Autonomous Real-Time Smoothness Control for Reliable DDQN-Based UAV Navigation Using Cellular Networks

Abstract

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 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 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.

Keywords

• UAV
• trajectory planning
• cellular networks
• reliability
• smoothness control
• machine learning