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Unit outline_

MTRX8700: Foundations of Robotics Research

Semester 1, 2023 [Normal day] - Camperdown/Darlington, Sydney

The goal of the course is to provide a comprehensive introduction to the basic knowledge and tools in the broad areas of robotics including sensing and vision, control and estimation, localisation and mapping, robotic learning, and planning and optimisation. Students will learn the basic principles, theories, and algorithms for these foundational blocks in robotics research accompanied with a series of training and practice in data acquisition, coding, and analytical methods for robotic systems. Students will also learn how these tools are pooled together to deliver robotic solutions that can solve important real-world problems, as well as the fundamental challenges and opportunities in robotic research frontiers. 

Unit details and rules

Academic unit Aerospace, Mechanical and Mechatronic
Credit points 6
Prerequisites
? 
None
Corequisites
? 
None
Prohibitions
? 
None
Assumed knowledge
? 

A demonstrated programming ability, familiarity with concepts in sensing and control systems and a background in either CS, Mechatronics or Electrical/Electronic Engineering

Available to study abroad and exchange students

Yes

Teaching staff

Coordinator Guodong Shi, guodong.shi@sydney.edu.au
Lecturer(s) Ian Manchester, ian.manchester@sydney.edu.au
Mitch Bryson, mitch.bryson@sydney.edu.au
Tejaswi Sundara Digumarti, tejaswi.digumarti@sydney.edu.au
Viorela Ila, viorela.ila@sydney.edu.au
Donald Dansereau, donald.dansereau@sydney.edu.au
Type Description Weight Due Length
Assignment Problems and Practices
Problem-solving and coding practices.
40% Multiple weeks N/A.
Outcomes assessed: LO2 LO3 LO4 LO5
Presentation Research Project
Presentation of a proposal for a robotics research project.
60% Week 13
Due date: 27 May 2022 at 23:59
N/A.
Outcomes assessed: LO1 LO8 LO7 LO6 LO2

Assessment summary

  • Assignments: Problems and practices corresponding to the fundamental modules. 
  • Research Project: Students will be asked to define a clear research problem in the area of robotics and outline the expected approaches, outcomes, and significance. The pitching session helps students gain feedback of the initial problem definition; the final presentation and report present the full scope of the proposed project. 
  • Student Lectures: Students will be asked to select a topic and prepare and present a short lecture of 15min  on a specific tool, concept, or paper in the area of robotics. 

Detailed information for each assessment can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name

Mark range

Description

High distinction

85 - 100

 

Distinction

75 - 84

 

Credit

65 - 74

 

Pass

50 - 64

 

Fail

0 - 49

When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see guide to grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

  • Deduction of 5% of the maximum mark for each calendar day after the due date.
  • After ten calendar days late, a mark of zero will be awarded.

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

WK Topic Learning activity Learning outcomes
Multiple weeks An average student should spend a total of 90 hours of independent study over the semester, including work on assessment tasks. Independent study (90 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8
Week 01 Introduction to Robotics Lecture (2 hr) LO1
Week 02 Robotic Vision Lecture (2 hr) LO3
Week 03 Robotic Imaging Lecture (2 hr) LO3
Vision and Imaging Tutorial (2 hr) LO3 LO7
Week 04 Robotic Control and Estimation Lecture (2 hr) LO4 LO5
Week 05 Robotic Control and Estimation Lecture (2 hr) LO4 LO5
Robotic Control and Estimation Tutorial (2 hr) LO4 LO5
Week 06 Research Methodologies Lecture (2 hr) LO1 LO2 LO7 LO8
Week 07 Localization and Mapping Lecture (2 hr) LO2 LO5
Research Project Pitching Presentation (2 hr) LO1 LO6
Week 08 Localization and Mapping Lecture (2 hr) LO2 LO5
Localization and Mapping Tutorial (2 hr) LO2 LO5
Week 09 Robotics Learning Lecture (3 hr) LO1 LO2
Student Lectures Presentation (2 hr) LO1 LO6
Week 10 Robotics Learning Lecture (2 hr) LO1 LO2
Robotics Learning Tutorial (2 hr) LO1 LO2
Week 11 Planning and Optimisation Lecture (2 hr) LO2
Week 12 Planning and Optimisation Lecture (2 hr) LO2
Planning and Optimization Tutorial (2 hr) LO1 LO2
Week 13 State of the Art Lecture (2 hr) LO7 LO8
Research Project Presentation Presentation (2 hr) LO1 LO7

Attendance and class requirements

  • Laboratory: Material covered in lectures is illustrated through experimental laboratory assignments. By applying the techniques they have learned, students will be given the opportunity to contextualise their learning. Application of the concepts will encourage a deeper approach to learning. Labs will be conducted once a week in the Mechatronics Lab.
  • Lecture: The series of lectures will cover robot fundamentals and case studies examining practical robot systems. Experts in the field will be invited to present guest lectures to give students a broad exposure to robotic systems in both research and industrial contexts.

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

At the completion of this unit, you should be able to:

  • LO1. apply a systematic approach to the design process for robotic systems
  • LO2. examine advanced topics in robotics including obstacle avoidance, path planning, robot architectures, multi-robot systems and learning as applied to robotic systems
  • LO3. demonstrate familiarity with sensor technologies relevant to robotic systems, specifically working with laser and vision data and understanding techniques for processing these data
  • LO4. implement navigation, sensing and control algorithms on a practical robotic system
  • LO5. understand conventions used in robot kinematics and dynamics
  • LO6. clearly express technical ideas in both oral and written form
  • LO7. develop the capacity to think independently and creatively about design problems
  • LO8. undertake independent research and analysis, thinking creatively about engineering problems.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1 Depth of disciplinary expertise

Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.

GQ2 Critical thinking and problem solving

Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.

GQ3 Oral and written communication

Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.

GQ4 Information and digital literacy

Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.

GQ5 Inventiveness

Generating novel ideas and solutions.

GQ6 Cultural competence

Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.

GQ7 Interdisciplinary effectiveness

Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.

GQ8 Integrated professional, ethical, and personal identity

An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.

GQ9 Influence

Engaging others in a process, idea or vision.

Outcome map

Learning outcomes Graduate qualities
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

This section outlines changes made to this unit following staff and student reviews.

Feedback from students is an important part of the continuous improvement of this course. We have updated the lecture plan for this year.

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.