In this work we address the inverse kinetics problem of motion planning of the soft actuators driven by three chambers. Although the mathematical model describing inverse dynamics of this kind of actuator can been employed, this model is still a complex system. On the one hand, the differential equations are nonlinear, therefore, it is very difficult and time consuming to get the analytical solutions. Since the exact solutions of the mechanical model are not available, the elements of the Jacobian matrix cannot be calculated. On the other hand, material model is a complicated system with significant nonlinearity, non-stationarity, and uncertainty, making it challenging to develop an appropriate system model. To overcome these intrinsic problems, we propose a back-propagation (BP) neural network learning the inverse kinetics of the soft manipulator moving in three-dimensional space. After the training, the BP neural network model can represent the relation between the manipulator tip position and the pressures applied to the chambers. The proposed algorithm is very precise, and computationally efficient. The results show that a desired terminal position can be achieved with a degree of accuracy of 2.59% relative average error with respect to the total actuator length, demonstrate the ability of the model to realize inverse kinematic control.
翻译:在这项工作中,我们解决了由三个会议厅驱动的软动动动动器运动规划反动动的问题。虽然可以使用描述这种动动器反动的数学模型,但这一模型仍然是一个复杂的系统。一方面,差异方程式是非线性,因此,获得分析解决方案非常困难和耗时。由于机械模型的精确解决方案不存在,因此无法计算雅各布式矩阵的元素。另一方面,材料模型是一个复杂的系统,具有明显的非线性、非静止性和不确定性,因此开发一个适当的系统模型具有挑战性。为了克服这些内在问题,我们提议了一个反向调整(BP)神经网络,学习在三维空间移动的软操纵器的反动动性。在培训后,BP神经网络模型可以代表操纵器端的位置和对会议厅的压力之间的关系。拟议的算法非常精确,并且计算有效。结果显示,一个理想的终端位置可以实现,其模型的精确度为2.59%的相对控制能力,显示动作控制能力的总的精确度。