Fast and Reliable Autonomous Surgical Debridement with Cable-Driven Robots Using a Two-Phase Calibration Procedure

Automating precision subtasks such as debridement (removing dead or diseased tissue fragments) with Robotic Surgical Assistants (RSAs) such as the da Vinci Research Kit (dVRK) is challenging due to inherent non-linearities in cable-driven systems. We propose and evaluate a novel two-phase coarse-to-fine calibration method. In Phase I (coarse), we place a red calibration marker on the end effector and let it randomly move through a set of open-loop trajectories to obtain a large sample set of camera pixels and internal robot end-effector configurations. This coarse data is then used to train a Deep Neural Network (DNN) to learn the coarse transformation bias. In Phase II (fine), the bias from Phase I is applied to move the end-effector toward a small set of specific target points on a printed sheet. For each target, a human operator manually adjusts the end-effector position by direct contact (not through teleoperation) and the residual compensation bias is recorded. This fine data is then used to train a Random Forest (RF) to learn the fine transformation bias. Subsequent experiments suggest that without calibration, position errors average 4.55mm. Phase I can reduce average error to 2.14mm and the combination of Phase I and Phase II can reduces average error to 1.08mm. We apply these results to debridement of raisins and pumpkin seeds as fragment phantoms. Using an endoscopic stereo camera with standard edge detection, experiments with 120 trials achieved average success rates of 94.5%, exceeding prior results with much larger fragments (89.4%) and achieving a speedup of 2.1x, decreasing time per fragment from 15.8 seconds to 7.3 seconds. Source code, data, and videos are available at https://sites.google.com/view/calib-icra/.Comment: Code, data, and videos are available at https://sites.google.com/view/calib-icra/. Final version for ICRA 201

Skill-based human-robot cooperation in tele-operated path tracking

This work proposes a shared-control tele-operation framework that adapts its cooperative properties to the estimated skill level of the operator. It is hypothesized that different aspects of an operatorâ\u80\u99s performance in executing a tele-operated path tracking task can be assessed through conventional machine learning methods using motion-based and task-related features. To identify performance measures that capture motor skills linked to the studied task, an experiment is conducted where users new to tele-operation, practice towards motor skill proficiency in 7 training sessions. A set of classifiers are then learned from the acquired data and selected features, which can generate a skill profile that comprises estimations of userâ\u80\u99s various competences. Skill profiles are exploited to modify the behavior of the assistive robotic system accordingly with the objective of enhancing user experience by preventing unnecessary restriction for skilled users. A second experiment is implemented in which novice and expert users execute the path tracking on different pathways while being assisted by the robot according to their estimated skill profiles. Results validate the skill estimation method and hint at feasibility of shared-control customization in tele-operated path tracking

Design of A Virtual Laboratory for Automation Control

In the past, only students who studied on campus were able to access laboratory equipment in traditional lab courses; distance learning students, enrolled in online courses, were at a disadvantage for they could learn basic lab experiment principles but could never experience hands-on learning. Modeling and simulation can be a powerful tool for generating virtual laboratories for distance learning students. This thesis describes the design and development of a virtual laboratory for automation control using mechanical, electrical, and pneumatic components for an automation and control course at Old Dominion University. This virtual laboratory application was implemented for two platforms — Windows personal computers and Android smartphones. The virtual lab serves as pre-lab session for on-campus students and a virtual lab tool for distance-learning students to gain some “hands-on” lab experience. Utilizing the virtual learning environment as a supplement to engineering-based laboratories is also beneficial for students to prepare for the physical experiment and obtain a “hands-on,” practical lab experience without the hazards present in the physical lab. Such a methodology can also be applied to experiments in different fields such chemistry, etc

Toward Image-Guided Automated Suture Grasping Under Complex Environments: A Learning-Enabled and Optimization-Based Holistic Framework

To realize a higher-level autonomy of surgical knot tying in minimally invasive surgery (MIS), automated suture grasping, which bridges the suture stitching and looping procedures, is an important yet challenging task needs to be achieved. This paper presents a holistic framework with image-guided and automation techniques to robotize this operation even under complex environments. The whole task is initialized by suture segmentation, in which we propose a novel semi-supervised learning architecture featured with a suture-aware loss to pertinently learn its slender information using both annotated and unannotated data. With successful segmentation in stereo-camera, we develop a Sampling-based Sliding Pairing (SSP) algorithm to online optimize the suture's 3D shape. By jointly studying the robotic configuration and the suture's spatial characteristics, a target function is introduced to find the optimal grasping pose of the surgical tool with Remote Center of Motion (RCM) constraints. To compensate for inherent errors and practical uncertainties, a unified grasping strategy with a novel vision-based mechanism is introduced to autonomously accomplish this grasping task. Our framework is extensively evaluated from learning-based segmentation, 3D reconstruction, and image-guided grasping on the da Vinci Research Kit (dVRK) platform, where we achieve high performances and successful rates in perceptions and robotic manipulations. These results prove the feasibility of our approach in automating the suture grasping task, and this work fills the gap between automated surgical stitching and looping, stepping towards a higher-level of task autonomy in surgical knot tying

Characterization and modeling of a planar ultrasonic piezoelectric transducer for periodontal scalers

Caries and periodontitis affect the majority of adults during their lifetime. Piezoelectric ultrasonic scalers offer great benefits during the prevention and treatment of periodontal diseases. Our group developed a novel ultrasonic periodontal scaler based on a planar piezoelectric transducer. However, similar to other piezoelectric configurations, the transducer’s characteristics are strongly influenced by operation conditions. In this study, we investigated the influence of driving voltage amplitude and loading force applied using physical calculus models on the novel planar transducer’s input impedance and vibration. Our results show that the resonance frequency, i.e. the frequency at which maximal deflection of the tip occurs, decreases with increasing driving voltage amplitude while it increases with increasing force. Additionally, decreasing driving voltage amplitudes and increasing force both increase the minimal magnitude and reduce the maximal phase of the input impedance near resonance. Based on these observations, we developed a procedure to extend the Butterworth–Van-Dyke (BVD) Model. The extended BVD models allow to simulate the transducer in realistic scenarios and may facilitate the development of dedicated control systems for planar piezoelectric transducers

Characterization and modeling of a planar ultrasonic piezoelectric transducer for periodontal scalers

Caries and periodontitis affect the majority of adults during their lifetime. Piezoelectric ultrasonic scalers offer great benefits during the prevention and treatment of periodontal diseases. Our group developed a novel ultrasonic periodontal scaler based on a planar piezoelectric transducer. However, similar to other piezoelectric configurations, the transducer’s characteristics are strongly influenced by operation conditions. In this study, we investigated the influence of driving voltage amplitude and loading force applied using physical calculus models on the novel planar transducer’s input impedance and vibration. Our results show that the resonance frequency, i.e. the frequency at which maximal deflection of the tip occurs, decreases with increasing driving voltage amplitude while it increases with increasing force. Additionally, decreasing driving voltage amplitudes and increasing force both increase the minimal magnitude and reduce the maximal phase of the input impedance near resonance. Based on these observations, we developed a procedure to extend the Butterworth-Van-Dyke (BVD) Model. The extended BVD models allow to simulate the transducer in realistic scenarios and may facilitate the development of dedicated control systems for planar piezoelectric transducers

Robot Assisted Object Manipulation for Minimally Invasive Surgery

Robotic systems have an increasingly important role in facilitating minimally invasive surgical treatments. In robot-assisted minimally invasive surgery, surgeons remotely control instruments from a console to perform operations inside the patient. However, despite the advanced technological status of surgical robots, fully autonomous systems, with decision-making capabilities, are not yet available. In 2017, a structure to classify the research efforts toward autonomy achievable with surgical robots was proposed by Yang et al. Six different levels were identified: no autonomy, robot assistance, task autonomy, conditional autonomy, high autonomy, and full autonomy. All the commercially available platforms in robot-assisted surgery is still in level 0 (no autonomy). Despite increasing the level of autonomy remains an open challenge, its adoption could potentially introduce multiple benefits, such as decreasing surgeons’ workload and fatigue and pursuing a consistent quality of procedures. Ultimately, allowing the surgeons to interpret the ample and intelligent information from the system will enhance the surgical outcome and positively reflect both on patients and society. Three main aspects are required to introduce automation into surgery: the surgical robot must move with high precision, have motion planning capabilities and understand the surgical scene. Besides these main factors, depending on the type of surgery, there could be other aspects that might play a fundamental role, to name some compliance, stiffness, etc. This thesis addresses three technological challenges encountered when trying to achieve the aforementioned goals, in the specific case of robot-object interaction. First, how to overcome the inaccuracy of cable-driven systems when executing fine and precise movements. Second, planning different tasks in dynamically changing environments. Lastly, how the understanding of a surgical scene can be used to solve more than one manipulation task. To address the first challenge, a control scheme relying on accurate calibration is implemented to execute the pick-up of a surgical needle. Regarding the planning of surgical tasks, two approaches are explored: one is learning from demonstration to pick and place a surgical object, and the second is using a gradient-based approach to trigger a smoother object repositioning phase during intraoperative procedures. Finally, to improve scene understanding, this thesis focuses on developing a simulation environment where multiple tasks can be learned based on the surgical scene and then transferred to the real robot. Experiments proved that automation of the pick and place task of different surgical objects is possible. The robot was successfully able to autonomously pick up a suturing needle, position a surgical device for intraoperative ultrasound scanning and manipulate soft tissue for intraoperative organ retraction. Despite automation of surgical subtasks has been demonstrated in this work, several challenges remain open, such as the capabilities of the generated algorithm to generalise over different environment conditions and different patients