In this paper, we propose a novel variable-length estimation approach for shape sensing of extensible soft robots utilizing fiber Bragg gratings (FBGs). Shape reconstruction from FBG sensors has been increasingly developed for soft robots, while the narrow stretching range of FBG fiber makes it difficult to acquire accurate sensing results for extensible robots. Towards this limitation, we newly introduce an FBG-based length sensor by leveraging a rigid curved channel, through which FBGs are allowed to slide within the robot following its body extension/compression, hence we can search and match the FBGs with specific constant curvature in the fiber to determine the effective length. From the fusion with the above measurements, a model-free filtering technique is accordingly presented for simultaneous calibration of a variable-length model and temporally continuous length estimation of the robot, enabling its accurate shape sensing using solely FBGs. The performances of the proposed method have been experimentally evaluated on an extensible soft robot equipped with an FBG fiber in both free and unstructured environments. The results concerning dynamic accuracy and robustness of length estimation and shape sensing demonstrate the effectiveness of our approach.
翻译:在本文中,我们提出了利用纤维布拉格格格格格(FBGs)对利用纤维布拉格格格格格(FBGs)对伸缩软软机器人进行形状感测的新型变长估计法。从FBG传感器对软机器人的形状的重建已经日益为软机器人开发了,而FBG纤维的狭窄拉伸展范围使得难以为外延机器人获得准确的遥感结果。为了达到这一限制,我们新采用了基于FBG的长度传感器,利用一个硬曲线通道,允许FBG在机器人身体延伸/压缩后,在机器人体内滑动,允许FBG在其身体延伸/压缩后,通过这一通道,允许FBG在机器人身体延伸/压缩后滑动。因此,我们可以在纤维中用具体的固定曲线曲线查找和匹配,以确定有效长度。从上述测量的混合中,提出了一种无模型过滤技术,因此,难以同时校准一个变量长模型和对机器人进行时间连续估计,使机器人能够仅使用FBGs;拟议方法的性性对在自由和非结构环境中安装FBG纤维的外软软机器人进行了实验性软机器人进行了实验性评估,结果显示。