EPN-V2

ACIT4820 Applied Robotics and Autonomous Systems Course description

Course name in Norwegian
Applied Robotics and Autonomous Systems
Study programme
Master's Programme in Applied Computer and Information Technology
Master's Programme in Applied Computer and Information Technology, Elective modules
Weight
10.0 ECTS
Year of study
2020/2021
Curriculum
FALL 2020
Schedule
Programme description
Course history

Introduction

Bestått-Ikke bestått.

Recommended preliminary courses

Knowledge of linear algebra, vector calculus and basic statistics and probability.

Required preliminary courses

The following required coursework must be approved before the student can take the exam:

Four assignments in groups of 1 - 3 students (1000 - 2000 words per assignment)

Learning outcomes

The assessment will be based on two part-exams:

1) Individual project report (4000-6000 words). The project report counts 80% of the final grade.

2) Individual project presentation (10 minutes). The oral examination counts 20% of the final grade

Both exams must be passed in order to pass the course.

The oral examination cannot be appealed.

New/postponed exam

In case of failed exam or legal absence, the student may apply for a new or postponed exam. New or postponed exams are offered within a reasonable time span following the regular exam. The student is responsible for applying for a new/postponed exam within the time limits set by OsloMet. The Regulations for new or postponed examinations are available in Regulations relating to studies and examinations at OsloMet.

Content

Topics covered in this course:

  • Robot kinematics and dynamics
  • Robotics sensors and actuators
  • Navigation, state estimation and filtering algorithms
  • Motion planning
  • Computer vision and visual servoing
  • ROS Robot Operating System
  • Introduction to ML/AI methods in robotics

Teaching and learning methods

All, computer with MATLAB and Simulink.

Course requirements

Two internal examiners. External examiner is used periodically.

Assessment

Associate Professor Tiina Komulainen

Permitted exam materials and equipment

Basic course in control theory, linear systems and system dynamics. Basic understanding of matrix algebra, statistics, and programming using MATLAB or similar.

Grading scale

  • Data driven dynamic modelling (system identification)
  • State estimation
  • Model-based PID-tuning methods
  • Multivariable control algorithms
  • Predictive control algorithms
  • Implementation, testing and validation with numerical simulation.

Examiners

Two internal examiners. External examiner is used periodically.