CS354 Schedule

Readings followed by (S) are supplemental. You should read them, but you won't be tested on that material.

Readings labeled "GIR" are from A Gentle Introduction to ROS. Jason M. O'Kane. 2014.

Readings labeled "PRR" are from Programming Robots with ROS . Morgan Quigley, Brian Gerkey, William D. Smart. 2015.

WEEK	DATE		READING	OUT	DUE
1	08-29	Introduction (.pdf) LAB - Teleoperation	GIR 1.1 About ROS PRR CH. 1 (S)	PA0
	08-31	ROS Command Line (.pdf) ROS Cheat Sheet (.pdf) LAB - ROS Command Line	Syllabus Video GIR 2.3-2.7 ROS Core Components PRR CH. 2 (S) GIR 2.1-2.2 (S)
2	09-05	rocket_bot.py thruster_w_globals.py thruster_oo.py thruster_oo_responder.py LAB - Python Publishers and Subscribers	Video ROS Tutorial 12 PRR CH. 3 (S)
	09-07	LAB - ROS Packages	ROS Tutorials 3 & 4 git tutorial github tutorial
3	09-12	LAB - PID Control	PID Tutorial (pdf) Video PID Wikipedia (S) ROS Tutorials 7 & 8 rospy parameter tutorial	PA1	PA0 (9/11 5:00PM)
	09-14	LAB - Numpy Sentry	Linear Algebra Video Linear Algebra Review and Reference (.pdf) sections 1-3.7, (skip 3.6) Numpy tutorial (only the numpy section) Fast Numpy Video Intro to Numpy and Scipy (S) (.pdf)		PA0 (9/11 5:00PM)
4	09-19	Coordinate Systems	Coordinate Frame Tutorial (.pdf) Jennifer Kay Kinematics Tutorial (.pdf) (Just sections 6-7)	hw1.pdf transforms.py kinematics_hw.py
	09-21	The tf System tf_demo.py tf2_demo.py	Video about tf tf Overview
5	09-26	Probability (.pdf)	Bayesian Brain, Doya et.al (.pdf) CH 1.1-1.2, 1.4-1.4.3 Video Set Theory Video (S) Bayes’ Rule Video (S)		Hw1 (9/26 5:00PM)
	09-28	Start Localization Localization (.pdf) Covariance Matrices +Multivariate Gaussian Distribution (.pdf)	Bayesian Filtering for Location Estimation, Fox et.al. (.pdf) p. 10-13 Mutltivariate Statistics Tutorial 6.5.1, 6.5.4-6.5.4.2	HW2 (.pdf)	Hw1 (9/26 5:00PM)
6	10-03	Kalman Filter (.pdf) kalman.py plot_gaussians.py kalman_demo.py	Kalman Filter Tutorial An Introduction to the Kalman Filter (S)	Bibliography	PA1 (10/2 5:00PM)
	10-05	Localization - Particle Filter (.pdf) particle_demo.py ROS Navigation Lab	Particle Filter Explained without Equations Bayesian Filtering for Location Estimation, Fox et.al. (.pdf) p. 13-15		PA1 (10/2 5:00PM)
7	10-10	Midterm Review			HW2 (10/9 5:00PM)
	10-12	MIDTERM			HW2 (10/9 5:00PM)
8	10-17	Mapping and SLAM Mapping and SLAM (.pdf)	Computational Principles of Mobile Robotics 9.2.2-9.2.3	PA2
	10-19	ROS Navigation (.pdf) ROS Navigation Lab	actionlib documentation 1-5, 6.2
9	10-24	Configuration Spaces (.pdf)	Computational Principles of Mobile Robotics CH 6-6.3.2 Configuration Space Visualization Configuration Space Videos (s)	Final Project	PA2A (10/23)
	10-26	Discrete Path Planning	Computational Principles of Mobile Robotics 6.3.3		PA2A (10/23)
10	10-31	Randomly Exploring Random Trees (.pdf)	Rapidly-exploring random trees: A new tool for path planning S. M. LaValle, 1998		PA2B(10/30) Bibliography (11/5)
	11-02	Computer Vision (.pdf) convolutions.py OpenCV Lab	Convolutions Python OpenCV Intro OpenCV Basic Operations OpenCV images OpenCV Video OpenCV Drawing (s) Computer Vision: Algorithms and ApplicationsCH 1 (s)		PA2B(10/30) Bibliography (11/5)
11	11-07	Object Recognition (.pdf) histogram.py sift_demo.py	Computer Vision: Algorithms and Applications CH 4-4.1.3	HW3 (.pdf)	Checkpoint 1 (11/7)
	11-09	ROS Vision Lab	Deep Learning (Accessible from JMU network)		Checkpoint 1 (11/7)
12	11-14	Finish Vision Paper Presentations			Checkpoint 2 (11/17 5:00PM)
	11-16	Paper Presentations Control Architectures			Checkpoint 2 (11/17 5:00PM)
13	11-21	THANKSGIVING
	11-23	THANKSGIVING
14	11-28	Paper Presentations and Final Project Work
	11-30	Paper Presentations and Final Project Work
15	12-05	Paper Presentations and Final Project Work			Checkpoint 3 (12/04)
	12-07	Exam			Checkpoint 3 (12/04)
	12-12	Final Project Presentations 10:30-12:30