发表机构
Massachusetts Institute of Technology; Politecnico di Milano; University of Illinois Urbana-Champaign; Universidad Politécnica de Madrid（麻省理工学院; 米兰理工大学; 伊利诺伊大学厄巴纳-香槟分校; 马德里理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文介绍DreamSat-Bench测试平台，集成机器人学习工具与硬件在环，评估基于生成式AI三维重建和FoundationPose的位姿估计，量化关键参数影响，验证物理部署可行性。
AI 中文摘要
本文介绍了DreamSat-Bench的开发与初步测试，这是一个模块化的交会与近距离操作测试平台，旨在对基于AI的相对导航技术进行基准测试。通过集成软件和硬件在环的机器人流水线，该平台实现了从数字仿真到物理现实的无缝过渡。DreamSat-Bench将MuJoCo、Isaac Lab和LeRobot等最先进的机器人学习工具统一到一个基准测试平台中，利用机械臂来追踪三维轨迹。该平台允许对轨道环境和光照进行广泛定制，以评估仿真到现实的差距。我们通过评估一个端到端的基于视觉的导航流水线来展示该测试平台的实用性，该流水线将DreamSat（一个用于单视图三维重建的生成式AI框架）与FoundationPose（用于对未见航天器进行零样本6自由度跟踪）配对。初步测试探索了具有任务代表性的轨道段，包括定点站位保持和飞越表征。通过一系列参数研究，我们量化了重建延迟、网格分辨率、轨道范围和光照几何对位姿估计精度的影响。最后，一项初步的硬件在环活动定性验证了该流水线的物理部署，确定目标对称性和累积跟踪漂移是稳健导航的关键因素。DreamSat-Bench为在没有先验几何模型的情况下，利用未准备的太空资产成熟自主导航提供了一个严格的框架。
英文摘要
This paper presents the development and initial testing of DreamSat-Bench, a modular rendezvous and proximity operation testbed designed to benchmark AI-based relative navigation techniques. By integrating a software- and hardware-in-the-loop robotic pipeline, the platform enables a seamless transition from digital simulation to physical reality. DreamSat-Bench unifies state-of-the-art robotic learning tools such as MuJoCo, Isaac Lab, and LeRobot into a single benchmarking platform, utilizing robotic arms to trace 3D trajectories. The platform allows for extensive customization of orbital environments and lighting to evaluate the simulation-to-reality gap. We demonstrate the testbed's utility by evaluating an end-to-end vision-based navigation pipeline that pairs DreamSat, a generative AI framework for single-view 3D reconstruction, with FoundationPose for zero-shot 6-DoF tracking of unseen spacecraft. Initial testing explores mission-representative orbital segments, including fixed-point station-keeping and fly-around characterization. Through a series of parametric studies, we quantify the impacts of reconstruction latency, mesh resolution, orbital range, and illumination geometry on pose estimation accuracy. Finally, a preliminary hardware-in-the-loop campaign qualitatively validates the physical deployment of the pipeline, identifying target symmetry and accumulated tracking drift as critical factors for robust navigation. DreamSat-Bench provides a rigorous framework for maturing autonomous navigation with unprepared space assets in the absence of prior geometric models.
