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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仿生章鱼吸盘具有附着力自感应功能，可实现心脏粘连。


这项研究开发了一种具有集成自感知功能的仿生软章鱼吸引装置，旨在提高心脏手术的精度和安全性。该设备的灵感来自于章鱼粘附各种表面的非凡能力及其复杂的本体感受系统，可以实时调整粘附力。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，实时传达有关接触环境的信息。使用灰狼优化器反向传播 (GWO-BP) 算法处理和解释来自软材料内传感器的信号，表现出复杂的应变特性。该组织稳定器具有仿生章鱼吸盘的自感知能力，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力测试以收集数据。 GWO-BP算法模型实时准确识别和预测吸盘附着力，误差率低于0.97%，平均预测时间为0.0027 s。集成该技术为心脏手术期间的智能监测和附着保证提供了一种新颖的方法。因此，潜在的心脏组织损伤的可能性降低了，未来可应用集成智能仿生粘合软机器人。