论文标题
基于趋势预测的技术报告，基于智能的无人机轨迹计划大规模动态场景
Technical Report for Trend Prediction Based Intelligent UAV Trajectory Planning for Large-scale Dynamic Scenarios
论文作者
论文摘要
无人驾驶飞机（UAV）的通信技术被认为是某些特殊应用方案的有效解决方案，在某些特殊的应用程序场景中，现有的陆地基础设施过载以提供可靠的服务。为了在满足QoS和能量限制的同时最大化无人机系统的实用性，无人机需要考虑场景的动态特征来计划其轨迹，该方案的动态特征被认为是马尔可夫决策过程（MDP）。为了解决上述问题，这里提出了基于深入的增强学习（DRL）的方案，这预测了动态场景的趋势，可以为无人机轨迹计划提供长期视图。仿真结果验证了我们提出的方案会更快地收敛，并在动态场景中实现更好的性能。
The unmanned aerial vehicle (UAV)-enabled communication technology is regarded as an efficient and effective solution for some special application scenarios where existing terrestrial infrastructures are overloaded to provide reliable services. To maximize the utility of the UAV-enabled system while meeting the QoS and energy constraints, the UAV needs to plan its trajectory considering the dynamic characteristics of scenarios, which is formulated as the Markov Decision Process (MDP). To solve the above problem, a deep reinforcement learning (DRL)-based scheme is proposed here, which predicts the trend of the dynamic scenarios to provide a long-term view for the UAV trajectory planning. Simulation results validate that our proposed scheme converges more quickly and achieves the better performance in dynamic scenarios.
