Dynamic Active Constraints for Surgical Robots using Vector Field Inequalities

Robotic assistance allows surgeons to perform dexterous and tremor-free procedures, but robotic aid is still underrepresented in procedures with constrained workspaces, such as deep brain neurosurgery and endonasal surgery. In these procedures, surgeons have restricted vision to areas near the surgical tooltips, which increases the risk of unexpected collisions between the shafts of the instruments and their surroundings. In this work, our vector-field-inequalities method is extended to provide dynamic active-constraints to any number of robots and moving objects sharing the same workspace. The method is evaluated with experiments and simulations in which robot tools have to avoid collisions autonomously and in real-time, in a constrained endonasal surgical environment. Simulations show that with our method the combined trajectory error of two robotic systems is optimal. Experiments using a real robotic system show that the method can autonomously prevent collisions between the moving robots themselves and between the robots and the environment. Moreover, the framework is also successfully verified under teleoperation with tool-tissue interactions.Comment: Accepted on T-RO 2019, 19 Page

Vitreoretinal Surgical Robotic System with Autonomous Orbital Manipulation using Vector-Field Inequalities

Vitreoretinal surgery pertains to the treatment of delicate tissues on the fundus of the eye using thin instruments. Surgeons frequently rotate the eye during surgery, which is called orbital manipulation, to observe regions around the fundus without moving the patient. In this paper, we propose the autonomous orbital manipulation of the eye in robot-assisted vitreoretinal surgery with our tele-operated surgical system. In a simulation study, we preliminarily investigated the increase in the manipulability of our system using orbital manipulation. Furthermore, we demonstrated the feasibility of our method in experiments with a physical robot and a realistic eye model, showing an increase in the view-able area of the fundus when compared to a conventional technique. Source code and minimal example available at https://github.com/mmmarinho/icra2023_orbitalmanipulation.Comment: 7 pages, 7 figures, accepted on ICRA202

Autonomous Robotic Drilling System for Mice Cranial Window Creation: An Evaluation with an Egg Model

Robotic assistance for experimental manipulation in the life sciences is expected to enable precise manipulation of valuable samples, regardless of the skill of the scientist. Experimental specimens in the life sciences are subject to individual variability and deformation, and therefore require autonomous robotic control. As an example, we are studying the installation of a cranial window in a mouse. This operation requires the removal of the skull, which is approximately 300 um thick, to cut it into a circular shape 8 mm in diameter, but the shape of the mouse skull varies depending on the strain of mouse, sex and week of age. The thickness of the skull is not uniform, with some areas being thin and others thicker. It is also difficult to ensure that the skulls of the mice are kept in the same position for each operation. It is not realistically possible to measure all these features and pre-program a robotic trajectory for individual mice. The paper therefore proposes an autonomous robotic drilling method. The proposed method consists of drilling trajectory planning and image-based task completion level recognition. The trajectory planning adjusts the z-position of the drill according to the task completion level at each discrete point, and forms the 3D drilling path via constrained cubic spline interpolation while avoiding overshoot. The task completion level recognition uses a DSSD-inspired deep learning model to estimate the task completion level of each discrete point. Since an egg has similar characteristics to a mouse skull in terms of shape, thickness and mechanical properties, removing the egg shell without damaging the membrane underneath was chosen as the simulation task. The proposed method was evaluated using a 6-DOF robotic arm holding a drill and achieved a success rate of 80% out of 20 trials.Comment: Accepted on IROS 2023, 8 page

MBAPose: Mask and Bounding-Box Aware Pose Estimation of Surgical Instruments with Photorealistic Domain Randomization

Surgical robots are controlled using a priori models based on robots' geometric parameters, which are calibrated before the surgical procedure. One of the challenges in using robots in real surgical settings is that parameters change over time, consequently deteriorating control accuracy. In this context, our group has been investigating online calibration strategies without added sensors. In one step toward that goal, we have developed an algorithm to estimate the pose of the instruments' shafts in endoscopic images. In this study, we build upon that earlier work and propose a new framework to more precisely estimate the pose of a rigid surgical instrument. Our strategy is based on a novel pose estimation model called MBAPose and the use of synthetic training data. Our experiments demonstrated an improvement of 21 % for translation error and 26 % for orientation error on synthetic test data with respect to our previous work. Results with real test data provide a baseline for further research.Comment: 8 pages, submitted to IROS202

Autonomous Coordinated Control of the Light Guide for Positioning in Vitreoretinal Surgery

Vitreoretinal surgery is challenging even for expert surgeons owing to the delicate target tissues and the diminutive workspace in the retina. In addition to improved dexterity and accuracy, robot assistance allows for (partial) task automation. In this work, we propose a strategy to automate the motion of the light guide with respect to the surgical instrument. This automation allows the instrument's shadow to always be inside the microscopic view, which is an important cue for the accurate positioning of the instrument in the retina. We show simulations and experiments demonstrating that the proposed strategy is effective in a 700-point grid in the retina of a surgical phantom. Furthermore, we integrated the proposed strategy with image processing and succeeded in positioning the surgical instrument's tip in the retina, relying on only the robot's geometric information and microscopic images.Comment: Accepted on T-MRB 2022, 16 page

Single-Shot Pose Estimation of Surgical Robot Instruments' Shafts from Monocular Endoscopic Images

Surgical robots are used to perform minimally invasive surgery and alleviate much of the burden imposed on surgeons. Our group has developed a surgical robot to aid in the removal of tumors at the base of the skull via access through the nostrils. To avoid injuring the patients, a collision-avoidance algorithm that depends on having an accurate model for the poses of the instruments' shafts is used. Given that the model's parameters can change over time owing to interactions between instruments and other disturbances, the online estimation of the poses of the instrument's shaft is essential. In this work, we propose a new method to estimate the pose of the surgical instruments' shafts using a monocular endoscope. Our method is based on the use of an automatically annotated training dataset and an improved pose-estimation deep-learning architecture. In preliminary experiments, we show that our method can surpass state of the art vision-based marker-less pose estimation techniques (providing an error decrease of 55% in position estimation, 64% in pitch, and 69% in yaw) by using artificial images.Comment: Accepted on ICRA 2020, 7 page

Active Constraints using Vector Field Inequalities for Surgical Robots

Robotic assistance allows surgeons to perform dexterous and tremor-free procedures, but is still underrepresented in deep brain neurosurgery and endonasal surgery where the workspace is constrained. In these conditions, the vision of surgeons is restricted to areas near the surgical tool tips, which increases the risk of unexpected collisions between the shafts of the instruments and their surroundings, in particular in areas outside the surgical field-of-view. Active constraints can be used to prevent the tools from entering restricted zones and thus avoid collisions. In this paper, a vector field inequality is proposed that guarantees that tools do not enter restricted zones. Moreover, in contrast with early techniques, the proposed method limits the tool approach velocity in the direction of the forbidden zone boundary, guaranteeing a smooth behavior and that tangential velocities will not be disturbed. The proposed method is evaluated in simulations featuring two eight degrees-of-freedom manipulators that were custom-designed for deep neurosurgery. The results show that both manipulator-manipulator and manipulator-boundary collisions can be avoided using the vector field inequalities.Comment: Accepted on ICRA 2018, 8 page

Virtual Fixture Assistance for Suturing in Robot-Aided Pediatric Endoscopic Surgery

The limited workspace in pediatric endoscopic surgery makes surgical suturing one of the most difficult tasks. During suturing, surgeons have to prevent collisions between tools and also collisions with the surrounding tissues. Surgical robots have been shown to be effective in adult laparoscopy, but assistance for suturing in constrained workspaces has not been yet fully explored. In this letter, we propose guidance virtual fixtures to enhance the performance and the safety of suturing while generating the required task constraints using constrained optimization and Cartesian force feedback. We propose two guidance methods: looping virtual fixtures and a trajectory guidance cylinder, that are based on dynamic geometric elements. In simulations and experiments with a physical robot, we show that the proposed methods achieve a more precise and safer looping in robot-assisted pediatric endoscopy.Comment: Accepted on RA-L/ICRA 2020, 8 Pages. Fixed a few typo