About this Research Topic
Machine learning and artificial intelligence play an increasingly important role in aerospace applications. This is particularly true for automated systems including space robotics and unmanned aerial vehicles, where a variety of technological opportunities have arisen, each requiring novel approaches and algorithms to its address corresponding technological challenges. Other applications include optimization algorithms in structural engineering for the design of fail-safe aerospace structures, as well as solving problems dealing with uncertainties in structural properties, unsteady aerodynamic loading and flow/flight control system parameters, to name a few.
The goal of this Research Topic is to illustrate applications of Machine Learning and Artificial Intelligence methods to problems in aerospace. Novel ML/AI algorithms and/or application of existing approaches to problems involving space robotics, UAV operations, flow and flight control, structural engineering, as well as other fields of aerospace engineering are examined. Military applications are out of scope for this collection.
Potential submission topics include, but are not limited to, the following:
• ML/AI and optimization methods in aerospace engineering
• ML/AI algorithms for robotic (semi)-autonomous space operations
• Human-robot interaction with shared autonomy in space robotics
• Sensors and actuators for robotic missions
• Application of ML/AI to problems with small amount of data
• Efficiency of multifidelity design of experiments with very few high fidelity analyses
• ML/AI for problems in flow/flight control including closed-loop control algorithms
• ML/AI for problems of increasing efficiency with expensive physical testing
• UAV-enabled massive multiple-input multiple-output (MIMO), internet-of-things (IoT) and non-orthogonal multiple access (NOMA)
• Hybrid satellite and UAV networks
• UAV cybersecurity and physical layer security of UAV-enabled networks
• Machine learning of UAV-enabled networks
• UAV trajectory optimization using machine learning
• Simultaneous Wireless Information and Power Transfer (SWIPT)-enabled UAVs
• Energy-efficient UAV communications
• Coexistence of UAV network and traditional cellular systems
• Protocols and system models of cellular-connected UAVs
• UAV with hardware impairment, caching, edge computing
• Prototypes and test bed for flying base stations and user equipment
Dr. Vladimir Balabanov is an Associate Technical Fellow at Boeing and is leading the application of Structural Optimization and related technologies at the Boeing Commercial Airplanes division. All other Topic Editors declare no competing interests with the theme of this Research Topic.
Image Credit: NASA
Keywords: Unmanned Aerial Vehicles, UAV, Flow and Flight Control, Structural Engineering, Machine Learning, Artificial Intelligence, space robotics
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.