Collision detection methods could reduce collision forces and improve safety during physical human-robot interaction without additional sensing devices. However, current collision detection methods result in an unavoidable trade-off between sensitivity to collisions, peaking value reduction near the initial time, and immunity to measurement noise. In this paper, a novel nonlinear extended state momentum observer (NESMO) is proposed for detecting collisions between a robot body and human under model uncertainties based on only position and current measurements. The collision detection method is divided into three steps. The first step is to identify the robot dynamic model. Then, we can deduce the generalized momentum-based state-space equations from the identified base dynamic parameters. The second step is to construct a NESMO. Benefiting from the fractional power function and the time-varying damping ratio, the NESMO achieves the required monitoring bandwidth with noise immunity. The last step is to design a novel time-varying threshold (TVT) to distinguish the collision signal from the estimated lumped disturbance. As with the dynamic model parameters, the coefficients of TVT could be obtained by offline identification. Combined with NESMO, the method can provide timely and reliable collision detection and estimation under model uncertainties. Simulation and experimental results obtained using a 6-DOF robot manipulator illustrate the effectiveness of the proposed method.

碰撞检测方法可以减少碰撞力并提高人机交互过程中的安全性，而无需额外的传感设备。然而，当前的碰撞检测方法导致在碰撞灵敏度、初始时间附近的峰值降低和对测量噪声的免疫力之间不可避免的权衡。在本文中，提出了一种新的非线性扩展状态动量观测器（NESMO），用于仅基于位置和电流测量来检测模型不确定性下机器人身体与人类之间的碰撞。碰撞检测方法分为三个步骤。第一步是识别机器人动力学模型。然后，我们可以从确定的基本动态参数中推导出基于广义动量的状态空间方程。第二步是构建一个NESMO。受益于分数功率函数和时变阻尼比，NESMO 实现了所需的具有抗噪性的监测带宽。最后一步是设计一个新颖的时变阈值 (TVT) 来区分碰撞信号和估计的集总扰动。与动态模型参数一样，TVT 的系数可以通过离线识别获得。结合NESMO，该方法可以在模型不确定性下提供及时可靠的碰撞检测和估计。使用 6 自由度机器人操纵器获得的仿真和实验结果说明了所提出方法的有效性。最后一步是设计一个新颖的时变阈值 (TVT) 来区分碰撞信号和估计的集总扰动。与动态模型参数一样，TVT 的系数可以通过离线识别获得。结合NESMO，该方法可以在模型不确定性下提供及时可靠的碰撞检测和估计。使用 6 自由度机器人操纵器获得的仿真和实验结果说明了所提出方法的有效性。最后一步是设计一个新颖的时变阈值 (TVT) 来区分碰撞信号和估计的集总扰动。与动态模型参数一样，TVT 的系数可以通过离线识别获得。结合NESMO，该方法可以在模型不确定性下提供及时可靠的碰撞检测和估计。使用 6 自由度机器人操纵器获得的仿真和实验结果说明了所提出方法的有效性。