Translational objects dynamic modeling and correction for point cloud augmented virtual reality–based teleoperation

Abstract

It is a challenging task for operators to interact with the remote environment without its geometric and dynamic knowledge during teleoperation. In this article, a novel system architecture for implementing the translational object modeling and correction during remote interaction is proposed to reconstruct the haptic interaction and predict the object motion at the local virtual reality–based teleoperation. First, a stress mutation analysis method is proposed for segmenting the translational object motion into static phase, critical phase, and sliding phase. And the static limiting friction is originally estimated in the teleoperation area. Meanwhile, mass-damper-spring model and adapted Karnopp friction model are adopted for dynamic modeling in each phase. Second, a novel adaptive forgetting factor recursive least square method is studied for high-accuracy parameter estimation. With the estimated model parameters, the motion of the translational object is predicted at the master side. Meanwhile, for model consistence between the real and virtual environments, a new correction strategy is used to adaptively update the environment model. According to the experimental results, the translational object can be accurately modeled in real time, and its motion at the master side can be predicted precisely and corrected promptly.