Design and analysis of a wire-driven flexible manipulator for bronchoscopic interventions

Bronchoscopic interventions are widely performed for the diagnosis and treatment of lung diseases. However, for most endobronchial devices, the lack of a bendable tip restricts their access ability to get into distal bronchi with complex bifurcations. This paper presents the design of a new wire-driven continuum manipulator to help guide these devices. The proposed manipulator is built by assembling miniaturized blocks that are featured with interlocking circular joints. It has the capability of maintaining its integrity when the lengths of actuation wires change due to the shaft flex. It allows the existence of a relatively large central cavity to pass through other instruments and enables two rotational degrees of freedom. All these features make it suitable for procedures where tubular anatomies are involved and the flexible shafts have to be considerably bent in usage, just like bronchoscopic interventions. A kinematic model is built to estimate the relationship between the translations of actuation wires and the manipulator tip position. A scale-up model is produced for evaluation experiments and the results validate the performance of the proposed mechanism

Implicit active constraints for safe and effective guidance of unstable concentric tube robots

Safe and effective telemanipulation of concentric tube robots is hindered by their complex, non-intuitive kinematics. In order for clinicians to operate these robots naturally, guidance schemes in the form of attractive and repulsive constraints can simplify task execution. The real-time seamless calculation and application of guidance, however, requires computationally efficient algorithms that solve the non-linear inverse kinematics of the robot and guarantee that the commanded robot configuration is stable and sufficiently away from the anatomy. This paper presents a multi-processor framework that allows on-the-fly calculation of optimal safe paths based on rapid workspace and roadmap precomputation. The realtime nature of the developed software enables complex guidance constraints to be implemented with minimal computational overhead. A clinically challenging user study demonstrates that the incorporated guiding constraints are highly beneficial for fast and accurate navigation with concentric tube robots

Achieving commutation control of an MRI-powered robot actuator

Actuators that are powered, imaged, and controlled by magnetic resonance (MR) scanners could inexpensively provide wireless control of MR-guided robots. Similar to traditional electric motors, the MR scanner acts as the stator and generates propulsive torques on an actuator rotor containing one or more ferrous particles. Generating maximum motor torque while avoiding instabilities and slippage requires closed-loop control of the electromagnetic field gradients, i.e., commutation. Accurately estimating the position and velocity of the rotor is essential for high-speed control, which is a challenge due to the low refresh rate and high latency associated with MR signal acquisition. This paper proposes and demonstrates a method for closed-loop commutation based on interleaving pulse sequences for rotor imaging and rotor propulsion. This approach is shown to increase motor torque and velocity, eliminate rotor slip, and enable regulation of rotor angle. Experiments with a closed-loop MR imaging actuator produced a maximum force of 9.4 N

A Non-Axisymmetric Parallel Manipulator for Head Stabilisation in Vitreoretinal Surgery

A non-axisymmetric parallel manipulator headrest design was previously proposed to counter patient head motion during ophthalmic surgery, and a non-motorized prototype was built. Custom linear actuators were designed, and installed to the headrest manipulator prototype in preparation for kinematic performance test. An inverse kinematic-based control algorithm was implemented, and initial kinematic testing was done. Finally, the future plans for the research are briefly explaine

Effective Semantic Segmentation in Cataract Surgery: What Matters Most?

Our work proposes neural network design choices that set the state-of-the-art on a challenging public benchmark on cataract surgery, CaDIS. Our methodology achieves strong performance across three semantic segmentation tasks with increasingly granular surgical tool class sets by effectively handling class imbalance, an inherent challenge in any surgical video. We consider and evaluate two conceptually simple data oversampling methods as well as different loss functions. We show significant performance gains across network architectures and tasks especially on the rarest tool classes, thereby presenting an approach for achieving high performance when imbalanced granular datasets are considered. Our code and trained models are available at https://github.com/RViMLab/MICCAI2021_Cataract_semantic_segmentation and qualitative results on unseen surgical video can be found at https://youtu.be/twVIPUj1WZM

A Linear Approach to Absolute Pose Estimation for Light Fields

This paper presents the first absolute pose estimation approach tailored to Light Field cameras. It builds on the observation that the ratio between the disparity arising in different sub-aperture images and their corresponding baseline is constant. Hence, we augment the 2D pixel coordinates with the corresponding normalised disparity to obtain the Light Field feature. This new representation reduces the effect of noise by aggregating multiple projections and allows for linear estimation of the absolute pose of a Light Field camera using the well-known Direct Linear Transformation algorithm. We evaluate the resulting absolute pose estimates with extensive simulations and experiments involving real Light Field datasets, demonstrating the competitive performance of our linear approach. Furthermore, we integrate our approach in a state-of-the-art Light Field Structure from Motion pipeline and demonstrate accurate multi-view 3D reconstruction

Implicit active constraints for concentric tube robots based on analysis of the safe and dexterous workspace

The use of concentric tube robots has recognized advantages for accessing target lesions while conforming to certain anatomical constraints. However, their complex kinematics makes their safe telemanipulation in convoluted anatomy a challenging task. Collaborative control schemes, which guide the operator through haptic and visual feedback, can simplify this task and reduce the cognitive burden of the operator. Guaranteeing stable, collision-free robot configurations during manipulation, however, is computationally demanding and, until now, either required long periods of pre-computation time or distributed computing clusters. Furthermore, the operator is often presented with guidance paths which have to be followed approximately. This paper presents a heterogeneous (CPU/GPU) computing approach to enable rapid workspace analysis on a single computer. The method is used in a new navigation scheme that guides the robot operator towards locations of high dexterity or manipulability of the robot. Under this guidance scheme, the user can make informed decisions and maintain full control of the path planning and manipulation processes, with intuitive visual feedback on when the robot's limitations are being reached