General Electric Company

Transportation IP Holdings, LLC

Ge Global Sourcing LLC

Romano Patrick

Huan Tan

Charles Burton Theurer

John Michael Lizzi

Balajee Kannan

Douglas Roy Forman

Omar Al Assad

Yonatan Gefen

Shiraj Sen

Viktor Holovashchenko

Arpit Jain

Steven Robert Gray

Justin Michael Foehner

John Robert Hoare

Frederick Wilson Wheeler

Li Zhang

Mauricio Castillo-Effen

Ghulam Ali Baloch

Shubao Liu

Sameer Vittal

Tai-Peng Tian

Johan Michael Reimann

Shuai Li

Pramod Sharma

Mark Bradshaw Kraeling

Achalesh Kumar Pandey

Jilin Tu

Eric Michael Gros

Subrat Nanda

Guangliang Zhao

Ana Dasilva

Matthew Francis Lemmon

Michael VanderLinden

Norman Wellings

Mark Bachman

Jonathan David White

Gary Cristofoli

A control system includes a task manager that can determine capability requirements to perform a task on the target object. The task has an associated series of sub-tasks, with the sub-tasks having one or more capability requirements. The task manager selects and assigns a first sequence of sub-tasks to a first robotic machine that has a first set of capabilities and operates according to a first mode of operation. The task manager selects and assigns a second sequence of sub-tasks to a second robotic machine that has a second set of capabilities and operates according to a second mode of operation. The task manager selects the first robotic machine based on the first set of capabilities and the first mode of operation of the first robotic machine, and selects the second robotic machine based on the second set of capabilities and the second mode of operation.

Control system with task manager

A system includes a first robotic machine having a first set of capabilities for interacting with a target object on stationary equipment; a second robotic machine having a second set of capabilities for interacting with the target object; and a task manager that can determine capability requirements to perform a task on the target object. The task has an associated series of sub-tasks. The task manager can assign a first sequence of sub-tasks for performance by the first robotic machine based on the first set of capabilities and a second sequence of sub-tasks for performance by the second robotic machine based on the second set of capabilities. The first and second robotic machines can 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.

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.

Integrated robotic system and method for autonomous vehicle maintenance

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.

Dynamically representing a changing environment over a communications channel

A system includes a first robotic machine having a first set of capabilities for interacting with a target object; a second robotic machine having a second set of capabilities for interacting with the target object; and a task manager that can determine capability requirements to perform a task on the target object. The task has an associated series of sub-tasks. The task manager can assign a first sequence of sub-tasks for performance by the first robotic machine based on the first set of capabilities and a second sequence of sub-tasks for performance by the second robotic machine based on the second set of capabilities. The first and second robotic machines can 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.

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.

Systems and method for robotic learning of industrial tasks based on human demonstration

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.

System and method for context-driven predictive simulation selection and use

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.

Systems and method for human-assisted robotic industrial inspection

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.

Systems and method for robotic industrial inspection system

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.

Autonomously-controlled inspection platform with model-based active adaptive data collection

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.

Control system and method for applying force to grasp a target object

A robot system is provided that includes a base, an articulable arm, a visual acquisition unit, and at least one processor. The articulable arm extends from the base and is configured to be moved toward a target. The visual acquisition unit is mounted to the arm or the base, and acquires environmental information. The at least one processor is operably coupled to the arm and the visual acquisition unit, the at least one processor configured to: generate an environmental model using the environmental information; select, from a plurality of planning schemes, using the environmental model, at least one planning scheme to translate the arm toward the target; plan movement of the arm toward the target using the selected at least one planning scheme; and control movement of the arm toward the target using the at least one selected planning scheme.

Systems and methods for control of robotic manipulation

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.

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Atlanta, GA, United States of America

Romano Patrick