基于深度学习的无人机空地对话指令理解技术
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UAV Air-to-ground Dialogue Command Understanding Technology Based on Deep Learning
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    摘要:

    为解决传统无人机(unmanned aerial vehicle,UAV)无法在融合空域中通过空管员进行指挥控制的问题, 提出一种基于深度学习的无人机空地对话指令理解技术。通过双向长短期记忆网络(bi-directional long short-term memory,Bi-LSTM)和条件随机场(conditional random fields,CRF)进行指令关键信息提取,得到无人机可直接执行 的结构化指令,实现空管员与无人机直接交互。实验结果表明:该方法能在一定程度解决传统交互模式的问题,达 到空管员直接通过语音操控无人机的目的。

    Abstract:

    In order to solve the problem that traditional unmanned aerial vehicle (UAV) can not be commanded and controlled by air traffic controllers in the fusion airspace, a deep learning based UAV air-to-ground dialogue command understanding technology is proposed. The bi-directional long short-term memory (Bi-LSTM) network and the conditional random fields (CRF) are used to extract the key information of the instruction. The structured instructions that can be directly executed by the UAV are obtained, and the direct interaction between the air traffic controller and the UAV is realized. The experimental results show that this method can solve the problem of traditional interaction mode to some extent, and achieve the purpose that air traffic controllers control UAV directly by voice.

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符 凯.基于深度学习的无人机空地对话指令理解技术[J].,2022,41(11).

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  • 收稿日期:2022-07-09
  • 最后修改日期:2022-08-13
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  • 在线发布日期: 2022-11-21
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