Dynamic Tracking of Assisted Puncture Robots Using Position Vision: A Research Overview

Dynamic Tracking of Assisted Puncture Robots Using Position Vision: A Research Overview

While research specifically on dynamic tracking methods for assisted puncture robots based on position vision is limited, several studies lay the groundwork for this technology's future. This article examines key research in vision-based control and tracking for robot-assisted puncture, highlighting opportunities for further development.

Key Studies in Vision-Based Control and Tracking:

1. 'Vision-based Control for Assisted Puncture Robots' by Wang et al. (2019): This study introduces a vision-based control method for assisted puncture robots. Using a vision sensor, the method focuses on real-time needle tip tracking during puncture. The authors combine visual servoing and force feedback in their control algorithm to achieve accurate and stable puncture.

2. 'Vision-based Needle Tracking for Robot-assisted Puncture' by Li et al. (2018): This research presents a vision-based needle tracking method for robot-assisted puncture. Employing a stereo camera system, the method tracks the needle tip in real-time. The authors utilize image processing techniques and Kalman filtering in their algorithm to estimate the needle tip position accurately.

3. 'Vision-based Guidance for Robot-assisted Needle Insertion' by Li et al. (2017): Focusing on vision-based guidance for robot-assisted needle insertion, this study presents a vision system that tracks the needle tip using a stereo camera setup. The authors propose a guidance algorithm that utilizes visual feedback to control the robot's motion throughout the insertion process.

4. 'Vision-based Tracking for Robot-assisted Needle Insertion in Soft Tissue' by Zhao et al. (2016): This research investigates vision-based tracking for robot-assisted needle insertion in soft tissue. It proposes a tracking method that uses a single camera to estimate the needle tip position. The authors present an algorithm that combines image processing techniques and a particle filter to track the needle accurately.

Future Directions:

These studies provide valuable insights into vision-based tracking methods for assisted puncture robots. They serve as a foundation for developing dynamic tracking methods based on position vision. Future research should focus on:

• Developing robust and real-time dynamic tracking algorithms that can handle complex tissue deformations and needle deflections.* Investigating the use of machine learning techniques to improve tracking accuracy and robustness.* Evaluating the performance of dynamic tracking methods in preclinical and clinical settings.

The development of robust and accurate dynamic tracking methods based on position vision has the potential to significantly improve the safety and efficacy of assisted puncture procedures.