Towards intelligent trajectory optimisation in astrodynamics
Author
Sprague, Christopher IliffeOther Contributors
Anderson, Kurt S.; Embrechts, Mark J.; Hicken, Jason; Oehlschlaeger, Matthew A.;Date Issued
2017-05Subject
Aeronautical engineeringDegree
MS;Terms of Use
This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.; Attribution-NonCommercial-NoDerivs 3.0 United StatesMetadata
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In this work trajectory optimisation is explored through four different avenues, namely: 1. Direct Methods, 2. Heuristics, 3. Reinforcement learning, 4. Supervised Learning Each method builds off of its preceding session. It is shown that each method poses the capability to be utilised for autonomous real-time optimal control in astrodynamics applications.; A housefly is a rather simple organism, yet it is able to independently make decisions to achieve its goals, such as navigating to a food-source and avoiding obstacles. Inspecting closer, a housefly is able to make these decisions instantaneously, such as in the case of being swatted at by a human. If one thinks about the descent of a landing capsule onto the Martian surface, the nature of the situation is quite the same. Because communication with Earth is prolonged, the lander must make decisions on its own in order to safely land on the surface. If a common housefly can independently make decisions in real-time, in uncertain dynamic environments, than surely a spacecraft should be able to do the same in an environment where the objective is clearly outlined.;Description
May 2017; School of EngineeringDepartment
Dept. of Mechanical, Aerospace, and Nuclear Engineering;Publisher
Rensselaer Polytechnic Institute, Troy, NYRelationships
Rensselaer Theses and Dissertations Online Collection;Access
CC BY-NC-ND. Users may download and share copies with attribution in accordance with a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License. No commercial use or derivatives are permitted without the explicit approval of the author.;Collections
Except where otherwise noted, this item's license is described as CC BY-NC-ND. Users may download and share copies with attribution in accordance with a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License. No commercial use or derivatives are permitted without the explicit approval of the author.