This paper proposes two simple schemes for adaptive control of robot manipulator, to achieve trajectory tracking. The state feedback controller consists of feedforward from the reference position trajectory, feedback ...
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This paper proposes two simple schemes for adaptive control of robot manipulator, to achieve trajectory tracking. The state feedback controller consists of feedforward from the reference position trajectory, feedback from the actual trajectory, and an auxiliary input. The feedforward/feedback controller is different from the state feedback controller in that it consists of feedforward from the reference position, velocity, and acceleration trajectory based on “inverse” dynamics of robot manipulator. The feedforward and feedback gains and the auxiliary input are adapted using adaptive control theory based on Lyapunov's direct method, and using only the local information of the corresponding joint. The proposed control schemes are computationally fast and do not require a priori knowledge of the parameter of the manipulator or the payload. Simulation results are presented in support of the proposed schemes.
An an application of robotics in physical rehabilitation therapy, a robotic system consisting of two planar robot arms, each with two degrees of freedom, is considered. This robotic system, when coupled across a human...
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An an application of robotics in physical rehabilitation therapy, a robotic system consisting of two planar robot arms, each with two degrees of freedom, is considered. This robotic system, when coupled across a human joint, provides a vehicle for rehabilitation of the joint following surgery or trauma. A novel approach for estimation of body segment parameters is formulated that uses state and output information from the robot system to improve these estimates. In addition, redundant sensors are used to improve the accuracy of the estimates. The dynamic equations for a single robot arm are provided and the system is simulated. Therapeutic applications of the robotic system are discussed and the sensitivity of the measured forces with respect to the robot arm joint angles is studied in order to find an optimum orientation of the system for the best possible estimation. The application of this system to both rehabilitation and sports medicine is also discussed.< >
The use of multisensor integration and fusion enables a multisensor-based mobile robot to operate in uncertain or unknown dynamic environments. After first distinguishing between multisensor integration and the more r...
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A flexible robotic system with intelligent sensory capability which can be used in an industrial environment to autonomously perform sophisticated manufacturing tasks that are typically done by manual labor is present...
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Issues concerning the effective integration of multiple sensors into the operation of intelligent systems are presented, and a description of some of the general paradigms and methodologies that address this problem i...
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In this paper, the usefulness of applying the complex as well as ordinary moment features for similar object recognition using a tactile array sensor has been explored. Some complex moment invariants have been derived...
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This paper describes a method for computing Gaussian and mean curvature maps from range data and a modification to this method aimed at facilitating its implementation as a VLSI circuit. The curvature computations con...
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This paper summarizes the design of a convolution processor card that is very low in cost, easy to use and most importantly, performs a 9 × 9 convolution in less than a second. Its high-performance is attributed ...
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The objective of this paper is to develop an innovative 3-D objects recognition algorithm which solves correspondence problem and reduces computational cost for recognition. Correspondency between features of unknown ...
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The authors address the problem of robot multisensor fusion and integration with special emphasis on optimal estimation of fused sensor data. The investigation is based on a Unimation PUMA 560 robot and various extern...
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The authors address the problem of robot multisensor fusion and integration with special emphasis on optimal estimation of fused sensor data. The investigation is based on a Unimation PUMA 560 robot and various external sensors. These include overhead vision, eye-in-hand vision, proximity, tactile array, position, force/torque, crossfire, overload, and slip sensing devices. The efficient fusion of data from different sources will enable the machine to respond promptly in dealing with the real world. Towards this goal, the general paradigm of a sensor data fusion system has been developed, and some simulation results as well as results from the actual implementation of certain concepts of sensor data fusion have been demonstrated.< >
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