The authors formulate the dextrous manipulation problem for a robot hand. First, dextrous manipulation is decomposed into coordinated manipulation, rolling motion, sliding motion, and finger relocation. Then the autho...
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The authors formulate the dextrous manipulation problem for a robot hand. First, dextrous manipulation is decomposed into coordinated manipulation, rolling motion, sliding motion, and finger relocation. Then the authors develop motion constraints for each of the manipulation modes and show that for finger motions that satisfy these constraints there exists a well-defined lift to the total space that links two contact configurations. Of special note is the incorporation of nonholonomic as well as holonomic and unilateral as well as bilateral constraints in motion planning.< >
Many algorithms have been proposed in the literature for control of multifingered robot hands. The authors compare the performance of several of these algorithms, as well as some extensions of more conventional manipu...
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Many algorithms have been proposed in the literature for control of multifingered robot hands. The authors compare the performance of several of these algorithms, as well as some extensions of more conventional manipulator control laws, in the case of planar grasping. Based on experiments performed on Styx, the most effective control laws are found to be the simple joint control law and the generalized computed torque law. The computed torque control law is shown to be an attractive alternative for position control of multifingered hands.< >
Consideration is given to switched linear resistive networks and nonlinear resistive networks for image smoothing and segmentation problems in robot vision. The latter network type is derived from the former by way of...
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Consideration is given to switched linear resistive networks and nonlinear resistive networks for image smoothing and segmentation problems in robot vision. The latter network type is derived from the former by way of an intermediate stochastic formulation, and a new result relating the solution sets of the two is given for the so-called zero-temperature limit. The authors present simulation studies of several continuation methods that can be gracefully implemented in analog VLSI and that seem to given good results for these nonconvex optimization problems.< >
A robotic system that can visually track and intercept an arbitrary object which is traveling in 2‐D at an unknown velocity on a conveyor is to be presented. An eye‐in‐hand vision system developed by the robotics a...
We study neural network strategies for the control of a dynamic, locomotive system using as a model of a one-legged hopping robot. The control task is to make corrections to the motion of the robot that serve to maint...
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We study neural network strategies for the control of a dynamic, locomotive system using as a model of a one-legged hopping robot. The control task is to make corrections to the motion of the robot that serve to maintain a fixed level of energy (and minimize energy losses), which yields a stable periodic limit cycle in the system's state space corresponding to periodic hopping to a prespecified height. The studied models are Michie and Chambers' BOXES system (1962), the ASE/ACE configuration of Barto and his coworkers (1983), and Anderson/Sutton's two-layered Connectionist model (1986.) Results are demonstrated through numerical simulations, and quantitatively compared to performance obtained by Raibert (1984) for the robotic leg, using full-state feefback. The main difference between Raibert's solution and the `neural' strategies presented here is that our system is not aware of the dynamical model of the plant which it is to control. It has to discover how to control the plant through a long sequence of trial and error experiments.
Results are presented on two neural network strategies for the control of dynamic locomotive systems, in particular a one-legged hopping robot. The control task is to make corrections to the motion of the robot that s...
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Results are presented on two neural network strategies for the control of dynamic locomotive systems, in particular a one-legged hopping robot. The control task is to make corrections to the motion of the robot that serve to maintain a fixed level of energy (and minimize energy losses), which yields a stable periodic limit cycle in the system's state space. Control of the robot is achieved by the use of artificial neural networks (ANNs) with a continuous learning memory. Through continuous reinforcement for past successes and failures, the control system develops a stable strategy for accomplishing the desired control objectives. The results are presented in the form of computer simulation that demonstrate the ability of two different ANNs to devise proper control signals that will develop a stable hopping strategy, and hence a stable limit cycle in the robot's state space, using imprecise knowledge of both the current state and the mathematical model of the robot leg.< >
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.
A new fast transform from curve samples to least-squares normalized B-spline control points is developed and their relation to Fourier descriptors is established. Contour control point sequence scale, translation, and...
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A new fast transform from curve samples to least-squares normalized B-spline control points is developed and their relation to Fourier descriptors is established. Contour control point sequence scale, translation, and rotation are known to produce identical shape model variations. It is shown that under the conditions of our transform, such sequences are also directly applicable to efficient computation of many useful shape features or characteristics. First, methods for computing boundary curvature and mean contour sample from control points are presented. Then, transforms from contour control points to shape moments and projections are developed and tested.
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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