# US 11,026,754 B2 · Motion execution of a robotic system > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,026,754 B2 - Document ID: US11026754B2 - Status: Granted - Kind: B2 - Application number: 16/878,411 - Filed: 2020-05-19 - Published: 2020-09-03 - Granted: 2021-06-08 - Earliest claimed: 2016-08-25 - Assignees: Verily Life Sciences LLC - Applicants: Verily Life Sciences LLC - Inventors: Joëlle K. Barral; Blake Hannaford - Web page: https://patentgrep.com/patent/US11026754B2 ## CPC - A61B 34/20: Surgical navigation systems; Devices for tracking or guiding surgical instruments, e.g. for frameless stereotaxis - A61B 34/30: Surgical robots - A61B 34/35: for telesurgery - A61B 34/37: Leader-follower robots (A61B34/35 takes precedence) - A61B 34/74: {Manipulators with manual electric input means} - B25J 9/163: {learning, adaptive, model based, rule based expert control} - B25J 9/1633: {compliant, force, torque control, e.g. combined with position control} - A61B 2034/2055: {Optical tracking systems} - A61B 2034/2059: {Mechanical position encoders} - A61B 2034/2065: {Tracking using image or pattern recognition} - A61B 2034/302: {specifically adapted for manipulations within body cavities, e.g. within abdominal or thoracic cavities} - A61B 2090/064: {for measuring force, pressure or mechanical tension} - A61B 2090/3937: {Visible markers} - A61B 2090/3941: {Photoluminescent markers} - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - B25J: MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES ({manipulators specially adapted for use in surgery A61B34/70; manipulators used in cleaning hollow articles B08B9/04}; manipulators associated with rolling mills B21B39/20; manipulators associated with forging machines B21J13/10; {manipulators associated with picking-up and placing mechanisms B23P19/007}; means for holding wheels or parts thereof B60B30/00; {vehicles with ground-engaging propulsion means, e.g. walking members B62D57/02, B62D57/032; devices for picking-up and depositing articles or materials between conveyors B65G47/90, B65G47/91; manipulators with gripping or holding means for transferring packages B65H67/065}; cranes B66C; {manipulators used in the protection or supervision of pipe-line installations F17D5/00; walking equipment adapted for nuclear steam-generators F22B37/006}; manipulators specially adapted for, or associated with, nuclear reactors G21C; {apparatus used for handling wafers during manufacture or treatment of semiconductor H10P72/50}) ## Abstract A method of surgical robot operation includes moving a surgical instrument through a medium using the surgical robot, where the surgical robot attempts to move the surgical instrument to a desired position. The method further includes measuring an actual position of the surgical instrument, and calculating a difference between the actual position and the desired position. The actual position of the surgical instrument is adjusted to the desired position using the surgical robot. ## Claims ### Claim 1 A non-transitory machine readable storage medium storing logic, which when executed by a processing apparatus, causes the processing apparatus to perform operations including: receiving a signal from a controller of a surgical robot; moving, with the surgical robot and in response to the signal, a surgical instrument through a first tissue along a trajectory that passes through the first tissue and a second tissue that differs from the first tissue; and adjusting, based upon a difference between an actual position and a desired position of the surgical instrument, the actual position of the surgical instrument to the desired position by moving the surgical instrument through the second tissue using the surgical robot, wherein adjusting comprises using a machine learning algorithm to instruct the surgical robot to apply to the surgical instrument at least a force that is sufficient to move the surgical instrument through the second tissue, wherein the machine learning algorithm recognizes a composition of the second tissue based upon the force that is sufficient to move the surgical instrument through the second tissue. ### Claim 2 (depends on 1) The non-transitory machine readable storage medium of claim 1, further comprising logic which when executed causes the processing apparatus to perform operations including: measuring the actual position of the surgical instrument; and calculating the difference between the actual position and the desired position. ### Claim 3 (depends on 1) The non-transitory machine readable storage medium of claim 1, further comprising logic which when executed causes the processing apparatus to perform operations including: using the machine learning algorithm to recognize the composition of the second tissue using at least a resistance signal output by a sensor coupled to the surgical robot and configured to sense a resistance when moving the surgical instrument through the second tissue. ### Claim 4 (depends on 3) The non-transitory machine readable storage medium of claim 3, wherein the sensor is a pressure sensor coupled to the surgical instrument. ### Claim 5 (depends on 1) The non-transitory machine readable storage medium of claim 1, wherein moving includes instructing the surgical robot to apply an initial force that is less than the force that is sufficient to move the surgical instrument from the actual position to the desired position through the second tissue. ### Claim 6 (depends on 1) The non-transitory machine readable storage medium of claim 1, further comprising logic which when executed causes the processing apparatus to perform operations including: motion scaling a movement of the surgical instrument through the second tissue, wherein motion scaling includes moving the surgical instrument less than indicated by the signal from the controller. ### Claim 7 (depends on 1) The non-transitory machine readable storage medium of claim 1, further comprising logic which when executed causes the processing apparatus to perform operations including: filtering sporadic actions of a user of the controller from the signal prior to moving the surgical instrument through the first tissue in response to the signal. ### Claim 8 (depends on 7) The non-transitory machine readable storage medium of claim 7, wherein the sporadic actions include tremor. ### Claim 9 (depends on 7) The non-transitory machine readable storage medium of claim 7, further comprising logic which when executed causes the processing apparatus to perform operations including: outputting a haptic feedback signal to the controller. ### Claim 10 (depends on 9) The non-transitory machine readable storage medium of claim 9, wherein the haptic feedback signal changes a physical resistance of the controller. ### Claim 11 (depends on 1) The non-transitory machine readable storage medium of claim 1, further comprising logic which when executed causes the processing apparatus to perform operations including: recording the composition of the second tissue and the force needed to move the surgical instrument through the second tissue. ### Claim 12 (depends on 11) The non-transitory machine readable storage medium of claim 11, wherein recording includes recording the composition of the second tissue and the force needed to move the surgical instrument through the second tissue in a database. ### Claim 13 (depends on 1) The non-transitory machine readable storage medium of claim 1, further comprising logic which when executed causes the processing apparatus to perform operations including: diagnosing a problem with the surgical robot using the difference between the actual position and the desired position, wherein the difference is attributed to at least one of a hardware failure in the surgical robot or a software failure. ### Claim 14 (depends on 1) The non-transitory machine readable storage medium of claim 1, further comprising logic which when executed causes the processing apparatus to perform operations including: training the machine learning algorithm based upon the composition of the second tissue and the force needed to move the surgical instrument through the second tissue. ### Claim 15 (depends on 1) The non-transitory machine readable storage medium of claim 1, wherein training the machine learning algorithm includes using the composition of the second tissue and the force needed to move the surgical instrument through the second tissue to train the machine learning algorithm to perform at least one of: instructing the surgical robot to apply to the surgical instrument at least the force that is sufficient to move the surgical instrument from the actual position through the second tissue, recognizing the composition of the second tissue, or associating the second tissue with the force that is sufficient to move the surgical instrument through the second tissue. ### Claim 16 (depends on 1) The non-transitory machine readable storage medium of claim 1, wherein training the machine learning algorithm includes using the composition of the second tissue and the force needed to move the surgical instrument through the second tissue to perform at least one of: training the machine learning algorithm to recognize the second tissue as a harder tissue and to apply a first force when moving the surgical instrument through the harder tissue, or training the machine learning algorithm to recognize the second tissue as a softer tissue and to apply a second force, which is less than the first force, when moving the surgical instrument through the softer tissue. ### Claim 17 (depends on 1) A surgical robot comprising the non-transitory machine readable storage medium of claim 1. ### Claim 18 A non-transitory machine readable storage medium storing logic, which when executed by a processing apparatus, causes the processing apparatus to perform operations including: receiving a signal from a controller of a surgical robot; and moving, with the surgical robot and in response to the signal, a surgical instrument to a desired position through a first tissue and through a second tissue that differs from the first tissue, wherein moving includes using a machine learning algorithm to instruct the surgical robot to apply to the surgical instrument at least a force that is sufficient to move the surgical instrument through the second tissue, wherein the machine learning algorithm recognizes a composition of the second tissue based upon at least the force that is sufficient to move the surgical instrument through the second tissue. ### Claim 19 (depends on 18) The non-transitory machine readable storage medium of claim 18, wherein moving includes moving an actual position of the surgical instrument to a desired position through the second tissue using the surgical robot, based upon a difference between the actual position and the desired position. ### Claim 20 (depends on 19) The non-transitory machine readable storage medium of claim 19, further comprising logic which when executed causes the processing apparatus to perform operations including: measuring the actual position of the surgical instrument; and calculating the difference between the actual position and the desired position.