SAM: Semi-Active Mechanism for Extensible Continuum Manipulator and Real-time Hysteresis Compensation Control Algorithm

Junhyun Park, Seonghyeok Jang, Myeongbo Park, Hyojae Park, Jeonghyeon Yoon, Minho Hwang·June 26, 2024

Summary

This paper presents a novel Semi-Active Mechanism (SAM) for cable-driven continuum manipulators in endoscopic surgery, addressing workspace limitations and hysteresis issues. The SAM extends workspace during translation without additional components and uses a hysteresis dataset collected with fiducial markers and RGBD sensing. A Temporal Convolutional Network (TCN) based control algorithm compensates for hysteresis, reducing it by up to 69.5% in joint space and 26% in tasks, improving control accuracy and surgical performance. The study designs the SAM, proposes a 1ms latency compensation method, and demonstrates its effectiveness in reducing hysteresis, with potential applications in minimally invasive surgeries. Other research referenced explores similar improvements in tendon-driven continuum robots through various techniques, including hysteresis compensation, machine learning, and calibration for enhanced surgical tool control.

Key findings

16

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

To provide a more accurate answer, I would need more specific information about the paper you are referring to. Please provide me with the title of the paper or a brief description of its topic so that I can assist you better.


What scientific hypothesis does this paper seek to validate?

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What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

I would be happy to help analyze the new ideas, methods, or models proposed in a paper. Please provide me with the specific details or key points from the paper that you would like me to analyze. The paper "SAM: Semi-Active Mechanism for Extensible Continuum Manipulator and Real-time Hysteresis Compensation Control Algorithm" introduces a novel Semi-Active Mechanism (SAM) for cable-driven continuum manipulators in endoscopic surgery, offering several key characteristics and advantages over previous methods .

  1. Workspace Extension: The SAM addresses workspace limitations by extending the workspace during translation without the need for additional components, enhancing the manipulator's reach and flexibility .

  2. Hysteresis Compensation: The SAM utilizes a hysteresis dataset collected with fiducial markers and RGBD sensing to compensate for hysteresis issues. Through a Temporal Convolutional Network (TCN) based control algorithm, hysteresis is reduced by up to 69.5% in joint space and 26% in tasks. This reduction in hysteresis improves control accuracy and surgical performance .

  3. Latency Compensation: The study proposes a 1ms latency compensation method, which contributes to the effectiveness of the SAM in reducing hysteresis and enhancing surgical tool control .

  4. Innovative Approach: The SAM design and the real-time hysteresis compensation control algorithm represent an innovative approach to addressing workspace limitations and hysteresis issues in cable-driven continuum manipulators, particularly in the context of minimally invasive surgeries .

  5. Comparative Advantages: Compared to previous methods, the SAM offers improved workspace extension, enhanced hysteresis compensation, reduced latency, and better control accuracy, making it a promising advancement in the field of endoscopic surgery and robotic manipulators .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

To provide you with information on related research and noteworthy researchers in a specific field, I would need more details about the topic or field you are referring to. Could you please specify the area of research or topic you are interested in so that I can assist you better?


How were the experiments in the paper designed?

To provide you with a detailed answer, I would need more specific information about the paper you are referring to. Could you please provide me with the title of the paper or some key details about the experiments so that I can assist you better?


What is the dataset used for quantitative evaluation? Is the code open source?

To provide you with accurate information, I need more details about the specific project or research you are referring to. Could you please provide more context or details about the dataset and code you are inquiring about?


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

To provide an accurate analysis, I would need more specific information about the paper, such as the title, authors, research question, methodology, and key findings. Without these details, it is challenging to assess the quality of support for the scientific hypotheses presented in the paper. If you can provide more context or specific details, I would be happy to help analyze the experiments and results in the paper.


What are the contributions of this paper?

To provide a more accurate answer, could you please specify which paper you are referring to?


What work can be continued in depth?

To delve deeper into the topic, further exploration can be conducted on the hysteresis compensation control algorithm proposed for the Semi-Active Mechanism (SAM) in the context of the extensible continuum manipulator. This algorithm leverages Temporal Convolutional Networks (TCN) to predict calibrated command joint angles based on physical joint angles, aiming to accurately reach desired joint angles . Additionally, investigating the impact of different input sequence lengths on the TCN models, such as L = 10, 50, 100, 150, can provide insights into capturing the history-dependent hysteresis behavior in continuum manipulators . Furthermore, exploring the design and performance evaluation of the TCN models for hysteresis compensation, as detailed in the context, can offer a comprehensive understanding of how these models enhance accuracy and stability in controlling the manipulator .

Tables

4

Introduction
Background
Current limitations in cable-driven continuum manipulators
Importance of workspace and hysteresis in endoscopic surgery
Objective
Development of SAM to address workspace and hysteresis issues
Aim to improve control accuracy and surgical performance
Method
Data Collection
Hysteresis Dataset
Fiducial markers and RGBD sensing for hysteresis measurement
Dataset generation and characteristics
Data Preprocessing
Cleaning and processing of collected data
Feature extraction for TCN model
Temporal Convolutional Network (TCN) Design
Architecture and implementation of the TCN
Hysteresis compensation algorithm
Latency Compensation
1ms Latency Compensation Method
Real-time control strategy for low latency
Impact on control performance
Experimental Validation
SAM integration with continuum manipulator
Hysteresis reduction results in joint space and task performance
Results
Hysteresis reduction percentages achieved
Improved control accuracy and surgical performance metrics
Comparison with Existing Research
Tendon-driven continuum robots improvements
Hysteresis compensation, machine learning, and calibration techniques
Applications and Future Directions
Potential use in minimally invasive surgeries
Opportunities for further research and development
Conclusion
Summary of SAM's contributions and implications for endoscopic surgery
Limitations and future work considerations
Basic info
papers
robotics
artificial intelligence
Advanced features
Insights
What are the potential applications of the SAM in surgical contexts?
How does the TCN-based control algorithm contribute to the paper's findings?
What problem does the Semi-Active Mechanism (SAM) address in cable-driven continuum manipulators?
What is the primary focus of the paper?

SAM: Semi-Active Mechanism for Extensible Continuum Manipulator and Real-time Hysteresis Compensation Control Algorithm

Junhyun Park, Seonghyeok Jang, Myeongbo Park, Hyojae Park, Jeonghyeon Yoon, Minho Hwang·June 26, 2024

Summary

This paper presents a novel Semi-Active Mechanism (SAM) for cable-driven continuum manipulators in endoscopic surgery, addressing workspace limitations and hysteresis issues. The SAM extends workspace during translation without additional components and uses a hysteresis dataset collected with fiducial markers and RGBD sensing. A Temporal Convolutional Network (TCN) based control algorithm compensates for hysteresis, reducing it by up to 69.5% in joint space and 26% in tasks, improving control accuracy and surgical performance. The study designs the SAM, proposes a 1ms latency compensation method, and demonstrates its effectiveness in reducing hysteresis, with potential applications in minimally invasive surgeries. Other research referenced explores similar improvements in tendon-driven continuum robots through various techniques, including hysteresis compensation, machine learning, and calibration for enhanced surgical tool control.
Mind map
Impact on control performance
Real-time control strategy for low latency
Hysteresis compensation algorithm
Architecture and implementation of the TCN
Dataset generation and characteristics
Fiducial markers and RGBD sensing for hysteresis measurement
Hysteresis reduction results in joint space and task performance
SAM integration with continuum manipulator
1ms Latency Compensation Method
Temporal Convolutional Network (TCN) Design
Hysteresis Dataset
Aim to improve control accuracy and surgical performance
Development of SAM to address workspace and hysteresis issues
Importance of workspace and hysteresis in endoscopic surgery
Current limitations in cable-driven continuum manipulators
Limitations and future work considerations
Summary of SAM's contributions and implications for endoscopic surgery
Opportunities for further research and development
Potential use in minimally invasive surgeries
Hysteresis compensation, machine learning, and calibration techniques
Tendon-driven continuum robots improvements
Improved control accuracy and surgical performance metrics
Hysteresis reduction percentages achieved
Experimental Validation
Latency Compensation
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Applications and Future Directions
Comparison with Existing Research
Results
Method
Introduction
Outline
Introduction
Background
Current limitations in cable-driven continuum manipulators
Importance of workspace and hysteresis in endoscopic surgery
Objective
Development of SAM to address workspace and hysteresis issues
Aim to improve control accuracy and surgical performance
Method
Data Collection
Hysteresis Dataset
Fiducial markers and RGBD sensing for hysteresis measurement
Dataset generation and characteristics
Data Preprocessing
Cleaning and processing of collected data
Feature extraction for TCN model
Temporal Convolutional Network (TCN) Design
Architecture and implementation of the TCN
Hysteresis compensation algorithm
Latency Compensation
1ms Latency Compensation Method
Real-time control strategy for low latency
Impact on control performance
Experimental Validation
SAM integration with continuum manipulator
Hysteresis reduction results in joint space and task performance
Results
Hysteresis reduction percentages achieved
Improved control accuracy and surgical performance metrics
Comparison with Existing Research
Tendon-driven continuum robots improvements
Hysteresis compensation, machine learning, and calibration techniques
Applications and Future Directions
Potential use in minimally invasive surgeries
Opportunities for further research and development
Conclusion
Summary of SAM's contributions and implications for endoscopic surgery
Limitations and future work considerations
Key findings
16

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

To provide a more accurate answer, I would need more specific information about the paper you are referring to. Please provide me with the title of the paper or a brief description of its topic so that I can assist you better.


What scientific hypothesis does this paper seek to validate?

I would be happy to help you with that. Please provide me with the title or some details about the paper you are referring to so I can assist you better.


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

I would be happy to help analyze the new ideas, methods, or models proposed in a paper. Please provide me with the specific details or key points from the paper that you would like me to analyze. The paper "SAM: Semi-Active Mechanism for Extensible Continuum Manipulator and Real-time Hysteresis Compensation Control Algorithm" introduces a novel Semi-Active Mechanism (SAM) for cable-driven continuum manipulators in endoscopic surgery, offering several key characteristics and advantages over previous methods .

  1. Workspace Extension: The SAM addresses workspace limitations by extending the workspace during translation without the need for additional components, enhancing the manipulator's reach and flexibility .

  2. Hysteresis Compensation: The SAM utilizes a hysteresis dataset collected with fiducial markers and RGBD sensing to compensate for hysteresis issues. Through a Temporal Convolutional Network (TCN) based control algorithm, hysteresis is reduced by up to 69.5% in joint space and 26% in tasks. This reduction in hysteresis improves control accuracy and surgical performance .

  3. Latency Compensation: The study proposes a 1ms latency compensation method, which contributes to the effectiveness of the SAM in reducing hysteresis and enhancing surgical tool control .

  4. Innovative Approach: The SAM design and the real-time hysteresis compensation control algorithm represent an innovative approach to addressing workspace limitations and hysteresis issues in cable-driven continuum manipulators, particularly in the context of minimally invasive surgeries .

  5. Comparative Advantages: Compared to previous methods, the SAM offers improved workspace extension, enhanced hysteresis compensation, reduced latency, and better control accuracy, making it a promising advancement in the field of endoscopic surgery and robotic manipulators .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

To provide you with information on related research and noteworthy researchers in a specific field, I would need more details about the topic or field you are referring to. Could you please specify the area of research or topic you are interested in so that I can assist you better?


How were the experiments in the paper designed?

To provide you with a detailed answer, I would need more specific information about the paper you are referring to. Could you please provide me with the title of the paper or some key details about the experiments so that I can assist you better?


What is the dataset used for quantitative evaluation? Is the code open source?

To provide you with accurate information, I need more details about the specific project or research you are referring to. Could you please provide more context or details about the dataset and code you are inquiring about?


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

To provide an accurate analysis, I would need more specific information about the paper, such as the title, authors, research question, methodology, and key findings. Without these details, it is challenging to assess the quality of support for the scientific hypotheses presented in the paper. If you can provide more context or specific details, I would be happy to help analyze the experiments and results in the paper.


What are the contributions of this paper?

To provide a more accurate answer, could you please specify which paper you are referring to?


What work can be continued in depth?

To delve deeper into the topic, further exploration can be conducted on the hysteresis compensation control algorithm proposed for the Semi-Active Mechanism (SAM) in the context of the extensible continuum manipulator. This algorithm leverages Temporal Convolutional Networks (TCN) to predict calibrated command joint angles based on physical joint angles, aiming to accurately reach desired joint angles . Additionally, investigating the impact of different input sequence lengths on the TCN models, such as L = 10, 50, 100, 150, can provide insights into capturing the history-dependent hysteresis behavior in continuum manipulators . Furthermore, exploring the design and performance evaluation of the TCN models for hysteresis compensation, as detailed in the context, can offer a comprehensive understanding of how these models enhance accuracy and stability in controlling the manipulator .

Tables
4
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