Abstract: An object attachment handler comprising a configurable gripper, configured to define an open and closed position, based on a software definition of an object to be attached, and a fastening tool, automatically configured to apply a defined force curve at defined positions, to complete attachment of the object, based on the software definition of the object.

Abstract: A vision analytics and validation (VAV) system for providing an improved inspection of robotic assembly, the VAV system comprising a trained neural network three-way classifier, to classify each component as good, bad, or do not know, and an operator station configured to enable an operator to review an output of the trained neural network, and to determine whether a board including one or more “bad” or a “do not know” classified components passes review and is classified as good, or fails review and is classified as bad. In one embodiment, a retraining trigger to utilize the output of the operator station to train the trained neural network, based on the determination received from the operator station.

Abstract: A tray feeder mechanism comprising a plurality of trays within a tray feeder, each tray including a tray handle, and a gripper positioned within a gripping area defined by the tray handles, wherein the gripper is configured to remove a tray from the tray feeder when the tray feeder stops.

Abstract: An angle screw feeding device comprises a screw feeding unit, a screw transporting unit, and a screw driving unit. The device is able to be used with small screws. Further, the device is able to perform an automatic screwing action at any selected angles.

Abstract: A vision analytics and validation (VAV) system for providing an improved inspection of robotic assembly, the VAV system comprising a trained neural network three-way classifier, to classify each component as good, bad, or do not know, and an operator station configured to enable an operator to review an output of the trained neural network, and to determine whether a board including one or more “bad” or a “do not know” classified components passes review and is classified as good, or fails review and is classified as bad. In one embodiment, a retraining trigger to utilize the output of the operator station to train the trained neural network, based on the determination received from the operator station.

Abstract: A tray feeder mechanism comprising a plurality of trays within a tray feeder, each tray including a tray handle, and a gripper positioned within a gripping area defined by the tray handles, wherein the gripper is configured to remove a tray from the tray feeder when the tray feeder stops.

Abstract: The present system is a software defined manufacturing (SDM) system that integrates several technologies and methods into a system that automates the process of engineering and operating automated manufacturing systems (aka “automating automation”). In one embodiment, some or all of the below aspects of the “automating automation” system are integrated: modular, configurable, reusable manufacturing cells; computer vision systems; autocalibration systems; a recipe-based programming environment; configuration management system; production analytics; and a marketplace for sharing recipes.

Abstract: A method for vision-based tool localization (VTL) in a robotic assembly system including one or more calibrated cameras, the method comprising capturing a plurality of images of the tool contact area from a plurality of different vantage points, determining an estimated position of the tool contact area based on an image, and refining the estimated position based on another image from another vantage point. The method further comprises providing the refined position to the robotic assembly system to enable accurate control of the tool by the robotic assembly system.

Abstract: The present system is a software defined manufacturing (SDM) system that integrates several technologies and methods into a system that automates the process of engineering and operating automated manufacturing systems (aka “automating automation”). In one embodiment, some or all of the below aspects of the “automating automation” system are integrated: modular, configurable, reusable manufacturing cells; computer vision systems; autocalibration systems; a recipe-based programming environment; configuration management system; production analytics; and a marketplace for sharing recipes.

Abstract: A configurable belt conveyor for manufacturing comprising first belt and an adaptable border adjacent to the belt. The adaptable border having a first configuration in which a first width of the belt is available for supporting a pallet on the conveyor belt, and a second configuration in which a portion of the belt is covered by the adaptable border, such that the second width of the belt is sized for supporting a printed circuit board (PCB). In one embodiment, the configurable belt conveyor may be configured to form a dual stream conveyor.

Abstract: A configurable belt conveyor for manufacturing comprising first belt and an adaptable border adjacent to the belt. The adaptable border having a first configuration in which a first width of the belt is available for supporting a pallet on the conveyor belt, and a second configuration in which a portion of the belt is covered by the adaptable border, such that the second width of the belt is sized for supporting a printed circuit board (PCB). In one embodiment, the configurable belt conveyor may be configured to form a dual stream conveyor.

Abstract: A robotic cell calibration method comprising a robotic cell system having elements comprising: one or more cameras, one or more sensors, components, and a robotic arm. The method comprises localizing positions of the one or more cameras and components relative to a position of the robotic arm using a common coordinate frame, moving the robotic arm in a movement pattern, and using the cameras and sensors to determine robotic arm position at multiple times during the movement. The method includes identifying a discrepancy in robotic arm position between a predicted position and the determined position in real time, and computing, by an auto-calibrator, a compensation for the identified discrepancy, the auto-calibrator solving for the elements in the robotic cell system as a system. The method includes modifying actions of the robotic arm in real time during the movement based on the compensation.

Abstract: An angle screw feeding device comprises a screw feeding unit, a screw transporting unit, and a screw driving unit. The device is able to be used with small screws. Further, the device is able to perform an automatic screwing action at any selected angles.

Abstract: The present system is a software defined manufacturing (SDM) system that integrates several technologies and methods into a system that automates the process of engineering and operating automated manufacturing systems (aka “automating automation”). In one embodiment, some or all of the below aspects of the “automating automation” system are integrated: modular, configurable, reusable manufacturing cells; computer vision systems; autocalibration systems; a recipe-based programming environment; configuration management system; production analytics; and a marketplace for sharing recipes.

Abstract: An object attachment handler comprising a configurable gripper, configured to define an open and closed position, based on a software definition of an object to be attached, and a fastening tool, automatically configured to apply a defined force curve at defined positions, to complete attachment of the object, based on the software definition of the object.

Abstract: A two-stage insertion system including a gripper to grip a module, a compliance element to provide movement in the XY axis, a first stage insertion control to insert the module into a socket, to a first level, and a second stage insertion control to complete the insertion of the module into the socket, when the first stage insertion control indicates that the module is aligned to the socket, the second level insertion control exerting enough force to complete the insertion of the module into the socket.

Abstract: A vision analytics and validation (VAV) system for providing an improved inspection of robotic assembly, the VAV system comprising a trained neural network three-way classifier, to classify each component as good, bad, or do not know, and an operator station configured to enable an operator to review an output of the trained neural network, and to determine whether a board including one or more “bad” or a “do not know” classified components passes review and is classified as good, or fails review and is classified as bad. In one embodiment, a retraining trigger to utilize the output of the operator station to train the trained neural network, based on the determination received from the operator station.