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

• URL: http://arxiv.org/abs/2609.02219v1
• Date: Wed, 02 Sep 2026 07:33:27 GMT
• Title: Hardware-Accelerated Instance Segmentation for Resource-Constrained Space Robotics with Criticality Analysis
• Abstract summary: We present a deployment-oriented instance segmentation framework for resource-constrained lunar robotics.<n>We jointly address quan- tization calibration and system-level fault exposure under strict compute constraints.<n>Results demonstrate an integrated approach and a blueprint for a reliable and safe AI perception framework under space deployment constraints.
• Score: 4.514832807541816
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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