Endovascular surgical robots (ESRs) are extensively researched because of their potential to minimize surgeons’ radiation exposure. However, during the surgical operation, it is difficult for ESR to achieve the same flexibility in manipulating the soft catheter and the slender guidewire as human fingers. The flexibility of ESR can be enhanced by incorporating a soft gripper, but the nonlinear deformation of soft material presents challenges in sensing and controlling the force. To address these issues, this study proposes a deep-learning-based force sensing method that enables the flexible ESR to measure the surgical force and torque. The proposed deep-learning model consists of multiple-layer long short-term memory (LSTM) modules and is trained using the robot’s motion and soft gripper deformation datasets. The well-trained LSTM model can predict the operating force and torque in real-time. The contrast experiments with other models demonstrate that our robot exhibits higher force measurement accuracy. Furthermore, a force control strategy is proposed for the application of LSTM-based ESR. In the force control strategy, the robot is encouraged to mimic the dexterity of the surgeon’s fingers and to maintain the force within a safe range. Finally, the proposed strategy is compared with other strategies in vascular phantom experiments and is proven to effectively enhance the safety and efficiency of surgical operations.

中文翻译：

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基于深度学习的柔性血管内手术机器人力传感方法

血管内手术机器人（ESR）因其有可能最大限度地减少外科医生的辐射暴露而受到广泛的研究。然而，在外科手术过程中，ESR很难达到与人类手指一样操纵柔软导管和细长导丝的灵活性。 ESR 的灵活性可以通过结合软夹具来增强，但软材料的非线性变形给力的传感和控制带来了挑战。为了解决这些问题，本研究提出了一种基于深度学习的力传感方法，使灵活的 ESR 能够测量手术力和扭矩。所提出的深度学习模型由多层长短期记忆（LSTM）模块组成，并使用机器人的运动和软夹具变形数据集进行训练。训练有素的 LSTM 模型可以实时预测操作力和扭矩。与其他模型的对比实验表明我们的机器人具有更高的力测量精度。此外，针对基于 LSTM 的 ESR 应用，提出了一种力控制策略。在力控制策略中，鼓励机器人模仿外科医生手指的灵巧性，并将力保持在安全范围内。最后，在血管体模实验中将所提出的策略与其他策略进行比较，证明该策略可以有效提高手术操作的安全性和效率。