Methods and systems of estimating location of an asset within material handling environment
Various embodiments disclose a method for tracking assets. Method includes determining one or more locations of an asset within an indoor environment based on metadata associated an RF signal received from an RF tag associated with the asset. The method further includes identifying a first set of locations of the one or more locations. The method further includes identifying a second set of locations of the one or more locations, wherein the second set of locations corresponds to uncalibrated locations of the asset within indoor environment. Additionally, the method includes receiving a third set of locations of the asset. Furthermore, the method includes training a machine learning (ML) model based on the first set of locations, the second set of locations, and the third set of locations, and the metadata associated with the RF signal. The ML model predicts a fourth set of locations of another asset.
1. A method for tracking assets, the method comprising:
determining, by a processor, one or more locations of an asset within an indoor environment based on metadata associated with a radio frequency (RF) signal received from a RF tag associated with the asset;
identifying, by the processor, a first set of locations of the one or more locations, wherein the first set of locations correspond to calibrated locations of the asset in which the RF tag on the asset is within a Line of Sight (LOS) of a RF beacon installed in the indoor environment;
identifying, by the processor, a second set of locations of the one or more locations, wherein the second set of locations corresponds to uncalibrated locations of the asset in which the RF tag on the asset is out of the LOS of the RF beacon within the indoor environment;
determining, by the processor, a third set of locations of the asset that are calibrated locations for the second set of locations based on association of a second timestamp associated with the second set of locations and a first timestamp associated with time series data of location data received from one of: a mobile computer and a machine, wherein the third set of locations is identified from the time series data of the location data such that the first timestamp associated with the third set of locations is the same as that of the second timestamp associated with the second set of locations; and
training, by the processor, a machine learning (ML) model based on the first set of locations, the second set of locations, the third set of locations, and the metadata associated with the RF signal, wherein the ML model is configured to predict a fourth set of locations of another asset within the indoor environment.
2. The method of claim 1 further comprising determining, by the processor, one or more locations of an other asset within the indoor environment.
3. The method of claim 2 , wherein the one or more locations of the other asset comprises the first set of locations that correspond to calibrated locations of the other asset in the indoor environment, and the second set of locations, wherein the second set of locations includes uncalibrated locations of the other asset in the indoor environment.
4. The method of claim 3 , wherein the fourth set of locations correspond to calibrated locations of the other asset for the second set of locations of the other asset.
5. The method of claim 1 further comprising categorizing, by the processor, the one or more locations of the asset as a set of traversal locations or a set of stationary locations based on a periodicity of reception of the RF signal from the RF tag.
6. The method of claim 5 , wherein the first set of locations and the second set of locations of the asset are identified from the set of traversal locations of the asset.
7. The method of claim 1 further comprising receiving, by the processor, accelerometer data from the RF tag.
8. The method of claim 7 further comprising categorizing, by the processor, the one or more locations of the asset as a set of traversal locations or a set of stationary locations based on the accelerometer data.
9. The method of claim 1 further comprising receiving an input corresponding to scanning a barcode on the asset.
10. The method of claim 9 further comprising determining the one or more locations of the asset based on the metadata associated with the RF signal received, from the RF tag on the asset, during a predetermined time period prior to a first time instant.
11. A central server for tracking assets, the central server comprising:
a processor;
a memory device communicatively coupled to the processor, the memory device comprising a set of instructions executable by the processor to:
determine one or more locations of an asset within an indoor environment based on metadata associated with a radio frequency (RF) signal received from a RF tag associated with the asset;
identify a first set of locations of the one or more locations, wherein the first set of locations correspond to locations within the indoor environment where the RF tag on the asset is within a Line of Sight (LOS) of a RF beacon installed in the indoor environment;
identify a second set of locations of the one or more locations, wherein the second set of locations corresponds to locations within the indoor environment where the RF tag on the asset is out of the LOS of the RF beacon, wherein the second set of locations correspond to uncalibrated locations of the RF tag within the indoor environment;
determine a third set of locations of the asset within the indoor environment based on association of timestamp associated with the second set of locations and timestamp associated with time series data of a location data received from one of: a mobile computer and a machine, wherein the third set of locations correspond to calibrated locations for the second set of locations, wherein a first timestamp associated with the second set of locations is the same as a second timestamp associated with the time series data of the location data; and
train a machine learning (ML) model based on the first set of locations, the second set of locations, the third set of locations, and the metadata associated with the ML model, wherein the ML model is configured to predict a fourth set of locations of another asset within the indoor environment when another RF tag on the another asset is out of the LOS of the RF beacon.
12. The central server of claim 11 , wherein the processor is further configured to determine one or more locations of an other asset within the indoor environment.
13. The central server of claim 12 , wherein the one or more locations of the other asset comprises the first set of locations that correspond to the calibrated locations of the other asset in the indoor environment, and the second set of locations, wherein the second set of locations includes uncalibrated locations of the other asset in the indoor environment.
14. The central server of claim 13 , wherein the fourth set of locations correspond to calibrated locations of the other asset for the second set of locations of the other asset.
15. The central server of claim 11 , wherein the processor is further configured to categorize the one or more locations of the asset as a set of traversal locations or a set of stationary locations based on a periodicity of reception of the RF signal from the RF tag.
16. The central server of claim 15 , wherein the first set of locations of the asset and the second set of locations of the asset are identified from the set of traversal locations of the asset.
17. The central server of claim 11 , wherein the processor is further configured to receive accelerometer data from the RF tag.
18. The central server of claim 17 , wherein the processor is further configured to categorize the one or more locations of the asset as a set of traversal locations or a set of set of stationary locations based on the accelerometer data.
19. The central server of claim 11 , wherein the processor is further configured to receive an input corresponding to scanning a barcode on the asset.
20. The central server of claim 19 , wherein the processor is further configured to determine the one or more locations of the asset based on the metadata associated with the RF signal received, from the RF tag on the asset, during a predetermined time period prior to a first time instant.
21. A method for tracking assets, the method comprising:
determining, by a processor, one or more locations of an asset within an indoor environment based on metadata associated with a radio frequency (RF) signal received from an RF tag associated with the asset, wherein the one or more locations include a first set of locations and a second set of locations, wherein the first set of locations corresponds to calibrated locations of the asset in which the RF tag on the asset is within a Line of Sight (LOS) of a RF beacon installed in the indoor environment, and wherein the second set of locations corresponds to uncalibrated locations of the asset in which the RF tag on the asset is out of the LOS of the RF beacon within the indoor environment;
predicting, by the processor, a fourth set of locations based on the first set of locations, the second set of locations, and a third set of locations determined based on a location data received from a mobile computer by utilizing a machine learning (ML) model, wherein the third set of locations correspond to the calibrated locations for the second set of locations, wherein a first timestamp associated with the second set of locations is the same as a second timestamp associated with time series data of the location data received from one of: the mobile computer and a machine; and
predicting a location of an aisle where the asset has been stored based on the fourth set of locations.