本文提出了一种由一组相同的电缆驱动连续体关节模块(CDCJM)组成的电缆驱动连续体机器人(CDCR)。CDCJM仅通过控制驱动电缆长度来产生2自由度弯曲运动。在每个CDCJM中,基于图案的柔性骨架被用作被动顺应性关节以产生2自由度弯曲偏转,可以用两个联合变量来表征,即,弯曲方向角度和弯曲角度。然而,由于弯曲挠度不仅取决于驱动电缆的长度,还取决于重力和有效载荷,用运动学模型计算两个关节变量是不准确的。在这项工作中,采用两个可拉伸电容传感器来测量柔性主干的弯曲形状,以便准确地确定两个关节变量。与基于FBG和基于视觉的形状传感方法相比,所提出的具有可拉伸电容传感器的方法具有对骨干的弯曲挠度高灵敏度的优点,易于实施,和成本效益。可拉伸传感器的初始位置通常由其在没有弯曲的情况下在主干的表面上的两个端点位置限定。一个通用的形状传感模型,即,传感器读数和两个联合变量之间的关系,是根据骨干的2自由度弯曲挠度制定的。为了进一步提高形状传感模型的精度,提出了一种校准方法来补偿可拉伸传感器的位置误差。基于校准的形状传感模型,为CDCR实现了一种基于滑模的闭环控制方法。为了验证所提出的闭环控制方法的有效性,基于圆形轨迹进行CDCR的轨迹跟踪精度实验,其中圆的半径为55mm。Qualisys运动捕捉系统在开环和闭环控制下测得的CDCR的平均跟踪误差为49.23和8.40mm,分别,减少了82.94%。
A Cable-Driven Continuum Robot (CDCR) that consists of a set of identical Cable-Driven Continuum Joint Modules (CDCJMs) is proposed in this paper. The CDCJMs merely produce 2-DOF bending motions by controlling driving cable lengths. In each CDCJM, a pattern-based flexible backbone is employed as a passive compliant joint to generate 2-DOF bending deflections, which can be characterized by two joint variables, i.e., the bending direction angle and the bending angle. However, as the bending deflection is determined by not only the lengths of the driving cables but also the gravity and payload, it will be inaccurate to compute the two joint variables with its kinematic model. In this work, two stretchable capacitive sensors are employed to measure the bending shape of the flexible backbone so as to accurately determine the two joint variables. Compared with FBG-based and vision-based shape-sensing methods, the proposed method with stretchable capacitive sensors has the advantages of high sensitivity to the bending deflection of the backbone, ease of implementation, and cost effectiveness. The initial location of a stretchable sensor is generally defined by its two endpoint positions on the surface of the backbone without bending. A generic shape-sensing model, i.e., the relationship between the sensor reading and the two joint variables, is formulated based on the 2-DOF bending deflection of the backbone. To further improve the accuracy of the shape-sensing model, a calibration method is proposed to compensate for the location errors of stretchable sensors. Based on the calibrated shape-sensing model, a sliding-mode-based closed-loop control method is implemented for the CDCR. In order to verify the effectiveness of the proposed closed-loop control method, the trajectory tracking accuracy experiments of the CDCR are conducted based on a circle trajectory, in which the radius of the circle is 55mm. The average tracking errors of the CDCR measured by the Qualisys motion capture system under the open-loop and the closed-loop control are 49.23 and 8.40mm, respectively, which is reduced by 82.94%.