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: The present approach relates to navigation (e.g., route planning and movement) of robots in an indoor environment shared with humans. The present approach includes detecting human activity over time, including but not limited to human motion; modeling human activities using the historical human activity, and using the modeled human activity to plan robotic motion or movement.

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 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: 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: In accordance with certain implementations of the present approach, a reduced, element-by-element, data set is transmitted between a robot having a sensor suite and a control system remote from the robot that is configured to display a representation of the environment local to the robot. Such a scheme may be useful in allowing a human operator remote from the robot to perform an inspection using the robot while the robot is on-site with an asset and the operator is off-site. In accordance with the present approach, an accurate representation of the environment in which the robot is situated is provided for the operator to interact with.

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: A locomotive control system may include first and second robotic machines and a task manager. The first and second robotic machines have respective first and second sets of capabilities for interacting with a surrounding environment. The task manager selects the first and second robotic machines from a group to perform a task based on the first and second sets of capabilities of the robotic machines. The task involves manipulating and/or inspecting a target object of a vehicle. The task manager assigns a first sequence of sub-tasks to be performed by the first robotic machine and a second sequence of sub-tasks to be performed by the second robotic machine. The first and second robotic machines are configured to coordinate performance of the first sequence of sub-tasks by the first robotic machine with performance of the second sequence of sub-tasks by the second robotic machine to accomplish the task.

Abstract: The present approach relates to streaming data derived from inspection data acquired using one or more robots performing inspections of an asset or assets. Such inspections may be fully or partially automated, such as being controlled by one or more computer-based routines, and may be planned or dynamically altered in response to inputs or requirements associated with an end-user of the inspection data, such as a subscriber to the data in a publication/subscription distribution scheme. Thus, an inspection may be planned or altered based on the data needs or subscription levels of the user or customers.

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: 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: A robotic system includes a controller configured to obtain image data from one or more optical sensors and to determine one or more of a location and/or pose of a vehicle component based on the image data. The controller also is configured to determine a model of an external environment of the robotic system based on the image data and to determine tasks to be performed by components of the robotic system to perform maintenance on the vehicle component. The controller also is configured to assign the tasks to the components of the robotic system and to communicate control signals to the components of the robotic system to autonomously control the robotic system to perform the maintenance on the vehicle component.

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: A robot system and method are provided that move an articulable arm relative to a target object. Perception information corresponding to a position of the arm relative to the target object is acquired. Movement of the arm is controlled based on the perception information. After movement of the arm, predicted position information representative of a predicted positioning of the arm is provided using the perception information and control signal information. The arm is subsequently controlled using the predicted position information.

Abstract: A locomotive control system may include first and second robotic machines and a task manager. The first and second robotic machines have respective first and second sets of capabilities for interacting with a surrounding environment. The task manager selects the first and second robotic machines from a group to perform a task based on the first and second sets of capabilities of the robotic machines. The task involves manipulating and/or inspecting a target object of a vehicle. The task manager assigns a first sequence of sub-tasks to be performed by the first robotic machine and a second sequence of sub-tasks to be performed by the second robotic machine. The first and second robotic machines are configured to coordinate performance of the first sequence of sub-tasks by the first robotic machine with performance of the second sequence of sub-tasks by the second robotic machine to accomplish the task.

Abstract: Provided are systems and methods for autonomous robotic localization. In one example, the method includes receiving ranging measurements from a plurality of fixed anchor nodes that each have a fixed position and height with respect to the asset, receiving another ranging measurement from an aerial anchor node attached to an unmanned robot having a dynamically adjustable position and height different than the fixed position and height of each of the plurality of anchor nodes, and determining a location of the autonomous robot with respect to the asset based on the ranging measurements received from the fixed anchor nodes and the aerial anchor node, and autonomously moving the autonomous robot about the asset based on the determined location.

Abstract: Provided are systems and methods for monitoring an asset via an autonomous model-driven inspection. In an example, the method may include storing an inspection plan including a virtually created three-dimensional (3D) model of a travel path with respect to a virtual asset that is created in virtual space, converting the virtually created 3D model of the travel path about the virtual asset into a physical travel path about a physical asset corresponding to the virtual asset, autonomously controlling vertical and lateral movement of the unmanned robot in three dimensions with respect to the physical asset based on the physical travel path and capturing data at one or more regions of interest, and capturing data at one or more regions of interest, and storing information concerning the captured data about the asset.

Abstract: A robotic machine assembly includes a movable robotic arm configured to perform an assigned task that involves moving toward, engaging, and manipulating a target object of a vehicle. The assembly also includes a communication circuit configured to receive manipulation parameters for the target object from a remote database. The manipulation parameters are specific to at least one of the vehicle or a vehicle class in which the vehicle belongs. The assembly also includes one or more processors configured to generate a task performance plan for the robotic arm based on the manipulation parameters. The task performance plan includes prescribed forces to be exerted by the robotic arm on the target object to manipulate the target object. The one or more processors also are configured to drive movement of the robotic arm during performance of the assigned task according to the task performance plan.

Abstract: A robotic system validates brake bleeding by detecting one or more forces generated by a machine assembly acting to move a brake lever of a vehicle in order to open a valve of an air brake system of the vehicle. The system also detects displacement of the machine assembly as the machine assembly acts to move the brake lever, monitors one or more sounds generated one or more of during or after the machine assembly acts to move the brake lever, and determines that the brake lever has been moved to a position to open the valve of the air brake system to release the air brake system based on the one or more forces that are detected, the displacement that is detected, and/or the one or more sounds that are monitored.

Abstract: A manipulation platform includes a navigation system, manipulation arm, and one or more area sensors. The navigation unit locates a position of the manipulation platform, and a manipulation arm has a device or a collection sensor. The area sensors acquire data representative of at least a portion of an area in which the manipulation platform is located. Processors determine or predict a presence of an external object within a manipulation range of the manipulation arm using the data acquired by the one or more area sensors. The processors respond to a determination of the external body being, or being predicted to be, within the manipulation range by controlling one or more of the manipulation arm or the manipulation platform.