Robotics and AI

ICSE Robotics and AI Class 09 Syllabus 2023-24

Part 1:  Robotics

1. Introduction to Robotics

2. Robot as a System

Part 2: Artificial Intelligence (AI)

1. Introduction to Artificial Intelligence (AI)

2. Role of Data and Information, Evolution of Computing

3. Introduction to Data and Programming with Python

4. AI Concepts and AI Project Framework"

Part 1: Robotics

1. Introduction to Robotics

(i) Understanding Robots.

Basic understanding of what a robot is; definition and characteristics; benefits of using robots (with respect to humans): increased quality, increased productivity, increased efficiency, longer working span, working in hazardous environments, improved workplace.

(ii) Evolution of Robots; Laws of Robotics.

Brief history of Robots with respect to their evolution from 1900’s till date. Definition of Robotics, the three Laws of Robotics by Isaac Asimov (statements only).

(iii) Classification of Robots.

Classification of Robots as: field/terrain based (arial, ground, underwater) and control based (manual, automatic): Meaning and examples of each. Bio-inspired robots: meaning, purpose and examples (humanoids, birds, snakes and insects).

(iv) Real-world Robots and their applications.

Application of robots in different fields (domestic, industry, medical, defense, entertainment and agriculture) with at least one example of each.

2. Robot as a System

(i) Building blocks of Robots.

General block diagram of a robot. A detailed study of the building blocks of a robot.

Concept of a robot is having mechanical, electronic and computational blocks; with the functioning and working principle of each block. Design aspects using examples of humanoid, aerial, underwater and mobile robots.

(ii) Identification of Robots.

Identification of robots (through demonstration/ video/graphic details).

Illustration using an industrial robot (e.g., Industrial Robotic Arm), humanoid and mobile robot. The idea that a mechanical body can be of any form must be emphasized.

3. Concepts in Robotics

(i) Types of motion; motion in one-dimension and two-dimension; types of joints and links.

Types of motion (linear, angular, and circular); a brief understanding of motion in one-dimension and two-dimension; types of joints (prismatic, revolute, and spherical); types of links (rigid and soft). Relevant examples for each of the above.

(ii) Using links and joints to create specific motion.

A detailed study of how links and joints help create specific motion. Identification of links and joints used in a given system. Examples of the demonstration can include Industrial Robot Arms.

(iii) Degree of freedom of a robot

Definition; identification through illustration.

Part II: Artificial Intelligence (AI)

1. Introduction to Artificial Intelligence (AI)

(i) Meaning and brief history.

Definition of Artificial Intelligence; brief account of the history of AI since the time John McCarthy first coined the term in 1956; Turing Test, its use and importance.

(ii) Applications and Benefits of AI.

Applications of AI in different fields: commercial, industry, medical/health care, defense, banking, entertainment, transport, security and agriculture. Commonly used AI applications in daily life such as online shopping, search engines, chatbots, voice assistants, entertainment portals, facial recognition, driver assisting vehicles, and augmented/ virtual reality.

Benefits of using AI - Automation, smart decision making, assisting humans, remote patient monitoring & and monitoring the progression of contagious diseases, analysis of data for research and development, efficiently solving complex problems, speedy disaster recovery strategy, performing recurring business tasks, reducing the chances of manual errors, ensuring 24-hour service availability with the same performance and consistency throughout the day.

(iii) Ethical considerations in AI.

A brief understanding of ethics in artificial intelligence including bias, prejudice, fairness, accountability, transparency, interpretability and explainability.

2. Role of Data and Information, Evolution of Computing

(i) Data and Information: Types of Data (audio, visual, numeric, text); Data to Information.

Understanding that data is pivotal to Artificial Intelligence. A brief introduction to how relevant data is identified, acquired, and explored, as a precursor to the AI Project Cycle.

(ii) Evolution of Computing: Pre-AI/ML Binary Logic System, Conditional Gates, Deterministic computing for deterministic problems.

An introduction to the above-mentioned topics, with the emphasis that earlier computing was suited for only deterministic problems; explaining deterministic computing and deterministic problems and giving relevant examples. Illustrating the limitations of deterministic computing in solving real-life problems, Comparison between deterministic and probabilistic nature of real-life problems.

Note: Explanation of how AI can solve a new class of problems, based on a probabilistic paradigm. Hence Need for AI: Probabilistic, real-life problems; AI Discretion (AI is not needed for solving deterministic problems) for example - is the difference in description of temperatures by a machine and a human. A machine would make a discrete distinction between cool and hot at a given temperature for instance if 35°C is hot, then any temperature 34.9 °C and below would be cool. Humans would, however, describe the temperature in a range of ‘cool, pleasant, warm, hot’ and so on based on their subjective experience of the temperature.

3. Introduction to Data and Programming with Python

(i) Familiarization with Python.

Introduction to Python and its elementary concepts: object-oriented, high-level, general-purpose programming language. Uses and advantages of Python.

(ii) Introduction to data types and variables.

Introduction to a simple Python program structure and the concept of indentation in Python, different data types in Python - numeric (int, float), Boolean, sequence type (tuple, list, strings), sets and dictionary, an understanding of what kind of data types should be used in different use cases.

Introduction to variables and assignment of values.

(iii) Introduction to Operators.

Usage of different operators (arithmetic, logical, assignment, comparison, identity, membership) on data types, and kind of statements which can be executed in Python.

(iv) Conditional Statements

 Introduction to blocks in Python, if conditions, if else conditions, nested if conditions, if-else-if (elif) conditional block, case and switch. Shorthand conditional statements.

(v) Control Statements.

Meaning and use of loops in Python. Different types of loops (while, for), nested loops, the syntax used. ‘for’ loop for different types of iterable (list, tuple, string, dictionary) along with the idea of break, continue and pass statements, ‘while’ loop and their use cases.

(vi) Functions

An understanding of both built-in and user-defined functions; the importance of functions to maintain modularity; arguments given to a function (fixed and variable length); the concept of default arguments and return type of a function.

4. AI Concepts and AI Project Framework

(i) AI Concepts

Broad and narrow AI, strong versus weak AI. Expert systems in AI (for e.g., Eliza). Computer vision (CV), Natural Language Processing (NLP) and Neural Network (NN).

(ii) Components and Stages (alias AI Project Cycle).

Understanding of AI Project Framework, Stages involved in AI project: Problem Scoping, Data Acquisition, Data Exploration, Modelling and Evaluation (brief understanding of each).

Enhance Technology Skills

Essential Resources

• https://drive.google.com/file/d/1FBJFfrXP5KHn3qFBCRzaBHE_HDXlAEiy/view?usp=sharing   

English

• https://icseportal.in/class-09/syllabus/robotics-and-ai