Trajectory Design for Space Systems (EN.530.626)
Course Description
This course is an introduction to the techniques and methods used to design and synthesize trajectories for a broad class of space systems. In particular, we will focus on optimization-based techniques for trajectory generation and study optimal control formulations for solving trajectory optimization and model predictive control problems. Applications of interest will include interplanetary trajectory optimization, rocket entry-descent-landing, asteroid proximity operations, and planetary rover path planning. A strong emphasis will be placed on practical applications through coding implementations in Python and evaluation in simple simulation environments. Finally, a course project will be included to allow students to gain further experience on an algorithm or application of their choice.
Instructors
Course Assistants
Mark Gonzales
Arnab Chatterjee
Meeting Times
Lectures will be held on Tuesdays and Thursdays from 1:30-2:45PM in Hodson 216.
Office Hours
Office hours will begin from the second week of the semester. Office hours will be held regularly at the following times, but please see the calendar at the bottom of the page for the most up-to-date hours:
- Monday 3-4PM: Arnab Chatterjee (Hackerman 111)
- Wednesday 1-2PM: Prof. Abhishek Cauligi (Hackerman 117)
- Thursday 11AM-12PM: Mark Gonzales (Hackerman 111)
Syllabus
The syllabus for the course can be found here.
Final Project
This class will culminate with a final project that will allow students to explore topics of their interest and pursue potential research applications. Details on the final project can be found here.
Schedule
| Week | Date | Topics Covered | Notes | Suggested Readings |
|---|---|---|---|---|
| 1 | 08/26 | Intro: linear algebra & differential equations review | Learn git, Learn shell, Docker tutorial | |
| 08/28 | Linear systems theory | Lecture 2 Notes | 1, 2 | |
| 2 | 09/02 | Optimization fundamentals | Lecture 3 Notes, HW1 Released | 1 |
| 09/04 | Constrained optimization (Pt. 1) | Lecture 4 Notes | 1, 2 | |
| 3 | 09/09 | Constrained optimization (Pt. 2) | Lecture 5 Notes | 1, 2 |
| 09/11 | Constrained optimization (Pt. 3) | HW1 Due, HW2 Released, Lecture 6 Notes | 1, 2 | |
| 4 | 09/16 | Constrained optimization (Pt. 4) | Lecture 7 Notes | |
| 09/18 | Off-the-shelf trajectory optimization | Lecture 8 Slides | 1, 2 | |
| 5 | 09/23 | From continuous to discrete optimal control | Lecture 9 Notes | |
| 09/25 | Powered descent guidance | Lecture 10 Notes, Final project proposal due | 1, 2 | |
| 6 | 09/30 | Planning over orientations (Pt. 1) | Lecture 11 Notes | 1 |
| 10/02 | Planning over orientations (Pt. 2) | |||
| 7 | 10/07 | Combinatorial planning with integer programs | Lecture 13 Notes, HW2 Due, HW3 Released | 1, 2 |
| 10/09 | Sampling-based motion planning | Lecture 14 Slides | ||
| 8 | 10/14 | Surface rover path planning | ||
| 10/16 | No Lecture (Fall Break) | |||
| 9 | 10/21 | Inverse classroom (mid-semester checkpoint) | HW3 Due, HW4 Released, Lecture 17 Notes | |
| 10/23 | Long and short range planner hierarchies | Lecture 18 Slides | ||
| 10 | 10/28 | Derivative-free methods for trajectory optimization | 1, 2, 3 | |
| 10/30 | Uncertainty propagation | 1, 2 | ||
| 11 | 11/04 | Stochastic optimal control (Pt. 1) | HW4 Due | 1, 2, 3 |
| 11/06 | Midterm Exam | HW5 Released | ||
| 12 | 11/11 | Guest lecture (Dr. Bobby Braun) | 1, 2 | |
| 11/13 | Stochastic optimal control (Pt. 2) | Lecture 24 Slides | ||
| 13 | 11/18 | Learning value functions | Lecture 25 Slides | |
| 11/20 | Differentiable MPC | HW5 Due, Lecture 26 Slides | 1, 2 | |
| 14 | 11/25 | No Lecture (Thanksgiving Break) | ||
| 11/27 | No Lecture (Thanksgiving Break) | |||
| 15 | 12/02 | Final project presentations | ||
| 12/04 | Final project presentations |