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Learning high-performance deep neural networks for dynamic modeling of high Degree-Of-Freedom (DOF) robots remains challenging due to the sampling complexity. Typical unknown system disturbance caused by unmodeled dynamics (such as internal compliance, cables) further exacerbates the problem. In this letter, a novel framework characterized by both high data efficiency and disturbance-adapting capability is proposed to address the problem of modeling gravitational dynamics using deep nets in feedforward gravity compensation control for high-DOF master manipulators with unknown disturbance. In particular, Feedforward Deep Neural Networks (FDNNs) are learned from both prior knowledge of an existing analytical model and observation of the robot system by Knowledge Distillation (KD). Through extensive experiments in high-DOF master manipulators with significant disturbance, we show that our method surpasses a standard Learning-from-Scratch (LfS) approach in terms of data efficiency and disturbance adaptation. Our initial feasibility study has demonstrated the potential of outperforming the analytical teacher model as the training data increases.

The use of intelligent feedback modalities to control and react to interaction forces during surgical procedures is an important factor in enabling safe and precise surgery. We explore the use of a model-mediated telemanipulation framework to enhance a user’s situational awareness using assistive virtual fixtures and semi-automated task execution for safe and intuitive environment interaction during robotic laparoscopic surgery. The framework allows stiffness mapping with semi-autonomous excitation, hybrid position-force control, and model updates during soft geometry contact. A 24-person study was carried out at 3 sites in simulated ablation and palpation of phantom anatomy. Compared to methods lacking intelligent feedback and guidance, the proposed framework improved task execution metrics (force regulation, completion time, path-following error) and reduced user effort.

This paper introduces an integrated real-time intraoperative surgical guidance system, in which an endoscope camera of da Vinci surgical robot and a transrectal ultrasound (TRUS) transducer are co-registered using photoacoustic markers that are detected in both fluorescence (FL) and photoacoustic (PA) imaging. The co-registered system enables the TRUS transducer to track the laser spot illuminated by a pulsed-laser-diode attached to the surgical instrument, providing both FL and PA images of the surgical region-of- interest (ROI). As a result, the generated photoacoustic marker is visualized and localized in the da Vinci endoscopic FL images, and the corresponding tracking can be conducted by rotating the TRUS transducer to display the PA image of the marker. A quantitative evaluation revealed that the average registration and tracking errors were 0.84 mm and 1.16°, respectively. This study shows that the co-registered photoacoustic marker tracking can be effectively deployed intraoperatively using TRUS+PA imaging providing functional guidance of the surgical ROI.

Robotic-assisted surgery is now well-established in clinical practice and has become the gold standard clinical treatment option for several clinical indications. The field of robotic-assisted surgery is expected to grow substantially in the next decade with a range of new robotic devices emerging to address unmet clinical needs across different specialities. A vibrant surgical robotics research community is pivotal for conceptualizing such new systems as well as for developing and training the engineers and scientists to translate them into practice. The da Vinci Research Kit (dVRK), an academic and industry collaborative effort to re-purpose decommissioned da Vinci surgical systems (Intuitive Surgical Inc, CA, USA) as a research platform for surgical robotics research, has been a key initiative for addressing a barrier to entry for new research groups in surgical robotics. In this paper, we present an extensive review of the publications that have been facilitated by the dVRK over the past decade. We classify research efforts into different categories and outline some of the major challenges and needs for the robotics community to maintain this initiative and build upon it.

Introduction: In this paper, we present and assess a proof of concept platform for semi-autonomous, cooperative robotic surgery. The platform is easily reproducible thanks to simple hardware components and open-source software. Moreover, the design accommodates open, soft tissue surgeries that recent advancements in surgical robotics do not generally focus on. Methods: The system is made up of an inexpensive robotic manipulator, a navigation system and a software interface. Accuracy measurement is performed on a rigid phantom that mimics the conditions of breast conserving surgery (BCS) as an example of a surgical use case. Results: The average target registration error (TRE) and fiducial registration error (FRE) of the system is within 1 mm. This indicates that the navigation system is sufficient for certain surgical applications such as BCS. The platform can also be easily replicated and used in a lab or home environment.

External disturbance forces caused by nonlinear springy electrical cables in the Master Tool Manipulator (MTM) of the da Vinci Research Kit (dVRK) limits the usage of the existing gravity compensation methods. Significant motion drifts at the MTM tip are often observed when the MTM is located far from its identification trajectory, preventing the usage of these methods for the entire workspace reliably. In this paper, we propose a general and systematic framework to address the problems of the gravity compensation for the MTM of the dVRK. Particularly, high order polynomial models were used to capture the highly nonlinear disturbance forces and integrated with the Multi-step Least Square Estimation (MLSE) framework. This method allows us to identify the parameters of both the gravitational and disturbance forces for each link sequentially, preventing residual error passing among the links of the MTM with uneven mass distribution. A corresponding gravity compensation controller was developed to compensate the gravitational and disturbance forces. The method was validated with extensive experiments in the majority of the manipulator's workspace, showing significant performance enhancements over existing methods. Finally, a deliverable software package in MATLAB and C++ was integrated with dVRK and published in the dVRK community for open-source research and development.

Robot-assisted minimally invasive surgery has made a substantial impact in operating rooms over the past few decades with their high dexterity, small tool size, and impact on adoption of minimally invasive techniques. In recent years, intelligence and different levels of surgical robot autonomy have emerged thanks to the medical robotics endeavors at numerous academic institutions and leading surgical robot companies. To accelerate interaction within the research community and prevent repeated development, we propose the Collaborative Robotics Toolkit (CRTK), a common API for the RAVEN-II and da Vinci Research Kit (dVRK) - two open surgical robot platforms installed at more than 40 institutions worldwide. CRTK has broadened to include other robots and devices, including simulated robotic systems and industrial robots. This common API is a community software infrastructure for research and education in cutting edge human-robot collaborative areas such as semi-autonomous teleoperation and medical robotics. This paper presents the concepts, design details and the integration of CRTK with physical robot systems and simulation platforms.

Ground-based teleoperation of robot manipulators for on-orbit servicing of spacecraft represents an example of high-payoff, high-risk operations that are challenging to perform due to high latency communications, with telemetry time delays of several seconds. In these scenarios, confidence of operating without failure is paramount. We report the development of an Interactive Planning and Supervised Execution (IPSE) system that takes advantage of accurate 3D reconstruction of the remote environment to enable operators to plan motions in the virtual world, evaluate and adjust the plan, and then supervise execution with the ability to pause and return to the planning environment at any time. We report the results of an experimental evaluation of a representative on-orbit telerobotic servicing task from NASA’s upcoming OSAM-1 mission to refuel a satellite in low earth orbit; specifically, to change the robot tool to acquire the fuel supply line and then to insert it into the satellite fill/drain valve. Results of a pilot study show that the operators preferred, and were more successful with, the IPSE system when compared to a conventional teleoperation implementation.

Robot-Assisted systems for Minimally Invasive Surgery enhance the surgeon capability, however, direct control over both the surgical tools and the endoscope results in an increased workload that leads to longer operation times. This work investigates the introduction of SCAN (System for Camera Autonomous Navigation) to overcome this limitation. An experimental study involving 12 participants was carried out with the da Vinci Research Kit. Each user tested two novel camera control modalities, autonomous and semi-autonomous, as well as the current manual control of the camera, while carrying out a dry-lab task. Among the camera control modalities, the autonomous navigation achieved better objective performances and the highest user confidence. Moreover, the autonomous control (along with the semi-autonomous one) was able to optimize some metrics related to the robotic surgery workflow.

High-resolution real-time intraocular imaging of retina at the cellular level is very challenging due to the vulnerable and confined space within the eyeball as well as the limited availability of appropriate modalities. A probe-based confocal laser endomicroscopy (pCLE) system, can be a potential imaging modality for improved diagnosis. The ability to visualize the retina at the cellular level could provide information that may predict surgical outcomes. The adoption of intraocular pCLE scanning is currently limited due to the narrow field of view and the micron-scale range of focus. In the absence of motion compensation, physiological tremors of the surgeons' hand and patient movements also contribute to the deterioration of the image quality. Therefore, an image-based hybrid control strategy is proposed to mitigate the above challenges. The proposed hybrid control strategy enables a shared control of the pCLE probe between surgeons and robots to scan the retina precisely, with the absence of hand tremors and with the advantages of an image-based auto-focus algorithm that optimizes the quality of pCLE images. The hybrid control strategy is deployed on two frameworks - cooperative and teleoperated. Better image quality, smoother motion, and reduced workload are all achieved in a statistically significant manner with the hybrid control frameworks., Comment: Accepted in IEEE TMRB

Since the first reports of a novel coronavirus (SARS-CoV-2) in December 2019, over 33 million people have been infected worldwide and approximately 1 million people worldwide have died from the disease caused by this virus, COVID-19. In the US alone, there have been approximately 7 million cases and over 200,000 deaths. This outbreak has placed an enormous strain on healthcare systems and workers. Severe cases require hospital care, and 8.5\% of patients require mechanical ventilation in an intensive care unit (ICU). One major challenge is the necessity for clinical care personnel to don and doff cumbersome personal protective equipment (PPE) in order to enter an ICU unit to make simple adjustments to ventilator settings. Although future ventilators and other ICU equipment may be controllable remotely through computer networks, the enormous installed base of existing ventilators do not have this capability. This paper reports the development of a simple, low cost telerobotic system that permits adjustment of ventilator settings from outside the ICU. The system consists of a small Cartesian robot capable of operating a ventilator touch screen with camera vision control via a wirelessly connected tablet master device located outside the room. Engineering system tests demonstrated that the open-loop mechanical repeatability of the device was 7.5\,mm, and that the average positioning error of the robotic finger under visual servoing control was 5.94\,mm. Successful usability tests in a simulated ICU environment were carried out and are reported. In addition to enabling a significant reduction in PPE consumption, the prototype system has been shown in a preliminary evaluation to significantly reduce the total time required for a respiratory therapist to perform typical setting adjustments on a commercial ventilator, including donning and doffing PPE, from 271 seconds to 109 seconds., Comment: 22 pages, 9 figures, 1 table; submitted to Frontiers in Robotics; in review

Teleoperation in extreme environments can be hindered by limitations in telemetry and in operator perception of the remote environment. Often, the primary mode of perception is visual feedback from remote cameras, which do not always provide suitable views and are subject to telemetry delays. To address these challenges, we propose to build a model of the remote environment and provide an augmented virtuality visualization system that augments the model with projections of real camera images. The approach is demonstrated in a satellite servicing scenario, with a multisecond round-trip telemetry delay between the operator on Earth and the satellite on orbit. The scene modeling enables both virtual fixtures to assist the human operator and augmented virtuality visualization that allows the operator to teleoperate a virtual robot from a convenient virtual viewpoint, with the delayed camera images projected onto the three-dimensional model. Experiments on a ground-based telerobotic platform, with software-created telemetry delays, indicate that the proposed method leads to better teleoperation performance with 30% better blade alignment and 50% reduction in task execution time compared to the baseline case where visualization is restricted to the available camera views.

This letter reports the development of a vision-based calibration method for dual remote center-of-motion (RCM) based robot arms in a human-robot collaborative minimally invasive surgery (MIS) scenario. The method does not require any external tracking sensors and directly uses images captured by the endoscopic camera and the robot encoder readings as calibration data, which leads to a minimal and practical system in the operating room. By taking advantage of the motion constraints imposed by the RCM-based kinematics of the robotic surgical tools and cameras, we can find unique relationships between the endoscope and the surgical tool using camera perspective projection geometry without the geometric information of the tool. A customized vision-based centerline detection algorithm is also proposed, which provides robust estimation of centerline positions for a variety of settings. We validate the method through simulations and an experimental study in a simulated MIS scenario, in which the first generation da Vinci surgical system controlled by the open source da Vinci research kit electronics and cisst/SAW software environment is used.

In this letter, we propose a new method to fully control complete 4-image-DoF manipulation of laparoscopic instruments [with remote center of motion (RCM) mechanism] based on the geometric features of a designed marker in a 2-D image. Our marker encodes the configuration of the instruments by computing geometric features among the projected image points from segmented areas in hue-saturation-value (HSV) space. We can then construct an image geometric feature vector to locally characterize the configuration of a laparoscopic instrument. Furthermore, we design an image-based kinematic controller to asymptotically track a planned trajectory using the constructed feature vector as the feedback. We evaluate our integration of rotation distinguishing marker and kinematic controller by several experiments in terms of illumination-invariance, rotation angle accuracy, and controller performance.