UAV Trajectory Planning Based on Deep Q-Networkfor Internet of Things
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摘要: 随着无人机技术的广泛应用，基于无人机辅助数据收集的物联网架构扩展了物联网的应用范围，尤其适用于军事战场、灾害救援等极端场景。针对上述场景，该文提出一种基于深度Q网络(Deep Q-Network, DQN)框架的无人机飞行路径规划算法。该算法以无人机飞行周期内收集信息的平均信息年龄(Age of Information, AoI)为优化目标，来保证无人机收集数据的时效性。仿真结果表明，所提算法可以有效降低无人机单个飞行周期内收集数据的平均AoI。与随机算法、基于最大AoI的贪心算法、最短路径算法以及基于AoI的路径规划算法(AoI-based Trajectory Planning, ATP)相比，平均AoI分别降低了约81%, 67%, 56%和39%。该研究实现了无人机辅助物联网系统中，数据的高效、低时延采集。Abstract: With the wide application of Unmanned Aerial Vehicle (UAV), the UAV-assisted Internet of Things (IoT) data collection architecture has expanded IoT’s application scope, which is especially suitable for extreme scenarios like military battlefields or disaster rescue. This paper proposes a UAV trajectory planning algorithm based on Deep Q-Network (DQN) framework for the above scenarios. The proposed algorithm takes the Age of Information (AoI) of collected data in a UAV’s flight cycle as the optimization goal to maintain data freshness. The simulation results show that this algorithm can effectively reduce the average AoI of the collected data. Compared with the random algorithm, the greedy algorithm based on the maximum AoI, the shortest path algorithm and the AoI-based Trajectory Planning (ATP) algorithm, the proposed algorithm can reduce AoI by about 81%, 67%, 56% and 39%, respectively. This paper has realized the efficient and low-latency data collection in the UAV-assisted IoT system.
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表 1 基于DQN的无人机路径规划算法
输入：学习速率$ \alpha $；打折率$ \gamma $；随机选取动作的参数$ \varepsilon $和$ \mu $；步长
$ w $；(1) 初始化 网络$ {Q_r} $和$ {Q_t} $的参数，并令$ {\theta _r}{\text{ = }}{\theta _t} $ (2) for 每一个训练回合 do (3) 初始化状态$ {s_k} $ (4) while $ {T_k} < {T_{\max }} $ do (5) 以$ \varepsilon $的概率随机选择动作$ {a_k} $，否则选择
$ {a_k} = \arg {\max _a}{Q_r}(s,a;{\theta _r}) $(6) 执行动作$ {a_k} $，按照式(2)和式(5)更新状态，计算奖
励，得到$ \left( {{s_k},{a_k},{r_k},{s_{k + 1}}} \right) $并存储在经验池中(7) if 经验池已存储满 do (8) 随机抽取$ {N_b} $个样本按照式(11)训练 (9) End if (10) if $ k\bmod w = 0 $ do (11) $ {\theta _r}{\text{ = }}{\theta _t} $ (12) End if (13) $ {s_k} \leftarrow {s_{k + 1}} $ (14) $ \varepsilon \leftarrow \varepsilon - \mu $ (15) end while (16) end for 
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表 2 DQN算法参数
参数名称 学习速率 折扣因子 随机概率 衰减因子 超参数 经验池 训练批次 更新步长 参数符号 $ \alpha $ $ \gamma $ $ \varepsilon $ $ \mu $ $ \lambda $ $ {\text{|}}D{\text{|}} $ $ {\text{|}}B{\text{|}} $ $ w $ 数值 0.001 0.9 0.95 0.000 1 10 3000 128 100
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表 3 不同算法的AoI性能对比(s)
算法名称 DQN ATP 最短路径 贪心法 随机算法 仿真场景1 7.7 12.7 17.8 23.3 42.1 仿真场景2 8.8 14.3 19.3 27.6 45.1
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