基于深度学习的无人机空地对话指令理解技术  

UAV Air-to-ground Dialogue Command Understanding Technology Based on Deep Learning

作　　者：符凯 朱雪耀[1,2] 吕全喜 姜超[1,2] Fu Kai;Zhu Xueyao;LYU Quanxi;Jiang Chao(AVIC Xi’an Flight Automatic Control Research Institute,Xi’an 710065,China;Key Laboratory of Aviation Science and Technology on Aircraft Control,Xi’an 710065,China)

机构地区：[1]航空工业西安飞行自动控制研究所,西安710065 [2]飞行控制航空科技重点实验室,西安710065

出　　处：《兵工自动化》2022年第11期84-88,共5页Ordnance Industry Automation

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。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.

关 键 词：无人机 空地对话 深度学习 指令理解 槽填充 双向长短期记忆网络 条件随机场 

分 类 号：V279[航空宇航科学与技术—飞行器设计]