The Boeing Company

Phillip Haeusler

Martin Alexander Szarski

David Michael Bain

Andrew K Glynn

Peter N Steele

Jason John Cochrane

Richard James Bain

Dominic Wierzbicki

Alexandre Desbiez

Systems and methods are provided for automatic intrinsic and extrinsic calibration for a robot optical sensor. An implementation includes an optical sensor; a robot arm; a calibration chart; one or more processors; and a memory storing instructions that cause the one or more processors to perform operations that includes: determining a set of poses for calibrating the first optical sensor; generating, based at least on the set of poses, pose data comprising three dimensional (3D) position and orientation data; moving, based at least on the pose data, the robot arm into a plurality of poses; at each pose of the plurality of poses, capturing a set of images of the calibration chart with the first optical sensor and recording a pose; calculating intrinsic calibration parameters, based at least on the set of captured images; and calculating extrinsic calibration parameters, based at least on the set of captured images.

Automatic calibration for a robot optical sensor

A method of localizing an object includes capturing a first image of the object from a first camera position, performing edge detection on the first image to form an edge-detected second image, and performing a distance transform on the second image to form a distance transformed third image. A virtual camera is positioned in virtual space relative to a virtual model of the object, and an edge-detected fourth image of the virtual model of the object is generated. An alignment cost is generated of the edge-detected fourth image of the virtual model relative to the edge-detected second image of the object, and a position of the virtual camera is transformed to a position having a lower alignment cost than the alignment cost. A physical location of the object is estimated based on the transformed virtual camera position.

Computer-implemented methods and system for localizing an object

In an example, a system for registering a three-dimensional (3D) pose of a workpiece relative to a robotic device is disclosed. The system comprises the robotic device, where the robotic device comprises one or more mounted lasers. The system also comprises one or more sensors configured to detect laser returns from laser rays projected from the one or more mounted lasers and reflected by the workpiece. The system also comprises a processor configured to receive a tessellation of the workpiece, wherein the tessellation comprises a 3D representation of the workpiece made up of cells, convert the laser returns into a 3D point cloud in a robot frame, based on the 3D point cloud, filter visible cells of the tessellation of the workpiece to form a tessellation included set, and solve for the 3D pose of the workpiece relative to the robotic device based on the tessellation included set.

Methods and systems for registering a three-dimensional pose of an object

A robotic system includes a robot and a processor configured to autonomously generate a master plan for movement of an end effector of the robot for operating on an operating surface of a workpiece in a physical environment. The master plan is based on a computer aided design (CAD) model of the workpiece and defines movement paths of the end effector based on a reach distance of a robotic arm of the robot at each of a plurality of working waypoints defined in the master plan and corresponding to fiducial markers on the workpiece. The processor registers the robot to the workpiece, iteratively adjusts an approximate workpiece position in a world map until matching an actual workpiece position in the physical environment, and constructs and executes a robotic arm trajectory causing the end effector to operate on the operating surface along the movement paths defined in the master plan.

Robotic system and method for operating on a workpiece

A method and apparatus for performing an automated validation of a condition of assembly for a structure. A plurality of images of the structure are registered to a computer model of the structure in which an image in the plurality of images captures a portion of the structure. Each image in the plurality of images is segmented based on registration of the plurality of images to the computer model to form a plurality of image sections. A final score is generated for the condition of assembly of the structure based on whether each image section in the plurality of image sections meets a corresponding condition in which the final score indicates whether the condition of assembly is valid.

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Melbourne, Australia

Phillip Haeusler