Soft resistive network sensor and universal robotic gripper in research

• Main Author: Khaw, Choo Kean
• Other Authors: Yifan Wang
• Format: Final Year Project
• Language: English
• Published: Nanyang Technological University 2023
• Subjects: Engineering::Mechanical engineering
• Online Access: https://hdl.handle.net/10356/168019
• Institution: Nanyang Technological University

Soft robotics has a lot of important roles in various fields such as the medical, electronic industry and food industry. With soft robotics, infinite degree of freedom mechanism or robot arms can be realized and built. There are a lot of soft grippers which have been built in many forms such as the octopus’ legs and more unimaginable forms with the aid of hard frame as support. Hence, to build up an entire soft robot arm, soft robotic sensors are essential to sense the changing of any input from the surroundings and give desire output to the users. A resistive sensor that is made up of soft materials entirely is the first step to step into the soft robotic arm and differentiate itself from the normal sensors that are made of hard materials such as resistors, capacitors, and inductors. In this project, the resistive network sensor was invented and used to detect changes of the environment. The sensor is built up by silicon and carbon grease. Carbon grease has a high resistor value and is sensitive towards external force applied to it when covered up by silicon layers. The resistive network sensor is sensitive to stretch and elongation happening on itself. A soft resistive network sensor function is to identify the shape of the object by touching the surface of the object. By Kirchhoff’s law Matrix, the soft resistive network sensor can have unlimited N-matrix networks but due to the limitation of the testing equipment, a maximum of 4x4 matrix network sensors can only be identified. Therefore, the author will present 1-Directional(1-D), 2x2 network sensors and soft resistive network robot gripper. All the matrix network mentioned are 2-Directional(2-D) sensors.