Automated Design of CubeSats and Small Spacecrafts
Himangshu Kalita, Jekan Thangavelautham

TL;DR
This paper introduces an automated, machine learning-based method for designing CubeSats and small spacecrafts, enabling the discovery of near-optimal designs more efficiently than traditional human-led approaches.
Contribution
It presents a novel evolutionary algorithm approach for spacecraft design that automates the search for optimal configurations using a virtual component warehouse.
Findings
Automated design process finds near-optimal spacecraft configurations.
Method outperforms traditional human design in exploring design options.
Enables evaluation of many more designs than manual methods.
Abstract
The miniaturization of electronics, sensors and actuators has enabled the growing use of CubeSats and sub-20 kg spacecraft. Their reduced mass and volume has the potential to translate into significant reductions in required propellant and launch mass for interplanetary missions, earth observation and for astrophysics applications. There is an important need to optimize the design of these spacecraft to better ascertain their maximal capabilities by finding optimized solution, where mass, volume and power is a premium. Current spacecraft design methods require a team of experts, who use their engineering experience and judgement to develop a spacecraft design. Such an approach can miss innovative designs not thought of by a human design team. In this work we present a compelling alternative approach that extends the capabilities of a spacecraft engineering design team to search for and…
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Taxonomy
TopicsSpacecraft Design and Technology · Modular Robots and Swarm Intelligence · Space Satellite Systems and Control
