基于操作者表现的机器人遥操作方法

doi:10.13973/j.cnki.robot.180087

摘要
图/表
参考文献
相关文章 (15)

全文: PDF (940 KB) HTML ( ) 输出: BibTeX \| EndNote (RIS)
引用本文:
刘爽, 朱国栋. 基于操作者表现的机器人遥操作方法[J]. 机器人, 2018, 40(4): 540-550.DOI: 10.13973/j.cnki.robot.180087. LIU Shuang, ZHU Guodong. A Performance of Teleoperator Based Method for Telerobotic System. ROBOT, 2018, 40(4): 540-550. DOI: 10.13973/j.cnki.robot.180087.

摘要针对遥操作机器人，提出了一种操作者表现（PoT）的在线识别方法以及基于PoT的遥操作移动机器人控制框架.首先，通过分析遥操作者的EEG（脑电信号）获得5个PoT指标，并使用BP（反向传播）神经网络对其进行建模，从而实现对遥操作者表现的在线识别.随后，设计了一种基于PoT动态调节遥操作共享控制系统中的控制权重的策略.选取3名不同遥操作者进行了在线PoT识别及基于PoT的共享控制遥操作实验，实验结果证明该方法能够有效地在线识别PoT，同时基于PoT的控制框架提升了遥操作的效率和安全性.

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	刘爽
	朱国栋

关键词 ：遥操作, 操作者表现, 脑电信号, 共享控制

Abstract：For telerobotic system, an online PoT (performance of teleoperator) identification method and a PoT based control framework for the mobile telerobotic system are proposed. Firstly, 5 PoT indicators are extracted by analyzing electroencephalography (EEG) signals of the teleoperator, and BP (backpropagation) neural network is used to model those indicators, so that the PoT can be identified online. Then, a dynamic adjustment strategy of control weights in a teleoperation shared control system is proposed based on PoT. Finally, experiments on the online PoT identification and the PoT based shared control teleoperation are performed with 3 teleoperators. The results demonstrate the effectiveness of the proposed method in PoT online identification, as well as the enhancement of teleoperation efficiency and safety by the PoT based control framework.

Key words： teleoperation performance of teleoperator (PoT) electroencephalography (EEG) shared control

收稿日期: 2018-01-29 录用日期: 2018-06-20

TP24

基金资助:国家自然科学基金（51405236）；上海浦江人才计划（16PJ1402300）

通讯作者: 刘爽,shuangliu@ecust.edu.cn E-mail: shuangliu@ecust.edu.cn

作者简介: 刘爽(1982-),男,博士,副教授.研究领域:机器人人机交互,路径规划.
朱国栋(1993-),男,硕士生.研究领域:人机交互,信号处理,轨迹规划.

链接本文:

https://robot.sia.cn/CN/10.13973/j.cnki.robot.180087 或 https://robot.sia.cn/CN/Y2018/V40/I4/540

[1] 王天然.机器人技术的发展[J].机器人,2017,39(4):385-386.Wang T R. The development of robot technology[J]. Robot, 2017, 39(4):385-386.
[2] 魏青,崔龙.基于时延预测的遥操作机器人预测显示方法[J].机器人,2017,39(3):298-306.Wei Q, Cui L. Predictive display for telerobot based on time-delay prediction[J]. Robot, 2017, 39(3):298-306.
[3] Takhmar A, Polushin I G, Talasaz A, et al. Cooperative teleoperation with projection-based force reflection for MIS[J]. IEEE Transactions on Control Systems Technology, 2015, 23(4):1411-1426.
[4] 化建宁,崔玉洁,贾琪,等.基于MDL的机器人网络遥操作系统控制方法[J].机器人,2013,35(5):615-622.Hua J N, Cui Y J, Jia Q, et al. MDL-based control method for tele-robotic systems[J]. Robot, 2013, 35(5):615-622.
[5] 席旭刚,罗志增.具有触觉临场感和肌电仿生控制功能的遥控机械手研究[J].机器人,2009,31(3):270-275.Xi X G, Luo Z Z. A tele-manipulator with tactile tele-presence and myoelectric bionic control[J]. Robot, 2009, 31(3):270-275.
[6] Jia Y Y, Xi N, Liu S, et al. Quality of teleoperator adaptive control for telerobotic operations[J]. International Journal of Robotics Research, 2014, 33(14):1765-1781.
[7] Benedetto S, Pedrotti M, Minin L, et al. Driver workload and eye blink duration[J]. Transportation Research, Part F:Traffic Psychology and Behaviour, 2011, 14(3):199-208.
[8] Patel M, Lal S K L, Kavanagh D, et al. Applying neural network analysis on heart rate variability data to assess driver fatigue[J]. Expert Systems with Applications, 2011, 38(6):7235-7242.
[9] Zhang X, Wang B, Wang X Y, et al. Human intention extracted from electromyography signals for tracking motion of meal assistance robot[C]//IEEE/ICME International Conference on Complex Medical Engineering. Piscataway, USA:IEEE, 2007:1384-1387.
[10] Liu S, Xi N, Jia Y Y. On-line operator skill assessment for tele-robot operation using electroencephalo-graph (EEG)[C]//IEEE International Conference on Robotics and Biomimetics. Piscataway, USA:IEEE, 2012:1598-1603.
[11] Li Y, Liu S, Xi N, et al. A study of the relationship between brain states and skill level of teleoperator[C]//IEEE International Conference on Robotics and Biomimetics. Piscataway, USA:IEEE, 2013:390-395.
[12] 王斐,王少楠,王惜慧,等.基于脑电图识别结合操纵特征的驾驶疲劳检测[J].仪器仪表学报,2014,35(2):398-404.Wang F, Wang S N, Wang X H, et al. Driving fatigue detection based on EEG recognition and vehicle handling characteristics[J]. Chinese Journal of Scientific Instrument, 2014, 35(2):398-404.
[13] Jia Y Y, Xi N, Wang Y X, et al. Online identification of quality of teleoperator (QoT) for performance improvementof telerobotic operations[C]//IEEE International Conference on Robotics and Automation. Piscataway, USA:IEEE, 2012:451-456.
[14] Lee K H, Mehmood U, Ryu J H. Development of the human interactive autonomy for the shared teleoperation of mobile robots[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway, USA:IEEE, 2016:1524-1529.
[15] Franchi A, Secchi C, Ryll M, et al. Shared control:Balancing autonomy and human assistance with a group of quadrotor UAVs[J]. IEEE Robotics & Automation Magazine, 2012, 19(3):57-68.
[16] 张晗,陈卫东,王景川.多机器人探索系统的人机共享控制[J].机器人,2015,37(1):17-24.Zhang H, Chen W D, Wang J C. Human-robot shared control for multi-robot exploration system[J]. Robot, 2015, 37(1):17-24.
[17] Itoh M, Inagaki T, Tanaka H. Haptic steering direction guidance for pedestrian-vehicle collision avoidance[C]//IEEE International Conference on Systems, Man, and Cybernetics. Piscataway, USA:IEEE, 2012:3327-3332.
[18] 段纪丁.脑机控制智能轮椅[D].广州:华南理工大学,2016.Duan J D. The control of intelligent wheelchair using brain computer interface[D]. Guangzhou:South China University of Technology, 2016.
[19] Li Q N, Chen W D, Wang J C. Dynamic shared control for human-wheelchair cooperation[C]//IEEE International Conference on Robotics and Automation. Piscataway, USA:IEEE, 2011:4278-4283.
[20] Tonin L, Leeb R, Tavella M, et al. The role of shared-control in BCI-based telepresence[C]//IEEE International Conference on Systems, Man, and Cybernetics. Piscataway, USA:IEEE, 2010:1462-1466.
[21] Kim J, Ladjal H, Folio D, et al. Evaluation of telerobotic shared control strategy for efficient single-cell manipulation[J]. IEEE Transactions on Automation Science and Engineering, 2012, 9(2):402-406.
[22] Carlson T, Leeb R, Chavarriaga R, et al. Online modulation of the level of assistance in shared control systems[C]//IEEE International Conference on Systems, Man, and Cybernetics. Piscataway, USA:IEEE, 2012:3339-3344.
[23] Song K T, Jiang S Y, Lin M H. Interactive teleoperation of a mobile manipulator using a shared-control approach[J]. IEEE Transactions on Human-Machine Systems, 2016, 46(6):834-845.
[24] Liu Y, Jiang X, Cao T, et al. Implementation of SSVEP based BCI with Emotiv EPOC[C]//IEEE International Conference on Virtual Environments, Human-Computer Interfaces, and Measurement Systems. Piscataway, USA:IEEE, 2012:34-37.
[25] Lin C T, Chuang C H, Huang C S, et al. Wireless and wearable EEG system for evaluating driver vigilance[J]. IEEE Transactions on Biomedical Circuits and Systems, 2014, 8(2):165-176.
[26] 焦李成,杨淑媛,刘芳,等.神经网络七十年:回顾与展望[J].计算机学报,2016,39(8):1697-1716.Jiao L C, Yang S Y, Liu F, et al. Seventy years beyond neural networks:retrospect and prospect[J]. Chinese Journal of Computers, 2016, 39(8):1697-1716.
[27] 范佳妮,王振雷,钱锋.BP人工神经网络隐层结构设计的研究进展[J].控制工程,2005,12(S1):109-113.Fan J N, Wang Z L, Qian F. Research progress structural design of hidden layer in BP artificial neural networks[J]. Control Engineering of China, 2005, 12(S1):109-113.
[28] Hüsken M, Jin Y, Sendhoff B. Structure optimization of neural networks for evolutionary design optimization[J]. Soft Computing, 2005, 9(1):21-28.
[29] Saida K, Suzuki S, Maeda Y, et al. Skill analysis in human tele-operation using dynamic image[C]//IECON Proceedings. Piscataway, USA:IEEE, 2006:4528-4533.