A Spatiotemporal U-Net-Based Data Preprocessing Pipeline for Sun-Synchronous Path Planning in Lunar South Polar Exploration
Yang Chen, Guangfei Wei, Hao Zhang, Jianfeng Lu, Fuchuan Pang · Remote Sensing · 2025
The dynamic illumination conditions in the Moon’s polar region present challenges for future rover explorations, which require enhanced efficiency and intelligent data preprocessing for Sun-synchronous path planning. Within the Chang’E-7 polar exploration mission context, this study investigates automated, intelligent preprocessing of 2.5D illumination data from high-resolution Digital Elevation Models for polar rover global path planning. A preprocessing pipeline is developed using a Sun-synchronous spatiotemporal U-Net,3STU-Net, incorporating time-slice and time-series sub-networks, to streamline data handling and identify regions with favorable illumination. Subsequently, an enhanced A* algorithm named 3ST-A*, leveraging preprocessed data, is applied in a designated area of interest for global path-planning experimental validation. The findings significantly improve illumination data processing efficiency and advance path-planning research, offering valuable support for future lunar exploration missions.