# A Survey on Artificial Intelligence Trends in Spacecraft Guidance Dynamics and Control

arXiv: https://arxiv.org/abs/1812.02948 · HTML: https://mlsanity.com/paper/1812.02948/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Dec 2018.

## Built on by (most cited first, 14 of 14)

- [Generalizable Spacecraft Trajectory Generation via Multimodal Learning with Transformers](https://mlsanity.com/paper/2410.11723/) (2024)
- [Advances in Trajectory Optimization for Space Vehicle Control](https://mlsanity.com/paper/2108.02335/) (2021)
- [Optimality Principles in Spacecraft Neural Guidance and Control](https://mlsanity.com/paper/2305.13078/) (2023)
- [Deep Learning Warm Starts for Trajectory Optimization on the International Space Station](https://mlsanity.com/paper/2505.05588/) (2025)
- [Asteroid Flyby Cycler Trajectory Design Using Deep Neural Networks](https://mlsanity.com/paper/2111.11858/) (2021)
- [Fast solver for J2-perturbed Lambert problem using deep neural network](https://mlsanity.com/paper/2201.02942/) (2022)
- [Feasible Low-thrust Trajectory Identification via a Deep Neural Network Classifier](https://mlsanity.com/paper/2202.04962/) (2022)
- [Probabilistic Foundations for Metacognition via Hybrid-AI](https://mlsanity.com/paper/2502.05398/) (2025)
- [Memristor-Based Neural Network Accelerators for Space Applications: Enhancing Performance with Temporal Averaging and SIRENs](https://mlsanity.com/paper/2509.04506/) (2025)
- [Tight Constraint Prediction of Six-Degree-of-Freedom Transformer-based Powered Descent Guidance](https://mlsanity.com/paper/2501.00930/) (2025)
- [Comparing Behavioural Cloning and Reinforcement Learning for Spacecraft Guidance and Control Networks](https://mlsanity.com/paper/2507.19535/) (2025)
- [Guidance and Control Neural Network Acceleration using Memristors](https://mlsanity.com/paper/2509.02369/) (2025)
- [Learning Adaptive Multi-Task Guidance, Navigation, and Control via Hypernetworks](https://mlsanity.com/paper/2607.24292/) (2026)
- [Reachability-Informed Reinforcement Learning for Multi-Impulse Interplanetary Transfers](https://mlsanity.com/paper/2610.01344/) (2026)

## Abstract (arXiv)

The rapid developments of Artificial Intelligence in the last decade are influencing Aerospace Engineering to a great extent and research in this context is proliferating. We share our observations on the recent developments in the area of Spacecraft Guidance Dynamics and Control, giving selected examples on success stories that have been motivated by mission designs. Our focus is on evolutionary optimisation, tree searches and machine learning, including deep learning and reinforcement learning as the key technologies and drivers for current and future research in the field. From a high-level perspective, we survey various scenarios for which these approaches have been successfully applied or are under strong scientific investigation. Whenever possible, we highlight the relations and synergies that can be obtained by combining different techniques and projects towards future domains for which newly emerging artificial intelligence techniques are expected to become game changers.