AI/Machine Learning in Aerospace Autonomy

Dear Colleagues,

As autonomous systems (ASs) become increasingly ubiquitous in complementing and supplementing humans and human-operated aerospace systems, our dependence on them is correspondingly growing. Applications and implementations of ASs within the aerospace domain will soon provide a spectrum of safety-critical, service-critical, and cost-critical functionalities. As such, their through-life evolution, adaptation, resilience, and security to unexpected inputs and events is essential to the trustworthiness of these systems. This dynamic nature of ASs presents us with new challenge frontiers for defining, analyzing, designing, and embedding the aforementioned features into their respective designs. To achieve this complex evolving behavior, the major paradigm shift that we currently face is the transition from design-time automated or sand-boxed autonomous systems to artificial intelligence (AI)-enabled self-aware and learning autonomous systems. AI-enabled autonomous systems within the aerospace domain operate in complex and unpredictable environments, while (a) accomplishing goals while providing through-life resilience and security against anomalies, failures and adversaries; and (b) learning and evolving through diverse experiences, often in contested environments. In that sense, a significant theoretical and methodological leap is required in developing learning-enabled systems within the aerospace domain with trustworthy and assured autonomy.

This Special Issue focuses on novel methods for applying artificial-intelligence-driven autonomy concepts to the design and execution of guidance, navigation, and control algorithms for aerospace vehicles. Topic areas of interest include the design, application, and implementation of AI technologies towards flight control system design, intelligent path/mission planning, sensor/data fusion and perception, situational awareness, classification and reconstruction, goal-based autonomy, multi-agent tactics development, target-task assignment, human–machine teaming, digital twins and data-driven modelling, model-free guidance and control, AI-driven testing and evaluation, AI hardware and software, dynamic verification and validation, and exploring pathways to the qualification and certification of learning-enabled designs.

Prof. Dr. Gokhan Inalhan
Guest Editor

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