Full 3D motion control for programmable bevel-tip steerable needles

Minimally invasive surgery has been in the focus of many researchers due to its reduced intra- and post-operative risks when compared to an equivalent open surgery approach. In the context of minimally invasive surgery, percutaneous intervention, and particularly, needle insertions, are of great importance in tumour-related therapy and diagnosis. However, needle and tissue deformation occurring during needle insertion often results in misplacement of the needles, which leads to complications, such as unsuccessful treatment and misdiagnosis. To this end, steerable needles have been proposed to compensate for placement errors by allowing curvilinear navigation. A particular type of steerable needle is the programmable bevel-tip steerable needle (PBN), which is a bio-inspired needle that consists of relatively soft and slender segments. Due to its flexibility and bevel-tip segments, it can navigate through 3D curvilinear paths. In PBNs, steering in a desired direction is performed by actuating particular PBN segments. Therefore, the pose of each segment is needed to ensure that the correct segment is actuated. To this end, in this thesis, proprioceptive sensing methods for PBNs were investigated. Two novel methods, an electromagnetic (EM)-based tip pose estimation method and a fibre Bragg grating (FBG)-based full shape sensing method, were presented and evaluated. The error in position was observed to be less than 1.08 mm and 5.76 mm, with the proposed EM-based tip tracking and FBG-based shape reconstruction methods, respectively. Moreover, autonomous path-following controllers for PBNs were also investigated. A closed-loop, 3D path-following controller, which was closed via feedback from FBG-inscribed multi-core fibres embedded within the needle, was presented. The nonlinear guidance law, which is a well-known approach for path-following control of aerial vehicles, and active disturbance rejection control (ADRC), which is known for its robustness within hard-to-model environments, were chosen as the control methods. Both linear and nonlinear ADRC were investigated, and the approaches were validated in both ex vivo brain and phantom tissue, with some of the experiments involving moving targets. The tracking error in position was observed to be less than 6.56 mm.Open Acces

Pose Measurement of Flexible Medical Instruments Using Fiber Bragg Gratings in Multi-Core Fiber

Accurate navigation of flexible medical instruments like catheters require the knowledge of its pose, that is its position and orientation. In this paper multi-core fibers inscribed with fiber Bragg gratings (FBG) are utilized as sensors to measure the pose of a multi-segment catheter. A reconstruction technique that provides the pose of such a fiber is presented. First, the measurement from the Bragg gratings are converted to strain then the curvature is deduced based on those strain calculations. Next, the curvature and the Bishop frame equations are used to reconstruct the fiber. This technique is validated through experiments where the mean error in position and orientation is observed to be less than 4.69 mm and 6.48 degrees, respectively. The main contributions of the paper are the use of Bishop frames in the reconstruction and the experimental validation of the acquired pose

High-performance parallel hexapod-robotic light abrasive grinding using real-time tool deflection compensation and constant resultant force control

In robotic grinding, significant tool deflection occurs due to the lower stiffness of the manipulator and tool, compared with operation by universal grinding machines. Tool deflection during robotic grinding operation causes geometrical errors in the workpiece cross section. Also, it makes difficult to control the grinding cutting depth. In this study, a method is proposed for calculation of the tool deflection in normal and tangential directions based on grinding force feedback in these directions. Based on calculated values, a real-time tool deflection compensation (TDC) algorithm is developed and implemented. Force interaction between the tool and workpiece is significant for grinding operation. Implementing grinding with constant normal force is a well-known approach for improving surface quality. Tool deflection in the robotic grinding causes orientation between the force sensor reference frame and tool reference frame. This means that the measured normal and tangential forces by the sensor are not actual normal and tangential interaction forces between the tool and workpiece. In order to eliminate this problem, a resultant grinding force control strategy is designed and implemented for a parallel hexapod-robotic light abrasive surface grinding operation. Due to the nonlinear nature of the grinding operation, a supervised fuzzy controller is designed where the reference input is identified by the developed grinding force model. This grinding model is optimized for the robotic grinding operation considering setup stiffness. Evaluation of the experimental results demonstrates significant improvement in grinding operation accuracy using the proposed resultant force control strategy in parallel with a real-time TDC algorithm