Autonomous Robotics

MWF 13:30-14:20, THO 125

Instructor: Chris Mavrogiannis (cmavro at cs dot uw dot edu)
OH: Tuesdays, 1:00-2:00pm, CSE1 436

TA: Kay Ke (kayke at cs dot uw dot edu) (OH: Tuesdays, 4:00-5:00, CSE1 022)
TA: Gilwoo Lee (gilwoo at cs dot uw dot edu) (OH: Thursdays, 4:00-5:00, CSE1 022)
TA: Matt Schmittle (schmttle at cs dot uw dot edu) (OH: Fridays, 4:00-5:00, CSE1 022)

Recitation: Thursday 1/9, 13:30-14:20, CSE1 022, rest of quarter on need-only basis

Canvas: https://canvas.uw.edu/courses/1352219
Piazza: https://piazza.com/class/k3tbbeofifd6ai

Overview

Mobile Robots delves into the building blocks of autonomous systems that operate in the wild. We will cover topics related to state estimation (bayes filtering, probabilistic motion and sensor models), control (feedback, Lyapunov, LQR, MPC), planning (roadmaps, heuristic search, incremental densification) and online learning. Students will be forming teams and implementing algorithms on 1/10th sized rally cars as part of their assignments. Concepts from all of the assignments will culminate into a final project with a demo on the rally cars. The course will involve programming in a Linux and Python environment along with ROS for interfacing to the robot.

Prerequisites: CSE 332 (required), MATH 308 (recommended), CSE 312 (recommended)

Grading: Homeworks (60%), Project (30%), Participation (10%)

Textbooks (optional):

• Probabilistic Robotics, S. Thrun, W. Burgard, and D. Fox. MIT Press, Cambridge, MA, 2005.
• Underactuated Robotics: Algorithms for Walking, Running, Swimming, Flying, and Manipulation, Russ Tedrake, Course Notes for MIT 6.832.
• Planning Algorithms, Steven M. LaValle. Cambridge University Press.

Learning Objectives:

• Construct a mathematical model of a robot's dynamics and sensors.
• Design a Bayes filter to estimate the state of the robot and the environment.
• Derive a controller to robustly track a given path according to stability or optimality criteria.
• Construct a planning roadmap and apply search techniques to compute a dynamically feasible, collision-free path between two locations.
• Assemble all 3 components - localization, planning and control - to enable a robot to navigate in an environment in a safe, efficient manner.
• Apply all of the above on a real RC-car; Program these modules in Python, use ROS for inter-module communications and execute onboard the robot.
• Conduct experiments in a principled manner - create hypotheses, conduct tests, infer outcomes, debug software.

Assignments

All assignments are to be done as a team of 3. Each team gets one robot. Homework assignments will have both written and programming components. Each team will submit one writeup and code. All team members must be listed on each submission. Assignments are due online by 11:59 PM on the listed date.

Linked is a reference for the cars. It is intended to allow you to get familiar with the car. It will be update throughout the quarter with useful troubleshooting help and general FAQ.

• Lab 0: Introduction, due Jan 17 (files)
• Lab 1: Localization due Jan 31 (files)
• Lab 2: Control due Feb 21 (files)
• Lab 3: Planning Mar 6 (files)

Writeups must be submitted as a PDF via Canvas. LaTeX is preferred, but other typesetting methods are acceptable. Code for the programming component must be submitted in a zip archive via Canvas.

Project

• Final Project Demo March 12, Deliverables and Writeup March 16 (files)

Policies

Collaboration Policy: All assignments are to be done as a group. It's okay to discuss with other groups, but each group should do their own writeup/programmed work. It's okay to look at online resources as long as sources are cited and code is not copied. Feel free to talk through struggles with your peers as long as you follow the academic misconduct warnings that have been relayed in every course you've taken thus far. Here's a reference in case you need a refresher.

Late Day Policy: Each group gets four late days to use over the course of the quarter. The late days do not apply to the final project. If an assignment is submitted after exhausting the late days, it is subject to a 10-point penalty for each day over the budget. Leftover late days will not contribute to your grade in any way.

If unusual circumstances truly beyond your control prevent you from submitting an assignment, you should discuss this with the instructor, preferably in advance. (Even if you’re sick in bed at home, you should still be able to send an email.) If you contact the instructor well in advance of the deadline, we may be able to show more flexibility in some cases.

Extra Credit Policy: We will keep track of any extra features you implement in the assignments. You won’t see these affecting your grades for individual projects, but they will be accumulated over all projects and used to bump up borderline grades at the end of the quarter. The bottom line is that these will only have a small effect on your overall grade (possibly none if you are not on a borderline) and you should be sure you have completed the non-extra credit portions of the homework in perfect form before attempting any extra credit. They are meant to be fun extensions to the assignments.

Getting Help: Please don’t be afraid to ask for help if you don’t understand something. Meaningful ways to get help:

• Come to office hours
• Ask someone on course staff questions before/after lecture
• Post on Piazza asking a question

Schedule

Acknowledgments: We are thankful to Sanjiban Choudhury for gratefully providing the materials from the Spring '19 version of the course, and to the MuSHR team for the software and hardware development on the MuSHR racecar.