# Automated Design of CubeSats using Evolutionary Algorithm for Trade Space Selection
## 1 Introduction  
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 COTS inventory. For these COTS assembled spacecrafts, the availability of rideshare opportunities on small spacecraft launchers and the prospect of being launched as a secondary payload on larger launch vehicles has led to the rapid advancement of COTS components and expansion of the small spacecraft market. As such, the buses for these small spacecrafts are designed as modular platforms and the components designed to be as diverse and capable as possible, which can be assembled with a payload in a short amount of time, ready to fly [1]. One such platform is the CubeSat platform, which weighs only a few kilograms and is based on a form factor of a \(10\times 10\times 10\) cm\({}^{3}\). CubeSats can be composed of a single cube (a \"1U\" CubeSat) or several cubes combined, forming, for instance, 3U or 6U units [2]. Although the COTS components are readily available, designing these spacecrafts is a long, expensive endeavor, where a team of experts work together to design the system for a specific mission using their engineering judgement. An alternative design approach is required that shortens and simplifies the spacecraft design process. In this paper, we propose a tool for automated design of a spacecraft using machine learning algorithms that utilize modular, interchangeable components from a COTS inventory to develop a spacecraft design. In our approach, the spacecraft design is presented in the form of a gene that selects subsystem components from an inventory, and a numerical goal function is specified along with constraints, based on which the spacecraft design is evaluated. An evolutionaryalgorithm (EA) is used to generate a population of candidate solutions, which undergo evolutionary operations such as selection, crossover, and mutation, and the fittest individuals are carried to the next generation, while the unfit individuals are culled off. This method of evolution is performed over hundreds of generations until the fittest individual in the population meets a desired performance metric [3]. This approach can be very useful to a design team to make informed system design decisions by finding a set of near-optimal solutions that represent the trade-offs among conflicting objectives, such as mass, cost, and performance. Automated design of engineering systems is not new. When compared to human created designs, computationally derived evolutionary designs have shown competitive advantages in terms of robustness, performance, and creativity. Design optimization through evolutionary computation has been studied for decades. Work has been done to design several satellite subsystems. One such example is an X-band antenna for NASA's Space Technology 5 (ST5) spacecraft, which is in fact the first computer-evolved hardware in space [4]. Other work includes design optimization of a power subsystem [5], low-thrust orbit transfer [6], spacecraft structure [7], and a corner retroreflector for satellites [8], which has shown potential improvements over traditional methods. An evolutionary approach has also 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 also been successfully applied to the design of mobile robots [15] and water desalination systems [16]. Automated design is in sharp contrast to current spacecraft design methods. Current design methods use engineering experience and judgement of a team of experts to identify candidate designs.