Affiliations: State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, Heilongjiang, China
Correspondence:
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Corresponding authors. Yanhe Zhu and Jie Zhao, State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, Heilongjiang, China. E-mails: [email protected] (Yanhe Zhu), [email protected] (Jie Zhao).
Abstract: Collision detection is the core issue in physical human–robot interactions, and many detection methods based on robot dynamic models have been proposed. However, model uncertainties, especially complicated friction, seriously affect the collision detection performance of these methods. In this paper, a nonlinear disturbance observer (NDO) originally proposed for friction estimation is applied for the first time in robot collision detection. To verify that the collision detection performance of the NDO is better than that of the classical generalized momentum observer (GMO), the detection sensitivity, robustness and external torque estimation accuracy of each method are compared and analyzed. Then, to eliminate the effects of friction uncertainties on the collision detection results, a modified nonlinear disturbance observer (MNDO) based on neural networks is proposed to improve the collision detection performance. To verify the effectiveness of the algorithm, simulations and experiments are conducted with a 6-DOF robot and two single-joint platforms. The results indicate that the proposed algorithm is accurate and effective.
