Barragan, Chanci win prize at IEEE's RO-MAN 2021

Robot surgeon, robotic equipment. Minimally invasive surgical innovation with three-dimensional overview.
Recent MSIE graduate, Juan Antonio Barragan, and Daniela Chanci Arrubla won the RSJ/KROS Distinguished Interdisciplinary Research Award at IEEE's RO-MAN 2021 for their work, titled "SACHETS: Semi-Autonomous Cognitive Hybrid Emergency Teleoperated Suction".

IEEE International Conference on Robot and Human Interactive Communication, or RO-MAN, was held virtually August 8-12, 2021. The theme of the conference was trustworthiness and cooperation. The Distinguished Interdisciplinary Research Award was sponsored by The Robotics Society of Japan and the Korean Robotics Society.

Barragan and Arrubla were advised by Professor Juan Wachs of Purdue University's School of Industrial Engineering  and Professor Denny Yu served on Barragan's committee. Please join us in congratulating this team on their recognition.



Blood suction and irrigation are among the most critical support tasks in robotic-assisted minimally invasive surgery (RMIS). Usually, suction/irrigation tools are controlled by a surgical assistant to maintain a clear view of the surgical field. Thus, the assistant’s contribution to other emergency support tasks is limited. Similarly, when the surgical assistant is not available to perform the blood suction, the leading surgeon must take over this task, which in a complex surgical procedure can result in an unnecessary increment in the cognitive load. To alleviate this problem, we have developed a semi-autonomous robotic suction assistant, which was integrated with a Da Vinci Research Kit (DVRK). At the heart of the algorithm, there is an autonomous control based on a deep learning model to segment and identify the location of blood accumulations. This system provides automatic suction allowing the leading surgeon to focus exclusively on the main task through the control of key instruments of the robot. We conducted a user study to evaluate the user’s workload demands and performance while doing a surgical task under two modalities: (1) autonomous suction action and (2) a surgeon-controlled-suction. Our results indicate that users working with the autonomous system completed the task 161 seconds faster than in the surgeon-controlled-suction modality. Furthermore, the autonomous modality led to a lower percentage of bleeding in the surgical field and workload demands on the users (p-value<0.05). These results show how leveraging state-of-the-art AI algorithms can reduce cognitive demands and enhance performance.

This work was made possible by the dVRK Research Community and Intuitive Surgical Inc. for the donation of the dVRK system. Research was funded by the National Institutes of Health under grant R21EB026177. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.


J. A. Barragan, D. Chanci, D. Yu and J. P. Wachs, "SACHETS: Semi-Autonomous Cognitive Hybrid Emergency Teleoperated Suction," 2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN), 2021, pp. 1243-1248, doi: 10.1109/RO-MAN50785.2021.9515517.