Flexible continuum manipulators are valued for minimally invasive surgery, offering access to confined spaces through nonlinear paths. However, cable-driven manipulators face control difficulties due to hysteresis from cabling effects such as friction, elongation, and coupling. These effects are difficult to model due to nonlinearity and the difficulties become even more evident when dealing with long and coupled, multi-segmented manipulator. This paper proposes a data-driven approach based on Deep Neural Networks (DNN) to capture these nonlinear and previous states-dependent characteristics of cable actuation. We collect physical joint configurations according to command joint configurations using RGBD sensing and 7 fiducial markers to model the hysteresis of the proposed manipulator. Result on a study comparing the estimation performance of four DNN models show that the Temporal Convolution Network (TCN) demonstrates the highest predictive capability. Leveraging trained TCNs, we build a control algorithm to compensate for hysteresis. Tracking tests in task space using unseen trajectories show that the proposed control algorithm reduces the average position and orientation error by 61.39% (from 13.7mm to 5.29 mm) and 64.04% (from 31.17{\deg} to 11.21{\deg}), respectively. This result implies that the proposed calibrated controller effectively reaches the desired configurations by estimating the hysteresis of the manipulator. Applying this method in real surgical scenarios has the potential to enhance control precision and improve surgical performance.

翻译：柔性连续体机械臂在微创手术中具有重要价值，可通过非线性路径进入受限空间。然而，线缆驱动机械臂因摩擦、伸长及耦合等线缆效应产生的磁滞现象，导致控制困难。这些效应由于非线性特性难以建模，尤其当涉及长距离、耦合多段机械臂时更为显著。本文提出一种基于深度神经网络的数据驱动方法，以捕获线缆驱动的非线性及历史状态依赖特性。通过RGBD感知与7个基准标记，我们根据指令关节构型采集实际关节构型数据，建立所提机械臂的磁滞模型。在四种深度神经网络模型的估计性能对比研究中，结果显示时间卷积网络（TCN）展现出最优预测能力。利用训练后的TCN，我们构建了磁滞补偿控制算法。采用未训练轨迹的工作空间跟踪测试表明，该控制算法使平均位置误差降低61.39%（从13.7毫米降至5.29毫米），平均姿态误差降低64.04%（从31.17°降至11.21°）。该结果表明，所提校准控制器通过估计机械臂磁滞效应，可有效达到期望构型。将该方法应用于实际手术场景，有望提升控制精度并改善手术效果。