This study develops a biomimetic soft octopus suction device with integrated self-sensing capabilities designed to enhance the precision and safety of cardiac surgeries. The device draws inspiration from the octopus's exceptional ability to adhere to various surfaces and its sophisticated proprioceptive system, allowing for real-time adjustment of adhesive force. The research integrates thin-film pressure sensors into the soft suction cup design, emulating the tactile capabilities of an octopus's sucker to convey information about the contact environment in real time. Signals from sensors within soft materials exhibiting complex strain characteristics are processed and interpreted using the grey wolf optimizer-back propagation (GWO-BP) algorithm. The tissue stabilizer is endowed with the self-sensing capabilities of biomimetic octopus suckers, and real-time feedback on the adhesion state is provided. The embedding location of the thin-film pressure sensors is determined through foundational experiments with flexible substrates, standard spherical tests, and biological tissue trials. The newly fabricated suction cups undergo compression pull-off tests to collect data. The GWO-BP algorithm model accurately identifies and predicts the suction cup's adhesion force in real time, with an error rate below 0.97% and a mean prediction time of 0.0027 s. Integrating this technology offers a novel approach to intelligent monitoring and attachment assurance during cardiac surgeries. Hence, the probability of potential cardiac tissue damage is reduced, with future applications for integrating intelligent biomimetic adhesive soft robotics.

中文翻译：

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仿生章鱼吸盘，具有心脏粘连的附着力自感应功能。


本研究开发了一种具有集成自感应功能的仿生软章鱼抽吸装置，旨在提高心脏手术的精度和安全性。该设备的灵感来自章鱼粘附在各种表面的非凡能力和复杂的本体感觉系统，允许实时调整粘附力。该研究将薄膜压力传感器集成到软吸盘设计中，模拟章鱼吸盘的触觉能力，以实时传达有关接触环境的信息。来自表现出复杂应变特性的软材料中传感器的信号使用灰狼优化器反向传播 （GWO-BP） 算法进行处理和解释。组织稳定器具有仿生章鱼吸盘的自感应能力，并提供粘附状态的实时反馈。薄膜压力传感器的嵌入位置是通过柔性基板的基础实验、标准球形测试和生物组织试验来确定的。新制造的吸盘经过压缩拉脱测试以收集数据。GWO-BP 算法模型实时准确识别和预测吸盘的粘附力，误差率低于 0.97%，平均预测时间为 0.0027 s。集成这项技术为心脏手术期间的智能监测和附件保证提供了一种新的方法。因此，降低了潜在心脏组织损伤的可能性，未来将应用于集成智能仿生胶粘剂软机器人。