NL2032291B1 - Attitude determination method for drilling tool of drilling robot for rockburst prevention based on redundant inertial units - Google Patents

Attitude determination method for drilling tool of drilling robot for rockburst prevention based on redundant inertial units Download PDF

Info

Publication number
NL2032291B1
NL2032291B1 NL2032291A NL2032291A NL2032291B1 NL 2032291 B1 NL2032291 B1 NL 2032291B1 NL 2032291 A NL2032291 A NL 2032291A NL 2032291 A NL2032291 A NL 2032291A NL 2032291 B1 NL2032291 B1 NL 2032291B1
Authority
NL
Netherlands
Prior art keywords
inertial
drilling
data
redundant
rockburst
Prior art date
Application number
NL2032291A
Other languages
Dutch (nl)
Other versions
NL2032291A (en
Inventor
Dai Jianbo
Si Lei
Wang Hao
Wang Zhongbin
Gu Jinheng
Yan Haifeng
Li Jiahao
Lu Xuliang
Tan Chao
Wei Dong
Original Assignee
Univ China Mining
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Univ China Mining filed Critical Univ China Mining
Publication of NL2032291A publication Critical patent/NL2032291A/en
Application granted granted Critical
Publication of NL2032291B1 publication Critical patent/NL2032291B1/en

Links

Classifications

    • E—FIXED CONSTRUCTIONS
    • E21—EARTH DRILLING; MINING
    • E21B—EARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B7/00—Special methods or apparatus for drilling
    • E21B7/02—Drilling rigs characterized by means for land transport with their own drive, e.g. skid mounting or wheel mounting
    • E21B7/025—Rock drills, i.e. jumbo drills
    • E—FIXED CONSTRUCTIONS
    • E21—EARTH DRILLING; MINING
    • E21B—EARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B44/00—Automatic control systems specially adapted for drilling operations, i.e. self-operating systems which function to carry out or modify a drilling operation without intervention of a human operator, e.g. computer-controlled drilling systems; Systems specially adapted for monitoring a plurality of drilling variables or conditions
    • E—FIXED CONSTRUCTIONS
    • E21—EARTH DRILLING; MINING
    • E21B—EARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B15/00—Supports for the drilling machine, e.g. derricks or masts
    • E21B15/003—Supports for the drilling machine, e.g. derricks or masts adapted to be moved on their substructure, e.g. with skidding means; adapted to drill a plurality of wells
    • E—FIXED CONSTRUCTIONS
    • E21—EARTH DRILLING; MINING
    • E21B—EARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B15/00—Supports for the drilling machine, e.g. derricks or masts
    • E21B15/04—Supports for the drilling machine, e.g. derricks or masts specially adapted for directional drilling, e.g. slant hole rigs
    • E—FIXED CONSTRUCTIONS
    • E21—EARTH DRILLING; MINING
    • E21B—EARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B47/00—Survey of boreholes or wells
    • E21B47/02—Determining slope or direction
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/084—Backpropagation, e.g. using gradient descent
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/086—Learning methods using evolutionary algorithms, e.g. genetic algorithms or genetic programming
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation

Abstract

A method for determining an attitude determination of a drilling tool of drilling robot for rockburst prevention based on redundant inertial units. The method includes the following steps: five paths 5 of inertia units are fixedly connected to vertices and a center of a tetrahedron respectively to form an inertia sensor; data in the five paths of the inertial units are calculated by a self-def1ned fusion formula according to a set ratio; an attitude error model measured by the inertial sensor is imported into a neural network for training to obtain a well-trained neural network prediction model; data measured by the inertial sensor are imported into the well-trained neural network prediction model 10 for a error prediction; and a neural network prediction error is imported into a calculation result of the inertial sensor; and the neural network prediction error is compensated. FIG.1

Description

ATTITUDE DETERMINATION METHOD FOR DRILLING TOOL OF
DRILLING ROBOT FOR ROCKBURST PREVENTION BASED ON
REDUNDANT INERTIAL UNITS
TEAHNICAL FIELD
The present disclosure relates to the technical field of an attitude determination method for drilling tool of drilling robot for rockburst prevention based on redundant inertial units, in particular, to an attitude determination method for drilling tool of drilling robot for rockburst prevention based on redundant inertial units.
BACKGROUND
With the development of science and technology, the automation degree of downhole drilling equipment has been significantly improved. Coal mine anti-impact drilling robots are used for the constructions of various engineering holes such as anti-impact pressure relief holes. Conventional coal mine drilling rigs usually adjust the attitudes of the drilling rigs in a fixed position, and at the same time, one or two persons are required to assist to observe the operation situations of the frames, and therefore, the drilling efficiency is low and the labor intensity of construction personnel is high, which cannot meet the requirements of fast anti-impact pressure relief drilling.
In order to improve the performance of the drilling rigs, reduce manpower and increase efficiency, it 1s necessary for the drilling rigs to adjust the attitudes of the drilling tools automatically, and the accurate attitudes of the drilling tools are the basis for the automatic adjustments of the drilling tools.
Therefore, an attitude determination method for drilling tool of drilling robot for rockburst prevention based on redundant inertial units is urgently needed to solve the above problems.
SUMMARY
In order to solve the above problems comprehensively, especially in view of the deficiencies in the prior art, the present disclosure provides a attitude determination method for drilling tool of drilling robot for rockburst prevention based on redundant inertial units, which can solve the above problems comprehensively.
In order to achieve above objective, the following technical solutions are adopted by the present disclosure.
An attitude determination method for drilling tool of drilling robot for rockburst prevention based on redundant inertial units includes the following steps.
In S1, five paths inertial units are fixedly and respectively connected to vertices and a center of a tetrahedron to form an inertial sensor.
In S2, data in five paths of the inertial units is calculated by a self-defined fusion formula according to a set ratio.
In S3, an attitude error model measured by the inertial sensor is imported into a neural network for training, and a well-trained neural network prediction model is obtained.
In S4, data measured by the inertial sensor is imported into the well-trained neural network prediction model for a error prediction.
In S5, prediction error of the neural network is imported into the calculation result of inertial sensor and compensated.
Preferably, the inertial sensor is fixedly connected to a lateral part of a motion mechanism of the anti-impact drilling robot, and the inertial sensor collects an angular velocity, an acceleration and magnetic force information of the motion mechanism.
The inertial sensor is communicated and connected with a microcomputer through a communication line, and the microcomputer reads and stores the data collected by the inertial sensor.
The microcomputer is communicated with a PC upper computer, the data stored by the microcomputer is sent to the PC upper computer, the PC upper computer fuses data in the five paths, conducts denoising and filtering operations on fused data, and display the data.
Preferably, the anti-impact drilling robot comprises an anti-impact drilling robot base, an azimuth angle rotary mechanism being rotatably connected to a top end of the anti-impact drilling robot base, a pitch angle motion mechanism is arranged at a top end of the azimuth angle rotary mechanism, the pitch angle motion mechanism is connected to the azimuth angle rotary mechanism in a lifting manner through a hydraulic cylinder, the pitch angle motion mechanism is further connected to the azimuth angle rotary mechanism in a guiding manner through a guide column, a drill rod is arranged outside the pitch angle motion mechanism, the drilling rod is connected to the pitch angle motion mechanism through a frame, and the inertial sensor is connected to a side wall of the pitch angle motion mechanism.
Preferably, the inertial units of MPU9250 produced by InvernSense Company are adopted as the redundant inertial units.
Preferably, the microcomputer is a Raspberry Pi, and Raspberry P14 Model B produced by
Amazon is adopted as the Raspberry Pi.
Preferably, the inertial units of MPU9250 communicate with a microcomputer through an I2C protocol.
Preferably, the microcomputer is simultaneously connected with five inertial units through an 12C interface, and the microcomputer accesses each of the inertial units of MPU9250 one by one to read data to collect the data of the inertial sensor.
Preferably, the microcomputer transmits data stored by the microcomputer to the PC upper computer through a serial port line.
Preferably, a fusion of the data in the five paths is calculated according to the self-defined fusion formula in a ratio of 4:1.5:1.5:1.5: 1.5.
Preferably, a wavelet threshold denoising is adopted as the denoising algorithm.
The beneficial effects of the present disclosure are that: the present disclosure is capable of acquiring the angular velocity, acceleration and magnetic force information of the motion mechanism of the anti-impact drilling robot through the inertial sensor without manually observing the operation of the frame. The inertial sensor transmits the collected information to the microcomputer through the communication line, and the microcomputer sends the obtained data information to the PC upper computer. The PC upper computer conducts the fusion of the data in the five paths, denoises and filters the fused data, and displays the data. The drilling rig can quickly and accurately adjust the attitude of the drilling tool through the final obtained data, so that the number of workers is effectively reduced, the amount of labor is effectively reduced, the drilling efficiency is effectively improved, and furthermore, the requirement of rapid anti-impact pressure- relief drilling can be met.
BRIEF DESCRIPTION OF THE DRAWINGS
In order to better illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present disclosure, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of a attitude determination method for drilling tool of drilling robot for rockburst prevention based on redundant inertial units according to the present disclosure.
FIG. 2 is a distribution diagram of a redundant arrangement of five inertial units according to the present disclosure.
FIG. 3 is a first schematic diagram of the anti-impact drilling robot according to the present disclosure.
FIG. 4 1s a second schematic diagram of the anti-impact drilling robot according to the present disclosure.
FIG. 5 is a flow chart of an inertial unit error compensation algorithm for optimizing a BP neural network based on a genetic algorithm according to the present disclosure.
DETAILED DESCRIPTION OF THE EMBODIMENTS
The technical solutions of the present disclosure will be described clearly and completely with reference to the accompanying drawings, and it will be apparent that the described embodiments are some, but not all embodiments of the present disclosure. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present disclosure.
In the description of the present disclosure, it should be noted that the orientations or positional relationships indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., are those based on the orientations or positional relationships illustrated in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present disclosure. Furthermore, the terms "first," "second," and "third" are used for descriptive objectives only and are not to be construed as indicating or implying relative importance.
In the description of the present disclosure, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected with," and "connected to" should be understood in a broad sense, for example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection or an electrical connection; it may be a direct connection or may also be indirectly connected through an intermediate medium, and it may be the internal communication of two elements. The specific meanings of the above terms in the present disclosure can be understood in specific situations for a person skilled in the art.
With reference to FIGS. 1 to 5, the present disclosure provides a attitude determination method for drilling tool of drilling robot for rockburst prevention based on redundant inertial units. The anti-impact drilling robot includes an anti-impact drilling robot base 11, an azimuth angle rotary mechanism 12 being rotatably connected is arranged at a top end of the anti-impact drilling robot base 11, a pitch angle motion mechanism 17 is arranged at a top end of the azimuth angle rotary mechanism 12, the pitch angle motion mechanism 17 is connected to the azimuth angle rotary mechanism 12 in a lifting manner through a hydraulic cylinder 16, the pitch angle motion mechanism 17 is further connected to the azimuth angle rotary mechanism 12 in a guiding manner through a guide column 13, a drilling rod 15 is arranged outside the pitch angle motion mechanism 17, the drilling rod 15 is connected to the pitch angle motion mechanism 17 through a frame 14.
Firstly, the five paths of the inertial units are arranged at the vertices and the center of a tetrahedron to form an inertial sensor 18, the inertial sensor 18 is fixedly connected to the side wall of a pitch angle motion mechanism 17 of a drilling tool of an anti-impact drilling robot, then the inertial sensor 18 is communicated and connected with a Raspberry Pi through a communication line, and then the Raspberry Pi is connected with a PC upper computer. The platform collects an angular velocity, an acceleration and magnetic force information through the inertial sensor 18 installed on the frame of the anti-impact drilling robot, reads and stores the data collected by the inertial sensor 18 through the Raspberry Pi, and then sends the data stored by the Raspberry Pi to the PC upper computer, the PC upper computer fuses the data in the five paths, conducts denoising and filtering operations on the fused data, and display the data. The inertial units of MPU9250 5 produced by InvernSense Company are adopted as the redundant inertial units, and the Raspberry
P14 Model B produced by Amazon is adopted as the Raspberry Pi The inertial units of MPU9250 communicate with a microcomputer through an I2C protocol. The Raspberry Pi is connected with five inertial units of MPU9250 through an I2C interface, and accesses each of the inertial units of
MPU9250 one by one to read data to collect the data of the inertial sensor. The Raspberry Pi processor transmits the stored data to the PC upper computer through a serial port line.
And a fusion of the data in the five paths is calculated according to the self-defined fusion formula in a ratio of 4:1.5:1.5:1.5: 1.5 according to the principle that the inertial units measure more accurately at a center of a carrier.
A wavelet threshold denoising is adopted as the denoising algorithm, the principle of whom is that a threshold is set for wavelet coefficients, the wavelet coefficients higher than the threshold are completely reserved or reserved after proper contraction, all of the wavelet coefficients lower than the threshold are reset to zero, and then a wavelet reconstruction signal which is not zero is selected to obtain a denoised signal.
With reference to FIG. 2, each circle in the figure represents an inertial unit of MPU9250, according to the right-hand screw rule, if there is a cross in the circle, the cross in the circle indicates that when the four fingers of the right hand are turned from the x-axis to the y-axis, the thumb points inward, that is, the z-axis points from the outside to the inside. If there is a dot in the circle, it means that when the four fingers of the right hand are turned from the x-axis to the y-axis, the thumb is outward, that is, the z-axis points from the inside to the outside. As can be seen from the figure, the sensitive axes of the inertia units on the four vertices of the Mitsubishi cone are not completely consistent in pointing direction, and the included angle between the two inconstant axes is a straight angle. The structure greatly reduces the fixed offset errors caused by external factors such as temperature and vibration during the measurement on the inertial units, which 1s installed on the drilling tool, can also greatly offsets the undetermined errors during the measurement on the inertial units, and can also eliminate various errors such as some cone errors, thereby greatly improving the measurement accuracy of the inertial system.
According to the tetrahedral symmetrical installation layout mode adopted by the inertial units and the measurement principle of the inertial units, a fusion equation of the sensor can be obtained:
A fusion equation for the data of an angular velocity in the inertial unit of MPU9250 is as follows:
Oy Dy 2 Dy Dy Ds a = ee a oe omne oma
O0. a, 0, OR o,, 0.
A fusion equation for an acceleration in the inertial unit of MPU9250 is as follows: a, a, ad ag ayy dys in = oe orn aon Lorn co oa a, as 4: Ay a, as
After the above formulas are established, the parameters of the carrier measured by the redundant inertial units can be obtained.
As illustrated in FIG. 5, the specific flow of an error compensation algorithm of the inertial units is as follows: (1) A determining part of BP neural network structure is determined according to the number of input and output parameters of the fitting function, and the length of the genetic algorithm individual is further determined. The weight and the threshold of the BP neural network are optimized by using a genetic algorithm, and each individual in the population includes the weight and the threshold of one network. The individual calculates the individual fitness value through a fitness function, and the genetic algorithm finds the individual corresponding to the optimal fitness value through selection, intersection and variation operations. The prediction on BP neural network uses the genetic algorithm to get the optimal individual to assign the weights and thresholds of the network initial test, and the network predicts the function output after being trained. (2) The data fused by the multiple redundant inertial unit sensors are imported into the trained neural network, and then measurement error prediction values of the inertial units are obtained. (3) The fused data are compensated according to the predicted measurement errors of the inertial units to obtain more accurate data, and the obtained accurate data are imported into an attitude calculation program to obtain a more accurate attitude of the drilling tool of the anti-impact drilling robot.
The present disclosure is illustrated by way of example and not by way of limitation. It will be apparent to those skilled in the art that, although it is not necessary and impossible to list all the implementations here, other variations and modifications may be made in the foregoing disclosure without departing from the spirits or essential characteristics of all implementations, and that the obvious variations or modifications derived therefrom still fall within the protection scope of the present disclosure.

Claims (10)

CONCLUSIESCONCLUSIONS 1. Methode voor het bepalen van een positie van een boorgereedschap van een boorrobot voor de preventie van een rotsuitbarsting op basis van redundante traagheidseenheden, gekenmerkt doordat ze de volgende stappen omvat: sl, het op vaste wijze verbinden van vijf banen van de traagheidseenheden met de basis van de respectievelijke hoekpunten en een middelpunt van een tetraëder om een traagheidssensor te vormen; s2, waarbij de gegevens in de vijf banen van de traagheidseenheden worden berekend met een zelfbepaalde fusieformule volgens een vastgestelde verhouding; s3, het importeren van een door de traagheidssensor gemeten standaard afwijkingsmodel in een neuraal netwerk voor training om een goed getraind neuraal netwerkvoorspellingsmodel te verkrijgen; s4, het invoeren van door de traagheidssensor gemeten gegevens in het goedgetrainde neurale netwerkvoorspellingsmodel voor een foutvoorspelling; en s5, het invoeren van een voorspellingsfout van het neurale netwerk in een berekeningsresultaat van de traagheidssensor en het compenseren van de voorspellingsfout van het neurale netwerk.1. Method for determining a position of a drilling tool of a drilling robot for the prevention of a rockburst based on redundant inertial units, characterized in that it comprises the following steps: sl, fixedly connecting five paths of the inertial units to the base of the respective vertices and a center of a tetrahedron to form an inertial sensor; s2, where the data in the five orbits of the inertial units are calculated with a self-determined fusion formula according to a fixed ratio; s3, importing a standard deviation model measured by the inertial sensor into a neural network for training to obtain a well-trained neural network prediction model; s4, inputting data measured by the inertial sensor into the well-trained neural network prediction model for error prediction; and s5, inputting a prediction error of the neural network into a calculation result of the inertial sensor and compensating for the prediction error of the neural network. 2. Methode voor het bepalen van de positie van het boorgereedschap van een boorrobot voor de preventie van rotsuitbarsting op basis van redundante traagheidseenheden volgens conclusie 1, gekenmerkt doordat de traagheidssensor vast verbonden is met een lateraal deel van een bewegingsmechanisme van de anti-impact boorrobot en de traagheidssensor een hoeksnelheid, een versnelling en magnetische krachtinformatie van het bewegingsmechanisme verzamelt; de traagheidssensor verbonden is met een microcomputer en communiceert via een communicatielijn en de microcomputer de door de traagheidssensor verzamelde gegevens leest en opslaat; en de microcomputer in verbinding staat met een pc-hoofdcomputer, de door de microcomputer opgeslagen gegevens naar de pc-hoofdcomputer worden gezonden, de pc-hoofdcomputer de gegevens in de vijf paden samenvoegt en denoising- en filterbewerkingen uitvoert op de samengevoegde gegevens en hierbij de gegevens weergeeft op een scherm.Method for determining the position of the drilling tool of a drilling robot for rockburst prevention based on redundant inertial units according to claim 1, characterized in that the inertial sensor is fixedly connected to a lateral part of a movement mechanism of the anti-impact drilling robot and the inertial sensor collects angular velocity, acceleration and magnetic force information from the motion mechanism; the inertial sensor is connected to a microcomputer and communicates via a communication line and the microcomputer reads and stores the data collected by the inertial sensor; and the microcomputer is connected to a host PC computer, the data stored by the microcomputer is sent to the host PC computer, the host PC computer merges the data in the five paths and performs denoising and filtering operations on the merged data, thereby displays data on a screen. 3. Methode voor het bepalen van de positie van het boorgereedschap van een boorrobot voor de preventie van rotsuitbarsting op basis van redundante traagheidseenheden volgens conclusie 2, gekenmerkt doordat de anti-impact boorrobot bestaat uit een basis van de anti-impact boorrobot, een roterend mechanisme in de azimuthoek die roterend wordt verbonden aan het hoogste uiteinde van de basis van de anti-effect boorrobot, een bewegingsmechanisme aan de steekhoek die via een hydraulische cilinder die op een liftende manier gemonteerd is aan een hoogste uiteinde van het roterend mechanisme in de azimuthoek, het bewegende mechanisme van de steekhoek verder op geleidende wijze via een geleidende kolom verbonden is met het roterende mechanisme van de azimuthoek, een boorstang aan de buitenzijde van het bewegende mechanisme van de steekhoek bevestigd is, de boorstang via een frame verbonden is met het bewegingsmechanisme van de steekhoek en de traagheidssensor verbonden is aan de zijkant van de bewegingsmechanisme van de steekhoek.3. Method for determining the position of the drilling tool of a drilling robot for the prevention of rock burst based on redundant inertia units according to claim 2, characterized in that the anti-impact drilling robot consists of a base of the anti-impact drilling robot, a rotating mechanism in the azimuth angle rotatingly connected to the highest end of the base of the anti-impact drilling robot, a moving mechanism at the pitch angle connected through a hydraulic cylinder mounted in a lifting manner to a highest end of the rotating mechanism in the azimuth angle, the moving mechanism of the pitch angle is further conductively connected to the rotating mechanism of the azimuth angle through a conductive column, a drill rod is attached to the outside of the moving mechanism of the pitch angle, the drill rod is connected to the moving mechanism of the pitch angle and the inertial sensor is connected to the side of the pitch angle movement mechanism. 4. Methode voor het bepalen van de positie van het boorgereedschap van een boorrobot voor de preventie van rotsuitbarsting op basis van redundante traagheidseenheden volgens conclusie 4, gekenmerkt doordat de traagheidssensor van MPU9250 geproduceerd door het bedrijf InvernSense Company worden gebruikt als redundante traagheidssensoren.Method for determining the position of the drilling tool of a drilling robot for the prevention of rockburst based on redundant inertial units according to claim 4, characterized in that the inertial sensors of MPU9250 produced by the company InvernSense Company are used as redundant inertial sensors. 5. Methode voor het bepalen van de positie van het boorgereedschap van een boorrobot voor de preventie van rotsuitbarsting op basis van redundante traagheidseenheden volgens conclusie 4, gekenmerkt doordat de microcomputer een Raspberry Pi is en een Rapsberry PI4 Model B geproduceerd door Amazon wordt gebruikt als Raspberry Pi.Method for determining the position of the drilling tool of a drilling robot for rockburst prevention based on redundant inertial units according to claim 4, characterized in that the microcomputer is a Raspberry Pi and a Rapsberry PI4 Model B produced by Amazon is used as Raspberry Pi. 6. Methode voor het bepalen van de positie van het boorgereedschap van een boorrobot voor de preventie van rotsuitbarsting op basis van redundante traagheidseenheden volgens conclusie 5, gekenmerkt doordat de traagheidssensoren van de MPU9250 via een I2C-protocol communiceren met een microcomputer.Method for determining the position of the drilling tool of a drilling robot for rockburst prevention based on redundant inertial units according to claim 5, characterized in that the inertial sensors of the MPU9250 communicate with a microcomputer via an I2C protocol. 7. Methode voor het bepalen van de positie van het boorgereedschap van een boorrobot voor de preventie van rotsuitbarsting op basis van redundante traagheidseenheden volgens conclusie 6, gekenmerkt doordat de microcomputer tegelijk via een I2C-interface verbonden is met de vijf traagheidseenheden en de microcomputer elk van de traagheidseenheden van de MPU9250 een voor een kan openen door de gegevens te lezen om de gegevens van de traagheidssensor te lezen.Method for determining the position of the drilling tool of a drilling robot for rockburst prevention based on redundant inertial units according to claim 6, characterized in that the microcomputer is simultaneously connected via an I2C interface to the five inertial units and the microcomputer controls each of can open the inertial units of the MPU9250 one by one by reading the data to read the data from the inertial sensor. 8. Methode voor het bepalen van de positie van het boorgereedschap van een boorrobot voor de preventie van rotsuitbarsting op basis van redundante traagheidseenheden volgens conclusie 7, gekenmerkt doordat de microcomputer de gegevens die door de microprocessor wordt bewaard, via een seriële poort doorstuurt naar de pc-hoofdcomputer.Method for determining the position of the drilling tool of a drilling robot for rockburst prevention based on redundant inertial units according to claim 7, characterized in that the microcomputer transmits the data kept by the microprocessor to the PC via a serial port -main computer. 9. Methode voor het bepalen van de positie van het boorgereedschap van een boorrobot voor de preventie van rotsuitbarsting op basis van redundante fusie volgens conclusie 8, gekenmerkt doordat de fusie van de gegevens in de vijf paden wordt berekend volgens de zelf bepaalde fusieformule in een verhouding van 4:1,5:1,5:1,5: 1.5.Method for determining the position of the drilling tool of a drilling robot for the prevention of rock burst based on redundant fusion according to claim 8, characterized in that the fusion of the data in the five paths is calculated according to the self-determined fusion formula in a ratio of 4:1.5:1.5:1.5: 1.5. 10. Methode voor het bepalen van de positie van het boorgereedschap van een boorrobot voor de preventie van rotsuitbarsting op basis van redundante traagheidseenheden volgens conclusie 9, gekenmerkt doordat een wavelet threshold denoising wordt gebruikt als denoising-algoritme.Method for determining the position of the drilling tool of a drilling robot for rockburst prevention based on redundant inertial units according to claim 9, characterized in that a wavelet threshold denoising is used as the denoising algorithm.
NL2032291A 2021-07-06 2022-06-27 Attitude determination method for drilling tool of drilling robot for rockburst prevention based on redundant inertial units NL2032291B1 (en)

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110760994.7A CN113431550B (en) 2021-07-06 2021-07-06 Anti-impact drilling robot drilling tool attitude determination method based on redundant inertial unit

Publications (2)

Publication Number Publication Date
NL2032291A NL2032291A (en) 2023-01-10
NL2032291B1 true NL2032291B1 (en) 2023-12-14

Family

ID=77759030

Family Applications (1)

Application Number Title Priority Date Filing Date
NL2032291A NL2032291B1 (en) 2021-07-06 2022-06-27 Attitude determination method for drilling tool of drilling robot for rockburst prevention based on redundant inertial units

Country Status (2)

Country Link
CN (1) CN113431550B (en)
NL (1) NL2032291B1 (en)

Family Cites Families (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6315062B1 (en) * 1999-09-24 2001-11-13 Vermeer Manufacturing Company Horizontal directional drilling machine employing inertial navigation control system and method
CN202562485U (en) * 2012-03-02 2012-11-28 江阴中科矿业安全科技有限公司 Drill carriage attitude measurement system based on monocular vision
CN103291216B (en) * 2012-03-02 2015-05-20 江阴中科矿业安全科技有限公司 Orientation system for horizontal drill of deep-hole drill carriage
CN107390246A (en) * 2017-07-06 2017-11-24 电子科技大学 A kind of GPS/INS Combinated navigation methods based on genetic neural network
CN109186589B (en) * 2018-07-19 2020-08-11 中国矿业大学 Coal mining machine positioning method based on array type inertia unit
CN109059909A (en) * 2018-07-23 2018-12-21 兰州交通大学 Satellite based on neural network aiding/inertial navigation train locating method and system

Also Published As

Publication number Publication date
CN113431550B (en) 2023-07-14
NL2032291A (en) 2023-01-10
CN113431550A (en) 2021-09-24

Similar Documents

Publication Publication Date Title
CN111442759B (en) Unified monitoring system for pose of fully-mechanized coal mining face equipment
CN108032868A (en) A kind of train rail detecting system and method
US20140316708A1 (en) Oriented Wireless Structural Health and Seismic Monitoring
CN108957405A (en) A kind of detection method of coal face scraper conveyor straightness
JPWO2006022201A1 (en) Robot evaluation system and evaluation method
CN102917844A (en) Methods and systems for inspection sensor placement
CN109540135B (en) Method and device for detecting pose and extracting yaw angle of paddy field tractor
CN103226398A (en) Data gloves based on micro-inertial sensor network technique
EP3348983B1 (en) Data transformation for a virtual asset
CA2812062A1 (en) Real-time definitive survey while drilling
EP3425329A1 (en) Cnc machine geometry error and accuracy monitoring and evaluation
NL2032291B1 (en) Attitude determination method for drilling tool of drilling robot for rockburst prevention based on redundant inertial units
JP2012125871A (en) Robot control setting support device
CN115790387A (en) Bridge displacement corner synchronous real-time monitoring method and system based on online camera
CN204727371U (en) A kind of cage guide automatic checkout equipment and real-time remote monitoring system
CN109115115B (en) Tunnel displacement detection system and detection calculation method
CN113340305A (en) Hydraulic support attitude monitoring method based on MEMS
CN100458803C (en) System for monitoring displacement of inclined plane
Dichev et al. A gyro-free system for measuring the parameters of moving objects
CN113295688A (en) Detection system and detection method for fatigue strength of welding part
CN108107882B (en) Automatic calibration and detection system of service robot based on optical motion tracking
CN108955629B (en) Antenna attitude precision measurement system and measurement method
CN108168517B (en) Method and system for automatically measuring inclination angle of building
CN114622617B (en) Method and device for judging rotation parameters of working machine and working machine
CN115727871A (en) Track quality detection method and device, electronic equipment and storage medium