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Kinematical and dynamical modeling of a multipurpose upper limbs rehabilitation robot

机译:多功能上肢康复机器人的动力学和动力学建模

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Knowing accurate model of a system is always beneficial to design a robust and safe control while allowing reduction of sensors-related cost as the system outputs are predictable using the model. In this context, this paper addresses the kinematical and dynamical model identification of the multipurpose rehabilitation robot, Universal Haptic Pantograph (UHP), and present experimental validations of the identified models. The UHP is a Pantograph based innovative robot actuated by two SEAs (Series Elastic Actuator), aiming at training impaired upper limbs after a stroke. This novel robot, thanks to its lockable/unlockable joints, can change its mechanical structure so that it enables stroke patient to perform different training exercises of the shoulder, elbow and wrist. This work focuses on the ARM mode, which is a training mode used to rehabilitate elbow and shoulder. The kinematical model of UHP is identified based on the loop vector equations, while the dynamical model is derived based on the Lagrangian formulation. To demonstrate the accuracy of the models, several experimental tests were performed. The results reveal that the mean position error between estimated values with the model and actual measured values stays in 3 mm (less than 2% of the maximum motion range). Moreover, the error between estimated and measured interaction force is smaller than 10% of maximum force range. So, the developed models can be adopted to estimate motion and force of UHP as well as control it without the need of additional sensors such as a force sensor, resulting in the reduction of total robot cost.
机译:知道系统的准确模型总是有益于设计鲁棒和安全的控制,同时由于使用该模型可以预测系统输出,因此可以减少与传感器相关的成本。在这种情况下,本文讨论了多功能康复机器人通用触觉受电弓(UHP)的运动学和动力学模型识别,并提出了所识别模型的实验验证。 UHP是一款基于受电弓的创新型机器人,由两个SEA(系列弹性致动器)驱动,旨在训练中风后受损的上肢。这款新颖的机器人由于具有可锁定/不可锁定的关节,可以改变其机械结构,从而使中风患者能够进行肩部,肘部和腕部的不同训练。这项工作的重点是ARM模式,这是一种用于恢复肘部和肩膀的训练模式。基于回路矢量方程确定了超高压的运动学模型,而基于拉格朗日公式推导了动力学模型。为了证明模型的准确性,进行了几次实验测试。结果表明,模型估计值与实际测量值之间的平均位置误差保持在3 mm(小于最大运动范围的2%)。此外,估计和测量的相互作用力之间的误差小于最大作用力范围的10%。因此,无需额外的传感器(例如力传感器),就可以采用开发的模型来估算UHP的运动和力以及对其进行控制,从而降低了机器人的总成本。

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