Developing Intelligent Space Systems: A Survey and Rubric for Future Missions

Space exploration continues to inspire the development of advanced technologies to explore the universe. From moon landings and the Mars rovers to the recent successful deployment of the James Webb Telescope, space exploration continues to push the limits of what is possible in both science and technology and to pave new ways for the discovery of our galaxy. While we have seen great advancements and barriers broken, we remain limited in our ability to optimize science discovery without onboard intelligent capabilities to enable complex system missions. In this paper, we focus on AI technologies and cross-disciplinary directions for sparking research and development for space applications. This paper attempts to bridge two disparate fields of research, to enhance the development of intelligent space systems by providing a comprehensive survey of existing technologies and showcasing a strategic rubric for future advancements

Advancing Aircraft Operations in a Net-Centric Environment with the Incorporation of Increasingly Autonomous Systems and Human Teaming

NextGen has begun the modernization of the nations air transportation system, with goals to improve system safety, increase operation efficiency and capacity, provide enhanced predictability, resilience and robustness. With these improvements, NextGen is poised to handle significant increases in air traffic operations, more than twice the number recorded in 2016, by 2025.1 NextGen is evolving toward collaborative decision-making across many agents, including automation, by use of a Net-Centric architecture, which in itself creates a very complex environment in which the navigation and operation of aircraft are to take place. An intricate environment such as this, coupled with the expected upsurge of air traffic operations generates concern respecting the ability of the human-agent to both fly and manage aircraft within. Therefore, it is both necessary and practical to begin the process of increasingly autonomous systems within the cockpit that will act independently to assist the human-agent achieve the overall goal of NextGen. However, the straightforward technological development and implementation of intelligent machines into the cockpit is only part of what is necessary to maintain, at minimum, or improve human-agent functionality, as desired, while operating in NextGen. The full integration of Increasingly Autonomous Systems (IAS) within the cockpit can only be accomplished when the IAS works in concert with the human, formulating trust between the two, thereby establishing a team atmosphere. Imperative to cockpit implementation is ensuring the proper performance of the IAS by the development team and the human-agent with which it will be paired when given a specific piloting, navigation, or observational task. Described in this paper are the steps taken, at NASA Langley Research Center, during the second and third phases of the development of an IAS, the Traffic Data Manager (TDM), its verification and validation by human-agents, and the foundational development of Human Autonomy Teaming (HAT) between the two

Autonomous System-Level Fault Diagnosis in Satellites Using Housekeeping Telemetry

To continue the growing success of scientific discovery through deep space exploration, missions need to be able to be able to travel further and operate more efficiently than ever before. To ensure resilience in this capability, on-board autonomous fault mitigation methods must be developed and matured. To this end, we present a system for cross-subsystem fault diagnosis of satellites using spacecraft telemetry. Our system leverages a combination of Kalman Filters, Autoencoders, and Causality algorithms. We test our system for accuracy against three data sets of varying complexity levels, along with baseline testing. Additionally, we perform an ablation study to evaluate on-board tractability

Predicting Satellite Close Approaches Using Statistical Parameters in the Context of Artificial Intelligence

In order to ensure a sustainable use of low earth orbit in particular and near Earth space in general, reliable and effective close approach prediction be-tween space objects is key. Only this allows for efficient and timely colli-sion avoidance. Space Situational Awareness (SSA) for commercial and government missions will be facing the rapidly growing amount of small and potentially less agile satellites as well as debris in the near earth realm, such as the increase in CubeSat launches and upcoming large constellations. At the same time, space object detection capabilities are expected to increase significantly, allowing for the reliable detection of smaller objects, e.g. when the Air Force Space Fence radar becomes operational. In combination, the space object catalog is expected to increase tremendously in size. In this paper, we introduce an investigative approach based on the latest capabili-ties in artificial intelligence in fostering the potential for fast and accurate close approach predictions. We consider the study of statistical and infor-mation theory parameters in contrast and complementary to the classical probability of collision computation alone, in order to determine the feasi-bility of reliably predicting close approaches

A Framework for Multi-Agent Fault Reasoning in Swarm Satellite Systems

Complex distributed systems are critical to scientific discovery in space. Future missions will take place in increasingly remote planetary environments where human intervention will neither by feasible, nore scalable toward fleet-wide mission control. To this end, autonomous onboard fault mitigation will be necessary. The unique topology of fleet systems offers opportunities for high-level contextual understanding of faults and coordinated fault mitigation not possible for single agents. We present a framework that augments single-agent fault mitigation with the context provided by a fleet. Multi-Agent Anomaly Detection (MAAD) operates on time-series sensor data to build a unidimensional distribution against which we can compare individual agents in order to detect faulty sensing hardware