1,804 research outputs found
Cooperative Object Manipulation with Force Tracking on the da Vinci Research Kit
The da Vinci Surgical System is one of the most established robot-assisted surgery device commended for its dexterity and ergonomics in minimally invasive surgery. Conversely, it inherits disadvantages which are lack of autonomy and haptic feedback. In order to address these issues, this work proposes an industry-inspired solution to the field of force control in medical robotics. This approach contributes to shared autonomy by developing a controller for cooperative object manipulation with force tracking utilizing available manipulators and force feedback. To achieve simultaneous position and force tracking of the object, master and slave manipulators were assigned then controlled with Cartesian position control and impedance control respectively. Because impedance control requires a model-based feedforward compensation, we identified the lumped base parameters of mass, inertias, and frictions of a three degree-of-freedom double four-bar linkage mechanism with least squares and weighted least squares regression methods. Additionally, semidefinite programming was used to constrain the parameters to a feasible physical solution in standard parameter space. Robust stick-slip static friction compensation was applied where linear Viscous and Coulomb friction was inadequate in modeling the prismatic third joint. The Robot Operating System based controller was tested in RViz to check the cooperative kinematics of up to three manipulators. Additionally, simulation with the dynamic engine Gazebo verified the cooperative controller applying a constant tension force on a massless spring-damper virtual object. With adequate model feedback linearization, the cooperative impedance controller tested on the da Vinci Research Kit yielded stable tension force tracking while simultaneously moving in Cartesian space. The maximum force tracking error was +/- 0.5 N for both a compliant and stiff manipulated object
A CoppeliaSim Dynamic Simulator for the Da Vinci Research Kit
The design of a physics-based dynamic simulator of a robot requires to properly integrate the robot kinematic and dynamic properties in a virtual environment. Naturally, the closer is the integrated information to the real robot properties, the more accurate the simulator predicts the real robot behaviour. A reliable robot simulator is a valuable asset for developing new research ideas; its use dramatically reduces the costs and it is available to all researchers. This letter presents a dynamic simulator of the da Vinci Research Kit (dVRK) patient-side manipulator (PSM). The kinematic and dynamic properties of the simulator rely on the parameters identified in Wang et al. With respect to the kinematic simulator previously developed by some of the authors, this work: (i) redefines the kinematic architecture and the actuation model by modeling the double parallelogram and the counterweight mechanism, to reflect the structure of the real robot; (ii) integrates the identified dynamic parameters in the simulation model. The obtained simulator enables the design and validation of control strategies relying on the robot dynamic model, including interaction force estimation and control, that are fundamental to guarantee safety in many surgical tasks
Caveats on the first-generation da Vinci Research Kit: latent technical constraints and essential calibrations
Telesurgical robotic systems provide a well established form of assistance in
the operating theater, with evidence of growing uptake in recent years. Until
now, the da Vinci surgical system (Intuitive Surgical Inc, Sunnyvale,
California) has been the most widely adopted robot of this kind, with more than
6,700 systems in current clinical use worldwide [1]. To accelerate research on
robotic-assisted surgery, the retired first-generation da Vinci robots have
been redeployed for research use as "da Vinci Research Kits" (dVRKs), which
have been distributed to research institutions around the world to support both
training and research in the sector. In the past ten years, a great amount of
research on the dVRK has been carried out across a vast range of research
topics. During this extensive and distributed process, common technical issues
have been identified that are buried deep within the dVRK research and
development architecture, and were found to be common among dVRK user feedback,
regardless of the breadth and disparity of research directions identified. This
paper gathers and analyzes the most significant of these, with a focus on the
technical constraints of the first-generation dVRK, which both existing and
prospective users should be aware of before embarking onto dVRK-related
research. The hope is that this review will aid users in identifying and
addressing common limitations of the systems promptly, thus helping to
accelerate progress in the field.Comment: 15 pages, 7 figure
Learning Deep Nets for Gravitational Dynamics with Unknown Disturbance through Physical Knowledge Distillation: Initial Feasibility Study
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
paper, 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
Toward Force Estimation in Robot-Assisted Surgery using Deep Learning with Vision and Robot State
Knowledge of interaction forces during teleoperated robot-assisted surgery
could be used to enable force feedback to human operators and evaluate tissue
handling skill. However, direct force sensing at the end-effector is
challenging because it requires biocompatible, sterilizable, and cost-effective
sensors. Vision-based deep learning using convolutional neural networks is a
promising approach for providing useful force estimates, though questions
remain about generalization to new scenarios and real-time inference. We
present a force estimation neural network that uses RGB images and robot state
as inputs. Using a self-collected dataset, we compared the network to variants
that included only a single input type, and evaluated how they generalized to
new viewpoints, workspace positions, materials, and tools. We found that
vision-based networks were sensitive to shifts in viewpoints, while state-only
networks were robust to changes in workspace. The network with both state and
vision inputs had the highest accuracy for an unseen tool, and was moderately
robust to changes in viewpoints. Through feature removal studies, we found that
using only position features produced better accuracy than using only force
features as input. The network with both state and vision inputs outperformed a
physics-based baseline model in accuracy. It showed comparable accuracy but
faster computation times than a baseline recurrent neural network, making it
better suited for real-time applications.Comment: 7 pages, 6 figures, submitted to ICRA 202
An Asynchronous Simulation Framework for Multi-User Interactive Collaboration: Application to Robot-Assisted Surgery
The field of surgery is continually evolving as there is always room for improvement in the post-operative health of the patient as well as the comfort of the Operating Room (OR) team. While the success of surgery is contingent upon the skills of the surgeon and the OR team, the use of specialized robots has shown to improve surgery-related outcomes in some cases. These outcomes are currently measured using a wide variety of metrics that include patient pain and recovery, surgeon’s comfort, duration of the operation and the cost of the procedure. There is a need for additional research to better understand the optimal criteria for benchmarking surgical performance. Presently, surgeons are trained to perform robot-assisted surgeries using interactive simulators. However, in the absence of well-defined performance standards, these simulators focus primarily on the simulation of the operative scene and not the complexities associated with multiple inputs to a real-world surgical procedure. Because interactive simulators are typically designed for specific robots that perform a small number of tasks controlled by a single user, they are inflexible in terms of their portability to different robots and the inclusion of multiple operators (e.g., nurses, medical assistants). Additionally, while most simulators provide high-quality visuals, simplification techniques are often employed to avoid stability issues for physics computation, contact dynamics and multi-manual interaction. This study addresses the limitations of existing simulators by outlining various specifications required to develop techniques that mimic real-world interactions and collaboration. Moreover, this study focuses on the inclusion of distributed control, shared task allocation and assistive feedback -- through machine learning, secondary and tertiary operators -- alongside the primary human operator
Autonomous pick-and-place using the dVRK.
PURPOSE: Robotic-assisted partial nephrectomy (RAPN) is a tissue-preserving approach to treating renal cancer, where ultrasound (US) imaging is used for intra-operative identification of tumour margins and localisation of blood vessels. With the da Vinci Surgical System (Sunnyvale, CA), the US probe is inserted through an auxiliary access port, grasped by the robotic tool and moved over the surface of the kidney. Images from US probe are displayed separately to the surgical site video within the surgical console leaving the surgeon to interpret and co-registers information which is challenging and complicates the procedural workflow. METHODS: We introduce a novel software architecture to support a hardware soft robotic rail designed to automate intra-operative US acquisition. As a preliminary step towards complete task automation, we automatically grasp the rail and position it on the tissue surface so that the surgeon is then able to manipulate manually the US probe along it. RESULTS: A preliminary clinical study, involving five surgeons, was carried out to evaluate the potential performance of the system. Results indicate that the proposed semi-autonomous approach reduced the time needed to complete a US scan compared to manual tele-operation. CONCLUSION: Procedural automation can be an important workflow enhancement functionality in future robotic surgery systems. We have shown a preliminary study on semi-autonomous US imaging, and this could support more efficient data acquisition
Accelerating Surgical Robotics Research: A Review of 10 Years With the da Vinci Research Kit
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
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