Robotics and Machine Vision
Elective III
Teaching Scheme                             Examination Scheme
Lectures: 3 Hrs./Week                     CIE + SEE
Credit: 3
Course Outcomes:
Learning fundamental practical aspects of robotics.
Understanding image processing techniques.
Using image processing techniques for machine vision applications.
Robotics – Introduction–Basic Structure– Classification of robot and Robotic systems –laws of
robotics – robot motions – work space, precision of movement.
Drives and control systems: Hydraulic systems, power supply – servo valve – sump – hydraulic motor –
DC servo motors – stepper motors – operation.
Mechanical Components of Robots: Power transmission systems: Gear transmission. Belt drives, cables,
Roller Chains, Link – Road Systems, Rotary to linear motion conversion, Ract and pinion drives, ball
bearing screws, speed reducers, Harmonic drives.
Kinematics of Robot: Introduction, Matrix Representation, Homogeneous transformation, forward and
inverse Kinematics, Inverse Kinematics Programming, Degeneracy, dexterity, velocity and static forces,
velocity transformation force control systems, Basics of Trajectory planning.
Robot End Effectors: Types of end effectors – Mechanical grippers – Types of Gripper mechanisms –
Grippers force analysis – Other types of Grippers – Vacuum cups – Magnetic Grippers – Adhesive
Grippers – Robot end effector interface.
Sensors: Position sensors – Potentiometers, encoders – LVDT, Velocity sensors, Acceleration Sensors,
Force, Pressure and Torque sensors, Touch and Tactile sensors, Proximity, Range and sniff sensors, RCC,
VOICE recognition and synthesizers.
Machine Vision: Introduction – Image processing Vs image analysis, image Acquisition, digital Images –
Sampling and Quantization – Image definition, levels of Computation.
UNIT 5 Image processing Techniques: Data reduction – Windowing, digital conversion. Segmentation –
Thresholding, Connectivity, Noise Reduction, Edge detection, Segmentation, Region growing and Region
Splitting, Binary Morphology and grey morphology operations.
Feature Extraction: Geometry of curves – Curve approximation, Texture and texture analysis, Image
resolution – Depth and volume, Color processing, Object recognition by features, Depth measurement,
specialized lighting techniques. Segmentation using motion – Tracking. Image Data Compression, Real
time Image processing, Application of Vision systems.

1.Saeed B. Niku, Introduction to Robotics: Analysis, Systems, Applications, 2nd edition, Pearson
Education India, PHI 2003 (ISBN 81-7808-677-8)

1. M.P. Groover, Industrial Robotics – Technology, Programming and Applications, McGraw-Hill, USA,