Course Description
This lecture-based course comprises four modules that present both theory and practice of mobile robot algorithms. These modules will be associated with four assignments through which students will build a autonomy software package. In the first lab, we will add vehicle dynamics simulation and linear control capabilities. The second assignment aims at adding mapping and state estimation capability. The third assignment will enable students to add motion planners. Finally, the fourth assignment combines the capabilities from the previous modules into an exploration system. The course will end with an individual student project that leverages the software system developed through the four assignments and adds additional mobile robot algorithms of student's choice.
Learning Objectives
When you complete this course, you will be able to:
- Aerial Robot Autonomy: Implement a framework for autonomous quadrotor navigation and exploration.
- Development Skills: Plan software development efforts that address robotics applications.
- Software Artifacts: Develop a nontrivial mobile robot application.
- Algorithmic Familiarity: Implement key probabilisitc algorithms in mobile robotics.
Prerequisites
Undergraduate-level understanding of probability, statistics, and algorithms is assumed. Experience with Python and basic familiarity with linear algebra, probability theory, and ordinary differential equations will benefit the student throughout the semester.
Learning Resources
There is no textbook required for this course. Slides and additional references for further reading will be provided with each lecture on the course website.
Assessments
This course implements software for mobile robots. Consequently, the assessments depend heavily on programming. We will be using the Python and C++ programming languages throughout the course. Your final grade in this course will be assessed according to:
- 70% Homework
- 25% Project
- 5% Participation
Homework
Four mandatory assignments will be provided during the semester. Students will have at least two weeks to complete each assignment. All homework will be distributed using GitHub and collected using AutoLab. AutoLab will enable auto-grading and feedback for students to help them finalize submissions. Solutions and grades will be returned within one week of homework due dates.
Office Hours
Office hours will be held by one of the instructors after every lecture, 1100 - 1200 EST.
Outside of office hours, Piazza will be used for all communication. Use public posts to ask questions that you would like answered by the course staff or your classmates. Use public posts to share any course related content with course staff and your classmates. Post privately to the teaching assistant(s) if you have specific questions regarding your performance on homeworks and the course. Post privately to the instructors for anything else. If these private posts are not answered within 24 hours, please email with the subject line starting with [16-362 Student].
