基于事件相关电位的水下机械手脑电波控制

发布时间：2018-01-04 22:14

本文关键词：基于事件相关电位的水下机械手脑电波控制　出处：《机器人》2017年04期 　论文类型：期刊论文

【摘要】：为了解放水下机械手操作人员的双手,本文将脑-机接口(BCI)技术应用到水下机器人作业中,通过解析脑电信号并将其映射为具体指令从而控制机械手.现有的脑电波控制机械手方法在实时性、准确性方面无法满足实际的水下作业要求,提出了基于视觉诱发模式的ERP(事件相关电位)脑电信号来控制水下机械手的策略.通过融合脑电波控制与水下机械手作业的各自特点和优化ERP视觉诱发界面,使操作人员能够快速地完成给定任务.8位被试被邀请在建立的实验平台上进行控制实验,最终得到的辨识操作人员意图平均准确率、系统信息传输率与完成任务平均控制时间分别为91.5%、27.7 bits/min与90.1 s.与同类系统相比,本文所提控制策略系统性能更好,且作业效率满足实际作业要求.
[Abstract]:In order to liberate the hands of underwater manipulator, the brain-computer interface (BCI) technology is applied to the underwater robot operation in this paper. By analyzing the EEG signal and mapping it to specific instructions, the existing methods of EEG control manipulator can not meet the actual underwater operation requirements in real time and accuracy. ERP (event related potential) based on visual evoked model is proposed. Electroencephalogram (EEG) is used to control the underwater manipulator. The ERP visual evoked interface is optimized by combining the characteristics of the EEG control and the underwater manipulator. So that the operator can quickly complete a given task of the 8-bit subjects were invited to conduct control experiments on the established experimental platform, the final identification operator intention average accuracy. The average control time between system information transmission rate and task completion is 91.5 / 27.7 bits/min and 90.1 / s, respectively. The control strategy proposed in this paper has better performance, and the operational efficiency meets the practical requirements.
【作者单位】：中国科学院沈阳自动化研究所机器人学国家重点实验室;中国科学院大学;
【基金】：国家重点研发计划项目(2016YFC0300801) 中国科学院前沿科学重点研究项目(QYZDY-SSW-JSC005) 国家自然科学基金重点项目(61233013)
【分类号】：TP241
【正文快照】： 1引言(Introduction)水下机械手是水下作业不可或缺的一部分,被广泛配置于HOV(human occupied vehicle)、ROV(remotely operated vehicle)等水下机器人上,用以代替人在水下进行复杂、艰苦甚至危险工作[1-4],其作业性能很大程度上决定了水下机器人系统整体的作业能力.水下机械

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