This paper introduces a new method for controlling wearable exoskeletons that do not need predefined joint trajectories. Instead, it only needs basic gait descriptors such as step length, swing duration, and walking speed. End point Model Predictive Control (MPC) is used to generate the online joint trajectories based on these gait parameters. Real-time ability and control performance of the method during the swing phase of gait cycle is studied in this paper. Experiments are performed by helping a human subject swing his leg with different patterns in the LOPES gait trainer. Results show that the method is able to assist subjects to make steps with different step length and step duration without predefined joint trajectories and is fast enough for real-time implementation. Future study of the method will focus on controlling the exoskeletons in the entire gait cycle.

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There is a great effort during the last decades towards building robotic devices that are worn by humans. These devices, called exoskeletons, are used mainly for support and rehabilitation, as well as for augmentation of human capabilities. Providing a control interface for exoskeletons, that would guarantee comfort and safety, as well as efficiency and robustness, is still an issue. This paper presents a methodology for estimating human arm motion and force exerted, using electromyographic (EMG) signals from muscles of the upper limb. The proposed method is able to estimate motion of the human arm as well as force exerted from the upper limb to the environment, when the motion is constrained. Moreover, the method can distinguish the cases in which the motion is constrained or not (i.e. exertion of force versus free motion) which is of great importance for the control of exoskeletons. Furthermore, the method provides a continuous profile of estimated motion and force, in contrast to other methods used in the literature that can only detect initiation of movement or intention of force. The system is tested in an orthosis-like scenario, during planar movements, through various experiments. The experimental results prove the system efficiency, making the proposed methodology a strong candidate for an EMG-based control scheme applied in robotic exoskeletons.

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It is the goal of this paper to introduce an analytical model that allows predicting and interpreting the characteristics of constraint forces generated by misaligned joint axes between human operators and wearable robots during physical human-robot interaction (pHRI). The pHRI model is based on geometric parameters that describe the combined human-robot system. It is applied in this paper to measured constraint forces from a pHRI experiment. The model is validated with the experimental data. The geometrical model parameters are identified from force and position measurements by non-linear parameter optimization. The attachment stiffness and the actual offsets between the exoskeleton and the human joints are estimated. For the tested subject, the stiffness reaches 222 N/m and constraint forces are shown to be in the order of plusmn 10 N. It is shown in this paper how an ergonomically designed wearable robot with passive compensation joints can reduce such interaction forces.

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Инженеры из Нидерландов разработали роботизированные протезы ног, при помощи которых пациенты с нарушениями в опорно-двигательной системе могут перемещаться и вновь отрабатывать навыки передвижений.

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Категория: LOPES | Нет комментариев »