arXiv:2605.29021v1 Announce Type: new 
Abstract: Active tether-net systems are a promising solution for capturing large non-cooperative targets, such as space debris, by deploying a flexible net manipulated by maneuverable units (MUs). However, concurrent systematic explorations of design and control choices of the tether-net system to understand its full potential remain limited, partly due to the complex, constrained, nonlinear optimization problem that it presents -- one that involves a mixture of continuous, integer and categorical variables, with the latter two arising from net connectivity and component choices, respectively. Classical binary encoding methods are often ineffective for solving highly nonlinear and multimodal Mixed Combinatorial Nonlinear Programmings (MCNLPs) in engineering design, while integer coding approaches can introduce spurious relations among combinations. Given the graph-structured characteristics of the combinatorial space, this paper adopts and extends a new graph-learning-aided optimization approach to solve this MCNLP problem. Here, a Graph Neural Network (GNN) is trained to score (as output) and thereof recommend candidate combinations represented as nodes in a graph, with the continuous variable vector portion of a candidate design given as input. As a result, the MCNLP optimization reduces to an NLP, which can be solved using standard solvers. While this reduction approach is agnostic to the choice of the NLP solver, here a state-of-the-art Particle Swarm Optimization (PSO) algorithm with gradient-based fine-tuning is used as the solver. Demonstrated on the problem of concurrently designing the morphology of the net, choice of mass and thrusters in the MUs and aiming points used by the controller of the tether-net system, the GNN-based recommender is shown to provide significantly faster convergence to similar optimal solutions, compared to direct solution of the MCNLP problem.

يتم تطوير أنظمة الشبكات النشطة كحل لالتقاط الأهداف الكبيرة غير التعاونية مثل الحطام الفضائي، باستخدام شبكات مرنة يتم التحكم فيها بواسطة وحدات قابلة للمناورة. يتناول هذا النهج التحديات المعقدة في تحسين تصميم هذه الأنظمة والتحكم فيها، والتي تتضمن مزيجًا من المتغيرات المستمرة والعددية والفئوية.

Se están desarrollando sistemas de redes activas como solución para capturar grandes objetivos no cooperativos, como los desechos espaciales, utilizando redes flexibles manipuladas por unidades maniobrables. Este enfoque aborda los complejos desafíos de optimización inherentes al diseño y control de tales sistemas, que implican una mezcla de variables continuas, enteras y categóricas.

Des systèmes de filets actifs sont en cours de développement comme solution pour capturer de grandes cibles non coopératives telles que les débris spatiaux, utilisant des filets flexibles manipulés par des unités manœuvrables. Cette approche répond aux défis complexes d'optimisation inhérents à la conception et au contrôle de tels systèmes, qui impliquent un mélange de variables continues, entières et catégorielles.

Active tether-net systems are being developed as a solution for capturing large non-cooperative targets like space debris, utilizing flexible nets manipulated by maneuverable units. This approach addresses the complex optimization challenges inherent in designing and controlling such systems, which involve a mix of continuous, integer, and categorical variables.