Robotic Systems and CIM
Objectives
To provide students with:
1) Knowledge on: a) Modeling of robotic cells in the perspective of agile manufacturing systems. b) Key organizational principles of manufacturing systems. c) Capability to deal with complex problems.
2) Know-how on: a) Formulating new problems and implementation strategies for robotic cells. b) Development of synthesis and critical analysis skills. c) Improving the capability of implementing heterogeneous robotic systems. d) Capability of selecting tools.
3) Non-technical competences: a) Team-work and improvement of the oral and written communication skills. b) Develop the innovation capabilities.
General characterization
Code
2363
Credits
6.0
Responsible teacher
José António Barata de Oliveira, Luís Manuel Camarinha de Matos
Hours
Weekly - 4
Total - 65
Teaching language
Português
Prerequisites
Available soon
Bibliography
1 – Robotic systems and CIM – Course handouts.
2 - Introduction to Multiagents: Michael Wooldridge, Wiley.
Teaching method
Theoretical part: Concept explanation lectures followed by examples and discussion.
Laboratorial component: For each experiment: Introduction to the planned work, tutorial on the technologies / tools to be used, discussion of the method of work, development of the experiments by the students supervised by a teaching assistant; elaboration of a report.
Evaluation method
The evaluation is made through the realization of 3 lab developments followed by a discussion. For each work a minimum grade of 9.5 is required.
Subject matter
- Fundamental Concepts of Manufacturing
- The World of Manufacturing
- Historical Vision
- Current challenges
- Manufacturing Systems Paradigms
- Highly reconfigurable systems
- RMS
- Holonic Manufacturing
- Cyber-Physical Production Systems
- Industry 4.0
- Emerging Paradigms
- Complexity Theory
- Self-organization
- Self-x
- Modelling
- SysML, ISA-95, Automation-ML
- Ontologies
- Analysis Dimension
- Control Perspective
- Cyber-Physical Systems / IIOT
- SOA based Manufacturing
- Agent based Manufacturing
- RAMI 4.0
- Information Treatment and Learning Perspective
- Sensors and Data Volume
- Contextualized Perception
- Learning
- Applications in Industry