Design of Heavy-Load Soft Robots Based on a Dual Biomimetic Structure

This study first draws inspiration from the dual biomimetic design of plant cell walls and honeycomb structures, drawing on their structural characteristics to design a flexible shell structure that can achieve significant deformation and withstand large loads. Based on the staggered bonding of this flexible shell structure, we propose a new design scheme for a large-load pneumatic soft arm and establish a mathematical model for its flexibility and load capacity. The extension and bending deformation of this new type of soft arm come from the geometric variability of flexible shell structures, which can be controlled through two switches, namely, deflation and inflation, to achieve extension or bending actions. The experimental results show that under a driving pressure within the range of 150 kpa, the maximum elongation of the soft arm reaches 23.17 cm, the maximum bending angle is 94.2 degrees, and the maximum load is 2.83 N. This type of soft arm designed based on dual bionic inspiration can have both a high load capacity and flexibility. The research results provide new ideas and methods for the development of high-load soft arms, which are expected to expand from laboratories to multiple fields.

Bio-inspired magnetic-driven folded diaphragm for biomimetic robot

Functional soft materials, exhibiting multiple types of deformation, have shown their potential/abilities to achieve complicated biomimetic behaviors (soft robots). Inspired by the locomotion of earthworm, which is conducted through the contraction and stretching between body segments, this study proposes a type of one-piece-mold folded diaphragm, consisting of the structure of body segments with radial magnetization property, to achieve large 3D and bi-directional deformation with inside-volume change capability subjected to the low homogeneous magnetically driving field (40 mT). Moreover, the appearance based on the proposed magnetic-driven folded diaphragm is able to be easily customized to desired ones and then implanted into different untethered soft robotic systems as soft drivers. To verify the above points, we design the diaphragm pump providing unique properties of lightweight, powerful output and rapid response, and the soft robot including the bio-earthworm crawling robot and swimming robot inspired by squid to exhibit the flexible and rapid locomotion excited by single homogeneous magnetic fields.

Design of robot finger based on flexible tactile sensor

In this article, a flexible tactile sensor that made of conductive silicone rubber for dexterous robot hand is designed. The tactile sensor is made up of four microsensors. The maximum gripping force is simulated when the degree of a robot finger joint is 138. Meanwhile, a control system to analyze the creep and hysteresis characteristics and a processing system of the tactile sensor is designed. We also demonstrated an experiment for the application of robot grasp object, showing the finger’s flexibility and sensitivity. Then the feedback data is sent to control system to provide precise grasp action changes for the robot hand.

Optimal control research on a manipulator’s combined feedback device by the variational method genetic algorithm radial basis function method

This article aims to improve the accuracy of each joint in a manipulator and to ensure the high-speed and real-time requirements. A method called the variational method genetic algorithm radial basis function, which is based on a combination feedback controller, is proposed to solve the optimal control problem. It is proposed a combined feedback with a linear part and a nonlinear part. We reconstruct the manipulator’s kinematics and dynamics models with a feedback control. In this model, the optimal trajectory, which was solved by the variation method, is regarded as the desired output. The other one is also established an improved genetic algorithm radial basis function neural network model. The optimal trajectory is rapidly solved by using the desired output and the improved genetic algorithm radial basis function neural network. This method can greatly improve the speed of the calculation and guarantee real-time performance while simultaneously ensuring accuracy.