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<a id="nametitle" href="index.html#body">Ramviyas Nattamai Parasuraman<br />       <span class="subtitle">ASSISTANT PROFESSOR, <a class="External" href="http://www.cs.uga.edu">UGA CS</a></span></a>
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<h1 class=""><strong><font color="white">CSCI/ARTI 4530/6530: Introduction to Robotics (Fall 2019)</font></strong> <br><br>
</div>
</div>
</div>
</div>
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<b>Instructor: <a class="External" href="index.html"> Ramviyas N. Parasuraman</a> </b>
<br> </p>
</div>
</div>
</div>
</div>
<div class="py-0">
<div class="container">
<div class="row">
<div class="col-md-12">
<p class="lead1">
<b>Time and Location of Lectures:</b>
</p>
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</div>
<div class="row">
<div class="col-md-12">
<p class="">
Monday (2:30-3:20 PM), Tuesday & Thursday (2:00-3:15 PM), Boyd #222 </p>
</div>
</div>
<div class="row">
<div class="col-md-12">
<p class="lead1">
<b>Office Hours and Location </b>
</p>
</div>
</div>
<div class="row">
<div class="col-md-12">
<p class="">Boyd 803, Tuesday & Thursday 1-2 PM or by an email appointment
</div>
</div>
<div class="row">
<div class="col-md-12">
<p class="lead1">
<b>Course Overview </b>
</p>
</div>
</div>
<div class="row">
<div class="col-md-12">
<p class="">
Students completing this course will gain an understanding of the hardware and software involved in robotics, with a focus on programming algorithms. Students will learn various mathematical and statistical models, associated algorithms, and their implementations, which are making modern-day robotics possible. They will understand the past, present and future of robotics, and the main challenges that make robotics difficult.
<br> </p>
<p class="">
<strong>Textbook: "Introduction to Autonomous Mobile Robots" by Roland Siegwart, Illah Reza Nourbakhsh, and Davide Scaramuzza, MIT Press (2nd Ed. 2011) </strong> <br>
<strong>Reference book:</strong> "Probabilistic Robotics" by Sebastian Thrun, Wolfram Burgard, and Dieter Fox. MIT Press (2005) <br>
<br> </p>
<p class="">
<strong>Note: </strong> We will follow the structure and content of the <strong><a href="https://www.edx.org/course/autonomous-mobile-robots" class="External">EdX course on Autonomous Mobile Robots</a></strong> and heavily use their slides in this class (all copyrights of the slides belong to the authors and EdX). It's an excellent course from the <strong><a href="http://www.ethz.ch/en.html" class="External">ETH Zurich</a></strong> team. The idea is that the student always has an additional venue for learning and reference. <br> </p>
</div>
</div>
<div class="row">
<div class="col-md-12">
<p class="lead1">
<b>Course Topics </b>
</p>
</div>
</div>
<div class="row">
<div class="col-md-12">
<p class="">
The course topics presented here is a general plan for the course; deviations announced to the class by the instructor may be necessary.<br>
<strong> I. Overview of Robotics:</strong> Introduction, Robot Hardware, Robotic Software Architectures, Probability Theory, Field Applications <br>
<strong> II. Robotic Perception:</strong> Range Finders and Camera models <br>
<strong> III. Robotic Motion:</strong> Kinematics, Velocity Motion Model, Odometry Motion Model, Motion and Maps <br>
<strong> IV. Localization:</strong> State Estimation under Uncertainty, Filters: Bayes, Kalman, Extended Kalman, and Monte Carlo, Localization methods: Markov, Extended Kalman Filter, Particle Filter, Monte Carlo framework, etc. <br>
<strong> V. Mapping:</strong> Occupancy Grid Mapping, Learning Inverse Measurement Model, Simultaneous Localization and Mapping (SLAM) solutions <br>
<br>
The course is composed of both theoretical and practical components. There will be four assignments during this course and each assignment will have theoretical questions and practical programming problems. Likewise, the midterm and final exam has written quizzes and programming projects. Therefore, it is necessary to learn and perform well in both theoretical and practical parts of this course to pass the course.
</p>
</div>
</div>
<div class="row">
<div class="col-md-12">
<p class="lead1">
<b>Grading </b>
</p>
</div>
</div>
<table width="200" class="table" >
<thead>
<tr>
<th><font color="yellow">Grading Item</font></th>
<th><font color="yellow">Percent</font></th>
</tr>
</thead>
<tbody>
<tr>
<td>Class participation </td>
<td>5%</td>
</tr>
<tr>
<td>Assignments (Each 10%)</td>
<td>40%</td>
</tr>
<tr>
<td>Midterm Exam + Midterm Project</td>
<td>20%</td>
</tr>
<tr>
<td>Final Exam + Final Project</td>
<td>35%</td>
</tr>
</tbody>
</table>
<br><br>
<p class="lead1 text-secondary">
<b>Class Schedule</b>
</p>
<table width="400" class="table">
<thead>
<tr>
<th><font color="yellow">Date</font></th>
<th><font color="yellow">Day</font></th>
<th><font color="yellow">Topics</font></th>
<th><font color="yellow">Comments/Lecture Notes</font></th>
</tr>
</thead>
<tbody>
<tr>
<!--<td>1</td>-->
<td>08-15-2019</td>
<td>Thursday</td>
<td>Course Introduction</td>
<td></td>
</tr>
<tr>
<td>08-19-2019</td>
<td>Monday</td>
<td>Overview of Robotics,<br />
History of Robotics,<br />
Sensors, and Hardware</td>
<td></td>
</tr>
<tr>
<td>08-20-2019</td>
<td>Tuesday</td>
<td>Wheeled kinematics - Introduction <br /> ROS - Introduction</td>
<td></td>
</tr>
<tr>
<td>08-22-2019</td>
<td>Thursday</td>
<td>ROS - Setup</td>
<td></td>
</tr>
<tr>
<td>08-26-2019</td>
<td>Monday</td>
<td>Kinematics - Continued</td>
<td></td>
</tr>
<tr>
<td>08-27-2019</td>
<td>Tuesday</td>
<td>Probability theory - Basics</td>
<td></td>
</tr>
<tr>
<td>08-29-2019</td>
<td>Thursday</td>
<td>Sensors Model - Rangefinders</td>
<td></td>
</tr>
<tr>
<td>09-02-2019</td>
<td>Monday</td>
<td><span class="blue">Holiday - Labor Day</span></td>
<td></td>
</tr>
<tr>
<td>09-03-2019</td>
<td>Tuesday</td>
<td>ROS: Nodes, Topics, Publisher, and Subscriber</td>
<td></td>
</tr>
<tr>
<td>09-05-2019</td>
<td>Thursday</td>
<td>Sensors Model: cameras</td>
<td></td>
</tr>
<tr>
<td>09-09-2019</td>
<td>Monday</td>
<td>Sensors Model: Conclusion <br> Robot Perception: Introduction </td>
<td></td>
</tr>
<tr>
<td>09-10-2019</td>
<td>Tuesday</td>
<td>Perception: Computer Vision - fundamentals <br> ROS: Subscriber and Custom Messages</td>
<td></td>
</tr>
<tr>
<td>09-12-2019</td>
<td>Thursday</td>
<td>Perception: Image processing basics</td>
<td> </td>
</tr>
<tr>
<td>09-16-2019</td>
<td>Monday</td>
<td>ROS: Services and Parameters</td>
<td></td>
</tr>
<tr>
<td>09-17-2019</td>
<td>Tuesday</td>
<td>Perception: Feature extraction, edge detection, corner detection, SIFT features, etc.</td>
<td></td>
</tr>
<tr>
<td>09-19-2019</td>
<td>Thursday</td>
<td>Computer Vision: Line Extraction <br> Localization: Introduction to Markovian Approach </td>
<td></td></tr>
<tr>
<td>09-23-2019</td>
<td>Monday</td>
<td>ROS: Assignment 1 discussions, running ROS across different machines</td>
<td></td>
</tr>
<tr>
<td>09-24-2019</td>
<td>Tuesday</td>
<td>Localization: Markovian and Kalman Filter Approaches</td>
<td></td> </tr>
<tr>
<td>09-26-2019</td>
<td>Thursday</td>
<td>Localization - Probabilistic map-based localization, Markov localization, Kalman filter localization</td>
<td></td>
</tr>
<tr>
<td>09-30-2019</td>
<td>Monday</td>
<td>ROS: rosbag and working with simulated data <br> Example implementation of EKF localization in Python</td>
<td></td>
</tr>
<tr>
<td>10-01-2019</td>
<td>Tuesday</td>
<td>Localization: Particle Filters</td>
<td></td>
</tr>
<tr>
<td>10-03-2019</td>
<td>Thursday</td>
<td>Localization: Examples </td>
<td></td>
</tr>
<tr>
<td>10-07-2019</td>
<td>Monday</td>
<td> <span class="blue">Mid-term exam (theory). <br> Practical Mid-term project will be annouced in the class </span></td>
<td></td>
</tr>
<tr>
<td>10-08-2019</td>
<td>Tuesday</td>
<td>SLAM: Introduction to the SLAM problem, challenges</td>
<td></td>
</tr>
<tr>
<td>10-10-2019</td>
<td>Thursday</td>
<td>Discussion of midterm questions and solutions </td>
<td></td>
</tr>
<tr>
<td>10-14-2019</td>
<td>Monday</td>
<td>SLAM: Continued</td>
<td> </td>
</tr>
<tr>
<td>10-15-2019</td>
<td>Tuesday</td>
<td>ROS: Simulation in Gazebo</td>
<td></td>
</tr>
<tr>
<td>10-17-2019</td>
<td>Thursday</td>
<td>SLAM: Continued</td>
<td></td>
</tr>
<tr>
<td>10-21-2019</td>
<td>Monday</td>
<td>Machine Learning (Guest Lecture)</td>
<td></td>
</tr>
<tr>
<td>10-22-2019</td>
<td>Tuesday</td>
<td>Cloud Computing and IoT (Guest Lecture)</td>
<td></td>
</tr>
<tr>
<td>10-24-2019</td>
<td>Thursday</td>
<td>Demonstration of mid-term projects</td>
<td></td>
</tr>
<tr>
<td>10-28-2019</td>
<td>Monday</td>
<td>ROS - rosparameters, joy teleop, laser scan data</td>
<td></td>
</tr>
<tr>
<td>10-29-2019</td>
<td>Tuesday</td>
<td>SLAM - Recap and Conclusion <br>
Navigation and Motion Planning - Introduction and Collision Avoidance </td>
<td></td>
</tr>
<tr>
<td>10-31-2019</td>
<td>Thursday</td>
<td>Navigation and Motion Planning - Collision Avoidance using Potential Fields </td>
<td></td>
</tr>
<tr>
<td>11-04-2019</td>
<td>Monday</td>
<td> ROS transform frames (tf) - static and dynamic transform <br> tf broadcaster and listener example in Python </td>
<td></td>
</tr>
<tr>
<td>11-05-2019</td>
<td>Tuesday</td>
<td>Navigation and Motion Planning - Potential Fields (contd.) <br>
Motion Planning - Graph Construction and Graph-based Search methods - Dijkstra, A*, RRT algorithms </td>
<td></td>
</tr>
<tr>
<td>11-07-2019</td>
<td>Thursday</td>
<td>Navigation and path planning - A* Algorithm Example and Conclusion </td>
<td></td></tr>
<tr>
<td>11-11-2019</td>
<td>Monday</td>
<td>ROS - actionlib, working with real robots</td>
<td></td>
</tr>
<tr>
<td>11-12-2019</td>
<td>Tuesday</td>
<td>Robot Motion Models - Odometry and Velocity Models </td>
<td></td>
</tr>
<tr>
<td>11-14-2019</td>
<td>Thursday</td>
<td>ROS Examples - SLAM <br> Discussion on Final Project </td>
<td></td>
</tr>
<tr>
<td>11-18-2019</td>
<td>Monday</td>
<td>ROS Examples - Transfer Frames (TF) <br> Robot Applications in Radioactive Environments - Examples </td>
<td></td>
</tr>
<tr>
<td>11-19-2019</td>
<td>Tuesday</td>
<td>ROS Practical - Hector SLAM </td>
<td></td>
</tr>
<tr>
<td>11-21-2019</td>
<td>Thursday</td>
<td>Robot Planning - Markov Decision Process (MDP) </td>
<td></td>
</tr>
<tr>
<td>11-25-2019</td>
<td>Monday</td>
<td> Uncovered Topic </td>
<td></td>
</tr>
<tr>
<td>11-26-2019</td>
<td>Tuesday</td>
<td> Uncovered Topic </td>
<td></td>
</tr>
<tr>
<td>11-28-2019</td>
<td>Thursday</td>
<td><span class="blue">Thanksgiving Break</span></td>
<td></td>
</tr>
<tr>
<td>12-02-2019</td>
<td>Monday</td>
<td><span class="blue">Final Project Demonstrations</span></td>
<td></td>
</tr>
<tr>
<td>12-02-2019</td>
<td>Monday</td>
<td><span class="blue">Final Project Demonstrations</span></td>
<td></td>
</tr>
<td>TBA</td>
<td>-</td>
<td><span class="blue">Final Exam </span></td>
<td></td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
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