Hardware-Accelerated Instance Segmentation for Resource-Constrained Space Robotics with Criticality Analysis

2026-09-02

AI Summary

该研究针对月球机器人在资源受限条件下的实时感知问题，提出了一种硬件加速的实例分割框架，通过量化校准和系统级故障暴露分析来提高模型的准确性和可靠性。

Abstract

Autonomous lunar missions require real-time per- ception under three coupled constraints: extreme low-light conditions, limited onboard compute, and radiation-induced hardware faults that can silently corrupt inference. We present a deployment-oriented instance segmentation framework for resource-constrained lunar robotics that jointly addresses quan- tization calibration and system-level fault exposure under strict compute constraints. First, we introduce Activation Variance Informative Sampling (AVIS), a label-free calibration strategy that deterministically selects calibration samples based on activation variance statistics. Second, we deploy a YOLO-based segmentation model on a Deep Learning Processor Unit (DPU) with architectural modifications that reduce CPU fallback paths and enable statically compiled execution with bounded latency in low-lighting conditions. We further introduce a software-level criticality analysis to estimate fault exposure and guide mitigation under radiation-constrained operation. On a lunar micro-rover platform, AVIS with bias correction recovers 69.8% of quantization-induced accuracy loss while achieving 309 ms inference latency and 5.7 W power consumption. Targeted mitigation reduces global criticality by 31.7%. The results demonstrate an integrated approach and a blueprint for a reliable and safe AI perception framework under space deployment constraints.

Problem

• hardware-accelerated
• instance segmentation
• resource-constrained
• space robotics
• criticality analysis

Innovation

• Activation Variance Informative Sampling
• quantization calibration
• fault exposure mitigation
• Deep Learning Processor Unit (DPU)
• criticality analysis