Yang Chen

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.

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.