Honors in Design and Dynamics - Minor in Robotics - 3rd to 8th Semester Syllabus (2023-24)

Aug 31, 2026

Curriculum and syllabi for the Minor in Robotics under the B.Tech programme in Robotics and Artificial Intelligence (Honors in Design and Dynamics) for the 2023-24 admission batch. Covers four linked modules focusing on elementary numerical methods, applied partial differential equations, basic probability with discrete and continuous distributions, and applied statistics including estimation, confidence intervals, hypothesis testing, regression and correlation. Defines course objectives, measurable outcomes (CO1-CO4), module-wise hours, prerequisites, and aligned reference texts for engineering-level learning.

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Pages126

Reading Time44 min

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Honors in Design and Dynamics

Minor in Robotics - Syllabus Structure (Admission Batch: 2023-24)

Course: Objectives and Outcomes

Module 1: Elementary Numerical Methods

Module 2: Applied PDE’s

Module 3: Basic Probability and Probability Distributions

Module 4: Applied Statistics

Text Books and Reference Books

ODISHA UNIVERSITY OF TECHNOLOGY AND RESEARCH Techno Campus, Mahalaxmi Vihar, Ghatikia, Bhubaneswar-751029. School of Mechanical Sciences CURRICULUM AND SYLLABI (2023-24) B. Tech. Robotics and Artificial Intelligence ODISHA UNIVERSITY OF TECHNOLOGY AND RESEARCH Techno Campus, Mahalaxmi Vihar, Ghatikia, Bhubaneswar-751029. Syllabus Structure (Admission Batch: 2023-24) School of Mechanical Sciences Programme: Robotics and Artificial Intelligence Abbreviation used: 3rd Semester 4th Semester *Minimum 4 weeks of Summer Course / Training / Internship / Skill Course / etc. after 4th Semester Abbreviation used: 5th Semester 6th Semester FOURTH YEAR (SEVENTH SEMESTER) *Project / Seminar / Internship FOURTH YEAR (EIGHTH SEMESTER) *Project / Seminar / Internship Honors in Design and Dynamics Minor in Robotics School/ Department: School of Mechanical Sciences Programme: Robotics and Artificial Intelligence Abbreviation used: 3rd Semester Prerequisite: Basic knowledge of calculus, linear algebra, differential equations, and elementary probability is required. Course Objectives : Apply numerical methods for solving equations, interpolation, and numerical integration. Solve applied partial differential equations using separation of variables and Fourier series. Explain probability concepts and standard probability distributions. Perform parameter estimation, hypothesis testing, regression, and correlation analysis. Course Outcome : At the end of the course the student will be able to: CO1: Demonstrate the use of common numerical methods such as numerical solutions of equations, interpolation, differentiation and integration etc. CO2: Recognize mathematical model of heat equations, wave equations and their solution by appropriate method. CO3: Understand the different terminology in probability and have clear concept on probability distribution functions both in continuous and discrete case. CO4: Select appropriate statistical tools to investigate a research hypothesis, perform data analysis by applying relevant methodology and interpret result in a variety of settings. Module-1: Elementary Numerical Methods (7Hours) Solution of algebraic and transcendental equations by Newton-Raphson and secant method. Interpolation: Lagrange’s method, divided difference method, Newton’s forward and backward method. Numerical Integration: Trapezoidal and Simpson’s Rule. Module- 2: Applied PDE’s (7 Hours) Elementary PDE’s: separation of variables method to simple problems. One dimensional wave equation: solution by separation of variables and use of Fourier series, D' Alembert's solution of wave equation. Normal forms of PDE’s.One dimensional heat equation: solution by Fourier series. Module-3: Basic Probability and Probability Distributions (7 Hours) Probability spaces, conditional probability, independence, Random variables (discrete and continuous), probability mass and density functions, cumulative distribution functions, moments of random variables, mean and variance. Discrete Probability distributions: Binomial, Poisson and hyper-geometric distributions. Continuous Probability distributions: exponential, uniform and normal distributions. Module-4: Applied Statistics (7 Hours) Random sampling, estimation of parameters, maximum likelihood estimation, confidence intervals, testing of hypotheses for mean and variance. Regression and correlation analysis: fitting of straight lines (method of lest squares), correlation coefficient with basic properties. Text Books: Advanced Engineering Mathematics by E. Kreyszig, John Willey & Sons Inc. 10th Edition. Ronald E. Walpole, Raymond H. Myers, Sharon L. Myers & Keying Ye, “Probability & Statistics for Engineers & Scientists", Eighth Edition, 2007, Pearson Education Inc., New Delhi. Reference Books: Ordinary and Partial Differential equations by J. Sinha Roy and S. Padhy, Kalyani Publishers. Higher Engineering Mathematics by B. V. Ramana, McGraw Hill Education. Engineering Mathematics by Pal

FAQs

What are the course prerequisites for this syllabus?

Students need basic knowledge of calculus, linear algebra, differential equations, and elementary probability. For the robotics part, basic understanding of physics, mathematics (geometry and vectors), and fundamentals of mechanics is required.

Which topics are included in Module 1 (Elementary Numerical Methods)?

The module covers Newton-Raphson and secant methods for solving equations, interpolation using Lagrange/divided difference and Newton forward/backward methods, and numerical integration using Trapezoidal and Simpson’s Rule.

What learning outcomes are expected by the end of the course?

Students should demonstrate numerical methods, recognize mathematical models for heat and wave equations and their solution methods, understand probability terminology and distribution functions, and select statistical tools for hypothesis testing and data analysis with interpretation.

Aug 31, 2026

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