用强化学习优化无人机远距飞行路径，提升通信连接质量。
Maximizing UAV Cellular Connectivity with Reinforcement Learning for BVLoS Path Planning
- 用通信质量作奖励信号，训练智能体规划飞行路径。
- 仿真显示路径可行，能有效减少飞行距离并保持强连接。
- 适合用于远程无人机管控系统，提升安全与可靠性。
本文提出一种基于强化学习（RL）的路径规划方法，用于实现超视距（BVLoS）运行的蜂窝连接无人机（UAV）。目标是在考虑真实空域覆盖约束和采用实测空中信道模型的前提下，最小化飞行距离的同时最大化蜂窝链路质量。该方案通过将无人机与基站（BS）之间的通信链路质量作为奖励函数，训练智能体生成可行路径。仿真结果表明，该方法能有效训练智能体并生成高质量路径计划。所提方法解决了无人机蜂窝通信受限带来的挑战，强调了该领域研究的必要性。强化学习算法高效识别最优路径，确保与地面基站的最大化连接，保障安全可靠的超视距飞行。此外，该方案可作为离线路径规划模块集成至未来地面控制站（GCS）中，增强其功能与安全性，适用于复杂长距离无人机应用，推动蜂窝连接无人机路径规划技术发展。
原文摘要 · Abstract (English)
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.
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