Systems and methods of sensor data fusion
Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.
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 and at least one fusion engine;
at least one first sensor operable to capture a first position measurement of a robotic component; and
at least one second sensor operable to capture a second position measurement 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 curation engine is operable to curate the first position measurement and the second position measurement, and the at least one fusion engine is operable to fuse the first position measurement and the second position measurement;
wherein the at least one curation engine is operable to curate the first position measurement by categorizing the first position measurement into at least one first position property and/or at least one first position sub-property;
wherein the at least one curation engine is operable to curate the second position measurement by categorizing the second position measurement into at least one second position property and/or at least one second position sub-property;
wherein the at least one fusion engine is operable to fuse the curated first position measurement and the curated second position measurement, wherein the at least one fusion engine creates at least one new data set; and
wherein the at least one computer processor is operable to instruct the robotic component based on the at least one new data set.
2 . The system of claim 1 , wherein the at least one first position property and/or the at least one first position sub-property and the at least one second position property and/or the at least one second position sub-property includes a timestamp and/or a measurement over a common period of time.
3 . The system of claim 2 , wherein the at least one curation engine is further operable to curate the first position measurement and the second position measurement based in part on the timestamp and/or measurement over the common period of time.
4 . The system of claim 1 , wherein the at least one curation engine is operable to use artificial intelligence to automatically categorize the first position measurement into the at least one first position property and/or the at least one first position sub-property and the second position measurement into the at least one second position property and/or the at least one second position sub-property based in part on historical accuracy of previous categorizations by an artificial intelligence engine.
5 . The system of claim 1 , wherein the at least one curation engine is operable to curate heterogeneous, partially heterogeneous, or homogeneous properties and/or sub-properties.
6 . The system of claim 1 , wherein the at least one computer processor is located on or in a machine, an edge device, at least one server, and/or a cloud.
7 . The system of claim 1 , wherein the at least one first sensor and/or the at least one second sensor is operable to include an accelerometer, a gyroscope, a force/torque sensor, a proximity sensor, a gear sensor, a magnetic field sensor, angle sensor, and/or a 6-axis combo inertial sensor.
8 . The system of claim 1 , wherein the at least one first position property and/or the at least one first position sub-property is operable to include acceleration, tilt, and a position of the robotic component, wherein the at least on second position property and/or the at least one second position sub-property is operable to include a change in magnetic field strength and the position of the robotic component.
9 . 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 and at least one fusion engine;
at least one first sensor capturing a first position measurement of a robotic component;
at least one second sensor capturing a second position measurement of the robotic component;
analyzing by the at least one computer processor the first position measurement and the second position measurement;
curating by the at least one curation engine the first position measurement and the second position measurement, and fusing by the at least one fusion engine the first position measurement and the second position measurement;
curating by the at least one curation engine the first position measurement by categorizing the first position measurement into at least one first position property and/or at least one first position sub-property;
curating by the at least one curation engine the second position measurement by categorizing the second position measurement into at least one second position property and/or at least one second position sub-property;
fusing by the at least one fusion engine the curated first position measurement and the curated second position measurement, wherein the at least one fusion engine creates at least one new data set; and
instructing the robotic component by the at least one computer processor based on the at least one new data set.
10 . The method of claim 9 , wherein the at least one first sensor and/or the at least one second sensor is operable to include an accelerometer, a gyroscope, a force/torque sensor, a proximity sensor, a gear sensor, a magnetic field sensor, angle sensor, and/or a 6-axis combo inertial sensor.
11 . The method of claim 9 , further comprising categorizing via the at least one curation engine using artificial intelligence the at least one first position property and/or the at least one first position sub-property and the at least one second position property and/or the at least one second position sub-property based in part on historical accuracy of previous categorizations by the at least one curation engine.
12 . The method of claim 9 , further comprising curating the first position measurement and the second position measurement in real-time.
13 . The method of claim 9 , wherein the at least one first position property and/or the at least one first position sub-property and the at least one second position property and/or the at least one second position sub-property includes a timestamp and/or a measurement over a common period of time.
14 . The method of claim 13 , further comprising curating the first position measurement and the second position measurement based in part on the timestamp and/or measurement over the common period of time.
15 . The method of claim 9 , wherein the at least one first position property and/or the at least one first position sub-property and/or the at least one second position property and/or the at least one second position sub-property is not associated with time.
16 . The method of claim 9 , further comprising curating heterogeneous, partially heterogeneous, or homogeneous properties and/or sub-properties.
17 . 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 and at least one fusion engine; and
at least two sensors, each of the at least two sensors operable to measure a first velocity of a robotic component and a second velocity of the robotic component;
wherein the at least one computer processor is operable to analyze the first velocity and the second velocity;
wherein the at least one curation engine is operable to curate the first velocity and the second velocity, and the at least one fusion engine is operable to fuse the first velocity and the second velocity;
wherein the at least one curation engine is operable to curate the first velocity by categorizing the first velocity into at least one first velocity property and/or at least one first velocity sub-property based in part on at least one query;
wherein the at least one curation engine is operable to curate the second velocity by categorizing the second velocity into at least one second velocity property and/or at least one second velocity sub-property based in part on the at least one query;
wherein the at least one curation engine is operable to filter the first velocity and the second velocity;
wherein the at least one fusion engine is operable to fuse the curated first velocity and the curated second velocity, wherein the at least one fusion engine creates at least one new data set; and
wherein the at least one computer processor is operable to instruct the robotic component based on the at least one new data set.
18 . The system of claim 17 , wherein the at least one curation engine is further operable to curate the first velocity and the second velocity based in part on a timestamp and/or a measurement over a common period of time.
19 . The system of claim 17 , wherein the at least one curation engine is operable to curate heterogeneous, partially heterogeneous, or homogeneous properties and/or sub-properties.
20 . The system of claim 17 , wherein the at least one curation engine is operable to use artificial intelligence to automatically categorize the first velocity into the at least one first velocity property and/or the at least one first velocity sub-property and the second velocity into the at least one second velocity property and/or the at least one second velocity sub-property based in part on historical accuracy of previous categorizations by an artificial intelligence engine.