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.

The lunar south polar region’s extreme illumination conditions impose strict energy constraints for solar-powered rover operations. Traditional Sun-synchronous path planning relies on dynamic time-dependent illumination evaluation, leading to high computational costs. We present CIRsE-Net, a spatiotemporal deep learning model that generates 72 h continuous illumination maps from hourly sequential illumination data. The model integrates a lightweight SST-VGG encoder (a customized 14-layer CNN for spatial feature extraction), BiGRU temporal modelling (a bidirectional recurrent network for capturing forward and backward temporal dependencies), and a consistency-aware spatiotemporal attention mechanism. On three different lunar illumination datasets (20 m/pixel, 5 m/pixel, and 20 m/pixel with a 2 m panel height), the model achieves Dice scores up to 0.983 and accuracy up to 0.985. When integrated with an enhanced 3ST-A* planner, the framework converts dynamic path planning into a static search task, reducing computational overhead while preserving path optimality and satisfying slope and illumination constraints. This work provides a validated methodological framework for Chang’E-7 mission planning and future lunar polar exploration missions by transforming dynamic path planning into a static search task.

Taking the connecting ridge between Shackleton and de Gerlache craters as the research area, based on real-time illumination simulation data from November 1, 2026, to February 28, 2027, a dynamic illumination dataset with a spatial resolution of 20 m/pixel and a temporal resolution of 1 hour was constructed. A deep-learning framework is proposed to recognize regions with continuous 3-day illumination, in which an improved VGG network extracts illumination-friendly regions from each temporal frame, a bidirectional GRU network captures temporal illumination characteristics, and a consistent temporal-spatial attention mechanism highlights key spatiotemporal illumination features. An output head network integrates these features to generate target regions. Based on the extracted regions and an eight-direction rover mobility model, a Sun-synchronous A* path planning algorithm is further optimized to enable illumination-aware navigation. Simulation results demonstrate that the proposed method accurately recognizes 3-day consecutive illumination-friendly regions in the 20 m/pixel dynamic dataset and effectively supports efficient rover path planning in well-illuminated areas of the lunar south pole.