Manufacturing

Fundamentals Of Robotic Grasping And Fixturing (Series on by Caihua Xiong

By Caihua Xiong

This e-book offers a basic wisdom of robot greedy and fixturing (RGF) manipulation. For RGF manipulation to turn into a technological know-how instead of an artwork, the content material of the booklet is uniquely designed for an intensive figuring out of the RGF from the multifingered robotic hand seize, uncomplicated fixture layout precept, and comparing and making plans of robot grasping/fixturing, and makes a speciality of the modeling and purposes of the RGF. in comparison with present courses, this quantity concentrates extra on summary formula, i.e. mathematical modeling of robot greedy and fixturing. therefore, it will likely be an exceptional reference textual content for tutorial researchers, production and commercial engineers and a textbook for engineering graduate scholars. The ebook presents readers an total photograph and medical foundation of RGF, the great info and mathematic types of constructing and utilizing RGF in undefined, and offers long-term useful info that is crucial and will be utilized by technical professions as an outstanding reference.

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Extra resources for Fundamentals Of Robotic Grasping And Fixturing (Series on Manufacturing Systems and Technology) (Series on Manufacturing Systems and Technology)

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31] Lang S. Differential and Riemannian Manifolds. Berlin: Springer, 1996. [32] Rockafellar R. T. Convex Analysis. Princeton: Princeton University Press, 1970. Chapter 2: Qualitative Analysis and Quantitative Evaluation 29 [33] Nee A. Y. , Senthil A. Advanced Fixture Design for FMS. London: Springer, 1995. [34] Xiong C. , Li Y. , Rong Y. , Xiong Y. L. Qualitative Analysis and Quantitative Evaluation of Fixturing. Robotics and Computer Integrated Manufacturing, 18(5-6), pp. 335-342. 2002. Chapter 3 Stability Index and Contact Configuration Planning of Force-Closure Grasping/Fixturing It is necessary to plan the contact configuration to guarantee a stable grasp.

1, by testing a number of variations and selecting the ones having the best rate of convergence over the entire training set. It is interesting to observe the effects of varing the number of hidden units on the performance of the network. Fig. e. without hidden layer), 6 hidden units and 10 hidden units network. The relationship between the number of training and the elapsed time with respect to different sizes of hidden layer is shown in Fig. 4. Figs. 4 show that the BP-network with 6 hidden units converges more quickly to the smaller error.

10) k where t j is the teacher signal (or desired output). 11) where η is the learning rate, α is the momentum coefficient. According to Hecht-Nielsen [10], for any function F and any ε > 0 , there exists a three-layer BP-network that can approximate F to within ε MSE accuracy. Based on this result, we have constructed a three-layer BP-network to plan the force in the multifingered hand. Sizes of input layer and output layer are decided by the problem. The input unit is 2 (corresponding to ra and rb ) and output unit is 9 (corresponding to the joint driven torques: τ11 , τ12 , τ13 , , τ31 , τ 32 , τ33 ) for our force planning problem.

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