Three-dimensional Morphological Reconstruction of Millimeter-Scale Soft Continuum Robots based on Dual-Stereo-Vision

TL;DR

This paper introduces a dual-stereo-vision method for accurately reconstructing the 3D morphology of millimeter-scale soft continuum robots, enabling detailed modeling of asymmetric structures for advanced robotic applications.

Contribution

It presents a novel dual stereo vision approach using stationary binocular cameras and geometry-based point cloud relocation for precise morphological reconstruction of tiny NTCRs.

Findings

Successfully reconstructed NTCR with 14 out of 16 notch features

Captured morphological details with measurements around 1.5 mm

Demonstrated feasibility for 3D reconstruction of millimeter-scale soft robots

Abstract

Continuum robots can be miniaturized to just a few millimeters in diameter. Among these, notched tubular continuum robots (NTCR) show great potential in many delicate applications. Existing works in robotic modeling focus on kinematics and dynamics but still face challenges in reproducing the robot's morphology -- a significant factor that can expand the research landscape of continuum robots, especially for those with asymmetric continuum structures. This paper proposes a dual stereo vision-based method for the three-dimensional morphological reconstruction of millimeter-scale NTCRs. The method employs two oppositely located stationary binocular cameras to capture the point cloud of the NTCR, then utilizes predefined geometry as a reference for the KD tree method to relocate the capture point clouds, resulting in a morphologically correct NTCR despite the low-quality raw point cloud collection. The method has been proved feasible for an NTCR with a 3.5 mm diameter, capturing 14 out of 16 notch features, with the measurements generally centered around the standard of 1.5 mm, demonstrating the capability of revealing morphological details. Our proposed method paves the way for 3D morphological reconstruction of millimeter-scale soft robots for further self-modeling study.