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A GPU-Accelerated Model-Based Tracker for Untethered Submillimeter Grippers.

Stefano Scheggi1, ChangKyu Yoon2, Arijit Ghosh2

  • 1Department of Biomechanical Engineering, University of Twente, 7522 NB, The Netherlands.

Robotics and Autonomous Systems
|September 5, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for tracking the 3D position and orientation of tiny, untethered grippers using marker-less visual data. The approach enables precise online tracking for micromanipulation and minimally invasive surgery.

Keywords:
Graphics Processing UnitMiniaturized grippersModel-based trackerParticle Swarm Optimization

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Area of Science:

  • Robotics and Automation
  • Computer Vision
  • Biomedical Engineering

Background:

  • Untethered miniaturized grippers are crucial for micromanipulation, microassembly, and minimally invasive surgery.
  • Accurate estimation of gripper configuration is essential for robust task performance.
  • Previous methods primarily focused on 2D tracking using optical images.

Purpose of the Study:

  • To develop a novel solution for estimating and tracking the 3D position, orientation, and configuration of submillimeter grippers.
  • To enable marker-less visual tracking of miniaturized agents from camera and ultrasound images.
  • To provide online tracking capabilities for real-time applications.

Main Methods:

  • The problem is framed as an optimization task solved using a variant of the Particle Swarm Optimization algorithm.
  • The approach is implemented on a Graphics Processing Unit (GPU) for online processing.
  • Evaluation involves image sequences from cameras and B-mode ultrasound, including soft and hard grippers of various sizes.

Main Results:

  • The method successfully tracks submillimeter grippers (750 microns to 4 mm) in diverse scenarios, including grasping biological material.
  • Quantitative analysis on synthetic data shows a mean position error of 25 ± 7 μm and orientation error of 1.7 ± 1.3 degrees.
  • Qualitative results demonstrate robust tracking across different gripper materials, shapes, and sizes.

Conclusions:

  • The proposed marker-less visual tracking technique accurately estimates the full 3D configuration of submillimeter grippers.
  • This method offers a significant advancement for controlling and utilizing miniaturized robotic agents.
  • The approach is adaptable for various stimuli-responsive miniaturized agents in complex environments.