@conference{Sayols2020,
title = {Global/local motion planning based on Dynamic Trajectory Reconfiguration and Dynamical Systems for autonomous surgical robots},
author = {Narcís Sayols, Alessio Sozzi, Nicola Piccinelli, Albert Hernansanz, Alicia Casals, Marcello Bonfè, and Riccardo Muradore,},
editor = {IEEE},
doi = {10.1109/ICRA40945.2020.9197525},
year = {2020},
date = {2020-09-15},
booktitle = {2020 IEEE International Conference on Robotics and Automation (ICRA)},
abstract = {This paper addresses the generation of collision-free trajectories for the autonomous execution of assistive tasks in Robotic Minimally Invasive Surgery (R-MIS). The proposed approach takes into account geometric constraints related to the desired task, like for example the direction to approach the final target and the presence of moving obstacles. The developed motion planner is structured as a two-layer architecture: a global level computes smooth spline-based trajectories that are continuously updated using virtual potential fields; a local level, exploiting Dynamical Systems based obstacle avoidance, ensures collision free connections among the spline control points. The proposed architecture is validated in a realistic surgical scenario.},
keywords = {assistive tasks, autonomous execution, autonomous surgical, Collision avoidance, collision free connections, collision-free trajectories, desired task, developed motion planner, dynamical systems based obstacle avoidance, final target, geometric constraints, global level computes smooth spline-based trajectories, Medical robotics, mobile robots, motion control, moving obstacles, realistic surgical scenario, Robots, splines (mathematics), Surgery, surgery INSPEC: Non-Controlled Indexing robotic minimally invasive surgery, Task analysis, Tools, Trajectory, two-layer architecture},
pubstate = {published},
tppubtype = {conference}
}

@conference{Rossi2020,
title = {Cognitive Robotic Architecture for Semi-Autonomous Execution of Manipulation Tasks in a Surgical Environment},
author = {Giacomo De Rossi, Marco Minelli, Alessio Sozzi, Nicola Piccinelli, Federica Ferraguti, Francesco Setti, Marcello Bonfé, Christian Secchi, Riccardo Muradore},
editor = {IEEE International Intelligent Robots and Systems (IROS)},
doi = {10.1109/IROS40897.2019.8967667},
isbn = {978-1-7281-4004-9},
year = {2020},
date = {2020-01-27},
booktitle = {2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
publisher = {IEEE},
abstract = {The development of robotic systems with a certain level of autonomy to be used in critical scenarios, such as an operating room, necessarily requires a seamless integration of multiple state-of-the-art technologies. In this paper we propose a cognitive robotic architecture that is able to help an operator accomplish a specific task. The architecture integrates an action recognition module to understand the scene, a supervisory control to make decisions, and a model predictive control to plan collision-free trajectory for the robotic arm taking into account obstacles and model uncertainty. The proposed approach has been validated on a simplified scenario involving only a da VinciO surgical robot and a novel manipulator holding standard laparoscopic tools.},
keywords = {Artificial Intelligence, Collision avoidance, Manipulators, Medical robotics, mobile robots, predictive control, Robot, Robot vision, Surgery, trajectory control, uncertain systems},
pubstate = {published},
tppubtype = {conference}
}