Abstract-In robot design, how to allocate tolerances for parts in manufacturing and assembling of robot is very important because this directly affects product quality and manufacturing cost. This paper introduces a technique using the Generalized Reduced Gradient algorithm optimization to allocate tolerances into robot parts. This method consists of three steps. First, based on the particular structure of robot, various methods are considered before the best method suitable for modeling the associated equation is chosen. Then, a mathematical model for tolerance allocation is formulated and transferred into the non-linear multi-variable optimization problem. Finally, this optimization problem is solved by using the Generalized Reduced Gradient algorithm. Two examples are used to verify the feasibility of the proposed method; the accuracy and effectiveness of the proposed method in producing the tolerance allocations is also illustrated via calculation and simulation results.

This paper proposed a new method to downgrade the kinematic mathematical model of parallel robots. A technique of complement mathematical models uses constraints to change the form of objective functions. An equivalent structure is used to replace the original structure of investigated robots. The difficulties encountered in solving problems having the transcendental form can be avoided by downgrading formula of the new mathematical model. The original formula which is usually in quaternary order can be downgraded to quadratic form. The main advantages of this method are understandable mathematical basis, high accuracy, and quick convergence. Carrying out solutions for pracitical kinematic problems of parallel robots becomes very promissable.

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In order to improve the slow response and weak robustness of fuzzy control for machining process, combining qualitative knowledge expressiveness of fuzzy control with excellent local property of wavelet analysis and quantitative learning ability of neural network, a new kind of fuzzy wavelet neural network controller (FWNNC) is presented and a generalized entropy square error (GESE) function is also defined. The FWNNC is then applied to the on-line control of the cutting force under variable cutting conditions. Simulation results show that the proposed controller is superior to the fuzzy control or the neural network control for machining process and it has better static, dynamic performance. Experimental examples are also given to demonstrate the effectiveness of the proposed controller.

[abstFig src='/00280003/17.jpg' width=""300"" text='Stewart Gough robot and the equivalent substitutional configuration' ] This paper proposes a new method of solving the kinematic problems for parallel robots. The paper content aims to solve nonlinear optimization problems with constraints rather than to directly solve high-order nonlinear systems of equations. The nonlinear optimization problems shall be efficiently solved by applying the Generalized Reduced Gradient algorithm and appropriate downgrade techniques. This new method can be able to find exact kinematic solutions by assigning constraints onto the parameters. The procedure can be done without filtering control results from mathematical solution, from which the control time of manipulators can be reduced. The numerical simulation results in this paper shall prove that the method can be applied to solve kinematic problems for a variety of parallel robots regardless of its structures and degree of freedom (DOF). There are several advantages of the proposed method including its simplicity leading to a shorter computing time as well as achieving high accuracy, high reliability, and quick convergence in final results. Hence, the applicability of this method in solving kinematic problems for parallel manipulators is remarkably high.