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Implementation of real-time motion and force capturing system for tele-manipulation based on sEMG signals and IMU motion data

机译:基于sEMG信号和IMU运动数据的远程操纵力实时捕捉系统的实现

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In this paper, we present a real-time motion and force capturing system for tele-operated robotic manipulation that combines surface-electromyogram (sEMG) pattern recognition with an inertia measurement unit(IMU) for motion calculation. The purpose of this system is to deliver the human motion and intended force to a remote robotic manipulator and to realize multi-fingered activities-of-daily-living (ADL) tasks that require motion and force commands simultaneously and instantaneously. The proposed system combines two different sensors: (i) the IMU captures arm motion, (ii) and the sEMG detects the hand motion and force. We propose an algorithm to calculate the human arm motion using IMU sensors and a pattern recognition algorithm for a multi-grasp myoelectric control method that uses sEMG signals to determine the hand postures and grasping force information. In order to validate the proposed motion and force capturing system, we used the in-house developed robotic arm, K-Arm, which has seven degrees-of-freedom (three for shoulder, one for elbow, and three for wrist), and a sixteen degrees-of-freedom robotic hand. Transmission Control Protocol Internet Protocol (TCP/IP)-based network communication was implemented for total system integration. The experimental results verified the effectiveness of the proposed method, although some open problems encountered.
机译:在本文中,我们提出了一种用于遥控机器人操纵的实时运动和力捕获系统,该系统结合了表面肌电图(sEMG)模式识别和惯性测量单元(IMU)进行运动计算。该系统的目的是将人类的运动和预期的力传递到远程机器人操纵器,并实现需要多指的日常活动(ADL)任务,这些任务需要同时并即时地获得运动和力命令。拟议的系统结合了两个不同的传感器:(i)IMU捕获手臂运动,(ii)和sEMG检测手的运动和力。我们提出了一种使用IMU传感器计算人体手臂运动的算法,以及一种针对多抓取肌电控制方法的模式识别算法,该方法使用sEMG信号确定手部姿势和抓握力信息。为了验证提议的运动和力捕获系统,我们使用了内部开发的机器人手臂K-Arm,该手臂具有七个自由度(三个用于肩部,一个用于肘部,三个用于腕部),并且十六个自由度的机械手。基于传输控制协议Internet协议(TCP / IP)的网络通信已实现整个系统的集成。实验结果验证了该方法的有效性,尽管遇到了一些开放性问题。

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