{"id":1543,"date":"2026-02-27T11:26:57","date_gmt":"2026-02-27T10:26:57","guid":{"rendered":"https:\/\/staging.gamethinkers.it\/portfolio\/synthetic-target-spacecraft-generator\/"},"modified":"2026-03-11T16:15:19","modified_gmt":"2026-03-11T15:15:19","slug":"synthetic-target-spacecraft-generator","status":"publish","type":"portfolio","link":"https:\/\/staging.gamethinkers.it\/en\/portfolio\/synthetic-target-spacecraft-generator\/","title":{"rendered":"Synthetic Target Spacecraft Generator: infinite satellites, real simulations"},"content":{"rendered":"\n<h2 class=\"wp-block-heading\">A Procedural System to Train Artificial Intelligence to Recognise Satellites<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The ASIMOV project aims to design, develop, verify, and validate a space autopilot for mapping non-cooperative objects in low Earth orbit (LEO). Within this joint initiative, our contribution focused on a specific and strategic challenge: the intelligent space autopilot needed realistic training data, in potentially unlimited quantities.<br>We responded by building a procedural 3D satellite generator, a system capable of automatically composing plausible, geometrically consistent, and industrially credible space configurations.<\/p>\n\n\n\n<figure class=\"wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\n<iframe loading=\"lazy\" title=\"Synthetic Target Spacecraft Generator: satelliti infiniti, simulazioni reali\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube.com\/embed\/Fatm1TTcICM?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe>\n<\/div><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">The Project<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Customer need<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Every generated configuration had to comply with strict dimensional controls and consistency criteria between components. Not a simple random combination of parts, but a tool that enables the automatic composition of space configurations that are coherent from a geometric, dimensional, and functional standpoint, while maintaining a high degree of controlled variability.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Identified solution<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Configurable framework. <\/strong>The solution is based on a framework configurable through external parameter sheets, which allow rules and constraints to be defined without touching the code, ensuring flexibility and scalability. The generation process is guided by a global characteristic size that ensures compliance with the dimensional limits defined by the scenario, while central bodies and components are generated within controlled ranges and with explicit geometric constraints.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>The evolution of the algorithm.<\/strong> The algorithm evolved toward a component-first approach, enabling more direct control over the requirements of individual elements and more robust management of the overall composition. In parallel, it was further refined to align with real-world industrial logic, introducing consistency across repeated components, control over models, dimensions and orientation, and support for composite bases.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>More components, more variability.<\/strong> Finally, we expanded the 3D library and improved the materials system to increase visual variability while maintaining quality and performance.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">A Replicable Model Beyond the Space Sector<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The ASIMOV project demonstrates that Game Thinking methodologies are not a privilege of the entertainment world. They are engineering tools applicable wherever there is a need to generate simulated environments, synthetic datasets, or AI training systems. If your organisation faces similar challenges &#8211; in defence, robotics, automotive, logistics, or training &#8211; the framework we developed for ASI can serve as the starting point for your own solution.<br><\/p>\n","protected":false},"featured_media":1576,"parent":0,"menu_order":0,"template":"","settore":[87],"class_list":["post-1543","portfolio","type-portfolio","status-publish","has-post-thumbnail","hentry","settore-aerospace"],"meta_box":{"portfolio_achievements":"","portfolio_description":"<p>Procedural Satellite Generation 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