Anomaly detection and resolution
AssigneeMicron Technology, Inc.
InventorsDmitry Vengertsev, Zahra Hosseinimakarem, Marta Egorova
Life of this patent
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Abstract
Methods, apparatuses, and systems associated with anomaly detection and resolution are described. Examples can include detecting, via a sensor of a robot, an object in a path of the robot while the robot is performing a task in an environment and classifying the object as an anomaly or a non-anomaly and the environment as anomalous or non-anomalous using a machine learning model. Examples can include proceeding with the task responsive to classification of the object as a non-anomaly and the environment as non-anomalous and resolving the anomaly or the anomalous environment and proceeding with the task responsive to classification of the object as an anomaly or the environment as anomalous.
Claims20
A method, comprising:
- detecting at a processing resource and via a sensor of a robot, an object in a path of the robot while the robot is performing a task in an environment;
- classifying at the processing resource the object as an anomaly or a non-anomaly and the environment as anomalous or non-anomalous using a machine learning model;
- proceeding with the task responsive to the processing resource's classification of the object as a non-anomaly and the environment as non-anomalous;
- and
- resolving the anomaly or the anomalous environment and proceeding with the task responsive to the processing resource's classification of the object as an anomaly or the environment as anomalous.
An apparatus, comprising:
- a processing resource;
- and
- a memory resource in communication with the processing resource having instructions executable to:
- detect, via a sensor of the apparatus, an object in a path of the apparatus while the apparatus is performing a cleaning task in an environment;
- classify the object in the path of the apparatus as an anomaly or a non-anomaly and the environment as anomalous or non-anomalous using a machine learning model based on historical data associated with an object previously detected by the sensor;
- and
- determine a response to the object in the path of the apparatus based on the classification and the machine learning model based on historical resolution data associated with the object previously detected by the sensor, wherein the response comprises instructions executable to:
- remove the object in the path of the apparatus and proceed with the cleaning task responsive to classification of the object in the path of the apparatus as a non-anomaly and the environment as non-anomalous;
- and
- resolve the anomaly or the anomalous environment and proceed with the cleaning task responsive to classification of the object in the path of the apparatus as an anomaly or the environment as anomalous.
A system, comprising:
- a plurality of delivery robots communicatively coupled to one another, wherein each one of the plurality of delivery robots comprises:
- a processing resource;
- and
- a memory resource in communication with the processing resource having instructions executable to:
- detect, via a sensor, an object in a path of the delivery robot while the delivery robot is performing a delivery task in an environment;
- classify the object as an anomaly or a non-anomaly and the environment as anomalous or non-anomalous using a machine learning model based on historical data associated with an object previously detected by the sensor and historical data received from other delivery robots of the plurality of delivery robots;
- determine a response to the object based on the classification and the machine learning model based on resolution data associated with the object previously detected by the sensor in the environment and historical data received from other delivery robots of the plurality of delivery robots, wherein the response comprises instructions executable to:
- proceed with the delivery task responsive to classification of the object in the path of the delivery robot as a non-anomaly and the environment as non-anomalous;
- resolve the anomaly or the environment responsive to classification of the object in the path of the delivery robot as an anomaly or the environment as anomalous;
- and
- update the machine learning model based on the classification and determined response.
Dependencies are inferred from the claim text.