Information technology asset type identification using a mobile vision-enabled robot

Patent metadata

Field	Value
Publication number	US-9744671-B2
Application number	US-201615074168-A
Country	US
Kind code	B2
Filing date	Mar 18, 2016
Priority date	May 20, 2014
Publication date	Aug 29, 2017
Grant date	Aug 29, 2017

Abstract

Mechanisms are provided for classifying an obstacle as an asset type. The mechanisms receive a digital image of an obstacle from an image capture device of an automated robot. The mechanisms perform a classification operation on the digital image of the obstacle to identify a proposed asset type classification for the obstacle. The mechanisms determine a final asset type for the obstacle based on the proposed asset type classification for the obstacle. The mechanisms update a map data structure for a physical premises in which the obstacle is present based on the final asset type.

First claim

What is claimed is:

1. A method, in a data processing system comprising a processor and a memory, for classifying an obstacle as an asset type, comprising:
receiving, by the data processing system, a digital image of an obstacle from an image capture device of an automated robot;
performing, by the data processing system, a classification operation on the digital image of the obstacle to identify a proposed asset type classification for the obstacle;
determining, by the data processing system, a final asset type for the obstacle based on the proposed asset type classification for the obstacle; and
updating, by the data processing system, a map data structure for a physical premises in which the obstacle is present based on the final asset type, wherein performing the classification operation further comprises:
gathering additional sensor information from one or more other sensors provided on either the robot or in the physical premises; and
performing the classification operation based on a classification of characteristics of the obstacle obtained from analysis of the digital image and analysis of the additional sensor information, wherein the additional sensor information comprises information indicative of environmental conditions within a vicinity of the obstacle, that together with the digital image of the obstacle, are indicative of an asset type classification of the obstacle.

2. The method of claim 1 , wherein determining a final asset type for the obstacle based on the proposed asset type classification for the obstacle comprises:
calculating, by the data processing system, a confidence value indicating a confidence in the identification of the proposed asset type classification for the obstacle; and
in response to the confidence value being less than a predetermined threshold value:
presenting, by the data processing system, the proposed asset type classification and the digital image to a human user via a user interface output on a computing device associated with the human user, wherein the user interface identifies the proposed asset type classification, the digital image, and provides a mechanism for user feedback input indicating whether or not the proposed asset type classification is correct or incorrect; and
determining, by the data processing system, the final asset type for the obstacle based on input from the human user via the user interface.

3. The method of claim 1 , wherein the method is implemented automatically without human intervention.

4. The method of claim 1 , wherein the additional sensor information comprises at least one of temperature information in proximity to the obstacle or humidity information in proximity to the obstacle, and at least one of infrared profile information of the obstacle, proximity information indicating a proximity of the obstacle to other known types of assets, or configuration information for other known types of nearby assets relative to the obstacle.

5. The method of claim 1 , wherein performing the classification operation on the digital image of the obstacle comprises utilizing a plurality of classification algorithms that each classify the obstacle with regard to one or more different characteristics of the obstacle as identified in at least one of the digital image or the additional sensor information.

6. The method of claim 1 , further comprising:
outputting, by the data processing system, a user interface representing the map of the physical premises in which assets in the physical premises are distinguished by asset type.

7. The method of claim 6 , wherein outputting the user interface representing the map of the physical premises comprises outputting the user interface with user interface elements for filtering assets in the physical premises to accentuate or remove assets from the map based on associated asset type.

8. The method of claim 1 , wherein the physical premises is a data center and the obstacle is a physical asset of the data center.

9. The method of claim 1 , wherein performing the classification operation further comprises:
performing an initial asset classification operation based on analysis of the digital image to identify a subset of asset types in which the obstacle is classifiable; and
performing a refined asset classification operation based on the additional sensor information, at least by comparing the additional sensor information to known patterns of additional sensor information for asset type classifications to identify a particular asset type classification within the subset of asset types.

10. The method of claim 1 , further comprising:
training the data processing system to perform the classification operation by performing iterative machine learning comprising:
analyzing a digital image and additional sensor information associated with an obstacle present in a physical premises used for training to generate a proposed asset type classification of the obstacle;
determining an actual asset type classification of the obstacle based on an asset identifier tag affixed to the obstacle;
comparing the proposed asset type classification of the obstacle with the actual asset type classification of the obstacle; and
modifying at least one parameter of the classification operation based on the results of comparing the proposed asset type classification of the obstacle with the actual asset type classification of the obstacle indicating a difference between the proposed asset type classification and the actual asset type classification.

11. A computer program product comprising a non-transitory computer readable medium having a computer readable program stored therein, wherein the computer readable program, when executed on a computing device, causes the computing device to:
receive a digital image of an obstacle from an image capture device of an automated robot;
perform a classification operation on the digital image of the obstacle to identify a proposed asset type classification for the obstacle;
determine a final asset type for the obstacle based on the proposed asset type classification for the obstacle; and
update a map data structure for a physical premises in which the obstacle is present based on the final asset type, wherein the computer readable program further causes the computing device to perform the classification operation at least by:
gathering additional sensor information from one or more other sensors provided on either the robot or in the physical premises; and
performing the classification operation based on a classification of characteristics of the obstacle obtained from analysis of the digital image and analysis of the additional sensor information, wherein the additional sensor information comprises information indicative of environmental conditions within a vicinity of the obstacle, that together with the digital image of the obstacle, are indicative of an asset type classification of the obstacle.

12. The computer program product of claim 11 , wherein the computer readable program further causes the computing device to determine a final asset type for the obstacle based on the proposed asset type classification for the obstacle at least by:
calculating a confidence value indicating a confidence in the identification of the proposed asset type classification for the obstacle; and
in response to the confidence value being less than a predetermined threshold value:
presenting the proposed asset type classification and the digital image to a human user via a user interface output on a computing device associated with the human user, wherein the user interface identifies the proposed asset type classification, the digital image, and provide

Assignees

• IBM

Inventors

• Connell Ii Jonathan H
• Kephart Jeffrey O
• Lenchner Jonathan

Classifications

• G06V10/764

  using classification, e.g. of video objects · CPC title

• G06F18/24

  Classification techniques · CPC title

• G06V20/20

  in augmented reality scenes · CPC title

• G05B2219/39369

  Host and robot controller and vision processing · CPC title

• G05B2219/40005

  Vision, analyse image at one station during manipulation at next station · CPC title