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A new robot collision detection method: A modified nonlinear disturbance observer based-on neural networks

机译：一种新的机器人碰撞检测方法：基于神经网络的修改非线性干扰观察者

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摘要

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.
机译：碰撞检测是物理人员机器人相互作用中的核心问题，并提出了许多基于机器人动态模型的检测方法。然而，模型不确定性，特别是复杂的摩擦，严重影响这些方法的碰撞检测性能。在本文中，在机器人碰撞检测中首次应用最初提出用于摩擦估计的非线性干扰观察者（NDO）。为了验证NDO的碰撞检测性能优于经典广义的动量观察者（GMO）的碰撞检测性能，比较和分析了各种方法的检测灵敏度，鲁棒性和外部扭矩估计精度。然后，为了消除摩擦不确定性对碰撞检测结果的影响，提出了一种基于神经网络的修改的非线性干扰观察者（MNDO）以提高碰撞检测性能。为了验证算法的有效性，使用6-DOF机器人和两个单个联合平台进行仿真和实验。结果表明，所提出的算法是准确有效的。

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