系统仿真学报 ›› 2020, Vol. 32 ›› Issue (3): 464-471.doi: 10.16182/j.issn1004731x.joss.18-0167

• 仿真模型/系统置信度评估技术 • 上一篇    下一篇

基于HMM和RNN的无人机语音控制方案与仿真研究

周楠, 艾剑良   

  1. 复旦大学航空航天系,上海 200433
  • 收稿日期:2018-03-24 修回日期:2018-09-12 出版日期:2020-03-18 发布日期:2020-03-25
  • 作者简介:周楠(1991-),男,江西鹰潭,博士生,研究方向为飞行控制与飞行仿真技术;艾剑良(1965-),男,江西临川,博士,教授,博导,研究方向为飞行力学与飞行控制及飞行仿真技术。

Speech Control Scheme Design and Simulation for UAV Based on HMM and RNN

Zhou Nan, Ai Jianliang   

  1. Department of Aeronautics and Astronautics, Fudan University, Shanghai 200433, China
  • Received:2018-03-24 Revised:2018-09-12 Online:2020-03-18 Published:2020-03-25

摘要: 为简化无人机操作,避免误操作,设计了一套基于隐马尔可夫模型(Hidden Markov Model,HMM)和循环神经网络(Recurrent Neural Networks,RNN)的无人机语音控制方案。该方案采用HMM识别无人机语音指令;同时采用RNN对多套无人机操作指令串进行训练,并对当前时刻指令进行预测,通过计算二者的相关性判断是否执行。仿真结果表明,该方案对HMM识别错误指令的辨别率达到61.90%,使整体错误率降至1.43%,表明该方案具有较为优异的性能。

关键词: 无人机, 语音控制, 隐马尔可夫模型, 循环神经网络

Abstract: In order to simplify the operation and avoid the misoperation of UAVs, Based on Hidden Markov Model and Recurrent Neural Networks, a speech control scheme for UAVs is designed. In this scheme, HMM is used to train and recognize the speech command samples of UAVs. HMM is used to pick out the error commands, RNN is used to train the sets of UAVs commands, and the next command based on the training result is predicted. It is determined whether to execute or not by calculating the correlation between commands recognized by HMM and predicted by RNN. The simulation results show the recognition rate of wrong command is as high as 61.90%, and the overall error rate is down to 1.43%. All show the excellent performance of this scheme.

Key words: UAVs(Unmanned Aerial Vehicles), speech control, HMM(Hidden Markov Model), RNN(Recurrent Neural Networks)

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