Contact force and torque sensing for serial manipulator based on an adaptive Kalman filter with variable time period

Abstract

• Mode-switching moving average reduces current noise and improve torque confidence. • Time period adapts to achieve tradeoff between response time and precision. • Calibration routines take motor current noise and speed errors into account. • Adaptive Kalman filter with variable time period facilitates force/torque estimation. Contact force and torque sensing approaches enable manipulators to cooperate with humans and to interact appropriately with unexpected collisions. In this paper, a mode-switching moving average with variable time period is proposed to reduce the effects of measured motor current noise and thus provide improved confidence in joint output torque estimation. The time period of the filter adapts continuously to achieve optimal tradeoff between response time and precision of estimation in real-time. An adaptive Kalman filter that consists of the proposed moving average and the classical Kalman filter is proposed. Calibration routines for the adaptive Kalman filter take the measured motor current noise and errors in the speed data from the individual joints into account. The combination of the proposed adaptive Kalman filter with variable time period and its calibration method facilitates force and torque estimation without force/torque sensors. Contact force/torque sensing and response time assessments from the proposed approach were performed on the Universal Robot 5 manipulator with differing unexpected end effector loads. The combined force and torque sensing method led to a reduction of the estimation errors and response time in comparison with the pioneering method, and the effect is further improved as the payload rises. The proposed method can be applied to any robotic manipulators as long as the motor information (current, joint position, and joint velocities) are available and consequently the cost will be reduced dramatically from methods that require load cells.