DETERMINING A LOAD STATUS OF A PLATFORM

• US 20180038991A1
• Filed: 08/05/2016
• Published: 02/08/2018
• Est. Priority Date: 08/05/2016
• Status: Active Grant

• Current Assignee: Blackberry Limited
• Original Assignee: Blackberry Limited
• Inventors: Chennakeshu, Sandeep, Gao, Yu, Stolpman, Victor James, Bennett, Jesse William
• Granted Patent: US 10,228,488 B2
• Time in Patent Office: Days
• CPC Class Codes: G01S 17/04 Systems determining the presence of a target
• CPC Class Codes: G01S 17/08 for measuring distance only indirect measurement G01S17/46; active triangulation systems G01S17/48
• CPC Class Codes: G01S 17/88 Lidar systems specially adapted for specific applications
• CPC Class Codes: G01S 7/4808 Evaluating distance, position or velocity data
• CPC Class Codes: G01S 7/497 Means for monitoring or calibrating
• CPC Class Codes: G01V 8/12 using one transmitter and one receiver
• CPC Class Codes: G06Q 10/087 Inventory or stock management, e.g. order filling, procurement or balancing against orders

Abstract

Based on a relationship between measurement data from at least one sensor and a baseline, a load status of a platform is determined. In response to the determined load status, such as determining that the platform is empty, the baseline is refined using a baseline estimator to produce a refined baseline that can be used to determine a further load status of the platform.

Claims

1. A method performed by at least one processor, comprising:

determining, based on a relationship between measurement data from at least one sensor and a baseline, a load status of a platform;

responsive to the determining, refining the baseline using a baseline estimator to produce a refined baseline; and

determining, based on further measurement data and the refined baseline, a further load status of the platform.

2. The method of claim 1, further comprising:

responsive to the determining of the further load status, further refining the refined baseline using the baseline estimator to produce a further refined baseline for use in another determination of the load status of the container.

3. The method of claim 1, wherein refining the baseline is based on the measurement data from the at least one sensor.

4. The method of claim 3, wherein refining the baseline is in response to the measurement data from the at least one sensor indicating that the platform is empty.

5. The method of claim 1, further comprising:

detecting closing of an entry barrier of the platform,

wherein the determining of the load status is performed in response to detecting the closing of the entry barrier of the container.

6. The method of claim 1, wherein the at least one sensor comprises a time-of-flight sensor that detects a reflected signal inside the platform, the reflected signal responsive to an emitted signal in the platform, the reflected signal being from a surface of a surface of the platform when the platform is empty, and the reflected signal being from a surface of a physical item when the platform is loaded with the physical item.

7. The method of claim 1, wherein the baseline estimator comprises a Kalman filter.

8. The method of claim 1, wherein determining the load status of the platform is based on comparing the measurement data to a threshold that is derived from the baseline.

9. The method of claim 1, wherein the determining of the load status and the refining of the baseline are performed by at least one processor of a sensor device that includes the at least one sensor, the method further comprising:

transmitting, by a communication component of the sensor device, an indication of the load status to a remote server.

10. The method of claim 1, wherein the determining of the load status and the refining of the baseline are performed by at least one processor of a server, the method further comprising:

receiving, over a network from a sensor device that includes the at least one sensor, the measurement data.

11. The method of claim 1, further comprising:

determining whether the refined baseline has breached a threshold; and

in response to determining that the refined baseline has breached the threshold, setting the refined baseline to a value different from that set by the baseline estimator.

12. An apparatus comprising:

at least one processor configured to;

execute an adaptive decision process to make a decision, based on measurement data from at least one sensor, of a load status of a platform; and

feedback the decision to refine the adaptive decision process to produce a refined adaptive decision process; and

execute the refined adaptive decision process to make a next decision, based on further measurement data from the at least one sensor, of a further load status of the platform.

13. The apparatus of claim 12, comprising a sensor device that comprises the at least one sensor and the at least one processor.

14. The apparatus of claim 12, comprising a server, the server including a communication component to receive the measurement data from the at least one sensor over a network.

15. The apparatus of claim 12, wherein the adaptive decision process comprises a baseline estimator to produce a baseline that is compared to the measurement data to determine the load status of the platform, wherein the refining of the adaptive decision process comprises refining the baseline.

16. The apparatus of claim 15, wherein the baseline estimator comprises a Kalman Filter.

17. The apparatus of claim 12, further comprising a sensor to detect closing of an entry barrier to the platform, wherein the executing of the adaptive decision process is responsive to detecting of the closing of the entry barrier.

18. The apparatus of claim 12, wherein the measurement data is from a time-of-flight sensor.

19. A non-transitory machine-readable storage medium storing instructions that upon execution cause a system to:

determine, based on a relationship between measurement data from at least one sensor and a baseline, a load status of a platform;

responsive to the determining, refine the baseline using a baseline estimator to produce a refined baseline; and

determine, based on further measurement data and the refined baseline, a further load status of the platform.

20. The non-transitory machine-readable storage medium of claim 19, wherein the determining of the load status of the platform is responsive to a sensor detecting that an entry barrier of the platform has been closed.