Collision Detection for Low-Cost Robot Manipulators Using Probabilistic Residual Torque Modeling

Abstract

With the advancement of robot manipulator technologies, developing custom-made low-cost manipulator arms has gained increasing popularity in the field of robotics. However, these low-cost systems often lack precise sensing and control capabilities, making reliable collision detection particularly critical to ensure safe operation. Popular methods of collision detection, which estimate external disturbances such as generalized Momentum Observer (MO) critically rely on an accurate dynamics model, and precise system identification requires joint torque sensors that are expensive for low-cost manipulators. This paper presents a novel approach to improve collision detection for low-cost robot manipulators where modeling error is present and torque sensing is not available. We propose a probabilistic framework using Gaussian Mixture Models (GMM) to capture friction and other unmodeled dynamics of a robot manipulator. Instead of explicitly identifying model parameters, GMM is created on the total residual torque of the MO while running an excitation trajectory. The GMM is then deployed in the main control loop to identify external disturbances from the residual torque of the same momentum observer. The approach is validated on a custom-made 4-Degrees-of-Freedom (DoF) robot arm with modeling error, unmodeled dynamics, and with only joint position measurement. Our method achieves reliable detection on both hard and soft collisions, demonstrating a reduction in the false positive rate by more than 50% compared to conventional MO-based methods with the same true positive rate on the same hardware.