Physics-Informed Bayesian Optimization Warm-Starts for Sequential Convex Programming in Asteroid Surface Hopping

Abstract

Surface hopping is an attractive mobility mode for small-body exploration, but designing fuel-optimal hops on asteroid 433~Eros requires solving a nonconvex optimal control problem with an irregular polyhedral gravity field, thrust--mass coupling, and collision-avoidance constraints. We show that a physics-informed Bayesian Optimization (BO) warm-start---a Gaussian-process search over a single Bézier control point that requires no offline training---provides a more reliable initialization for Sequential Convex Programming (SCP) than the standard straight-line guess. The straight chord penetrates the asteroid on every inter-site transfer considered here and degrades convergence. The physics-informed BO reference feeds an SCP stage in which a log-mass change of variables convexifies the thrust--mass coupling and nearest-facet half-spaces enforce collision avoidance, and an automated Pareto time-of-flight sweep selects fuel-priority solutions without introducing bilinear terms. Applied to all 20 ordered transfers among five representative surface sites and validated under 10 random seeds, the framework tracks every trajectory to meter-level terminal accuracy in closed-loop Monte Carlo simulation, completes a five-site tour for one fifth of the propellant budget, and reduces mean $\Delta V$ by roughly one third relative to an idealized two-impulse ballistic trajectory estimate. A straight-line ablation credits the warm-start with cutting the mean SCP iteration count from 8.8 to 5.9 and removing the one convergence failure. A target-perturbation analysis further shows that the solutions vary smoothly with the landing target, with no jumps between local basins.

In the 10th International Artificial Intelligence and Data Processing Symposium