Accurate dynamic model is essential for the model-based control of robotic systems. However, on the one hand, the nonlinearity of the friction is seldom treated in robot dynamics. On the other hand, few of the previous studies reasonably balance the calculation time-consuming and the quality for the excitation trajectory optimization. To address these challenges, this article gives a Lie-theory-based dynamic modeling scheme of multi-degree-of-freedom (DoF) serial robots involving nonlinear friction and excitation trajectory optimization. First, we introduce two coefficients to describe the Stribeck characteristics of Coulomb and static friction and consider the dependency of friction on load torque, so as to propose an improved Stribeck friction model. Whereafter, the improved friction model is simplified in a no-load scenario, a novel nonlinear dynamic model is linearized to capture the features of viscous friction across the entire velocity range. Additionally, a new optimization algorithm of excitation trajectories is presented considering the benefits of three different optimization criteria to design the optimal excitation trajectory. On the basis of the above, we retrieve a feasible dynamic parameter set of serial robots through the hybrid least square algorithm. Finally, our research is supported by simulation and experimental analyses of different combinations on the seven-DoF Franka Emika robot. The results show that the proposed friction has better accuracy performance, and the modified optimization algorithm can reduce the overall time required for the optimization process while maintaining the quality of the identification results.

Accurate online estimation of the payload parameters benefits robot control. In the existing approaches, however, on the one hand, only the linear friction model was used for online payload identification, which reduced the online estimation accuracy. On the other hand, the estimation models contain much noise because of using actual joint trajectory signals. In this article, a new estimation algorithm based on parameter difference for the payload dynamics is proposed. This method uses a nonlinear friction model for the online payload estimation instead of the traditionally linear one. In addition, it considers the commanded joint trajectory signals as the computation input to reduce the model noise. The main contribution of this article is to derive a symbolic relationship between the parameter difference and the payload parameters and then apply it to the online payload estimation. The robot base parameters without payload were identified offline and regarded as the prior information. The one with payload can be solved online by the recursive least squares method. The dynamics of the payload can be then solved online based on the numerical difference of the two parameter sets. Finally, experimental comparisons and a manual guidance application experiment are shown. The results confirm that our algorithm can improve the online payload estimation accuracy (especially the payload mass) and the manual guidance comfort.

The inertial parameters of payloads attached to the end effector of robots benefit to several robotics applications, such as the model-based control, the task optimization, and so on. These applications require the accurate estimation of the inertial parameters. In the existing payload estimation approaches, however, the data weighting technique, which can reduce the adverse effects of outliers and significantly improve the final results, has not been applied yet. In this article, an accurate identification method based on double weighting for inertial parameters of robot payloads is proposed. In order to obtain the weighting matrices, a modified dynamic parameter identification method with two loops is firstly proposed. Then, based on the identified results of dynamic parameters, a payload identification model based on double weighting is constructed. In addition, the variations of both nonlinear friction parameters and linear friction parameters caused by the payload are considered in this model. Finally, experimental comparisons between our method and another four methods are conducted. The results confirm that our method shows the best performance, especially on improving the identification accuracy of mass and center of mass of the payload.