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A dual neural network is presented for the bi-criteria kinematic control of redundant manipulators. To diminish the discontinuity of minimum infinity-norm solutions, the kinematiccontrol problem is formulated in the bi-criteria of the infinity and Euclidean norms. Physical constraints such as joint limits and joint velocity limits are also incorporated simultaneously into the proposed kinematic control scheme. The single-layer dual neural network model with a simple structure is developed for bi-criteria redundant resolution of redundant manipulators subject to robot physical constraints. The dual neural network is shown to be globally convergent to optimal solutions in the bi-criteria sense, and is demonstrated to be effective in controlling the PAl0 robot manipulator. [ABSTRACT FROM AUTHOR]

Studies the problem of modeling and control of multiple cooperative manipulators handling a rigid object. Holonomic property of multiple manipulators; Development of a trajectory tracking controller that yields asymptotic convergence of position errors and bounded interaction forces; Kinematic and dynamic modeling of multiple manipulators.

Modeling dynamic human control strategy (HCS), or human skill in response to real-time sensing is becoming an increasingly popular paradigm in many different research areas. We propose a stochastic similarity measure, based on hidden Markov model analysis, capable of comparing and contrasting stochastic, dynamic, multidimensional trajectories. We first derive and demonstrate properties of the similarity measure for stochastic systems. We then apply the similarity measure to real-time human driving data by comparing different control strategies among different individuals. We show that the proposed similarity measure out performs the more traditional Bayes classifier in correctly grouping driving data from the same individual. Finally, we illustrate how the similarity measure can be used in the validation of models which are learned from experimental data, and how we can connect model validation and model learning to iteratively improve our models of HCS. [ABSTRACT FROM PUBLISHER]