Amazon Technologies, Inc.

Lars Petter Johnsen

Nicholas A Smith

Techniques for mobile robots performing self-diagnostic fiducial classification are described herein. For example, a mobile robot can determine that an expected fiducial marker has not been detected as the mobile robot navigates via fiducial markers on a surface. In response, the mobile robot can generate an input to a machine learning model that can be executed on the mobile robot. The input can include images of the surface captured by the mobile robot. The machine learning model may be trained to generate an output comprising a classification of image data of the images based at least in part on the input. The mobile robot can determine an error event corresponding to the expected fiducial marker based at least in part on the classification of image data received from the machine learning model. The mobile robot can output a notification indicating the error event corresponding to the expected fiducial marker.

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Seattle, WA, United States of America

Lars Petter Johnsen