Courses

Automated Guided Systems

General

Course Contents

Theory:
1. Introduction to Autonomous Systems and Autonomous Vehicles
2. Introduction to the Python programming language
3. Basic concepts of routing and path finding algorithms
4. Python structures for implementing path finding algorithms
5. The ecosystem of Autonomous Vehicles (chassis, electrical and electronic components, hardware and software components, sensors)
6. Simulation tools for Autonomous Vehicles
7. Raspberry Pi and Linux
8. Robot Operating System
9. Simultaneous Localization and Mapping (SLAM) for creating the Occupancy Grid Map (OGM)
10. The Gazebo emulation tool
11. Mathematical models and tools for Autonomous Vehicles
12. Planning and Scheduling algorithms
13. Project: Python, Raspberry, ROS, Algorithms
Lab:
1. Introduction to python and python programs
2. Routing and path finding algorithms
3. Python for implementing routing algorithms
4. Raspberry Pi and Linux
5. Assembly of an autonomous vehicle prototype

Educational Goals

KNOWLEDGE
Introduction to the ecosystem of autonomous vehicles
Functionality of the basic principles of autonomous navigation
Functionality of the basic routing and path planning algorithms
Applications of indoor and outdoor autonomous vehicles
Technological tools for autonomous vehicles
ABILITIES
Identification, analysis, design and implementation of applied autonomous vehicles
Modelling of simple environments for navigation and path planning
Simulation and real-world environments for vehicle navigation
Assessment of hardware and software tools for autonomous vehicles
Programming in Python

General Skills

Search, analysis and synthesis of data and information, using corresponding technologies, Adaptation to new situations, Independent work, Teamwork – distribution of responsibilities.

Teaching Methods

Lectures, Exercises, Online guidance, Projected Presentations, E-mail communication, Online Synchronous and Asynchronous Teaching Platform (moodle).

Students Evaluation

Assessment Language: English / Greek. Theory:
Public Presentations
Practical mid-term examination
Final Written Examinations
Lab
Public Presentations
Final Examinations
Evaluation criteria:
– Ability to Identify and Describe the Operation / Applications of Autonomous Vehicles
– Ability to program in the Python programming language
– Simulation Skills for working with autonomous vehicles
– Skills for working with real-world equipment (raspberry, vehicle chassis)
– Skills of Assignment Preparation and Presentation

Recommended Bibliography

Automated Guided Vehicle Systems, Second revised and expanded edition, DOI 10.1007/978-3-662-44814-4, Günter Ullrich
Learning ROS for Robotics Programming, Aaron Martinez-Enrique Fernandez.
Lecture Notes