real-world inspired problems. You will be required to understand the problem scenarios, choose suitable algorithmic approaches, and implement them. Your implementations will be assessed automatically online.

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

Assessment: Computer Vision Problem Solving Task

You will 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. Your implementations will be assessed automatically online.

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

Academic Integrity

Students are expected to engage in learning and assessment at QUT with honesty, transparency and fairness. Maintaining academic integrity means upholding these principles and demonstrating valuable professional capabilities based on ethical foundations.

Failure to maintain academic integrity can take many forms. It includes cheating in examinations, plagiarism, self-plagiarism, collusion, and submitting an assessment item completed by another person (e.g. contract cheating). It can also include providing your assessment to another entity, such as to a person or website.

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.

Further details of QUT’s approach to academic integrity are outlined in the Academic integrity policy and the Student Code of Conduct. Breaching QUT’s Academic integrity policy is regarded as student misconduct and can lead to the imposition of penalties 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, P. Corke, 2nd edition, Springer 2017. Available in printed and as eBook.

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

Software

MATLAB, Robotics Toolbox for MATLAB, Machine Vision Toolbox for MATLAB

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: Robotics Problem Solving Task, Computer Vision Problem Solving Task
2. 
   Relates to: Robotics Problem Solving Task, Computer Vision Problem Solving Task

2: Engineering Application Ability

1. 
   Relates to: Robotics Team Project, Computer Vision Team Project
2. 
   Relates to: Robotics Team Project, Computer Vision Team Project , Robotics Problem Solving Task, Computer Vision Problem Solving Task
3. 
   Relates to: Robotics Team Project, Computer Vision Team Project
4. 
   Relates to: Robotics Team Project, Computer Vision Team Project

3: Professional and Personal Attributes

1. 
   Relates to: Robotics Problem Solving Task, Computer Vision Problem Solving Task
2. 
   Relates to: Robotics Team Project, Computer Vision Team Project
3. 
   Relates to: Robotics Team Project, Computer Vision Team Project