Assessing Accessibility of Main-Belt Asteroids Based on Gaussian Process Regression

The main-belt asteroids are of great scientific interest and have become one of the primary targets of planetary exploration. In this paper, the accessibility of more than 600,000 main-belt asteroids is investigated. A computationally efficient approach based on Gaussian process regression is proposed to assess the accessibility. Two transfer models consisting of globally optimal two-impulse and Mars gravity-assist transfers are established, which would serve as a source of training samples for Gaussian process regression. The multistart and deflection technologies are incorporated into the numerical optimization solver to avoid local minima, thereby guaranteeing the quality of the training samples. The covariance function, as well as hyperparameters, which dominate the regression process, are chosen elaborately in terms of the correlation between samples. Numerical simulations demonstrate that the proposed method can achieve the accessibility assessment within tens of seconds, and the average relative error is only 1.33%. Mars gravity assist exhibits significant advantage in the accessibility of main-belt asteroids because it reduces the total velocity increment by an average of 1.23 km/s compared with the two-impulse transfer. Furthermore, it is observed that 3976 candidate targets have potential mission opportunities with a total velocity increment of less than 6 km/s.