Survey of autonomous guidance methods for powered planetary landing

Abstract

This paper summarizes the autonomous guidance methods (AGMs) for pinpoint soft landing on celestial surfaces. We first review the development of powered descent guidance methods, focusing on their contributions for dealing with constraints and enhancing computational efficiency. With the increasing demand for reusable launchers and more scientific returns from space exploration, pinpoint soft landing has become a basic requirement. Unlike the kilometer-level precision for previous activities, the position accuracy of future planetary landers is within tens of meters of a target respecting all constraints of velocity and attitude, which is a very difficult task and arouses renewed interest in AGMs. This paper states the generalized three-and six-degree-of-freedom optimization problems in the powered descent phase and compares the features of three typical scenarios, i.e., the lunar, Mars, and Earth landing. On this basis, the paper details the characteristics and adaptability of AGMs by comparing aspects of analytical guidance methods, numerical optimization algorithms, and learning-based methods, and discusses the convexification treatment and solution strategies for non-convex problems. Three key issues related to AGM application, including physical feasibility, model accuracy, and real-time performance, are presented afterward for discussion. Many space organizations, such as those in the United States, China, France, Germany, and Japan, have also developed free-flying demonstrators to carry out related research. The guidance methods which have been tested on these demonstrators are briefly introduced at the end of the paper.

A Convex Programming Method for Rocket Powered Landing With Angle of Attack Constraint

“…In addition to the above two methods, Sun et al [21] utilized reasonable approximation and equivalent conversion method to convexify the nonlinear dynamics and nonconvex energy-optimal objective function respectively. However, it is difficult to eliminate the nonlinearity caused by aerodynamic force via approximation and the lossless convexification of the thrust constraint may not be compatible with the energy -optimal objective function [7].…”

“…Due to nonlinear dynamics and various nonconvex state and control constraints, the landing trajectory planning problem is highly nonconvex, which makes it difficult to be solved in real time via existing nonconvex optimization algorithms. In the study of fast trajectory optimization methods [3]- [7], the methods based on convex optimization have a great advantage in solution efficiency. Theoretically, as long as the problem can be formulated in the form of convex optimization, classical convex algorithms (such as primal-dual interior point algorithms) can be utilized to obtain the optimal solution in polynomial time [8].…”

Improving the efficiency of reinforcement learning for a spacecraft powered descent with Q-learning

“…Autonomous GNC for extra-terrestrial landing has been a well studied problem dating back to the Apollo lunar missions (Song et al 2020). These missions had tight constraints on computing power which necessitated simple and robust control algorithms to generate feasible trajectories online (Klumpp 1974).…”

Fuel-Optimal Ascent Trajectory Problem for Launch Vehicle via Theory of Functional Connections

“…This not only offers higher requirements for the reference trajectory but also annihilates the advantage of the convex optimization algorithm that does not rely on the good initial value. Nevertheless, considering the rapidity of the convex optimization algorithm in solving convex problems, in recent years, trajectory planning based on the convex optimization algorithm, such as planetary landing [14][15][16], rocket ascent guidance [17], and entry guidance [18], has been widely studied.…”