The Aalborg model - one way of dealing with PBL

1. The Aalborg model-one way of dealing with PBL Session 4: The Aalborg model. Topic 5: Directing the studies Agenda: • Study regulations • Examination

2. Directing the studies Study Regulations: • General regulations

5. Directing the studies Study Regulations: • General regulations • Sector’s, lines or specialization’s • Objectives and content

6. 4.6. INTELLIGENT AUTONOMOUS SYSTEMS Objectives and contents of the specialisation The objectives of the specialisation in Intelligent Autonomous Systems are summarised asfollows: to provide students with knowledge in modelling of mechanical systems such as spacecraft, ships, and mobile robots, enable the student to apply modern methods of control to problems related to autonomous systems, 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 andproficiency in English language for those who need it.

7. Directing the studies Study Regulations: • General regulations • Sector’s, lines or specialization’s • Objectives and content • Specific semesters • Theme

8. 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. Themodel of the mechanical system has to be derived. The vital part of the project is the choiceof the set of actuators and sensors for onboard application. Different control strategies haveto be investigated and compared. The supervisor system responsible for autonomy onboardhas to be designed. The chosen solution has to be implemented on a real time platform andtested, either by the computer simulations or dedicatedhardware. 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 studentis marked according to the 13-scale.

9. Directing the studies Study Regulations: • General regulations • Sector’s, lines or specialization’s • Objectives and content • Specific semesters • Theme • Projects

10. Model based tracking for navigation • Background • As part of an ongoing research project (with Computer Science AAU andThe Danish Institute of Agricultural Sciences) an autonomous vehicle isdeveloped which navigates autonomously in the field. The aim is toreduce the inputs to the field and monitor the growth of the individual plants, thereby providing obvious environmental and economicadvantages over more traditional farming. • Purpose • It is important in such applications to both navigate accurately in the fieldbut also to be able to identify individual plants. The aim in this project is touse perspective images captures from a camera mounted on the front ofthe vehicle to provide estimates of structure of the crop rows as well as position of the individual plants. The focus will not be on the imageanalysis but on sensor fusion with non-vision sensors mounted on thevehicle e.g. wheel encoders, differential GPS as well as integration ofinformation about the known structure of the field. • The aim is to use all available information on the autonomous vehicle inorder to achieve the best possible estimates of the vehicle and individualplant position (in the order of cm). • Methods • The project will include: • Modeling of vehicle system and plant pattern in the cameraimage • Prediction of the crop structure based on the systemmodels as well as previous measurements (images and • data from sensors) • Estimation of vehicle position and orientation as well asplant position • Algorithms are simulated in the laboratory on simplesetup. • If possible the algorithms are applied to data acquired inthe field.

11. Directing the studies Study Regulations: • General regulations • Sector’s, lines or specialization’s • Objectives and content • Specific semesters • Theme • Projects • Courses

12. Study related courses (SE): Fault Detection and Automated Systems Modelling of Mechanical Systems Controller Structures Modelling of Mechanical Systems II Engineering Responsibilities Project related courses (PE): Robust Control Optimal Control Supervisory Control Neural Networks and Fuzzy Logic

13. The aim of the study goals objectives Forms of the exams and criteria Assessment - the hidden curriculum Teaching and learning methods: PBL and project work

14. Basic assumptions • Examination is dominating the students learning approach • Criteria for examination are crucial for the learning process • Reflection is a basic part for the learning process

15. Examination - practice Peer group Project group Supervisor and (external) censor

16. Points • Criteria and methods of self and peer assessment has to be integrated in the summative examination • Evaluation/reflection has to be part of the learning environment • Supervisor has a role to play in facilitating students peer assessment