Contact force measurement of the compliant gripper based on a force sensing model.

Abstract

It is challenging for most existing grippers to accurately measure their contact force when grasping unstructured objects. To address this issue, a novel force sensing model is established. A compliant gripper derived by the topology optimization method is introduced, and its actual deformation is measured without contacting by OpenCV. Meanwhile, the hyperelastic constitutive model of flexible materials is further studied by the uniaxial compression test to improve the accuracy of its theoretical deformation. Subsequently, the force sensing model is established based on linear finite element theory and the deep neural network (DNN) algorithm. The nonlinear errors of actual deformation (input layer) and theoretical deformation (output layer) are compensated by the DNN algorithm. This compensated deformation is then input into the linear force sensing model to determine the contact force. Finally, experimental results show that the gripper has a high force sensing accuracy (average error less than 3%) in the middle part. While the force sensing accuracy at the end of the compliant gripper has declined, the contact force measurement of the model in the middle of the new compliant gripper has been effectively verified.