Abstract: An approach relates to manipulation of tools or instruments in the performance of a task by a robot. In accordance with this approach, sensor data is acquired and processed to identify a subset of instruments initially susceptible to manipulation. The instruments are then manipulated in the performance of the task based on the processed sensor data.

Abstract: A system for performing industrial tasks includes a robot and a computing device. The robot includes one or more sensors that collect data corresponding to the robot and an environment surrounding the robot. The computing device includes a user interface, a processor, and a memory. The memory includes instructions that, when executed by the processor, cause the processor to receive the collected data from the robot, generate a virtual recreation of the robot and the environment surrounding the robot, receive inputs from a human operator controlling the robot to demonstrate an industrial task. The system is configured to learn how to perform the industrial task based on the human operator's demonstration of the task, and perform, via the robot, the industrial task autonomously or semi-autonomously.

Abstract: A robotic body scanning system includes a robotic manipulator, a force sensor, a probe, a surface sensing system, and a computing device. The probe is attached to the robotic manipulator and configured to scan the portion of the human body. The surface sensing system is configured to detect a surface of the portion of the human body and generate data representing the portion of the human body. The computing device is configured to receive data representing the portion of the human body from said surface sensing system and generate two or three-dimensional representations of the portion of the human body. The computing device includes a trajectory generation module configured to generate an adapted trajectory for the probe to follow based on the two or three-dimensional representations. The robotic manipulator is configured to move the probe along the adapted trajectory along the portion of the human body.

Abstract: A system includes a robotic vehicle having a propulsion and a manipulator configured to perform designated tasks. The system also including a local controller disposed onboard the robotic vehicle and configured to receive input signals from an off-board controller. Responsive to receiving an input signal for moving in an autonomous mode, the local controller is configured to move the robotic vehicle toward one of the different final destinations by autonomously and iteratively determining a series of waypoints until the robotic vehicle has reached the one final destination. For each iteration, the local controller is configured to determine a next waypoint between a current location of the robotic vehicle and the final destination, determine movement limitations of the robotic vehicle, and generate control signals in accordance with the movement limitations.

Abstract: The example embodiments are directed to a system and method for controlling and commanding an unmanned robot using natural interfaces. In one example, the method includes receiving a plurality of sensory inputs from a user via one or more natural interfaces, wherein each sensory input is associated with an intention of the user for an unmanned robot to perform a task, processing each of the plurality of sensory inputs using a plurality of channels of processing to produce a first recognition result and a second recognition result, combining the first recognition result and the second recognition result to determine a recognized command, and generating a task plan assignable to the unmanned robot based on the recognized command and predefined control primitives.

Abstract: The present approach employs a context-aware simulation platform to facilitate control of a robot remote from an operator. Such a platform may use the prior domain/task knowledge along with the sensory feedback from the remote robot to infer context and may use inferred context to dynamically change one or both of simulation parameters and a robot-environment-task state being simulated. In some implementations, the simulator instances make forward predictions of their state based on task and robot constraints. In accordance with this approach, an operator may therefore issue a general command or instruction to a robot and based on this generalized guidance, the actions taken by the robot may be simulated, and the corresponding results visually presented to the operator prior to evaluate prior to the action being taken.

Abstract: A system including at least three robots. Each robot including a proximity sensor unit and an imaging device. At least one robot including a processor to perform a method of estimating a pose of a human, the method including obtaining a first pose estimate for the human, the first pose estimate based on proximity sensor information, obtaining a second pose estimate for the human, the second pose estimate based on imaging device information, and generating a refined pose estimate for the human by fusing the first pose estimate with the second pose estimate, where the first pose information provides predictive values and the second pose estimate provides correction values. The method including applying a deep neural network (DNN) human model, and applying a DNN human pose model. A method to generate a refined pose estimation for a human and a non-transitory computer readable medium are also disclosed.

Abstract: An asset inspection system includes a robot and a server. The robot collects inspection data corresponding to an asset. The server, includes a user interface, a processor, and a memory. The memory includes instructions that, when executed by the processor, cause the processor to receive the inspection data from the robot, display the inspection data via the user-interface, receive feedback on the inspection data via the user interface, generate a human-assisted inspection based on the received feedback, analyze the inspection data via a trained model, generate an automated inspection based on the analysis by the trained model, combine the automated inspection and the human-assisted inspection to generate an inspection report, and transmit the inspection report for review.

Abstract: A method of robot autonomous navigation includes capturing an image of the environment, segmenting the captured image to identify one or more foreground objects and one or more background objects, determining a match between one or more of the foreground objects to one or more predefined image files, estimating an object pose for the one or more foreground objects by implementing an iterative estimation loop, determining a robot pose estimate by applying a robot-centric environmental model to the object pose estimate by implementing an iterative refinement loop, associating semantic labels to the matched foreground object, compiling a semantic map containing the semantic labels and segmented object image pose, and providing localization information to the robot based on the semantic map and the robot pose estimate. A system and a non-transitory computer-readable medium are also disclosed.

Abstract: An asset inspection system includes a robot and a server. The server receives a request for data from the robot, wherein the requested data comprises an algorithm, locates the requested data in a database stored on the server, encrypts the requested data, and transmits the requested data to the robot. The robot is configured to collect inspection data corresponding to an asset based at least in part on the requested data and transmit the collected inspection data to the server.

Abstract: The present approach relates to integrated planning of robot navigation and manipulator motion in performing tasks. In particular, as discussed herein sensor information, such as visual information, may be used for the robot to locate a target object when it arrives at a working location. Adjustments may be made based on this information that may include moving the robot and planning a manipulator arm motion.

Abstract: Modifying a motion plan for an autonomously-operated inspection platform (AIP) includes obtaining sensor data for an industrial asset area of interest, analyzing the obtained sensor data during execution of an initial motion plan to determine if modification of the initial motion plan is required. If modification is required then performing a pose estimation on a first group of potential targets and a second group of potential targets, optimizing the results of the pose estimation to determine a modification to the initial motion plan, performing reactive planning to the initial motion plan to include the modification, the reactive planning providing a modified motion plan that includes a series of waypoints defining a modified path, and autonomously controlling motion of the AIP along the modified path. The analysis, pose estimation, optimization, and reactive planning occurring during movement of the AIP along a motion plan. A system and computer-readable medium are disclosed.

Abstract: A sensor calibration system includes a plurality of sensors and a user interface configured to receive user-provided locations of at least two sensors of the plurality of sensors. The sensor calibration system further includes a motorized apparatus including a drive system, at least one detector, and a localization system. The sensor calibration system further includes a controller communicatively coupled to the user interface and the motorized apparatus. The controller is configured to determine a whether each of at least two sensors is a key sensor based on the user-provided locations. The controller is configured to determine a path for the motorized apparatus based on the user-provided locations. The controller is further configured to determine a position of each sensor based on the location of the motorized apparatus when each sensor is detected by the at least one detector.

Abstract: Methods and systems for controlling a snake-arm robot. In an embodiment, a server computer receives real-time image data associated with at least one of an operating environment and a location of a workpiece from an optical sensor mounted on a robot head of a snake-arm robot, and receives, input data describing a desired pose of the robot head from a user device. The server computer then computes a desired velocity of the robot head using an image Jacobian, translates the desired velocity of the robot head into incremental displacement data and rotation data within a control cycle, computes a position of each of a plurality of links comprising a snake-arm of the snake-arm robot to follow motion of the robot head, computes a current position of each of the plurality of links utilizing a forward dynamics model, and computes force and torque data required to move at least one of a plurality of joints connecting the links to move the snake-arm robot to the desired pose.

Abstract: A system for polishing a surface of a component includes a motorized apparatus including a body, a drive system coupled to the body, and an arm including a proximal end coupled to the body and a distal end opposite the proximal end. The motorized apparatus further includes a tool coupled to the distal end of the arm. The tool is configured to polish the surface of the component. The motorized apparatus also includes an actuator coupled to the arm. The system also includes a controller configured to position the tool relative to the component by positioning at least one of the body and the arm relative to the component to reach the target area on the component while maintaining a distance between the tool and the body that is less than a threshold distance. The threshold distance is less than a full reach of the arm and is determined to prevent vibrations of the tool and the arm from exceeding a predefined level.

Abstract: A system for detecting the presence of an anomaly within a component includes a motorized apparatus configured to move around the component. The system also includes an excitation device and a camera mounted to the motorized apparatus. The excitation device is configured to emit an excitation signal toward the component to cause the anomaly within the component to generate a detectable reactionary thermal signal in response to the excitation signal. The camera is configured to capture thermal images of the component. The thermal images include the detectable reactionary thermal signal and indicate the presence of the anomaly within the component. The system further includes a controller communicatively coupled to the excitation device and the camera. The controller is configured to receive and analyze the thermal images to detect the presence of the anomaly within the component.

Abstract: System and methods may evaluate and/or improve target aiming accuracy for a sensor of an Unmanned Aerial Vehicle (“UAV”). According to some embodiments, a position and orientation measuring unit may measure a position and orientation associated with the sensor. A pose estimation platform may execute a first order calculation using the measured position and orientation as the actual position and orientation to create a first order model. A geometry evaluation platform may receive planned sensor position and orientation from a targeting goal data store and calculate a standard deviation for a target aiming error utilizing: (i) location and geometry information associated with the industrial asset, (ii) a known relationship between the sensor and a center-of-gravity of the UAV, (iii) the first order model as a transfer function, and (iv) an assumption that the position and orientation of the sensor have Gaussian-distributed noises with zero mean and a pre-determined standard deviation.

Abstract: Systems and methods are provided for an automation system. The systems and methods calculate a motion trajectory of a manipulator and an end-effector. The end-effector is configured to grasp a target object. The motion trajectory defines successive positions of the manipulator and the end-effector along a plurality of via-points toward the target object. The systems and methods further acquire force/torque (F/T) data from an F/T sensor associated with the end-effector, and adjusts the motion trajectory based on the F/T data.

Abstract: The example embodiments are directed to a system and method for cold start deployment of an ML model for an edge system associated with an industrial asset. In one example, the method may include one or more of storing an incremental ML model comprising a plurality increments which sequentially increase a complexity of a predictive function of the incremental ML model, receiving performance information from an edge system that processes incoming data of an industrial asset using a current increment of the incremental ML model, dynamically determining to modify the current increment of the incremental ML model used by the edge system with a next increment of the incremental ML model having increased complexity based on the received performance information, and transmitting the next increment of the incremental ML model to the edge system.

Abstract: The example embodiments are directed to a system and method for cold start deployment of an ML model for an edge system associated with an industrial asset. In one example, the method may include one or more of storing machine learning (ML) models and local edge information where the ML models are already deployed, receiving, via a network, meta information of an edge system associated with an industrial asset in response to a cold start of the edge system, dynamically determining an optimum ML model for the cold start of the edge system from among the already deployed ML models based on the received meta information and the local edge information, and transmitting the determined optimum ML model to the edge system.