A Convex Optimization Framework for 6-DOF Lunar Powered Descent with a Normalized Finite Rotation Parameterization
Year: 2026
Type: article
Abstract: There has been increasing interest in the Moon for deep space exploration missions in the last few decades. To accommodate fuel-optimal lunar landing missions, it is essential to develop a fast trajectory planning algorithm considering constrained six-degree-of-freedom (6-DOF) dynamics. On the one hand, the trajectory planning problem involves a coordination of the optimal fuel consumption and the vehicle’s position, velocity, and attitude, which requires computational efficiency. On the other hand, the initialization setup of the existing sequential convex optimization method provides the linear reference trajectory, which slows down the convergence of the iterative process. In this manuscript, an improved sequential convex programming algorithm is proposed to solve the minimum-fuel 6-DOF powered descent problem. Firstly, we suggest a trajectory planning method based on a normalized finite rotation formulation, which improves the efficiency of the computational processes. Secondly, we present an initial guess method that computes the projection-analogous gradient with respect to the terminal value, accelerating the convergence of the algorithm. The simulation results show that the proposed method improves computational efficiency, indicating the potential for future applications in autonomous landing missions.
Source: Aerospace
Authors: Yandi Qiao, Zexu Zhang
Institution: Harbin Institute of Technology
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
Award: G6461297872
OpenAlex ID: W7140161143