ELEC ENG 4107 - Autonomous Systems

North Terrace Campus - Semester 2 - 2024

This course provides an introduction to the perception, orientation, cognition, decision and motion control featured in autonomous systems. Topics include the operating principles of motion sensing, sensor fusion, state estimation, localisation, modelling and control of autonomous vehicles, and the theory and applications of reinforcement learning in autonomous operations. The course includes practicals to perform sensing and motion control of autonomous vehicles, and an assignment on reinforcement learning. Assessments include practicals and report, tests and a final exam.

  • General Course Information
    Course Details
    Course Code ELEC ENG 4107
    Course Autonomous Systems
    Coordinating Unit Electrical and Electronic Engineering
    Term Semester 2
    Level Undergraduate
    Location/s North Terrace Campus
    Units 3
    Contact Up to 5 hours per week
    Available for Study Abroad and Exchange Y
    Prerequisites ELEC ENG 2104 or MECH ENG 3032
    Incompatible ELEC ENG 3107
    Assumed Knowledge ELEC ENG 3101, MATHS 2201 or MATHS 2106, MATHS 2202 or MATHS 2107
    Course Description This course provides an introduction to the perception, orientation, cognition, decision and motion control featured in autonomous systems. Topics include the operating principles of motion sensing, sensor fusion, state estimation, localisation, modelling and control of autonomous vehicles, and the theory and applications of reinforcement learning in autonomous operations. The course includes practicals to perform sensing and motion control of autonomous vehicles, and an assignment on reinforcement learning. Assessments include practicals and report, tests and a final exam.
    Course Staff

    Course Coordinator: Dr Cheng-Chew Lim

    Course Timetable

    The full timetable of all activities for this course can be accessed from Course Planner.

  • Learning Outcomes
    Course Learning Outcomes
    On successful completion of this course students will be able to:

      
    1 Apply the basic operating principles of sensing, sensor fusion and control to operate small autonomous vehicles
    2 Explain the perception, localisation, cognition and motion control featured in autonomous systems
    3 Gain hands-on experience to perform small vehicle motion simulation and indoor deployment
    4 Comprehend the fundamentals for reinforcement learning in autonomous systems

     
    The above course learning outcomes are aligned with the Engineers Australia Entry to Practice Competency Standard for the Professional Engineer. The course develops the following EA Elements of Competency to levels of introductory (A), intermediate (B), advanced (C):  
     
    1.11.21.31.41.51.62.12.22.32.43.13.23.33.43.53.6
    C C C B B C C C C B B B B B B B
    University Graduate Attributes

    This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:

    University Graduate Attribute Course Learning Outcome(s)

    Attribute 1: Deep discipline knowledge and intellectual breadth

    Graduates have comprehensive knowledge and understanding of their subject area, the ability to engage with different traditions of thought, and the ability to apply their knowledge in practice including in multi-disciplinary or multi-professional contexts.

    1-4

    Attribute 2: Creative and critical thinking, and problem solving

    Graduates are effective problems-solvers, able to apply critical, creative and evidence-based thinking to conceive innovative responses to future challenges.

    1-4

    Attribute 7: Digital capabilities

    Graduates are well prepared for living, learning and working in a digital society.

    1-4
  • Learning Resources
    Required Resources
    A set of course notes, tutorial problems, instructions for laboratory practicals, computer simulations and other supporting materials are available for downloading from the MyUni course web site.
    Recommended Resources
    Reference books:

    R Siegwart, IR Nourbakhsh, D Scaramuzza and RC Arkin:  Introduction to Autonomous Mobile Robots, 2nd Ed, MIT Press, 2011, online book, available from BSL

    P Corke: Robotics, Vision and Control, 2ndEd, Springer, 2017, online book, available from BSL

    E Blasch, E Bosse and D Lambert: High-Level Information Fusion Management and Systems Design, Artech House, 2012, online book,
    available from BSL.

    RS Sutton and AG Barto: Reinforcement Learning: An Introduction, 2nd Ed, 2018, MIT Press, available from BSL.

    Online Learning
    Extensive use is made of the MyUni web site for this course, https://myuni.adelaide.edu.au/webapps/login.
    Coursenotes, tutorial problems and solutions, laboratory and computer simulation exercises and practice problems are available for downloading from the web site.
  • Learning & Teaching Activities
    Learning & Teaching Modes
    This course relies on lectures as the primary delivery mechanism for the material.

    Tutorials supplement the lectures by providing exercises and example problems to enhance the understanding obtained through lectures.

    Practicals provide hands-on experience for students to reinforce the theoretical concepts encountered in lectures.

    Computer simulations provide interactive learning for students to relate the mathematical concepts encountered in lectures. 

    Continuous assessment activities provide the formative assessment opportunities for students to gauge their progress and understanding.
    Workload

    The information below is provided as a guide to assist students in engaging appropriately with the course requirements.

    The information below is provided as a guide to assist students in engaging appropriately with the course requirements.

    Activity Contact hours Workload hours
    28 Lecture 28 70
    5 Tutorials 5 20
    4 Practical Sessions 12 34
    2 Tests 2 8
    1 Assignment 3 9
    1 Exam 1.5 16
    Total 51.5 157



    Learning Activities Summary
    The course explores the role of autonomous systems in engineering and study the topic areas fundamental to understanding and developing autonomous systems.

    Lectures
    The face-to-face lectures delivered from week 1 to week 11 cover the following topic areas:
    1. Introduction to autonomous systems in engineering applications
    2. Perception: sensors, sensing, extraction & sensor fusion
    3. Orientation: reference frame, position & localisation of vehicles
    4. Modelling: vehicle equations of motion
    5. Control: motion control of automonous vehicles
    6. Learning and decision: Multi-agent Markov decision process & reinforcement learning


    Tutorials
    The face-to-face tutorial classess are conducted in week 3, week 5, week 7, week 9, and week 11.

    Practicals
    Practical classes (face-to-face) are scheduled for week 4 to week 8. Students must attend their allocated practical class where further instructions on the operation of the laboratory session are provided.

    Computing & Simulation
    Comuting  classes are scheduled in Week 10 and Week 11. Students should attend their allocated class where further
    instructions on the computer programming session are provided.
  • Assessment

    The University's policy on Assessment for Coursework Programs is based on the following four principles:

    1. Assessment must encourage and reinforce learning.
    2. Assessment must enable robust and fair judgements about student performance.
    3. Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
    4. Assessment must maintain academic standards.

    Assessment Summary
    Assessment Task Weighting (%) Individual/ Group Formative/ Summative 
    Due (week)*
    Hurdle criteria Learning outcomes
    Practicals 8 & 20 Group (in-lab) & Individual (report) Formative 6 - 10 1-3
    Assignment 10 Individual Formative 12 2, 4
    Tutorials 2 Individual Formative 3, 5, 7, 9, 11 1-4
    Test1 & Test 2 10 & 10 Individual Formative 8 & 10 1-4
    Exam 40 Individual Summative Exam period min 40% 1- 4
    Total 100
    * The specific due date for the assessment task will be available on MyUni.
      
    This assessment breakdown is registered as an exemption to the University's Assessment for Coursework Programs Policy. The exemption is related to the Procedures clause(s): 1. a. ii    1. a. iv    
      
    This course has a hurdle requirement. Meeting the specified hurdle criteria is a requirement for passing the course.
    Assessment Related Requirements
    The examination is a hurdle requirement. It is necessary to achieve at least 40% in the exam. If this is not achieved, the total course mark
    will be limited to a maximum of 49.

    A hurdle requirement is defined by the University's Assessment for Coursework Programs policy as "...an assessment task mandating a minimumlevel of performance as a condition of passing the course. If a studentfails to meet a hurdle requirement (normally no less than 40%),and is assigned a total mark for the course in the range of 45-49, then the student is entitled to an offer of additional assessment of some type. The type of assessment is to be decided by the School Assessment Review Committee when determining final results. The student’s final total markwill be entered at no more than 49% and the offer of an additional assessment will be specified eg. US01. Once the additional assessment has been completed, this mark will be included in the calculation of thetotal mark for the course and the better of the two results will apply.Note however that the maximum final result for a course in which a student has sat an additional assessment will be a “50 Pass”.

    If astudent is unable to meet a hurdle requirement related to an assessmentpiece (may be throughout semester or at semester’s end) due to medical or compassionate circumstances beyond their control, then the student isentitled to an offer of replacement assessment of some type. An interimresult of RP will be entered for the student, and the student will be notified of the offer of a replacement assessment. Once the replacement assessment has been completed, the result of that assessment will be included in the calculation of the total mark for the course.
    Assessment Detail
    Practicals: students maintain a practical logbook in which they record practical preparation and the work completed during the practical
    sessions. This is assessed (i) during practicals on the basis of the student having completed the prescribed practical work and answered
    in-lab test questions, and (ii)  individual practical written reports presenting the results obtained from their practical work.

    Assignment: one individual submission presenting the work of the course assignment.

    Tests: Two 45-minute tests in class.

    Exam: a 1.5-hour examination is held at the end of the semester.

     

    Submission
    All written submissions to formative assessment activities are to be submitted online.

    Submission instruction will be provided.

    Late submissions: no extensions on the submission date for any reason outside those allowed by the MACA policy (i.e., medical or compassionate). The School's late submission policy of 20% reduction per day of original mark applies.
    Full details can be found at the School policies website:https://eleceng.adelaide.edu.au/current-students/undergraduate/
    Course Grading

    Grades for your performance in this course will be awarded in accordance with the following scheme:

    M10 (Coursework Mark Scheme)
    Grade Mark Description
    FNS   Fail No Submission
    F 1-49 Fail
    P 50-64 Pass
    C 65-74 Credit
    D 75-84 Distinction
    HD 85-100 High Distinction
    CN   Continuing
    NFE   No Formal Examination
    RP   Result Pending

    Further details of the grades/results can be obtained from Examinations.

    Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.

    Final results for this course will be made available through Access Adelaide.

  • Student Feedback

    The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.

    SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.

  • Student Support
  • Policies & Guidelines
  • Fraud Awareness

    Students are reminded that in order to maintain the academic integrity of all programs and courses, the university has a zero-tolerance approach to students offering money or significant value goods or services to any staff member who is involved in their teaching or assessment. Students offering lecturers or tutors or professional staff anything more than a small token of appreciation is totally unacceptable, in any circumstances. Staff members are obliged to report all such incidents to their supervisor/manager, who will refer them for action under the university's student’s disciplinary procedures.

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