Concentric-tube robots (CTR) consist of a series of pre-curved flexible tubes that make up the robot structure and provide the high dexterity required for performing surgical tasks in constrained environments. This special design introduces new challenges in shape sensing as large twisting is experienced by the torsionally compliant structure. In the literature, fiber Bragg grating (FBG) sensors are attached to needle-sized continuum robots for curvature sensing, but they are limited to obtaining bending curvatures since a straight sensor layout is utilized. For a CTR, in addition to bending curvatures, the torsion along the robots shaft should be determined to calculate the shape and pose of the robot accurately. To solve this problem, in our earlier work, we proposed embedding FBG sensors in a helical pattern into the tube wall. The strain readings are converted to bending curvatures and torsion by a strain-curvature model. In this paper, a modified strain-curvature model is proposed that can be used in conjunction with standard shape reconstruction algorithms for shape and pose calculation. This sensing technology is evaluated for its accuracy and resolution using three FBG sensors with 1 mm sensing segments that are bonded into the helical grooves of a pre-curved Nitinol tube. The results show that this sensorized robot can obtain accurate measurements: resolutions of 0.02 rad/m with a 100 Hz sampling rate. Further, the repeatability of the obtained measurements during loading and unloading conditions are presented and analyzed.
Ran Xu, Aaron Yurkewich, and Rajni V. Patel, "Shape sensing for torsionally compliant concentric-tube robots," Proc. SPIE 9702, Optical Fibers and Sensors for Medical Diagnostics and Treatment Applications XVI, 97020V (Presented at SPIE BiOS: February 14, 2016; Published: 7 March 2016); https://doi.org/10.1117/12.2213128.
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