Safety-Prioritized Optimization of Asteroid Sample Site Selection Using Genetic Algorithm and Variable Neighborhood Tabu Search

Sample site selection is crucial for asteroid sample return missions, requiring a strategy that considers factors such as terrain and illumination for mission safety. However, the lack of detailed prior knowledge regarding the target asteroid poses significant challenges for the selection process. Current methods often lack robust quantitative models and tend to become stuck in local optima. This study proposed a new sample site selection method that incorporates a multifactor weight analysis, genetic algorithm (GA), and variable neighborhood tabu search (VNTS) technique. This method leverages the global search capability of the GA for the initial coarse search of selection sites and refines the selection through more localized optimization with the VNTS. Experiments using the Ryugu asteroid dataset from the Hayabusa2 mission to validate the effectiveness of the proposed method. In addition, a simulation dataset is used to compare it with traditional methods and demonstrate its increase in the safety index of the selected sites, highlighting its potential to optimize landing site selection and provide a more efficient solution for asteroid exploration missions.