Contact Force Estimation by Fusing Current and Tactile Information Through KF for Robot Arm

Abstract

Accurate contact force estimation is crucial for interactions between the robot arm and its environment. Presently, contact force estimation based on current information often suffers from biases caused by unmodeled errors, noise, and various other factors. In contrast, tactile sensors information effectively complements current information, enhancing the estimation of contact forces in robot arm interactions. To improve the accuracy of force estimation without force/torque sensors, we propose an information fusion method utilizing Kalman Filter (KF) to incorporate motor current with the tactile sensor for a nursing robot arm with Differential Modular Joints (DMJ). The coupling dynamic model of the DMJ and the contact force estimation model of the tactile sensor are established respectively. Subsequently, a linear KF fusion method is introduced based on these two methods. The experimental results indicate that contact force can be reliably estimated without force/torque sensors, significantly enhancing the accuracy of force estimation