Autonomous Systems: IoT-Enabled UAV Path Optimization with Adaptive Fuzzy Control

In the wake of escalating tensions between Russia and Ukraine, Quadrotor Unmanned Aerial Vehicles (UAVs) are emerging as game-changers. These ensemble UAVs, with their variable fuel efficiencies and flight times, require efficient routing from origin to destination. However, factors like speed, angle, accuracy, timing, and situational control introduce uncertainty in trajectory prediction, further complicated by the Internet of Things (IoT) platform integration. This study presents an innovative adaptive fuzzy sliding mode controller optimized for UAV routing in an IoT environment. Using swarm intelligence algorithms in combination, the proposed model leverages the Dragonfly-Firefly algorithm to facilitate deviation-free path scheduling and target tracking. Through a MATLAB environment simulation, this research expects to yield comprehensive evaluation results, thereby demonstrating the potential efficacy of this approach. The simulation's graphical display enables a visual understanding of real-time path scheduling based on routing and target trajectory. This groundbreaking research paves the way for more efficient and intelligent preparation for potential conflicts, capitalizing on the increasing significance of Quadrotor UAVs. The proposed model could revolutionize the way we approach UAV routing within IoT platforms, marking a significant advancement in warfare technology.