Simultaneous Estimation of Shape and Contact Force along Highly Deformable Surgical Robots Using Sparse Fiber Bragg Grating Measurements

Core Concepts

A novel data-driven paradigm is proposed for simultaneously estimating the shape and contact force along highly deformable flexible surgical robots using sparse strain measurements from a single-core fiber Bragg grating sensor.

Abstract

The paper introduces a novel data-driven approach for simultaneously estimating the shape and contact force along highly deformable flexible surgical robots. A soft sensing tube with a helically wrapped single-core fiber Bragg grating (FBG) fiber is designed and fabricated to be integrated with a robotic-assisted flexible ureteroscope.
Three different learning models - fully connected (FC), long short-term memory (LSTM), and 1D convolutional neural network (Conv1D) - are proposed to encode the spatial FBG strain features and decode the shape (curvature and twist) as well as the contact force (magnitude and location).
Comprehensive experiments are conducted to evaluate the performance of the proposed methods in both free space without interactions and constrained environments with contact forces. The results demonstrate that the Conv1D network outperforms the other two models and the conventional model-based approach, achieving the smallest average errors in tip position (0.88%), force magnitude (12.39%), and force location (3.16%). The learning-based methods can effectively alleviate the requirement for precise FBG placement and exhibit better accuracy and robustness in dynamic conditions with unknown external interactions.

Stats

The flexible ureteroscope can bend up to 270° with a minimum radius of curvature of 7.5 mm.
The soft sensing tube has an outer diameter of 4 mm and a length of 120 mm, with a helical pitch of 30 mm.
The single-core FBG fiber has 14 FBG sensors with a spatial resolution of 3.3 mm.

Quotes

"Simultaneous perception of shape and contact force along flexible surgical robots is highly demanded for safety-critical surgical interventions."
"Machine learning methods have gained great attention for discovering the representation of highly nonlinear behavior, which can deal with the limitations of model-based approaches and account for increasing sensory noises due to spectral distortion of FBG fiber when bending large deformation."

Key Insights Distilled From

Simultaneous Estimation of Shape and Force along Highly Deformable Surgical Manipulators Using Sparse FBG Measurement

by Yiang Lu,Bin... at arxiv.org 04-29-2024

https://arxiv.org/pdf/2404.16952.pdf

Deeper Inquiries

How can the proposed learning-based methods be extended to handle 3D deformations and multi-axis force sensing for more complex surgical scenarios?

The proposed learning-based methods can be extended to handle 3D deformations and multi-axis force sensing by incorporating advanced sensor fusion techniques and more sophisticated neural network architectures. To address 3D deformations, the spatial encoding of the FBG strains can be enhanced to capture the full three-dimensional shape of the flexible surgical robot. This can involve using multi-core optical fibers with FBGs arranged in different orientations to cover a wider range of deformations. Additionally, the strain encoders can be modified to process 3D strain data and extract features that represent complex deformations accurately.
For multi-axis force sensing, the force estimation head of the network can be expanded to predict forces along multiple axes. This can be achieved by incorporating additional output nodes in the force estimation head corresponding to different force components. Training the network with labeled data that includes force vectors along multiple axes will enable it to learn the relationships between strain measurements and multi-axis forces. By optimizing the network architecture and training process, the model can effectively estimate forces in multiple directions, enhancing the robot's perception capabilities in complex surgical scenarios.

How can the proposed learning-based methods be extended to handle 3D deformations and multi-axis force sensing for more complex surgical scenarios?

The potential challenges and limitations in deploying the FBG-based soft sensing tube in real surgical procedures include issues related to biocompatibility, sterilization, integration with existing surgical instruments, and durability during prolonged use. To address these challenges, several strategies can be implemented:

Biocompatibility: Ensuring that the materials used in the soft sensing tube are biocompatible and safe for use inside the human body is crucial. Conducting thorough biocompatibility tests and using medical-grade materials can mitigate this challenge.

Sterilization: Developing sterilization protocols and ensuring that the soft sensing tube can withstand standard sterilization methods without compromising its functionality is essential. Using materials that are compatible with common sterilization techniques can help overcome this limitation.

Integration: Designing the soft sensing tube to seamlessly integrate with existing surgical instruments, such as endoscopes or robotic arms, is important for practical deployment. Ensuring compatibility with different surgical setups and providing easy attachment mechanisms can facilitate integration.

Durability: Enhancing the durability of the soft sensing tube to withstand the rigors of surgical procedures, including repeated bending, twisting, and insertion, is critical. Using robust materials and reinforcement techniques can improve the tube's longevity and reliability during surgical interventions.

By addressing these challenges through rigorous testing, material selection, and design optimization, the FBG-based soft sensing tube can be effectively deployed in real surgical procedures, providing accurate shape and force estimation for flexible surgical robots.

Can the simultaneous shape and force estimation be further integrated with other sensing modalities, such as vision and actuation data, to enhance the overall perception and control of flexible surgical robots?

Integrating simultaneous shape and force estimation with other sensing modalities, such as vision and actuation data, can significantly enhance the overall perception and control of flexible surgical robots. By combining multiple sources of information, the robot can achieve a more comprehensive understanding of its environment and improve its decision-making capabilities. Here are some ways in which integration with other modalities can enhance robot performance:

Vision Data: Combining shape and force estimation with vision data from cameras or endoscopes can provide visual feedback that complements the sensor-based information. Vision data can help validate the estimated shape, provide contextual information, and enable the robot to adapt to dynamic changes in the surgical environment.

Actuation Data: Integrating shape and force estimation with actuation data from the robot's motors or actuators can enable closed-loop control and feedback mechanisms. By correlating the estimated shape and external forces with the robot's movements, the control system can adjust the robot's behavior in real-time to optimize performance and ensure safety.

Sensor Fusion: Implementing sensor fusion techniques to combine data from multiple modalities can enhance the accuracy and robustness of the perception system. By fusing information from FBG sensors, vision systems, and actuation data, the robot can create a more holistic representation of its surroundings and make more informed decisions during surgical tasks.

Overall, integrating shape and force estimation with vision and actuation data can lead to a more intelligent and adaptive flexible surgical robot, capable of performing complex tasks with precision and efficiency. By leveraging the complementary strengths of different sensing modalities, the robot can enhance its perception capabilities and improve overall surgical outcomes.