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Inverse kinematics solution for 6R serial manipulator based on RBF neural network

机译:基于RBF神经网络的6R串行机械手逆运动学解决方案。

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Calculation of 6R serial manipulator's inverse kinematics is a highly complex nonlinear mapping problem. As the process of 6R serial manipulator's inverse kinematics calculating concerned, these traditional algorithms, like inverse transformation, geometry, pieper solution, etc., are very complex, and they often involve in the problem of multiple solutions and singularity. So, the calculation efficiency is very low. Artificial Neural Network (ANN) has a great advantage in solving these problems of nonlinear mapping, like parallel processing, self-adaptive, and fast, efficient handling of the multivariable system, etc. So, ANN has been widely used to solve the complex kinematics problems of the redundant robotic manipulators. This paper adopts a RBF-based neural network to solve the inverse kinematics problem of 6R serial manipulator. From the final simulation, we can find that, by using this new calculating algorithm, it can give an accurate inverse kinematics solution fast and with a high efficiency. Apart from these, when using in practice, it also reflects a perfect performance.
机译:6R串行机械手的逆运动学计算是一个高度复杂的非线性映射问题。作为6R串行机械手逆运动学计算的过程,这些传统算法,如逆变换,几何,微分方程等,都非常复杂,经常涉及多重解和奇异性问题。因此,计算效率非常低。人工神经网络(ANN)在解决非线性映射的这些问题方面具有巨大优势,例如并行处理,自适应以及对多变量系统的快速有效处理等。因此,ANN已被广泛用于解决复杂的运动学冗余机械手的问题。本文采用基于RBF的神经网络来解决6R串行机械手的逆运动学问题。从最终的仿真中我们可以发现,通过使用这种新的计算算法,它可以快速,高效地提供精确的逆运动学解决方案。除此之外,在实践中使用时,它还体现出了完美的性能。

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