Dexterous continuum manipulators (DCMs) can largely increase the reachable region and steerability for minimally and less invasive surgery. Many such procedures require the DCM to be capable of producing large deflections. The real-time control of the DCM shape requires sensors that accurately detect and report large deflections. We propose a novel, large deflection, shape sensor to track the shape of a 35 mm DCM designed for a less invasive treatment of osteolysis. Two shape sensors, each with three fiber Bragg grating sensing nodes is embedded within the DCM, and the sensors’ distal ends fixed to the DCM. The DCM centerline is computed using the centerlines of each sensor curve. An experimental platform was built and different groups of experiments were carried out, including free bending and three cases of bending with obstacles. For each experiment, the DCM drive cable was pulled with a precise linear slide stage, the DCM centerline was calculated, and a 2D camera image was captured for verification. The reconstructed shape created with the shape sensors is compared with the ground truth generated by executing a 2D–3D registration between the camera image and 3D DCM model. Results show that the distal tip tracking accuracy is 0.40 ± 0.30 mm for the free bending and 0.61 ± 0.15 mm, 0.93 ± 0.05 mm and 0.23 ± 0.10 mm for three cases of bending with obstacles. The data suggest FBG arrays can accurately characterize the shape of large-deflection DCMs.
Shape sensing techniques utilizing Fiber Bragg grating (FBG) arrays can enable real-time tracking and control of dexterous continuum manipulators (DCM) used in minimally invasive surgeries. For many surgical applications, the DCM may need to operate with much larger curvatures than what current shape sensing methods can detect. This paper proposes a novel shape sensor, which can detect a radius of curvature of 15 mm for a 35 mm long DCM. For this purpose, we used FBG sensors along with nitinol wires as the supporting substrates to form a triangular cross section. For verification, we assembled the sensor inside the wall of the DCM. Experimental results indicate that the proposed sensor can detect the DCM's curvature with an average error of 3.14%.
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