Granted Patent B2

US 12,461,997 · App. 19/016,257 · Granted Nov 4, 2025

Inventor: Armando Montalvo (Winter Garden, FL)

Assignee: Digital Global Systems, Inc.

G06F18/256B25J9/163B60R19/483B60T8/1755B60T8/3275B60W10/18B60W10/184B60W30/085B60W30/09B60W60/0018B60W60/00186B62D15/0285G01C3/00G01C21/3804G01C21/3811G01C21/3848G01C22/00G01S5/14G01S11/00G01S13/08G01S13/103G01S13/42G01S15/08G01S15/101G01S15/42G01S17/08G01S17/88G01S17/894G05B13/0205G06F7/14G06F7/16G06F16/24G06F16/245G06F16/2455G06F16/24556G06F16/2456G06F16/33G06F16/334G06F16/43G06F16/53G06F16/903G06F16/90335G06F16/9035G06F17/18G06F18/217G06F18/2431G06F18/25G06F18/251G06N3/02G06N3/0464G06N5/022G06N5/042G06N5/045G06N5/046G06N5/048G06T7/521G06V10/764G06V10/803G06V10/82G06V20/56H04L67/12H04W4/38B25J9/1664B25J9/1694B60T2201/00B60T2201/03B60W2050/0052B60W60/00B60W2420/00B60W2420/40B60W2420/403B60W2420/408B60W2420/50B60W2510/069B60W2510/18B60W2520/04B60W2540/12B60W2554/801B60W2554/802B60W2556/35B60W2710/18B60W2754/30G01C21/1652G01S7/4808G01S2013/93185G05D2101/15G05D2111/50G05D2111/67G06F18/213G06N5/04G06N20/00G06T2207/10028G06T2207/20024G06T2207/20084G06T2207/30252G06T2207/30264G06V10/80G08B29/188G08G1/0133G08G1/04G08G1/042

Abstract

Claims (64)

1 . A system for sensor data fusion for sensor management and utilization in robotics, comprising:

at least one computer processor including a memory;

at least one curation engine, at least one link engine, at least one fusion engine, at least one inference engine, and at least one validation engine;

at least one first position sensor operable to capture a first position measurement corresponding to a first position of a robotic component; and

at least one second position sensor operable to capture a second position measurement corresponding to the first position of the robotic component;

wherein the at least one computer processor is operable to analyze the first position measurement and the second position measurement;

wherein the at least one computer processor is operable to receive at least one query;

wherein the at least one curation engine is operable to curate the first position measurement and the second position measurement, the at least one link engine is operable to link the first position measurement and the second position measurement via calculating a conditional entropy between the first position measurement and the second position measurement, the at least one fusion engine is operable to fuse the first position measurement and the second position measurement, thereby creating fused data, the at least one inference engine is operable to determine at least one inference from the first position measurement and the second position measurement, and the at least one validation engine is operable to validate the first position measurement and the second position measurement;

wherein the fused data includes at least one new data set;

wherein the at least one inference engine determines the at least one inference by using artificial intelligence based in part on the at least one query and/or the at least one new data set;

wherein the at least one validation engine is operable to actively validate the fused data;

wherein the actively validating the fused data includes modification of position of the at least one first position sensor, and the at least one first position sensor is operable to capture a third position measurement at the modified position to generate a second data set;

wherein the actively validating the fused data further includes the at least one validation engine comparing the fused data to the second data set via a statistical comparison;

wherein the at least one inference engine is operable to respond to the at least one query based in part on the at least one inference;

wherein the at least one inference engine is operable to determine internal sensor damage for the at least one first position sensor and the at least one second position sensor based on the at least one inference; and

wherein the at least one computer processor is operable to instruct the robotic component to move to a second position based on the at least one new data set and the first position measurement and the second position measurement.

2 . The system of claim 1 , wherein the response to the at least one query includes a position of the robotic component.

3 . The system of claim 1 , wherein the at least one query is user and/or computer generated.

4 . The system of claim 1 , wherein the at least one inference engine is operable to determine which of the at least one first position sensor and/or the at least one second position sensor the at least one computer processor responds to based in part on the at least one inference.

5 . The system of claim 1 , wherein the at least one inference includes a numerical value.

6 . The system of claim 1 , wherein the at least one inference includes a prediction of a future event based in part on the fused data.

7 . A method for sensor data fusion for sensor management and utilization in robotics, comprising:

providing at least one computer processor including a memory;

providing at least one curation engine, at least one link engine, at least one fusion engine, at least one inference engine, and at least one validation engine;

at least one first position sensor capturing a first position measurement corresponding to a first position of a robotic component;

at least one second position sensor capturing a second position measurement corresponding to the first position of the robotic component;

analyzing by the at least one computer processor the first position measurement and the second position measurement;

receiving by the at least one computer processor at least one query;

curating by the at least one curation engine the first position measurement and the second position measurement, linking by the at least one link engine the first position measurement and the second position measurement via calculating a conditional entropy between the first position measurement and the second position measurement, fusing by the at least one fusion engine the first position measurement and the second position measurement, thereby creating fused data, determining at least one inference by the at least one inference engine from the first position measurement and the second position measurement, and validating by the at least one validation engine the first position measurement and the second position measurement;

wherein the fused data includes at least one new data set;

determining via the at least one inference engine the at least one inference by using artificial intelligence based in part on the at least one query and/or the at least one new data set;

actively validating by the at least one validation engine the fused data;

wherein the actively validating the fused data includes modifying position of the at least one first position sensor, and the at least one first position sensor is operable to capture a third position measurement at the modified position to generate a second data set;

comparing via the at least one validation engine as part of the actively validating the fused data to the second data set via a statistical comparison;

determining by the at least one validation engine if the statistical comparison exceeds a predefined threshold to automatically validate the at least one inference in real time;

responding via the at least one inference engine the at least one query based in part on the at least one inference;

determining via the at least one inference engine internal sensor damage for the at least one first position sensor and the at least one second position sensor based on the at least one inference;

instructing by the at least one computer processor movement of the robotic component to a second position based on the at least one new data set and the first position measurement and the second position measurement.

8 . The method of claim 7 , wherein responding via the at least one inference engine to the at least one query includes a position of the robotic component.

9 . The method of claim 7 , wherein the at least one inference includes a numerical value.

10 . The method of claim 7 , further comprising predicting via the at least one inference engine a future event based in part on the fused data.

11 . The method of claim 7 , wherein the at least one query is user and/or computer generated.

12 . The method of claim 7 , further comprising determining via the at least one inference engine which of the at least one first position sensor and/or the at least one second position sensor the at least one computer processor responds to based in part on the at least one inference.

13 . A system for sensor data fusion for sensor management and utilization in robotics, comprising:

at least one computer processor including a memory;

at least one curation engine, at least one link engine, at least one fusion engine, at least one inference engine, and at least one validation engine; and

at least two sensors, each of the at least two sensors operable to measure a first position of a robotic component and a second position of the robotic component;

wherein the at least one computer processor is operable to analyze the first position and the second position;

wherein the at least one computer processor is operable to receive at least one query;

wherein the at least one curation engine is operable to curate the first position and the second position, the at least one link engine is operable to link the first position and the second position via calculating a conditional entropy between the first position measurement and the second position measurement, the at least one fusion engine is operable to fuse the first position and the second position, thereby creating fused data, the at least one inference engine is operable to determine at least one inference from the first position and the second position, and the at least one validation engine is operable to validate the first position and the second position;

wherein the fused data includes at least one new data set;

wherein the at least one inference engine determines the at least one inference by using artificial intelligence based in part on the at least one query and/or the at least one new data set;

wherein the at least one inference engine is operable to respond to the at least one query based in part on the at least one inference;

wherein the at least one validation engine is operable to actively validate the fused data;

wherein the actively validating the fused data includes modification of position of the at least one first position sensor, and the at least one first position sensor is operable to capture a third position measurement at the modified position to generate a second data set;

wherein the actively validating the fused data further includes the at least one validation engine comparing the fused data to the second data set via a statistical comparison;

wherein the at least one inference engine is operable to determine internal sensor damage for the at least two sensors based on the at least one inference; and

wherein the at least one computer processor is operable to instruct the robotic component to move to the third position based on the at least one new data set and the first position and the second position.

14 . The system of claim 13 , wherein the response to the at least one query includes a position of the robotic component.

15 . The system of claim 13 , wherein the at least one inference engine is operable to determine which of the at least one first position sensor and/or the at least one second position sensor the at least one computer processor responds to based in part on the at least one inference.

16 . The system of claim 13 , wherein the at least one inference includes a numerical value.

17 . The system of claim 13 , wherein the at least one inference engine is operable to determine the at least one inference in real-time.

18 . The system of claim 13 , wherein the at least one inference includes a prediction of a future event based in part on the fused data.

19 . The system of claim 13 , wherein the at least one new data set includes an accuracy value for the at least two sensors.

Assignments (1)

Continuity (3)

References Cited (154)