NOVEL TECHNIQUE OF DISPLACEMENT AND ROTATION MEASUREMENT

• US 20160146600A1
• Filed: 11/24/2014
• Published: 05/26/2016
• Est. Priority Date: 11/22/2013
• Status: Abandoned Application

• Current Assignee: Shervin Taghavi Larigani
• Original Assignee: Shervin Taghavi Larigani
• Inventors: Taghavi Larigani, Shervin
• Application Number:
  US14/552,385
• Publication Number:
  US 20160146600A1
• Time in Patent Office: Days
• CPC Class Codes: G01B 11/002 for measuring two or more coordinates
• CPC Class Codes: G01B 11/14 for measuring distance or clearance between spaced objects or spaced apertures G01B11/26 takes precedence; rangefinders G01C3/00
• CPC Class Codes: G01B 21/16 for measuring distance of clearance between spaced objects
• CPC Class Codes: G01B 9/02048 Rough and fine measurement
• CPC Class Codes: G01C 3/10 using a parallactic triangle with variable angles and a base of fixed length in the observation station, e.g. in the instrument
• CPC Class Codes: G01S 5/16 using electromagnetic waves other than radio waves
• CPC Class Codes: G01S 5/163 Determination of attitude using inertial means G01C9/00; control of attitude G05D1/49
• CPC Class Codes: G01S 5/18 using ultrasonic, sonic, or infrasonic waves

Abstract

A simple and reliable novel method providing the ability to measure the spatial relative displacement and rotation of objects as well as the measurement of the different degrees of freedom of each object (i.e. rotations and translations) relative to an inertial frame. This novel technique relies on the measurement of the center of an energy pattern emanating from a source to a detector. Our technique can be used in diverse applications like remote sensing, as it applies to earth/planetary and geo sciences, oil/gas exploration, and mining, civil, structural, medical engineering, and homeland security & defense, among others.

Claims

General. General Claim

System. System Claim

1. A method for measuring displacement and rotation, comprising:

A network of objects composed of (one, two or many more) objects. From an object acting as a source emanates an energy beam pattern travelling toward another object, which acts as a detector. At the detector, the position of the center of the pattern of the beam of energy is measured and hence its position relative to the detector is measured using centroid algorithms. Thus the projection of the displacement of the source relative to the detector on the plane of the detector is measured. Following this line of thought the three dimensional relative displacement could be measured if another energy beam pattern emanating from the source at a different angle or the same energy beam but received at another angle at the detecting is monitored. The combination of the two latter measurements yields to deduce the three dimensional motion of the source relative to the detector.

2. The method of claim 1 where the energy beam pattern is composed is energy wave. As an example among others an energy wave pattern could include:

Any electromagnetic waves;

Several examples;

light.

Infra red

Black body radiation

Any other frequency spectrum

Heat waves.

Any mechanical wave

Several examples;

Any acoustic waves.

Ultrasound

Pressure waves.

3. The method of claim 1, where the emanating energy beam pattern is composed of particles. As an example among others an energy particle pattern could be composed of electrons, neutrons, positrons, ions, atoms, molecules or projectiles or any other particles.

4. The method of claim 1, wherein the emitted beam of energy and the corresponding energy pattern is the result of the reflection or scattering of an energy beam on the source. An example of such occurrence pertains to the image of an object with the presence of ambient light. The perceived image of the object is the scattered of ambient light by the object.

5. The method of claim 1, wherein the energy beam pattern is generated at the source.

6. The method of claim 1, wherein:

relative displacement of the energy pattern as small as

7. The method of claim 1 where each object or station is used as a multipurpose different measurement instruments calculating any output parameters that one could calculate from the fundamental variable measured at each station using our technique. Few example among many others are:

accelerometer, torque measurement, seismometer, strain meter, gravitymeter,

8. The method of claim 1, where a multitude of energy beam pattern from a single source travelled to a plural number of detectors. In other words the same source is at the same time the source or target of several different detectors.

9. The method of claim 1, where the detector monitors simultaneously a multitude of energy patterns from different sources or targets.

10. The method of claim 1, where the detector can contain both a precise measure technique allowing to reach its shot noise measurement limit, but as well processing a detecting scheme with large Field of View thus maximizing the maximum range of operation. By combining both technique together the detector could reach a measurement dynamic range (defined as the ratio between the largest and smallest possible measurable values) of 10 orders of magnitude, which is the dynamic range of Earth

11. The method of claim 1, where a single device automatically detects a feature on the target and from then on monitored it automatically

12. We claim a network of objects or stations performing the measurement of relative displacement and rotations between the stations or objects using the technique we introduced. The network is composed of unit stations or objects, as depicted in FIG. 15. It could be either composed of one, two or many more objects. One could envisage that the network is composed of homogenous unit stations as depicted in FIG. 2(a) but we also expect to have a hybrid inhomogeneous network. Each station or object of the network could be located either on a moving object such as (satellites, airplanes, balloons, or cars, or others etc. . . . ) or static, like stations on the ground, or composed of both moving and static objects. Each station or object of the network could be located underground, under sea, on ground, on sea in air or in space. Each one of them could be static or mobile and different that the others.

13. Method of claim [12] where an object or station of the network is defined:

i) As an entity that is a target for one or several other detectors on the network or

ii) A detector of one or several other targets (sources) in the network, or

iii) Both the combination of the two previous cases.

14. Method of claim [12] where one station is composed of an object where the user could select the target and the object automatically selects a feature on the target via various images processing feature detection for example similar as to face detection scheme on digital cameras. On mean of doing so would as an example consists of applying an image gradient operator on the filter. We could also add to that unit station of the network an Electronic Distance Measurement (EDM) which measures the range distance to the target, while our imaging scheme measures the transversal position of the target on the screen of the detector. By combing these two techniques in one station, we could monitor the relative displacement of the target relative to the detector as well as its rotations. Indeed if D is the diameter of the lens or the aperture at the detector, the Field Of View is proportional to

15. Method of claim [12] where the nature of the energy beam pattern emanating from the sources and detected by the detectors could be of different physical natures within the same network.

16. Method of claim [12] where each station or object could act as the source for one, two or as many other objects in the network.

17. Method of claim [12] where each station or object could act as the detector of one, two or many other objects in the network simultaneously.

18. Method of claim [12] where we design the network such the number of independent measurements is increased such its exceed the number of variables (composed of the rotations and translations of each single unit relative to a inertial frame) not only we would be able to calculate the relative displacements and rotations inside the network but we will be able to calculate absolute rotations and translations of single objects relative to a inertial reference.