20-D-O-TEC-01 ARTIFICIAL INTELLIGENCE FOR TERRAIN RELATIVE NAVIGATION IN UNKNOWN ENVIRONMENT (ATENA) PROJECT – EXPRO+
17, July 2020

ESA Open Invitation to Tender AO10356
Open Date: 16/07/2020
Closing Date: 15/09/2020 13:00:00

Status: ISSUED
Reference Nr.: 20.197.03
Prog. Ref.: Discovery;P3 – CPQ Other
Budget Ref.: E/0600-06 – Discovery;E/0910-06 – P3 – CPQ Other
Special Prov.: AT+BE+CZ+DE+EE+ES+FR+FI+GR+IT+IE+LU+NL+PL+PT+SE+GB+DK+HR+CY+LV+LT+HU+MT+RO+SK+BG+CA+SI+NO+CH
Tender Type: C
Price Range: 200-500 KEURO
Products: Satellites & Probes / AOCS & GNC / Sensors / Other
Technology Domains: Space System Control / AOCS/GNC Sensors and Actuators / AOCS/GNC Optical Sensors
Establishment: ESAHQ
Directorate: Directorate of Tech, Eng. & Quality
Department: Systems Department
Division: Adv. Concepts & Studies Office
Contract Officer: Pages, Christian
Industrial Policy Measure: N/A – Not apply
Last Update Date: 16/07/2020
Update Reason: Tender issue

Space missions benefit greatly by the capability of the on-board GNC system to adapt rapidly to unknown environment. Autonomous vision-based navigation is a particular technology under implementation in several ESA missions. Artificial Intelligence (AI) techniques could allow the spacecraft to improve the relative navigation performances when arriving to an unknown target. One of the most interesting applications in that field is the proximity operations around a small asteroid, like those in Hera.There are several challenges to be solved for application of AI techniques in autonomous GNC systems, for instance inadequate data sets for training, demonstrate generalization to different environment, capability to pre-train or transfer learning to speed up the training during the real operations, or modelling mathematically the performance behaviour under different environmental conditions.The goal of this activity is to develop an Artificial Intelligence based navigation algorithm with the capability to fly over an unknown terrain and achieve better navigation performances than current vision-based techniques based on unknown feature tracking.

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