Although robot-assisted diagnosis and minimally invasive surgery (MIS) brings distinct benefits, deficient multi-dimensional force feedback remains a noteworthy limitation and challenge in MIS. Aiming for a comprehensive high-fidelity perception of… Click to show full abstract
Although robot-assisted diagnosis and minimally invasive surgery (MIS) brings distinct benefits, deficient multi-dimensional force feedback remains a noteworthy limitation and challenge in MIS. Aiming for a comprehensive high-fidelity perception of tissue-instrument interactions, we present a Fiber Bragg Grating (FBG)-based 3-axis tactile sensing for surface reaction force mapping, identification and localization of tissue hard-inclusion. The tactile sensing probe consists of five optical fibers inscribed with FBGs and a force-sensitive 3D printed deformable body. All fibers are suspended inside the deformable body in a parallel manner, leading to the direct compression or tension of each FBG. Such configuration can effectively avoid the chirping failure of FBG compared with the pasting FBG-based sensors. A linearized difference model is proposed to calibrate the 3-axis force detection and enhance the resistance to nonlinear interferences. Hard-inclusion identification experiments with varied hard-inclusion sizes and depths have been implemented through discrete palpation and dragging palpation modes. Results indicate that the probe can effectively identify the presence and location of these small hard-inclusions from the force mapping. Furthermore, lengthy vessels embedded in the phantom can be accurately identified through dragging palpation with an arbitrary contact angle. Another novelty of the probe is the reconstruction of the surface profile of a non-planar tissue, which further allows hard-inclusion identification and 3D localization. Ex-vivo tissue palpation on a porcine kidney further validates the effectiveness and feasibility of the probe to map surface reaction forces and localize the hard-inclusions intraoperatively.
               
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