22MT90122656654+5+5+5+56sksjhshinon.pptx

Dr.V.Narasimharaj
Mechatronics Engineering
22MT901 Mobile Robotics(Elective)
Odd semester 2025 - 2026
Class : IV Year /VII Semester

DR.V.NARASIMHARAJ - FACULTY OF MECHATRONICS ENGINEERING - PRESENTATION - 04.01.2025
INDIVIDUAL TIME TABLE
2/11

22MT901 & MOBILE ROBOTICS
Course Objectives
• To familiarize the students with mobile robots
• To understand the basic methods for achieving mobility and autonomy
• To provide a practical understanding of robotic navigation and locomotion
3/11

Course Outcomes
• Describe different robot configuration for various applications
• Select various sensors used for perception, localization and mapping
• Outline control algorithms involved in mobile robots
• Apply the concept of kinematics in modeling mobile robots
•Apply various algorithms in path planning and navigation
4/11

Module I
Introduction to Mobile Robotics - Key issues for locomotion, Legged and Wheeled
mobile robots - Representing Robot position - Forward Kinematic models, Wheel
kinematic constraints – Robot kinematic constraints –Mobile Robot
Maneuverability-Mobile Robot Workspace - Domestic Robots - Education Robots.
5/11

Module 2
Sensors for Mobile Robots – Classification, characterizing sensor performance,
wheel/motor sensor, heading sensor, Ground-based beacons, Active ranging,
motion/speed sensor, Vision-based sensors- Representing Uncertainty- Feature
Extraction - Gyroscope – Compass – Force and Tactile Sensors – Global
Positioning System.
6/11

Module 3
Mobile Robot Localization, localization-based navigation versus programmed
solutions, Belief and Map representation, Probabilistic Map based Localization,
Examples, Landmark based Navigation, Route-based localization. Autonomous
map building. Introduction to Planning and Navigation, Path Planning, Obstacle
avoidance, bug algorithm, Vector Field Histogram, curvature velocity techniques,
Navigation architecture.
7/11

8/11
Mapping of Course Outcomes (CO) with Programme Outcomes (PO) Programme Specific
Outcomes (PSO)
COs
POs PSOs
PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 1 2
C901.1 3 2 2 2 3 3
C901.2 3 2 2 2 3 3
C901.3 3 2 2 2 3 3
C901.4 3 2 2 2 3 3
C901.5 3 2 2 2 3 3

9/11
Assessment Methods & Levels (Based on Blooms Taxanomy)
Formative Assessment based on Capstone Model
Course
Outcome
Blooms level Assessment Component Marks
C901.1 Remember Important terms and Definitions
test
20
C901.2 Understand Online Quiz 20
C901.3 Understand Seminar presentation 20
C901.4
C901.5
Apply Assignment 20

10/11
GCR Link Myklassroom link
https://classroom.google.com/c/
NzY5MTk4MTM5MDg0
Curriculum gaps Identified
Case studies shall be included at
the end of the syllabus
Sample case studies will be dealt
at the end of Module 3.
Troubleshooting in robots shall be
added
Basic troubleshooting will be
taught at the end of module 2.

11/11

12/11
Innovative methods planned for teaching
Module 1
Topics Teaching method
Introduction to Mobile robotics and Key
Issues for locomotion
Lecture with video
Legged and Wheeled mobile robots Flipped Classroom
Representing Robot position, Forward
Kinematic models, Wheel kinematic
constraints & Robot kinematic constraints
Lecture with demonstration using sketches
Mobile Robot Maneuverability & Mobile
Robot Workspace
Blended learning(Classroom learning and
Online learning)
Domestic Robots & Education Robots Inquiry based learning(Group activity) –
Students find the solutions

13/11
Module 2
Sensors for Mobile Robots, Classification
wheel/motor sensor, heading sensor,
Ground-based beacons, motion/speed
sensor, Vision-based sensors & Force/
Tactile Sensors
Flipped Classroom and working
demonstration using videos
Representing Uncertainty, Feature
Extraction, Sensor Integration, fusion and
state estimation & Global Positioning
System
Lecture with relevant videos
Bayes filter & Kalman filters Jigsaws(Students to other students)

14/11
Module 3
Mobile Robot Localization, localization
based navigation versus programmed
solutions
Lecture
Belief and Map representation, Probabilistic
Map based Localization
Demo videos and lecture
Landmark based Navigation, Route-based
localization & Autonomous map building
Peer teaching
Planning and Navigation, Path Planning,
Obstacle avoidance, bug algorithm, Vector
Field Histogram, curvature velocity
techniques, Navigation architecture.
Video lectures

15/11
Content beyond syllabus
• Applications Of Mobile Industrial Robots: From Manufacturing To Agriculture
• Mobile Robots for Narrow Space Applications
• Mobile robotics in smart farming

16/11
Assessment tools planned
https://kahoot.com/
https://get.plickers.com/
https://quizlet.com/
Online courses pertaining to the subject
https://onlinecourses.nptel.ac.in/noc21_me44/preview
https://www.coursera.org/learn/modernrobotics-course5

22MT90122656654+5+5+5+56sksjhshinon.pptx

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