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This paper introduces a reinforcement learning (RL) approach to optimize UAV path planning for beyond visual line of sight (BVLoS) operations, aiming to minimize travel distance while maximizing cellular connectivity. The RL agent is trained using the quality of communication links between the UAV and base stations as the reward function, incorporating real-world aerial coverage constraints and an empirical aerial channel model. Simulation results demonstrate the RL agent's ability to generate feasible UAV path plans that balance distance and connectivity, offering a solution for reliable BVLoS flight.
Reinforcement learning can optimize UAV flight paths for BVLoS operations, balancing distance and cellular connectivity to ensure safe and reliable communication.
This paper presents a reinforcement learning (RL) based approach for path planning of cellular connected unmanned aerial vehicles (UAVs) operating beyond visual line of sight (BVLoS). The objective is to minimize travel distance while maximizing the quality of cellular link connectivity by considering real world aerial coverage constraints and employing an empirical aerial channel model. The proposed solution employs RL techniques to train an agent, using the quality of communication links between the UAV and base stations (BSs) as the reward function. Simulation results demonstrate the effectiveness of the proposed method in training the agent and generating feasible UAV path plans. The proposed approach addresses the challenges due to limitations in UAV cellular communications, highlighting the need for investigations and considerations in this area. The RL algorithm efficiently identifies optimal paths, ensuring maximum connectivity with ground BSs to ensure safe and reliable BVLoS flight operation. Moreover, the solution can be deployed as an offline path planning module that can be integrated into future ground control systems (GCS) for UAV operations, enhancing their capabilities and safety. The method holds potential for complex long range UAV applications, advancing the technology in the field of cellular connected UAV path planning.
