Artificial Intelligence Approaches To UCAV Autonomy

TL;DR

This paper reviews AI techniques like neural networks, ensembling, and reinforcement learning to enhance the autonomy of Unmanned Combat Aerial Vehicles, analyzing current methods and proposing extensions.

Contribution

It introduces novel AI-based control strategies for UCAVs by integrating neural networks, ensembling, and reinforcement learning techniques.

Findings

AI techniques improve UCAV autonomy

Enhanced control strategies outperform traditional methods

Reinforcement learning enables adaptive decision-making

Abstract

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.