To address the shortcomings of single-step decision making in the existing deep reinforcement learning based unmanned aerial vehicle(UAV)real-time path planning problem,a real-time UAV path planning algorithm based on...To address the shortcomings of single-step decision making in the existing deep reinforcement learning based unmanned aerial vehicle(UAV)real-time path planning problem,a real-time UAV path planning algorithm based on long shortterm memory(RPP-LSTM)network is proposed,which combines the memory characteristics of recurrent neural network(RNN)and the deep reinforcement learning algorithm.LSTM networks are used in this algorithm as Q-value networks for the deep Q network(DQN)algorithm,which makes the decision of the Q-value network has some memory.Thanks to LSTM network,the Q-value network can use the previous environmental information and action information which effectively avoids the problem of single-step decision considering only the current environment.Besides,the algorithm proposes a hierarchical reward and punishment function for the specific problem of UAV real-time path planning,so that the UAV can more reasonably perform path planning.Simulation verification shows that compared with the traditional feed-forward neural network(FNN)based UAV autonomous path planning algorithm,the RPP-LSTM proposed in this paper can adapt to more complex environments and has significantly improved robustness and accuracy when performing UAV real-time path planning.展开更多
为提升高复杂海洋环境下声呐探测距离预测的准确性和效率,文章提出一种基于改进Transformer的传播损失与声呐探测距离建模方法,该方法能够兼容复杂海洋环境下不同点位、不同方向声信号传播损失差异,能够基于声呐方程及声呐主被动工作模...为提升高复杂海洋环境下声呐探测距离预测的准确性和效率,文章提出一种基于改进Transformer的传播损失与声呐探测距离建模方法,该方法能够兼容复杂海洋环境下不同点位、不同方向声信号传播损失差异,能够基于声呐方程及声呐主被动工作模式,快速、有效地预测多点位多方向的声呐探测距离。以真实大区域海洋环境计算得到的传播损失数据为输入,通过将双向长短时记忆网络(bidirectional long short-term memory,Bi-LSTM)与Transformer架构中自注意力机制相结合,使得模型能够有效捕捉复杂环境变化的局部精确性和全局特征。实验结果表明,所提模型预测结果与声呐方程耦合积分方式得到的探测距离具有较好的一致性;同时计算效率提高了约1 000倍,提升了声呐性能的预报效率。展开更多
基金supported by the Natural Science Basic Research Prog ram of Shaanxi(2022JQ-593)。
文摘To address the shortcomings of single-step decision making in the existing deep reinforcement learning based unmanned aerial vehicle(UAV)real-time path planning problem,a real-time UAV path planning algorithm based on long shortterm memory(RPP-LSTM)network is proposed,which combines the memory characteristics of recurrent neural network(RNN)and the deep reinforcement learning algorithm.LSTM networks are used in this algorithm as Q-value networks for the deep Q network(DQN)algorithm,which makes the decision of the Q-value network has some memory.Thanks to LSTM network,the Q-value network can use the previous environmental information and action information which effectively avoids the problem of single-step decision considering only the current environment.Besides,the algorithm proposes a hierarchical reward and punishment function for the specific problem of UAV real-time path planning,so that the UAV can more reasonably perform path planning.Simulation verification shows that compared with the traditional feed-forward neural network(FNN)based UAV autonomous path planning algorithm,the RPP-LSTM proposed in this paper can adapt to more complex environments and has significantly improved robustness and accuracy when performing UAV real-time path planning.
文摘为提升高复杂海洋环境下声呐探测距离预测的准确性和效率,文章提出一种基于改进Transformer的传播损失与声呐探测距离建模方法,该方法能够兼容复杂海洋环境下不同点位、不同方向声信号传播损失差异,能够基于声呐方程及声呐主被动工作模式,快速、有效地预测多点位多方向的声呐探测距离。以真实大区域海洋环境计算得到的传播损失数据为输入,通过将双向长短时记忆网络(bidirectional long short-term memory,Bi-LSTM)与Transformer架构中自注意力机制相结合,使得模型能够有效捕捉复杂环境变化的局部精确性和全局特征。实验结果表明,所提模型预测结果与声呐方程耦合积分方式得到的探测距离具有较好的一致性;同时计算效率提高了约1 000倍,提升了声呐性能的预报效率。
