2023
Stevenson, Emma; Rodríguez-Fernández, Víctor; Urrutxua, Hodei; Camacho, David
Benchmarking deep learning approaches for all-vs-all conjunction screening Journal Article
In: Advances in Space Research, 2023.
Abstract | Links | BibTeX | Tags: Conjunction Assessment, Deep Learning, Filtering, Machine Learning, Orbits, Space, Space Debris, Space Environment Management, Space Situational Awareness, Space Traffic Management, Time Series
@article{nokey,
title = {Benchmarking deep learning approaches for all-vs-all conjunction screening},
author = {Emma Stevenson and Víctor Rodríguez-Fernández and Hodei Urrutxua and David Camacho},
doi = {https://doi.org/10.1016/j.asr.2023.01.036},
year = {2023},
date = {2023-01-23},
urldate = {2023-01-23},
journal = {Advances in Space Research},
abstract = {The all-vs-all problem, for which conjunctions are screened for over all possible sets of catalogued objects, is crucial for space traffic management and space situational awareness, but is a computational challenge owing to the vast and growing number of possible conjunction pairs. In this work, we present the application of deep learning techniques to this problem, framing conjunction screening as a machine learning classification task. We investigate the performance of different input data representations and model architectures on a realistic all-vs-all dataset, generated using the CNES BAS3E space surveillance simulation framework, and consisting of 170 million object pairs over a 7-day screening period. These approaches are benchmarked against operationally used classical filters in both screening capability and computational efficiency, and the ability of deep learning algorithms to cope and aid with the scales required for current and future operational all-vs-all scenarios is demonstrated.},
keywords = {Conjunction Assessment, Deep Learning, Filtering, Machine Learning, Orbits, Space, Space Debris, Space Environment Management, Space Situational Awareness, Space Traffic Management, Time Series},
pubstate = {published},
tppubtype = {article}
}
The all-vs-all problem, for which conjunctions are screened for over all possible sets of catalogued objects, is crucial for space traffic management and space situational awareness, but is a computational challenge owing to the vast and growing number of possible conjunction pairs. In this work, we present the application of deep learning techniques to this problem, framing conjunction screening as a machine learning classification task. We investigate the performance of different input data representations and model architectures on a realistic all-vs-all dataset, generated using the CNES BAS3E space surveillance simulation framework, and consisting of 170 million object pairs over a 7-day screening period. These approaches are benchmarked against operationally used classical filters in both screening capability and computational efficiency, and the ability of deep learning algorithms to cope and aid with the scales required for current and future operational all-vs-all scenarios is demonstrated.
2022
Fernandez-Mellado, Luis Sánchez; Stevenson, Emma; Rodriguez-Fernandez,; Vasile, Massimiliano; Camacho, David
An Intelligent System for Robust Decision-Making in the All-vs-All Conjunction Screening Problem Proceedings Article
In: 3rd IAA Conference on Space Situational Awareness (ICSSA), Madrid, Spain, 2022.
Abstract | BibTeX | Tags: Artificial Intelligence, Collision Avoidance Manoeuvre, Conjunction Assessment, Robust Decision-making, Space Traffic Management
@inproceedings{stevenson2022_icssa,
title = {An Intelligent System for Robust Decision-Making in the All-vs-All Conjunction Screening Problem},
author = {Luis Sánchez Fernandez-Mellado and Emma Stevenson and Rodriguez-Fernandez and Massimiliano Vasile and David Camacho},
year = {2022},
date = {2022-04-01},
urldate = {2022-04-01},
booktitle = {3rd IAA Conference on Space Situational Awareness (ICSSA)},
address = {Madrid, Spain},
abstract = {The progressive increase of traffic in space demands new approaches for supporting automatic and robust operational decisions. CASSANDRA, Computational Agent for Space Situational Awareness aNd Debris Remediation Automation, is an intelligent system for Space Environment Management (SEM) intended to assist operators with the management of space traffic by providing robust decision-making support. This paper will present the automatic conjunction screening and collision avoidance manoeuvre pipeline within CASSANDRA, connecting the some of CASSANDRA's modules: Automated Conjunction Screening (ACS), Robust State Estimation (RSE), Intelligent Decision Support System (IDSS) and Collision Avoidance Manoeuvres (CAM). The pipelines allows to screen the catalogue to detect potential conjunctions, perform a detailed analysis of the encounter accounting for uncertainty (aleatory and epistemic) and new observations, provide robust decisions based on the available information and, if necessary, proposed robust optimal CAMs and analyse the impact of the new orbit on the background population. This paper will present the pipeline described above along with an example that illustrates how CASSANDRA can be used to generate robust decisions on the execution of CAMs in an automated way.},
keywords = {Artificial Intelligence, Collision Avoidance Manoeuvre, Conjunction Assessment, Robust Decision-making, Space Traffic Management},
pubstate = {published},
tppubtype = {inproceedings}
}
The progressive increase of traffic in space demands new approaches for supporting automatic and robust operational decisions. CASSANDRA, Computational Agent for Space Situational Awareness aNd Debris Remediation Automation, is an intelligent system for Space Environment Management (SEM) intended to assist operators with the management of space traffic by providing robust decision-making support. This paper will present the automatic conjunction screening and collision avoidance manoeuvre pipeline within CASSANDRA, connecting the some of CASSANDRA's modules: Automated Conjunction Screening (ACS), Robust State Estimation (RSE), Intelligent Decision Support System (IDSS) and Collision Avoidance Manoeuvres (CAM). The pipelines allows to screen the catalogue to detect potential conjunctions, perform a detailed analysis of the encounter accounting for uncertainty (aleatory and epistemic) and new observations, provide robust decisions based on the available information and, if necessary, proposed robust optimal CAMs and analyse the impact of the new orbit on the background population. This paper will present the pipeline described above along with an example that illustrates how CASSANDRA can be used to generate robust decisions on the execution of CAMs in an automated way.
2021
Stevenson, Emma; Rodriguez-Fernandez, Victor; Urrutxua, Hodei; Morand, Vincent; Camacho, David
Self-supervised machine learning based approach to orbit modelling applied to space traffic management Proceedings Article
In: 11th International Association for the Advancement of Space Safety Conference (IAASS), (Virtual), Osaka, Japan, 2021.
Abstract | BibTeX | Tags: Machine Learning, Orbits, Self-supervised Learning, Space Debris, Space Traffic Management
@inproceedings{stevenson2021_iaass,
title = {Self-supervised machine learning based approach to orbit modelling applied to space traffic management},
author = {Emma Stevenson and Victor Rodriguez-Fernandez and Hodei Urrutxua and Vincent Morand and David Camacho},
year = {2021},
date = {2021-10-01},
booktitle = {11th International Association for the Advancement of Space Safety Conference (IAASS)},
address = {(Virtual), Osaka, Japan},
abstract = {This paper presents a novel methodology for improving the performance of machine learning based space traffic management tasks through the use of a pre-trained orbit model. Taking inspiration from BERT-like self-supervised language models in the field of natural language processing, we introduce ORBERT, and demonstrate the ability of such a model to leverage large quantities of readily available orbit data to learn meaningful representations that can be used to aid in downstream tasks. As a proof of concept of this approach we consider the task of all vs. all conjunction screening, phrased here as a machine learning time series classification task. We show that leveraging unlabelled orbit data leads to improved performance, and that the proposed approach can be particularly beneficial for tasks where the availability of labelled data is limited.},
keywords = {Machine Learning, Orbits, Self-supervised Learning, Space Debris, Space Traffic Management},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper presents a novel methodology for improving the performance of machine learning based space traffic management tasks through the use of a pre-trained orbit model. Taking inspiration from BERT-like self-supervised language models in the field of natural language processing, we introduce ORBERT, and demonstrate the ability of such a model to leverage large quantities of readily available orbit data to learn meaningful representations that can be used to aid in downstream tasks. As a proof of concept of this approach we consider the task of all vs. all conjunction screening, phrased here as a machine learning time series classification task. We show that leveraging unlabelled orbit data leads to improved performance, and that the proposed approach can be particularly beneficial for tasks where the availability of labelled data is limited.