This letter investigates an unmanned aerial vehicle (UAV)-aided data collection system, where a UAV is deployed to gather information from terrestrial Internet of Things (IoT) devices. We aim to minimize the mission completion time by optimizing the UAV trajectory, while ensuring all the target data can be successfully collected with a given energy budget. Firstly, the problem is formulated as a constrained Markov Decision Process (CMDP). Then, a constrained soft actor-critic (CSAC) algorithm is proposed by incorporating Lagrangian primal-dual optimization (PDO) into the soft actor-critic (SAC) framework. Simulation results demonstrate that the proposed algorithm outperforms state-of-the-art benchmark algorithms in terms of the mission completion time. Particularly, it is able to learn an adaptive policy that outputs optimal trajectories for different device locations.

中文翻译：

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无人机辅助物联网网络中能量感知轨迹设计的约束软 Actor-Critic


这封信研究了无人机 (UAV) 辅助数据收集系统，其中部署了无人机来从地面物联网 (IoT) 设备收集信息。我们的目标是通过优化无人机轨迹来最大限度地缩短任务完成时间，同时确保在给定的能量预算下可以成功收集所有目标数据。首先，问题被表述为约束马尔可夫决策过程（CMDP）。然后，通过将拉格朗日原始对偶优化（PDO）纳入软演员批评家（SAC）框架中，提出了一种约束软演员批评家（CSAC）算法。仿真结果表明，所提出的算法在任务完成时间方面优于最先进的基准算法。特别是，它能够学习自适应策略，为不同的设备位置输出最佳轨迹。