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Learning the inverse kinetics of an octopus-like manipulator in three-dimensional space

机译：学习三维空间中章鱼形机械手的逆动力学

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This work addresses the inverse kinematics problem of a bioinspired octopus-like manipulator moving in three-dimensional space. The bioinspired manipulator has a conical soft structure that confers the ability of twirling around objects as a real octopus arm does. Despite the simple design, the soft conical shape manipulator driven by cables is described by nonlinear differential equations, which are difficult to solve analytically. Since exact solutions of the equations are not available, the Jacobian matrix cannot be calculated analytically and the classical iterative methods cannot be used. To overcome the intrinsic problems of methods based on the Jacobian matrix, this paper proposes a neural network learning the inverse kinematics of a soft octopus-like manipulator driven by cables. After the learning phase, a feed-forward neural network is able to represent the relation between manipulator tip positions and forces applied to the cables. Experimental results show that a desired tip position can be achieved in a short time, since heavy computations are avoided, with a degree of accuracy of 8% relative average error with respect to the total arm length.
机译：这项工作解决了在三维空间中运动的仿生章鱼式机械手的逆运动学问题。受生物启发的机械手具有圆锥形的柔软结构，赋予其像真正的章鱼手臂一样旋转物体的能力。尽管设计简单，但是通过非线性微分方程描述了由电缆驱动的软圆锥形操纵器，这在解析上很难解决。由于无法获得方程的精确解，因此无法解析地计算雅可比矩阵，也无法使用经典的迭代方法。为了克服基于雅可比矩阵的方法的内在问题，本文提出了一种神经网络，用于学习由电缆驱动的类似章鱼的机械手的逆运动学。在学习阶段之后，前馈神经网络能够表示操纵器尖端位置与施加到电缆上的力之间的关系。实验结果表明，由于避免了繁琐的计算，因此可以在短时间内获得所需的尖端位置，相对于总臂长的相对平均误差的准确度为8％。

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