Research on the Inverse Kinematics Prediction of a Soft Biomimetic Actuator via BP Neural Network

TL;DR

This paper presents a BP neural network approach to accurately predict the inverse kinematics of a soft biomimetic actuator, overcoming the limitations of traditional models in real-time applications.

Contribution

It introduces a data-driven neural network method for inverse kinematics prediction, improving accuracy and real-time applicability over analytical models.

Findings

Achieved 2.46% average error in terminal position prediction.

Demonstrated better precision than traditional analytical models.

Validated effectiveness in three-dimensional motion planning.

Abstract

In this work, we address the inverse kinetics problem of motion planning of soft biomimetic actuators driven by three chambers. Soft biomimetic actuators have been applied in many applications owing to their intrinsic softness. Although a mathematical model can be derived to describe the inverse dynamics of this actuator, it is still not accurate to capture the nonlinearity and uncertainty of the material and the system. Besides, such a complex model is time-consuming, so it is not easy to apply in the real-time control unit. Therefore, developing a model-free approach in this area could be a new idea. To overcome these intrinsic problems, we propose a back-propagation (BP) neural network learning the inverse kinetics of the soft biomimetic actuator moving in three-dimensional space. After training with sample data, the BP neural network model can represent the relation between the manipulator tip position and the pressure applied to the chambers. The proposed algorithm is more precise than the analytical model. The results show that a desired terminal position can be achieved with a degree of accuracy of 2.46% relative average error with respect to the total actuator length.