SARTRES: a semi-autonomous robot teleoperation environment for surgery

Rahman, Md. Masudur; Balakuntala, Mythra V.; Gonzalez, Glebys; Agarwal, Mridul; Kaur, Upinder; Venkatesh, Vishnunandan L. N.; Sánchez-Tamayo, Natalia; Xue, Yexiang; Voyles, Richard M.; Aggarwal, Vaneet; Wachs, Juan P.

doi:10.1080/21681163.2020.1834878

Cited by 8 publications

(3 citation statements)

References 32 publications

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“…When the position tracking and error graphs of the master and slave robots given in Figures 15 and 16 are examined, the angular position performance of the first joint varies between approximately ± 0.28 radians (rad). It is seen that the error value varies between approximately ± 5 × 10 −3 radians. When the angular position performance of the second joint is examined, it is seen that it changes between approximately +0.4 to−0.1 radians, respectively, and the error value changes between approximately ± 0.02 radians.…”

Section: Resultsmentioning

confidence: 97%

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Optimal adaptive computed torque control for haptic-teleoperation system with uncertain dynamics

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Soygüder

2021

Proceedings of the Institution of Mechanical Engineers, Part I:

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This study aimed to eliminate dynamic uncertainty, one of the main problems of haptic teleoperation robotic systems. The optimal adaptive computed torque control method was used to overcome this problem. As is known, excellent stability and transparency are required in teleoperation systems. However, dynamic uncertainty that causes stability problems in the control of these systems also causes poor performance. In conventional adaptive computed torque control methods, updating the parameters of the system is generally discussed, but updating the control coefficients of vital importance in the control of the system is not considered. In the proposed method, an adaptation rule has been created to update uncertain parameters. In addition, the gray wolf optimization algorithm, one of the current optimization algorithms, has been proposed and applied to obtain the control coefficients of the system. The position tracking stability of the system was examined by using Lyapunov stability analysis method. As a result, both simulation and real-time optimal adaptive computational torque control method were used and bilateral position and force control was performed and the performance results of the system are obtained graphically and examined. Optimal adaptive computed torque control method obtained using the gray wolf optimization algorithm was used first in the literature search and success results have been obtained. In this regard, the authors have the idea that this work is an innovative aspect of both simulation and real time with the optimal adaptive computed torque control method.

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Section: Resultsmentioning

confidence: 97%

“…From robotic surgery to the defense industry and simulators, it has a wide range of uses. 1–5 One of the most important uses of Haptic-Teleoperation systems is to leave surgery or diagnosis without touching the patient. The COVID-19 pandemic experienced today shows the importance of these studies.…”

Section: Introductionmentioning

confidence: 99%

Optimal adaptive computed torque control for haptic-teleoperation system with uncertain dynamics

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Soygüder

2021

Proceedings of the Institution of Mechanical Engineers, Part I:

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“…The translation from TSRSs to semi‐autonomous surgical systems is becoming clearer these days since numerous semi‐autonomous systems can execute surgical tasks with limited human intervention. [ 237 ] Hu et al., [ 238 ] for example, suggested a semiautonomous tumor ablation system that offers the operator a collection of potential execution plans. Once a strategy is chosen, the robot completes the task autonomously.…”

Section: Discussionmentioning

confidence: 99%

Advanced User Interfaces for Teleoperated Surgical Robotic Systems

Nguyễn

Wong

Thai

et al. 2023

Advanced Sensor Research

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In recent years, advances in modern technology have altered the practice of surgery from open to minimally invasive surgery (MIS) aided by robots. Teleoperated surgical robotic systems (TSRSs) provide numerous significant benefits for MIS over traditional approaches, including improved safety, more efficient and precise surgery, better cosmesis, shorter recovery time, and reduced postoperative pain. Existing TSRSs' master consoles, with improvements in vision systems, designs, and control methods, have significantly enhanced human-robot interactions, resulting in safer and more accurate medical intervention operations. Despite advances, haptic technologies, including sensors, machine assistance, and intuitive devices for user interfaces, are still limited, resulting in less effective usage of TSRSs for surgical operations. This review presents a summary of the emerging TSRSs with a focus on their user interfaces. In addition, advanced sensing, haptic, smart garments, and medical image artificial intelligence (AI) assistance technologies are shown with their potential for use in master consoles of the TSRSs are shown. Finally, a discussion on the need for a smart human-robot interface for TSRSs is given.

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ASAP-CORPS: A Semi-Autonomous Platform for COntact-Rich Precision Surgery

Balakuntala,

Gonzalez,

Wachs

et al. 2023

Military Medicine

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Introduction Remote military operations require rapid response times for effective relief and critical care. Yet, the military theater is under austere conditions, so communication links are unreliable and subject to physical and virtual attacks and degradation at unpredictable times. Immediate medical care at these austere locations requires semi-autonomous teleoperated systems, which enable the completion of medical procedures even under interrupted networks while isolating the medics from the dangers of the battlefield. However, to achieve autonomy for complex surgical and critical care procedures, robots require extensive programming or massive libraries of surgical skill demonstrations to learn effective policies using machine learning algorithms. Although such datasets are achievable for simple tasks, providing a large number of demonstrations for surgical maneuvers is not practical. This article presents a method for learning from demonstration, combining knowledge from demonstrations to eliminate reward shaping in reinforcement learning (RL). In addition to reducing the data required for training, the self-supervised nature of RL, in conjunction with expert knowledge-driven rewards, produces more generalizable policies tolerant to dynamic environment changes. A multimodal representation for interaction enables learning complex contact-rich surgical maneuvers. The effectiveness of the approach is shown using the cricothyroidotomy task, as it is a standard procedure seen in critical care to open the airway. In addition, we also provide a method for segmenting the teleoperator’s demonstration into subtasks and classifying the subtasks using sequence modeling. Materials and Methods A database of demonstrations for the cricothyroidotomy task was collected, comprising six fundamental maneuvers referred to as surgemes. The dataset was collected by teleoperating a collaborative robotic platform—SuperBaxter, with modified surgical grippers. Then, two learning models are developed for processing the dataset—one for automatic segmentation of the task demonstrations into a sequence of surgemes and the second for classifying each segment into labeled surgemes. Finally, a multimodal off-policy RL with rewards learned from demonstrations was developed to learn the surgeme execution from these demonstrations. Results The task segmentation model has an accuracy of 98.2%. The surgeme classification model using the proposed interaction features achieved a classification accuracy of 96.25% averaged across all surgemes compared to 87.08% without these features and 85.4% using a support vector machine classifier. Finally, the robot execution achieved a task success rate of 93.5% compared to baselines of behavioral cloning (78.3%) and a twin-delayed deep deterministic policy gradient with shaped rewards (82.6%). Conclusions Results indicate that the proposed interaction features for the segmentation and classification of surgical tasks improve classification accuracy. The proposed method for learning surgemes from demonstrations exceeds popular methods for skill learning. The effectiveness of the proposed approach demonstrates the potential for future remote telemedicine on battlefields.

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SARTRES: a semi-autonomous robot teleoperation environment for surgery

Cited by 8 publications

References 32 publications

Optimal adaptive computed torque control for haptic-teleoperation system with uncertain dynamics

Optimal adaptive computed torque control for haptic-teleoperation system with uncertain dynamics

Advanced User Interfaces for Teleoperated Surgical Robotic Systems

ASAP-CORPS: A Semi-Autonomous Platform for COntact-Rich Precision Surgery

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