INTELLIGENT AND AUTONOMOUS SYSTEMS
| Title | ΝΟΗΜΟΝΑ ΚΑΙ ΑΥΤΟΝΟΜΑ ΣΥΣΤΗΜΑΤΑ / INTELLIGENT AND AUTONOMOUS SYSTEMS |
| Code | ΔΜ1022 |
| Interdepartmental Programme | PPS Advanced Computer and Communication Systems |
| Collaborating Schools | Electrical and Computer Engineering Medicine Music Studies Journalism and Mass Communications |
| Cycle / Level | 2nd / Postgraduate |
| Teaching Period | Spring |
| Coordinator | Loukas Petrou |
| Common | No |
| Status | Active |
| Course ID | 600004433 |
Programme of Study: PPS Advanced Computer and Communication Systems
Registered students: 19
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| EUFYĪ KAI AUTONOMA SYSTĪMATA-METHODOLOGIES YPOLOGISTIKĪS NOĪMOSYNĪS KAI EFARMOGES | Compulsory Course | 2 | 1 | 5 |
| TECΗNOLOGIES DIADIKTYOU KAI DIADIKTYO TŌN PRAGMATŌN | Elective Courses | 2 | 1 | 5 |
Programme of Study: PPS Advanced Computer and Communication Systems
Registered students: 0
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| Eyfyī systīmata-Methodologíes ypologistikīs noīmosýnīs kai efarmogés | Compulsory Course | 2 | 1 | 5 |
| Diktyakī Ypologistikī- Īlektronikó Ebório | Compulsory Course | 2 | 1 | 5 |
| Academic Year | 2020 – 2021 |
| Class Period | Spring |
| Faculty Instructors | |
| Instructors from Other Categories | |
| Weekly Hours | 24 |
| Class ID | 600183819
|
Course Type 2016-2020
- Background
Course Type 2011-2015
General Foundation
Mode of Delivery
- Face to face
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600183819
- eLearning (Moodle): https://elearning.auth.gr/course/view.php?id=9863
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
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 130 | ||||
| Written assigments | 20 | ||||
| Total | 150 |
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
