On-Board Deep Learning for Payload Data Processing: Hardware Performance Comparison

The path towards a multi-planetary species passes through the implementation of disruptive technological innovation. Artificial Intelligence and autonomy on spacecraft will be a fundamental part of this future. Hence, leveraging on-the-edge AI accelerators, such as FPGAs, GPUs, VPUs, ASICs, will constitute an essential component of the spacecraft hardware of tomorrow. This work presents a comparative work, specifically targeted to the use of on-board satellites. The tested platforms are Intel Myriad X, Nvidia Jetson Nano, and CPU (x64 architecture

Innovations in the Field of On-Board Scheduling Technologies

Space missions are characterized by long distances, difficult or unavailable communication and high operating costs. Moreover, complexity has been constantly increasing in recent years. For this reason, improving the autonomy of space operators is an attractive goal to increase the mission reward with lower costs. This paper proposes an onboard scheduler, that integrates inside an onboard software framework for mission autonomy. Given a set of activities, it is responsible for determining the starting time of each activity according to their priority, order constraints, and resource consumption. The presented scheduler is based on linear integer programming and relies on the use of a branch-and-cut solver. The technology has been tested on an Earth Observation scenario, comparing its performance against the state-of-the-art scheduling technology

An AI-Based Goal-Oriented Agent for Advanced On-Board Automation

In the context of fierce competition arising in the space economy, the number of satellites and constellations that will be placed in orbit is set to increase considerably in the upcoming years. In such a dynamic environment, raising the autonomy level of the next space missions is key to maintaining a competitive edge in terms of the scientific, technological, and commercial outcome. We propose the adoption of an AI-based autonomous agent aiming to fully enable spacecraft’s goal-oriented autonomy. The implemented cognitive architecture collects input starting from the sensing of the surrounding operating environment and defines a low-level schedule of tasks that will be carried out throughout the specified horizon. Furthermore, the agent provides a planner module designed to find optimal solutions that maximize the outcome of the pursued objective goal. The autonomous loop is closed by comparing the expected outcome of these scheduled tasks against the real environment measurements. The entire algorithmic pipeline was tested in a simulated operational environment, specifically developed for replicating inputs and resources relative to Earth Observation missions. The autonomous reasoning agent was evaluated against the classical, non-autonomous, mission control approach, considering both the quantity and the quality of collected observation data in addition to the quantity of the observation opportunities exploited throughout the simulation time. The preliminary simulation results point out that the adoption of our software agent enhances dramatically the effectiveness of the entire mission, increasing and optimizing in-orbit activities, on the one hand, reducing events\u27 response latency (opportunities, failures, malfunctioning, etc.) on the other. In the presentation, we will cover the description of the high-level algorithmic structure of the proposed goal-oriented reasoning model, as well as a brief explanation of each internal module’s contribution to the overall agent’s architecture. Besides, an overview of the parameters processed as input and the expected algorithms\u27 output will be provided, to contextualize the placement of the proposed solution. Finally, an Earth Observation use case will be used as the benchmark to test the performances of the proposed approach against the classical one, highlighting promising conclusions regarding our autonomous agent’s adoption

NEURAL NETWORKS FOR PLUME DETECTION: INTERPLANETARY CUBESAT CASE STUDY

In the last few years, CubeSat missions have been pushed towards a new direction: interplanetary exploration. Several challenges undermine the feasibility and the good success of these missions: two of the most critical ones are limited data rates available on CubeSats, and the necessity of performing autonomous operations. This paper deals with the latter problem: autonomous on board operations for a CubeSat mission to an asteroid. In particular, the work presented focuses on external event detection, performed by processing images acquired by a camera sensor. The objective of this work is to demonstrate the capability of artificial neural networks to successfully detect the emission of plumes from a comet-like body, and to identify the orientation of such emissions, with the intent of providing information to the guidance, navigation and control system of the spacecraft, be it for avoidance manoeuvre planning or for enhanced science operations. Results of the paper demonstrate that employing neural networks for event detection is feasible and provides interesting outcomes. In addition, the proposed algorithm can be further coupled with GNC algorithms towards the development of an autonomous interplanetary small satellite

NEURAL NETWORKS FOR PLUME DETECTION: INTERPLANETARY CUBESAT CASE STUDY

In the last few years, CubeSat missions have been pushed towards a new direction: interplanetary exploration. Several challenges undermine the feasibility and the good success of these missions: two of the most critical ones are limited data rates available on CubeSats, and the necessity of performing autonomous operations. This paper deals with the latter problem: autonomous on board operations for a CubeSat mission to an asteroid. In particular, the work presented focuses on external event detection, performed by processing images acquired by a camera sensor. The objective of this work is to demonstrate the capability of artificial neural networks to successfully detect the emission of plumes from a comet-like body, and to identify the orientation of such emissions, with the intent of providing information to the guidance, navigation and control system of the spacecraft, be it for avoidance manoeuvre planning or for enhanced science operations. Results of the paper demonstrate that employing neural networks for event detection is feasible and provides interesting outcomes. In addition, the proposed algorithm can be further coupled with GNC algorithms towards the development of an autonomous interplanetary small satellite