A Novel Real-Time Modeling and Perception Method for Surgical Robots Based on Multisensory Data in a Dynamic Unstructured Environment

Abstract

Despite advancements in robotic surgery, recent developments in surgical robots have rarely focused on ensuring the safety of surgeons and other devices within the surgical environment. This paper introduces a novel real-time modeling and perception method tailored for surgical Dynamic Unstructured Environments, leveraging multisensory data. We propose the use of a uniform Cartesian grid (UCG) over the traditional radial non-uniform grid (RNUG) to establish a more accurate offline model of the surgical environment. Furthermore, our approach incorporates multisensory data fusion and UCGs for rapid online updates of environmental models. This dual strategy of offline modeling and online updating addresses both modeling accuracy and efficiency, making UCG modeling more suitable for multisensory fusion compared to RNUG. The proposed methodology enhances the overall safety and operational reliability of surgical robots.