AI Bibliography
AI Bibliography |
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Hagos, D. H., & Rawat, D. B. (2022). Recent advances in artificial intelligence and tactical autonomy: Current status, challenges, and perspectives. Sensors, 22(24), 9916. |
| Resource type: Journal Article BibTeX citation key: Hagos2022 View all bibliographic details  |
Categories: Artificial Intelligence, Cognitive Science, Complexity Science, Computer Science, Data Sciences, Decision Theory, Engineering, General, Military Science Subcategories: Air Force, Autonomous systems, Command and control, Decision making, Deep learning, Drones, JADC2, Machine learning, Military research, Networked forces, Psychology of human-AI interaction, Robotics, Strategy, Systems theory Creators: Hagos, Rawat Publisher: Collection: Sensors |
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| Abstract |
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This paper presents the findings of detailed and comprehensive technical literature aimed at identifying the current and future research challenges of tactical autonomy. It discusses in great detail the current state-of-the-art powerful artificial intelligence (AI), machine learning (ML), and robot technologies, and their potential for developing safe and robust autonomous systems in the context of future military and defense applications. Additionally, we discuss some of the technical and operational critical challenges that arise when attempting to practically build fully autonomous systems for advanced military and defense applications. Our paper provides the state-of-the-art advanced AI methods available for tactical autonomy. To the best of our knowledge, this is the first work that addresses the important current trends, strategies, critical challenges, tactical complexities, and future research directions of tactical autonomy. We believe this work will greatly interest researchers and scientists from academia and the industry working in the field of robotics and the autonomous systems community. We hope this work encourages researchers across multiple disciplines of AI to explore the broader tactical autonomy domain. We also hope that our work serves as an essential step toward designing advanced AI and ML models with practical implications for real-world military and defense settings.
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