Maximizing Limited Volume: A Genetic Algorithm-Based Approach to CubeSat Solar Panel and Antenna Deployment Pattern Design

CubeSats are increasingly being recognized as a cost-effective and efficient means of conducting space missions. However, several challenges are posed to their design and capabilities, particularly concerning volume efficiency due to their small size. The use of available space for components such as solar panels and antennas in CubeSats requires careful optimization. In this paper, a genetic algorithm-based approach to design the deployment pattern for CubeSat solar panels or antennas that maximizes volume efficiency is proposed. The approach involves the definition of a fitness function that evaluates the design based on several criteria, including the surface area of the deployed solar panel or antenna, the stability and reliability of the deployment mechanism, and the volume occupied by the deployed structure. The proposed approach is applied to a case study of a CubeSat antenna deployment pattern design, and its effectiveness in optimizing volume efficiency is demonstrated. The results indicate that a significant improvement in volume efficiency can be achieved with the proposed approach while meeting the design criteria. Specifically, deployment patterns that occupy less volume while still providing the necessary surface area can be generated using the approach. This approach provides a useful tool for engineers and designers to optimize the deployment pattern of CubeSat components while considering volume constraints, ultimately enhancing the capabilities of small satellites. The importance of volume efficiency in CubeSat design is highlighted in this paper, and a novel approach to optimize it using a genetic algorithm-based design methodology is proposed. The results demonstrate the potential of this approach to improve the efficiency and performance of CubeSats in space missions.