## 1 Introduction
This autonomous approach is crucial for maintaining operational integrity and addressing the complexities of space environments, where rapid adaptation is essential for survival and mission accomplishment [8; 9; 10; 11; 12]. Structural design is an intellectual endeavor rooted in the discernment and expertise of the designer, varying with their experience and overarching vision. The advent of topology optimization, pioneered by [PERSON] [29] through the solving structural optimization design utilizing [PERSON]'s lemma [30], has significantly advanced computer-aided design, enabling the creation of designs based on rigorous non-parametric optimization principles. While the complete automation of evaluation and design processes remains an aspirational goal, it is a crucial imperative for advancing future transportation technologies, particularly in the realm of deep space missions. This integration of sophisticated computational methods into the design process not only enhances precision and efficiency but also paves the way for innovations that are essential for overcoming the unique challenges of extraterrestrial exploration and transportation [31; 32; 33]. Topology optimization encompasses two main categories: non-parametric optimization and generalized shape optimization. It involves dividing a domain into discrete parts with defined spatial relationships, such as finite differences, boxes, elements, and volumes. Initially addressing layout challenges, topology optimization concentrates on configuring designated regions (design domains) within a specified space, each having fixed traction and support points [34; 35; 36; 37]. Numerous efforts have been invested in advancing topology optimization methodologies. A significant contribution to this field has been the utilization of numerical discretization of structures, as proposed by [PERSON] et al. [38], to enhance the efficiency and precision of topology optimization techniques. This approach involves the detailed numerical representation of structural elements, allowing for more accurate and refined optimization outcomes. [PERSON] [39] leveraged sequential unconstrained minimization and constrained steepest descent techniques to minimize structure weight. [PERSON] and [PERSON] [40] adopted the Prager-shield implementation to identify optimal design criteria for rigid-perfectly plastic systems under multiple loading conditions. [PERSON] and [PERSON] [41] investigated the optimal design of grillage-like continua, emphasizing spatial distribution within confined grillage units. [PERSON] and [PERSON] [42] utilized the finite element method to determine the optimal thickness of variable thickness sheets,addressing potential energy considerations for elastic sheet in-plane stress assumptions and pioneering shape optimization by introducing holes into plate structures. [PERSON] and [PERSON] [43] implemented the finite element method to optimize the thickness of annual plates with a stiffened approach, incorporating homogenization as an averaging method in topology optimization. The discretized continuous optimality criterion (DCOC) further advanced topology optimization, leading to the conceptualization of fictitious materials by [PERSON] and the subsequent development of the Solid Isotropic Material with Penalization (SIMP) method [44; 45; 46]. The SIMP method has been instrumental in optimizing structures with minimal stress concentrations, leveraging image processing techniques to construct design cases and transform them into the topology optimization process. Moreover, several other effective methods for topology optimization exist, including the Evolutionary Structural Optimization (ESO) method [47; 48] and the Metaheuristic Structure Binary-Distribution (MSB) method [35; 49]. Additionally, shape optimization methods that focus on the boundaries of the structure are currently showing promising results, such as the level-set method [50] and the H1 gradient method [51].