A 3D REM-Guided UAV Path Planning Method under Communication Connectivity Constraints

Abstract

With the emergence of a large number of smart devices, the radio environment in which unmanned aerial vehicles (UAVs) take tasks is becoming more and more complex, which puts forward higher requirements for UAVs situational awareness and autonomous obstacle avoidance capabilities. To tackle this issue, we propose a three-dimension (3D) UAV path planning method under communication connectivity constraints guided by radio environment maps (REMs), which are distributed by ground edge servers in the form of compressed global REMs and detailed local REMs. An interfered fluid dynamic system (IFDS) model is deployed on UAVs to allow them to avoid obstacles and plan paths. We propose a twin-delayed deep deterministic policy gradient- (TD3-) based deep reinforcement learning (DRL) method to optimize the reaction coefficients of UAVs to avoid obstacles and improve the signal to interference plus noise ratio (SINR). The simulation results show that the proposed algorithm can effectively avoid static obstacles and dynamic interference under communication connectivity constraints, significantly improve the communication stability with a higher receive signal SINR and reduce the cost of UAV performing tasks with the shortest path.