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.

Autonomous robotic suturing has the potential to improve surgery outcomes by leveraging accuracy, repeatability, and consistency compared to manual operations. However, achieving full autonomy in complex surgical environments is not practical and human supervision is required to guarantee safety. In this paper, we develop a confidence-based supervised autonomous suturing method to perform robotic suturing tasks via both Smart Tissue Autonomous Robot (STAR) and surgeon collaboratively with the highest possible degree of autonomy. Via the proposed method, STAR performs autonomous suturing when highly confident and otherwise asks the operator for possible assistance in suture positioning adjustments. We evaluate the accuracy of our proposed control method via robotic suturing tests on synthetic vaginal cuff tissues and compare them to the results of vaginal cuff closures performed by an experienced surgeon. Our test results indicate that by using the proposed confidence-based method, STAR can predict the success of pure autonomous suture placement with an accuracy of 94.74%. Moreover, via an additional 25% human intervention, STAR can achieve a 98.1% suture placement accuracy compared to an 85.4% accuracy of completely autonomous robotic suturing. Finally, our experiment results indicate that STAR using the proposed method achieves 1.6 times better consistency in suture spacing and 1.8 times better consistency in suture bite sizes than the manual results.

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.

During robotic teleoperation, the dynamics of the master manipulators and the control of remote-side instruments impose challenges on the motor system of the human operator, and may impact performance and learning. In teleoperated robot-assisted minimally invasive surgery, there is a clear correlation between patient outcomes and the surgeon's case experience. However, the effect of the teleoperator on human motor skills and the relationship between these motor skills and patient outcomes are unknown. We used the da Vinci Research Kit, a custom research version of the da Vinci Surgical System, to compare teleoperated and open needle-driving movements of experienced da Vinci surgeons and novices. The experimental protocol consisted of structured but unconstrained needle driving trials repeated 80 times to allow for computational modeling of movement coordination and learning. Kinematic analysis showed that teleoperation increases trial time but reduces path length, that the trial times and path lengths of experienced surgeons are smaller than those of novices. In addition, there are significant differences in learning between experienced surgeons and novice users. Modeling of the movements and learning processes of experienced and novice surgeons may be used in the design of novel controllers that will expand robotic surgery capabilities and improve robot-assisted surgical skill acquisition.

Teleoperated robot-assisted surgery (RAS) provides surgeons with improved dexterity, movement control, and visualization in comparison to standard minimally invasive surgery. However, there exists little quantitative understanding of the motor performance of human operators in RAS. Models of how users control their movements and how this control relates to surgical performance could provide inspiration for new robot or human interface designs, as well as more targeted training methods. Toward this end, we present a framework for the analysis of surgeon arm posture variability based on the uncontrolled manifold (UCM) concept, a method used in the study of human motor control for testing hypotheses about the coupling of control and task variables. We partition users' joint angle variability into variability that does and does not result in hand trajectory change. In a preliminary study applying this framework, we explored how expert and novice operators control planar reaching and reversal movements when moving freehand as well as using a teleoperated RAS system. We show that only movements in task-relevant directions are stabilized by the coordination of joint angles, and that this stabilization is stronger for expert movements than novice movements. We also show that stabilization is stronger in freehand than teleoperated movements, especially for the expert. These preliminary findings suggest that the proposed framework can be useful for: (1) assessment of teleoperator design and control that reveals how design parameters affect the ability of the user to exploit the UCM for stabilization of hand movement, and (2) skill assessment in RAS.