The shape of flexible endoscopic surgical robot should be obtained to increase control accuracy and prevent unwanted tissue damage. To estimate the shape of flexible manipulator, space efficiency, cost-effectiveness, system complexity, and ease of calibration should be considered to integrate sensors into the manipulator. In this article, we propose a real-time method to estimate the shape of a hyper-redundant manipulator having embedded coiled fiber sensors. The main advantage of this method is guaranteeing shape recognition even when the manipulator is subjected to an external load. The fiber sensors are highly flexible, compact, and inexpensive, as well as they can functionally measure both compressive and tensile strain of hyper-redundant manipulator. The sensor design was optimized to achieve durability and sensitivity. The numbers of sensor and the placements were determined by the analysis of the kinematics and moment distribution of the manipulator. The accuracy of shape estimation was validated experimentally under both free-loading and loading conditions. The proposed method achieved real-time estimating capability with a mean maximum error of each joint position smaller than 3.54% in free-loading condition and 5.47% in loading condition.

中文翻译：

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使用卷绕光纤传感器对超冗余柔性机械手进行实时形状估计。


应获得柔性内窥镜手术机器人的形状，以提高控制精度并防止不必要的组织损伤。为了估计柔性机械手的形状，应考虑空间效率、成本效益、系统复杂性和校准的便利性，以便将传感器集成到机械手中。在本文中，我们提出了一种实时方法来估计具有嵌入式卷绕光纤传感器的超冗余机械手的形状。这种方法的主要优点是即使在机械手受到外部负载时也能保证形状识别。光纤传感器高度灵活、紧凑且价格低廉，并且可以在功能上测量超冗余机械手的压缩应变和拉伸应变。传感器设计经过优化，以实现耐用性和灵敏度。传感器的数量和位置是通过分析机械手的运动学和力矩分布来确定的。在自由加载和加载条件下，形状估计的准确性都经过实验验证。该方法实现了实时估计能力，在自由载荷条件下每个关节位置的平均最大误差小于 3.54%，在载荷条件下小于 5.47%。