Search for: All records

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. 

Surgical robots have been introduced to operating rooms over the past few decades due to their high sensitivity, small size, and remote controllability. The cable-driven nature of many surgical robots allows the systems to be dexterous and lightweight, with diameters as low as 5mm. However, due to the slack and stretch of the cables and the backlash of the gears, inevitable uncertainties are brought into the kinematics calcu- lation [1]. Since the reported end effector position of surgical robots like RAVEN-II [2] is directly calculated using the motor encoder measurements and forward kinematics, it may contain relatively large error up to 10mm, whereas semi-autonomous functions being introduced into abdominal surgeries require position inaccuracy of at most 1mm. To resolve the problem, a cost-effective, real-time and data-driven pipeline for robot end effector position precision estimation is proposed and tested on RAVEN-II. Analysis shows an improved end effector position error of around 1mm RMS traversing through the entire robot workspace without high-resolution motion tracker. The open source code, data sets, videos, and user guide can be found at //github.com/HaonanPeng/RAVEN Neural Network Estimator. 

Surgical robots have been introduced to operating rooms over the past few decades due to their high sensitivity, small size, and remote controllability. The cable-driven nature of many surgical robots allows the systems to be dexterous and lightweight, with diameters as low as 5mm. However, due to the slack and stretch of the cables and the backlash of the gears, inevitable uncertainties are brought into the kinematics calcu- lation [1]. Since the reported end effector position of surgical robots like RAVEN-II [2] is directly calculated using the motor encoder measurements and forward kinematics, it may contain relatively large error up to 10mm, whereas semi-autonomous functions being introduced into abdominal surgeries require position inaccuracy of at most 1mm. To resolve the problem, a cost-effective, real-time and data-driven pipeline for robot end effector position precision estimation is proposed and tested on RAVEN-II. Analysis shows an improved end effector position error of around 1mm RMS traversing through the entire robot workspace without high-resolution motion tracker. The open source code, data sets, videos, and user guide can be found at //github.com/HaonanPeng/RAVEN Neural Network Estimator. 

The Raven I and the Raven II surgical robots, as open research platforms, have been serving the robotic surgery research community for ten years. The paper 1) briefly presents the Raven I and the Raven II robots, 2) reviews the recent publications that are built upon the Raven robots, aim to be applied to the Raven robots, or are directly compared with the Raven robots, and 3) uses the Raven robots as a case study to discuss the popular research problems in the research community and the trend of robotic surgery study. Instead of being a thorough literature review, this work only reviews the works formally published in the past three years and uses these recent publications to analyze the research interests, the popular open research problems, and opportunities in the topic of robotic surgery. 

Inverse kinematics solves the problem of how to control robot arm joints to achieve desired end effector positions, which is critical to any robot arm design and implemen- tations of control algorithms. It is a common misunderstanding that closed-form inverse kinematics analysis is solved. Popular software and algorithms, such as gradient descent or any multi-variant equations solving algorithm, claims solving inverse kinematics but only on the numerical level. While the numerical inverse kinematics solutions are rela- tively straightforward to obtain, these methods often fail, due to dependency on specific numerical values, even when the inverse kinematics solutions exist. Therefore, closed-form inverse kinematics analysis is superior, but there is no generalized automated algorithm. Up till now, the high-level logical reasoning involved in solving closed-form inverse kine- matics made it hard to automate, so it’s handled by human experts. We developed IKBT, a knowledge-based intelligent system that can mimic human experts’ behaviors in solving closed-from inverse kinematics using Behavior Tree. Knowledge and rules used by engineers when solving closed-from inverse kinematics are encoded as actions in Behavior Tree. The order of applying these rules is governed by higher level composite nodes, which resembles the logical reasoning process of engineers. It is also the first time that the dependency of joint variables, an important issue in inverse kinematics analysis, is automatically tracked in graph form. Besides generating closed-form solutions, IKBT also explains its solving strategies in human (engineers) interpretable form. This is a proof-of-concept of using Behavior Trees to solve high-cognitive problems. 

Dynamic 3D reconstruction of surgical cavities is essential in a wide range of computer-assisted surgical intervention applications, including but not limited to surgical guidance, pre-operative image registration and vision-based force estimation. According to a survey on vision based 3D reconstruction for abdominal minimally invasive surgery (MIS) [1], real-time 3D reconstruction and tissue deformation recovery remain open challenges to researchers. The main challenges include specular reflections from the wet tissue surface and the highly dynamic nature of abdominal surgical scenes. This work aims to overcome these obstacles by using multiple viewpoint and independently moving RGB cameras to generate an accurate measurement of tissue deformation at the volume of interest (VOI), and proposes a novel efficient camera pairing algorithm. Experimental results validate the proposed camera grouping and pair sequencing, and were evaluated with the Raven-II [2] surgical robot system for tool navigation, the Medtronic Stealth Station s7 surgical navigation system for real- time camera pose monitoring, and the Space Spider white light scanner to derive the ground truth 3D model. 

Monitoring surgical instruments is an essential task in computer-assisted interventions and surgical robotics. It is also important for navigation, data analysis, skill as- sessment and surgical workflow analysis in conventional surgery. However, there are no standard datasets and benchmarks for tool identification in neurosurgery. To this end, we are releasing a novel neurosurgical instrument seg- mentation dataset called NeuroID for advancing research in the field. Delineating surgical tools from the background requires accurate pixel-wise instrument segmentation. In this paper, we present a comparison between three encoder- decoder approaches to binary segmentation of neurosurgi- cal instruments, where we classify each pixel in the image to be either tool or background. A baseline performance was obtained by using heuristics to combine extracted features. We also extend the analysis to a publicly available robotic instrument segmentation dataset and include its results. The source code for our methods and the neurosurgical instru- ment dataset will be made publicly available1 to facilitate reproducibility. 

Compressing soft-obstacles secondary to a con- trolled motion task is common for human beings. While these tasks are nearly trivial for teleoperated robots, they remain a challenging problem in robotic autonomy. Addressing the problem is significant. For example, in Minimally Invasive Surgeries (MISs), safely compressing soft tissues ensures the surgical safety and decreases tissue removal, thus dramatically decreases surgical trauma and operating room time, and leads to improved surgical outcomes. In this work, we define the problem of soft-obstacle avoidance and project the safety motion constraints into the task space and the velocity space. We illustrate the significance of addressing this problem in the robotic surgery scenario. We present a Recurrent Neural Networks (RNNs) based solution, which for- mulates the problem as an inequality constrained optimization problem and solves it in its dual space. The application of the proposed method was demonstrated in the Raven II surgical robot. Experimental results demonstrated that the proposed method is effective in addressing the soft-obstacle avoidance problem.