Fast Monte Carlo Analysis for 6-DoF Powered-Descent Guidance via GPU-Accelerated Sequential Convex Programming

TL;DR

This paper presents a GPU-accelerated Monte Carlo framework for nonconvex trajectory optimization, enabling faster simulations for 6-DoF powered descent guidance with guaranteed constraint satisfaction and a matrix-inverse-free approach.

Contribution

It introduces the first GPU-based general-purpose nonconvex trajectory optimization solver using sequential convex programming with continuous-time constraints.

Findings

Faster Monte Carlo simulations with over 1000 runs on GPU

Guarantees continuous-time constraint satisfaction

First GPU implementation for general nonconvex trajectory optimization

Abstract

We introduce a GPU-accelerated Monte Carlo framework for nonconvex, free-final-time trajectory optimization problems. This framework makes use of the prox-linear method, which belongs to the larger family of sequential convex programming (SCP) algorithms, in conjunction with a constraint reformulation that guarantees inter-sample constraint satisfaction. Key features of this framework are: (1) continuous-time constraint satisfaction; (2) a matrix-inverse-free solution method; (3) the use of the proportional-integral projected gradient (PIPG) method, a first-order convex optimization solver, customized to the convex subproblem at hand; and, (4) an end-to-end, library-free implementation of the algorithm. We demonstrate this GPU-based framework on the 6-DoF powered-descent guidance problem, and show that it is faster than an equivalent serial CPU implementation for Monte Carlo simulations with over 1000 runs. To the best of our knowledge, this is the first GPU-based implementation of a general-purpose nonconvex trajectory optimization solver.