Shen Wenjun, He Jianhui, Yang Guilin, Kong Xiangjie, Bai Haotian, Fang Zaojun
Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China.
College of Materials Science and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing 100049, China.
Sensors (Basel). 2024 May 24;24(11):3385. doi: 10.3390/s24113385.
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%.
本文提出了一种由一组相同的索驱动连续体关节模块(CDCJM)组成的索驱动连续体机器人(CDCR)。CDCJM仅通过控制驱动索的长度来产生两自由度弯曲运动。在每个CDCJM中,采用基于模式的柔性主干作为被动柔顺关节,以产生两自由度弯曲挠度,该挠度可由两个关节变量来表征,即弯曲方向角和弯曲角度。然而,由于弯曲挠度不仅取决于驱动索的长度,还受重力和负载的影响,因此用其运动学模型计算这两个关节变量会不准确。在这项工作中,采用两个可拉伸电容式传感器来测量柔性主干的弯曲形状,以便准确确定这两个关节变量。与基于光纤光栅(FBG)和基于视觉的形状传感方法相比,所提出的可拉伸电容式传感器方法具有对主干弯曲挠度灵敏度高、易于实现和成本效益高的优点。可拉伸传感器的初始位置通常由其在无弯曲主干表面上的两个端点位置来定义。基于主干的两自由度弯曲挠度,建立了一个通用的形状传感模型,即传感器读数与两个关节变量之间的关系。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。基于校准后的形状传感模型,对CDCR实施了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,基于半径为55mm的圆形轨迹对CDCR进行了轨迹跟踪精度实验。在开环和闭环控制下,由Qualisys运动捕捉系统测量的CDCR的平均跟踪误差分别为49.23mm和8.40mm,误差降低了82.94%。