John B. Moore

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The problem is examined of estimating the state of a linear dy namical system in the presence of high measurement noise. It is coneluded that optimal filter design maybe simplified to the extent that it need not depend on the solution of a matrix Riccati differential equation )butonly onthesolutionof amatrix linear differential equation. For a related(More)
In this paper, sequential or “on-line” hidden Markov model (HMM) signal processing schemes are derived and their performance illustrated in simulation studies. The on-line algorithms are sequential expectation maximization (EM) schemes and are derived by using stochastic approximations to maximize the Kullback-Leibler information measure. The whemes can be(More)
A key goal in dextrous robotic hand grasping is to balance external forces and at the same time achieve grasp stability and minimum grasping energy by choosing an appropriate set of internal grasping forces. Since it appears that there is no direct algebraic optimization approach, a recursive optimization, which is adaptive for application in a dynamic(More)
We propose a novel algorithm to register multiple 3D point sets within a common reference frame using a manifold optimization approach. The point sets are obtained with multiple laser scanners or a mobile scanner. Unlike most prior algorithms, our approach performs an explicit optimization on the manifold of rotations, allowing us to formulate the(More)
There is a robotic balancing task, namely real-time dextrous-hand grasping, for which linearly constrained, positive definite programming gives a quite satisfactory solution from an engineering point of view. We here propose refinements of this approach to reduce the computational effort. The refinements include elimination of structural constraints in the(More)
This paper presents a Newton–like algorithm for solving systems of rank constrained linear matrix inequalities. Though local quadratic convergence of the algorithm is not a priori guaranteed or observed in all cases, numerical experiments, including application to an output feedback stabilization problem, show the effectiveness of the algorithm.
A detailed study of Oja's learning equation in neural networks is undertaken in this paper. Not only are such fundamental issues as existence, uniqueness, and representation of solutions completely resolved, but also the convergence issue is resolved. It is shown that the solution of Oja's equation is exponentially convergent to an equilibrium from any(More)
One of the central issues in dextrous robotic hand grasping is to balance external forces acting on the object and at the same time achieve grasp slability and minimum grasping effort. A companion paper shows that the nonlinear friction-force limit constraints on grasping forces are equivalent to the positive definiteness of a certain matrix subject to(More)