This work presents MONSTER, a Moon optical navigation robotic facility on simulated terrain, i.e., an experimental facility that can be used to simulate lunar navigation problems. The facility consists of a 3D Cartesian manipulator that, once fully operative, will be equipped with a spherical joint allowing to simulate both attitude and orbital dynamics. All the experiments that have been performed so far and are planned to be performed in the future are based on innovative and disruptive approaches using artificial intelligence (AI) algorithms. Indeed, a crater detection algorithm based on a fully convolutional neural network has been implemented, and a reinforcement learning approach is under development for prescribing the control policy of the simulated system. MONSTER enables hardware-in-the-loop simulations of landers and spacecrafts using AI hardware accelerators such a graphics processing unit (GPU), visual processing unit (VPU) and field programmable gate arrays (FPGA).
A Moon Optical Navigation Robotic Facility on Simulated TERrain: MONSTER / Latorre, Francesco; Carbone, Andrea; THOTTUCHIRAYIL SASIDHARAN, Sarathchandrakumar; Ciabatti, Giulia; Spiller, Dario; Curti, Fabio; Capobianco, Roberto. - In: THE JOURNAL OF THE ASTRONAUTICAL SCIENCES. - ISSN 2195-0571. - (2022). (Intervento presentato al convegno 2022 AAS/AIAA Astrodynamics Specialist Conference tenutosi a Charlotte, NC, USA).
A Moon Optical Navigation Robotic Facility on Simulated TERrain: MONSTER
Francesco Latorre
;Sarathchandrakumar Thottuchirayil Sasidharan;Giulia Ciabatti;Dario Spiller;Fabio Curti;Roberto Capobianco
2022
Abstract
This work presents MONSTER, a Moon optical navigation robotic facility on simulated terrain, i.e., an experimental facility that can be used to simulate lunar navigation problems. The facility consists of a 3D Cartesian manipulator that, once fully operative, will be equipped with a spherical joint allowing to simulate both attitude and orbital dynamics. All the experiments that have been performed so far and are planned to be performed in the future are based on innovative and disruptive approaches using artificial intelligence (AI) algorithms. Indeed, a crater detection algorithm based on a fully convolutional neural network has been implemented, and a reinforcement learning approach is under development for prescribing the control policy of the simulated system. MONSTER enables hardware-in-the-loop simulations of landers and spacecrafts using AI hardware accelerators such a graphics processing unit (GPU), visual processing unit (VPU) and field programmable gate arrays (FPGA).File | Dimensione | Formato | |
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