A High-Precision Adaptive Sequential Convex Programming Method for Non-Coplanar Transfer Trajectory Optimization in Constellation Aggregation

With the development of constellations and satellite chains, trajectory planning for constellation satellites has gradually attracted research attention. To address the infeasibility issues of trapezoidal sequential convex programming (T-SCP) in this strong non-convexity problem, this paper proposes high-precision adaptive SCP based on the Hermite Simpson method for Low-Earth-Orbit (LEO) constellation aggregation and aimed at enhanced local reconnaissance with practical constraints, including collision avoidance. The method introduces an adaptive trust region and slack variables, which avoid convergence failures due to strong nonlinearity or large state variations over long distances and accelerate the convergence rate. Furthermore, a third-order accurate Hermite Simpson method is adopted along with a adaptive iterative collision warning mechanism, improving accuracy while reducing computational cost. Simulation results demonstrate that the proposed method improves computational efficiency by 6% and 21% compared with the T-SCP and pseudospectral method, respectively. When the number of discrete nodes is 50, it achieves position errors of 284.7 m and velocity errors of 0.33 m/s, and these errors are far lower than those of T-SCP. Monte Carlo simulations across 10,000 scenarios validate the robustness of the proposed method with a 91% overall success rate and 100% for LEO.