INTELLIGENT AND AUTONOMOUS SYSTEMS

Course Information
TitleΝΟΗΜΟΝΑ ΚΑΙ ΑΥΤΟΝΟΜΑ ΣΥΣΤΗΜΑΤΑ / INTELLIGENT AND AUTONOMOUS SYSTEMS
CodeΔΜ1022
Interdepartmental ProgrammePPS Advanced Computer and Communication Systems
Collaborating SchoolsElectrical and Computer Engineering
Medicine
Music Studies
Journalism and Mass Communications
Cycle / Level2nd / Postgraduate
Teaching PeriodSpring
CoordinatorLoukas Petrou
CommonNo
StatusActive
Course ID600004433

Programme of Study: PPS Advanced Computer and Communication Systems

Registered students: 1
OrientationAttendance TypeSemesterYearECTS
Eyfyī systīmata-Methodologíes ypologistikīs noīmosýnīs kai efarmogésCompulsory Course215
Diktyakī Ypologistikī- Īlektronikó EbórioCompulsory Course215

Class Information
Academic Year2018 – 2019
Class PeriodSpring
Faculty Instructors
Instructors from Other Categories
Weekly Hours24
Class ID
600138565
Course Type 2016-2020
  • Background
Course Type 2011-2015
General Foundation
Mode of Delivery
  • Face to face
Prerequisites
General Prerequisites
Basic knowledge of programming, data structures and probability theory
Learning Outcomes
After the successful completion of the course, the students will be able to: - Know the basic types of sensors used in robotic systems - Know the basic types of motors/motion models used in robotic systems - Know the basic algorithms for path planning, mapping, localization, exploration and coverage applied in autonomous systems - Design the architecture and functionalities of an autonomous robotic system, given initial specifications
General Competences
  • Apply knowledge in practice
  • Retrieve, analyse and synthesise data and information, with the use of necessary technologies
  • Adapt to new situations
  • Make decisions
  • Work autonomously
  • Be critical and self-critical
  • Advance free, creative and causative thinking
Course Content (Syllabus)
The course aims at teaching what an autonomous system is and how it operates. Initially, an introduction to the autonomy and behaviors concepts is performed, as well as in their types of representations (Stimulus-Response diagrams, FSAs etc.), including their ways of encoding and combination (motor schema/subsumption). Next, the structure and design of basic autonomy systems architectures is presented (hierarchical, reactive, blackboard and hybrid), as well as the way behaviors are incorporated in them. Finally, an inspection of various algorithms that essentially are presented as behaviors is performed, including localization and mapping (SLAM), path planning, autonomous coverage and exploration, as well as applications of multiple robots and social robots.
Keywords
Autonomous and hybrid systems, artificial intelligence algorithms, architectures
Educational Material Types
  • Notes
  • Slide presentations
Course Organization
ActivitiesWorkloadECTSIndividualTeamworkErasmus
Lectures130
Written assigments20
Total150
Student Assessment
Student Assessment methods
  • Written Exam with Short Answer Questions (Summative)
  • Written Assignment (Summative)
  • Written Exam with Problem Solving (Summative)
Last Update
11-07-2018