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Third-Cycle Courses

Faculty of Engineering | Lund University

Details for the Course Syllabus for Course FRT165F valid from Autumn 2016

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General
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.
Contents
  • The course is organized in a collaboration between four universities in Sweden: KTH, Chalmers, Linköping, and Lund. The course consists of four modules:
    - Control and Decision making
    - Sensing and Perception
    - Learning and Knowledge
    - Interaction and Collaboration
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.
Types of Instruction
  • Lectures
  • Seminars
  • Laboratory exercises
  • Exercises
  • Project
  • The course is organized around five two day sessions with physical meetings:
    - Control and Decision Making
    - Sensing and Perception
    - Learning and Knowledge
    - Interaction and Collaboration
    - Final examination
    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 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.
  • Failed, pass
Admission Requirements
Assumed Prior Knowledge
Selection Criteria
  • PhD students admitted to the WASP Graduate School have priority in case of overbooking.
Literature
  •  
  • Lecture material (slides) and hand-in assignments are distributed via the course homepage.
Further Information
Course code
  • FRT165F
Administrative Information
  • 2017-06-01
  • Professor Thomas Johansson

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