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Considering the difficulty in regeneration the stroke patient's neurons by modern medicine, the rehabilitation robots are widely studied to help the patient reduce influence caused by the stroke illness. In this paper, the lower limb rehabilitation robot, which has eight degree of freedoms, is studied. The motion data of the lower limb rehabilitation robot are got by the great number of models provided...
In this paper we present an artificial neural network based motion and path planning system of a wheeled mobile robot navigating among stationary and moving obstacles. The neural network is aware of its distance sensor readings and its relative position from the target. The neural network is used in this system as a controller, and it is trained using a previously proposed extension of the backpropagation...
This paper analyzes cause of control errors for 5 DOF exoskeleton upper-limb rehabilitation robot. Based on the method of typical repetitive control, let multiple improved repetitive control loops with filtering parallel embed into closed-loop of control system in order to eliminate motion error of rehabilitation robot with multi-channel periodic input signal. The simulation results show that the...
We model the dynamics of non-linear point-to-point robot motions as a time-independent system described by an autonomous dynamical system (DS). We propose an iterative algorithm to estimate the form of the DS through a mixture of Gaussian distributions. We prove that the resulting model is asymptotically stable at the target. We validate the accuracy of the model on a library of 2D human motions and...
This paper describes a dynamic artificial neural network based mobile robot motion and path planning system. The method is able to navigate a robot car on flat surface among static and moving obstacles, from any starting point to any endpoint. The motion controlling ANN is trained online with an extended backpropagation through time algorithm, which uses potential fields for obstacle avoidance. The...
We present a neural network approach to early motor learning. The goal is to explore the needs for boot-strapping the control of hand movements in a biologically plausible learning scenario. The model is applied to the control of hand postures of the humanoid robot ASIMO by means of full upper body movements. For training, we use an efficient online scheme for recurrent reservoir networks consisting...
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