By M. Farber, R. Ghrist, M. Burger, D. Koditschek

ISBN-10: 0821842463

ISBN-13: 9780821842461

ISBN-10: 1341973115

ISBN-13: 9781341973116

ISBN-10: 2004120045

ISBN-13: 9782004120045

ISBN-10: 2319724354

ISBN-13: 9782319724358

Ever because the literary works of Capek and Asimov, mankind has been serious about the assumption of robots. sleek learn in robotics finds that besides many different branches of arithmetic, topology has a primary position to play in making those grand principles a truth. This quantity summarizes fresh growth within the box of topological robotics--a new self-discipline on the crossroads of topology, engineering and computing device technology. presently, topological robotics is constructing in major instructions. On one hand, it reviews natural topological difficulties encouraged by way of robotics and engineering. however, it makes use of topological rules, topological language, topological philosophy, and in particular constructed instruments of algebraic topology to resolve difficulties of engineering and machine technology. Examples of study in either those instructions are given via articles during this quantity, that's designed to be a mix of a variety of fascinating themes of natural arithmetic and sensible engineering

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Extra resources for Topology and Robotics: July 10-14, 2006, Fim Eth, Zurich

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8) for the given qLPV model using only a minimal number of components. In the nonexact case, a trade-off study between the number of components and the accuracy of the resulting TP model is also provided. The procedures of the TP model transformation involve the discretization of the given qLPV model, and then using higher-order singular value decomposition (HOSVD) to obtain the unique tensor product structure of the given model. Based on this tensor structure we can readily identify the various components of the resultant TP model.

IN × (m + k) × (m + l)) and that of Un as (Mn × In ). If we are executing CHOSVD, then In = Rn = rankn (SD ). If we are executing RHOSVD, then In < Rn at least for one n. Hence, normally we have In ≤ Mn for all n = 1, . . , N. ,iN , in = 1, . . , In as elements. One may further define matrix transformations Tn to transform singular matrices ¯ n: Un to U ¯ n Tn = Un . 16) where S¯ = S N n=1 Tn . 17) ¯ n certain If transformation Tn is defined in a special way, we can incorporate into U convexity types, and hence desirable convex hull, for the later control system design process.

N. ✐ ✐ ✐ ✐ ✐ ✐ ✐ ✐ CHAPTER 2. 2. Computationally, the n-mode product of a tensor by a matrix, A = B ×n U, can be obtained by first finding the n-mode matrix of B, B(n) , computing the product A(n) = UB(n) , and then converting A(n) to recover A. 1. Given the tensor A ∈ RI1 ×I2 ×···×IN and the matrices F ∈ R Jn ×In , G ∈ R Jm ×Im , n m, we have (A ×n F) ×m G = (A ×m G) ×n F = A ×n F ×m G. 2. Given the tensor A ∈ RI1 ×I2 ×···×IN and the matrices F ∈ R Jn ×In , G ∈ RKn ×Jn , we have (A ×n F) ×n G = A ×n (G · F).

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Topology and Robotics: July 10-14, 2006, Fim Eth, Zurich by M. Farber, R. Ghrist, M. Burger, D. Koditschek

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