Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing 100083, China.
The Fourth Medical Center of China General Hospital of People's Liberation Army, Beijing 100700, China.
Math Biosci Eng. 2024 Feb 5;21(3):3519-3539. doi: 10.3934/mbe.2024155.
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.
微创手术机器人系统中缺乏有效的夹持力反馈机制，阻碍了医生在手术过程中准确感知手术器械和人体组织之间的力，从而增加了手术风险。为了解决在现有系统中集成微创手术工具上的力传感器的挑战，提出了一种基于机械夹钳刀片运动参数的夹持力预测方法。通过对离体猪肾组织进行压缩实验，分析了夹持力、位移、压缩速度以及夹钳刀片压头的接触面积之间的相互关系。随后，使用麻雀搜索算法 (SSA) 优化的反向传播 (BP) 神经网络开发了一个预测模型。该模型能够实时预测夹持力，有助于更准确地估计手术过程中器械和组织之间的力。结果表明，SSA 优化的模型在准确性和收敛速度方面均优于传统的 BP 网络和遗传算法优化的 (GA) BP 模型。这项研究不仅为提高手术安全性和效率提供了技术支持，也为未来微创手术机器人力反馈系统的设计提供了新的研究方向。
