标题： ARTPS：深度增强的混合异常检测和可学习的好奇分值用于自主探测器目标优先级排序 显示英文标题

标题： ARTPS: Depth-Enhanced Hybrid Anomaly Detection and Learnable Curiosity Score for Autonomous Rover Target Prioritization

摘要： 我们提出ARTPS（自主探测器目标优先级系统），这是一种新颖的混合人工智能系统，结合了深度估计、异常检测和可学习的好奇性评分，用于行星表面的自主探索。 我们的方法将基于视觉变换器的单目深度估计与多组件异常检测以及平衡已知价值、异常信号、深度方差和表面粗糙度的加权好奇心得分相结合。 该系统在火星探测器数据集上实现了最先进的性能，AUROC为0.94，AUPRC为0.89，F1-Score为0.87。 我们通过消融研究展示了目标优先级准确性的显著改进，并提供了组件贡献的全面分析。 混合融合方法将误报率降低了23%，同时在各种地形类型中保持了高检测灵敏度。 显示英文摘要

摘要： We present ARTPS (Autonomous Rover Target Prioritization System), a novel hybrid AI system that combines depth estimation, anomaly detection, and learnable curiosity scoring for autonomous exploration of planetary surfaces. Our approach integrates monocular depth estimation using Vision Transformers with multi-component anomaly detection and a weighted curiosity score that balances known value, anomaly signals, depth variance, and surface roughness. The system achieves state-of-the-art performance with AUROC of 0.94, AUPRC of 0.89, and F1-Score of 0.87 on Mars rover datasets. We demonstrate significant improvements in target prioritization accuracy through ablation studies and provide comprehensive analysis of component contributions. The hybrid fusion approach reduces false positives by 23% while maintaining high detection sensitivity across diverse terrain types.