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dc.rights.licenseUsers 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.
dc.contributorAnderson, Kurt S.
dc.contributorEmbrechts, Mark J.
dc.contributorHicken, Jason
dc.contributorOehlschlaeger, Matthew A.
dc.contributor.authorSprague, Christopher Iliffe
dc.date.accessioned2021-11-03T08:48:11Z
dc.date.available2021-11-03T08:48:11Z
dc.date.created2017-07-03T14:05:36Z
dc.date.issued2017-05
dc.identifier.urihttps://hdl.handle.net/20.500.13015/1938
dc.descriptionMay 2017
dc.descriptionSchool of Engineering
dc.description.abstractIn 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.
dc.description.abstractA 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.
dc.language.isoENG
dc.publisherRensselaer Polytechnic Institute, Troy, NY
dc.relation.ispartofRensselaer Theses and Dissertations Online Collection
dc.subjectAeronautical engineering
dc.titleTowards intelligent trajectory optimisation in astrodynamics
dc.typeElectronic thesis
dc.typeThesis
dc.digitool.pid178165
dc.digitool.pid178166
dc.digitool.pid178167
dc.rights.holderThis electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.
dc.description.degreeMS
dc.relation.departmentDept. of Mechanical, Aerospace, and Nuclear Engineering


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