Robotics and Machine Vision
Elective III
Teaching Scheme                             Examination Scheme
Lectures: 3 Hrs./Week                     CIE + SEE
Credit: 3
Course Outcomes:

1. Illustrate components of robotic system.

2. Formulate position and motion of robot using kinematic equations.

3. Classify robot end effectors and sensors.

4. Describe machine vision system and its applications.

5. Apply image processing techniques to extract features essential to machine vision system

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,