Artificial Intelligence Approaches To UCAV Autonomy
It addresses autonomy challenges for UCAVs in military applications, but it appears incremental as it builds on current approaches without introducing a new paradigm.
The paper analyzes existing autonomous control methods for Unmanned Combat Aerial Vehicles (UCAVs) and explores how AI techniques like neural networks, ensembling, and reinforcement learning can enhance these strategies, but it does not report specific experimental results or numbers.
This paper covers a number of approaches that leverage Artificial Intelligence algorithms and techniques to aid Unmanned Combat Aerial Vehicle (UCAV) autonomy. An analysis of current approaches to autonomous control is provided followed by an exploration of how these techniques can be extended and enriched with AI techniques including Artificial Neural Networks (ANN), Ensembling and Reinforcement Learning (RL) to evolve control strategies for UCAVs.