This paper proposes an external force detection method for humanoid robot arm without using joint torque sensors, which can detect the external force of the joint space in real time during the operation of the robot. We first analyzed the structure of the humanoid robot arm we designed, and then established the external force detection model of the robot arm based on robot dynamics and motor dynamics. Subsequently, analyses were conducted on the error of the detection model and the dynamic model error of the robot arm is compensated by using the artificial neural network method to obtain more accurate external force value for the robot arm. In experiment, the accuracy test and the collision test were performed on the detected extern forces of the robot arm. The results show that the method can effectively improve the detection accuracy of the robot arm, and the robot arm can realize the real-time collision detection during its static and running states, which can ensure the safe operation of the robot.

中文翻译：

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基于BP神经网络误差补偿的机械臂无传感器外力检测方法

本文提出了一种不使用关节扭矩传感器的仿人机器人手臂外力检测方法，可以在机器人运行过程中实时检测关节空间的外力。我们首先分析了我们设计的仿人机器人手臂的结构，然后基于机器人动力学和电机动力学建立了机器人手臂的外力检测模型。随后，对检测模型的误差进行了分析，并采用人工神经网络的方法对机械臂的动态模型误差进行补偿，以获得更准确的机械臂外力值。在实验中，对检测到的机械臂外力进行了精度测试和碰撞测试。