带宽、延迟与4亿公里：火星本地计算的理由
Bandwidth, Latency, and 400 Million Kilometers: The Case for Mars-Local Compute
- University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对火星-地球链路带宽低、延迟高的问题，本文提出火星本地共享计算方案，通过两层轨道计算卫星部署实现数据就地处理，以覆盖90%火星并支持人类探索。
AI中文摘要：
近期有关于2030年代在火星建立人类定居点的提议。任何在火星上的人类活动都必须以广泛的机器人探索为前提。然而，火星探索受到低带宽、间歇性的火星-地球链路的瓶颈制约。例如，搭载在火星轨道器MRO上的高分辨率相机HiRISE在十一年间仅拍摄了火星不到3%的区域，而MRO的低分辨率背景相机在同一时期已绘制了火星超过99%的地图。我们提出了一个共享计算用于火星探索的系统性理由。这种火星本地计算，结合计算机视觉和人工智能的进步，可以实现在火星上收集和处理大量数据，同时向地球发送定期更新、洞察和选择性数据集。为了克服火星上缺乏地面基础设施的问题，我们提出了一种两层轨道计算卫星部署方案，以提供一致的覆盖和带宽。我们的分析表明，所提出的部署可以从较小规模开始：一个火星星体同步轨道节点即可使计算能力覆盖所有活跃的火星任务，增加两个火星星体同步轨道节点可将覆盖范围扩展到约90%的行星表面，而低火星轨道节点则在需求增长的地方增加高速率的地面链路和计算能力。
英文摘要：
There have been recent proposals for human settlements on Mars in 2030s. Any human activity on Mars must be preceded by extensive robotic exploration. However, Mars exploration is bottlenecked by the low bandwidth, intermittent Mars-Earth link. For example, HiRISE, a high-resolution camera onboard the Martian orbiter MRO imaged less than 3% of Mars over eleven years, even though MRO's low resolution Context Camera had mapped more than 99% of Mars in that time. We present a systems case for shared compute for Mars exploration. Such Mars-local compute, paired with advances in computer vision and AI, can enable large volumes of data to be collected and processed on Mars while sending periodic updates, insights, and selective datasets to Earth. To overcome the lack of surface infrastructure on Mars, we propose a two-tier in-orbit deployment of computational satellites that provides consistent coverage and bandwidth. Our analysis shows that the proposed deployment can start small: one areostationary node makes compute reachable from all active Mars missions, two additional areostationary nodes can extend this coverage to roughly 90% of the planet, while low-Mars-orbit nodes add high-rate surface links and compute capacity where demand grows.
