In soft tissue surgeries, such as tumor resections, achieving precision is of utmost importance. Surgeons conventionally achieve this precision through intraoperative adjustments to the cutting plan, responding to deformations from tool-tissue interactions. This study examines the integration of physics-based tissue cutting simulations into autonomous robotic surgery to preoperatively predict and compensate for such deformations, aiming to improve surgical precision and reduce the necessity for dynamic adjustments during autonomous surgeries. This study adapts a real-to-sim-to-real workflow. Initially, the Autonomous System for Tumor Resection (ASTR) was employed to evaluate its accuracy in performing preoperatively intended incisions along the irregular contours of porcine tongue pseudotumors. Following this, a finite element analysis-based simulation, utilizing the Simulation Open Framework Architecture (SOFA), was developed and tuned to accurately mimic these tissue and incision interactions. Insights gained from this simulation were applied to refine the robot’s path planning, ensuring a closer alignment of actual incisions with the initially intended surgical plan. The efficacy of this approach was validated by comparing surface incision precision on ex vivo porcine tongues, with the average absolute error reducing from 1.73mm to 1.46mm after applying simulation-driven path adjustments (p < 0.001). Additionally, our method not only demonstrated improvements in maintaining the intended cutting shapes and locations, with shape matching scores using Hu moments enhancing from 0.10 to 0.06 and centroid shifts decreasing from 2.09mm to 1.33mm, but it also potentially reduced the likelihood of adverse oncologic outcomes by preventing clinically suggested excessively close margins of 2.2mm. This feasibility study suggests that merging physics-based cutting simulations with autonomous robotic surgery could potentially lead to more accurate incisions.

This paper describes a novel tissue positioning system with an integrated suturing robot and demonstrates its ability to perform semi-automatic anastomoses of synthetic blood vessels. We began with a finite element analysis-based design consideration for achieving adequate grasping of blood vessels to demonstrate robust performance under expected clinical forces. We then conducted standardized positioning tests to measure the repeatability of the system and incorporated a high-resolution optical coherence tomography (OCT) fiber imaging sensor within the tip of the suturing tool to provide position feedback of the robot during a suturing task. Using the microvascular positioner and OCT sensor, the system performed semi-automatic suturing of synthetic 5 mm diameter blood vessels ($\mathrm{N}=4$), and the suture quality was evaluated for consistency in spacing, bite depth, percent lumen reduction, and maximum suture strength. The system completed the task in an average time of 31.75 minutes. The samples had zero missed stitches, average spacing of 1.64 mm, an average bite depth of 2.14 mm, an average lumen reduction of 57.98%, and an average suture strength of 3.13 N.

This article introduces a novel suture managing device (SMD) and new suture management controller to enable single-arm suture management during autonomous suturing with the Smart Tissue Autonomous Robot (STAR). The primary function of the SMD is to tension and manage the suture thread, a task that was previously carried out by a second manipulator or a human assistant. The SMD and its controller are integrated into STAR's autonomous suturing workflow. Experiments were conducted to quantify the tensioning force of SMD and to evaluate the suture quality of the new single-arm system. The prototype of SMD achieves 1.67N tensioning force with suturing time of 29.1±0.42 seconds per stitch. Our study results demonstrate that the single-arm STAR system with SMD achieves equivalent performance to our previous works in suturing efficiency where suture management was performed with either a dual-armed robotic system or by a human surgical assistant. The study's findings contribute to the field of medical robotics and to our knowledge represent the first known instance of single-arm suturing with suture management during autonomous anastomosis.

Surgical resection is the current clinical standard of care for treating squamous cell carcinoma. Maintaining an adequate tumor resection margin is the key to a good surgical outcome, but tumor edge delineation errors are inevitable with manual surgery due to difficulty in visualization and hand-eye coordination. Surgical automation is a growing field of robotics to relieve surgeon burdens and to achieve a consistent and potentially better surgical outcome. This paper reports a novel robotic supervised autonomous electrosurgery technique for soft tissue resection achieving millimeter accuracy. The tumor resection procedure is decomposed to the subtask level for a more direct understanding and automation. A 4-DOF suction system is developed, and integrated with a 6-DOF electrocautery robot to perform resection experiments. A novel near-infrared fluorescent marker is manually dispensed on cadaver samples to define a pseudotumor, and intraoperatively tracked using a dual-camera system. The autonomous dual-robot resection cooperation workflow is proposed and evaluated in this study. The integrated system achieves autonomous localization of the pseudotumor by tracking the near-infrared marker, and performs supervised autonomous resection in cadaver porcine tongues (N=3). The three pseudotumors were successfully removed from porcine samples. The evaluated average surface and depth resection errors are 1.19 and 1.83mm, respectively. This work is an essential step towards autonomous tumor resections.

Vertebral compression fractures (VCFs), the most common fragility fractures secondary to osteoporosis, affect more than 200 million individuals worldwide. Percutaneous vertebral augmentation is an effective interventional treatment option that is routinely performed across the world. Because fluoroscopy-guided vertebral augmentation is a well-established and safe minimally invasive technique, automating its delivery is among the most important next steps. In this work, we describe the design and evaluation of a novel cannula mounted vertebral augmentation robot in a simulated X-ray environment as a first step toward autonomous vertebral augmentation. The cannula robot employs a piezo stack with inchworm control to place surgical tools within the vertebral body, while X-ray imaging verifies the robot does not interfere with imaging. Finite element analysis of the robot confirms that radiolucent materials were rigid enough to be used in the robot design as expected deformations for the cannula drive, accessory drive, and locking mechanisms$(1.299 \pm 0.034 \ um, 1.280 \pm 0.027\ um$, and$1.960 \pm 0.218\ um$, respectively) did not exceed the stroke lengths of the piezo stacks. An in silico clinical trial based on a human anatomy model suffering from VCF validates that the cannula robot does not impede visualization of the critical anatomy and tool-to-tissue positioning. Together these results demonstrate the feasibility of a cannula mounted robot for vertebral augmentation.

This paper reports the design and evaluation of a novel piezo based actuator for needle drive in autonomous Deep Anterior Lamellar Keratoplasty (piezo-DALK). The actuator weighs less than 8g and is 20mm × 20mm × 10.5mm in size, making it ideal for eye-mounted applications. Mean open loop positional deviation was 1.17 ± 3.15um, and system repeatability and accuracy were 17.16um and 18.33um, respectively. Stall force was found to vary linearly with the cooling cycle and the actuator achieved a maximum drive force of 3.98N. When simulating the DALK procedure in synthetic corneal tissue, the piezo-DALK achieved a penetration depth of 643.56um which was equivalent to 92.1% of the total corneal thickness. This correlated closely with our desired depth of 90% ± 5% and took 2.5 hours to achieve. This work represents the first eye mountable actuator capable of "Big Bubble" needle drive for autonomous DALK procedures.

Patient-specific biodegradable grafts target to enhance surgical repairs of complex congenital heart defects (CHD). This study reports the design, simulation, and creation of bifurcated right ventricle-pulmonary artery (RVPA) conduit grafts for patients with CHD. The original right ventricle outflow tract and RVPA conduit-anatomies of two patients (n=2) who previously underwent Rastelli type surgical repair for their CHD were created using medical image segmentation software based on magnetic resonance imaging data. The pulsatile RVPA flow was simulated utilizing computational fluid dynamics (CFD) to calculate important hemodynamic parameters. The re-designed RVPA geometries for the patients were created by varying the radius and angle of the pulmonary artery bifurcation. The wall shear stress and power loss results of the re-designed RVPA models were compared to identify the best performing graft. The hemodynamic results demonstrated that the designed optimized grafts outperformed the original grafts. To test the feasibility of designed grafts in vivo, the bifurcated RVPA conduit of a pig was manufactured using a 3D printed mandrel and electrospinning technique before the implantation. The implanted graft allowed new tissue formation within weeks. The results of our study and simulations provide an insight into the creation of optimal performing tissue-engineered bifurcated grafts for the patients with CHD in the surgical planning process. Integration of flow simulations to support design and electrospinning technique to manufacture patient-specific biodegradable grafts has the potential to improve surgical outcomes in CHD.

Compared to open surgical techniques, laparoscopic surgical methods aim to reduce the collateral tissue damage and hence decrease the patient recovery time. However, constraints imposed by the laparoscopic surgery, i.e. the operation of surgical tools in limited spaces, turn simple surgical tasks such as suturing into time-consuming and inconsistent tasks for surgeons. In this paper, we develop an autonomous laparoscopic robotic suturing system. More specific, we expand our smart tissue anastomosis robot (STAR) by developing i) a new 3D imaging endoscope, ii) a novel actuated laparoscopic suturing tool, and iii) a suture planning strategy for the autonomous suturing. We experimentally test the accuracy and consistency of our developed system and compare it to sutures performed manually by surgeons. Our test results on suture pads indicate that STAR can reach 2.9 times better consistency in suture spacing compared to manual method and also eliminate suture repositioning and adjustments. Moreover, the consistency of suture bite sizes obtained by STAR matches with those obtained by manual suturing.

This paper reports a robotic laparoscopic surgery system performing electro-surgery on porcine cadaver kidney, and evaluates its accuracy in an open loop control scheme to conduct targeting and cutting tasks guided by a novel 3D endoscope. We describe the design and integration of the novel laparoscopic imaging system that is capable of reconstructing the surgical field using structured light. A targeting task is first performed to determine the average positioning error of the system as guided by the laparoscopic camera. The imaging system is then used to reconstruct the surface of a porcine cadaver kidney, and generate a cutting trajectory with consistent depth. The paper concludes by using the robotic system in open loop control to cut this trajectory using a multi degree of freedom electro-surgical tool. It is demonstrated that for a cutting depth of 3 mm, the robotic surgical system follows the trajectory with an average depth of 2.44 mm and standard deviation of 0.34 mm. The average positional accuracy of the system was 2.74±0.99 mm.

Autonomous robotic assisted surgery (RAS) systems aim to reduce human errors and improve patient outcomes leveraging robotic accuracy and repeatability during surgical procedures. However, full automation of RAS in complex surgical environments is still not feasible and collaboration with the surgeon is required for safe and effective use. In this work, we utilize our Smart Tissue Autonomous Robot (STAR) to develop and evaluate a shared control strategy for the collaboration of the robot with a human operator in surgical scenarios. We consider 2D pattern cutting tasks with partial blood occlusion of the cutting pattern using a robotic electrocautery tool. For this surgical task and RAS system, we i) develop a confidence-based shared control strategy, ii) assess the pattern tracking performances of manual and autonomous controls and identify the confidence models for human and robot as well as a confidence-based control allocation function, and iii) experimentally evaluate the accuracy of our proposed shared control strategy. In our experiments on porcine fat samples, by combining the best elements of autonomous robot controller with complementary skills of a human operator, our proposed control strategy improved the cutting accuracy by 6.4%, while reducing the operator work time to 44% compared to a pure manual control.

This paper specifies a surgical robot performing semi-autonomous electrosurgery for tumor resection and evaluates its accuracy using a visual servoing paradigm. We describe the design and integration of a novel, multi-degree of freedom electrosurgical tool for the smart tissue autonomous robot (STAR). Standardized line tests are executed to determine ideal cut parameters in three different types of porcine tissue. STAR is then programmed with the ideal cut setting for porcine tissue and compared against expert surgeons using open and laparoscopic techniques in a line cutting task. We conclude with a proof of concept demonstration using STAR to semi-autonomously resect pseudo-tumors in porcine tissue using visual servoing. When tasked to excise tumors with a consistent 4mm margin, STAR can semi-autonomously dissect tissue with an average margin of 3.67 mm and a standard deviation of 0.89mm.

Three-dimensional sensing of changing surgical scenes would improve the function of surgical robots. This paper explores the requirements and utility of a new type of depth sensor, the plenoptic camera, for surgical robots. We present a metric calibration procedure for the plenoptic camera and the registration of its coordinate frame to the robot (hand-eye calibration). We also demonstrate the utility in robotic needle insertion and application of sutures in phantoms. The metric calibration accuracy is reported as 1.14 ± 0.80 mm for the plenoptic camera and 1.57 ± 0.90 mm for hand-eye calibration. The accuracy of needle insertion task is 1.79 ± 0.35 mm for the entire robotic system. Additionally, the accuracy of suture placement with the presented system is reported at 1.80 ± 0.43 mm. Finally, we report consistent suture spacing with only 0.11 mm standard deviation between inter-suture distances. The measured accuracy of less than 2 mm with consistent suture spacing is a promising result to provide repeatable leak-free suturing with a robotic tool and a plenoptic depth imager.