Reliable Unmanned Aerial Vehicle (UAV) navigation in urban environments is a crucial prerequisite for major civilian and military applications.Many existing Global Positioning System (GPS)-based UAV navigation solutions here do not meet the performance requirements given their unreliability in urban environments.In this paper, we present a smooth trajectory planning approach to generate reliable UAV trajectories with less chatter and sharp turns.We propose to utilize broadcast signals from existing cellular networks to practically navigate the UAV from a given source to a destination in urban environments independent of GPS or other transmissible signals.
For this purpose, we formulate the smooth trajectory planning problem as an optimization problem to provide a probabilistic guarantee on the success of the UAV mission considering the vibrating table for chocolate UAV dynamic and kinematic constraints.We utilize proper optimization-based techniques to determine the optimal bound of the solution for benchmarking purposes.Next, we propose a machine learning based approach to provide a practical real-time solution to the formulated UAV navigation problem.Finally, we present an in-depth comparative analysis to evaluate the performance of the proposed double deep Q-network (DDQN)-based technique as compared to other solutions from the literature.