COMPSYS 732 : Mobile Autonomous Robotics

Engineering

2021 Semester Two (1215) (15 POINTS)

Course Prescription

Techniques and principles for designing and developing mobile robots that interact autonomously with their environment. Topics include sensors and actuators, kinematic analysis, computer vision, state estimation and planning. Includes significant hands-on experience through the design and development of a mobile robot.

Course Overview

This course will focus on how Artificial Intelligence (AI) can be used for building autonomous mobile robots. The course will explore how AI can be integrated into different aspects of robotics using the See-Think-Act cycle. The course theory will be supplemented by hands-on practical labs, where the students will learn to use TurtleBot robots in both simulated and real-world environments. The final project in the course will be to implement an autonomous robot for a real-world task.

Course Requirements

Prerequisite: 15 points from COMPSCI 230, 235, COMPSYS 302, ENGSCI 331, MECHENG 313, SOFTENG 306

Capabilities Developed in this Course

Capability 1: Disciplinary Knowledge and Practice
Capability 2: Critical Thinking
Capability 3: Solution Seeking
Capability 4: Communication and Engagement
Capability 5: Independence and Integrity

Learning Outcomes

By the end of this course, students will be able to:
  1. Evaluate the principles and theories of AI as they apply to robotics and automation (Capability 1 and 2)
  2. Compare and contrast different AI techniques for their applicability in different scenarios (Capability 2 and 3)
  3. Synthesise how different AI models would impact on the behaviour of a robot in a specified environment (Capability 1, 2 and 3)
  4. Design a robotic system that can safely navigate an environment autonomously (Capability 1, 2 and 3)
  5. Compare and contrast different approaches that a robot can use to understand its environment (Capability 3, 4 and 5)

Assessments

Assessment Type Percentage Classification
Labs 30% Individual Coursework
Design Report 55% Individual Coursework
Peer Reviews 15% Individual Coursework
Assessment Type Learning Outcome Addressed
1 2 3 4 5
Labs
Design Report
Peer Reviews

Workload Expectations

This course is a standard 15 point course and students are expected to spend 10 hours per week involved in each 15 point course that they are enrolled in.

For this course, you can expect 3 hours of lectures, 2 hours of labs, 1 hour of reading and thinking about the content and 4 hours of work on assignments and/or test preparation.

Delivery Mode

Campus Experience

Attendance is expected at scheduled activities including labs to complete components of the course.
Lectures will be available as recordings. Other learning activities including labs will not be available as recordings.
The course will not include live online events.
The activities for the course are scheduled as a standard weekly timetable.

Learning Resources

The recommended textbook for this course is Introduction to AI Robotics, second edition by Robin R. Murphy (2019).

Health & Safety

The labs will be using physical robots, as such there is a risk of harm. No food or drink is allowed in the robotics laboratories, and the teaching assistant instructions must be followed at all times. The first lab will include a health and safety induction to the laboratories, if this is missed students will be unable to attend the labs.

Student Feedback

At the end of every semester students will be invited to give feedback on the course and teaching through a tool called SET or Qualtrics. The lecturers and course co-ordinators will consider all feedback and respond with summaries and actions.

Your feedback helps teachers to improve the course and its delivery for future students.

Class Representatives in each class can take feedback to the department and faculty staff-student consultative committees.

Digital Resources

Course materials are made available in a learning and collaboration tool called Canvas which also includes reading lists and lecture recordings (where available).

Please remember that the recording of any class on a personal device requires the permission of the instructor.

The course will be using ROS (Robot Operating System). This software is installed on the lab computers, if students want to download it for themselves is is available at https://www.ros.org/.

Academic Integrity

The University of Auckland will not tolerate cheating, or assisting others to cheat, and views cheating in coursework as a serious academic offence. The work that a student submits for grading must be the student's own work, reflecting their learning. Where work from other sources is used, it must be properly acknowledged and referenced. This requirement also applies to sources on the internet. A student's assessed work may be reviewed against online source material using computerised detection mechanisms.

Class Representatives

Class representatives are students tasked with representing student issues to departments, faculties, and the wider university. If you have a complaint about this course, please contact your class rep who will know how to raise it in the right channels. See your departmental noticeboard for contact details for your class reps.

Inclusive Learning

All students are asked to discuss any impairment related requirements privately, face to face and/or in written form with the course coordinator, lecturer or tutor.

Student Disability Services also provides support for students with a wide range of impairments, both visible and invisible, to succeed and excel at the University. For more information and contact details, please visit the Student Disability Services’ website http://disability.auckland.ac.nz

Special Circumstances

If your ability to complete assessed coursework is affected by illness or other personal circumstances outside of your control, contact a member of teaching staff as soon as possible before the assessment is due.

If your personal circumstances significantly affect your performance, or preparation, for an exam or eligible written test, refer to the University’s aegrotat or compassionate consideration page https://www.auckland.ac.nz/en/students/academic-information/exams-and-final-results/during-exams/aegrotat-and-compassionate-consideration.html.

This should be done as soon as possible and no later than seven days after the affected test or exam date.

Learning Continuity

In the event of an unexpected disruption we undertake to maintain the continuity and standard of teaching and learning in all your courses throughout the year. If there are unexpected disruptions the University has contingency plans to ensure that access to your course continues and your assessment is fair, and not compromised. Some adjustments may need to be made in emergencies. You will be kept fully informed by your course co-ordinator, and if disruption occurs you should refer to the University Website for information about how to proceed.

Student Charter and Responsibilities

The Student Charter assumes and acknowledges that students are active participants in the learning process and that they have responsibilities to the institution and the international community of scholars. The University expects that students will act at all times in a way that demonstrates respect for the rights of other students and staff so that the learning environment is both safe and productive. For further information visit Student Charter https://www.auckland.ac.nz/en/students/forms-policies-and-guidelines/student-policies-and-guidelines/student-charter.html.

Disclaimer

Elements of this outline may be subject to change. The latest information about the course will be available for enrolled students in Canvas.

In this course you may be asked to submit your coursework assessments digitally. The University reserves the right to conduct scheduled tests and examinations for this course online or through the use of computers or other electronic devices. Where tests or examinations are conducted online remote invigilation arrangements may be used. The final decision on the completion mode for a test or examination, and remote invigilation arrangements where applicable, will be advised to students at least 10 days prior to the scheduled date of the assessment, or in the case of an examination when the examination timetable is published.

Published on 09/07/2021 12:29 p.m.