CS485 Autonomous Robotics (Fall 2018)

Class Information

Instructor Location and Time Office Hours

Amarda Shehu , Room #4452 ENG, amarda\AT\gmu.edu Art and Design Building 2026, MW, 9:00am - 10:15 am MW, 10:25 - 11:25 am

Class Information

Course Summary:This course covers topics from artificial intelligence, algorithms, and robotics for the design and practice of intelligent robotics systems. The main emphasis will be on planning algorithms for single and multi-robot systems in the presence of kinematic and dynamic constraints. Integration of sensory data will also be discussed. Selected topics will include manipulation planning, assembly planning, and planning under uncertainty.

Target audience: Junior- and senior-level students interested in artificial intelligence in general and robotics in particular. The course will allow students to implement sophisticated robotic algorithms. For samples of past student projects, visit this page .

Format:Material will be disseminated through class lectures. Homework programming projects and two exams will test comprehension of the basic material. Homeworks will allow students to plugin their implementations to provided platforms so emphasis is on algorithmic design rather than graphical rendering. Extra credit in homeworks will allow students that are interested in advanced topics and research to demonstrate their abilities. Extra credit will not account for more than 10% of the total grade. No programming is involved in the exams. No late homeworks or project deliverables will be accepted. A final research project will provide students with an opportunity to implement a published algorithm and present. An in-class presentation will demonstrate project progress in the form of a conference presentation. Check out samples of past student projects .

Prerequisites:CS 262, CS310, and Math 203. Students taking the class should be comfortable with linear algebra, calculus, and probability. Computer Vision and Analysis of Algorithms are desirable but not imperative.

Textbook(s):The course will combine topics from 1) "Principles of Robot Motion" by Howie Choset et al. and 2) Planning Algorithms (available online) by Steven M. Lavalle, Cambridge University Press, 1st Edition (2006). Students are encouraged to purchase "Principles of Robot Motion."

Outcomes:

• Understand basic notion of feedback control and apply and implement it in the context of mobile robot navigation.
• Be able to understand and implement basic algorithms for processing information from range and visual sensors, feature extraction and matching.
• Understand basic principles of motion planning in discrete and continuous state spaces.
• Demonstrate basic knowledge of different robotic architectures and their suitability for problems at hand.
• Be able to understand the role of probabilistic reasoning, basic formulation of Bayes filters and implement basic robot localization.
• Be able to define more advanced task for single or multiple robots, identify the components and implement it in the robot simulator of the choice.

Grade Breakdown

• Homeworks (3): 45% (15% each)
• Paper Presentation: 5%
• Exams (2): 30%
• Final Project: 20%

Preliminary Class Syllabus

Date	Topic	Lectures	Assignments
Aug. 27	Introduction and Course Overview

Basic Motion-Planning Algorithms and Foundations

Aug. 29	Bug0, Bug1		Hw1 Out
Sep. 05	Bug 2, Configuration Spaces
Sep. 10	Forward Kinematics
Sep. 12	Inverse Kinematics
Sep. 17	Inverse Kinematics contd
Sep. 19	Potential Fields		Hw1 Due
Sep. 24	Potential Fields contd		Hw2 Out
Sep. 26	Deterministic Roadmap Planners
Oct. 01	Deterministic Roadmap Planners contd
Oct. 03	Exam 1

Sampling-based and Probabilistic Motion Planning

Oct. 09	Probabilistic Roadmap
Oct. 10	Biased Sampling		Hw2 Due
Oct. 15	Random Trees		Hw3 Out
Oct. 17	Planning with Kinematic Constraints
Oct. 22	Wheeled Systems
Oct. 24	Planning with Kinodynamic Constraints

Advanced Motion Planning

Oct. 29	Multi-Robot Planning		Hw3 Due
Nov. 05	Multi-Robot Planning contd		Project Topics
Nov. 07	Manipulation Planning
Nov. 12	Paper Presentations

Probabilistic Robotics

Nov. 14	Perception, Range Sensing
Nov. 19	Exam 2
Nov. 26	Modeling Uncertainty
Nov. 28	Sensor Models and Hands-on Problems
Dec. 03	Action Model, Hands-on Problems, and Recursive Bayesian Filtering		Project Preliminary Report
Dec. 05	SLAM, Kalman Filtering, and Particle Filters
Dec. 17	Project Demos and Presentations

Honor Code

The class enforces the GMU Honor Code . Violations of academic honesty will not be tolerated. The CS department also has very specific honor code policies for projects. Please familiriaze yourselves with these policies.

Disability Statement

If a disability or other condition affects your academic performance, document it with the Office of Disability Services.

Class Homepage

Latest lectures and other course materials will be available at URL
http://www.cs.gmu.edu/~ashehu/?q=CS485_Fall2018