Abstract
The miniaturization of electronics, sensors, and actuators has enabled the growing use of nanosatellites for earth observation, astrophysics, and even interplanetary missions. This rise of nanosatellites has led to the development of an inventory of modular, interchangeable commercially-off-the-shelf (COTS) components by a multitude of commercial vendors. As a result, the capability of combining subsystems in a compact platform has considerably advanced in the last decade. However, to ascertain these spacecraft’s maximum capabilities in terms of mass, volume, and power, there is an important need to optimize their design. Current spacecraft design methods need engineering experience and judgements made by of a team of experts, which can be labor intensive and might lead to a sub-optimal design. In this work we present a compelling alternative approach using machine learning to identify near-optimal solutions to extend the capabilities of a design team. The approach enables automated design of a spacecraft that requires developing a virtual warehouse of components and specifying quantitative goals to produce a candidate design. The near-optimal solutions found through this approach would be a credible starting point for the design team that will need further study to determine their implementation feasibility.
Highlights
The development of commercially-off-the-shelf (COTS) components has led to the capability of developing compact spacecraft platforms by combining subsystems from the readily availableCOTS inventory
Two constraints are added: the first is that the average solar power generated per orbit, P, should be greater than the total power requirement of the CubeSat, P, and the second is that the state of charge of the battery subsystem should not drop below a minimum value
Two constraints are added: the first is that the average solar power generated per orbit, Pavg, should be greater than the total power requirement of the CubeSat, PT, and the second is that the state of charge of the battery subsystem should not drop below a minimum value
Summary
The development of commercially-off-the-shelf (COTS) components has led to the capability of developing compact spacecraft platforms by combining subsystems from the readily available. An evolutionary approach has been applied to design satellite constellations for continuous regional coverage [9], nontraditional constellations such as heterogeneous and rideshare constellations using COTS components to increase resiliency and responsiveness [10], reconfigurable satellite constellations [11], regional navigation satellite system constellations [12], pattern forming tasks using robot teams [13] and even teams of excavating robots for lunar base preparation [14], which have produced human-competitive designs This process has been successfully applied to the design of mobile robots [15] and water desalination systems [16].
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