Using Unmanned Aerial Vehicles (UAVs) in Search and rescue operations (SAR) to navigate challenging terrain while maintaining reliable communication with the cellular network is a promising approach. This paper suggests a novel technique employing a reinforcement learning multi Q-learning algorithm to optimize UAV connectivity in such scenarios. We introduce a Strategic Planning Agent for efficient path planning and collision awareness and a Real-time Adaptive Agent to maintain optimal connection with the cellular base station. The agents trained in a simulated environment using multi Q-learning, encouraging them to learn from experience and adjust their decision-making to diverse terrain complexities and communication scenarios. Evaluation results reveal the significance of the approach, highlighting successful navigation in environments with varying obstacle densities and the ability to perform optimal connectivity using different frequency bands. This work paves the way for enhanced UAV autonomy and enhanced communication reliability in search and rescue operations.

翻译：在搜救作业中利用无人机穿越复杂地形并保持与蜂窝网络的可靠通信，是一种极具前景的方法。本文提出一种基于强化学习多Q学习算法的新技术，用于优化此类场景下的无人机连接性能。我们引入战略性规划智能体来实现高效路径规划与碰撞感知，同时设计实时自适应智能体以维持与蜂窝基站的最佳连接。通过在仿真环境中采用多Q学习训练智能体，使其能够从经验中学习并依据不同地形复杂度与通信场景调整决策。评估结果表明了该方法的重要性，成功实现了在障碍物密度多变环境中的导航能力，并展示了利用不同频段维持最优连接的性能。本研究为提升搜救作业中无人机自主性与通信可靠性奠定了重要基础。