Variable Stiffness Control via External Torque Estimation Using LSTM

摘要

Stable contact and safe responses to the collision have been studied to develop interactive robots such as service and collaborative robots. Stable and safe interactions are usually achieved through the inherent compliance of a motion controller with external torque estimation. However, a fixed control gain would sacrifice either compliance or position tracking performance. Additionally, external torque estimation is susceptible to model errors. In this study, a novel variable stiffness control approach is proposed to achieve a high position tracking performance in free motion and compliant behavior in the contact state. For this purpose, a precise estimation of the external torque and control gains that change based on the external torque are required. To estimate the external torque precisely, a collision detecting learning algorithm that uses long short-term memory (LSTM) is adopted. Although this method uses only proprioceptive sensors, its torque estimation capability is comparable to that of methods that use additional sensors. Then, the stiffness of a motion controller is adjusted based on the external torque in the stable region. Moreover, by adopting the Operational Space Formulation considering joint elasticity for a motion controller, high position tracking performance can be achieved with only proprioceptive sensors. The performance of the proposed method was validated through comparative experiments with two degrees of freedom (DoF) manipulator.