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Robotica Medica

CFU: 9

Prerequisites

Basic knowledge of programming techniques; basic knowledge of closed loop control systems.

Preliminary Courses

Foundations of Robotics.

Learning Goals 

The course of Medical Robotics aims to provide notions and foundations for the design, construction and control of robotic systems for medical applications (e.g., in surgery and rehabilitation). In addition to the use of methods for modeling and control of robotic systems consisting of rigid kinematic chains, such as some manipulating robots currently used in surgery and rehabilitation, theoretical methods for modeling and controlling systems involving soft parts will be provided. The soft parts can be integrated into the structure or completely soft structures will be considered, this is the case of robots capable of reconfiguring and adapting to the environment, as well as wearable robots such as prostheses and exoskeletons. Basic knowledge of the most common software used for robot programming will be provided through practical applications by means of the use of simulators for case studies.

Expected Learning Outcomes 

Knowledge and understanding

The student must demonstrate knowledge of the robotic systems currently used in surgery and rehabilitation, in particular the mechanical structure and its characteristics, the typical control systems and software used for programming. The student must demonstrate knowledge of the problems relating to patient/robot-doctor/robot interaction and the consequent requirements necessary for the correct and effective operation of robotic systems. The student must demonstrate that he/she has acquired the modeling and control techniques of robotic systems characterized by soft structures that interact in close physical contact with the human being, including minimally invasive systems for surgery and wearable systems for rehabilitation.

Applying knowledge and understanding

The student must demonstrate that he/she is able to design a control system, chosen among the classical systems studied, to adapt it to a particular medical application using one of the most popular robotic systems in surgery and/or rehabilitation (such as the da Vinci, the KUKA, etc.). The student must be able to know how to implement this control system using simulation tools provided during the course. To this end, the student must demonstrate that he/she is able to use software tools typically used in robotics (including ROS, Gazebo, Matlab/Simulink, C ++, Python, CoppeliaSim). Furthermore, the student must demonstrate basic knowledge of typical open-source rapid prototyping, electronic and 3D printing tools.

Course Content - Syllabus 

  1. Introduction to medical robotics.
  2. Classification of surgical robots and applications.
  3. Interaction control (impedance and force control; variable impedance control, priority management of multiple tasks in redundant manipulators).
  4. Unilateral and bilateral teleoperation: passivity and stability.
  5. Haptic interfaces.
  6. Shared and semi-autonomous control.
  7. da Vinci robotic system and da Vinci Research Kit (kinematics, dynamics, control architecture, control software).
  8. Exercises on the da Vinci Research Kit system.
  9. Examples of learning techniques applied to the control of robots for surgical applications.
  10. Introduction to the use of continuous and snake-like robots in robotic surgery.
  11. Modeling of continuous mechanisms.
  12. Control of the interaction and locomotion of hyper-redundant continuous robots.
  13. Introduction to soft robotics.
  14. Modeling of soft robots.
  15. Control of soft robots.
  16. Introduction to rehabilitation robotics and assistive robotics.
  17. Exercises on the KUKA MED robot.
  18. Materials and methods for the measurement of physiological signals (EMG, EEG, ECoG, eye tracking).
  19. Exoskeletons and wearable robotics: construction principles and control strategies.
  20. Variable impedance actuators.
  21. Robotic prostheses: robotic hands and legs.
  22. Principles of control of locomotion.
  23. Mechanical design of robotic hands, modeling, sensors and actuation.
  24. Practical exercises on the PRISMA Hand II.
  25. Control of grasping and manipulation.
  26. Examples of learning techniques applied to manipulation and locomotion.

Readings/Bibliography

B. Siciliano, O. Khatib (Eds.), Springer Handbook of Robotics, 2nd Edition, Springer, Berlin, 2016, ISBN 978-3-319-32552-1.

K.M. Lynch, F.C. Park, Modern Robotics: Mechanics, Planning, and Control, Cambridge University Press, 2017, ISBN 9781107156302.

J. Rosen, B. Hannaford, R.M. Satava (Eds.), Surgical Robotics: Systems, Applications, and Visions, Springer, 2011 ISBN 9781441911261.

A. Schweikard, F. Ernst, Medical Robotics, Springer, 2015, ISBN 9783319228914.

Notes from the lectures, available to students enrolled in the course through Segrepass.

Teaching Method

The teacher will use: a) lectures for about 70% of the total hours, b) classroom exercises through the use of robot simulation tools known for surgery and rehabilitation, based on ROS, Gazebo and CoppeliaSim, c) 3/4 2-hour seminars held by doctors, robotic researchers and representatives of the medical robot industry.

Examination/Evaluation criteria

Exam type

 

Only oral and project discussion. The project, developed using a simulator, must be delivered to the teacher one week before the oral exam and then discussed in the oral exam through a presentation of the results obtained. The project aims to verify the student's ability to design simple control algorithms for medical robotics applications (surgical or rehabilitation, at the student's choice), using one of the simulators based on ROS, Gazebo and CoppeliaSim, which were introduced and used during the course exercises. The oral exam follows the discussion of the project and is aimed at a critical discussion of the solution(s) given by the student to the problems proposed in the project, and at the assessment of the acquisition of the concepts and contents introduced during lessons.

Evaluation pattern 

The project is mandatory to access the oral exam, and it contributes to 25% of the final evaluation.

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