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WO2024261752A1 - Systèmes de détection en temps réel de collision d'objet et/ou de mouvement d'objet - Google Patents

Systèmes de détection en temps réel de collision d'objet et/ou de mouvement d'objet Download PDF

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Publication number
WO2024261752A1
WO2024261752A1 PCT/IL2024/050595 IL2024050595W WO2024261752A1 WO 2024261752 A1 WO2024261752 A1 WO 2024261752A1 IL 2024050595 W IL2024050595 W IL 2024050595W WO 2024261752 A1 WO2024261752 A1 WO 2024261752A1
Authority
WO
WIPO (PCT)
Prior art keywords
processor
sensor
end effector
robotic arm
movement
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
PCT/IL2024/050595
Other languages
English (en)
Inventor
Itamar ESHEL
Hay Haim SHMULEVICH
Ran Shaham
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Mazor Robotics Ltd
Original Assignee
Mazor Robotics Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Mazor Robotics Ltd filed Critical Mazor Robotics Ltd
Publication of WO2024261752A1 publication Critical patent/WO2024261752A1/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

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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
    • A61B34/20—Surgical navigation systems; Devices for tracking or guiding surgical instruments, e.g. for frameless stereotaxis
    • 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
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • B25J9/1674—Programme controls characterised by safety, monitoring, diagnostic
    • B25J9/1676—Avoiding collision or forbidden zones
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • B25J9/1694—Programme controls characterised by use of sensors other than normal servo-feedback from position, speed or acceleration sensors, perception control, multi-sensor controlled systems, sensor fusion
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B17/00—Surgical instruments, devices or methods
    • A61B2017/00017—Electrical control of surgical instruments
    • A61B2017/00207—Electrical control of surgical instruments with hand gesture control or hand gesture recognition
    • 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
    • A61B34/20—Surgical navigation systems; Devices for tracking or guiding surgical instruments, e.g. for frameless stereotaxis
    • A61B2034/2046—Tracking techniques
    • A61B2034/2055—Optical tracking systems
    • 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
    • A61B34/20—Surgical navigation systems; Devices for tracking or guiding surgical instruments, e.g. for frameless stereotaxis
    • A61B2034/2046—Tracking techniques
    • A61B2034/2055—Optical tracking systems
    • A61B2034/2057—Details of tracking cameras
    • 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
    • A61B34/25—User interfaces for surgical systems
    • A61B2034/256—User interfaces for surgical systems having a database of accessory information, e.g. including context sensitive help or scientific articles

Definitions

  • the present disclosure is generally directed to detecting an object, and relates more particularly to real-time collision detection between an end effector and an object or detection of movement of the object.
  • Surgical robots may assist a surgeon or other medical provider in carrying out a surgical procedure, or may complete one or more surgical procedures autonomously.
  • Providing controllable linked articulating members allows a surgical robot to reach areas of a patient anatomy during various medical procedures.
  • Example aspects of the present disclosure include:
  • a system for detecting collision in real-time comprises a processor; and a memory storing data for processing by the processor, the data, when processed, causes the processor to: receive sensor data corresponding to a distance between an end effector and an object; process the sensor data to determine the distance; and determine if a collision will occur between the end effector and the object based on the distance.
  • determining the collision includes comparing the distance and a predetermined threshold distance, wherein the collision is determined to occur when the distance is less than the predetermined threshold distance.
  • the memory stores further data for processing by the processor that, when processed, causes the processor to: cause a robotic arm supporting and orienting the end effector to stop movement when the collision is determined to occur between the end effector and the object.
  • processing the sensor data to determine the distance includes measuring a round-trip time of a light signal emitted by a light source onto the object.
  • at least one time-of-flight sensor configured to measure a round-trip time of a light signal emitted by a light source onto the object to yield the sensor data.
  • any of the aspects herein further comprising: a robotic arm; and an end effector releasably secured to the robotic arm, wherein the at least one time-of-flight sensor is positioned on the end effector.
  • a system for detecting collision in real-time comprises an end effector; at least one sensor positioned on the end effector and configured to yield sensor data corresponding to a distance between the end effector and an object; a robotic arm configured to support and orient the end effector; a processor; and a memory storing data for processing by the processor, the data, when processed, causes the processor to: receive the sensor data corresponding to the distance between the end effector and the object; process the sensor data to determine the distance; and determine if a collision will occur between the end effector and the object based on the distance.
  • determining the collision includes comparing the distance and a predetermined threshold distance, wherein the collision is determined to occur when the distance is less than the predetermined threshold distance.
  • the memory stores further data for processing by the processor that, when processed, causes the processor to: cause a robotic arm supporting and orienting the end effector to stop movement when the collision is determined to occur between the end effector and the object.
  • processing the sensor data to determine the distance includes measuring a round-trip time of a light signal emitted by a light source onto the object.
  • the at least one sensor comprises at least one proximity sensor.
  • the at least one sensor comprises at least one time-of- flight sensor.
  • a system for detecting motion in real-time comprises a processor; and a memory storing data for processing by the processor, the data, when processed, causes the processor to: receive sensor data corresponding a plurality of distances between an object and an end effector; process the sensor data to yield the plurality of distances; and determine a movement of an object based on the plurality of distances.
  • the movement corresponds to a desired action of a robotic arm
  • the memory stores further data for processing by the processor that, when processed, causes the processor to: cause the robotic arm to perform the desired action.
  • the desired action comprises at least one of causing a robotic arm to stop movement, causing a robotic arm to change a trajectory of movement, or causing a robotic arm to continue movement on a trajectory.
  • the memory stores further data for processing by the processor that, when processed, causes the processor to: receive a three-dimensional (3D) image depicting at least the object; and update the 3D image to depict a current pose of the object based on the movement of the object.
  • 3D three-dimensional
  • each of the expressions “at least one of A, B and C”, “at least one of A, B, or C”, “one or more of A, B, and C”, “one or more of A, B, or C” and “A, B, and/or C” means A alone, B alone, C alone, A and B together, A and C together, B and C together, or A, B and C together.
  • each one of A, B, and C in the above expressions refers to an element, such as X, Y, and Z, or class of elements, such as XI -Xn, Yl-Ym, and Zl- Zo
  • the phrase is intended to refer to a single element selected from X, Y, and Z, a combination of elements selected from the same class (e.g., XI and X2) as well as a combination of elements selected from two or more classes (e.g., Y 1 and Zo).
  • FIG. 1 is a block diagram of a system according to at least one embodiment of the present disclosure
  • FIG. 2A is a block diagram of a system according to at least one embodiment of the present disclosure.
  • FIG. 2B is an illustration of a portion of the system of Fig. 2A according to at least one embodiment of the present disclosure
  • FIG. 2C is an illustration of an image received from a sensor according to at least one embodiment of the present disclosure.
  • FIG. 3 is a flowchart according to at least one embodiment of the present disclosure.
  • Fig. 4 is a flowchart according to at least one embodiment of the present disclosure.
  • Fig. 5 is a flowchart according to at least one embodiment of the present disclosure.
  • the described methods, processes, and techniques may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored as one or more instructions or code on a computer-readable medium and executed by a hardware-based processing unit. Alternatively or additionally, functions may be implemented using machine learning models, neural networks, artificial neural networks, or combinations thereof (alone or in combination with instructions).
  • Computer-readable media may include non-transitory computer-readable media, which corresponds to a tangible medium such as data storage media (e.g., RAM, ROM, EEPROM, flash memory, or any other medium that can be used to store desired program code in the form of instructions or data structures and that can be accessed by a computer).
  • processors such as one or more digital signal processors (DSPs), general purpose microprocessors (e.g., Intel Core i3, i5, i7, or i9 processors; Intel Celeron processors; Intel Xeon processors; Intel Pentium processors; AMD Ryzen processors; AMD Athlon processors; AMD Phenom processors; Apple A10 or 10X Fusion processors; Apple Al l, A12, A12X, A12Z, or A13 Bionic processors; or any other general purpose microprocessors), graphics processing units (e.g., Nvidia GeForce RTX 2000-series processors, Nvidia GeForce RTX 3000-series processors, AMD Radeon RX 5000-series processors, AMD Radeon RX 6000-series processors, or any other graphics processing units), application specific integrated circuits (ASICs), field programmable logic arrays (FPGAs), or other equivalent integrated or discrete logic circuit
  • DSPs digital signal processors
  • proximal and distal are used in this disclosure with their conventional medical meanings, proximal being closer to the operator or user of the system, and further from the region of surgical interest in or on the patient, and distal being closer to the region of surgical interest in or on the patient, and further from the operator or user of the system.
  • Robotic assisted or autonomous robotic surgical procedures using robotic surgical systems may include risks such as, for example, collisions between components such as robotic arm(s), object(s) in the surgical space, and/or user(s).
  • risks such as, for example, collisions between components such as robotic arm(s), object(s) in the surgical space, and/or user(s).
  • a preoperative working volume may be initially defined.
  • one or more sensors may be positioned on an end effector that is supported, oriented, and/or operated by a robotic arm.
  • the end effector may be fixed or releasably secured to the robotic arm and may be configured to facilitate or receive a tool changer unit.
  • the end effector may also include an imaging device capable of obtaining three-dimensional (3D) images.
  • the one or more sensors positioned on the end effector may be, for example, any proximity sensor capable of determining a distance between the sensor (and thus, the component to which the sensor is affixed or positioned on) and an object.
  • the one or more sensors includes time-of-flight (ToF) sensor(s).
  • ToF time-of-flight
  • three to five ToF sensors are positioned on the end effector and are spaced along a circumference of the end effector.
  • the one or more sensors may provide a cost effective and simple alternative to using a 3D imaging camera to detect a collision or movement of an object and can provide sensor data in real-time.
  • the one or more sensors may also be used to detect contactless gestures from a user such as, for example, a surgeon, which may be correlated to a command or input. For example, a surgeon may wave their hand in front of the one or more sensors to cause the robotic arm to stop movement.
  • the one or more sensors may be used to detect if an object has moved, which may be used to, for example, update a 2D or 3D image to reflect a current position of the object.
  • Embodiments of the present disclosure provide technical solutions to one or more of the problems of (1) real-time collision detection for a robotic surgical system, (2) real-time motion detection for a robotic surgical system, and (3) real-time collision and/or motion detection for a robotic surgical system.
  • Fig. 1 a block diagram of a system 100 according to at least one embodiment of the present disclosure is shown.
  • the system 100 may be used to detect a potential collision between an end effector and an object, movement of the object, and/or carry out one or more other aspects of one or more of the methods disclosed herein.
  • the system 100 comprises a computing device 102, one or more imaging devices 112, a robot 114, a navigation system 118, a database 130, and/or a cloud or other network 134.
  • Systems according to other embodiments of the present disclosure may comprise more or fewer components than the system 100.
  • the system 100 may not include the imaging device 112, the robot 114, the navigation system 118, one or more components of the computing device 102, the database 130, and/or the cloud 134.
  • the computing device 102 comprises a processor 104, a memory 106, a communication interface 108, and a user interface 110.
  • Computing devices according to other embodiments of the present disclosure may comprise more or fewer components than the computing device 102.
  • the processor 104 of the computing device 102 may be any processor described herein or any similar processor.
  • the processor 104 may be configured to execute instructions stored in the memory 106, which instructions may cause the processor 104 to carry out one or more computing steps utilizing or based on data received from the imaging device 112, the robot 114, the navigation system 118, the database 130, and/or the cloud 134.
  • the memory 106 may be or comprise RAM, DRAM, SDRAM, other solid-state memory, any memory described herein, or any other tangible, non-transitory memory for storing computer- readable data and/or instructions.
  • the memory 106 may store information or data useful for completing, for example, any step of the methods 300, 400, and/or 500 described herein, or of any other methods.
  • the memory 106 may store, for example, instructions and/or machine learning models that support one or more functions of the robot 114.
  • the memory 106 may store content (e.g., instructions and/or machine learning models) that, when executed by the processor 104, enable sensor processing 120, collision detection 122, and/or movement detection 124.
  • the sensor processing 120 enables the processor 104 to process sensor data received from, for example, a sensor 128.
  • the sensor data may be processed to obtain, for example, a distance between the sensor 128 (or an end effector 126 to which the sensor 128 is attached to) and an object. Such information may be used to determine if collision between the end effector 126 and an object may occur.
  • the sensor data may be processed to determine movement of an object (for example, motion of a user’s hand) in front of the sensor 128 or whether the object has moved.
  • the motion may be used to control, for example, a robotic arm 116 of the robot 114 or any component of the system 100. If the sensor data indicates that an object has moved (for example, a target anatomical element has moved or shifted), this may indicate that a 3D model or image or 2D image being used should be updated to reflect the movement of the object.
  • the collision detection 122 enables the processor 104 to determine whether a collision may occur between the end effector 126 and an object using the processed sensor data.
  • the processed sensor data comprises a distance between the object and the end effector 126
  • the distance may be compared to a predetermined distance.
  • a collision may be detected when the distance is equal to or less than the predetermined distance.
  • the movement detection 124 enables the processor 104 to determine a movement of an object within a field of vision of the sensor 128 (e.g., in front of the sensor 128) using the processed sensor data.
  • the movement may correspond to a desired action of the robot 114 and can cause the processor 104 to generate instructions for the robot 114 to cause the robot 114 to perform the desired action.
  • the movement can be used to control the robot 114 or any component of the system 100.
  • Such content may, in some embodiments, be organized into one or more applications, modules, packages, layers, or engines.
  • the memory 106 may store other types of content or data (e.g., machine learning models, artificial neural networks, deep neural networks, etc.) that can be processed by the processor 104 to carry out the various method and features described herein.
  • various contents of memory 106 may be described as instructions, it should be appreciated that functionality described herein can be achieved through use of instructions, algorithms, and/or machine learning models.
  • the data, algorithms, and/or instructions may cause the processor 104 to manipulate data stored in the memory 106 and/or received from or via the imaging device 112, the robot 114, the database 130, and/or the cloud 134.
  • the computing device 102 may also comprise a communication interface 108.
  • the communication interface 108 may be used for receiving image data or other information from an external source (such as the imaging device 112, the robot 114, the navigation system 118, the database 130, the cloud 134, and/or any other system or component not part of the system 100), and/or for transmitting instructions, images, or other information to an external system or device (e.g., another computing device 102, the imaging device 112, the robot 114, the navigation system 118, the database 130, the cloud 134, and/or any other system or component not part of the system 100).
  • an external system or device e.g., another computing device 102, the imaging device 112, the robot 114, the navigation system 118, the database 130, the cloud 134, and/or any other system or component not part of the system 100.
  • the communication interface 108 may comprise one or more wired interfaces (e.g., a USB port, an Ethernet port, a Firewire port) and/or one or more wireless transceivers or interfaces (configured, for example, to transmit and/or receive information via one or more wireless communication protocols such as 802.11a/b/g/n, Bluetooth, NFC, ZigBee, and so forth).
  • the communication interface 108 may be useful for enabling the device 102 to communicate with one or more other processors 104 or computing devices 102, whether to reduce the time needed to accomplish a computing-intensive task or for any other reason.
  • the computing device 102 may also comprise one or more user interfaces 110.
  • the user interface 110 may be or comprise a keyboard, mouse, trackball, monitor, television, screen, touchscreen, and/or any other device for receiving information from a user and/or for providing information to a user.
  • the user interface 110 may be used, for example, to receive a user selection or other user input regarding any step of any method described herein. Notwithstanding the foregoing, any required input for any step of any method described herein may be generated automatically by the system 100 (e.g., by the processor 104 or another component of the system 100) or received by the system 100 from a source external to the system 100.
  • the user interface 110 may be useful to allow a surgeon or other user to modify instructions to be executed by the processor 104 according to one or more embodiments of the present disclosure, and/or to modify or adjust a setting of other information displayed on the user interface 110 or corresponding thereto.
  • the computing device 102 may utilize a user interface 110 that is housed separately from one or more remaining components of the computing device 102.
  • the user interface 110 may be located proximate one or more other components of the computing device 102, while in other embodiments, the user interface 110 may be located remotely from one or more other components of the computer device 102.
  • the imaging device 112 may be operable to image one or more objects or components, anatomical feature(s) of a patient, etc.
  • “Image data” as used herein refers to the data generated or captured by an imaging device 112, including in a machine-readable form, a graphical/visual form, and in any other form.
  • the image data may be or comprise a preoperative image, an intraoperative image, a postoperative image, or an image taken independently of any surgical procedure.
  • a first imaging device 112 may be used to obtain first image data (e.g., a first image) at a first time
  • a second imaging device 112 may be used to obtain second image data (e.g., a second image) at a second time after the first time.
  • the imaging device 112 may be capable of taking a 2D image or a 3D image or volume to yield the image data. In embodiments where the imaging device 112 is capable of taking a 3D image or volume, the imaging device 112 may be used to obtain a preoperative working volume of an operative space. The preoperative working volume may be used in detecting and/or preventing collision between components of, for example, the system 100 and/or objects or other components within the working volume.
  • the imaging device 112 may be or comprise, for example, an ultrasound scanner (which may comprise, for example, a physically separate transducer and receiver, or a single ultrasound transceiver), a 3D imaging device, a stereo camera, an 0-arm, a C-arm, a G-arm, or any other device utilizing X-ray-based imaging (e.g., a fluoroscope, a CT scanner, or other X-ray machine), a magnetic resonance imaging (MRI) scanner, an optical coherence tomography (OCT) scanner, an endoscope, a microscope, an optical camera, a thermographic camera (e.g., an infrared camera), a radar system (which may comprise, for example, a transmitter, a receiver, a processor, and one or more antennae), or any other imaging device 112.
  • the imaging device 112 may be contained entirely within a single housing, or may comprise a transmitter/emitter and a receiver/detector that are in separate housings or are otherwise physically separated.
  • the imaging device 112 may comprise more than one imaging device 112.
  • a first imaging device may provide first image data and/or a first image
  • a second imaging device may provide second image data and/or a second image.
  • the same imaging device may be used to provide both the first image data and the second image data, and/or any other image data described herein.
  • the imaging device 112 may be operable to generate a stream of image data.
  • the imaging device 112 may be configured to operate with an open shutter, or with a shutter that continuously alternates between open and shut so as to capture successive images.
  • image data may be considered to be continuous and/or provided as an image data stream if the image data represents two or more frames per second.
  • the robot 114 may be any surgical robot or surgical robotic system.
  • the robot 114 may be or comprise, for example, the Mazor XTM Stealth Edition robotic guidance system.
  • the robot 114 may be configured to position an end effector such as the end effector 126 at one or more precise position(s) and orientation(s), and/or to return the end effector 126 to the same position(s) and orientation(s) at a later point in time.
  • the robot 114 may additionally or alternatively be configured to manipulate a surgical tool (whether based on guidance from the navigation system 118 or not) to accomplish or to assist with a surgical task.
  • the robot 114 may be configured to hold and/or manipulate an anatomical element during or in connection with a surgical procedure.
  • the robot 114 may comprise one or more robotic arms 116.
  • the robotic arm 116 may comprise a first robotic arm and a second robotic arm, though the robot 114 may comprise more than two robotic arms.
  • one or more of the robotic arms 116 may be used to hold and/or maneuver the end effector 126.
  • the end effector 126 comprises two or more physically separate components (e.g., a transmitter and receiver)
  • one robotic arm 116 may hold one such component
  • another robotic arm 116 may hold another such component.
  • Each robotic arm 116 may be positionable independently of the other robotic arm.
  • the robotic arms 116 may be controlled in a single, shared coordinate space, or in separate coordinate spaces.
  • the robot 114 together with the robotic arm 116, may have, for example, one, two, three, four, five, six, seven, or more degrees of freedom. Further, the robotic arm 116 may be positioned or positionable in any pose, plane, and/or focal point. The pose includes a position and an orientation. As a result, an imaging device 112, surgical tool, or other object held by the robot 114 (or, more specifically, by the robotic arm 116) may be precisely positionable in one or more needed and specific positions and orientations.
  • reference markers may be placed on the robot 114 (including, e.g., on the robotic arm 116), the imaging device 112, or any other object in the surgical space.
  • the reference markers may be tracked by the navigation system 118, and the results of the tracking may be used by the robot 114 and/or by an operator of the system 100 or any component thereof.
  • the navigation system 118 can be used to track other components of the system (e.g., imaging device 112) and the system can operate without the use of the robot 114 (e.g., with the surgeon manually manipulating the imaging device 112 and/or one or more surgical tools, based on information and/or instructions generated by the navigation system 118, for example).
  • the end effector(s) 126 may be positioned at an end of the robotic arm 116. It will be appreciated that in other instances, the end effector 126 may be positioned on any portion of the robotic arm 116 or the robot 114. In some embodiments the end effector 126 may be fixed to the robotic arm 116. In other embodiments, the end effector 126 may be releasably secured to the robotic arm 116. The end effector 126 may be configured to, for example, support, orient, and/or operate a surgical tool, an instrument, and/or a guide and/or may facilitate changing of the surgical tool, the instrument, and/or the guide.
  • the sensor(s) 128 may be configured to determine movement of an object or a distance between an object and the sensor 128.
  • the sensor 128 may be powered by, for example, the robotic arm 116 and/or the robot 114. In other instances, the sensor 128 may be self-powered or receive power from any power source.
  • the sensor 128 may include one or more or any combination of components that are electrical, mechanical, electro-mechanical, magnetic, electromagnetic, or the like.
  • the sensor 128 may include, for example, a proximity sensor.
  • the sensor 128 may be a ToF sensor configured to measure sensor data that can be used to determine a distance between the sensor 128 and an object.
  • the ToF sensor measures a round-trip time of light emitted from a light source (which may be part of the ToF sensor) onto an object.
  • the distance may be measured or resolved for each point of an image, as will be described in more detail in Fig. 2C.
  • the ToF sensor may also be used to determine a motion of an object such as, for example, a user’s hand.
  • the senor 128 may include a memory for storing sensor data. In still other examples, the sensor 128 may output signals (e.g., sensor data) to one or more sources (e.g., the computing device 102, the navigation system 114, and/or the robot 116). In some embodiments, the sensor 128 is positioned as a standalone component. The sensor 128 may include a plurality of sensors and each sensor may be positioned at the same location or a different location as any other sensor. It will be appreciated that in some embodiments the sensor(s) 128 can be positioned at or on any component of the system 100 or environment (e.g., on any portion of the navigation system 118, the robot 114, and/or any other component at the surgical site).
  • the navigation system 118 may provide navigation for a surgeon and/or a surgical robot during an operation.
  • the navigation system 118 may be any now-known or future-developed navigation system, including, for example, the Medtronic StealthStationTM S8 surgical navigation system or any successor thereof.
  • the navigation system 118 may include one or more cameras or other sensor(s) for tracking one or more reference markers, navigated trackers, or other objects within the operating room or other room in which some or all of the system 100 is located.
  • the one or more cameras may be optical cameras, infrared cameras, or other cameras.
  • the navigation system 118 may comprise one or more electromagnetic sensors.
  • the navigation system 118 may be used to track a position and orientation (e.g., a pose) of the end effector 126, the robot 114 and/or robotic arm 116, and/or one or more surgical tools (or, more particularly, to track a pose of a navigated tracker attached, directly or indirectly, in fixed relation to the one or more of the foregoing).
  • the navigation system 118 may include a display for displaying one or more images from an external source (e.g., the computing device 102, imaging device 112, or other source) or for displaying an image and/or video stream from the one or more cameras or other sensors of the navigation system 118.
  • the system 100 can operate without the use of the navigation system 118.
  • the navigation system 118 may be configured to provide guidance to a surgeon or other user of the system 100 or a component thereof, to the robot 114, or to any other element of the system 100 regarding, for example, a pose of one or more anatomical elements, whether or not a tool is in the proper trajectory, and/or how to move a tool into the proper trajectory to carry out a surgical task according to a preoperative or other surgical plan.
  • the database 130 may store information that correlates one coordinate system to another (e.g., one or more robotic coordinate systems to a patient coordinate system and/or to a navigation coordinate system).
  • the database 130 may additionally or alternatively store, for example, one or more surgical plans (including, for example, pose information about a target and/or image information about a patient’s anatomy at and/or proximate the surgical site, for use by the robot 114, the navigation system 118, and/or a user of the computing device 102 or of the system 100); one or more images useful in connection with a surgery to be completed by or with the assistance of one or more other components of the system 100; and/or any other useful information.
  • one or more surgical plans including, for example, pose information about a target and/or image information about a patient’s anatomy at and/or proximate the surgical site, for use by the robot 114, the navigation system 118, and/or a user of the computing device 102 or of the system 100
  • the database 130 may be configured to provide any such information to the computing device 102 or to any other device of the system 100 or external to the system 100, whether directly or via the cloud 134.
  • the database 130 may be or comprise part of a hospital image storage system, such as a picture archiving and communication system (PACS), a health information system (HIS), and/or another system for collecting, storing, managing, and/or transmitting electronic medical records including image data.
  • the cloud 134 may be or represent the Internet or any other wide area network.
  • the computing device 102 may be connected to the cloud 134 via the communication interface 108, using a wired connection, a wireless connection, or both.
  • the computing device 102 may communicate with the database 130 and/or an external device (e.g., a computing device) via the cloud 134.
  • the system 100 or similar systems may be used, for example, to carry out one or more aspects of any of the methods 300, 400, and/or 500 described herein.
  • the system 100 or similar systems may also be used for other purposes.
  • FIGs. 2A and 2B a block diagram of a system 200 and an illustration of a robotic arm 216 of the system 200 according to at least one embodiment of the present disclosure are respectfully shown.
  • the system 200 includes a computing device 202 (which may be the same as or similar to the computing device 102 described above), a navigation system 218 (which may be the same as or similar to the navigation system 118 described above), and a robot 214 (which may be the same as or similar to the robot 114 described above).
  • the system 200 may be used with the system 100 in some embodiments.
  • Systems according to other embodiments of the present disclosure may comprise more or fewer components than the system 200.
  • the system 200 may not include the navigation system 218.
  • the robot 214 includes a robotic arm 216 which may be the same as or similar to the robotic arm 116.
  • the robotic arm 216 may comprise one or more members 216A connected by one or more joints 216B extending from a base 240.
  • the base 240 may be stationary or movable.
  • the robot 214 may include one robotic arm or two or more robotic arms. In embodiments where the robot 214 includes more than two robotic arms, the robotic arms may operate in a shared or common coordinate space.
  • an end effector 226, which may be the same as or similar to the end effector 126, may be disposed or supported on an end of the robotic arm 216.
  • the end effector 226 may be disposed or secured to any portion of the robotic arm 216. As previously described, the end effector 226 may be fixed to the robotic arm 216 or may be releasably secured to the robotic arm 216. At least one sensor 228, which may be the same as or similar to the sensor 128, may be positioned on or integrated with the end effector 226. The end effector 226 may be used to support, for example, a surgical tool or instrument for use on a patient 210. [0079] Turning to Fig. 2B, an illustration of the robotic arm 216, the end effector 226, and the sensor 228 is shown.
  • the sensor 228 may comprise two or more sensors 228 disposed on the end effector 226 and spaced around a circumference of the end effector 226. It will be appreciated that in other embodiments, the sensor(s) 228 may be spaced or positioned on any portion or surface of the end effector 226. By having multiple sensors 228 spaced around the circumference of the end effector 226, at least one sensor 228 is likely to detect an object near the end effector 226 in any direction. More specifically, a field of view of each sensor 228 may overlap or be within proximity of each other so as to cover a range around the end effector 226.
  • the sensors 228 may include proximity sensors such as ToF sensors 230 as previously described, which will be described in more detail below.
  • the ToF sensor 230 is configured to measure sensor data that can be used to determine a distance between the sensor 128 and an object. More specifically, the ToF sensor measures a roundtrip time of light emitted from a light source (which may be part of the ToF sensor) onto an object.
  • the light source may be, for example, and one or more light emitting diodes (LEDs) or a laser.
  • the distance may be measured or resolved for each point of the image 232.
  • the sensor data is outputted or processed and outputted as a matrix where each cell represents a zone.
  • a first set of cells 234 may indicate no object and a second set of cells 236 may indicate an object.
  • the second set of cells 236 may be color coded, include a distance from the ToF sensor 230 to the object, or a combination thereof.
  • the second set of cells 236 may be used to determine a potential collision between the end effector 226 to which the ToF sensor (or any sensor) 230 is affixed to and an object or can be used to determine a movement of the object, as will be described below.
  • Fig. 3 depicts a method 300 that may be used, for example, detect a collision between an end effector such as the end effector 126, 226 and an object.
  • the method 300 (and/or one or more steps thereof) may be carried out or otherwise performed, for example, by at least one processor.
  • the at least one processor may be the same as or similar to the processor(s) 104 of the computing device 102, 202 described above.
  • the at least one processor may be part of a robot (such as a robot 114, 214) or part of a navigation system (such as a navigation system 118, 218).
  • a processor other than any processor described herein may also be used to execute the method 300.
  • the at least one processor may perform the method 300 by executing elements stored in a memory such as the memory 106.
  • the elements stored in memory and executed by the processor may cause the processor to execute one or more steps of a function as shown in method 300.
  • One or more portions of a method 300 may be performed by the processor executing any of the contents of memory, such as sensor processing 120, collision detection 122, and/or movement detection 124.
  • the method 300 also comprises receiving sensor data (step 304).
  • the sensor data may be received or obtained from a sensor such as the sensor 128, 228 which may be any sensor capable of measuring sensor data for determining a distance between a component and an object within, for example, a working volume.
  • the component may be, for example, an end effector such as the end effector 126, 226 disposed at an end of a robotic arm such as the robotic arm 116, 216 of a robot such as the robot 114, 214.
  • the sensor data may also be generated by and/or uploaded to any other component of the system.
  • the sensor data may be indirectly received via any other component of the system or a node of a network to which the system is connected.
  • the sensor data may be received in or near real-time.
  • the sensor data may be received via a user interface such as the user interface 110 and/or a communication interface such as the communication interface 108 of a computing device such as the computing device 102, and may be stored in a memory such as the memory 106 of the computing device.
  • the method 300 also comprises processing the sensor data (step 308).
  • the sensor data received in, for example, the step 304 may be processed by a processor such as the processor 104 using a sensor processing such as the sensor processing 120.
  • the sensor processing enables the processor to process the sensor data to obtain, for example, a distance between the sensor (which correlates to an end effector to which the sensor is attached to) and an object. Such information may be used to determine if collision between the end effector and an object may occur.
  • the sensor data may be processed to determine a motion of an object (for example, motion of a user’s hand) in front of the sensor as will be described in Fig. 4.
  • the motion may be used to control, for example, a robotic arm of the robot or any component of the system.
  • the method 300 also comprises determining if a collision between the end effector and the object will occur (step 312). Determining if the collision may occur may include the processor using a collision detection such as the collision detection 122. The collision detection enables the processor to determine whether a collision may occur between the end effector and an object using the processed sensor data. In examples where the processed sensor data comprises a distance between the object and the end effector, the distance may be compared to a predetermined distance. A collision may be detected when the distance is equal to or less than the predetermined distance. [0086] The method 300 also comprises causing the robotic arm to stop movement (step 316).
  • the robotic arm may be stopped to prevent the end effector or any portion of the robotic arm from the potential collision.
  • the robotic arm may be stopped using any mechanical (e.g., brakes) or electro-mechanical means (e.g., using a controller to stop a motor of the robotic arm).
  • the processor may generate and transmit instructions to the robotic arm to cause it to stop movement.
  • the processor may generate and transmit instructions to cause the robotic arm to reverse direction from the object or to travel in a reverse direction along a trajectory.
  • the method 300 also comprises generating a notification (step 320).
  • the notification may be generated when a potential collision is detected or determined in, for example, the step 312 above.
  • the notification may be a visual notification, an audible notification, or any type of notification communicated to a user.
  • the notification may be communicated to the user via a user interface such as the user interface 110.
  • the notification may be automatically generated by the processor.
  • the notification may be automatically generated by any component of a system such as the system 100.
  • the notification may be generated when a measured distance between the sensor and the object is equal to or less than a predetermined distance.
  • the predetermined distance may be determined automatically using artificial intelligence and training data (e.g., historical cases) in some embodiments.
  • the predetermined distance may be or comprise, or be based on, surgeon input received via the user interface.
  • the predetermined distance may be determined automatically using artificial intelligence, and may thereafter be reviewed and approved (or modified) by a surgeon or other user.
  • a notification may be generated for each measured distance that meets or exceeds the corresponding predetermined distance. The notification may alert a surgeon or user of an expected collision between the end effector and an object that the surgeon or other user may wish to avoid or otherwise mitigate.
  • the steps 316 and 320 may occur simultaneously together, may occur separately, or one step may occur without the other step.
  • the robotic arm may stop movement upon detection of potential collision with an object without a notification.
  • the notification may be generated and the robotic arm may not stop movement.
  • the robotic arm may stop movement (and/or move in a reverse direction to a safe position) and the notification may be generated.
  • the present disclosure encompasses embodiments of the method 300 that comprise more or fewer steps than those described above, and/or one or more steps that are different than the steps described above.
  • Fig. 4 depicts a method 400 that may be used, for example, to determine movement of an object such as, for example, obstacles, a user’s hand (e.g., hand gestures) or object held by a user, etc. It will be appreciated that in some embodiments, the object may not be supported by, for example, a robotic arm.
  • an object such as, for example, obstacles, a user’s hand (e.g., hand gestures) or object held by a user, etc.
  • the object may not be supported by, for example, a robotic arm.
  • the method 400 (and/or one or more steps thereof) may be carried out or otherwise performed, for example, by at least one processor.
  • the at least one processor may be the same as or similar to the processor(s) 104 of the computing device 102 described above.
  • the at least one processor may be part of a robot (such as a robot 114) or part of a navigation system (such as a navigation system 118).
  • a processor other than any processor described herein may also be used to execute the method 400.
  • the at least one processor may perform the method 400 by executing elements stored in a memory such as the memory 106.
  • the elements stored in memory and executed by the processor may cause the processor to execute one or more steps of a function as shown in method 400.
  • One or more portions of a method 400 may be performed by the processor executing any of the contents of memory, such as sensor processing 120, collision detection 122, and/or movement detection 124.
  • the method 400 also comprises receiving sensor data (step 404).
  • the step 404 may be the same as or similar to the step 304 of the method 300 described above.
  • the method 400 also comprises processing the sensor data (step 408).
  • the step 408 may be the same as or similar to the step 308 of the method 400 described above.
  • the method 400 also comprises determining a movement of the object (step 412). Determining the movement of the object may use the processed sensor data of, for example, the step 408 by a processor such as the processor 104 using a movement detection such as the movement detection 124.
  • the movement detection enables the processor to determine a movement of an object within a field of vision of a sensor such as the sensor 128, 228 (e.g., in front of the sensor) using the processed sensor data.
  • the movement may be used to trigger or control a desired action of a robotic arm such as the robotic arm 116 of a robot such as the robot 114, 214. Such trigger can cause the processor to generate instructions for the robot to cause the robot to perform the desired action.
  • the movement can be used to control the robot (or any component of the system 100, 200).
  • the movement can include, for example, a user moving their hand in a direction such as up and down, left and right, or diagonally.
  • the movement can include the user moving their hand in a pattern such as, for example, moving their hand towards the sensor or away from the sensor, rotating their hand, etc.
  • the method 400 also comprises causing a robotic arm to perform the desired action (step 416) . If a movement of an object such as a user’ s hand (or an instrument held by the user) is detected in, for example, the step 412 above, the robotic arm may perform an action associated or correlated with the detected movement.
  • the processor may generate and transmit instructions to the robotic arm to cause it to perform the desired action. Alternatively or additionally, the processor may generate and transmit instructions to cause the robotic arm to perform the desired action.
  • the action may include causing a robotic arm to stop movement, causing a robotic arm to change a trajectory of movement, and/or causing a robotic arm to continue movement on a trajectory.
  • the method 400 also comprises receiving an image depicting at least one object (step 420).
  • the at least one object may be, for example, a target anatomical element and the image may be used during, for example, a surgical procedure.
  • the image may be registered to the patient coordinate space, the navigation coordinate space, and/or the robotic coordinate space.
  • the image may be received via a user interface such as the user interface 110 and/or a communication interface such as the communication interface 108 of a computing device such as the computing device 102, and may be stored in a memory such as the memory 106 of the computing device.
  • the image may also be received from an external database or image repository (e.g., a hospital image storage system, such as a picture archiving and communication system (PACS), a health information system (HIS), and/or another system for collecting, storing, managing, and/or transmitting electronic medical records including image data), and/or via the Internet or another network.
  • a hospital image storage system such as a picture archiving and communication system (PACS), a health information system (HIS), and/or another system for collecting, storing, managing, and/or transmitting electronic medical records including image data
  • the image may be received or obtained from an imaging device such as the imaging device 112, which may be any imaging device such as an MRI scanner, a CT scanner, any other X-ray based imaging device, or an ultrasound imaging device.
  • the image may also be generated by and/or uploaded to any other component of a system such as the system 100.
  • the image may be indirectly received via any other component of the system or a node of a network to which the system is connected.
  • the image may be a 2D image or a 3D image or a set of 2D and/or 3D images.
  • the image may depict a patient’s anatomy or portion thereof.
  • the image may be captured preoperatively (e.g., before surgery) and may be stored in a system and/or one or more components thereof (e.g., a database 130).
  • the stored image may then be received (e.g., by a processor 104), as described above, preoperatively (e.g., before the surgery) and/or intraoperatively (e.g., during surgery).
  • the image may depict multiple anatomical elements associated with the patient anatomy, including incidental anatomical elements (e.g., ribs or other anatomical objects on which a surgery or surgical procedure will not be performed) in addition to target anatomical elements (e.g., vertebrae or other anatomical objects on which a surgery or surgical procedure is to be performed).
  • incidental anatomical elements e.g., ribs or other anatomical objects on which a surgery or surgical procedure will not be performed
  • target anatomical elements e.g., vertebrae or other anatomical objects on which a surgery or surgical procedure is to be performed.
  • the method 400 also comprises updating the image (step 424).
  • the image may be updated based on detecting a movement of an object in, for example, the step 412.
  • movement of an object such as, for example, a target anatomical element depicted in the image may cause the system to automatically update the image received in, for example, the step 420.
  • the image is updated so as to depict an updated or current position and/or orientation of the object. It will be appreciated that in some embodiments, the method 400 may not include the steps 420 and/or 424.
  • the method 400 also comprises generating a notification (step 428).
  • the step 428 may be the same as or similar to the step 320 of the method 300 described above.
  • the present disclosure encompasses embodiments of the method 400 that comprise more or fewer steps than those described above, and/or one or more steps that are different than the steps described above.
  • Fig. 5 depicts a method 500 that may be used, for example, to construct a work volume.
  • the method 500 (and/or one or more steps thereof) may be carried out or otherwise performed, for example, by at least one processor.
  • the at least one processor may be the same as or similar to the processor(s) 104 of the computing device 102 described above.
  • the at least one processor may be part of a robot (such as a robot 114) or part of a navigation system (such as a navigation system 118).
  • a processor other than any processor described herein may also be used to execute the method 500.
  • the at least one processor may perform the method 500 by executing elements stored in a memory such as the memory 106.
  • the elements stored in memory and executed by the processor may cause the processor to execute one or more steps of a function as shown in method 500.
  • One or more portions of a method 500 may be performed by the processor executing any of the contents of memory, such as sensor processing 120, collision detection 122, and/or movement detection 124.
  • the method 500 comprises positioning a sensor (step 504).
  • the sensor may include one or more sensors.
  • the sensor(s) may be the same as or similar to the sensor 128 and may be disposed on or integrated with an end effector such as the end effector 126.
  • the end effector may be supported, oriented, and/or operated by a robotic arm such as the robotic arm 116 of a robot such as the robot 114.
  • the sensor(s) may be positioned on or integrated with the robotic arm and/or the end effector.
  • the method 500 also comprises receiving sensor data (step 508).
  • the step 508 may be the same as or similar to the step 304 of the method 300 described.
  • the sensor data may be used to determine at least a portion of a work volume and to identify one or more objects and/or components in the work volume.
  • the method 500 also comprises repositioning the sensor (step 512).
  • the sensor may be repositioned by the robotic arm to obtain sensor data at another pose or location such that other objects and/or components can be identified in the work volume.
  • the method 500 also comprises receiving additional sensor data (step 516).
  • the step 516 may be the same as or similar to the step 304 of the method 400 described above.
  • the steps 504-516 may be repeated until sensor data of a desired portion or an entirety of the working volume is obtained.
  • the sensor may be repositioned multiple times at a plurality of different poses and sensor data may be obtained at each pose until the working volume or a sufficient portion of the working volume is captured by the sensor data.
  • the method 500 also comprises processing the sensor data (step 520).
  • the step 520 may be the same as or similar to the step 308 of the method 400 described above.
  • the method 500 comprises constructing a working volume (step 524).
  • the working volume may be used in detecting and/or preventing collision between components of, for example, a system such as the system 100, 200 and/or objects or other components within the working volume.
  • the working volume may be constructed by, for example, a processor such as the processor 104 using the sensor data obtained in, for example, the steps 508 and 516 (and any subsequent iterations of the steps 508 and/or 516) and processed in the step 520.
  • the sensor data may comprise, for example, sensor data data from a ToF sensor, image data obtained from an imaging device such as the imaging device 112 or an imaging device of a navigation system such as the navigation system 118, 218.
  • the sensor data may depict, for example, a surgical or operating space and any component within the room including any component of the system.
  • a subset volume of the surgical or operating space (or an entirety of the surgical and/or operating space) can be defined as the working volume.
  • the one or more components and/or objects may be defined and tracked within the working volume by, for example, the navigation system.
  • the method 500 also comprises inputting the work volume into the system (step 528).
  • the work volume may be inputted into the system or any component of the system such as, for example, a computing device such as the computing device 102 and/or the navigation system.
  • the system may use the working volume to detect and/or identify component(s) and/or object(s) in the working volume for use with, for example, collision detection or for identifying target component(s) and/or object(s).
  • the present disclosure encompasses embodiments of the method 500 that comprise more or fewer steps than those described above, and/or one or more steps that are different than the steps described above.
  • the present disclosure encompasses methods with fewer than all of the steps identified in Figs. 3, 4, and 5 (and the corresponding description of the methods 300, 400, and 500), as well as methods that include additional steps beyond those identified in Figs. 3, 4, and 5 (and the corresponding description of the methods 300, 400, and 500).
  • the present disclosure also encompasses methods that comprise one or more steps from one method described herein, and one or more steps from another method described herein. Any correlation described herein may be or comprise a registration or any other correlation.
  • Example 1 A system for detecting collision in real-time comprising: a processor; and a memory storing data for processing by the processor, the data, when processed, causes the processor to: receive sensor data corresponding to a distance between an end effector and an object; process the sensor data to determine the distance; and determine if a collision will occur between the end effector and the object based on the distance.
  • Example 2 The system of example 1, wherein determining the collision includes comparing the distance and a predetermined threshold distance, wherein the collision is determined to occur when the distance is less than the predetermined threshold distance.
  • Example 3 The system of example 1, wherein the memory stores further data for processing by the processor that, when processed, causes the processor to: cause a robotic arm supporting and orienting the end effector to stop movement when the collision is determined to occur between the end effector and the object.
  • Example 4 The system of example 1, wherein processing the sensor data to determine the distance includes measuring a round-trip time of a light signal emitted by a light source onto the object.
  • Example 5 The system of example 4, further comprising at least one time-of-flight sensor configured to measure the round-trip time of the light.
  • Example 7 The system of example 6, further comprising: a robotic arm; and an end effector releasably secured to the robotic arm, wherein the at least one time-of- flight sensor is positioned on the end effector.
  • Example 8 The system of example 7, wherein the at least one time-of-flight sensor comprises at least three time-of-flight sensors positioned on and spaced around a circumference of the end effector.
  • Example 9 A system for detecting collision in real-time comprising: an end effector; at least one sensor positioned on the end effector and configured to yield sensor data corresponding to a distance between the end effector and an object; a robotic arm configured to support and orient the end effector; a processor; and a memory storing data for processing by the processor, the data, when processed, causes the processor to: receive the sensor data corresponding to the distance between the end effector and the object; process the sensor data to determine the distance; and determine if a collision will occur between the end effector and the object based on the distance.
  • Example 12 The system of example 9, wherein processing the sensor data to determine the distance includes measuring a round-trip time of a light signal emitted by a light source onto the object.
  • Example 14 The system of example 9, wherein the at least one sensor comprises at least one time-of-flight sensor.
  • Example 15 The system of example 9, wherein the robotic arm provides power to the at least one sensor.
  • Example 16 A system for detecting motion in real-time comprising: a processor; and a memory storing data for processing by the processor, the data, when processed, causes the processor to: receive sensor data corresponding a plurality of distances between an object and an end effector; process the sensor data to yield the plurality of distances; and determine a movement of an object based on the plurality of distances.
  • Example 17 The system of example 16, wherein the movement corresponds to a desired action of a robotic arm, and wherein the memory stores further data for processing by the processor that, when processed, causes the processor to: cause the robotic arm to perform the desired action.
  • Example 18 The system of example 17, wherein the object is a user’s hand and the motion is a motion of the user’s hand.

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  • Life Sciences & Earth Sciences (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Biomedical Technology (AREA)
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Abstract

L'invention concerne des systèmes et des procédés de détection de collision en temps réel et/ou de détection de mouvement d'un objet. Des données de capteur correspondant à une distance entre un effecteur terminal et un objet peuvent être reçues. Les données de capteur peuvent être traitées pour déterminer la distance. Il est possible de déterminer si une collision se produira entre l'effecteur terminal et l'objet sur la base de la distance.
PCT/IL2024/050595 2023-06-20 2024-06-18 Systèmes de détection en temps réel de collision d'objet et/ou de mouvement d'objet Pending WO2024261752A1 (fr)

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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102017202004A1 (de) * 2017-02-08 2018-08-09 Thyssenkrupp Ag Vorrichtung zur Absicherung eines maschinell gesteuerten Handhabungsgeräts und Verfahren
US20200180162A1 (en) * 2017-08-28 2020-06-11 Fogale Nanotech Multi-distance detection device for a robot, and robot equipped with such (a) device(s)
US20220130147A1 (en) * 2019-02-22 2022-04-28 Fogale Nanotech Method and device for monitoring the environment of a robot

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102017202004A1 (de) * 2017-02-08 2018-08-09 Thyssenkrupp Ag Vorrichtung zur Absicherung eines maschinell gesteuerten Handhabungsgeräts und Verfahren
US20200180162A1 (en) * 2017-08-28 2020-06-11 Fogale Nanotech Multi-distance detection device for a robot, and robot equipped with such (a) device(s)
US20220130147A1 (en) * 2019-02-22 2022-04-28 Fogale Nanotech Method and device for monitoring the environment of a robot

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