Introduction to Problem based Learning The AAU

Introduction to Problem based Learning – The AAU Way Program: • Monday : Structure, teaching task's, courses, • Tuesday : The Aalborg model - trying it out, PBL/Project Work and Assessment • Wednesday : Supervision Coffee break's at ap. 10. 00 and 14. 00 - Lunch at 12. 00 1

Introduction to Problem based Learning – The AAU Way Program for to day, Structure, teaching task's, courses : • • • Welcome Introduction and short presentation Structure and conditions Teaching task's Courses Exercises 2

Structure of Aalborg University Welcome to Aalborg University 3

Structure of Aalborg University Senate Rectorate Faculty of Humanities Faculty of Engineering and Sc. Departments Faculty of Social Science Study Programmes 4

Structure of Aalborg University Senate Rectorate Faculty of Humanities Faculty of Engineering and Sc. Institute of Electronic Systems Faculty of Social Science Study Programmes • Secretary and labs • Research • Teaching 5

Structure of Aalborg University Senate Rectorate Faculty of Humanities Faculty of Engineering and Sc. Institute of Electronic Systems • Secretary and labs • Research • Teaching Faculty of Social Science Computer Eng. Electronic and. • Project work • Course activities 6

Working tasks for VIP’s Research Professor Associated Assistent Ph. D. Professor student 40% 45% 83% Teaching 60% 55% 17% Teaching In hours 497 h 456 h 140 h 7

Study board for Electronics and Information Technology 8

Controlling the studies Study Regulations: • General regulations 9

4. 6. INTELLIGENT AUTONOMOUS SYSTEMS Controlling the studies Objectives and contents of the specialisation The objectives of the specialisation in Intelligent Autonomous Systems are summarised as follows: Study Regulations: • General regulations to provide students with knowledge in modelling of mechanical systems such as spacecraft, ships, and mobile robots, • Sector’s, lines or specialization’s enable the student to apply modern methods of control to problems related to autonomous systems, and content – Objectives to analyse methods of state observation, parameter estimation and sensor fusion in mechanical systems, to provide students with a comprehension of supervisory control, fault-tolerant control and fault detection, to let students analyse software architectures for autonomous systems. The courses include necessary general theoretical topics within process control for autonomous systems but modules are also made available in scientific communication and proficiency in English language for those who need it. 10

SPRING Semester – Intelligent Autonomous Systems THEME: Modelling and Control PERIOD: 1 February - 30 June PURPOSE: To give knowledge and comprehension of optimal and robust control theory. To give the students the ability to analyse modern control methods for multi input/multi output systems. To give students the ability to apply modelling methods and control synthesis for advanced mechanical systems. CONTENTS: The project is based on a problem of control and supervision of an autonomous system. – Objectives and content The model of the mechanical system has to be derived. The vital part of the project is the choice of the set of actuators and sensors for onboard application. Different control strategies have to be investigated and compared. The supervisor system responsible for – Theme autonomy onboard has to be designed. The chosen solution has to be implemented on a real time platform and tested, either by the computer simulations or dedicated hardware. COURSES: Courses will be given in the field of modelling of mechanical systems, supervisory and fault tolerant control, and modern control theory. EXAM: The external oral examination is based on the prepared project documentation. Each 11 student is marked according to the 13 -scale. Controlling the studies Study Regulations: • General regulations • Sector’s, lines or specialization’s • Specific semesters

Model based tracking for navigation Controlling the studies Background As part of an ongoing research project (with Computer Science AAU and The Danish Institute of Agricultural Sciences) an autonomous vehicle is developed which navigates autonomously in the field. The aim is to reduce the inputs to the field and monitor the growth of the individual plants, thereby providing obvious environmental and economic advantages over more traditional farming. Study Regulations: Purpose • General regulations It is important in such applications to both navigate accurately in the field but also to be able to identify individual plants. The aim in this project is to use perspective images captures from a camera mounted on the front of the vehicle to provide estimates of structure of the crop rows as well as position of the individual • Sector’s, lines or specialization’s plants. The focus will not be on the image analysis but on sensor fusion with non-vision sensors mounted on – Objectives and GPS as well the vehicle e. g. wheel encoders, differentialcontent as integration of information about the known structure of the field. The aim is to use all available information on the autonomous vehicle in order to achieve the best possible estimates of the vehicle and individual plant position (in the order of cm). • Specific semesters – Theme Methods – Projects The project will include: • Modeling of vehicle system and plant pattern in the camera image • Prediction of the crop structure based on the system models as well as previous measurements (images and data from sensors) • Estimation of vehicle position and orientation as well as plant position • Algorithms are simulated in the laboratory on simple setup. • If possible the algorithms are applied to data acquired in the field. 12

Study related courses (SE): Controlling the studies Fault Detection and Automated Systems Study of Mechanical Systems Modelling Regulations: Controller Structures • General regulations Modelling of Mechanical Systems II Engineering Responsibilities specialization’s • Sector’s, lines or – Objectives and content Project related courses (PE): • Specific semesters – Control Robust. Theme Optimal Control – Projects Supervisory Control – Courses Neural Networks and Fuzzy Logic Project Management and Team Building 13

Controlling the studies Study Regulations: • General regulations • Sector’s, lines or specialization’s – Objectives and content • Specific semesters – – Theme Projects Courses Semester group 14

Teaching task’s Structure of a semester: Project courses lectures seminar 50% - 33% Lecturer/instructor Study courses and lectures Lecturer/instructor Examination Examinor Project 50% - 67% Supervisor: Advisor and facilitator Examinor/censor Examination 15

Course Description Optimal Control Theory Courses Purpose: To give the students knowledge in optimal control and practical experience with optimal control strategies based on minimisation of a • Description performance index. Contents: Dynamic programming LQ control Introduction of reference and disturbance conditions Introduction of integral conditions Use of observer, LQG control The position of closed loop poles Prerequisites: Analogue and Digital Control (FP 6 -4, PR 6 -1, PR 6 -2), Stochastic systems (FP 6 -3, FP 8 -5) Duration: 1 module Category: Project theme course (PE- course) 16

Courses • Description • Placed in a timetable for the semester 17

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Courses • Description • Placed in a timetable for the semester • Syllabus 20

Courses Each lesson/lecture (Mini module): • Duration 3 hours 45 minutes (½ day) • 2 lectures app. 45 min each • Exercises in groups, app. 2 hours – The lecturer is now instructor The purpose of the combination of lectures/exercises is to increase the comprehension of the curriculum 21

Courses What kind of exercises would you chose? • Promote comprehension and methodical ness How will you act as instructor during the exercises in the groups? • Ask questions about how they have made their solution • Make sure that they have understood the basic principles of the problems 22

Courses Differences between project course (PE) and study course (SE) • Examination – PE has no formal examination by the lecturer, it is examined during the project examination by the supervisor – SE is examined by the lecturer, normally as a written examination (passed/non passed) • Exercises – PE is used in the project, exercises is examples – In SE the student must learn to solve examination exercises 23

Lunch until 12. 45 24

Course exercise • Think of an engineering subject you all know something about • Suppose that you have to do a course on that subject • Make a short exercise that will learn the students the major point of a specific part of your course 25

Course exercise continued For one group at a time do: • Give your exercise to the other three groups who starts solving it • Prepare how you will instruct the groups during their problem solving • After 5 -10 minutes of problem solving 2 persons from your group enters each of the other groups and starts acting as an instructor 26