Sayols, Narcís; Sozzi, Alessio; Piccinelli, Nicola; Hernansanz, Albert; Casals, Alicia; Bonfè, Marcello; Muradore, Riccardo A hFSM based cognitive control architecture for assistive task in R-MIS Conference 10th Conference on New Technologies for Computer/Robot Assisted Surgery (CRAS), 2020. Abstract | Links | BibTeX | Tags: Cognitive control, hierarchical finite state machine, Robotic surgery Oleari, Elettra; Leporini, Alice; Trojaniello, Diana; Sanna, Alberto; Capitanio, Umberto; Deho, Federico; Larcher, Alessandro; Montorsi, Francesco; Salonia, Andrea; Setti, Francesco; Muradore, Riccardo Enhancing Surgical Process Modeling for Artificial Intelligence Development in Robotics the SARAS Case Study for Minimally Invasive Procedures Journal Article pp. 1-6, 2019, ISBN: 978-1-7281-2342-4. Abstract | Links | BibTeX | Tags: Artificial Intelligence, Autonomy, Cognitive control, Cognitive functions, Decision making, Laparoscopes, Laparoscopy, Laparoscopy, learning systems, machine learning, Medical robotics, multirobots teleoperated platform, Robotic surgery, Surgery, Surgical robots, Teleoperation Setti, Francesco; Oleari, Elettra; Leporini, Alice; Trojaniello, Diana; Sanna, Alberto; Capitanio, Umberto; Montorsi, Francesco; Salonia, Andrea; Muradore, Riccardo 2019, ISBN: 978-1-5386-7825-1. Abstract | Links | BibTeX | Tags: Artificial Intelligence, Cognitive control, Computer Science, Laparoscopy, Laparoscopy, machine learning, Robot, Robotic surgery, Surgery, Teleoperation
2020
title = {A hFSM based cognitive control architecture for assistive task in R-MIS },
author = {Narcís Sayols and Alessio Sozzi and Nicola Piccinelli and Albert Hernansanz and Alicia Casals and Marcello Bonfè and Riccardo Muradore},
url = {https://zenodo.org/record/5770464#.YbIo_VXMLIU},
year = {2020},
date = {2020-09-29},
booktitle = {10th Conference on New Technologies for Computer/Robot Assisted Surgery (CRAS)},
pages = {44-45},
abstract = {Nowadays, one of the most appealing and debated challenge in robotic surgery is the introduction of certain levels of autonomy in robot behaviour [1] implying technical advances in scene understanding and situation awareness, decision making, collision-free motion planning and environment interaction. The growth of R&D projects for autonomous surgical robotics (e.g. EU funded I-SUR, MURAB and SARAS) demonstrates the confidence and the expectations of the medical community on the benefits of such technologies. SARAS aims to develop assistive surgical robots for laparoscopic MIS, autonomously operating in the same workspace of either a teleoperated surgical robot or a manually driven surgical tool. The auxiliary robots autonomously decide which task perform to assist the main surgeon, planning
motions for executing the task considering the dynamics of human driven tools and patient's organs (predictable
only within a short time horizon). This paper proposes a control architecture for surgical robotic assistive tasks in
MIS using a hierarchical multi-level Finite State Machine (hFSM) as the cognitive control and a two-layered motion planner for the execution of the task. The hFSM models the operation starting from atomic actions to progressively build up more complex levels. The twolayer architecture of the motion planner merges the benefits of an offline geometric path construction method with those of online trajectory reconfiguration and reactive adaptation. At a global level, the path is built according to the initial knowledge of the operating scene and the requirements of the surgical tasks. Then, the path is reconfigured with respect to the dynamic environment using artificial potential fields [2]. Finally, a local level computes the robot trajectory, preserving collision-free property even in presence of obstacles with small diameter (i.e. the manually driver surgical instruments), by enforcing a velocity modulation technique derived from the Dynamical Systems (DS) based approach of [3].},
keywords = {Cognitive control, hierarchical finite state machine, Robotic surgery},
pubstate = {published},
tppubtype = {conference}
}
motions for executing the task considering the dynamics of human driven tools and patient's organs (predictable
only within a short time horizon). This paper proposes a control architecture for surgical robotic assistive tasks in
MIS using a hierarchical multi-level Finite State Machine (hFSM) as the cognitive control and a two-layered motion planner for the execution of the task. The hFSM models the operation starting from atomic actions to progressively build up more complex levels. The twolayer architecture of the motion planner merges the benefits of an offline geometric path construction method with those of online trajectory reconfiguration and reactive adaptation. At a global level, the path is built according to the initial knowledge of the operating scene and the requirements of the surgical tasks. Then, the path is reconfigured with respect to the dynamic environment using artificial potential fields [2]. Finally, a local level computes the robot trajectory, preserving collision-free property even in presence of obstacles with small diameter (i.e. the manually driver surgical instruments), by enforcing a velocity modulation technique derived from the Dynamical Systems (DS) based approach of [3].
2019
title = {Enhancing Surgical Process Modeling for Artificial Intelligence Development in Robotics the SARAS Case Study for Minimally Invasive Procedures},
author = {Elettra Oleari and Alice Leporini and Diana Trojaniello and Alberto Sanna and Umberto Capitanio and Federico Deho and Alessandro Larcher and Francesco Montorsi and Andrea Salonia and Francesco Setti and Riccardo Muradore},
editor = {IEEE},
doi = {10.1109/ISMICT.2019.8743931},
isbn = {978-1-7281-2342-4},
year = {2019},
date = {2019-05-09},
pages = {1-6},
abstract = {Nowadays Minimally Invasive Surgery (MIS) is playing an increasingly major role in the clinical practice also thanks to a rapid evolution of the available medical technologies, especially surgical robotics. A new challenge in this respect is to equip robots with cognitive capabilities, in order to make them able to act autonomously and cooperate with human surgeons. In this paper we describe the methodological approach developed to comprehensively describe a specific surgical knowledge, to be transferred to a complex Artificial Intelligence (AI) integrating Perception, Cognitive and Planning modules. Starting from desk researches and a strict cooperation with expert surgeons, the surgical process is framed on a high-level perspective, which is then deepened into a granular model through a Surgical Process Modelling approach, so as to embed all of the needed information by the AI to properly work. The model is eventually completed adding the corresponding Process Risk Analysis. We present the results obtained with the application of the aforementioned methodology to a Laparoscopic Radical Nephrectomy (LRN) procedure and discuss on the next technical implementation of this model.},
keywords = {Artificial Intelligence, Autonomy, Cognitive control, Cognitive functions, Decision making, Laparoscopes, Laparoscopy, Laparoscopy, learning systems, machine learning, Medical robotics, multirobots teleoperated platform, Robotic surgery, Surgery, Surgical robots, Teleoperation},
pubstate = {published},
tppubtype = {article}
}
title = {A Multirobots Teleoperated Platform for Artificial Intelligence Training Data Collection in Minimally Invasive Surgery},
author = {Francesco Setti and Elettra Oleari and Alice Leporini and Diana Trojaniello and Alberto Sanna and Umberto Capitanio and Francesco Montorsi and Andrea Salonia and Riccardo Muradore},
editor = {IEEE},
url = {http://bmvc2018.org/contents/papers/0593.pdf},
doi = {10.1109/ISMR.2019.8710209},
isbn = {978-1-5386-7825-1},
year = {2019},
date = {2019-05-09},
pages = {1-7},
abstract = {Dexterity and perception capabilities of surgical robots may soon be improved by cognitive functions that can support surgeons in decision making and performance monitoring, and enhance the impact of automation within the operating rooms. Nowadays, the basic elements of autonomy in robotic surgery are still not well understood and their mutual interaction is unexplored. Current classification of autonomy encompasses six basic levels: Level 0: no autonomy; Level 1: robot assistance; Level 2: task autonomy; Level 3: conditional autonomy; Level 4: high autonomy. Level 5: full autonomy. The practical meaning of each level and the necessary technologies to move from one level to the next are the subject of intense debate and development. In this paper, we discuss the first outcomes of the European funded project Smart Autonomous Robotic Assistant Surgeon (SARAS). SARAS will develop a cognitive architecture able to make decisions based on pre-operative knowledge and on scene understanding via advanced machine learning algorithms. To reach this ambitious goal that allows us to reach Level 1 and 2, it is of paramount importance to collect reliable data to train the algorithms. We will present the experimental setup to collect the data for a complex surgical procedure (Robotic Assisted Radical Prostatectomy) on very sophisticated manikins (i.e. phantoms of the inflated human abdomen). The SARAS platform allows the main surgeon and the assistant to teleoperate two independent two-arm robots. The data acquired with this platform (videos, kinematics, audio) will be used in our project and will be released (with annotations) for research purposes.},
keywords = {Artificial Intelligence, Cognitive control, Computer Science, Laparoscopy, Laparoscopy, machine learning, Robot, Robotic surgery, Surgery, Teleoperation},
pubstate = {published},
tppubtype = {conference}
}
A hFSM based cognitive control architecture for assistive task in R-MIS Conference 10th Conference on New Technologies for Computer/Robot Assisted Surgery (CRAS), 2020. Enhancing Surgical Process Modeling for Artificial Intelligence Development in Robotics the SARAS Case Study for Minimally Invasive Procedures Journal Article pp. 1-6, 2019, ISBN: 978-1-7281-2342-4. 2019, ISBN: 978-1-5386-7825-1.
2020
2019