Improving Computational Efficiency for Powered Descent Guidance via Transformer-based Tight Constraint Prediction (2311.05135v2)

Abstract: In this work, we present Transformer-based Powered Descent Guidance (T-PDG), a scalable algorithm for reducing the computational complexity of the direct optimization formulation of the spacecraft powered descent guidance problem. T-PDG uses data from prior runs of trajectory optimization algorithms to train a transformer neural network, which accurately predicts the relationship between problem parameters and the globally optimal solution for the powered descent guidance problem. The solution is encoded as the set of tight constraints corresponding to the constrained minimum-cost trajectory and the optimal final time of landing. By leveraging the attention mechanism of transformer neural networks, large sequences of time series data can be accurately predicted when given only the spacecraft state and landing site parameters. When applied to the real problem of Mars powered descent guidance, T-PDG reduces the time for computing the 3 degree of freedom fuel-optimal trajectory, when compared to lossless convexification, from an order of 1-8 seconds to less than 500 milliseconds. A safe and optimal solution is guaranteed by including a feasibility check in T-PDG before returning the final trajectory.

• The paper presents Transformer-based Powered Descent Guidance (T-PDG), a novel application of transformer neural networks to significantly improve the computational efficiency of trajectory calculation for spacecraft powered descent.
• T-PDG achieved a mean computation time of 373.25 milliseconds for Mars landing scenarios, drastically faster than the 1.673 seconds required by traditional lossless convexification methods, while guaranteeing feasibility.
• This reduction in computation time enables real-time trajectory re-optimization on flight-grade processors and opens possibilities for applying similar machine learning techniques to various aerospace guidance problems.

A Technical Analysis of "Improving Computational Efficiency for Powered Descent Guidance via Transformer-based Tight Constraint Prediction"

This paper presents a novel application of transformer neural networks, termed Transformer-based Powered Descent Guidance (T-PDG), aimed at enhancing the computational efficiency of the direct optimization formulation of the spacecraft powered descent guidance problem. By leveraging transformers, the authors address the computational challenges associated with solving the fuel-optimal powered descent guidance problem, specifically for Martian landings. T-PDG effectively predicts the set of tight constraints and optimal final time for the guidance problem, thereby allowing for real-time trajectory computations with significantly reduced latency compared to traditional methods such as lossless convexification (LCvx).

The methodology comprises three primary components: (i) the sampling of problem parameters and their corresponding optimal strategies, (ii) the training and testing of NNs to predict tight constraints and optimal final time, and (iii) a real-time application of T-PDG for trajectory computation. The utility of transformer networks, known for their effectiveness in sequence modeling and parallel computation capabilities, underpins the success of T-PDG.

The paper empirically evaluates T-PDG on a 3-degree-of-freedom (DoF) Mars landing scenario. The results are noteworthy, with T-PDG achieving a mean computation time of 373.25 milliseconds, a significant reduction from the 1.673 seconds required by the traditional LCvx algorithm. This improvement is achieved while maintaining a 100% feasibility guarantee through a feasibility check embedded within T-PDG.

From a theoretical standpoint, T-PDG enhances understanding of the relationship between problem parameters and optimal solutions in powered descent guidance through the lens of machine learning. The ability of T-PDG to efficiently reduce problem size by predicting tight constraints offers both practical computational benefits and deeper insights into the nature of constraint satisfaction in trajectory optimization problems.

In terms of implications, the reduction in computational time opens the possibility for T-PDG to be implemented on flight-grade processors, facilitating real-time trajectory re-optimization during critical mission phases. Moreover, the interpretability afforded by transformer-based approaches, as demonstrated through t-SNE visualizations, augments the understanding of how different problem configurations influence optimal strategy decisions.

Looking forward, the paper suggests several avenues for future exploration. These include the extension of T-PDG to nonconvex problems, where the prediction of feasible yet computationally efficient trajectories can be invaluable. Additionally, the application of transfer learning and active learning could further improve the adaptability and robustness of T-PDG across various mission scenarios and spacecraft configurations.

In conclusion, this research represents a significant step toward the integration of advanced neural network architectures in space mission planning. By reducing computational times and maintaining solution feasibility, T-PDG not only addresses current methodological limitations but also sets the stage for broad applications of machine learning techniques in real-time aerospace guidance systems. The convergence of computational efficiency and theoretical insights evidenced in this work is expected to spur further research into data-driven optimization techniques in control engineering and trajectory design.