Contact force and torque estimation for collaborative manipulators based on an adaptive Kalman filter with variable time period.

Contact force and torque sensing approaches enable manipulators to cooperate with humans and to interact appropriately with unexpected collisions. In this thesis, various moving averages are investigated and Weighted Moving Averages and Hull Moving Average are employed to generate a mode-switching moving average to support force sensing. The proposed moving averages with variable time period were used 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 an optimal trade-off between response time and precision of estimation in real-time. An adaptive Kalman filter that consists of the proposed moving averages and the conventional Kalman filter is proposed. Calibration routines for the adaptive Kalman filter interpret the measured motor current noise and errors in the speed data from the individual joints into. The combination of the proposed adaptive Kalman filter with variable time period and its calibration method facilitates force and torque estimation without direct measurement via force/torque sensors. Contact force/torque sensing and response time assessments from the proposed approach are performed on both the single Universal Robot 5 manipulator and the collaborative UR5 arrangement (dual-arm robot) with differing unexpected end effector loads. The combined force and torque sensing method leads to a reduction of the estimation errors and response time in comparison with the pioneering method (55.2% and 20.8 %, respectively), and the positive performance of the proposed approach is further improved as the payload rises. The proposed method can potentially be applied to any robotic manipulators as long as the motor information (current, joint position, and joint velocities) are available. Consequently the cost of implementation will be significantly lower than methods that require load cells

Joint Dynamics and Adaptive Feedforward Control of Lightweight Industrial Robots

The use of lightweight strain-wave transmissions in collaborative industrial robots leads to structural compliance and a complex nonlinear behavior of the robot joints. Furthermore, wear and temperature changes lead to variations in the joint dynamics behavior over time. The immediate negative consequences are related to the performance of motion and force control, safety, and lead-through programming.This thesis introduces and investigates new methods to further increase the performance of collaborative industrial robots subject to complex nonlinear and time-varying joint dynamics behavior. Within this context, the techniques of mathematical modeling, system identification, and adaptive estimation and control are applied. The methods are experimentally validated using the collaborative industrial robots by Universal Robots.Mathematically, the robot and joint dynamics are considered as two coupled subsystems. The robot dynamics are derived and linearly parametrized to facilitate identification of the inertial parameters. Calibrating these parameters leads to improvements in torque prediction accuracy of 16.5 %-28.5 % depending on the motion.The joint dynamics are thoroughly analyzed and characterized. Based on a series of experiments, a comprehensive model of the robot joint is established taking into account the complex nonlinear dynamics of the strain-wave transmission, that is the nonlinear compliance, hysteresis, kinematic error, and friction. The steady-state friction is considered to depend on angular velocity, load torque, and temperature. The dynamic friction characteristics are described by an Extended Generalized Maxwell-Slip (E-GMS) model which describes in a combined framework; hysteresis characteristics that depend on angular position and Coulomb friction that depend on load torque. E-GMS model-based feedforward control improves the torque prediction accuracy by a factor 2.1 and improve the tracking error by a factor 1.5.An E-GMS model-based adaptive feedforward controller is developed to address the issue of friction changing with wear and temperature. The adaptive control strategy leads to improvements in torque prediction of 84 % and tracking error of 20 %