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This paper introduces a conceptual algorithm for simulating realistic UAV behavior within a battlefield environment using Unreal Engine's built-in AI tools. The simulation incorporates flight dynamics, wind disturbances, terrain obstacles, and ambient conditions to create a credible 3D environment. The proposed approach enables autonomous decision-making for the UAV, such as collision avoidance and route optimization, facilitating operator training and mission rehearsal without physical UAV deployment.
Train drone operators in realistic battlefield environments without ever leaving the simulator, thanks to Unreal Engine's built-in AI.
Ongoing technological advances in unmanned aerial vehicles (UAVs) have heightened the relevance of systems that integrate augmented reality (AR) and artificial intelligence (AI) to simulate UAV behaviour in realistic three-dimensional environments. AR-based solutions can substantially enhance an operator’s situational awareness by improving environmental perception and enabling more proactive flight-path planning. To be credible and operationally useful, such a simulation model should capture key physical and environmental properties, including flight dynamics, responses to wind disturbances, terrain obstacles, and time-varying ambient conditions. Integrating AI allows an appropriately implemented 3D UAV model – here referred to as a drone simulant – to make autonomous decisions, such as collision avoidance, selection of an optimal route, and real-time adaptation to scenario changes. In response to these needs, this paper proposes a conceptual algorithm for simulating realistic UAV behaviour in a battlefield environment using Unreal Engine and its built-in AI toolset. The proposed approach is intended to support training and mission rehearsal under conditions that approximate real-world operations without requiring physical UAV deployment, thereby enabling operator training, evaluation of flight strategies, and mission design while maintaining full immersion and safety.
