Research

Artificially-Intelligent 3D-Printed Soft Grippers for Ripeness and Stiffness Identification

Project Details

Abstract

Soft robotics has recently begun to play a significant role, as its compliant, flexible structure, which is capable of delicately interacting with the environment, allows it to handle delicate objects such as ripe fruits and vegetables with ease. This work focuses on an artificially intelligent 3D printed soft robotic gripper with embedded pneumatic sensing chambers capable of categorizing tomatoes during harvesting. The ripeness identification process involves two stages: a data collection stage and a classification stage. In the first stage, a closed-loop pressure/force control is used to squeeze the tomato with the gripper, and the resulting pressure versus displacement data is recorded and fed to the custom-designed neural network (NN) in the second stage. The developed NN is a sequential model based on a 1D convolutional neural network (CNN) architecture. The model is trained with an Adam optimizer and tuned hyperparameters, and achieves an accuracy of 87.5% with real-time deployment, achieving an accuracy of 80.55%. This two-stage approach mimics human behavior of assessing the ripeness of fruit, which involves gently applying pressure to the fruit to identify its stiffness through touch and then handling the produce accordingly. This proposed gripper and the developed NN present a reliable and nondestructive solution for produce handling both in the harvesting and quality control stages.