In this paper, we propose a three-degree-of-freedom (3 DOF) mobile exoskeleton robot for rehabilitation and motion assist in daily activity for physically weak persons. This exoskeleton robot is activated based on the...
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For worm robots applied to pipe inspection and colonoscopy, earthworm-like robots with a locomotion pattern in backward wave or green caterpillar-like robots with a locomotion pattern in forward wave have been studied...
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in this paper, the concept of forcefree control is extended to realize flexible motion of industrial articulated robot arms under the virtual operational circumstances such as free of inertia, friction and gravity thr...
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In this paper, characteristic of the previously proposed forcefree control with independent compensation, in which inertia, friction and gravity torque were compensated independently, was investigated. The proposed fo...
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Hopfield networks are symmetric recurrent neural networks which exhibit motions in the state space which converge to minima of energy. Hopfield networks can be used to solve practical complex problems such as implemen...
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Hopfield networks are symmetric recurrent neural networks which exhibit motions in the state space which converge to minima of energy. Hopfield networks can be used to solve practical complex problems such as implement associative memory, linear programming solvers and optimal guidance problems. In such practical problems, the Hopfield network, may be subject to disturbance signals which can be modelled as finite energy signals. In this paper, we adopt the Lur'e - Postnikov systems approach to analyze Hopfield networks and suggest a training algorithm leading to minimum L2 gain from the disturbance signals to the error of the network with respect and to its equilibrium points. The suggested algorithm is applied to a numerical example from the field of magnetic heading determination.
One of control methods for underactuated manipulators is known as a switching control which selects a partially-stable controller using a prespecified switching rule. A switching computed torque control with a fuzzy e...
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One of control methods for underactuated manipulators is known as a switching control which selects a partially-stable controller using a prespecified switching rule. A switching computed torque control with a fuzzy energy region method has been already proposed. In this approach, some partly stable controllers are designed by the computed torque method, and a switching rule is based on fuzzy energy regions. Design parameters related to boundary curves of fuzzy energy regions are optimized offline by a genetic algorithm (GA). In this paper, we discuss about the controlled system using the proposed method with parameters designed by GA.
Exoskeletons are expected to be used as wearable haptic devices and power assist robot systems. We have been studying exoskeletons to assist the human motion in daily activity and rehabilitation. The EMG (electromyogr...
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Exoskeletons are expected to be used as wearable haptic devices and power assist robot systems. We have been studying exoskeletons to assist the human motion in daily activity and rehabilitation. The EMG (electromyogram), which directly reflects the human motion intention, has been used to control the exoskeleton without any special control equipment. This paper presents a strategy that realizes the effective adaptation of the EMG-based exoskeleton controller (i.e., robot-human interface) to any persons.
A novel online inverse kinematics solution of redundant manipulator to avoid joint limits is presented. A Widrow-Hoff neural network (NN) with a learning algorithm derived by applying Lyapunov approach is introduced f...
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A novel online inverse kinematics solution of redundant manipulator to avoid joint limits is presented. A Widrow-Hoff neural network (NN) with a learning algorithm derived by applying Lyapunov approach is introduced for this problem. Since the inverse kinematics has infinite number of joint angle vectors, a fuzzy neural network (FNN) is designed to provide an approximate value for that vector. This vector is fed into the NN as a hint input vector to guide the output of the NN within the self-motion. This FNN is designed based on cooperatively controlling each joint angle of the manipulator. The joint velocity limits as well as the joint limits are incorporated into this method. Experiments are conducted for the PA-10 redundant arm to demonstrate the efficacy of the proposed control system. A comparative study is made with the gradient projection method.
For Internet-based teleoperation systems, user-friendly natural interfaces are advantageous because those systems are intended to be used by non-experts. In developing user friendly interfaces, natural language commun...
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For Internet-based teleoperation systems, user-friendly natural interfaces are advantageous because those systems are intended to be used by non-experts. In developing user friendly interfaces, natural language communication is mandatory. This work presents a system in which a sub-set of natural language is used to command a tele-robot manipulator doing an object sorting task. The paper discusses about referring to objects with natural language commands such as "pick the small red cube". This is achieved by learning individual lexical symbols that refer to colors, shapes, and sizes independently, and then inferring the meaning of a combination of them.
This paper describes an adaptive control for nonholonomic mobile robots, which are subjected to a suddenly changed disturbance due to the change of payloads. We adopt a control architecture based on a two-degrees-of-f...
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This paper describes an adaptive control for nonholonomic mobile robots, which are subjected to a suddenly changed disturbance due to the change of payloads. We adopt a control architecture based on a two-degrees-of-freedom design, where the feedforward controller is constructed by a neural network (NN) to acquire an inverse dynamical model of the robot, whereas the feedback controller is designed by two methods: one is a conventional PD compensator and the other is an adaptive fuzzy compensator. A concept of virtual master-slave robots is applied to obtain an inverse model of a nonholonomic robot. A compensator needs to be used to reduce the effect of the NN mapping errors or to suppress the effect of a sudden change of payloads. It is demonstrated by several simulations that the present approach is effective for controlling a nonholonomic mobile robot in a navigation of trajectory tracking problem for the positions and azimuth.
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