Low degree-of-freedom parallel robot multi-index optimization design method based on interval analysis

Abstract

The invention relates to a low degree-of-freedom parallel robot multi-index optimization design method based on interval analysis. The method comprises the following steps: 1. conducting mathematical modeling of property indexes, determining basic geometric parameters and initial range of a parallel robot in needed of optimal designation, and determining compulsory design indexes on a work space, parasitic movement and precision modeling; 2. optimally designing the work space, the parasitic movement and the precision modeling based on an interval analysis algorithm to respectively obtain almost all solution intervals of the geometric parameters which can respectively satisfy the work space, the amplitude limit of the parasitic movement and the requirements of precision design; 3. calculating feasible solutions which can satisfy all compulsory requirements; and 4. carrying out central sampling and calculating compromising design indexes on sampling points. The invention provides a new thought capable of systematically solving the multi-index optimization problem, thereby a weight value is not needed to be selected, extremely small local is not entrapped; and the method has an incremental and opening algorithm structure.

Description

technical field [0001] The invention relates to a robot design method, in particular to a multi-index optimization design method for a low-degree-of-freedom parallel robot based on interval analysis, and belongs to the technical field of intelligent robots. Background technique [0002] The multi-degree-of-freedom movement of a parallel robot is based on the mechanical design of the parallel mechanism, and its mechanical structure is mainly composed of a moving platform, a fixed platform and a kinematic chain connecting the two platforms. The moving platform and the fixed platform are distributed up and down, and the degree of freedom of the parallel robot is directly related to the number of kinematic chains. Parallel robots have many advantages, such as stronger rigidity, higher positioning accuracy (small cumulative error), high speed, high load capacity, etc. Therefore, the research on parallel mechanisms has received extensive attention. However, most of the landmark r...

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Problems solved by technology

However, this assumption is difficult to satisfy for parallel robots. For example, the design requirement that the working space of parallel robots must contain a cuboid with a given volume cannot be accurately described mathematically.

On the other hand, the calculation of the optimization index also becomes inaccurate due to computational complexity and numerical errors

[0010] 2. The weighted evaluation function method has the local minimum value problem of numerical optimization

The mechanism model of the parallel robot has strong nonlinearity, and there are many local minimum points. The local minimum points will cause the design result to deviate, resulting in a design result that is very different from the optimal mechanism, which cannot meet the design requirements.

[0011] 3. The most fatal shortcoming of the weighted evaluation function method is how to select the weight

But so far, there is no mature and intuitive method for how to select weights.

[0012] 4. Another disadvantage of the weighted evaluation function method is that it can only provide one optimization result

[0015] 5. In the usual evaluation criterion function method, the factor of post-processing error cannot be considered

If the evaluation criterion function is very sensitive to design errors, the final design results will be far from the theoretical analysis results due to the existence of post-processing errors

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