A robotic system that can visually track and intercept an arbitrary object which is traveling at an unknown velocity on a conveyor has been presented. A fiber-optic eye-in-hand vision system developed at NCSU is used ...
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A robotic system that can visually track and intercept an arbitrary object which is traveling at an unknown velocity on a conveyor has been presented. A fiber-optic eye-in-hand vision system developed at NCSU is used as an integral part of the entire tracking system. The eye-in-hand system is used to characterize the object trajectory in real time, using a modified optical flow approach. A control strategy has been developed which utilizes the kinematic data that are extracted by the tracking algorithm to intercept the moving object. An overall system configuration and its basic principles are described. The demonstration of the initial results is presented.< >
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.< >
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.< >
The robotic manipulator control performance database has been expanded by experimental evaluation of a joint space model-reference adaptive control technique. The trajectory tracking accuracy of the evaluated Model-Re...
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The robotic manipulator control performance database has been expanded by experimental evaluation of a joint space model-reference adaptive control technique. The trajectory tracking accuracy of the evaluated Model-Reference Adaptive Control technique was insufficient for gross motion control of a six degree of freedom PUMA manipulator. The adaption mechanism did not force the manipulator joints to track the reference model output. The Model-Referenced Adaptive Controller demonstrated efficacy inferior to dynamics based control techniques. Velocity reference input and friction compensation have been identified as essential components of any adaptive PUMA controller. Evaluation results provide valuable insight into further development of adaptive manipulator control methods.
This paper proposes a near-optimum control algorithm for a highly nonlinear robot manipulator model via a parameter sensitivity method. The method uses Pontrvagin's maximum principle and the Riccati formulation of...
This paper proposes a near-optimum control algorithm for a highly nonlinear robot manipulator model via a parameter sensitivity method. The method uses Pontrvagin's maximum principle and the Riccati formulation of the linear state regulator problem. The effectiveness of the proposed method is verified by simulation results for a two-link planar robot.
Vertical integration of transformation of abstract assembly task descriptions into task execution commands is needed to overcome the fundamental factors which limit the widespread application of current robotic system...
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Vertical integration of transformation of abstract assembly task descriptions into task execution commands is needed to overcome the fundamental factors which limit the widespread application of current robotic systems. These factors are the requirements for precise control of the robot task environment, custom-designed fixtures and end-effectors, and on-site robot control programming. These requirements arise because current robotic systems do not have an understanding of the robot cell environment. Further, these systems are not able to react rationally to non-deterministic events which occur during task execution. A research plan which is aimed at the systematic incorporation of limited amounts of machine intelligence into robot task planning and execution is outlined.
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