The sharp, intricate contours of lunar regolith particles hold critical clues to the Moon's geological evolution and inform engineering applications from habitat construction to spacecraft design, making their precise segmentation a task of significant scientific and engineering value. However, this task exposes a weakness in deep learning models known as spectral bias, an inherent tendency to learn smooth, low-frequency functions which causes them to systematically erase the very high-frequency boundary details that are of primary interest. To resolve this conflict, we propose a framework to deeply seek object boundaries. First, we propose High-Frequency Initialized LoRA (HiFi-LoRA) to counteract spectral bias. By initializing the LoRA adaptation matrices as the optimal low-rank approximation of a high-pass filter, it fundamentally enhances the model's high-frequency perception and injects a strong preference for edges. Second, we propose the Wavelet Energy Modulation (WEM) regularizer. It guides the model to learn the intrinsic correlation between contour complexity and mask area, forcing the model to build a geometric understanding of contour morphology upon its high-frequency perception, thereby enabling the generation of boundary details commensurate with the object's scale. Experimentally, we constructed the Lunar Regolith Segmentation Dataset (LRSD), the first large-scale benchmark with expert-annotated contours. Extensive experiments demonstrate that our method sets a new state of the art on this challenging benchmark, not only achieving top performance on regional metrics like mIoU and DSC but, more critically, drastically outperforming existing models on boundary accuracy. This work not only provides a powerful computational tool for lunar science but also offers a robust and synergistic design pattern for other fine-grained segmentation challenges.

Drilling & coring is considered as an effective way to acquire deep sample on the moon. Since the lunar regolith environment in depth is unknown, sampling drill should be developed to adapt to the undetermined drilling medium on the moon. Once mechanical system of sampling robot was finished, control strategy is a key to realize the high-efficiency drilling process. Since composition of lunar regolith is complicated, it's not easy to evaluate all the physical parameters to judge the drilling difficulty level. This paper proposes a novel idea of lunar regolith drillability which is established on the rate of penetration under the given standard terms. Drillability is selected to describe the drilling difficulty level which can be identified online by use of pattern recognition method of SVM. Control algorithm tunes the drilling parameters to adapt to the recognized medium. Experiments are conducted to verify the drillability online recognition based intelligent control strategy can make sampling robot adapt to complicated drilling media.