基于稳态视觉诱发电位的智能轮椅半自主导航控制

doi:10.13973/j.cnki.robot.180771

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张亚徽, 王斐, 李景宏, 刘玉强, 吴仕超. 基于稳态视觉诱发电位的智能轮椅半自主导航控制[J]. 机器人, 2019, 41(5): 620-627,636.DOI: 10.13973/j.cnki.robot.180771. ZHANG Yahui, WANG Fei, LI Jinghong, LIU Yuqiang, WU Shichao. Semi-autonomous Navigation Control of Intelligent Wheelchair Based on Steady State Visual Evoked Potential. ROBOT, 2019, 41(5): 620-627,636. DOI: 10.13973/j.cnki.robot.180771.

摘要针对现有基于脑－机接口（BCI）控制的智能轮椅因交互不协调、识别准确率低、执行效率差而造成用户疲劳等问题，提出了一种人机协同智能控制方法，设计并实现了一种基于BCI与层级地图结合的智能轮椅半自主导航控制系统．首先根据实际需要构建栅格－拓扑－意图3级层级地图．然后采用基于典型相关性分析的1维卷积神经网络对人的意图进行识别分类，并通过BCI系统发送至导航控制部分．最后经融合决策给出控制指令实现智能轮椅的导航．实验中所提方法的平均准确率为91.576%，对脑电信号的识别准确率高，控制系统的稳定性好．结果表明该方法可灵活控制轮椅运动方向并按照人的控制意图无碰撞地到达目标地点．

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	王斐
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关键词 ：智能轮椅, 半自主导航, 典型相关性分析, 1维卷积神经网络, 融合决策

Abstract：Current brain-computer interface (BCI) control based intelligent wheelchairs are facing the problem of users' fatigue caused by uncoordinated interaction, low recognition accuracy, low execution efficiency. A method of human-machine collaborative intelligent control is proposed to solve the problem, and a semi-autonomous navigation control system based on BCI and hierarchical map is designed and implemented for intelligent wheelchair. Firstly, a three-level raster-topology-intention map is constructed according to actual needs. Then, a one dimensional convolutional neural network (1D-CNN) based on canonical correlation analysis (CCA) is used to identify and classify the human's intention, which is sent to the navigation control section through BCI system. Finally, the control command is given to control the navigation of the intelligent wheelchair by fusion decision. The experimental results show that the proposed method achieves high recognition accuracies on electroencephalography (EEG) signals with an average of 91.576%, and the control system demonstrates a good stability. The method can flexibly control the motion direction of intelligent wheelchair and reach the target location without collision according to the human's control intention.

Key words： intelligent wheelchair semi-autonomous navigation CCA (canonical correlation analysis) 1D-CNN (one dimensional convolutional neural network) fusion decision

收稿日期: 2018-12-27 录用日期: 2019-05-13

TP24

基金资助:中央高校基本科研业务费（N172608005，N182612002）；辽宁省自然科学基金（20180520007）

通讯作者: 王斐,wangfei@mail.neu.edu.cn E-mail: wangfei@mail.neu.edu.cn

作者简介: 张亚徽(1995-),女,硕士生.研究领域:脑机接口,机器学习.
王斐(1974-),男,博士,副教授.研究领域:人机交互感知与协作理论与技术,仿人机器人理论与技术.
李景宏(1965-),男,硕士,副教授.研究领域:电路与系统.

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https://robot.sia.cn/CN/10.13973/j.cnki.robot.180771 或 https://robot.sia.cn/CN/Y2019/V41/I5/620

[1] 王行愚,金晶,张宇,等.脑控:基于脑-机接口的人机融合控制[J].自动化学报,2013,39(3):208-221.Wang X Y, Jin J, Zhang Y, et al. Brain control:Human-computer integration control based on brain-computer interface[J]. Acta Automatica Sinica, 2013, 39(3):208-211.
[2] 赵丽,薛仲林,王宣方.基于SSVEP的高传输速率脑机拨号系统[J].计算机技术与发展,2017,27(10):185-188.Zhao L, Xue Z L, Wang X F. A high ITR BCI dial system based on SSVEP[J]. Computer Technology and Development, 2017, 27(10):185-188.
[3] 谢宏,刘婧,夏斌.基于SSVEP的音频播放器控制系统[C]//全国第21届计算机技术与应用学术会议(CACIS. 2010)暨全国第2届安全关键技术与应用学术会议论文集.合肥,中国:中国科学技术大学出版社,2010:256-260.Xie H, Liu J, Xia B, Audio player control system based on SSVEP[C]//Proceedings of the 21st National Academic Conference on Computer Technology and Application (CACIS. 2010) and the 2nd National Academic Conference on Critical Security Technology and Application. Hefei, China:University of Science and Technology of China Press, 2010:256-260.
[4] Su W B, Li Z J. Brain-computer interface based stochastic navigation and control of a semiautonomous mobile robot in an indoor environment[C]//2nd International Conference on Advanced Robotics and Mechatronics. Piscataway, USA:IEEE, 2017:718-723.
[5] 张毅,杨柳,李敏,等.基于AR和SVM的运动想象脑电信号识别[J].华中科技大学学报(自然科学版),2011, 39(s2):103-106.Zhang Y, Yang L, Li M, et al. Recognition of motor imagery EEG based on AR and SVM[J]. Journal of Huazhong University of Science and Technology (Natural Science Edition), 2011, 39(s2):103-106.
[6] 孟丽霞,陶霖密,孙富春,等.基于脑机接口与双激光雷达的移动车导航系统[J].机器人,2012,34(4):449-454.Meng L X, Tao L M, Sun F C, et al. Vehicle navigation system based on BCI and dual laser radar[J]. Robot, 2012, 34(4):449-454.
[7] Iturrate I, Antelis J M, Kubler A, et al. A noninvasive brain-actuated wheelchair based on a P300 neurophysiological protocol and automated navigation[J]. IEEE Transactions on Robotics, 2009, 25(3):614-627.
[8] Choi B J, Jo S H. A low-cost EEG system-based hybrid brain-computer interface for humanoid robot navigation and recognition[J]. PLoS ONE, 2013, 8(9). DOI:10.1371/journal. pone.0074583.
[9] 汪霖,曹建福,韩崇昭.基于粒子群优化的机器人多传感器自标定方法[J].机器人,2009,31(5):391-396.Wang L, Cao J F, Han C Z. A robot multi-sensor self-calibration method based on particle swarm optimization[J]. Robot, 2009, 31(5):391-396.
[10] Rebsamen B, Guan C T, Zhang H H, et al. A brain controlled wheelchair to navigate in familiar environments[J]. IEEE Transactions on Neural System and Rehabilitation Engineering, 2010, 18(6):590-598.
[11] 俞建成,张进,李伟.基于事件相关电位的水下机械手脑电波控制[J].机器人,2017,39(4):395-404.Yu J C, Zhang J, Li W. Controlling an underwater manipulator via event-related potentials of brainwaves[J]. Robot, 2017, 39(4):395-404.
[12] 邓志东,李修全,郑宽浩.一种基于SSVEP的仿人机器人异步脑机接口控制系统[J].机器人,2010,33(2):129-135.Deng Z D, Li X Q, Zheng K H. A humanoid robot control system with SSVEP-based asynchronous brain-computer interface[J]. Robot, 2010, 33(2):129-135.
[13] 史添玮.基于混合计算机接口的多旋翼飞行器3维空间目标搜索[J].机器人,2018,40(5):734-741, 749.Shi T W. Three-dimensional space target search based on hybrid computer interfacefor multi-rotor aircraft[J]. Robot, 2018, 40(5):734-741, 749.
[14] Röfer T, Mandel C, Laue T. Controlling an automated wheelchair via joystick/head joystick supported by smart driving assistance[C]//IEEE 11th International Conference on Rehabilitation Robotics. Piscataway, USA:IEEE, 2009:743-748.
[15] Zhang Y, Zhou G X, Jin J, et al. Frequency recognition in SSVEP-based BCI using multiset canonical correlation analysis[J]. International Journal of Neural Systems, 2014, 24(4). DOI:10.1142/S0129065714500130.
[16] 笪铖璐,陈志阳,黄丽亚.基于CCA的SSVEP性能研究[J].计算机技术与发展,2015,25(5):52-55,59.Da C L, Chen Z Y, Huang L Y. Study on performance of SSVEP based on CCA[J]. Computer Technology and Development, 2015, 25(5):52-55,59.
[17] Rasiwasia N, Mahajan D, Mahadevan V, et al. Cluster canonical correlation analysis[C/OL].[2018-05-04]. http://pdfs.semanticscholar.org/3c56/53a8e607f436465ade84e3d6134df99cc728.pdf.
[18] Bin G Y, Gao X R, Yan Z, et al. An online multi-channel SSVEP-based brain-computer interface using a canonical correlation analysis method[J]. Journal of Neural Engineering, 2009, 6(4). DOI:10.1088/1741-2560/6/4/046002.
[19] 王炜,陈卫东,王勇.基于概率栅格地图的移动机器人可定位性估计[J].机器人, 2012, 34(4):485-491,512. Wang W, Chen W D, Wang Y. Probabilistic grid map based localizability estimation for mobile robots[J]. Robot, 2012, 34(4):485-491,512.
[20] Grisetti G, Stachniss C, Burgard W. Improved techniques for grid mapping with Rao-Blackwellized particle filters[J]. IEEE Transactions on Robotics, 2007, 23(1):34-46.
[21] Pan J, Li Y, Zhang R, et al. Discrimination between control and idle states in asynchronous SSVEP-based brain switches:A pseudo-key-based approach[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2013, 21(3):435-443.