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

Yongli Yan¹, Tiansheng Sun², Teng Ren³

¹Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing 100083, China.

Mathematical Biosciences and Engineering : MBE

|March 29, 2024

Summary

Minimally invasive surgery robots lack force feedback, increasing risks. A new method predicts clamping force using clamp motion, improving surgical safety and instrument-tissue interaction perception.

Keywords:

BP neural network Sparrow search algorithm clamp force estimation minimally invasive surgical robot

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Area of Science:

Robotics
Surgical Technology
Biomedical Engineering

Background:

Minimally invasive surgical robots lack effective gripping force feedback.
This deficiency hinders accurate force perception between instruments and tissues, elevating surgical risks.
Integrating force sensors into existing systems presents a significant challenge.

Purpose of the Study:

To propose a novel clamping force prediction method for minimally invasive surgical tools.
To address the challenge of integrating force feedback into existing robotic systems.
To enhance surgical safety and efficiency through improved force estimation.

Main Methods:

Developed a clamping force prediction model using mechanical clamp blade motion parameters.
Analyzed the relationship between clamping force, displacement, compression speed, and contact area via compression experiments on pig kidney tissue.
Utilized a backpropagation (BP) neural network optimized by the Sparrow Search Algorithm (SSA) for real-time force prediction.

Main Results:

The Sparrow Search Algorithm (SSA)-optimized BP model demonstrated superior accuracy and convergence speed compared to traditional BP and genetic algorithm-optimized (GA) BP models.
The proposed method enables real-time prediction of clamping force.
Accurate force estimation between surgical instruments and tissues was facilitated.

Conclusions:

The SSA-optimized BP neural network provides an effective solution for real-time clamping force prediction in minimally invasive surgery.
This approach offers technical support for enhancing surgical safety and efficiency.
Presents a novel research direction for developing force feedback systems in surgical robotics.