We introduce a novel shape-sensing method using Resistive Flex Sensors (RFS) embedded in cable-driven Continuum Dexterous Manipulators (CDMs). The RFS is predominantly sensitive to deformation rather than direct forces, making it a distinctive tool for shape sensing. The RFS unit we designed is a considerably less expensive and robust alternative, offering comparable accuracy and real-time performance to existing shape sensing methods used for the CDMs proposed for minimally-invasive surgery. Our design allows the RFS to move along and inside the CDM conforming to its curvature, offering the ability to capture resistance metrics from various bending positions without the need for elaborate sensor setups. The RFS unit is calibrated using an overhead camera and a ResNet machine learning framework. Experiments using a 3D printed prototype of the CDM achieved an average shape estimation error of 0.968 mm with a standard error of 0.275 mm. The response time of the model was approximately 1.16 ms, making real-time shape sensing feasible. While this preliminary study successfully showed the feasibility of our approach for C-shape CDM deformations with non-constant curvatures, we are currently extending the results to show the feasibility for adapting to more complex CDM configurations such as S-shape created in obstructed environments or in presence of the external forces.

翻译：我们提出了一种使用嵌入在缆驱连续体灵巧机械臂（CDM）中的电阻式柔性传感器（RFS）的新型形状感知方法。RFS主要对变形敏感而非直接受力，因此成为形状感知的独特工具。我们设计的RFS单元是一种成本显著更低且更坚固的替代方案，在用于微创手术的CDM上，其精度和实时性能与现有形状感知方法相当。我们的设计使RFS能够沿CDM内部移动并贴合其曲率，从而无需复杂的传感器配置即可从不同弯曲位置捕获电阻指标。RFS单元使用顶置摄像头和ResNet机器学习框架进行校准。利用3D打印的CDM原型进行的实验显示，平均形状估计误差为0.968 mm，标准误差为0.275 mm。模型响应时间约为1.16 ms，这使得实时形状感知成为可能。尽管这项初步研究成功证明了我们的方法在处理非恒定曲率C型CDM变形时的可行性，但我们目前正在扩展结果，以验证其在更复杂CDM构型（如受阻环境或外力作用下产生的S型构型）中的适应性。