SURGICAL ROBOT SYSTEM AND CONTROL METHOD THEREOF

• US 20140046128A1
• Filed: 07/17/2013
• Published: 02/13/2014
• Est. Priority Date: 08/07/2012
• Status: Active Grant

• Current Assignee: Samsung Electronics Co. Ltd.
• Original Assignee: Samsung Electronics Co. Ltd.
• Inventors: LEE, Min Hyung, SHIM, Young Bo, KWON, Young Do, ROH, Kyung Shik, CHOI, Byung June
• Granted Patent: US 9,687,301 B2
• Time in Patent Office: Days
• US Class Current: 600/102
• CPC Class Codes: A61B 1/000096 using artificial intelligence
• CPC Class Codes: A61B 1/00149 using articulated arms
• CPC Class Codes: A61B 1/04 combined with photographic or television appliances
• CPC Class Codes: A61B 17/0469 Suturing instruments for use in minimally invasive surgery, e.g. endoscopic surgery
• CPC Class Codes: A61B 17/062 Needle manipulators
• CPC Class Codes: A61B 34/30 Surgical robots
• CPC Class Codes: A61B 34/37 Master-slave robots A61B34/35 takes precedence
• CPC Class Codes: A61B 5/055 involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
• CPC Class Codes: A61B 5/11 Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb for measuring pulse A61B5/02 ;A61B5/1038 takes precedence; motion detection to correct for motion artifacts in physiological signals A61B5/721
• CPC Class Codes: A61B 5/1124 Determining motor skills
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: A61B 5/742 using visual displays A61B5/339 takes precedence
• CPC Class Codes: A61B 6/032 Transmission computed tomography [CT]

Abstract

A control method may be applied to a surgical robot system including a slave robot having a robot arm to which a main surgical tool and an auxiliary surgical tool are coupled, and a master robot having a master manipulator to manipulate the robot arm. The control method includes acquiring data regarding a motion of the master manipulator, predicting a basic motion to be performed by an operator based on the acquired motion data and results of learning a plurality of motions constituting a surgical task, and adjusting the auxiliary surgical tool so as to correspond to the operator basic motion based on the predicted basic motion. The control method allows an operator to perform surgery more comfortably and to move or fix all required surgical tools to or at an optimized surgical position.

Claims

1. A control method of a robot system, the control method comprising:

acquiring data regarding a motion of a master manipulator;

predicting a basic motion to be performed by an operator based on the acquired motion data and results of learning a plurality of motions constituting a task; and

adjusting a tool so as to correspond to the operator basic motion based on the predicted basic motion.

2. The control method according to claim 1, wherein the data regarding the motion of the master manipulator includes at least one of interactive force data between the master manipulator and the operator, position data of the master manipulator, and speed data of the master manipulator.

3. The control method according to claim 1, wherein the task includes one or more of suturing, passing, running, and cannulation.

4. The control method according to claim 1, wherein the plurality of motions constituting the task includes one or more of orient, push, and pull motions.

5. The control method according to claim 1, wherein the learning of the plurality of motions constituting the task is performed via machine learning.

6. The control method according to claim 5, wherein the machine learning comprises supervised learning in which the data regarding the motion of the master manipulator is acquired beforehand and is an input, and learning is performed in a state in which a target value for the input is known.

7. A control method of a robot system, the control method comprising:

acquiring data regarding a motion of a master manipulator, bio-data of an operator, and bio-data of a patient;

predicting a basic motion to be performed by the operator based on the acquired motion data, bio-data of the operator, bio-data of the patient, and results of learning a plurality of motions constituting a task; and

adjusting a tool so as to correspond to the operator basic motion based on the predicted basic motion.

8. The control method according to claim 7, wherein the data regarding the motion of the master manipulator includes at least one of interactive force data between the master manipulator and the operator, position data of the master manipulator, and speed data of the master manipulator.

9. A robot system comprising a slave robot having a robot arm to which at least one tool is coupled, and a master robot to control the slave robot,

wherein the master robot includes:

a master manipulator to manipulate the robot arm;

a motion data detector to detect data regarding a motion of the master manipulator; and

a controller to predict a basic motion to be performed by an operator based on the motion data detected via the motion data detector and results of learning a plurality of motions constituting a task, and to control the at least one tool so as to correspond to the operator basic motion based on the predicted basic motion.

10. The system according to claim 9, wherein the motion data detector includes at least one of a force/torque sensor to detect interactive force data between the master manipulator and the operator, a position sensor to detect a position of the master manipulator, and a speed sensor to detect a speed of the master manipulator.

11. The system according to claim 9, wherein the task includes one or more of suturing, passing, running, and cannulation.

12. The system according to claim 9, wherein the plurality of motions constituting the task includes one or more of orient, push, and pull motions.

13. The system according to claim 9, wherein the controller includes:

a first control signal generator to generate a first control signal to control a motion of the slave robot based on the data regarding the motion of the master manipulator acquired via the motion data detector;

a learner to learn the plurality of motions constituting the task based on previously acquired data regarding the motion of the master manipulator;

a predictor to predict the operator basic motion or the task based on the motion data detected via the motion data detector and the results of learning performed via the learner;

a second control signal generator to generate a second control signal to control the motion of the slave robot and/or the master manipulator having a redundant degree of freedom (DOF) based on the predicted basic motion or task; and

an image processor to process an image input from an endoscope coupled to the robot arm, and to output a picture image.

14. The system according to claim 9, further comprising a storage to store the results of learning the plurality of motions constituting the task acquired via the learner, and a reference image including at least one of an X-ray image, a Computed Tomography (CT) image, and a Magnetic Resonance Imaging (MRI) image.

15. The system according to claim 9, further comprising a display unit to display an endoscopic image corresponding to an image signal provided from an endoscope coupled to the robot arm.