Architectures and techniques are for significantly improving operation of unmanned underwater vehicles (UUVs). For example, a UUV can have modular interfaces that can be configured to interchangeably connect different types of sensors to facilitate different UUV applications, to interchangeably connect different types of clamping devices that can be configured for different types or sizes of underwater pipe, and can comprise a mother ship interface that can be used to exchange information and supply a fluid for the clamping device. The UUV can comprise a PID controller that can be used for autonomous navigation to a target location of the underwater pipe and autonomous coupling, via the clamping device, to the underwater pipe.

An omnidirectional surface vehicle (OSV) for use in a water-borne environment is described. The OSV can comprise a platform of interconnected buoyant compartments having incorporated position thrusters to navigate the OSV. The thrusters are connected to a series of ducts and ports to enable navigation of the OSV by fluid intake/ejection. An onboard camera system can be configured to capture imagery of a subsurface structure such as a net in an aquaculture facility. AI and ML technologies can be applied to enable detection of a potential anomaly/hole in the net structure. Location of the anomaly can be determined based on any of a current position/location of the OSV, a current field of view of the camera, a position/focal length of a lens in the camera, etc. Control/operation of the OSV can be performed autonomously by an onboard computer/controller. The OSV can be further configured to communicate with a remote device.

Semantic three-dimensional scan is performed for the multi-phase inspection of a structure using an unmanned aerial vehicle (UAV). The multi-phase inspection includes a first inspection phase and a second inspection phase. A UAV performs the first inspection phase of the structure to determine a semantic understanding of components associated with the structure and pose information of the components. Based on the semantic understanding of the components and the pose information, a flight path indicating capture points and camera poses associated with the capture points is determined. The UAV then performs the second inspection phase of the structure according to the flight path, in which all or some of the components are inspected.

An unmanned aerial vehicle (UAV) performs operations to semantically understand components of a structure under inspection. During an exploration inspection of the structure, a camera of the UAV captures images of the structure. Components of the structure are determined based on the images and a taxonomy associated with the structure, for example, using a computer vision process and a machine learning model. A visual representation of the components (e.g., a semantic scene graph, such as a three-dimensional graphical representation of a hierarchical text representation) is generated and output to a user device in communication with the UAV to enable selections, via a graphical user interface output for display at the user device, of ones of the components for further inspection using the UAV.

A method for controlling an unmanned aerial vehicle using a control apparatus, comprises: executing a navigation process by: obtaining a live video moving image from a navigation camera device of the UAV; and generating a navigation display interface for display on a display device of the control apparatus, the navigation display interface comprising a plurality of navigation augmented reality display elements related to a determined waypoint superimposed over the live video moving image; and when the UAV reaches the determined waypoint, executing a precision landing process by: generating a precision landing display interface for display on the display device, the precision landing display interface comprising a plurality of precision landing AR display elements related to a landing target associated with the determined waypoint superimposed over the live video moving image obtained from a precision landing camera device of the UAV.

This disclosure relates to embodiments of a radio frequency identification (RFID) system. An embodiment includes a reader/recorder (active element) in autonomous robotic underwater vehicles (AUVs) and an identifying TAG (RFID) (passive element) with memory for recording an ID (identification/code). The ID is read by the reader/recorder and immediately updates its position and identification of the equipment, system, or underwater pipeline for inspection by AUVs. TAGs are placed on equipment, pipelines, and existing underwater materials in the oil field area by the AUVs themselves, or onshore. For positioning and inspections, the codes of these RFID TAGs are linked to their location in the underwater oil field at its facility.

Robotic systems and associated methods are described herein. The robotic system may collect measurements from various sensors corresponding to motion of the robotic system, the surrounding environment of the robotic system, or both. The robotic system may generate measurement data based on the collected measurements. Measurements from a particular sensor may be processed in conjunction with different sensors of the robotic system, which may facilitate more accurate or more useful measurement data. The systems and methods of the present disclosure enable the detection, labeling, and locating of features in real time or near real time using the robotic system with little or no reliance on human interaction to detect and map the features. The disclosure provides enhanced accuracy and efficiency as it enhances the functionality and reduces the reliance on human detection of features.

A robot sized and shaped for reception in a pipe includes a chassis configured for movement of the robot on the pipe, a tool supported by the chassis for movement relative to the chassis, a plurality of sensors including an inertial measurement unit (IMU), an encoder and a light detection and ranging sensor (LIDAR) associated with the robot, and a sensor fusion system operable to combine readings from the IMU, the encoder and LIDAR to determine a position of the robot within the pipe.

A method includes creating a digital map of an environment, loading the digital map on a moveable robot, wherein the robot is placed in the environment, generating a trajectory path plan from a current position to a desired position using the digital map, the trajectory path having a plurality of waypoints, causing the robot to traverse within the environment in accordance with the trajectory path plan, collecting sensor data in real time while the robot is traversing within the environment, detecting, based on the collecting step, at each waypoint, whether an anomaly is present between an existing waypoint and a subsequent waypoint, and performing a corrective action of the robot based on the detecting step.

A method for assessing, by a robot, a feature of an environment based on data from one of the plurality of sensors, wherein the robot is positioned in the environment includes comparing, by the robot, the feature of the environment to an expected feature of the environment; creating, by the robot, a feedback loop based on the comparing step; and adjusting an operational condition of the robot based on the feedback loop.