Updates
Course Overview
 Instructor: Prof. Mangal Kothari
 Office Hours: Tuesday, Friday (03:30PM — 04:30PM)
 Class Venue: L11, Lecture Hall Complex
 Class Timings: Monday, Wednesday (5:10PM — 6:30PM)
 T.A. Hours: Mayank Mittal (Friday, 4:30PM — 5:30PM), Krishnraj Singh Gaur (by appointment)
This 9credit graduate level course is designed to teach the essentials of robotics. The course would deal with dynamics and state estimation for various robotic systems, mainly focusing on the kalman filter and its family. It would also cover path planning and SLAM algorithms for these systems. Towards the end of the course advanced topics like cooperative localization may be discussed.
Course PreRequisites
There are no formal prerequisties for the course. However, prior knowledge of dynamics, probability and statistics is necessary. The students also need to be proficient in objectoriented programming (Python/C++), and MATLAB. Familiarity of robotic hardware and ROS would be beneficial.
NOTE: The course is being offered to advanced undergraduate and graduate students only.
Schedule
Week  Topic  Summary  

Introduction to Robot Operating System  
Week 1  08/01/2018 10/01/2018 
System Integration using ROS Framework Lecture
Homework

Slides References

Mayank Mittal 

Dynamics and Control  
Week 2  15/01/2018 17/01/2018 
Modelling and Control of Quadrotor Lecture

Slides References 
Mangal Kothari 

Week 3  20/01/2018 22/01/2018 
Robotic System Modelling Lecture

Slides References 
Mangal Kothari 

Week 5  31/01/2018 05/02/2018 
Localization Problem Lecture

References 
Mangal Kothari 

Week 6  07/02/2018 12/02/2018 
Localization Problem (Contd.) Lecture


Mangal Kothari 

Review Week  
Week 7  05/03/2018 07/03/2018 
Review of Topics Taught So Far Lecture


Mayank Mittal 

Simultaneous Localization and Mapping (SLAM)  
Week 8  12/03/2018 14/03/2018 
Solving the SLAM problem Lecture


Mangal Kothari 

Week 9  19/03/2018 07/03/2018 
Solving the SLAM problem (Contd.) Lecture


Mangal Kothari 

Sensing  
Week 8  26/03/2018 28/03/2018 
Visionbased Sensing Lecture


Harsh Sinha 

Week 9  19/03/2018 21/03/2018 
Inertial Navigation Lecture

Slides 
Mangal Kothari 

Path Planning  
Week 10  02/04/2018 04/04/2018 
Overview of Concepts in Path Planning Lecture

Slides 
Mangal Kothari 

MultiRobots System  
Week 13  09/04/2018 11/04/2018 
UAVUGV Collaboration Lecture


Mangal Kothari 

Project Presentation  
Week 14  14/04/2018 16/04/2018 
Presentation of Course Projects 

Grading
This course is graded on a straight scale, it will not be curved. There are a total of 100 possible points in this course. The tentative breakup of the points is as follows: Homework Assignments (40 points), Project (35 points), Quizzes (5 points) and EndSemester Examination (20 points). Each section has modest opportunity for extra credit that can cover missing points elsewhere.
Homework (40% / 40 points)
There would be 4 homework assignments in the course. Each homework assignment is worth 10 points, and would comprise of implementation and theory based questions. There might be bonus questions in each assignment carrying extra points. Any points over 40 points are worth half their value.
NOTE:
 All assignments have to submitted in LaTeX following this template.
 Late assignment submissions may lead to penalty.
Project (35% / 35 points)
The course heavily emphasizes on the project aspect. You would be required to work in a team of 23 members. The project has to be an implementation of a recent work (from 2013 onwards) in robotics appearing in conferences like RSS, ICRA, or IROS. The source code for the same should not be publicly available. Points would be given on the basis of code readibilty, documentation, and presentation.
Quizzes and Participation (5% / 5 points)
Attendance is not compulsory for this course. However, the lectures might include some quizzes used to verify comprehension. These quizzes could be in form of class assignments based on implementation or quick two to three theoretical questions. We reserve the right to experiment with this mechanism a bit throughout the semester.
Use of Unfair Means
The use of unfair means by any student would be severely punished. The following document would be referred to while deciding the penalty imposed: General Instructions and Guidelines
Course Staff
For general issues, email the course staff at ae640a@gmail.com. For sensitive issues, please email Prof. Mangal Kothari directly.
References
[1] Probabilistic Robotics. Sebastian Thrun, Wolfram Burgard and Dieter Fox. MIT press, 2005.
[2] Principles of Robot Motion: Theory, Algorithms and Implementations, Howie Choset et al.. MIT Press, 2005.