Abstract
Hand-assisted laparoscopic surgery is a Minimally Invasive Surgery technique that is based on the insertion of one surgeon’s hand inside the abdominal cavity. In this scenario, a robotic assistant can properly collaborate with the surgeon, working side by side with him/her. This paper presents a robotic system for this kind of technique, based on a cognitive architecture that makes possible an efficient collaboration with the surgeon, thanks to a better understanding of the environment and the learning mechanisms included. This architecture includes a hand gesture recognition module and two different autonomous movement of the robotic arms, one for the camera motion and the other for the tool movement. All of these modules take advantage of the cognitive learning mechanisms of the architecture, fitting their behavior to the current user and procedure.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Vogel, J.D., et al.: Hand-assisted laparoscopic right colectomy: how does it compare to conventional laparoscopy? J. Am. Coll. Surg. 212(3), 367–372 (2011)
Haddadin, S., et al.: Towards the Robotic Co-Worker. Robotic Research. Springer Tracts in Advance Robotics, vol. 70, pp. 261–282 (2011)
Chui, C.K., et al.: Development of a cognitive engine for balancing automation and human control of surgical robotic system. In: 2014 11th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), pp. 13–17 (2015)
Weede, O., et al.: Towards cognitive medical robotics in minimal invasive surgery. In: Proceedings of Conference on Advances in Robotics, pp. 1–8 (2013)
Fahn, D.S., et al.: Development of a fingertip glove equipped with magnetic tracking sensors. Sensors 10(2), 1119–1140 (2010)
Galka, J., et al.: Inertial motion sensing glove for sign language gesture acquisition and recognition. IEEE Sens. J. 16(16), 6310–6316 (2016)
Rossi, M., et al.: Hybrid EMG classifier based on HMM and SVM for hand gesture recognition in prosthetics. In: IEEE Conference Industrial Technology, pp. 1700–1705 (2015)
Plouffe, G., et al.: Static and dynamic hand gesture recognition in depth data using dynamic time warping. IEEE Trans. Instrum. Meas. 65(2), 305–316 (2016)
Bautista, M.A., et al.: A gesture recognition system for detecting behavioral patterns of ADHD. IEEE Trans. Cybern. 46(1), 136–146 (2016)
Valdastri, P., et al.: A magnetic internal mechanism for precise orientation of the camera in wireless endoluminal applications. Endoscopy 42(6), 481–486 (2010)
Sutton, R.S., et al.: Reinforcement Learning: An Introduction, vol. 1(1). MIT Press, Cambridge (2012)
Laird, J.E.: Tuning Procedural Knowledge: Reinforcement Learning. The Soar Cognitive Architecture. MIT Press, pp. 181–202 (2012). ISBN:9780262301145
Laird, J.E.: The Soar Cognitive Architecture. MIT Press (2012)
Koubaa, A.: Robotic Operating System (ROS). SCI, vol. 625. Springer (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
López-Casado, C., Bauzano, E., Rivas-Blanco, I., Muñoz, V.F., Fraile, J.C. (2018). Collaborative Robotic System for Hand-Assisted Laparoscopic Surgery. In: Ollero, A., Sanfeliu, A., Montano, L., Lau, N., Cardeira, C. (eds) ROBOT 2017: Third Iberian Robotics Conference. ROBOT 2017. Advances in Intelligent Systems and Computing, vol 694. Springer, Cham. https://doi.org/10.1007/978-3-319-70836-2_45
Download citation
DOI: https://doi.org/10.1007/978-3-319-70836-2_45
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-70835-5
Online ISBN: 978-3-319-70836-2
eBook Packages: EngineeringEngineering (R0)