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M-TECH in Robotics Mobility Systems at Indian Institute of Technology Jodhpur
Jodhpur, Rajasthan
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About the Specialization
What is Robotics & Mobility Systems at Indian Institute of Technology Jodhpur Jodhpur?
This Robotics & Mobility Systems M.Tech program at IIT Jodhpur focuses on the interdisciplinary aspects of robotics, autonomous systems, and intelligent mobility solutions. It addresses the growing demand for skilled professionals in India''''s rapidly evolving automation, automotive, and defense sectors. The program distinguishes itself with a strong emphasis on practical applications and emerging technologies relevant to Indian industry, preparing students for cutting-edge challenges in smart manufacturing, self-driving vehicles, and aerial robotics.
Who Should Apply?
This program is ideal for engineering graduates holding a B.Tech/B.E. in Electrical, Electronics, Computer Science, Mechanical, Mechatronics, or Aerospace disciplines, eager to specialize in robotics and autonomous systems. It caters to fresh graduates aspiring for careers in R&D, design, and deployment of robotic technologies, as well as working professionals seeking to upskill and transition into advanced roles in automation, intelligent systems, and mobility solutions within the Indian market. A strong analytical and mathematical background is beneficial.
Why Choose This Course?
Graduates of this program can expect diverse India-specific career paths in companies like TCS, Mahindra, Tata Motors, DRDO, ISRO, and startups. Roles typically include Robotics Engineer, Autonomous Vehicle Engineer, AI/ML for Robotics Specialist, and R&D Engineer. Entry-level salaries range from INR 6-12 LPA, with experienced professionals earning significantly higher. The program provides a strong foundation for pursuing advanced research or entrepreneurship in India''''s tech landscape, aligning with industry demand for certifications in ROS, AI, and control systems.
Student Success Practices
Foundation Stage
Master Core Robotics Fundamentals- (Semester 1-2)
Dedicating extra effort to grasp core concepts in Linear Control Systems, Robot Kinematics and Dynamics, and Machine Learning for Robotics. This involves rigorous problem-solving, understanding theoretical underpinnings, and connecting concepts across courses to build a strong foundational knowledge.
Tools & Resources
NPTEL courses on Control Systems and Robotics, Online problem-solving platforms like HackerRank for ML concepts, Textbooks by Siciliano, Ogata, Bishop
Career Connection
A solid foundation in these areas is crucial for excelling in technical interviews and for understanding the advanced topics required for research or industry roles in robotics R&D.
Hands-on Lab Competence with ROS- (Semester 1-2)
Actively participating in Robotics and Mobility Systems Lab-I and II. Focus on gaining proficiency in Robot Operating System (ROS), sensor integration, motor control, and simulation tools. Proactively undertake mini-projects beyond lab assignments to explore different robotic platforms and algorithms.
Tools & Resources
Official ROS Tutorials, Gazebo simulator, TurtleBot or custom robot kits, GitHub for collaborative coding
Career Connection
Practical skills in ROS and robot hardware interfacing are highly valued by Indian companies for roles in robot development, testing, and deployment. This builds a strong portfolio for internships.
Engage in Peer Learning and Study Groups- (Semester 1-2)
Forming study groups with peers to discuss challenging concepts, solve problems collaboratively, and prepare for exams. Utilize the diverse backgrounds of fellow students to gain different perspectives on robotics problems, fostering a strong academic community.
Tools & Resources
Campus study rooms, Online collaboration tools like Discord/Slack, Whiteboards for brainstorming
Career Connection
Developing strong communication and teamwork skills is essential for working in interdisciplinary robotics teams in industry, making students more attractive to potential employers.
Intermediate Stage
Specialized Skill Development through Electives- (Semester 2-3)
Strategically choose program electives based on specific career interests, such as Advanced Control Systems, Deep Learning, or Autonomous Mobility. Dedicate time to deeply explore these specialized areas through advanced coursework, independent study, and mini-projects.
Tools & Resources
Research papers and journals (e.g., IEEE Robotics & Automation Letters), Online specialized courses (Coursera, edX), Advanced textbooks in chosen elective areas
Career Connection
Specialized knowledge makes candidates highly desirable for niche roles in automotive, defense, or industrial automation sectors, allowing them to carve out a distinct career path.
Pursue Internships and Industry Projects- (Semester 2-3)
Actively seek and complete internships during summer breaks or semester-long projects with robotics companies, research labs (like DRDO, ISRO), or startups. Focus on gaining exposure to real-world industrial challenges, professional work ethics, and practical application of learned theories.
Tools & Resources
IIT Jodhpur Placement Cell, LinkedIn, Internshala for internship search, Networking events and career fairs
Career Connection
Internships are critical for bridging the gap between academia and industry in India, often leading to pre-placement offers and providing invaluable experience that boosts resume strength for final placements.
Participate in Robotics Competitions- (Semester 2-3)
Join or form teams to participate in national/international robotics competitions (e.g., RoboCup, IndiaSkills, DRDO''''s Robotic Competitions). This provides hands-on experience in design, development, and testing under pressure, simulating real-world engineering challenges.
Tools & Resources
University robotics club, Competition rulebooks and past challenge archives, Access to workshop facilities and mentoring
Career Connection
Such participation demonstrates practical problem-solving skills, teamwork, and resilience, which are highly regarded by recruiters looking for innovative and capable engineers.
Advanced Stage
Focused M.Tech Project Work- (Semester 3-4)
Choose a challenging M.Tech project (Part I & II) that aligns with your specialization and career goals. Dedicate significant time to thorough research, innovative design, rigorous experimentation, and high-quality thesis writing. Aim for publishing research papers if applicable.
Tools & Resources
Access to specialized lab equipment, Faculty mentorship, Academic databases (IEEE Xplore, ACM Digital Library), Thesis writing guides
Career Connection
A strong M.Tech project, especially if it leads to publications or patents, significantly enhances a student''''s profile for R&D roles, PhD admissions, and prestigious job offers in core robotics companies.
Placement Preparation and Networking- (Semester 3-4)
Begin comprehensive placement preparation focusing on aptitude tests, technical interviews in robotics, data structures/algorithms, and soft skills. Network with alumni, industry professionals, and recruiters through workshops, seminars, and professional platforms.
Tools & Resources
Online platforms for aptitude and coding practice (GeeksforGeeks, LeetCode), Mock interview sessions, LinkedIn for professional networking, Placement cell resources
Career Connection
Thorough preparation and a robust professional network are key to securing top placements in India''''s competitive job market for M.Tech graduates in Robotics and Mobility Systems.
Continuous Learning and Emerging Tech Adoption- (Throughout the program, intensifying in Semester 3-4)
Stay updated with the latest advancements in robotics, AI, and mobility systems through online courses, webinars, tech blogs, and industry reports. Focus on adopting new tools and frameworks (e.g., advanced deep learning libraries, new simulation platforms) to remain competitive.
Tools & Resources
arXiv, TechCrunch, IEEE Spectrum, Kaggle for data science competitions, Professional development courses
Career Connection
The robotics field evolves rapidly. Demonstrating a commitment to continuous learning makes graduates adaptable and valuable assets for companies seeking long-term talent in India''''s innovative ecosystem.
Program Structure and Curriculum
Eligibility:
- B.Tech. / B.E. degree in Electrical Engineering, Electronics Engineering, Computer Science Engineering, Mechanical Engineering, Aerospace Engineering, Mechatronics, or equivalent disciplines with a minimum of 60% marks or 6.5/10 CGPA (55% or 6.0/10 CGPA for SC/ST/PwD candidates). Valid GATE score in appropriate discipline is required.
Duration: 2 years (4 semesters)
Credits: Minimum 60 credits Credits
Assessment: Assessment pattern not specified
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| EEL5301 | Linear Control Systems | Core | 6 | Mathematical models of physical systems, Feedback characteristics of control systems, Stability analysis (Routh-Hurwitz, Nyquist criteria), Root locus techniques, Frequency response methods (Bode, Polar, Nichols plots), State-space analysis and design |
| EEL5302 | Robot Kinematics and Dynamics | Core | 6 | Rigid body transformations and representations, Forward and inverse kinematics for serial manipulators, Differential kinematics, Jacobians, singularities, Robot dynamics (Newton-Euler and Lagrangian formulations), Trajectory generation and planning, Force and impedance control basics |
| EEL5303 | Machine Learning for Robotics | Core | 6 | Introduction to machine learning paradigms, Supervised learning (regression, classification), Unsupervised learning (clustering, dimensionality reduction), Reinforcement learning fundamentals (MDPs, Q-learning), Deep learning basics (neural networks, CNNs), Applications of ML in robot perception, control, and planning |
| EEL5304 | Robotics and Mobility Systems Lab-I | Lab | 3 | Robot programming and simulation environments, Sensor interfacing and data acquisition, Actuator control and motor drivers, Robot Operating System (ROS) basics, Kinematics implementation and verification, Basic navigation and mapping experiments |
| PE1 | Program Elective I | Elective (Program) | 6 | Chosen from the Program Electives Pool |
| OE1 | Open Elective I | Elective (Open) | 6 | Chosen from other M.Tech specializations/departments/programs |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| EEL5305 | Control of Robotic Systems | Core | 6 | Robot control architectures, Joint space and task space control, Nonlinear control methods for robots, Adaptive and robust control strategies, Force and impedance control, Learning control for robotic systems |
| EEL5306 | Sensing and Perception for Robotics | Core | 6 | Robot sensor technologies (vision, lidar, radar, IMU), Feature extraction and descriptor generation, Object detection and recognition algorithms, Robot localization and mapping techniques, Simultaneous Localization and Mapping (SLAM), Sensor fusion and data interpretation |
| EEL5307 | Autonomous Mobility Systems | Core | 6 | Vehicle modeling and dynamics, Perception for autonomous vehicles, Path planning algorithms, Motion control and navigation strategies, Intelligent transportation systems (ITS), Safety and ethical considerations in autonomous driving |
| EEL5308 | Robotics and Mobility Systems Lab-II | Lab | 3 | Advanced ROS programming and navigation stack, Vision-based perception and object detection, Implementation of SLAM algorithms, Autonomous driving simulations, Hardware integration for mobile robots, Control experiments on robotic platforms |
| PE2 | Program Elective II | Elective (Program) | 6 | Chosen from the Program Electives Pool |
| PE3 | Program Elective III | Elective (Program) | 6 | Chosen from the Program Electives Pool |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PE4 | Program Elective IV | Elective (Program) | 6 | Chosen from the Program Electives Pool |
| PE5 | Program Elective V | Elective (Program) | 6 | Chosen from the Program Electives Pool |
| OE2 | Open Elective II | Elective (Open) | 6 | Chosen from other M.Tech specializations/departments/programs |
| EEP5301 | M.Tech Project Part-I | Project | 12 | Problem identification and definition, Comprehensive literature review, Development of methodology and experimental design, Preliminary results and analysis, Technical report writing, Presentation and defense of initial progress |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| EEP5302 | M.Tech Project Part-II | Project | 12 | Advanced experimental setup and data collection, Detailed analysis and interpretation of results, Algorithm refinement and optimization, Thesis writing and documentation, Final project presentation and defense, Potential for research publication |
Semester electives
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| EEL5309 | Advanced Control Systems | Elective (Program) | 6 | Nonlinear control systems theory, Adaptive control techniques, Robust control design methods, Optimal control and dynamic programming, Model predictive control (MPC), Sliding mode control |
| EEL5310 | Cyber Physical Systems | Elective (Program) | 6 | CPS architecture and design principles, Sensor and actuator networks, Real-time operating systems for CPS, Security and privacy in CPS, Modelling and verification of CPS, Applications in smart grids, healthcare, and manufacturing |
| EEL5311 | Machine Vision and Image Processing | Elective (Program) | 6 | Image formation and perception, Image enhancement and restoration, Feature extraction and description, Image segmentation techniques, Object detection and recognition, 3D vision and stereo imaging |
| EEL5312 | Advanced Robotics | Elective (Program) | 6 | Human-robot interaction and collaboration, Mobile robot navigation and path planning, Swarm robotics and multi-robot systems, Soft robotics and compliant mechanisms, Medical and surgical robotics, Legged locomotion and bio-inspired robotics |
| EEL5313 | Deep Learning and its Applications | Elective (Program) | 6 | Fundamentals of neural networks, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and LSTMs, Generative Adversarial Networks (GANs), Transfer learning and fine-tuning, Applications in computer vision, natural language processing, and robotics |
| EEL5314 | Human Robot Interaction | Elective (Program) | 6 | Paradigms of human-robot interaction, Social robotics and expressive robots, Affective computing and emotion recognition, Robot ethics and safety in HRI, User interfaces for human-robot collaboration, Psychological and sociological aspects of HRI |
| EEL5315 | Multi-robot Systems | Elective (Program) | 6 | Swarm intelligence and collective behavior, Multi-agent planning and coordination, Task allocation and resource management, Formation control and distributed control, Distributed perception and mapping, Communication and networking in MRS |
| EEL5316 | Robot Operating System (ROS) | Elective (Program) | 6 | ROS architecture (nodes, topics, services, actions), Catkin workspace and package development, ROS communication mechanisms, ROS navigation stack (localization, global/local planning), Gazebo simulation environment, Interfacing with hardware using ROS |
| EEL5317 | Reinforcement Learning | Elective (Program) | 6 | Markov Decision Processes (MDPs), Dynamic programming (policy iteration, value iteration), Monte Carlo methods, Temporal-difference learning (Q-learning, SARSA), Function approximation and deep reinforcement learning, Applications in game playing, control, and robotics |
| EEL5318 | Industrial Automation and Robotics | Elective (Program) | 6 | Programmable Logic Controllers (PLCs), Supervisory Control and Data Acquisition (SCADA), Industrial sensors and transducers, Robot manipulators and end-effectors, Robot programming languages and interfaces, Automated manufacturing systems and flexible automation |
| EEL5319 | Vehicle Dynamics and Control | Elective (Program) | 6 | Longitudinal and lateral vehicle dynamics, Tire models and road-tire interaction, Vehicle stability control (ABS, ESP, Traction Control), Active suspension systems, Powertrain control and driveline dynamics, Dynamics and control of electric and hybrid vehicles |
| EEL5320 | Navigation and Localization | Elective (Program) | 6 | Global Positioning System (GPS) principles, Inertial Measurement Units (IMUs) and odometry, Kalman filters and extended Kalman filters, Particle filters and Bayesian localization, Visual odometry and dead reckoning, Simultaneous Localization and Mapping (SLAM) |
| EEL5321 | Optimal Control | Elective (Program) | 6 | Calculus of variations for optimal control, Pontryagin''''s Minimum/Maximum Principle, Dynamic programming and Hamilton-Jacobi-Bellman equation, Linear Quadratic Regulator (LQR), Constrained optimal control problems, Numerical methods for optimal control |
| EEL5322 | Estimation and Filtering | Elective (Program) | 6 | Probability theory and random processes review, Bayesian estimation and maximum likelihood estimation, Kalman filter and its variants (EKF, UKF), Particle filters and sequential Monte Carlo methods, State estimation for nonlinear systems, Sensor fusion techniques |
| EEL5323 | Computer Vision | Elective (Program) | 6 | Image formation and camera models, Image processing fundamentals (filters, enhancement), Feature detection and matching (SIFT, SURF, ORB), Image segmentation and object recognition, Stereo vision and 3D reconstruction, Motion analysis and optical flow |
| EEL5324 | Digital Signal Processing | Elective (Program) | 6 | Discrete-time signals and systems, Z-transform and its properties, Discrete Fourier Transform (DFT) and FFT algorithms, Digital filter design (IIR, FIR), Adaptive filters and their applications, Multirate signal processing |
| EEL5325 | Embedded Systems for Robotics | Elective (Program) | 6 | Microcontrollers and microprocessors architectures, Real-time operating systems (RTOS) for embedded systems, Sensor integration and data acquisition, Actuator interfacing and motor control, Communication protocols (I2C, SPI, UART, CAN), Embedded system design for robotic applications |
| EEL5326 | Mathematical Methods for Engineers | Elective (Program) | 6 | Linear algebra (vector spaces, eigenvalues), Vector calculus and applications, Ordinary and partial differential equations, Optimization techniques (linear, nonlinear), Numerical methods for engineering problems, Probability and statistics for data analysis |
