新处理器让航天器自主导航提速百倍，还自带故障自检能力。
Enhancing Fault-Tolerant Space Computing: Guidance Navigation and Control (GNC) and Landing Vision System (LVS) Implementations on Next-Gen Multi-Core Processors
- 用多核芯片并行运行导航与着陆视觉算法，提升实时性。
- 着陆图像处理快15倍，轨迹优化快250倍，较旧硬件显著提升。
- 新增异步冗余检测机制，可实时发现并修复核心故障，适合深空任务。
未来行星探测任务需要高性能、高容错的计算能力，以实现进入、下降和着陆（EDL）阶段的自主导航与控制（GNC）及着陆视觉系统（LVS）运行。本文评估了GNC与LVS算法在新一代多核处理器（HPSC、Snapdragon VOXL2、AMD Xilinx Versal）上的部署效果，显示着陆视觉图像处理最高提速15倍，燃料最优大转向轨迹优化（GFOLD）提速超过250倍，相较传统航天硬件。为保障计算可靠性，提出ARBITER（异步冗余行为检测与可信执行恢复）机制，一种多核投票（MV）方法，可在冗余核心间实现实时故障检测与纠正。该机制在静态优化任务（GFOLD）与动态闭环控制（姿态控制系统）中均得到验证。故障注入实验发现，GFOLD中的梯度计算阶段对比特级错误最敏感，从而推动采用选择性保护与基于向量的输出仲裁策略。本工作建立了一种可扩展、低功耗的架构，适用于火星样本返回、土卫二轨道着陆器及谷神星采样返回等任务，这些任务对机上自主性、低延迟和故障韧性要求极高。
原文摘要 · Abstract (English)
Future planetary exploration missions demand high-performance, fault-tolerant computing to enable autonomous Guidance, Navigation, and Control (GNC) and Lander Vision System (LVS) operations during Entry, Descent, and Landing (EDL). This paper evaluates the deployment of GNC and LVS algorithms on next-generation multi-core processors--HPSC, Snapdragon VOXL2, and AMD Xilinx Versal--demonstrating up to 15x speedup for LVS image processing and over 250x speedup for Guidance for Fuel-Optimal Large Divert (GFOLD) trajectory optimization compared to legacy spaceflight hardware. To ensure computational reliability, we present ARBITER (Asynchronous Redundant Behavior Inspection for Trusted Execution and Recovery), a Multi-Core Voting (MV) mechanism that performs real-time fault detection and correction across redundant cores. ARBITER is validated in both static optimization tasks (GFOLD) and dynamic closed-loop control (Attitude Control System). A fault injection study further identifies the gradient computation stage in GFOLD as the most sensitive to bit-level errors, motivating selective protection strategies and vector-based output arbitration. This work establishes a scalable and energy-efficient architecture for future missions, including Mars Sample Return, Enceladus Orbilander, and Ceres Sample Return, where onboard autonomy, low latency, and fault resilience are critical.
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