A Machine Learning Empowered Shape Memory Alloy Gripper With Displacement-Force-Stiffness Self-Sensing

Abstract

This paper proposes a multiple self-sensing gripper (MssGripper) driven by the shape memory alloy (SMA) and empowered by machine learning algorithms. The MssGripper can identify objects without external sensors. A single SMA wire can drive the gripper for self-sensing accurately. This paper confirms the resistance of the SMA can reflect the phase transition and can be used for displacement and force prediction, as well as for object stiffness prediction. Through machine learning, a backpropagation neural network (BPNN) and long-short-term-memory (LSTM) are used to establish multiple self-sensing models for prediction. The robustness experimental results show that the self-sensing models based on LSTM have higher prediction accuracy. The average root-mean-square errors of displacement prediction and force prediction are 0.063 mm and 0.236 N, respectively, and the stiffness prediction error is less than 9.4%. Moreover, the accuracy of the classifier in stiffness identification is 97.2%. The MssGripper can accurately predict the displacement, force, and stiffness and identify objects such as springs, rubber bars and steel bars. The establishment of the models expands the novel idea of gripper sensing, which is beneficial to promoting miniaturization and compactness.