Manipulator residual estimation and its application in collision detection. Issue 3 (21st May 2018) - Guo, Mingming; Zhang, Hua; Feng, Chuncheng; Liu, Manlu; Huo, Jianwen
Abstract : Purpose: This paper aims to present a method to improve the sensitive and low probabilities of false alarm of a manipulator in a human–robot interaction environment, which can improve the performance of the system owing to non-linear uncertainty in the model of the robot controller. Design/methodology/approach: A novel collision detection method based on adaptive residual estimation is proposed, promoting the detection accuracy of the collision of the manipulator during operation. First, a general momentum residual estimator is designed to incorporate the non-linear factors of the manipulator (e.g. joint friction, speed and acceleration) into the residual-related uncertainty of the model. Second, model parameters are estimated through gradient correction. The residual filter is used to determine the dynamic threshold, resulting in higher detection accuracy. Finally, the performance of the residual estimation scheme is evaluated by comparing the dynamic threshold with residual in real-time experiments where a single Universal Robot 5 robot end–effector collides with the obstacle. Findings: Experimental results demonstrate that the collision detection system can improve sensitivity and lead to low probabilities of false alarm of non-linear uncertainty in the model. Practical implications: The method proposed in this article can be applied to industry and human–robot interaction area. Originality/value: An adaptive collision detection method is proposed in this paper to address non-linear uncertainties of the model in industrial application.