Print Email Facebook Twitter Assessment of Reinforcement Learning for CubeSat concept generation Title Assessment of Reinforcement Learning for CubeSat concept generation Author Krijnen, Bas (TU Delft Aerospace Engineering) Contributor Guo, J. (mentor) Degree granting institution Delft University of Technology Programme Aerospace Engineering Date 2020-12-17 Abstract The growing need for CubeSats could present strong demands for the use of automated systems during the early stage of the design cycle. Automated design tools that are able to incorporate the entire design space offered by the commercial-off-the-shelf (COTS) components for CubeSats may potentially improve the design of a CubeSat, compared to manual methods. This thesis sets out to develop and assess such a design tool. The design tool that is developed makes use of reinforcement learning (RL) for automated CubeSat concept generation. Concepts are generated by selecting components from a manually created hypothetical components database. The capability of the design tool to create feasible CubeSat concepts is tested through a case study, where the results from a manual approach are compared to the results from the design tool. It is investigated whether the RL-based design tool shows promise for automated CubeSat concept generation. Subject CubeSatsDesign ToolPreliminary designReinforcement LearningSpace Systems Engineering To reference this document use: http://resolver.tudelft.nl/uuid:60957970-2e7d-420c-94ae-0247dec7a3bd Part of collection Student theses Document type master thesis Rights © 2020 Bas Krijnen Files PDF MSc_Thesis_V1.0_BasKrijnen.pdf 14.55 MB Close viewer /islandora/object/uuid:60957970-2e7d-420c-94ae-0247dec7a3bd/datastream/OBJ/view