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: Provided are systems and methods for generating an autonomous 3D inspection plan for an unmanned robot. In an example, the method may include receiving a selection of a plurality of regions of interest with respect to a virtual asset displayed in virtual space, detecting a 3D position of the regions of interest within a coordinate frame of the virtual space, auto-generating a travel path about a physical asset corresponding to the virtual asset by generating a virtual 3D travel path with respect to the virtual asset based on the detected 3D positions of the selected regions of interest within the coordinate frame, aligning the virtual 3D travel path in the virtual space with a physical travel path in a physical space, and outputting a robotic inspection plan comprising the auto-generated physical travel path for the unmanned robot.

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: 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: A three-dimensional model data store may contain a three-dimensional model of an industrial asset, including points of interest associated with the industrial asset. An inspection plan data store may contain an inspection plan for the industrial asset, including a path of movement for an autonomous inspection robot. An industrial asset inspection platform may receive sensor data from an autonomous inspection robot indicating characteristics of the industrial asset and determine a current location of the autonomous inspection robot along the path of movement in the inspection plan along with current context information. A forward simulation of movement for the autonomous inspection robot may be executed from the current location, through a pre-determined time window, to determine a difference between the path of movement in the inspection plan and the forward simulation of movement along with future context information.

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 system includes a processing system comprising at least one processor. The processor generates a plan to monitor the asset. The plan comprises one or more tasks to be performed by the at least one robot. The processor receives sensor data from at least one sensor indicating one or more characteristics of the asset. The processor adjusts the plan to monitor the asset by adjusting or adding one or more tasks to the plan based on one or both of the quality of the acquired data or a potential defect of the asset. The adjusted plan causes the at least one robot to acquire additional data related to the asset when executed.

Abstract: Provided are systems and methods for generating an autonomous 3D inspection plan for an unmanned robot. In an example, the method may include receiving a selection of a plurality of regions of interest with respect to a virtual asset displayed in virtual space, detecting a 3D position of the regions of interest within a coordinate frame of the virtual space, auto-generating a travel path about a physical asset corresponding to the virtual asset by generating a virtual 3D travel path with respect to the virtual asset based on the detected 3D positions of the selected regions of interest within the coordinate frame, aligning the virtual 3D travel path in the virtual space with a physical travel path in a physical space, and outputting a robotic inspection plan comprising the auto-generated physical travel path for the unmanned robot.

Abstract: Provided are systems and methods for generating an autonomous 3D inspection plan for an unmanned robot. In an example, the method may include receiving a selection of a plurality of regions of interest with respect to a virtual asset displayed in virtual space, detecting a 3D position of the regions of interest within a coordinate frame of the virtual space, auto-generating a travel path about a physical asset corresponding to the virtual asset by generating a virtual 3D travel path with respect to the virtual asset based on the detected 3D positions of the selected regions of interest within the coordinate frame, aligning the virtual 3D travel path in the virtual space with a physical travel path in a physical space, and outputting a robotic inspection plan comprising the auto-generated physical travel path for the unmanned robot.

Abstract: A method includes receiving, via at least one sensor of a robot, sensor data indicating one or more characteristics of an asset. The method includes detecting, based on the sensor data, an existing or imminent defect of the asset. The method includes fabricating a part suitable for use in correcting the defect. The structure of the part is derived using one or both of a digital representation of the asset generated using the sensor data or stored reference data related to the asset.

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: 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: 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: 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: A system, medium, and method, including receiving a set of formalized requirements for accomplishing a mission; allocating, by the processor using architecture synthesis, constraint solving, and compositional verification techniques, a role to each of a plurality of assets comprising a team of autonomous entities, the team to execute specific tasks according to their role to accomplish the mission; and generating, by the processor using controller synthesis and verification techniques, automata for accomplishing the mission for the plurality of assets, the automata being encoded to confer an ability to dynamically react to external inputs during a run-time execution of the automata by the plurality of assets.

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: A three-dimensional model data store may contain a three-dimensional model of an industrial asset, including points of interest associated with the industrial asset. An inspection plan data store may contain an inspection plan for the industrial asset, including a path of movement for an autonomous inspection robot. An industrial asset inspection platform may receive sensor data from an autonomous inspection robot indicating characteristics of the industrial asset and determine a current location of the autonomous inspection robot along the path of movement in the inspection plan along with current context information. A forward simulation of movement for the autonomous inspection robot may be executed from the current location, through a pre-determined time window, to determine a difference between the path of movement in the inspection plan and the forward simulation of movement along with future context information.

Abstract: Provided are systems and methods for generating an autonomous 3D inspection plan for an unmanned robot. In an example, the method may include receiving a selection of a plurality of regions of interest with respect to a virtual asset displayed in virtual space, detecting a 3D position of the regions of interest within a coordinate frame of the virtual space, auto-generating a travel path about a physical asset corresponding to the virtual asset by generating a virtual 3D travel path with respect to the virtual asset based on the detected 3D positions of the selected regions of interest within the coordinate frame, aligning the virtual 3D travel path in the virtual space with a physical travel path in a physical space, and outputting a robotic inspection plan comprising the auto-generated physical travel path for the unmanned robot.

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 method, medium, and system to receive flight parameter data relating to a plurality of flights, the flight parameter data including indications of aircraft performance based navigation (PBN) capabilities, flight plan information, an aircraft configuration, and an airport configuration for the plurality of flights; assign probabilistic properties to the flight parameter data; receive accurate and current position and predicted flight plan information for a plurality of aircraft corresponding to the flight parameter data; determine a probabilistic trajectory for two of the plurality of aircraft based on a combination of the probabilistic properties of the flight parameter data and the position and predicted flight plan information, the probabilistic trajectory being specific to the two aircraft and including a target spacing specification to maintain a predetermined spacing between the two aircraft at a target location with a specified probability; and generate a record of the probabilistic trajectory for the