Note

Subject to change based on class size, group formation, and lab availability

Wk Date Day Topic Description Labs and Assignments START Labs and Assignments END
1 Jan 12 M Introduction to Robotics Syllabus review & course structure Reading for Quiz 1  
1 Jan 14 W ROS Overview ROS publishers/subscribers, services, params Quiz 0 for Lab 0 Reading for Quiz 1
1 Jan 16 F     Lab 1: Setup (VM and ROS Basics)  
2 Jan 19 M NO CLASS Holiday: Martin Luther King Jr. Day    
2 Jan 21 W A Primer on Coordinate Frames Coordinate Frames and Transformation matrices    
3 Jan 26 M Coordinate Frames in ROS Quaternions and ROS TF   Lab 1: Setup (VM and ROS Basics)
3 Jan 28 W A Primer on Kinematics and Dynamics Overview of Kinematics, Dynamics and Control Systems Lab 2: Simulation  
4 Feb 2 M A Primer on Perception Overview of Robot Sensors and Levels of Perception    
4 Feb 4 W Range Sensors Line fitting (RANSAC, Hough Transform)   Lab 2: Simulation
5 Feb 9 M Robot Setup Robot Setup Lab 3: Robot Setup  
5 Feb 11 W Image Processing Color spaces, filters, edge detection    
6 Feb 16 M Feature Matching SIFT, ORB    
6 Feb 17 T     Lab 4: Sensor-Motor Loop  
6 Feb 18 W 3D Point Clouds and Filtering Voxel grid, statistical outlier, pass-through filters    
6 Feb 19 Th     Report 1: Initial Ideas  
7 Feb 23 M Segmentation and Registration Euclidean Cluster Extraction & ICP    
7 Feb 24 T       Lab 3: Robot Setup
7 Feb 25 W Local Planning Motion Planning, BUG Algorithms, Dynamic Window Approach (DWA)   Lab 4: Sensor-Motor Loop
8 Mar 2 M Project and Assignment Discussion Project and Assignment Discussion Assignment 1: Perception  
8 Mar 4 W Global Planning I Map Representations & Occupancy Grids    
8 Mar 6 F       Report 1: Initial Ideas
9 Mar 9 M NO CLASS Spring Break    
9 Mar 11 W NO CLASS Spring Break    
10 Mar 16 M Global Planning II Greedy, A*, Dijkstra's Algorithms    
10 Mar 18 W Sampling-based Planners RRT and PRM Report 2: Refine Ideas  
10 Mar 20 F       Assignment 1: Perception
11 Mar 23 M Bayes Filter Uncertainty and Gaussian noise models    
11 Mar 25 W Bayes Filter (Cont.) Covariance propagation    
12 Mar 30 M Sensor Model Beam model and likelihood field model    
12 Mar 31 T     Assignment 2: Motion Planning  
12 Apr 1 W Motion Model Odometry and velocity models    
13 Apr 6 M Kalman Filter Linear Kalman filter    
13 Apr 8 W Extended Kalman Filter EKF localization & Nonlinear state estimation    
14 Apr 13 M        
14 Apr 15 W Particle Filter Non-parametric distributions & importance sampling    
13 Apr 17 F       Assignment 2: Motion Planning
15 Apr 20 M Brief Introduction to SLAM Introduction to SLAM Assignment 3: Bayes Filter Report 2: Refine Ideas
15 Apr 22 W Algorithms Review Class Review    
16 Apr 27 M Ethics in AI Theoretical concepts and Discussion Report 3: Finalize Ideas  
16 Apr 29 W Ethics in AI Responsibilities and Case Studies    
17 May 7 Th       Assignment 3: Bayes Filter
17 May 8 F Final Presentations 2:30 pm - 4:20 pm   Report 3: Finalize Ideas