Bright Machines, Inc.

Oytun Akman

Brian Philip Mathews

Konstantinos Boulis

Melinda Varga

Julio Soldevilla Estrada

Ronald Poelman

Barrett Clark

Matthew Brown

Emmanuel Gallo

Ali Shafiekhani

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.

Method and apparatus for vision-based tool localization

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.

Image analysis system for testing in manufacturing

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.

Method and apparatus for improved auto-calibration of a robotic cell

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.

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Oakland, CA, United States of America

Oytun Akman