Enhanced grip force estimation in robotic surgery: A sparrow search algorithm-optimized backpropagation neural network approach

Yongli Yan; Tiansheng Sun; Teng Ren; Li Ding; Yongli Yan; Tiansheng Sun; Teng Ren; Li Ding

doi:10.3934/mbe.2024155

Mathematical Biosciences and Engineering

2024, Volume 21, Issue 3: 3519-3539. doi: 10.3934/mbe.2024155

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Research article

Enhanced grip force estimation in robotic surgery: A sparrow search algorithm-optimized backpropagation neural network approach

1.
Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing 100083, China
2.
The Fourth Medical Center of China General Hospital of People's Liberation Army, Beijing 100700, China
3.
School of Mechanical Engineering, Shenyang University of Technology, Shenyang 110870, China
4.
School of Biological Science and Medical Engineering, Beihang University, Beijing 100083, China

Academic Editor: Yang Kuang

Received: 17 November 2023 Revised: 16 January 2024 Accepted: 22 January 2024 Published: 05 February 2024

The absence of an effective gripping force feedback mechanism in minimally invasive surgical robot systems impedes physicians' ability to accurately perceive the force between surgical instruments and human tissues during surgery, thereby increasing surgical risks. To address the challenge of integrating force sensors on minimally invasive surgical tools in existing systems, a clamping force prediction method based on mechanical clamp blade motion parameters is proposed. The interrelation between clamping force, displacement, compression speed, and the contact area of the clamp blade indenter was analyzed through compression experiments conducted on isolated pig kidney tissue. Subsequently, a prediction model was developed using a backpropagation (BP) neural network optimized by the Sparrow Search Algorithm (SSA). This model enables real-time prediction of clamping force, facilitating more accurate estimation of forces between instruments and tissues during surgery. The results indicate that the SSA-optimized model outperforms traditional BP networks and genetic algorithm-optimized (GA) BP models in terms of both accuracy and convergence speed. This study not only provides technical support for enhancing surgical safety and efficiency, but also offers a novel research direction for the design of force feedback systems in minimally invasive surgical robots in the future.
- minimally invasive surgical robot,
- clamp force estimation,
- BP neural network,
- Sparrow search algorithm
Citation: Yongli Yan, Tiansheng Sun, Teng Ren, Li Ding. Enhanced grip force estimation in robotic surgery: A sparrow search algorithm-optimized backpropagation neural network approach[J]. Mathematical Biosciences and Engineering, 2024, 21(3): 3519-3539. doi: 10.3934/mbe.2024155

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Abstract

The absence of an effective gripping force feedback mechanism in minimally invasive surgical robot systems impedes physicians' ability to accurately perceive the force between surgical instruments and human tissues during surgery, thereby increasing surgical risks. To address the challenge of integrating force sensors on minimally invasive surgical tools in existing systems, a clamping force prediction method based on mechanical clamp blade motion parameters is proposed. The interrelation between clamping force, displacement, compression speed, and the contact area of the clamp blade indenter was analyzed through compression experiments conducted on isolated pig kidney tissue. Subsequently, a prediction model was developed using a backpropagation (BP) neural network optimized by the Sparrow Search Algorithm (SSA). This model enables real-time prediction of clamping force, facilitating more accurate estimation of forces between instruments and tissues during surgery. The results indicate that the SSA-optimized model outperforms traditional BP networks and genetic algorithm-optimized (GA) BP models in terms of both accuracy and convergence speed. This study not only provides technical support for enhancing surgical safety and efficiency, but also offers a novel research direction for the design of force feedback systems in minimally invasive surgical robots in the future.

References

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