This paper addresses rock hazard detection for in-situ resource utilization (ISRU) robotic navigation in the challenging visual environment of the lunar south pole (LSP). We evaluate three state-of-the-art instance segmentation mod-els-Mask R-CNN, YOLOv8, and SAM-using a novel, synthetically generated dataset that simulates LSP-specific illumination challenges at sun angles of$2.5^{\circ}, 5^{\circ}$, and 7.5°. Additionally, we evaluate these approaches in both up and downsun driving with low solar angle light. This study highlights the potential of deep learning-based approaches for improving ISRU operations by reliably identifying visual surface hazards, such as rocks, which may impede robotic navigation and excavation in future lunar missions.

To support sustainable infrastructure on the Moon, NASA is developing the In-Situ Resource Utilization (ISRU) Pilot Excavator (IPEx) to extract and transport lunar regolith for processing and construction. During its mission, IPEx will execute various driving patterns, primarily cycling between excavation and unloading sites, with additional ma-neuvers such as circular traverses around the lander and raster scans for environmental mapping. In this work, dynamic move-ment primitives (DMPs) are used to represent these patterns. We augment the DMPs with a vision-based real-time obstacle avoidance system to navigate surface hazards, such as rocks, encountered during traversal. Our approach is evaluated in a high-fidelity simulation replicating the challenging environment of the lunar south pole to demonstrate IPEx's ability to adapt to surface hazards while fulfilling its operational tasks.