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WO2025027463A1 - Système et procédé de traitement de flux de données combinés de robots chirurgicaux - Google Patents

Système et procédé de traitement de flux de données combinés de robots chirurgicaux Download PDF

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Publication number
WO2025027463A1
WO2025027463A1 PCT/IB2024/057222 IB2024057222W WO2025027463A1 WO 2025027463 A1 WO2025027463 A1 WO 2025027463A1 IB 2024057222 W IB2024057222 W IB 2024057222W WO 2025027463 A1 WO2025027463 A1 WO 2025027463A1
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WIPO (PCT)
Prior art keywords
robotic arm
data
instrument
robotic
surgical
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PCT/IB2024/057222
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English (en)
Inventor
Christina O. INSAM
Uwe REINECKE
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Covidien LP
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Covidien LP
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Publication of WO2025027463A1 publication Critical patent/WO2025027463A1/fr
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Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B34/00—Computer-aided surgery; Manipulators or robots specially adapted for use in surgery
    • A61B34/30—Surgical robots
    • A61B34/37—Leader-follower robots
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B34/00—Computer-aided surgery; Manipulators or robots specially adapted for use in surgery
    • A61B34/30—Surgical robots
    • A61B2034/302—Surgical robots specifically adapted for manipulations within body cavities, e.g. within abdominal or thoracic cavities

Definitions

  • Surgical robotic systems are currently being used in a variety of surgical procedures, including minimally invasive medical procedures.
  • Some surgical robotic systems include a surgeon console controlling a surgical robotic arm and a surgical instrument having an end effector (e.g., forceps or grasping instrument) coupled to and actuated by the robotic arm.
  • the robotic arm In operation, the robotic arm is moved to a position over a patient and then guides the surgical instrument into a small incision via a surgical port or a natural orifice of a patient to position the end effector at a work site within the patient’s body.
  • the surgical robotic systems are extremely complex and use a variety of sensors to monitor their operation. Such sensors may be used in analyzing performance of the robotic systems. However, sensor data without context may be ambiguous. Thus, there is a need for data analysis to improve reliability and performance of surgical robotic systems.
  • the present disclosure provides a system and method of analyzing combined data streams of sensor data with image and/or video data in an online and/or offline manner.
  • the combined data streams analysis is used to control a surgical robotic system during its use.
  • the combined data streams may be used as input for processes controlling operation of robotic arms, instruments, and other components of the surgical robotic system or to display information on a graphical user interface (GUI) observable by the surgeon and the operating room (OR) staff.
  • GUI graphical user interface
  • control processes include responses to dynamic situations, where the system is configured to detect an event based on the combined data stream and react to or avoid the event. Examples of such events include collisions, where a collision is detected before it occurs.
  • the system is configured to determine whether the collision is avoidable and if so, the system takes evasive action, i.e., moves one or more of the robotic arms and/or the instrument. The determination is based on intent (i.e., fulfill surgeon’s intent based on user input), expected hardware damage, optimization of motion, and reduction of mechanical loads on instruments and hardware components using null-space motion (i.e., motion that changes the overall configuration of the robotic arm without changing the position of the end effector).
  • the system may also improve performance of the system and user experience based on the combined data stream.
  • the system may use data to support the surgeon’s decisions and facilitate steps during a surgical procedure by automating certain non-critical movements of the instruments during navigation.
  • Combined data streams are also used offline (i.e., when the surgical system is not performing surgical procedures).
  • the combined data streams may be used to improve reliability with predictive maintenance by optimizing maintenance intervals, for example, recorded component-specific loading history may be used to identify required component exchange.
  • the data is also used to analyze use cases to determine potential misuse.
  • Data analysis of the combined data streams may be performed using machine learning (ML) and/or artificial intelligence (Al) algorithms, or physics-based modeling.
  • ML machine learning
  • Al artificial intelligence
  • a surgical robotic system combines video data (e.g., from an endoscope or external vision system) with sensor data to identify causes and provide context for the sensor data.
  • video data e.g., from an endoscope or external vision system
  • sensor data provides for an improved understanding of dynamic phenomena, root causes, and/or use conditions (e.g., misuse, etc.) of operation of the robotic system.
  • certain detected errors e.g., increase in joint torque corresponding to a collision, may be supplemented with video data to identify the collision.
  • a surgical robotic system includes: a first robotic arm having a first instrument and a first sensor; a second robotic arm may include a second instrument and a second sensor; a laparoscopic camera configured to capture intraoperative imaging data; and a surgeon console having a handle controller configured to receive user input to actuate at least one of the first robotic arm or the second robotic arm.
  • the system also includes a controller configured to: control at least one of the first robotic arm or of the second robotic arm based on the user input and a control algorithm; receive a plurality of data streams, which includes sensor data from the first sensor and the second sensor, operational data which may include user input data, external vision data, and internal imaging data from the laparoscopic camera; predict occurrence of an event based on the plurality of data streams; and adjust the control algorithm based on the predicted occurrence of the event.
  • a controller configured to: control at least one of the first robotic arm or of the second robotic arm based on the user input and a control algorithm; receive a plurality of data streams, which includes sensor data from the first sensor and the second sensor, operational data which may include user input data, external vision data, and internal imaging data from the laparoscopic camera; predict occurrence of an event based on the plurality of data streams; and adjust the control algorithm based on the predicted occurrence of the event.
  • the surgical robotic system may include an external vision system configured to generate external vision data that may include a position of the first robotic arm and a position of the second robotic arm.
  • the internal imaging data may include preoperative imaging data.
  • the event may be a collision of one of the first robotic arm or the second robotic arm with one of the first instrument or the second instrument.
  • the adjustment to the control algorithm may include lowering torque or velocity output to one of the first robotic arm, the second robotic arm, the first instrument, or the second instrument.
  • the adjustment to the control algorithm may include selecting a kinematic solution from a plurality of kinematic solutions in a null space of at least one of the first robotic arm, the second robotic arm, the first instrument, or the second instrument.
  • the adjustment to the control algorithm may include stopping movement of one of the first robotic arm, the second robotic arm, the first instrument, or the second instrument.
  • the controller may be further configured to determine reliability of components of the first and second robotic arm and the first and second instruments based on the plurality of data streams.
  • the controller may be further configured to classify severity of the event and to adjust the control algorithm based on the severity of the event.
  • a method for controlling a surgical robotic system includes capturing through a laparoscopic camera a video stream of a first robotic instrument of a first robotic arm and a second robotic instrument of a second robotic arm. The method also includes measuring at least one parameter of the first robotic arm or the second robotic arm and identifying, at a video processing device, the first robotic instrument and the second robotic instrument in at least one frame of the video stream. The method further includes determining, at a controller, whether an instrument collision has occurred between the first instrument and the second instrument based on the at least one measured parameter and confirming, at the controller, collision occurrence using the video stream. The method additionally includes adjusting operation of the first robotic arm or the second robotic arm in response to confirmation of the collision.
  • Implementations of the above embodiment may include one or more of the following features.
  • the method may further include determining, at the controller, whether the collision is accidental.
  • the method may also include receiving at a surgeon console user input for actuating at least one of the first robotic arm or the second robotic arm and determining, at the controller, whether the collision is accidental based on the user input.
  • the method may further include tracking the positions of the first robotic arm and the second robotic arm at an external vision system and receiving external vision data at the controller from the external vision system.
  • the method also includes determining whether an arm collision has occurred between the first robotic arm and the second robotic arm based on the at least one measured parameter.
  • the method also includes confirming, at the controller, collision occurrence using the external vision data.
  • the at least one parameter may be torque imparted on the first robotic arm or the second robotic arm.
  • the method may also include displaying a graphical user interface (GUI) on a screen.
  • GUI graphical user interface
  • the method may also include outputting a prompt on the GUI in response to confirmation of the collision.
  • a surgical robotic system includes a first robotic arm having a first instrument and a first sensor and a second robotic arm having a second instrument and a second sensor.
  • the system also includes a surgeon console having a handle controller configured to receive user input to actuate at least one of the first robotic arm or the second robotic arm.
  • the system further includes a controller configured to determine whether an instrument collision has occurred between the first instrument and the second instrument based on at least one parameter measured by one of the first sensor or the second sensor.
  • the controller is further configured to receive a combined data stream that includes intraoperative imaging data and external vision data and confirm collision occurrence using the intraoperative imaging data and the external vision data.
  • the controller is additionally configured to adjust operation of the first robotic arm or the second robotic arm in response to confirmation of the collision.
  • the surgical robotic system may also include a screen configured to display a graphical user interface (GUI), where the controller may be further configured to output a prompt on the GUI in response to confirmation of the collision.
  • GUI graphical user interface
  • the system is also configured to process and analyze combined data streams to determine impact of use on reliability.
  • the reliability data as well as the loading history of each component can be stored and sent to a database. The loading history helps to optimize component exchange intervals.
  • FIG. 1 is a perspective view of a surgical robotic system including a control tower, a console, and one or more surgical robotic arms each disposed on a mobile cart according to an embodiment of the present disclosure
  • FIG. 2 is a perspective view of a surgical robotic arm of the surgical robotic system of FIG. 1 according to an embodiment of the present disclosure
  • FIG. 3 is a perspective view of a mobile cart having a setup arm with the surgical robotic arm of the surgical robotic system of FIG. 1 according to an embodiment of the present disclosure
  • FIG. 4 is a schematic diagram of a computer architecture of the surgical robotic system of FIG. 1 according to an embodiment of the present disclosure
  • FIG. 5 is a plan schematic view of the surgical robotic system of FIG. 1 positioned about a surgical table according to an embodiment of the present disclosure
  • FIG. 6 is a schematic diagram of a system for determining phases of a surgical procedure according to an embodiment of the present disclosure
  • FIG. 7 is a schematic diagram of data streams of the surgical robotic system of FIG.
  • FIG. 8 is a flow chart of a method for processing combined of data streams of surgical robots.
  • FIG. 9 is a flow chart of a method for controlling a surgical robotic system according to an embodiment of the present disclosure.
  • a surgical robotic system 10 includes a control tower 20, which is connected to all of the components of the surgical robotic system 10 including a surgeon console 30 and one or more mobile carts 60.
  • Each of the mobile carts 60 includes a robotic arm 40 having a surgical instrument 50 removably coupled thereto.
  • the robotic arms 40 also couple to the mobile carts 60.
  • the robotic system 10 may include any number of mobile carts 60 and/or robotic arms 40.
  • the surgical instrument 50 is configured for use during minimally invasive surgical procedures.
  • the surgical instrument 50 may be configured for open surgical procedures.
  • the surgical instrument 50 may be an electrosurgical forceps configured to seal tissue by compressing tissue between jaw members and applying electrosurgical current thereto.
  • the surgical instrument 50 may be a surgical stapler including a pair of jaws configured to grasp and clamp tissue while deploying a plurality of tissue fasteners, e.g., staples, and cutting stapled tissue.
  • the surgical instrument 50 may be a surgical clip applier including a pair of jaws configured to apply a surgical clip onto tissue.
  • One of the robotic arms 40 may include a laparoscopic camera 51 configured to capture video of the surgical site.
  • the laparoscopic camera 51 may be a stereoscopic endoscope configured to capture two side-by-side (i.e., left and right) images of the surgical site to produce a video stream of the surgical scene.
  • the laparoscopic camera 51 is coupled to an image processing device 56, which may be disposed within the control tower 20.
  • the image processing device 56 may be any computing device configured to receive the video feed from the laparoscopic camera 51 and output the processed video stream.
  • the surgeon console 30 includes a first screen 32, which displays a video feed of the surgical site provided by camera 51 of the surgical instrument 50 disposed on the robotic arm 40, and a second screen 34, which displays a user interface for controlling the surgical robotic system 10.
  • the first screen 32 and second screen 34 may be touchscreens allowing for displaying various graphical user inputs.
  • the surgeon console 30 also includes a plurality of user interface devices, such as foot pedals 36 and a pair of hand controllers 38a and 38b which are used by a user to remotely control robotic arms 40.
  • the surgeon console further includes an armrest 33 used to support clinician’s arms while operating the hand controllers 38a and 38b.
  • the control tower 20 includes a screen 23, which may be a touchscreen, and outputs on the graphical user interfaces (GUIs).
  • GUIs graphical user interfaces
  • the control tower 20 also acts as an interface between the surgeon console 30 and one or more robotic arms 40.
  • the control tower 20 is configured to control the robotic arms 40, such as to move the robotic arms 40 and the corresponding surgical instrument 50, based on a set of programmable instructions and/or input commands from the surgeon console 30, in such a way that robotic arms 40 and the surgical instrument 50 execute a desired movement sequence in response to input from the foot pedals 36 and the hand controllers 38a and 38b.
  • the foot pedals 36 may be used to enable and lock the hand controllers 38a and 38b, repositioning camera movement and electrosurgical activation/deactivation.
  • the foot pedals 36 may be used to perform a clutching action on the hand controllers 38a and 38b. Clutching is initiated by pressing one of the foot pedals 36, which disconnects (i.e., prevents movement inputs) the hand controllers 38a and/or 38b from the robotic arm 40 and corresponding instrument 50 or camera 51 attached thereto. This allows the user to reposition the hand controllers 38a and 38b without moving the robotic arm(s) 40 and the instrument 50 and/or camera 51. This is useful when reaching control boundaries of the surgical space.
  • Each of the control tower 20, the surgeon console 30, and the robotic arm 40 includes a respective computer 21, 31, 41.
  • the computers 21 , 31 , 41 are interconnected to each other using any suitable communication network based on wired or wireless communication protocols.
  • Suitable protocols include, but are not limited to, transmission control protocol/intemet protocol (TCP/IP), datagram protocol/intemet protocol (UDP/IP), and/or datagram congestion control protocol (DC).
  • Wireless communication may be achieved via one or more wireless configurations, e.g., radio frequency, optical, Wi-Fi, Bluetooth (an open wireless protocol for exchanging data over short distances, using short length radio waves, from fixed and mobile devices, creating personal area networks (PANs), ZigBee® (a specification for a suite of high level communication protocols using small, low-power digital radios based on the IEEE 122.15.4-1203 standard for wireless personal area networks (WPANs)).
  • wireless configurations e.g., radio frequency, optical, Wi-Fi, Bluetooth (an open wireless protocol for exchanging data over short distances, using short length radio waves, from fixed and mobile devices, creating personal area networks (PANs), ZigBee® (a specification for a suite of high level communication protocols using small, low-power digital radios based on the IEEE 122.15.4-1203 standard for wireless personal area networks (WPANs)).
  • PANs personal area networks
  • ZigBee® a specification for a suite of high level communication protocols using small, low-power digital radios
  • the computers 21, 31, 41 may include any suitable processor (not shown) operably connected to a memory (not shown), which may include one or more of volatile, nonvolatile, magnetic, optical, or electrical media, such as read-only memory (ROM), random access memory (RAM), electrically-erasable programmable ROM (EEPROM), non-volatile RAM (NVRAM), or flash memory.
  • the processor may be any suitable processor (e.g., control circuit) adapted to perform the operations, calculations, and/or set of instructions described in the present disclosure including, but not limited to, a hardware processor, a field programmable gate array (FPGA), a digital signal processor (DSP), a central processing unit (CPU), a microprocessor, and combinations thereof.
  • FPGA field programmable gate array
  • DSP digital signal processor
  • CPU central processing unit
  • microprocessor e.g., microprocessor
  • each of the robotic arms 40 may include a plurality of links 42a, 42b, 42c, which are interconnected at joints 44a, 44b, 44c, respectively.
  • the joint 44a is configured to secure the robotic arm 40 to the mobile cart 60 and defines a first longitudinal axis.
  • the mobile cart 60 includes a lift 67 and a setup arm 61, which provides a base for mounting of the robotic arm 40.
  • the lift 67 allows for vertical movement of the setup arm 61.
  • the mobile cart 60 also includes a screen 69 for displaying information pertaining to the robotic arm 40.
  • the robotic arm 40 may include any type and/or number of joints.
  • the setup arm 61 includes a first link 62a, a second link 62b, and a third link 62c, which provide for lateral maneuverability of the robotic arm 40.
  • the links 62a, 62b, 62c are interconnected at joints 63a and 63b, each of which may include an actuator (not shown) for rotating the links 62b and 62b relative to each other and the link 62c.
  • the links 62a, 62b, 62c are movable in their corresponding lateral planes that are parallel to each other, thereby allowing for extension of the robotic arm 40 relative to the patient (e.g., surgical table).
  • the robotic arm 40 may be coupled to the surgical table (not shown).
  • the setup arm 61 includes controls 65 for adjusting movement of the links 62a, 62b, 62c as well as the lift 67.
  • the setup arm 61 may include any type and/or number of joints.
  • the third link 62c may include a rotatable base 64 having two degrees of freedom.
  • the rotatable base 64 includes a first actuator 64a and a second actuator 64b.
  • the first actuator 64a is rotatable about a first stationary arm axis which is perpendicular to a plane defined by the third link 62c and the second actuator 64b is rotatable about a second stationary arm axis which is transverse to the first stationary arm axis.
  • the first and second actuators 64a and 64b allow for full three-dimensional orientation of the robotic arm 40.
  • the actuator 48b of the joint 44b is coupled to the joint 44c via the belt 45 a, and the joint 44c is in turn coupled to the joint 46b via the belt 45b.
  • Joint 44c may include a transfer case coupling the belts 45a and 45b, such that the actuator 48b is configured to rotate each of the links 42b, 42c and a holder 46 relative to each other. More specifically, links 42b, 42c, and the holder 46 are passively coupled to the actuator 48b which enforces rotation about a pivot point “P” which lies at an intersection of the first axis defined by the link 42a and the second axis defined by the holder 46. In other words, the pivot point “P” is a remote center of motion (RCM) for the robotic arm 40.
  • RCM remote center of motion
  • the actuator 48b controls the angle 0 between the first and second axes allowing for orientation of the surgical instrument 50. Due to the interlinking of the links 42a, 42b, 42c, and the holder 46 via the belts 45a and 45b, the angles between the links 42a, 42b, 42c, and the holder 46 are also adjusted in order to achieve the desired angle 0. In embodiments, some or all of the joints 44a, 44b, 44c may include an actuator to obviate the need for mechanical linkages.
  • the joints 44a and 44b include an actuator 48a and 48b configured to drive the joints 44a, 44b, 44c relative to each other through a series of belts 45a and 45b or other mechanical linkages such as a drive rod, a cable, or a lever and the like.
  • the actuator 48a is configured to rotate the robotic arm 40 about a longitudinal axis defined by the link 42a.
  • the holder 46 defines a second longitudinal axis and configured to receive an instrument drive unit (IDU) 52 (FIG. 1).
  • the IDU 52 is configured to couple to an actuation mechanism of the surgical instrument 50 and the camera 51 and is configured to move (e.g., rotate) and actuate the instrument 50 and/or the camera 51.
  • the holder 46 transfers actuation forces from its actuators to the surgical instrument 50 to actuate components an end effector 49 of the surgical instrument 50.
  • the holder 46 includes a sliding mechanism 46a, which is configured to move the IDU 52 along the second longitudinal axis defined by the holder 46.
  • the holder 46 also includes a joint 46b, which rotates the holder 46 relative to the link 42c.
  • the instrument 50 may be inserted through a laparoscopic access port 55 (FIG. 3) held by the holder 46.
  • the holder 46 also includes a port latch 46c for securing the access port 55 to the holder 46 (FIG. 2).
  • the robotic arm 40 also includes a plurality of manual override buttons 53 (FIG. 1) disposed on the IDU 52 and the setup arm 61, which may be used in a manual mode. The user may press one or more of the buttons 53 to move the component associated with the button 53.
  • each of the computers 21, 31, 41 of the surgical robotic system 10 may include a plurality of controllers, which may be embodied in hardware and/or software.
  • the computer 21 of the control tower 20 includes a controller 21a and safety observer 2 lb.
  • the controller 21a receives data from the computer 31 of the surgeon console 30 about the current position and/or orientation of the hand controllers 38a and 38b and the state of the foot pedals 36 and other buttons.
  • the controller 21a processes these input positions to determine desired drive commands for each joint of the robotic arm 40 and/or the IDU 52 and communicates these to the computer 41 of the robotic arm 40.
  • the controller 2 la also receives the actual joint angles measured by encoders of the actuators 48a and 48b and uses this information to determine force feedback commands that are transmitted back to the computer 31 of the surgeon console 30 to provide haptic feedback through the hand controllers 38a and 38b.
  • the safety observer 21b performs validity checks on the data going into and out of the controller 21a and notifies a system fault handler if errors in the data transmission are detected to place the computer 21 and/or the surgical robotic system 10 into a safe state.
  • the controller 21a is coupled to a storage 22a, which may be non-transitory computer-readable medium configured to store any suitable computer data, such as software instructions executable by the controller 21a.
  • the controller 21a also includes transitory memory 22b for loading instructions and other computer readable data during execution of the instructions.
  • other controllers of the system 10 include similar configurations.
  • the computer 41 includes a plurality of controllers, namely, a main cart controller 41a, a setup arm controller 41b, a robotic arm controller 41c, and an instrument drive unit (IDU) controller 4 Id.
  • the main cart controller 41a receives and processes joint commands from the controller 21a of the computer 21 and communicates them to the setup arm controller 41b, the robotic arm controller 41c, and the IDU controller 41d.
  • the main cart controller 41a also manages instrument exchanges and the overall state of the mobile cart 60, the robotic arm 40, and the IDU 52.
  • the main cart controller 41a also communicates actual joint angles back to the controller 21a.
  • Each of joints 63a and 63b and the rotatable base 64 of the setup arm 61 are passive joints (i.e., no actuators are present therein) allowing for manual adjustment thereof by a user.
  • the joints 63a and 63b and the rotatable base 64 include brakes that are disengaged by the user to configure the setup arm 61.
  • the setup arm controller 41b monitors slippage of each of joints 63a and 63b and the rotatable base 64 of the setup arm 61, when brakes are engaged or can be freely moved by the operator when brakes are disengaged, but do not impact controls of other joints.
  • the robotic arm controller 41c controls each joint 44a and 44b of the robotic arm 40 and calculates desired motor torques required for gravity compensation, friction compensation, and closed loop position control of the robotic arm 40.
  • the robotic arm controller 41c calculates a movement command based on the calculated torque.
  • the calculated motor commands are then communicated to one or more of the actuators 48a and 48b in the robotic arm 40.
  • the actual joint positions are then transmitted by the actuators 48a and 48b back to the robotic arm controller 41c.
  • the IDU controller 4 Id receives desired joint angles for the surgical instrument 50, such as wrist and jaw angles, and computes desired currents for the motors in the IDU 52.
  • the IDU controller 4 Id calculates actual angles based on the motor positions and transmits the actual angles back to the main cart controller 41a.
  • the surgical robotic system 10 is setup around a surgical table 90.
  • the system 10 includes mobile carts 60a-d, which may be numbered “1” through “4.”
  • each of the carts 60a-d are positioned around the surgical table 90.
  • Position and orientation of the carts 60a-d depends on a plurality of factors, such as placement of a plurality of access ports 55a-d, which in turn, depends on the surgery being performed.
  • the access ports 55a-d are inserted into the patient, and carts 60a-d are positioned to insert instruments 50 and the laparoscopic camera 51 into corresponding ports 55a-d.
  • each of the robotic arms 40a-d is attached to one of the access ports 55a- d that is inserted into the patient by attaching the latch 46c (FIG. 2) to the access port 55 (FIG. 3).
  • the IDU 52 is attached to the holder 46, followed by the SIM 43 being attached to a distal portion of the IDU 52.
  • the instrument 50 is attached to the SIM 43.
  • the instrument 50 is then inserted through the access port 55 by moving the IDU 52 along the holder 46.
  • the SIM 43 includes a plurality of drive shafts configured to transmit rotation of individual motors of the IDU 52 to the instrument 50 thereby actuating the instrument 50.
  • the SIM 43 provides a sterile barrier between the instrument 50 and the other components of robotic arm 40, including the IDU 52.
  • the SIM 43 is also configured to secure a sterile drape (not shown) to the IDU 52.
  • a surgical procedure may include multiple phases, and each phase may include one or more surgical actions.
  • phase represents a surgical event that is composed of a series of steps (e.g., closure).
  • a “surgical action” may include an incision, a compression, a stapling, a clipping, a suturing, a cauterization, a sealing, or any other such actions performed to complete a phase in the surgical procedure.
  • a “step” refers to the completion of a named surgical objective (e.g., hemostasis).
  • certain surgical instruments 50 e.g., forceps
  • the surgical robotic system 10 may include a machine learning (MU) processing system 310 that processes the surgical data using one or more MU models to identify one or more features, such as surgical phase, instrument, anatomical structure, etc., in the surgical data.
  • the MU processing system 310 includes a MU training system 325, which may be a separate device (e.g., server) that stores its output as one or more trained MU models 330.
  • the MU models 330 are accessible by a MU execution system 340.
  • the MU execution system 340 may be separate from the MU training system 325, namely, devices that “train” the models are separate from devices that “infer,” i.e., perform real-time processing of surgical data using the trained MU models 330.
  • System 10 includes a data reception system 305 that collects surgical data, including the video data and surgical instrumentation data.
  • the data reception system 305 can include one or more devices (e.g., one or more user devices and/or servers) located within and/or associated with a surgical operating room and/or control center.
  • the data reception system 305 can receive surgical data in real-time, i.e., as the surgical procedure is being performed.
  • the ML processing system 310 may further include a data generator 315 to generate simulated surgical data, such as a set of virtual images, or record the video data from the video processing device 56, to train the ML models 330 as well as other sources of data, e.g., user input, arm movement, etc.
  • Data generator 315 can access (read/write) a data store 320 to record data, including multiple images and/or multiple videos.
  • the ML processing system 310 also includes a phase detector 350 that uses the ML models to identify a phase within the surgical procedure (“procedure”).
  • Phase detector 350 uses a particular procedural tracking data structure 355 from a list of procedural tracking data structures.
  • Phase detector 350 selects the procedural tracking data structure 355 based on the type of surgical procedure that is being performed. In one or more examples, the type of surgical procedure is predetermined or input by user.
  • the procedural tracking data structure 355 identifies a set of potential phases that may correspond to a part of the specific type of procedure.
  • the procedural tracking data structure 355 may be a graph that includes a set of nodes and a set of edges, with each node corresponding to a potential phase.
  • the edges may provide directional connections between nodes that indicate (via the direction) an expected order during which the phases will be encountered throughout an iteration of the procedure.
  • the procedural tracking data structure 355 may include one or more branching nodes that feed to multiple next nodes and/or may include one or more points of divergence and/or convergence between the nodes.
  • a phase indicates a procedural action (e.g., surgical action) that is being performed or has been performed and/or indicates a combination of actions that have been performed.
  • a phase relates to a biological state of a patient undergoing a surgical procedure.
  • the biological state may indicate a complication (e.g., blood clots, clogged arteries/veins, etc.), pre-condition (e.g., lesions, polyps, etc.).
  • pre-condition e.g., lesions, polyps, etc.
  • the ML models 330 are trained to detect an “abnormal condition,” such as hemorrhaging, arrhythmias, blood vessel abnormality, etc.
  • the phase detector 350 outputs the phase prediction associated with a portion of the video data that is analyzed by the ML processing system 310.
  • the phase prediction is associated with the portion of the video data by identifying a start time and an end time of the portion of the video that is analyzed by the ML execution system 340.
  • the phase prediction that is output may include an identity of a surgical phase as detected by the phase detector 350 based on the output of the ML execution system 340.
  • the phase prediction in one or more examples, may include identities of the structures (e.g., instrument, anatomy, etc.) that are identified by the ML execution system 340 in the portion of the video that is analyzed.
  • the phase prediction may also include a confidence score of the prediction. Other examples may include various other types of information in the phase prediction that is output.
  • sensor data 100 may include torque, angle, and/or motion data pertaining to movement of all of the components of the robotic system 10, which is provided by one or more sensors 70 (FIG. 2) disposed in the components of the robotic arms 40.
  • Actuators of the robotic arm 40 e.g., actuators 48a and 48b
  • the setup arm 61, and the IDU 52 include a plurality of sensors for monitoring their performance to provide for feedback and control thereof.
  • One of the sensors is an encoder coupled to a motor.
  • the encoder may be any device that provides a sensor signal indicative of the number of rotations of the motor, such as a mechanical encoder or an optical encoder.
  • the motor may also include other sensors, such as a current sensor configured to measure the current draw of the motor, a motor torque sensor for measuring motor torque, and the like.
  • the number of rotations may be used to determine the speed and/or position control of individual joints 44a, 44b, 44c.
  • Parameters which are measured and/or determined by the encoder may include speed, distance, revolutions per minute, position, and the like.
  • One of the sensors may be a joint torque sensor, which may be any force or strain sensor including one or more strain gauges configured to convert mechanical forces and/or strain into a sensor signal indicative of the torque imparted by the motor and/or other transmission components.
  • the joint torque sensor is configured to measure torque that is caused by weight, robot inertia, friction, external forces/torques.
  • the sensor signals from the encoder and the joint torque sensor are transmitted to the computer 41, which then controls the speed, angle, and/or position of each of the joints 44a, 44b, 44c of the robotic arm 40 based on the sensor signals.
  • additional position sensors may also be used to determine movement and orientation of the robotic arm 40 and the setup arm 62. Additional sensors include, but are not limited to, potentiometers coupled to movable components and configured to detect travel distances, Hall Effect sensors, IMU, accelerometers, and gyroscopes.
  • Operational data 102 may include movement commands and outputs from the ML processing system 310 (e.g., detected phase). Movement command data may include the pose of the handle controller controlling the robotic arm 40 and the instrument 50 or camera 51, e.g., the handle controller 38a. The pose of the handle controller 38a/38b is transformed into a desired pose of the robotic arm 40 through a hand eye transform function executed by the main cart controller 41a. The hand eye function, as well as other functions described herein, is/are embodied in software executable by the controller 21a or any other suitable controller described herein.
  • the pose of one of the handle controller 38a/38b may be embodied as a coordinate position and role -pitch-yaw (“RPY”) orientation relative to a coordinate reference frame, which is fixed to the surgical console 30.
  • the desired pose of the instrument 50 is relative to a fixed frame on the robotic arm 40.
  • the pose of the handle controller 38a is then scaled by a scaling function executed by the main cart controller 41a.
  • the coordinate position is scaled down and the orientation is scaled up by the scaling function.
  • the main cart controller 41a also executes a clutching function, which disengages the handle controller 38a/38b from the robotic arm 40.
  • the main cart controller 41a stops transmitting movement commands from the handle controller 38a to the robotic arm 40 if certain movement limits or other thresholds are exceeded and in essence acts like a virtual clutch mechanism, e.g., limits mechanical input from effecting mechanical output.
  • User inputs is also used to determine user intent for event detection and/or prediction.
  • the desired pose of the robotic arm 40 is based on the pose of the handle controller 38a and is then passed by an inverse kinematics function executed by the main cart controller 41a.
  • the inverse kinematics function calculates angles for the joints 44a, 44b, 44c of the robotic arm 40 that achieve the scaled and adjusted pose input by the handle controller 38a.
  • the calculated angles are then passed to the robotic arm controller 41c, which includes a joint axis controller.
  • the joint axis controller includes a proportional- derivative (PD) controller, a friction estimator module, a gravity compensator module, and a two-sided saturation block, which is configured to limit the commanded torque of the motor.
  • the joint axis controller also includes an environment torque estimator module, which determines the force and/or torque that the robotic arm 40 and the surgical instrument 50 is applying to the environment, such as the patient, other robotic arms 40, operating table, bedside operator, and other objects or people.
  • External vision data 104 may be provided by an external vision tracking unit 170 (FIG. 1) having one or more cameras or infrared sensors or other imaging devices configured to track the locations of the robotic arms 40, which may have a marker 172 disposed thereon.
  • the tracking unit 170 may be disposed on the control tower 20 or any other location in the operating room.
  • the tracking unit 170 may include one or more position sensors, which may be any suitable white light or infrared cameras, electromagnetic sensors, magnetoresistance sensors, radio frequency sensors, or any other sensor adapted to sufficiently sense the position of a navigation marker.
  • the tracking unit 170 may be configured to detect markers 172 disposed on the robotic arm 40, i.e., the holder 46.
  • the markers 172 may be passive tracking elements (e.g., reflectors) for transmitting light signals (e.g., reflecting light emitted from the tracking unit 170). Alternatively, the markers 172 may include a radio opaque material that is identified and trackable by the tracking unit 170. In other configurations, active tracking markers can be employed.
  • the active tracking markers can be, for example, light emitting diodes transmitting light, such as infrared light. Active and passive arrangements are possible.
  • the markers may be arranged in a defined or known position and orientation relative to the other markers in order to allow the tracking unit 170 to determine the position of the robotic arms 40, and in particular the holders 46, relative to each other. Tracking the position and orientation of the holders 46 allows the surgical robotic system 10 to determine the position and/or orientation of the instruments 50 and the camera 51 within a defined space, such as the surgical field.
  • the external vision data 104 may include video data, 3D map coordinates, point clouds, etc. and other external data of the robotic arms 40, the mobile carts 60, the setup arm 61, etc.
  • the external vision data 104 may also include location data of other external objects and persons in the operating room.
  • the external vision data 104 is used along with other data described below to provide context to sensor data 100 and detect collisions.
  • Internal imaging data 106 may include preoperative and/or intraoperative image data.
  • Preoperative imaging data may include computed tomography (CT), magnetic resonance imaging (MRI), etc.
  • Intraoperative image data may include ultrasound and laparoscopic images obtained during surgery. Laparoscopic images are provided by the camera 51, which captures images (e.g., video stream) of the surgical site including the instruments 50. The individual frames of the video stream are processed at the video processing device 56 using any suitable computer vision algorithm suitable for identifying objects, e.g., machine learning algorithms trained on data including images of instruments.
  • Instruments 50 may include a fiducial marker having a plurality of geometric shapes identifiable by the computer vision algorithm executed by the video processing device 56.
  • the fiducial marker allows the video processing device 56 to calculate the shape, size, orientation, position, and other physical parameters of the instrument 50 in relation to the surgical site and other objects captured by the camera 51.
  • the fiducial marker may be detected in one or more frames of the video stream.
  • the video processing device 56 may collect and process a sufficient number of frames, e.g., 16 or more, until the fiducial marker is identified and in each of the sampled frames to allow the video processing device 56 to track the fiducial marker across all frames of the video stream.
  • the laparoscopic video data is processed by the video processing device 56 to determine instrument-to-instrument and instrument-to-tissue collision or contact.
  • the sensor data 100, the operational data 102, the external vision data 104, and the internal imaging data 106 are analyzed and processed into combined data 108.
  • the data 100-106 includes time stamps for each of the data entries.
  • the combined data 108 includes correlation of all data streams and may be generated by performing data analysis.
  • the ML processing system 310 may receive as input each of the data 100-106 and output correlations therebetween to determine phase, collisions, and other events occurring during the surgical procedure. Correlation between data streams indicative of collisions may be determined using an artificial intelligence or machine learning (AI/ML) algorithm.
  • the AI/ML algorithm may be based on statistical ML that is configured to develop a statistical model and draw inferences therefrom.
  • Suitable statistical ML models include, but are not limited to, linear regression, logistic regression, decision trees, random forest, Naive Bayes, ensemble methods, support vector machines, K-Nearest Neighbor, and the like.
  • the AI/ML algorithm may be a deep learning algorithm that incorporates neural networks in successive layers.
  • Suitable deep learning models include, but are not limited to, convolutional neural network, recurrent neural network, deep reinforcement network, deep belief network, transformer network, and the like.
  • the input provided to train the models may be previously collected operation data of the system 10, including data streams from previous procedures.
  • FIG. 8 a flow chart of a method for analyzing multiple data streams from the surgical robotic system 10 (FIGS. 1-6) to improve its reliability and performance is shown.
  • the method of FIG. 8 may be embodied as software instructions executable by a processor such as the controller 21a or any other suitable controller, e.g., cloud server.
  • the system utilizes AI/ML to provide an overall understanding of the loading conditions of the surgical robotic system 10 as well as their influence on component lifetime. This information is then used to improve reliability of the robotic system 10.
  • Data stream inputs are collected.
  • Data stream inputs may include two or more of sensor data 100, operational data 102, external vision data 104, and internal imaging data 106 of FIG. 7.
  • the sensor data 100 includes data from position sensors, joint torque/force sensors, as well as additional sensors added on the robot and/or instrument (e.g., force, torque, acceleration, strain gauges, tactile, etc.).
  • Operational data 102 includes user inputs through handles and other input devices, procedural knowledge from the phase detector, as well as surgeon intention based on inputs and eye tracking.
  • External vision data 104 also includes data from cameras mounted on the robot system 10 or in the operating room.
  • Internal vision data 106 includes endoscope and laparoscopic imaging as well as pre- or intra-operative imaging.
  • Data inputs may also include voice and sound recording from the operating room. Additional data inputs also may include information on hardware components and instruments, such as specifications, requirements, test results of components, load history of component, etc. Further data inputs may include patient health information scrubbed of personally identifiable information to comply with local privacy laws.
  • correlation between data and reliability of the robotic system 10 is determined.
  • Correlation between robot data and hardware component and instrument reliability may be determined using AI/ML, mathematical correlations, and/or robot dynamics.
  • Hardware component/instrument reliability includes component lifetime, wear, and/or probability of failure based on load history (i.e., severity).
  • Components of interest include instruments, cameras, as well as hardware components in the robotic arm, setup arm, and cart (e.g., bearings, guides, sensors, structure, links, etc.)
  • the correlation between the data and the reliability parameters of the components of the robotic system 10 is used to update service intervals, update designs and provide efficient and effective refurbishment of these components.
  • step 3 data is monitored and analyzed, and the results are used to predict various events.
  • the system determines if one or more critical events has occurred or predicts whether the events will occur based on the data. If the upcoming occurrence of a critical event is detected, a control algorithm decides whether adaptations of the robot motion are required as described below in step 4.
  • Events that are detected or predicted include collisions such as instrument-to-instrument, robot-to-robot, instrument-to-tissue, collisions with other objects, such as cameras and access ports, external impacts, e.g., robot with operating room obstructions or staff, and/or high load and stress events approaching or above the specification limits.
  • Occurrence of an event is predicted using one or more of the following data inputs: surgeon’s intention, procedural knowledge based on detected phase, kinematic relations (e.g., orientation of robots with respect to each other, the patient, and the table), videos, images, robot velocities/positions, robot dynamics, and previous load histories of components.
  • the data analysis outputs the expected loads and the severity of the event, e.g., collision, if it would occur as predicted.
  • the severity of the event depends on the expected loads as well as the load history.
  • the system calculates a priority factor for the requested motion of any component of the robotic system. The priority factor is calculated based on the surgeon’s input, patient health information, and procedural knowledge.
  • the system outputs the amount of time until a critical event occurs (i.e., “time to react” value).
  • analysis of the data also includes categorization or classification of predicted events based on severity.
  • Event severity is categorized as adequate (i.e., unlikely that components fail), high loads with low risk of damage, high loads with high risk of damage, in combination with the information as to whether the expected event is intended or accidental.
  • the priority to execute the motion could also be categorized into high priority (i.e., high patient risk if motion is restricted), required priority (i.e., for patient’s safety and surgeon’s comfort), or low priority (i.e., no patient risk if motion is altered or slowed down).
  • the operation and/or behavior of the surgical robotic system 10 is modified based on the data processing of step 3.
  • a corresponding control algorithm is activated.
  • the control algorithm performs the following changes of the robot motion based on the event severity, motion execution priority factor, and time to react value.
  • Patient health supersedes robot health and thus this parameter overrules any decisions made to preserve the longevity of the robotic components.
  • this controller feature may be deactivated, e.g., by a button on a GUI feature.
  • Specific adaptations of robot behavior include minimizing torques of the motors, or the velocity of robotic arm and instruments.
  • the modifications also include calculating multiple robotic arm and instrument positions to achieve the same motion of the instrument while changing the configuration of the robot that results in the lower load on the robotic system in the null space of the robotic arm being controlled.
  • the null space is the region of state space where there is a redundancy of solutions; the system can move in a number of ways and still not affect the completion of the goals of the end effector.
  • the algorithm selects a kinematic solution from a plurality of kinematic solutions in a null space of the robotic arm to achieve the desired position that avoids collisions or occurrence of another adverse event.
  • the robotic system or certain components may be completely stopped to overrule user input.
  • step 3 if at step 3 a large motion is detected that is required to achieve the next procedural step that would result in a collision, an alternative configuration is provided that avoids the collision while moving the end effectors into the commanded position. If no such alternative is possible, then the robotic system is stopped. In another embodiment, if the predicted event will lead to high loads, has a small risk of damage, and the motion is intended, then the loads are reduced by limiting torques/velocity of the robot. [0070] Another embodiment of modifying robot behavior is described below with respect to FIG. 9 where collisions are detected based on various sensor inputs.
  • collisions are detected based on severity of sensor inputs, such as a steep change of joint torque or force values while filtering sensor measurements caused merely by robot movement, e.g., rapid acceleration.
  • the algorithm also uses image data to confirm the collision.
  • the final step of the collision detection algorithm includes using the identified location and robot dynamics to compute the amount of load imparted on the robotic system.
  • steps 1-4 occur during operation of the surgical robotic system 10 and are used to modify behavior of the system 10
  • steps 5 and 6 described below may be performed online or offline.
  • the system determines event types and extracts details pertaining to the events recorded by the data input streams. Events that are detected may include collisions, high loads, high stresses, changes in robot dynamics (e.g., characteristic frequency, damping of vibrations, friction, etc.), and changes in robot performance (e.g., power consumption, etc.)
  • information to be extracted may include, but is not limited to, event type, severity of the event, impact on the robotic components, etc.
  • the extracted data is then used for reliability assessment of the system.
  • Determining that an event has occurred is based on data pertaining to robot dynamics and performance properties and are compared to past performance and dynamics. If changes are detected, then the system saves the data corresponding to the event. The system process event information to extract the information to allow for identification of the event in algorithms controlling the robotic system 10.
  • specific data that is monitored includes the frequency spectrum of the robotic arm, which includes characteristic frequency and damping.
  • This information may be extracted from the following data streams: joint torque/force/position sensors and/or videos and/or voice/sound recording and/or acceleration sensors.
  • the system may determine friction coefficients using sensor data.
  • the system stores the event information such that the extracted information from step 5, which can be used to improve user experience and update and/or generate control algorithms.
  • the information could be stored, displayed, sent, processed, etc.
  • the extracted information may be saved in a cloud or locally on any component of the robotic system 10.
  • the information may be displayed on any of the displays of the system 10 and may include prompts in response to the detected event, such as when component exchange is recommended, how the robotic setup should be adjusted, etc.
  • the extracted information may also be sent to service providers when maintenance is required.
  • a method for detecting and/or predicting collisions of various components of the system 10 is disclosed.
  • the method may be implemented as software instructions executable by the controller 21a and executed during operation of the system 10.
  • input movement commands for moving the robotic arm 40, instrument 50, and/or camera 51 are received by the controller 21a.
  • the movement commands are transformed to move the robotic arm 40, instrument 50, and/or camera 51 and at step 204 the robotic arm 40 and the attached instrument (e.g., instrument 50, camera 51, etc.) are actuated.
  • the process of receiving and transforming movement commands via inverse kinematics is described above with respect to the operational data 102 in FIG. 7.
  • sensor data 100 is provided to the controller 21a at step 206.
  • the controller 21a determines whether the sensor data 100 is indicative of an actual collision or a predicted collision between any portion of the robotic arms 40 including the instrument 50 and any other object in the operating room or at the surgical site, e.g., the camera 51, access port 50, tissue structure, etc. If the collision is not detected or predicted, the operation of the robotic system 10 continues, i.e., receiving and transforming movement commands. Collision may be determined based on measured torque exceeding a preset threshold or by determining that the movement would result in a collision by calculating inverse kinematics prior to executing the movement command.
  • the controller 21a receives combined data stream 106, including external vision data 104 and internal imaging data 106. If the collision is detected based on the sensor data 100 then at step 212, the controller 21a confirms that collision has occurred or predicts that a collision may occur based on external vision data 104 and internal imaging data 106. Steps 210 and 212 may be executed in parallel with steps 206 and 208, such that different data streams are analyzed in parallel. Imaging data may be used to detect or predict collisions based on objects contacting each other or about to contact each other in the image frames. If a collision or a predicted collision is not confirmed, then the operation of the robotic system 10 continues, i.e., receiving and transforming movement commands.
  • the controller 21a may output GUI indicators or prompts on the screens 32, 34, and/or 23 informing of the collision at step 214.
  • the user may dismiss the prompts depending on severity of the warning, i.e., medium or extreme warnings regarding hard impacts cannot be dismissed, whereas warning regarding slight contact may be dismissed.
  • severity of the warning i.e., medium or extreme warnings regarding hard impacts cannot be dismissed, whereas warning regarding slight contact may be dismissed.
  • the robotic system 10 may take corrective action on its own in response to detected or predicted collisions.
  • the influence on the reliability is evaluated and used in the future.
  • the reliability data can be stored, analyzed, and transmitted for subsequent use because the information that an event (e.g., a collision) happened is important for the entire load history and hence for determination of reliability.
  • Intended collisions may be distinguished from unintended collisions based on the operational data 102 and outputs from the ML processing system 310 (e.g., detected phase).
  • the contextual collision information is used for the potential correction, whether the correcting action is performed manually or automatically.
  • Automatic correction by the system 10 may include stopping any movement of the robotic arms 40 and/or lowering torque and/or speed limits of the actuators in the joints 44a, 44b, 44c, holder 46 of the robotic arm 40 and the IDU 52 at step 216.
  • the torque limits prevent the actuators from operating at full torque, thus preventing damage to the instruments 50 after collision is detected.
  • Manual collision correction may include movement of the arms in reverse given the information pertaining to the collision. In particular, each collision may have a particular set of circumstances and require a specific reversal movement, the specific information provided on the GUI allows for the operator to reverse the collision.
  • the system 10 is configured to gather different data streams and store information that enables a unique interpretation of sensor data and dynamic phenomena (e.g., collisions).
  • the correlations from the data processing may also be used to improve product reliability.
  • the combined data streams may also be used to predict failure events by providing maintenance intervals, or failure events (e.g., usage time, failure type, etc.) of the components of the robotics system 10.
  • a surgical robotic system comprising: a first robotic arm including a first instrument and a first sensor; a laparoscopic camera configured to capture intraoperative imaging data; a surgeon console including a handle controller configured to receive user input to actuate the first robotic arm; and a controller configured to: control the first robotic arm based on the user input and a control algorithm; receive a plurality of data streams including sensor data from the first sensor, operational data including user input data, and internal imaging data from the laparoscopic camera; predict occurrence of an event based on the plurality of data streams; and adjust the control algorithm based on the predicted occurrence of the event.
  • Example 2 The surgical robotic system according to Example 1, further comprising: a second robotic arm including a second instrument and a second sensor; and an external vision system configured to generate external vision data including position of the first robotic arm and the second robotic arm.
  • Example 3 The surgical robotic system according to Example 1, wherein the internal imaging data further includes preoperative imaging data.
  • Example 4 The surgical robotic system according to Example 2, wherein the event is a collision of one of the first robotic arm or the second robotic arm with one of the first instrument or the second instrument.
  • Example 5 The surgical robotic system according to Example 2, wherein the adjustment to the control algorithm includes lowering torque or velocity output to one of the first robotic arm, the second robotic arm, the first instrument, or the second instrument.
  • Example 6 The surgical robotic system according to Example 2, wherein the adjustment to the control algorithm includes selecting a kinematic solution from a plurality of kinematic solutions in a null space of at least one of the first robotic arm or the second robotic arm.
  • Example 7 The surgical robotic system according to Example 2, wherein the adjustment to the control algorithm includes stopping movement of one of the first robotic arm, the second robotic arm, the first instrument, or the second instrument.
  • Example 8 The surgical robotic system according to Example 1, wherein the controller is further configured to determine reliability of components of the first and second robotic arm and the first and second instruments based on the plurality of data streams.
  • Example 9 The surgical robotic system according to Example 1, wherein the controller is further configured to classify severity of the event and to adjust the control algorithm based on the severity of the event.
  • Example 10 The surgical robotic system according to Example 9, wherein the controller is further configured to determine effect of the severity of the event on reliability of the first robotic arm.
  • Example 11 A method for controlling a surgical robotic system, the method comprising: capturing, through a laparoscopic camera, a video stream of a first robotic instrument of a first robotic arm and a second robotic instrument of a second robotic arm; measuring at least one parameter of the first robotic arm or the second robotic arm; identifying, at a video processing device, the first robotic instrument and the second robotic instrument in at least one frame of the video stream; determining, at a controller, whether an instrument collision has occurred based on the at least one measured parameter; and confirming, at the controller, collision occurrence using the video stream.
  • Example 12 The method according to Example 11, further comprising: adjusting an operation of the first robotic arm or the second robotic arm in response to confirmation of the collision.
  • Example 13 The method according to Example 11, further comprising determining, at the controller, whether the collision is accidental.
  • Example 14 The method according to Example 11, further comprising: receiving, at a surgeon console, user input for actuating at least one of the first robotic arm or the second robotic arm; and determining, at the controller, whether the collision is accidental based on the user input.
  • Example 15 The method according to Example 11, further comprising: tracking a position of the first robotic arm and a position of the second robotic arm at an external vision system; and receiving external vision data at the controller from the external vision system.
  • Example 16 The method according to Example 15, further comprising: determining whether an arm collision has occurred between the first robotic arm and the second robotic arm based on the at least one measured parameter.
  • Example 17 The method according to Example 16, further comprising: confirming, at the controller, collision occurrence using the external vision data.
  • Example 18 The method according to Example 11, wherein the at least one parameter is torque imparted on the first robotic arm or the second robotic arm.
  • Example 19 The method according to Example 11, further comprising displaying a graphical user interface (GUI) on a screen.
  • GUI graphical user interface
  • Example 20 The method according to Example 19, further comprising: outputting a prompt on the GUI in response to confirmation of the collision.
  • Example 21 A surgical robotic system comprising: a first robotic arm including a first instrument and a first sensor; a second robotic arm including a second instrument and a second sensor; a surgeon console including a handle controller configured to receive user input to actuate at least one of the first robotic arm or the second robotic arm; and a controller configured to: determine whether at least one the first instrument, the second instrument, the first robotic arm, or the second robotic arm had a collision based on at least one parameter measured by one of the first sensor or the second sensor; receive a combined data stream including intraoperative imaging data and external vision data; confirm collision occurrence using the intraoperative imaging data and the external vision data; and adjust operation of the first robotic arm or the second robotic arm in response to confirmation of the collision.
  • Example 22 The surgical robotic system according to Example 21, further comprising a screen configured to display a graphical user interface (GUI), wherein the controller is further configured to output a prompt on the GUI in response to confirmation of the collision.
  • GUI graphical user interface
  • Example 23 The surgical robotic system according to Example 21, wherein the controller is further configured to analyze the combined data stream to determine load exerted on at least one the first instrument, the second instrument, the first robotic arm, or the second robotic arm from the combined data stream.
  • Example 24 A method for operating a surgical robotic system, the method comprising: detecting occurrence of an event during movement of a robotic arm including a surgical instrument; assessing severity of the event on one of the robotic arm or the surgical instrument; and combining the severity of event with a previous loading history of the robotic arm or the surgical instrument.
  • Example 25 The method according to Example 24, wherein the previous loading history includes at least one a specification parameter or reliability test data of the robotic arm or the surgical instrument.
  • Example 26 The method according to Example 24, further comprising: calculating risk of failure of the robotic arm or the surgical instrument based on comparison of the severity of event to the previous loading history.

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  • Health & Medical Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Robotics (AREA)
  • Surgery (AREA)
  • Biomedical Technology (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Medical Informatics (AREA)
  • Molecular Biology (AREA)
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  • General Health & Medical Sciences (AREA)
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Abstract

L'invention concerne un système robotique chirurgical incluant un premier bras robotique ayant un premier instrument et un premier capteur ; un second bras robotique peut inclure un second instrument et un second capteur ; une caméra laparoscopique configurée pour capturer des données d'imagerie peropératoire ; et une console de chirurgien ayant une manette de commande configurée pour recevoir une entrée d'utilisateur afin d'actionner au moins l'un du premier bras robotique ou du second bras robotique. Le système inclut également un dispositif de commande configuré pour : commander au moins l'un du premier bras robotique ou du second bras robotique sur la base de l'entrée d'utilisateur et d'un algorithme de commande ; recevoir une pluralité de flux de données, qui peuvent inclure des données de capteur provenant du premier capteur et du second capteur, des données opérationnelles, des données d'entrée d'utilisateur, des données de vision externe, et/ou des données d'imagerie interne provenant de la caméra laparoscopique ; prédire l'occurrence d'un événement sur la base de la pluralité de flux de données ; et ajuster l'algorithme de commande sur la base de l'occurrence prédite de l'événement.
PCT/IB2024/057222 2023-08-01 2024-07-25 Système et procédé de traitement de flux de données combinés de robots chirurgicaux Pending WO2025027463A1 (fr)

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CN120078518A (zh) * 2025-05-06 2025-06-03 华中科技大学同济医学院附属协和医院 一种腹腔镜手术机器人的多维交互方法、系统及存储介质

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