Abstract: A method of operation of an unmanned autonomous vehicle includes autonomously maneuvering the unmanned autonomous vehicle about a facility, collecting gas concentration data using one or more sensors carried on or integrated with the unmanned autonomous vehicle, and monitoring the collected gas concentration data to detect a high concentration due to a gas leak. The method further includes, if a high concentration is detected, estimating a location of the gas leak and maneuvering the unmanned autonomous vehicle toward the estimated location of the gas leak.

Abstract: A system for inspecting one or more gas plumes in a facility, including a processor and a memory having instructions executable by the processor, wherein the instructions are configured to obtain one or more images from one or more cameras, wherein the one or more images include a gas plume. The instructions are further configured to focus the one or more images on an area of interest with an object detection model to generate a boundary box around the gas plume, and focus the one or more images on the area of interest with a segmentation model to generate a mask around the gas plume based on the boundary box. The instructions are further configured to obtain one or more masked images from the segmentation model, and analyze the one or more masked images to obtain an image analysis of one or more parameters of the gas plume.

Abstract: Methods, systems, and computer program products are provided for defect detection in industrial inspections. In one embodiment, captured images are ingested from a robotic platform equipped with imaging sensors. The ingested images are analyzed using an image analysis pipeline and a plurality of labeled images to generate a plurality of segmentation masks. In an environment shown in at least a subset of the plurality of ingested images, a plurality of environmental conditions are simulated to create an augmented plurality of labeled images. A defect analysis model is then trained with the augmented plurality of labeled images and the plurality of segmentation masks.

Abstract: The disclosed techniques are directed to using unmanned autonomous vehicles to perform inspections of, for example, gas sensors or other assets located within a processing facility. For example, the unmanned autonomous vehicles may autonomously navigate through a processing facility to perform the inspections. In addition, one or more properties of data captured by the unmanned autonomous vehicles may be controlled based on real-time conditions to optimize the inspection of the assets of the processing facility. Furthermore, the unmanned autonomous vehicles may be configured to perform calibration of the assets when anomalies readings are collected. In addition, the unmanned autonomous vehicles may be self-learning autonomous devices configured to learning from data collected during previous inspections of assets.

Abstract: A method for producing multi-dimensional spectral models of a production system. Input data is received from a plurality of spectral markers that are disposed in the production system, the spectral markers providing reference points for multiple readings taken by mobile or stationary sensors to be matched. Each of the spectral markers within the three-dimensional space are read to determine a unique spectral signature corresponding to each one of the spectral markers, the spectral signature having a pattern of spectral values that is unique to each one of the corresponding spectral markers. The determined spectral signatures of each of the plurality of spectral markers are associated with a unique identification which are then provided to a robot within the three-dimensional space. The multi-dimensional spectral model is then reconstructed using the assigned locations. The spectral markers also double as sensors and can transmit readings along with their unique spectral signatures.

Abstract: Methods, computing devices, and computer-readable storage media are provided. A computing device receives an image of a respective area of a facility, detects signage within the image, and maps the detected signage to the respective area represented in a map to produce a map of signs that is stored in a spatial data structure. Natural language processing with references to well-known symbols semantically understands information included on the signage, which is then stored. When one or more anomalies are determined to exist in the signage, information regarding the one or more anomalies is presented. An impact area of the signage is determined, if any. The received image is compared with a previously received image of the respective area to determine a difference. The computing device presents an indication of the difference.

Abstract: Approaches to characterizing methane emission at an industrial facility using an unmanned air vehicle (UAV) equipped with navigational instruments and methane gas sensors. A scan area associated with the industrial facility is defined. The scan area can be logically partitioned into a plurality of pixels based on information provided by an intelligent agent. A flight plan for a flight of the UAV can be created to cover the pixels of the scan area. The UAV can execute the flight plan and collect time-series navigational data and time-series sensor data as the UAV executes the flight plan. The flight path of the UAV can be dynamically adjusted during the flight by an agent to ensure that the flight path of the UAV covers the scan area. A two-dimensional (2D) image of methane concentration over the scan area can be constructed from the time-series navigational data and the time-series sensor data.

Abstract: Safety control algorithms for surgical devices, including safety control algorithms for surgical stapler devices, and associated systems, devices, and methods. In one embodiment, a method of operating a surgical stapler comprises tracking a position of a distal end of a blade assembly of a reloadable cartridge assembly of the surgical stapler as the distal end of the blade assembly is moved, via actuation of a motor and a corresponding drivetrain, along a stroke of the blade assembly. The method can further comprise adjusting a current limit for current levels in the drivetrain based on the position of the distal end of the blade assembly along the stroke.

Abstract: A system includes one or more sensors to obtain three-dimensional (3D) data in a facility and a processor communicatively coupled to the one or more sensors. The processor is configured to execute instruction to generate a 3D model of the facility based on a first 3D data received from the one or more sensors at a first time and receive a second 3D data from the one or more sensors at a second time after the first time. The processor is further configured to execute instructions to determine an event based on the second 3D data and the 3D model and output a notification via an electronic device based on the event.

Abstract: A system for inspecting one or more gas plumes in a facility, including a processor and a memory having instructions executable by the processor, wherein the instructions are configured to obtain one or more images from one or more cameras, wherein the one or more images include a gas plume. The instructions are further configured to focus the one or more images on an area of interest with an object detection model to generate a boundary box around the gas plume, and focus the one or more images on the area of interest with a segmentation model to generate a mask around the gas plume based on the boundary box. The instructions are further configured to obtain one or more masked images from the segmentation model, and analyze the one or more masked images to obtain an image analysis of one or more parameters of the gas plume.

Abstract: This disclosure generally relates to systems and methods for inspecting one or more equipment of a facility. The system may be configured to obtain one or more images including an equipment from one or more cameras, wherein the one or more cameras are fixed within the facility or are attached to an unmanned autonomous vehicle; focus the one or more images on an area of interest with an object detection model to generate a boundary box around the equipment; focus the one or more images on the area of interest with a segmentation model to generate a mask around the equipment based on the boundary box; obtain one or more masked images from the segmentation model; and analyze the one or more masked images to obtain an image analysis of one or more parameters of the equipment.

Abstract: A method of operation of an unmanned autonomous vehicle includes autonomously maneuvering the unmanned autonomous vehicle about a facility, collecting gas concentration data using one or more sensors carried on or integrated with the unmanned autonomous vehicle, and monitoring the collected gas concentration data to detect a high concentration due to a gas leak. The method further includes, if a high concentration is detected, estimating a location of the gas leak and maneuvering the unmanned autonomous vehicle toward the estimated location of the gas leak.

Abstract: Safety control algorithms for surgical devices, including safety control algorithms for surgical stapler devices, and associated systems, devices, and methods. In one embodiment, a method of operating a surgical stapler comprises tracking a position of a distal end of a blade assembly of a reloadable cartridge assembly of the surgical stapler as the distal end of the blade assembly is moved, via actuation of a motor and a corresponding drivetrain, along a stroke of the blade assembly. The method can further comprise adjusting a current limit for current levels in the drivetrain based on the position of the distal end of the blade assembly along the stroke.

Abstract: A hydraulic motor includes a top sub for connecting to a tubing string, a main barrel and a bottom bit sub; an impeller housing connected to the top sub and disposed within the main barrel, the impeller housing defining an annular intake plenum and at least one inlet port for directing fluid in a direction substantially transverse to a longitudinal axis of the motor, and at least one exhaust port for exhausting fluid from the impeller housing into an annular exhaust plenum; an impeller disposed within the impeller housing, connected to an impeller shaft, the impeller comprising a hub bearing a plurality of outwardly extending vanes, wherein fluid directed onto the impeller through the at least one inlet port causes rotation of the impeller; and a bottom bearing assembly for supporting an adapter shaft disposed within the bit sub.

Abstract: Methods, systems, and computer program products are provided for defect detection in industrial inspections. In one embodiment, captured images are ingested from a robotic platform equipped with imaging sensors. The ingested images are analyzed using an image analysis pipeline and a plurality of labeled images. to generate a plurality of segmentation masks. In an environment shown in at least a subset of the plurality of ingested images, a plurality of environmental conditions are simulated to create an augmented plurality of labeled images. A defect analysis model is then trained with the augmented plurality of labeled images and the plurality of segmentation masks.

Abstract: Approaches to characterizing methane emission at an industrial facility using an unmanned air vehicle (UAV) equipped with navigational instruments and methane gas sensors. A scan area associated with the industrial facility is defined. The scan area can be logically partitioned into a plurality of pixels based on information provided by an intelligent agent. A flight plan for a flight of the UAV can be created to cover the pixels of the scan area. The UAV can execute the flight plan and collect time-series navigational data and time-series sensor data as the UAV executes the flight plan. The flight path of the UAV can be dynamically adjusted during the flight by an agent to ensure that the flight path of the UAV covers the scan area. A two-dimensional (2D) image of methane concentration over the scan area can be constructed from the time-series navigational data and the time-series sensor data.