AUTOMATED ASSET POSITIONING FOR LOCATION AND INVENTORY TRACKING USING MULTIPLE POSITIONING TECHNIQUES

• US 20070222674A1
• Filed: 03/26/2007
• Published: 09/27/2007
• Est. Priority Date: 03/24/2006
• Status: Active Grant

• Current Assignee: Mi-Jack Products Incorporated (Lanco International, Inc.)
• Original Assignee: ContainerTrac, Inc. (Lanco International, Inc.)
• Inventors: Warf, Gregory Keith, Tan, Han-Shue, Henry, Larry
• Granted Patent: US 7,646,336 B2
• Time in Patent Office: Days
• US Class Current: 342/357.320
• CPC Class Codes: G01S 19/485 whereby the further system is an optical system or imaging system
• CPC Class Codes: G01S 19/49 whereby the further system is an inertial position system, e.g. loosely-coupled
• CPC Class Codes: G06Q 10/08 Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
• CPC Class Codes: G06Q 10/087 Inventory or stock management, e.g. order filling, procurement or balancing against orders

Abstract

A system and method is provided for tracking and maintaining a highly accurate inventory of shipping containers that are stored within container storage facilities. The invention includes using multiple complementary real-time and post-processing positioning techniques associated with various positioning sensors that are associated with inventory pieces or equipment. Examples of such positioning techniques are DGPS, GPS with RTK, DGPS loosely-coupled with INS, DGPS tightly-coupled with INS, and DGPS deeply-coupled with INS. Data correction and fusion techniques are applied to these positioning stages to re-compute a calibrated position with an improved accuracy. An additional trajectory can be iteratively determined using the fusing technique until the position data becomes statistically trustworthy. Further, combinations of multiple real-time positioning techniques combined with past position error correction algorithms provide a high accuracy needed for inventory tracking.

Claims

1. An improved position tracking system comprising:

position sensor systems, each sensor system including one or more sensors that provide signals indicating a location of a mobile object that are used to determine position data for the mobile object;

a decision making module for receiving the position data output from each of the position sensor systems, and for performing an analysis to enable combining the position data outputs to provide position solution data with a higher degree of accuracy than the position data provided from a single one of the position sensor systems; and

a fusion module for combining the data according to the analysis of the decision making module.

2. The system of claim 1, further comprising:

a data storage unit for storing at least part of the position data outputs from each of the position sensor systems as past position data,

wherein the decision making module further receives the past position data from the data storage unit and performs the analysis using the past position data.

3. The system of claim 2, wherein the fusion module provides an output after iteratively adjusting data from the position sensor systems and the data storage unit using mathematical formula a number of times until data obtained from the mathematical formula is statistically trustworthy based on a predetermined criteria.

4. The system of claim 1, wherein the position sensor systems comprise a combination of at least a portion of:

a tightly coupled DGPS/INS integration filter, DGPS data determined using receiver software, an integrated DGPS and motion sensors, DGPS combined with dead reckoning sensors, DGPS with an RTK algorithm, RFID tag with triangulation algorithm, digital map with imaging processing locating algorithm, Real Time Locating System (RTLS) with a DGPS validation algorithm, and RTLS combined with DGPS.

5. The system of claim 1, wherein the decision making module further uses at least one of an RFID tag, a compass, a magnetometer, an altimeter, a laser, a camera, a radar, and a RF beacon transmitter to perform their analysis.

6. The system in claim 1, wherein the decision making module further uses at least one of the following data to perform the analysis:

(a) a digital map, (b) rules relating operation of the mobile object, (c) inventory information indicating a location of the mobile object, (d) an output from a sensor providing information identifying the mobile object, (e) an output from a sensor indicating the mobile object is arriving at a specific location, and (f) an output from a sensor indicating a specific operation relating to the mobile object has taken place.

7. The system of claim 1, wherein the decision making module further uses operational rules for position location in the analysis, including at least one of:

identification codes, storage height, storage row number, storage isle number, surrounding environment that can cause movement blockage, a dynamic map of current inventory, and positions of nearby vehicles.

8. The system of claim 1, wherein the decision making module or the fusion module performs filtering of the combined data outputs to provide the position solution data.

9. The system of claim 8, wherein the filter comprises a recursive state estimation filter.

10. The system of claim 8, wherein the recursive state estimation filter comprises a Kalman filter.

11. The system of claim 8, wherein the filter uses a method including at least one of a probabilistic data association method, fuzzy logic rules, neural network, information-based or cognitive-based algorithm, and rule based voting.

12. The system of claim 1, wherein the decision making module performs a weighting to combine percentages of each of the combined data outputs to form the position solution data.

13. The system of claim 1, wherein the decision making module performs a selection to determine which of the combined data outputs are used to provide the position solution data.

14. The system of claim 1, wherein the position sensor systems each provide data from a combination of two or more systems, and wherein at least two of the position sensor systems provide a different combination to data from a same set of sensors.

15. The system of claim 2,

an error correction module, wherein the fusion module provides the combined data to the error correction module, and wherein the error correction module further receives the past position data from the data storage unit and performs an analysis to provide error corrected data.

16. The system of claim 15, wherein the error correction module provides the error corrected data to the data storage unit to provide additional past position data.

17. The system in claim 15, wherein the error correction module further uses at least one of the following data to generate error corrected data:

(a) a digital map, (b) rules relating operation of the mobile object, (c) inventory information indicating a location of the mobile object, (d) an output from a sensor providing information identifying the mobile object, (e) an output from a sensor indicating the mobile object is arriving at a specific location, and (f) an output from a sensor indicating a specific operation relating to the mobile object has taken place.

18. The system in claim 15, wherein the error correction module generates the error correction data by iteratively adjusting the data from the fusion module and the data storage unit using mathematical formula a number of times until data obtained from the mathematical formula is statistically trustworthy based on a predetermined criteria.

19. The system of claim 1, further comprising a central positioning control module wherein the position sensor systems, decision making module and fusion module reside in the mobile object, and wherein the fusion module is wirelessly linked to the central positioning control module.

20. The system of claim 1, further comprising a central positioning control unit, wherein the position sensor systems reside in the mobile object and are wirelessly linked to the decision making module, wherein the decision making module and fusion module reside in the central positioning control module.

21. A position tracking system comprising:

positioning sensors associated with a first mobile object, each positioning sensor for providing first position data for the mobile object;

combining modules, each combining module receiving the first position data from at least some of the position sensors and providing a combined data output;

a correlation module for receiving the combined data output from each of the combining modules, and for performing an analysis to enable combining the combined data outputs to provide position solution data with a higher degree of accuracy than a single one of the combined data outputs; and

a fusion module for combining the data according to the analysis of the correlation module.

22. The position tracking system of claim 21, wherein the positioning sensors comprise at least some of GPS, INS, DGPS, RTLS, laser/RF/optical range sensors, motion sensors, and attitude sensors.

23. The position tracking system of claim 21, wherein the combining modules provide a combination of at least some of:

tightly coupled DGPS/INS, Dual Antenna DGPS, Loosely coupled DGPS/INS, DGPS with motion sensors from a vehicle, DGPS and motion heuristic integration, DGPS and RFID integration, position sensors and digital map integration, and INS motion sensor dead reckoning.

24. A method for providing position data for a mobile object comprising:

obtaining position data indicative of a position of the mobile object;

correlating the position data from each of the sensing systems to analyze data segments and provide an indication of the accuracy of each of the data segments relative to the others; and

fusing the data according to the indication provided by the correlation step.

25. The method of claim 24, wherein the position data is provided from a combination of at least some of the following sensing systems:

tightly coupled DGPS/INS, Dual Antenna DGPS, Loosely coupled DGPS/INS, DGPS with motion sensors from a vehicle, DGPS and motion heuristic integration, DGPS with map integration, DGPS and RFID integration, motion sensor with map association, and INS motion sensor dead reckoning.