Fragility impact of RL based advanced air mobility under gradient attacks and packet drop constraints

The increasing utilization of unmanned aerial vehicles (UAVs) in advanced air mobility (AAM) necessitates highly automated conflict resolution and collision avoidance strategies. Consequently, reinforcement learning (RL) algorithms have gained popularity in addressing conflict resolution strategies among UAVs. However, increasing digitization introduces challenges related to packet drop constraints and various adversarial cyber threats, rendering AAM fragile. Adversaries can introduce perturbations into the system states, reducing the efficacy of learning algorithms. Therefore, it is crucial to systematically investigate the impact of increased digitization, including adversarial cyber-threats and packet drop constraints to study the fragile characteristics of AAM infrastructure. This study examines the performance of artificial intelligence(AI) based path planning and conflict resolution strategies under different adversarial and stochastic packet drop constraints in UAV systems. The fragility analysis focuses on the number of conflicts, collisions and fuel consumption of the UAVs with respect to its mission, considering various adversarial attacks and packet drop constraint scenarios. The safe deep q-networks (DQN) architecture is utilized to navigate the UAVs, mitigating the adversarial threats and is benchmarked with vanilla DQN using the necessary metrics. The findings are a foundation for investigating the necessary modification of learning paradigms to develop antifragile strategies against emerging adversarial threats.