Robotics and Computer Vision




COURSE OUTCOMES:

1. Represent mathematically, the position and orientation information of the object in an environment.

2. Explain the techniques to estimate the location of robot and navigate.

3. Apply and implement the Kinematics and dynamics concepts required to manipulate and control the links and joints.

4. Explain the Image formation mechanism and apply the suitable image processing algorithms.

5. Integrate image processing and Robotic control techniques to navigate the robots in a given environment.

6. Implement robotic vision and control algorithms by working in a group, document and present the results in professional manner.


SYLLABUS

UNIT 1:

Representing position & orientation: Pose in 2-dimensions, Pose in 3-dimensions, orthonormal rotation matrices, homogeneous transformation matrices, Euler angles, roll-pitch-yaw angles, gimbal lock, quaternions

Time & motion Trajectories: 1-dimensional, multi-dimensional, multi-segment, Interpolation of rotation, Smooth Cartesian motion, Time-varying coordinate frames, angular velocity, Inertial navigation solution

8 hours

UNIT 2:

Mobile Robot Vehicles: Mobility, Car-like vehicles, moving to a point, line & pose, Flying robots (Quadrotors)

Navigation: Reactive navigation, Braitenberg vehicles, Bug* automata, Distance transform, D*Roadmap methods, EKF(E-based dead reckoning Map based Creating a map Localization & mapping Monte-Carlo approach. 8 hours

UNIT 3:

Kinematics: Forward kinematics, Inverse kinematics, Trajectories Assigning Denavit-Hartenberg parameters, Applications

Velocity relationships Manipulator Jacobians, Resolve-rate motion control Force relationships, under and over actuated manipulators,

Dynamics & Control: Independent Joint control, Rigid body equations of motion: gravity, inertia, Coriolis Forward dynamics, rigid body dynamics compensation

8 hours

Unit 4

Computer Vision Fundamentals: Light & color Spectral representation of light Color, color spaces, color gamut, color consistency, White balance Gamma correction, Image formation Perspective imaging

Image processing: Acquiring images from files, cameras and the web, Image histograms, Monadic operation, Diadic operations, Spatial operations: convolution, template matching, rank filtering Morphology: image cleanup, skeletonization, hit-or-miss transform Shape changing: cropping, resizing, warping

8 hours

Unit 5

Image feature extraction: Region features: segmentation, thresholding, MSER, graph-based Line features: Hough transform Point features: Harris, SURF

Visual Servoing: Position-based visual servoing (PBVS), Image feature motion due to camera motion, Controlling feature motion — image-based visual servoing (IBVS), estimating depth.

8 hours

SLE Component: Study the recent Journal paper: 3-D Mapping with RGB-D Camera by Felix Endres et.al., IEEE Transaction on Robotics, Vol 30 (1), 2014.

References:

1. Peter Corke, Robotics, Vision and Control: Fundamental Algorithms In MATLAB, Second Edition, Springer, 2017

2. Saeed B Niku, Introduction to Robotics: Analysis, Control, Applications, Wiley Student Edition, 2011

3. Mark Spong, M. Vidyasagar, Robot Dynamics and Control, Wiley Student Edition 2004.

4. R. K. Mittal and I. J. Nagarath: Robotics and Control, 6th Reprint, Tata Mcgraw-Hill Education, Delhi 2007.

5. Video Lecture: https://robotacademy.net.au/masterclass/introduction-to-robotics/





LessonPlan_Robotics_Block1.pdf

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