Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE/RSJ International Conference on Intelligent Robots and Systems最新文献

Lumbar injection is an image-guided procedure performed manually for diagnosis and treatment of lower back pain and leg pain. Previously, we have developed and verified an MR-Conditional robotic solution to assisting the needle insertion process. Drawing on our clinical experiences, a virtual remote center of motion (RCM) constraint is implemented to enable our robot to mimic a clinician's hand motion to adjust the needle tip position in situ. Force and image data are collected to study the needle behavior in gel phantoms during this motion, and a mechanics-based needle-tissue interaction model is proposed and evaluated to further examine the underlying physics. This work extends the commonly-adopted notion of an RCM for flexible needles, and introduces new motion parameters to describe the needle behavior. The model parameters can be tuned to match the experimental result to sub-millimeter accuracy, and this proposed needle manipulation method presents a safer alternative to laterally translating the needle during in situ needle adjustments.

This paper studies the contact stability and contact safety of a robotic intravascular cardiac catheter under blood flow disturbances while in contact with tissue surface. A probabilistic blood flow disturbance model, where the blood flow drag forces on the catheter body are approximated using a quasi-static model, is introduced. Using this blood flow disturbance model, probabilistic contact stability and contact safety metrics, employing a sample based representation of the blood flow velocity distribution, are proposed. Finally, the contact stability and contact safety of a MRI-actuated robotic catheter are analyzed using these models in a specific example scenario under left pulmonary inferior vein (LIV) blood flow disturbances.

Robot-assisted retinal surgery has become increasingly prevalent in recent years in part due to the potential for robots to help surgeons improve the safety of an immensely delicate and difficult set of tasks. The integration of robots into retinal surgery has resulted in diminished surgeon perception of tool-to-tissue interaction forces due to robot's stiffness. The tactile perception of these interaction forces (sclera force) has long been a crucial source of feedback for surgeons who rely on them to guide surgical maneuvers and to prevent damaging forces from being applied to the eye. This problem is exacerbated when there are unfavorable sclera forces originating from patient movements (dynamic eyeball manipulation) during surgery which may cause the sclera forces to increase even drastically. In this study we aim at evaluating the efficacy of providing warning auditory feedback based on the level of sclera force measured by force sensing instruments. The intent is to enhance safety during dynamic eye manipulations in robot-assisted retinal surgery. The disturbances caused by lateral movement of patient's head are simulated using a piezo-actuated linear stage. The Johns Hopkins Steady-Hand Eye Robot (SHER), is then used in a multi-user experiment. Twelve participants are asked to perform a mock retinal surgery by following painted vessels inside an eye phantom using a force sensing instrument while auditory feedback is provided. The results indicate that the users are able to handle the eye motion disturbances while maintaining the sclera forces within safe boundaries when audio feedback is provided.

In magnetic resonance imaging (MRI) guided robotic catheter ablation procedures, reliable tracking of the catheter within the MRI scanner is needed to safely navigate the catheter. This requires accurate registration of the catheter to the scanner. This paper presents a differential, multi-slice image-based registration approach utilizing active fiducial coils. The proposed method would be used to preoperatively register the MRI image space with the physical catheter space. In the proposed scheme, the registration is performed with the help of a registration frame, which has a set of embedded electromagnetic coils designed to actively create MRI image artifacts. These coils are detected in the MRI scanner's coordinate system by background subtraction. The detected coil locations in each slice are weighted by the artifact size and then registered to known ground truth coil locations in the catheter's coordinate system via least-squares fitting. The proposed approach is validated by using a set of target coils placed withing the workspace, employing multi-planar capabilities of the MRI scanner. The average registration and validation errors are respectively computed as 1.97 mm and 2.49 mm. The multi-slice approach is also compared to the single-slice method and shown to improve registration and validation by respectively 0.45 mm and 0.66 mm.

Robot-assisted vitreoretinal surgery can filter surgeons' hand tremors and provide safe, accurate tool manipulation. In this paper, we report the design, optimization, and evaluation of a novel tilt mechanism for a new Steady-Hand Eye Robot (SHER). The new tilt mechanism features a four-bar linkage design and has a compact structure. Its kinematic configuration is optimized to minimize the required linear range of motion (LRM) for implementing a virtual remote center-of-motion (V-RCM) while tilting a surgical tool. Due to the different optimization constraints for the robots at the left and right sides of the human head, two configurations of this tilt mechanism are proposed. Experimental results show that the optimized tilt mechanism requires a significantly smaller LRM (e.g. 5.08 mm along Z direction and 8.77 mm along Y direction for left side robot) as compared to the slider-crank tilt mechanism used in the previous SHER (32.39 mm along Z direction and 21.10 mm along Y direction). The feasibility of the proposed tilt mechanism is verified in a mock bilateral robot-assisted vitreoretinal surgery. The ergonomically acceptable robot postures needed to access the surgical field is also determined.

This paper proposes a magnetic needle steering controller to manipulate mesoscale magnetic suture needles for executing planned suturing motion. This is an initial step towards our research objective: enabling autonomous control of magnetic suture needles for suturing tasks in minimally invasive surgery. To demonstrate the feasibility of accurate motion control, we employ a cardinally-arranged four-coil electromagnetic system setup and control magnetic suture needles in a 2-dimensional environment, i.e., a Petri dish filled with viscous liquid. Different from only using magnetic field gradients to control small magnetic agents under high damping conditions, the dynamics of a magnetic suture needle are investigated and encoded in the controller. Based on mathematical formulations of magnetic force and torque applied on the needle, we develop a kinematically constrained dynamic model that controls the needle to rotate and only translate along its central axis for mimicking the behavior of surgical sutures. A current controller of the electromagnetic system combining with closed-loop control schemes is designed for commanding the magnetic suture needles to achieve desired linear and angular velocities. To evaluate control performance of magnetic suture needles, we conduct experiments including needle rotation control, needle position control by using discretized trajectories, and velocity control by using a time-varying circular trajectory. The experiment results demonstrate our proposed needle steering controller can perform accurate motion control of mesoscale magnetic suture needles.

A motion-planning problem's setup can drastically affect the quality of solutions returned by the planner. In this work we consider optimizing these setups, with a focus on doing so in a computationally-efficient fashion. Our approach interleaves optimization with motion planning, which allows us to consider the actual motions required of the robot. Similar prior work has treated the planner as a black box: our key insight is that opening this box in a simple-yet-effective manner enables a more efficient approach, by allowing us to bound the work done by the planner to optimizer-relevant computations. Finally, we apply our approach to a surgically-relevant motion-planning task, where our experiments validate our approach by more-efficiently optimizing the fixed insertion pose of a surgical robot.

In this paper, a novel semi-autonomous control framework is presented for enabling probe-based confocal laser endomicroscopy (pCLE) scan of the retinal tissue. With pCLE, retinal layers such as nerve fiber layer (NFL) and retinal ganglion cell (RGC) can be scanned and characterized in real-time for an improved diagnosis and surgical outcome prediction. However, the limited field of view of the pCLE system and the micron-scale optimal focus distance of the probe, which are in the order of physiological hand tremor, act as barriers to successful manual scan of retinal tissue. Therefore, a novel sensorless framework is proposed for real-time semi-autonomous endomicroscopy scanning during retinal surgery. The framework consists of the Steady-Hand Eye Robot (SHER) integrated with a pCLE system, where the motion of the probe is controlled semi-autonomously. Through a hybrid motion control strategy, the system autonomously controls the confocal probe to optimize the sharpness and quality of the pCLE images, while providing the surgeon with the ability to scan the tissue in a tremor-free manner. Effectiveness of the proposed architecture is validated through experimental evaluations as well as a user study involving 9 participants. It is shown through statistical analyses that the proposed framework can reduce the work load experienced by the users in a statistically-significant manner, while also enhancing their performance in retaining pCLE images with optimized quality.

When robotic assistance is present into vitreoretinal surgery, the surgeon will experience reduced sensory input that is otherwise derived from the tool's interaction with the eye wall (sclera). We speculate that disconnecting the surgeon from this sensory input may increase the risk of injury to the eye and affect the surgeon's usual technique. On the other hand, robot autonomous motion to enhance patient safety might inhibit the surgeons tool manipulation and diminish surgeon comfort with the procedure. In this study, to investigate the parameters of patient safety and surgeon comfort in a robot-assisted eye surgery, we implemented three different approaches designed to keep the scleral force in a safe range during a synergic eye manipulation task. To assess the surgeon comfort during these procedures, the amount of interference with the surgeons usual maneuvers has been analyzed by defining quantitative comfort metrics. The first two utilized scleral force control approaches are based on an adaptive force control method in which the robot actively counteracts any excessive force on the sclera. The third control method is based on a virtual fixture approach in which a virtual wall is created for the surgeon in the unsafe directions of manipulation. The performance of the utilized approaches was evaluated in user studies with two experienced retinal surgeons and the outcomes of the procedure were assessed using the defined safety and comfort metrics. Results of these analyses indicate the significance of the opted control paradigm on the outcome of a safe and comfortable robot-assisted eye surgery.

We present a method that plans motions for a concentric tube robot to automatically reach surgical targets inside the body while avoiding obstacles, where the patient's anatomy is represented by point clouds. Point clouds can be generated intra-operatively via endoscopic instruments, enabling the system to update obstacle representations over time as the patient anatomy changes during surgery. Our new motion planning method uses a combination of sampling-based motion planning methods and local optimization to efficiently handle point cloud data and quickly compute high quality plans. The local optimization step uses an interior point optimization method, ensuring that the computed plan is feasible and avoids obstacles at every iteration. This enables the motion planner to run in an anytime fashion, i.e., the method can be stopped at any time and the best solution found up until that point is returned. We demonstrate the method's efficacy in three anatomical scenarios, including two generated from endoscopic videos of real patient anatomy.