A Fully Autonomous On-Board GNC Methodology for Small-Body Environments Based on CNN Image Processing and MPCs
Year: 2024
Type: article
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.
Source: Aerospace
Authors: Pelayo Peñarroya, Alfredo Escalante, Thomas Frekhaug, Manuel Sanjurjo
Institution: Universidad Carlos III de Madrid
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,728
OpenAlex ID: W4404534109