Li Jie, Cao Shuang, Liu Xianjie, Yu Ruiyun, Wang Xingwei
School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China.
School of Software, Northeastern University, Shenyang, Liaoning, China.
Front Neurorobot. 2023 Jan 24;16:1076338. doi: 10.3389/fnbot.2022.1076338. eCollection 2022.
Communication infrastructure is damaged by disasters and it is difficult to support communication services in affected areas. UAVs play an important role in the emergency communication system. Due to the limited airborne energy of a UAV, it is a critical technical issue to effectively design flight routes to complete rescue missions. We fully consider the distribution of the rescue area, the type of mission, and the flight characteristics of the UAV. Firstly, according to the distribution of the crowd, the PSO algorithm is used to cluster the target-POI of the task area, and the neural collaborative filtering algorithm is used to prioritize the target-POI. Then we also design a Trans-UTPA algorithm. Based on MAPPO 's policy network and value function, we introduce transformer model to make Trans-UTPA's policy learning have no action space limitation and can be multi-task parallel, which improves the efficiency and generalization of sample processing. In a three-dimensional space, the UAV selects the emergency task to be performed (data acquisition and networking communication) based on strategic learning of state information (location information, energy consumption information, etc.) and action information (horizontal flight, ascent, and descent), and then designs the UAV flight path based on the maximization of the global value function. The experimental results show that the performance of the Trans-UTPA algorithm is further improved compared with the USCTP algorithm in terms of the success rate of each UAV reaching the target position, the number of collisions, and the average reward of the algorithm. Among them, the average reward of the algorithm exceeds the USCTP algorithm by 13%, and the number of collisions is reduced by 60%. Compared with the heuristic algorithm, it can cover more target-POIs, and has less energy consumption than the heuristic algorithm.
通信基础设施会因灾害而受损，受灾地区的通信服务难以得到保障。无人机在应急通信系统中发挥着重要作用。由于无人机的机载能量有限，有效设计飞行路线以完成救援任务是一个关键技术问题。我们充分考虑救援区域的分布、任务类型以及无人机的飞行特性。首先，根据人群分布，采用粒子群优化算法对任务区域的目标兴趣点进行聚类，并使用神经协同过滤算法对目标兴趣点进行优先级排序。然后我们还设计了一种跨无人机路径规划算法（Trans-UTPA算法）。基于多智能体近端策略优化算法（MAPPO）的策略网络和价值函数，引入变换器模型，使Trans-UTPA算法的策略学习无动作空间限制且可多任务并行，提高了样本处理的效率和泛化能力。在三维空间中，无人机基于对状态信息（位置信息、能耗信息等）和动作信息（水平飞行、上升和下降）的策略学习，选择要执行的应急任务（数据采集和组网通信），然后基于全局价值函数的最大化设计无人机飞行路径。实验结果表明，在各无人机到达目标位置的成功率、碰撞次数以及算法平均奖励方面，与联合稀疏通信传输协议算法（USCTP算法）相比，Trans-UTPA算法的性能有进一步提升。其中，算法平均奖励比USCTP算法高出13%，碰撞次数减少了60%。与启发式算法相比，它能覆盖更多的目标兴趣点，且能耗比启发式算法更低。
