基于肌电图的机器人腿关节角度估计与控制

Ulrik Mamikoglu, G. Andrikopoulos, G. Nikolakopoulos, Ulrik Roijezon, Mascha Pauelsen, T. Gustafsson
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引用次数: 5

摘要

基于肌电图(EMG)的肌肉骨骼建模在物理治疗和生物启发机器人技术中有许多应用。在这篇文章中,将建立一种新的方法来模拟驱动人类踝关节运动的拮抗肌肉对的动力学。正如将要介绍的那样,肌肉骨骼模型基于多输入单输出(MISO)自回归综合外源输入移动平均(ARIMAX)模型,该模型以综合肌电测量作为输入并估计相应的关节角度。基于该方法,设计了一种模拟人类踝关节屈伸运动的气动人造肌肉(PAM)机器人腿装置,以复制人类运动。实验结果验证了该模型在基于肌电图的机器人腿关节角度估计和控制中的有效性。
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Electromyography based joint angle estimation and control of a robotic leg
Musculoskeletal modeling based on Electromyography (EMG) has many applications in physiotherapy and biologically-inspired robotics. In this article, a novel methodology for the modeling of the dynamics of an antagonistic muscle pair that actuates the human ankle joint movements will be established. As it will be presented, the musculoskeletal model is based on a multi input single output (MISO) auto-regressive integrated moving average with exogenous input (ARIMAX) model, which takes the integrated EMG measurements as input and estimates the corresponding joint angles. Based on this methodology, a Pneumatic Artificial Muscle (PAM) robotic leg setup that mimics the flexion/extension movement of human ankle joint is controlled to replicate the human movement. The experimental results demonstrate the performance of EMG-based joint angle estimation and control of the robotic leg with the proposed model.
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