Syllabus - Robotics and Embedded Systems (CB-606 (A))

Computer Science and Business System (CSBS)

Robotics and Embedded Systems (CB-606 (A))

VI

UNIT-I

Industrial Applications

INTRODUCTION TO MODERN DAY ROBOTICS AND THEIR technologies: Industry 4.0 Concept: Background implementation patterns in manufacturing companies-Evolution of Industrial Robots and their Applications- dvancements in Robotics and Its Future Uses-Types of robotics in various fields for applications and Overview-Industry 4.0 Technologies essential for Cognitive Robotics: Computer systems and Technologies relevant to modern day robotics- Robotic Process Automation: Overview of RPA and its applications-RPA, AI, and Cognitive Technologies for Leaders- Introduction to Robotics: Analysis, Control, Applications

UNIT-II

Basics of Robotic Operating System

Basics of Robotic operating System: ROS for beginners an overview- Introduction to the Robot Operating System (ROS) Middleware - Secure communication for the Robot Operating System - An Introduction to Robot Operating System: The Ultimate Robot Application Framework by Adnan, Quality of Service and Cyber security Communication Protocols -Analysis for the Robot Operating System Robotics systems communication- Threat modelling using ROS Towards cloud robotic system: A case study of online co-localization for fair resource competence-A Case Study on Model-Based Development of Robotic Systems using MontiArc with Embedded Automata

UNIT-III

AI in the Context of Cognitive Robotics and Role of AI in Robotics

Foundation for Advanced Robotics and AI- A Concept for a Practical Robot Design Process- Demo to train A Robot Using AI - Deep learning core applications-Deep learning business applications Introduction to computer vision and application of Vision Systems in Robotics: Concepts of computer vision and the how vision systems are becoming essential part of Robotics-Computer Vision: Models, Learning, and Inference - Mastering Computer Vision with TensorFlow 2.x: Build advanced computer vision applications using machine learning and deep learning techniques- Machine Vision Applications- Application areas for vision systems-Robot inspection case study-Autonomous driving using 3D imaging case study.

UNIT-IV

Data Science and Big Data in the Context of Cognitive Robotics

Cognitive Technologies: The Next Step Up for Data and Analytics in robotics-Cognitive Deep Learning Technology for Big Data Cognitive Assistant Robots for Reducing Variability in Industrial Human-Robot Activities Introduction to Python and R Programming in the context of Robotics: Introduction to Python - Python Functions for Data Science-Basic ROS Learning Python for robotics- An introduction to R -The R in Robotics rosR: A New Language Extension for the Robot Operating System Artificial Intelligence and Robotics - The Review of Reliability Factors Related to Industrial Robots -Failure analysis of mature robots in automated production- Data Analytics for Predictive Maintenance of Industrial Robots - Failure Is an Option: How the Severity of Robot Errors Affects Human-Robot Interaction.

UNIT-V

Concepts of Cloud Computing, Cloud Platforms and IT Applications in Robotics

Learning Cloud Computing: Core Concepts - Cloud Computing: Private Cloud Platforms - Robot as a Service in Cloud Computing -Cloud Computing Technology and Its Application in Robot Control - A Comprehensive Survey of Recent Trends in Cloud Robotics Architectures and Applications - Google's cloud robotics and high computing needs of industrial automation and systems-The role of cloud and open source software in the future of robotics-The Power of Cloud Robotics by Robotics Industry Association

Course Objective

To provide students with a comprehensive understanding of robotics and embedded systems, including their industrial applications, operating systems, AI integration, data science, cloud computing, and practical implementation.

Course Outcome

Upon completion of this course, students will be able to: 1. Understand the concepts and technologies of modern-day robotics and their industrial applications. 2. Gain knowledge of the basics of the Robotic Operating System (ROS) and its secure communication. 3. Explore the role of AI in cognitive robotics and its applications. 4. Learn about data science and big data in the context of cognitive robotics. 5. Understand the concepts of cloud computing, cloud platforms, and their applications in robotics. 6. Apply the learned concepts through practical experiments and projects.

Practicals

  • Build a Self-Driving Robot that can automatically follow a line.

  • Build a basic obstacle-avoiding robot and improve the design to help it avoid getting stuck.

  • Build a Humanoid Robot.

  • Autonomous Robot Navigation using Computer Vision for exhaustive path-finding.

  • A Mobile Autonomous Chemical Detecting Robot.

  • Build a voice controlled robot.

  • Web-Controlled Mobile Video-Enabled Robotic Litter Collection Device.

  • Utilizing Artificial Neural Networks to Create a Learning Robot.

  • Hospital Sanitizing Robot.

  • Autonomous Robotic Vehicle: Saving lives, preventing accidents one at a time.

  • Build a robot with Python and 3D Printed Robotic Arm.

  • Build an Intelligent Irrigation Control System.

  • AI-powered Hearing Aid.

  • Fire Extinguishing Robot.

  • Remote Operated Spy Robot Circuit.

Reference Books

  • Saeed Benjamin Niku, “Introduction to Robotics: Analysis, Control, Applications”, Wiley Publishers, 2nd edition, 2011.

  • Simon J. D. Prince, “Computer Vision: Models, Learning, and Inference”, Cambridge University Press, 2012.

  • Francis X. Govers,” Artificial Intelligence for Robotics: Build Intelligent Robots that Perform Human Tasks Using AI Techniques”, Packt publishing, 2018.

  • Krishnendu Kar, “Mastering Computer Vision with TensorFlow 2.x: Build Advanced Computer Vision Applications Using Machine Learning and Deep Learning Techniques”, Packt publishing, 2020.

  • Armando Vieira, Bernardete Ribeiro,” Introduction to Deep Learning Business Applications for Developers from Conversational Bots in Customer Service to Medical Image processing”,Apress, 2018.

  • Steve Heath, "Embedded System Design 2nd Edition", EDN Series for Design Engineers, 2003