PreprintArticleVersion 1This version is not peer-reviewed
A Fully-Autonomous on-Board GNC Methodology for Small-Body Environments Based on Convolutional Neural Network (CNN) Image Processing and Model Predictive Control (MPC)s
Version 1
: Received: 14 October 2024 / Approved: 14 October 2024 / Online: 15 October 2024 (10:19:19 CEST)
How to cite:
Peñarroya, P.; Escalante, A.; Frekhaug, T.; Sanjurjo, M. A Fully-Autonomous on-Board GNC Methodology for Small-Body Environments Based on Convolutional Neural Network (CNN) Image Processing and Model Predictive Control (MPC)s. Preprints2024, 2024101117. https://doi.org/10.20944/preprints202410.1117.v1
Peñarroya, P.; Escalante, A.; Frekhaug, T.; Sanjurjo, M. A Fully-Autonomous on-Board GNC Methodology for Small-Body Environments Based on Convolutional Neural Network (CNN) Image Processing and Model Predictive Control (MPC)s. Preprints 2024, 2024101117. https://doi.org/10.20944/preprints202410.1117.v1
Peñarroya, P.; Escalante, A.; Frekhaug, T.; Sanjurjo, M. A Fully-Autonomous on-Board GNC Methodology for Small-Body Environments Based on Convolutional Neural Network (CNN) Image Processing and Model Predictive Control (MPC)s. Preprints2024, 2024101117. https://doi.org/10.20944/preprints202410.1117.v1
APA Style
Peñarroya, P., Escalante, A., Frekhaug, T., & Sanjurjo, M. (2024). A Fully-Autonomous on-Board GNC Methodology for Small-Body Environments Based on Convolutional Neural Network (CNN) Image Processing and Model Predictive Control (MPC)s. Preprints. https://doi.org/10.20944/preprints202410.1117.v1
Chicago/Turabian Style
Peñarroya, P., Thomas Frekhaug and Manuel Sanjurjo. 2024 "A Fully-Autonomous on-Board GNC Methodology for Small-Body Environments Based on Convolutional Neural Network (CNN) Image Processing and Model Predictive Control (MPC)s" Preprints. https://doi.org/10.20944/preprints202410.1117.v1
Abstract
The increasing need for autonomy for space exploration missions is becoming more and more relevant in the design of missions to small bodies. The long communication latencies and sensitivity of the system to unplanned environment perturbations pose autonomous methods as a key design block for this type of mission. In this work, a fully autonomous gnc methodology is introduced. This methodolody relies on published cnn-based techniques for surface recognition and pose estimation and on also existing mpc-based techniques for the design of a trajectory to perform a soft landing on an asteroid. Combining hda with relative navigation systems, a gsm is built on-the-fly as images are acquired. These gsms provide the gnc system with information about feasible landing spots and populate a longitude-latitude map with safe/hazardous labels that are later processed to find an optimal landing spot based on mission requirements and a distance-from-hazard metric. The methodology is exemplified using Bennu as the body of interest and a gsm is built for an arbitrary reconnaissance orbit.
Keywords
gnc; Autonomous systems; Machine Learning; Asteroid; Space Exploration
Subject
Engineering, Aerospace Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
