Search

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.

Photodynamic therapy has the potential to not only treat tumors directly but also to reduce incidental damage caused by large surgical margins and radiation therapy. In this study, a novel robotic system of delivering light was developed using a cartesian robot. Human input was limited to a computer input and no physical positioning of the light delivery system was required during testing. Error analysis was conducted to ensure the system’s applicability to a clinical environment. Error involved in both the outlining and coverage of the targeted areas was examined. The average outlining error and standard deviation were 0.23 +/− 0.16mm, and the coverage time error was below 4%. These results indicate that a robotic light delivery system for photodynamic therapy can consistently provide light delivery with sub-millimeter errors when testing with ex-vivo phantoms.

Trauma is among the leading causes of death in the United States with up to 29% of pre-hospital trauma deaths attributed to uncontrolled hemorrhages. This paper reports a semi-autonomous robotic system capable of assessing trauma using 2D and 3D image analysis and enabling remote focused assessment with sonography for trauma (FAST) en route to the hospital for earlier trauma diagnosis and faster initialization of life saving care. The system was able to accurately calculate FAST scan positions of patient specific phantoms using the measured phantom sizes and positions of the umbilicus. The system was capable of accurately classifying and localizing wounds, so they can be avoided during the ultrasound scan. These objects were localized with an accuracy of 0.94 ± 0.179cm and FAST exam locations were estimated with an accuracy of 2.2 ± 1.88cm. A radiologist successfully completed a remote FAST scan of the phantom using the system with improved image quality over manual scans, demonstrating feasibility of the system.

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.

Mutli-robot teleoperation systems benefit from human's capabilities in adaptation to new environments and decision-making in the presence of uncertainty and complexity. In these applications, usually a human operator determines the general behaviour of the multi-robot team via selecting and controlling a leader robot while the follower robots coordinate with the team autonomously. Trust is a major factor in human's allocation of autonomy among the team of robots. A trust-based online leader selection strategy is proposed in this work for multi-robot bilateral haptic teleoperation with applications in collective position tracking and synchronization. Human-to-robot trust is computed and utilized as a dynamic criterion to select the leader. Also, robot-to-human trust is used to dynamically scale the haptic force feedback cues so that the operator receives smaller force feedback when his/her performance in leading the team is higher. This reduces the physical workload of the operator. Due to the trust-based leader switching and force feedback scaling, the stability of the closed-loop system may be lost. We guarantee this stability via passivity-based techniques. Finally, our experimental results indicate that the trust-based leader selection strategy can reduce task completion time by 35.25% and formation error by 41.64% compared to a non-leader switching strategy.

Recently, lightweight and flexible robots have been introduced for human-robot collaborative manufacturing. However, most of the research in this field focuses on the physical aspects of the interaction only. Nevertheless, psychological and social impact of human-robot collaboration (HRC) on manufacturing needs to be addressed and embedded in the design as well such that the robot actions become acceptable and comfortable for the human. Motivated by this need, we propose an integrated physical and social HRC framework for assembly tasks in a hybrid manufacturing cell. To address human physical demands, the robot motion control will be developed to keep pace with human motion during the task. Furthermore, psychological human factors will also be taken into account into the robot motion control for better HRC. More specifically, we consider a computational model of a human worker's trust in his/her robot partner and use the trust evaluation as a constraint in an optimal control problem. Finally, we run a pilot study to test our proposed framework.

A trust-based mixed-initiative scheme for the bilateral teleoperation of mobile robotic systems is proposed. In a mixed-initiative control scheme, the control task is shared between human and an in-situ autonomous robot controller. In a bilateral haptic teleoperation scheme, a human operator controls a robot remotely through some control device while receiving haptic force feedback cues. The bilateral haptic teleoperation is implemented with mixed-initiative control, by introducing computational two-way trust models, to increase the performance of the joint system. Mixed-initiative is enabled by scaling the manual and autonomous control inputs with a function of computational human-to-robot trust. The haptic force feedback is dynamically scaled with a function of computational robot-to-human trust and hence reduces the physical workload of operator. Passivity based techniques are developed for the proposed control scheme in the presence of such time-varying scales as well as communication delays. The trust-based mixed-initiative teleoperation scheme has been experimentally tested. The results indicate the effectiveness of our proposed scheme in improving task performance by 31% and reducing operator workload by 23.9% compared to the exclusively bilateral teleoperation.

Market forecasts for the civilian use of unmanned aerial vehicles (UAVs) show a sustained growth in the long term. To increase awareness for operators, a three degree-of-freedom haptic interface is introduced to provide helpful operational assistance for UAV motion control. Furthermore, an adjustable feedback is implemented in the interface with different combinations of force. In this paper, the kinematics of the haptic manipulator are analyzed first. Specifically, by studying the relation between angular and linear motion, a kinematic model is established to derive the Jacobian matrix and the inverse kinematics of the manipulator. Then, the Newton-Euler equations are introduced to characterize the system dynamics. Finally, these dynamics are incorporated into the adjustable feedback control strategy which accounts for the stiffness and damping effects. The control strategy is evaluated based on the performance of human operators. It is demonstrated that by adding adjustable feedback, the operators' performance can be improved by 16% in position error and 45.7% in test completion time in comparison to non-feedback haptic devices.

In this paper, we investigate shared autonomous and manual control of robotic systems. A trust and self-confidence based autonomy allocation scheme is proposed based on objective and unbiased measures of human-robot collaboration (HRC) systems. We also provide analytical tools to evaluate the long-term effects of the proposed allocation scheme on the overall robot performance and human workload. Experiments are designed to test and show that the proposed trust and self-confidence based allocation scheme can capture human autonomy allocation pattern. Furthermore, we develop a pattern correction algorithm using nonlinear model predictive control (NMPC) to help the human gradually adapt to a modified autonomy allocation pattern for better overall performance. The effectiveness of this scheme is shown in simulations.

This paper proposes an optimal control approach to power management and pacing in an electric bicycle ride with the objective of minimizing travel time. We assume prior knowledge of upcoming terrain. Furthermore human pedaling force constraints are estimated by using a phenomenological fatigue dynamics model. Using upcoming terrain information, estimated rider's state of fatigue (SOF), measured velocity, cadence, and state of charge (SOC) of the battery, the optimal solution to the problem is obtained via a three-state dynamic programming (DP) approach. The proposed solution guides the rider by suggesting an optimal reference velocity and also optimally adjusts the electric-human power split. The optimal solution is compared to aggressive and conservative rule-based strategies that we have devised. Simulation results show that travel time can be significantly reduced with the optimal control approach.