Autonomous Systems Part 2
Autonoma system del 2

FRT200F, 6 credits

Valid from: Autumn 2018
Decided by: Professor Thomas Johansson
Date of establishment: 2019-09-12

General Information

Division: Automatic Control
Course type: Third-cycle course
Teaching language: English

Aim

Autonomous Systems is a core course within Wallenberg Autonomous Systems and Software Program (WASP), whose purpose is to give a broad understanding of the wide area of autonomous systems and foundational knowledge in the topic areas required to understand and develop autonomous systems.

Goals

Knowledge and Understanding

For a passing grade the doctoral student must

• explain what autonomy is and what makes a system autonomous.
• explain how an autonomous system works and describe important components.
• explain optimal control and model predictive control and how they relate to autonomous systems.
• explain common methods for visual object detection and tracking (background modelling, appearance-based detectors, region tracking, object tracking).
• explain common types of positioning systems (e.g. GNSS, UWB, dead reckoning, inertial navigation, signals of opportunity, magnetic field SLAM), their strengths and weaknesses.
• explain common methods and techniques for reinforcement learning, neural networks and deep learning, especially for autonomous systems.

Competences and Skills

For a passing grade the doctoral student must

• analyze how different components contribute to the autonomy of a system.
• analyze the relations between and integration of control, motion planning, task planning and decision making.
• analyze the effect of measurement errors on the accuracy and dependability of positioning systems.
• design and program a system for human-robot interaction involving visual information.

Judgement and Approach

For a passing grade the doctoral student must

• analyze the industrial and societal impact of autonomous systems.
• reflect upon the ethical and moral issues related to autonomous systems.

Course Contents

The course is organized in a collaboration between four universities in Sweden: KTH, Chalmers, Linköping, and Lund. The course consists of two modules: - Learning and Knowledge - Interaction and Collaboration

Course Literature

Lecture material (slides) and hand-in assignments are distributed via the course homepage.

Instruction Details

Types of instruction: Lectures, seminars, laboratory exercises, exercises, project. The course is organized around two two day sessions with physical meetings: - Learning and Knowledge - Interaction and Collaboration Each session consists of lectures, invited talks and seminars. The main content of each module is presented at a session and then examined at a seminar at the following session. The first session will also give an introduction to autonomous systems and autonomy. Between the sessions there will be local activities at the four main sites (Göteborg, Linköping, Lund and Stockholm). These will be mainly student driven.

Examination Details

Examination formats: Written report, written assignments, seminars given by participants, miscellaneous. The examination consists of exercises, labs and projects. There is also a final examination in the form of a two-day seminar at the end of the course.
Grading scale: Failed, pass
Examiner:

Admission Details

Selection criteria: PhD students admitted to the WASP Graduate School have priority in case of overbooking.

Course Occasion Information

Contact and Other Information

Course coordinators:
Web page: https://kth.instructure.com/courses/5148