資源制約のある宇宙ロボティクス向けハードウェア高速化インスタンスセグメンテーションと重要度解析
Hardware-Accelerated Instance Segmentation for Resource-Constrained Space Robotics with Criticality Analysis
月面探査ロボット向けに、量子化キャリブレーション手法AVISとDPU上のYOLOベースセグメンテーションモデルを導入し、低照度・計算資源制約・放射線故障下でのリアルタイム推論を実現。ソフトウェアレベルの重要度解析で故障影響を推定し、精度損失の69.8%回復と消費電力5.7Wを達成した。
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著者: Siddhant Shete, Hilmi Dogu Kücüker, Udo Frese, Frank Kirchner
分類: cs.RO, cs.AR, cs.CV, cs.LG
原文アブストラクト
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.