Description

Theory
2

Laboratory
3

Instructors

Ricardo Severino

Contents

PO1. Fundamentals: Introduction to robotics and autonomous systems history, concepts and architecture. Robotic development frameworks and simulation tools. Kinematics and basic control approaches. Hardware components and systems.
PO2. Sensors and Perception: Working principles and usage of most common sensors. Fundamentals of computer vision. Application of Artificial Neural Networks for image processing and classification.
PO3. Localization and Mapping: Localization strategies: relative and absolute, Maps and sensor fusion techniques, Kalman Filter, Monte-Carlo Localization, Particle filters, Simultaneous Localization and Mapping.
PO4. Planning, Navigation and Control: A*, reinforcement learning, PID and MPC control.
PO5. Cooperation: Strategies for problem solving in cooperative systems, including task allocation, planning and coordination. Review of relevant communication systems and application to cooperation use cases.

Learning Outcomes

By the end of this course, the student should master the supporting technologies and operation of most common autonomous systems, and be able to apply such knowledge in the design of one inteligent autonomous system functionality.
In particular, the student must be able to:
CO1. Understand the fundamentals of autonomous systems operation.
CO2. Make adequate architectural design choices to meet application requirements, in regards to system components commonly used in robotic platforms, including sensors.
CO3. Understand and apply state-of-the art strategies, frameworks and algorithms employed in the development of intelligent autonomous systems
CO4. Analyse the impact of a communication system's performance in the critical operation of a cooperative task
CO5, Implement a sub-system of an autonomous cooperating application with safety/mission critical concerns.