A memory-based robotic control paradigm which learns relationships between a control effort and a change of state is introduced. It has been used to develop a learning control system which implements step responses in...
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A memory-based robotic control paradigm which learns relationships between a control effort and a change of state is introduced. It has been used to develop a learning control system which implements step responses in one dimension on a robotic gripper, with partial success. It was found that velocity as well as positional feedback were required to complete even simple movements. It is believed that aspects of this approach would readily extend to a tactile sensing system.< >
A method is developed for the measurement of short-range visual motion in image sequences, making use of the motion of image features such as edges and points. Each feature generates a Gaussian activation profile in a...
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A method is developed for the measurement of short-range visual motion in image sequences, making use of the motion of image features such as edges and points. Each feature generates a Gaussian activation profile in a spatiotemporal neighborhood of specified scale around the feature itself; this profile is then convected with motion of the feature. The authors show that image velocity estimates can be obtained from such dynamic activation profiles using a modification of familiar gradient techniques. The resulting estimators can be formulated in terms of simple ratios of spatiotemporal filters (i.e. receptive fields) convolved with image feature maps. A family of activation profiles of varying scale must be utilized to cover a range of possible image velocities. They suggest a characteristic speed normalization of the estimate obtained from each filter in order to decide which estimate is to be accepted. They formulate the velocity estimators for dynamic edges in 1-D and 2-D image sequences, as well as that for dynamic feature points in 2-D image sequences.< >
Real-time expert system techniques and applications to robot manipulator control systems are discussed. A novel type of intelligent controller structure, the expert learning controller prototype ELEC (expert learning ...
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Real-time expert system techniques and applications to robot manipulator control systems are discussed. A novel type of intelligent controller structure, the expert learning controller prototype ELEC (expert learning controller), is developed for the trajectory tracking control in repeat operations. ELEC, acting as an intelligent real-time controller in a closed-loop system, can modify the control series in a human-expert-like manner using the experience of previous operations in order to force the system output to converge to the prespecified desired trajectory. ELEC does not require the knowledge of system models, so it can be used in a fairly wide range of control problems. A numerical example for a two-link robot manipulator is given which shows the satisfactory performance of ELEC.< >
A paradigm for optimum estimation of fused multiple sensor data is developed in order to best use the sensor information in the time evolving environment. Two basic approaches have been developed: dynamic moving quadr...
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A paradigm for optimum estimation of fused multiple sensor data is developed in order to best use the sensor information in the time evolving environment. Two basic approaches have been developed: dynamic moving quadratic curve fitting and weighted least mean square error. These two approaches are shown to be advantageous in terms of accuracy, speed, and versatility. The theoretical frameworks presented are supported by sets of simulation data.< >
A decentralized adaptive control is proposed to stabilize and track the nonlinear interconnected subsystems with unknown parameters. The adaptation of the controller gain is derived by using model reference adaptive c...
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A decentralized adaptive control is proposed to stabilize and track the nonlinear interconnected subsystems with unknown parameters. The adaptation of the controller gain is derived by using model reference adaptive control theory based on Lyapunov's direct method. The adaptive gains consist of sigma , proportional, and integral combination of the measured and reference values of the corresponding subsystem. The proposed control is applied to the joint control of a two-link robot manipulator, and the performance in computer simulation corresponds with what is expected in theoretical development.< >
PAR (principal axis representation) for rotational parts (i.e., solids of revolution) is proposed as an internal representation scheme for constructive solid geometry (CSG). They key idea of PAR is to represent an obj...
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PAR (principal axis representation) for rotational parts (i.e., solids of revolution) is proposed as an internal representation scheme for constructive solid geometry (CSG). They key idea of PAR is to represent an object uniquely by its principal axis and a set of boundary curves. Since PAR is in an evaluated form, geometrical properties of parts can be computed more directly and efficiently from this evaluated representation than from the original CSG tree. The scheme is described, and operations (e.g. union) on PARs are defined. The uniqueness of the scheme is proved. An algorithm that converts a CSG representation into PAR is presented along with examples. The equivalence of PAR to the CSG scheme for rotational parts is proved.
The paper summarizes the current research in the computervision Research laboratory at the University of Michigan. The laboratory concentrates on developing generic vision algorithms for industrial applications. Gene...
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We have taken up the challenge of integrating telemanipulation technology and autonomous system technology. We are seeking methods for integration at a fundamental rather than an ad-hoc level. We believe that success ...
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A decentralized adaptive control scheme is presented for multilink robot arms. The structure of dynamic and force interactions in the robot arm allows local high gain feedback control to stabilize the overall system, ...
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A decentralized adaptive control scheme is presented for multilink robot arms. The structure of dynamic and force interactions in the robot arm allows local high gain feedback control to stabilize the overall system, and cause the arm to track reference trajectories to any accuracy desired. The adaptive law is locally stable with an attractive set that is adjustable a-priori using minimal knowledge of robot parameters. Robot manipulators control using decentralized approach is highly desirable in that it facilitates real-time implementation.
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