CN105928514A - AGV composite guiding system based on image and inertia technology - Google Patents
AGV composite guiding system based on image and inertia technology Download PDFInfo
- Publication number
- CN105928514A CN105928514A CN201610237820.1A CN201610237820A CN105928514A CN 105928514 A CN105928514 A CN 105928514A CN 201610237820 A CN201610237820 A CN 201610237820A CN 105928514 A CN105928514 A CN 105928514A
- Authority
- CN
- China
- Prior art keywords
- module
- agv
- information
- data
- inertia
- Prior art date
- Legal status (The legal status 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 status listed.)
- Pending
Links
- 238000005516 engineering process Methods 0.000 title claims abstract description 27
- 239000002131 composite material Substances 0.000 title claims abstract description 21
- 230000000007 visual effect Effects 0.000 claims abstract description 24
- 230000004044 response Effects 0.000 claims description 28
- 238000000034 method Methods 0.000 claims description 23
- 230000004927 fusion Effects 0.000 claims description 19
- 238000005259 measurement Methods 0.000 claims description 17
- 230000003044 adaptive effect Effects 0.000 claims description 16
- 230000006870 function Effects 0.000 claims description 12
- 230000002452 interceptive effect Effects 0.000 claims description 11
- 239000000203 mixture Substances 0.000 claims description 8
- 230000001133 acceleration Effects 0.000 claims description 6
- 230000005540 biological transmission Effects 0.000 claims description 5
- 230000007613 environmental effect Effects 0.000 claims description 5
- 230000010354 integration Effects 0.000 claims description 5
- 230000002093 peripheral effect Effects 0.000 claims description 4
- 230000008901 benefit Effects 0.000 claims description 3
- 238000006243 chemical reaction Methods 0.000 claims description 3
- 238000005265 energy consumption Methods 0.000 claims description 3
- 238000002955 isolation Methods 0.000 claims description 3
- 230000005055 memory storage Effects 0.000 claims description 2
- 230000008878 coupling Effects 0.000 claims 1
- 238000010168 coupling process Methods 0.000 claims 1
- 238000005859 coupling reaction Methods 0.000 claims 1
- 230000003287 optical effect Effects 0.000 claims 1
- 238000004422 calculation algorithm Methods 0.000 abstract description 27
- 238000001914 filtration Methods 0.000 abstract description 12
- 239000013589 supplement Substances 0.000 abstract description 3
- 230000003993 interaction Effects 0.000 abstract 1
- 239000011159 matrix material Substances 0.000 description 12
- 238000013461 design Methods 0.000 description 3
- 230000006978 adaptation Effects 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 230000019771 cognition Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000005183 dynamical system Methods 0.000 description 2
- 230000005611 electricity Effects 0.000 description 2
- 238000007667 floating Methods 0.000 description 2
- 230000007246 mechanism Effects 0.000 description 2
- 230000008447 perception Effects 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 230000005654 stationary process Effects 0.000 description 2
- 230000033228 biological regulation Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 230000002596 correlated effect Effects 0.000 description 1
- 230000000875 corresponding effect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000005674 electromagnetic induction Effects 0.000 description 1
- 238000012905 input function Methods 0.000 description 1
- 238000011068 loading method Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000007620 mathematical function Methods 0.000 description 1
- 238000002156 mixing Methods 0.000 description 1
- 238000000465 moulding Methods 0.000 description 1
- 230000037361 pathway Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 230000006641 stabilisation Effects 0.000 description 1
- 238000011105 stabilization Methods 0.000 description 1
- 239000003381 stabilizer Substances 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
Classifications
-
- 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
- G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
- G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
- G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
-
- 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
- G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
- G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
- G01C21/18—Stabilised platforms, e.g. by gyroscope
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0227—Control of position or course in two dimensions specially adapted to land vehicles using mechanical sensing means, e.g. for sensing treated area
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
- G05D1/0246—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using a video camera in combination with image processing means
Landscapes
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- General Physics & Mathematics (AREA)
- Aviation & Aerospace Engineering (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Multimedia (AREA)
- Electromagnetism (AREA)
- Navigation (AREA)
Abstract
The invention discloses an AGV composite guiding system based on an image and inertia technology. The system comprises an inertia guiding module, which can intelligently sense the positions and moving information of AGV in a vehicle coordinate system in every moment through a plurality of inertia sensors; a visual guiding module, which can intelligently sense the position and environment information of AGV through a visual sensor, when AGV move to a preset position in a reference coordinate system; an information interaction module, which efficiently passing information among the inertia guiding module, the visual guiding module, and a movement control module, and the movement control module, wherein the movement control module obtains the data of the sensors of the inertia guiding module and the visual guiding module, then the data of each sensor are fused by a Sage-Husa self-adaption Kalman filtering algorithm, according to the obtained fused data, AGV is controlled, and the accumulated errors of the inertia guiding module are corrected. Based on visual guiding and inertia guiding technologies, multiple information sources supplement each other, and an AGV composite guiding system, which has redundancy and higher navigation accuracy, is constructed.
Description
Technical field
The present invention relates to automatic field, be specifically related to AGV composite guide based on image Yu inertial technology
Draw system.
Background technology
At present in AGV (Automated Guided Vehicle, the homing guidance of industrial occasions application
Transport vehicle) mostly use rail guidance technology, rail guidance technology refers to arrange on the path travelled
The information media thing guided, by sensor, AGV detects that the information of information media thing is led
Drawing, concrete circuit can be the guide cable in way of electromagnetic induction, the track in mechanical system, magnetic
Magnetic stripe in guide mode and the reflective tape etc. in light guide mode.It is mainly characterized by technology and more becomes
Ripe, but early stage construction is loaded down with trivial details, working method and the number of fixing, move robot of working line
Amount is very restricted, it is difficult to meet the demand in the following field such as intelligence manufacture, Intelligent logistics.And
Trackless guidance system often uses single guidance system, is limited to the limitation of self, causes applying model
Being with limit, such as visual guidance need not artificially arrange physical pathway, but real-time is poor, in background
Complicated occasion is inapplicable；Inertial guide real-time is good, and precision is high, but there is accumulated error.
In view of this, it is badly in need of providing one to have redundancy and navigation accuracy higher AGV composite guide
Draw system.
Summary of the invention
The technical problem to be solved is that design one has redundancy and navigation accuracy is higher
AGV composite guide system.
In order to solve above-mentioned technical problem, the technical solution adopted in the present invention is to provide a kind of based on figure
As with the AGV composite guide system of inertial technology, including inertial guide module, visual guidance module,
Information interactive module and motion-control module,
Described inertial guide module by multiple inertial sensor Intellisense AGV per time be engraved in vehicle sit
Position under mark system and movable information；
Described visual guidance module is moved to relevant position by AGV described in multiple sensor intelligent perception
Position under the frame of reference and environmental information during calibration point；
Described information interactive module realizes described inertial guide module, described visual guidance module and institute
State the transmission information of real-time high-efficiency between motion-control module；
Described motion-control module obtains described inertial guide module and institute by described information interactive module
State the data of each sensor of visual guidance module, and use Sage-Husa adaptable Kalman filter to calculate
Method carries out fusion treatment to the data of each sensor, carries out described AGV according to the fused data obtained
Control, revise the accumulated error of described inertial guide module.
In technique scheme, described inertial guide module is by Inertial Measurement Unit, angular encoder
And the embedded microprocessor composition of technical grade,
Described Inertial Measurement Unit is by three axis MEMS gyro, mems accelerometer and three axle magnetometer groups
Becoming, described three axis MEMS gyro, described mems accelerometer and described three axle magnetometers rely on height
Resolution analog digital quantizer carries out digital-to-analogue conversion, and has temperature-compensating, dynamic compensation and acceleration
Degree calibration function；
Described angular encoder is a kind of incremental encoder based on transmission-type grating, and resolution ratio reaches
3600P/R, maximum permissible revolution 6000RPM, in conjunction with the input of described embedded microprocessor timer
Function obtains the rotary speed of motor, angle and direction；
Described embedded microprocessor is the ARM of STM32 series, has that peripheral hardware is abundant, chip integration
Advantage high and low in energy consumption.
In technique scheme, described movable information includes but not limited to acceleration and angular speed.
In technique scheme, described visual guidance module is by Quick Response Code identification decoding unit and two dimension
Code vision positioning unit composition, described Quick Response Code vision positioning unit moves to corresponding positions at described AGV
Position deviation and the angle of the center of Quick Response Code center and described AGV is exported when putting calibration point；
The described Quick Response Code identification decoding unit environmental information decoding data to Quick Response Code record.
In technique scheme, described Quick Response Code vision positioning unit include embedded image processor,
Digital camera and CS annular light source；
Described embedded image processor is the super low-power consumption float-point DSP processor that TI promotes mainly, local TI
Floating-point DSP, standard configuration technical grade, integrated uPP, EMIF and USB2.0 interface；
Described digital camera is the 1300000 pixel camera heads of COMS, image in 2 D code data to be collected
Gathered by COMS imageing sensor, described embedded image processor read image in 2 D code data also
Being stored in external memory storage SDRAM, carry out image procossing, result is by the mutual mould of described information
Block is transferred to described motion-control module.
In technique scheme, described motion-control module is adaptive by PID controller, Sage-Husa
Answer Kalman filter, electric power management circuit, high-performance embedded processor DSP and light-coupled isolation electricity
Road forms, and the data of each sensor are merged by described Sage-Husa adaptive Kalman filter
Process；Described PID controller is according to the fusion number of described Sage-Husa adaptive Kalman filter
It is controlled according to described AGV.
In technique scheme, described information interactive module is CAN.
In technique scheme, described fusion treatment includes attitude information fusion, horizontal direction speed
Under information fusion and the frame of reference, positional information merges.
The present invention, is difficult to meet mobile robot flexibility application needs for current rail guidance technology,
By the exploitation module such as inertial guide, visual guidance, motion control, carry out environment comprehensive cognition technology,
Placement technology, multisensor Data Fusion technology, fault-toleranr technique etc. are studied, by visual guidance and
Inertial guide technical combinations together, forms a kind of view-based access control model and guides the AGV with inertial guide technology
Composite guide system, utilizes complementing each other of Multiple Information Sources, constitutes one and has redundancy and navigation
The degree of accuracy higher AGV composite guide system.
Accompanying drawing explanation
Fig. 1 is the system block diagram of the present invention；
Fig. 2 is the flowchart of the present invention；
Fig. 3 is the principle schematic of the present invention；
Fig. 4 is two counter circuits of Kalman filter algorithm.
Detailed description of the invention
Visual guidance together with inertial guide technical combinations, is utilized the mutual of Multiple Information Sources by the present invention
Supplement mutually, constitute one and there is redundancy and navigation accuracy higher AGV composite guide system,
Solve techniques below problem: (1) trackless guides problem: trackless guidance technology is at image in 2 D code
On the basis of location technology, Quick Response Code identification decoding technique and inertial survey technique merge, by two dimension
Code identifies the accumulated error of decoding technique correction inertial survey technique, it is ensured that AGV composite guide system
Convergence, it is achieved the trackless of AGV guides, and meets flexibility application needs；(2) comprehensive perception problems:
The comprehensive cognition technology of AGV composite guide system can provide environment sensing, coordinate setting, motion simultaneously
The parameter of information, overcomes rail guidance technology and is only provided that the shortcoming of single parameter, beneficially AGV
Composite guide system grasps more fully information, improves the comprehensive using effect of AGV；(3) fault-tolerant ask
Topic: on the premise of multi sensor combination is applied, application Sage-Husa self adaptation extension Kalman filter
Ripple algorithm On-line Estimation sensor variance, dynamically adjusts filtering gain, significantly reduces error message
Impact on AGV composite guide system.
Below in conjunction with specification drawings and specific embodiments, the present invention is described in detail.
Embodiments provide a kind of AGV composite guide system based on image Yu inertial technology,
As it is shown in figure 1, be the system block diagram of the present invention, including:
Inertial guide module 1, by multiple inertial sensor Intellisense AGV per time be engraved in vehicle coordinate
Position under Xi and movable information, it is achieved the first time of AGV is positioned.
Visual guidance module 2, is moved to relevant position calibration point by vision sensor Intellisense AGV
Time position under the frame of reference and environmental information, it is achieved the second time of AGV is positioned.
Information interactive module 3, i.e. CAN, it is achieved inertial guide module 1, visual guidance module 2
And the transmission information of real-time high-efficiency between motion-control module 4, complete Synchronization Control mutual with information
Work.
Above-mentioned movable information includes but not limited to acceleration and angular speed.
Motion-control module 4, obtains inertial guide module 1 and visual guidance by information interactive module 3
The data of each sensor of module 2, and use Sage-Husa Adaptive Kalman Filtering Algorithm to each biography
The data of sensor carry out fusion treatment: attitude information merges, horizontal direction velocity information merges and base
Under conventional coordinates, positional information merges；On-line Estimation each sensor variance, dynamically adjusts filtering gain,
AGV is controlled by the fused data according to obtaining, and revises the accumulated error of inertial guide module 1.
Inertial guide module 1 by Inertial Measurement Unit 10, angular encoder 11 (E6B2-CWZ5G) and
Embedded microprocessor 12 (STM32F103RBT7) composition of technical grade；Inertial Measurement Unit 10 is main
It is made up of, often three axis MEMS gyro 101, mems accelerometer 102 and three axle magnetometers 103
Individual inertial sensor relies on high resolution A/D C (analog-digital converter) to carry out digital-to-analogue conversion, and carries
The functions such as temperature-compensating, dynamic compensation and acceleration calibration, in order to the motion adapted under varying environment is special
Property, the measurement scope of each inertial sensor and filtered band adjustable；Angular encoder 11 is a kind of base
In the incremental encoder of transmission-type grating, resolution ratio reaches 3600P/R, maximum permissible revolution
6000RPM, can obtain the rotation of motor in conjunction with the input function of embedded microprocessor 12 timer
The movable information such as speed, angle and direction；Embedded microprocessor 12 belongs to the ARM of STM32 series,
It has the features such as peripheral hardware is abundant, chip integration is high and low in energy consumption, has been widely used in industry control
System and data Treatment stations close.
Visual guidance module 2 is by Quick Response Code identification decoding unit 20 and Quick Response Code vision positioning unit 21
Composition；Quick Response Code vision positioning unit 21 exports Quick Response Code when AGV moves to relevant position calibration point
The position deviation of the center of center and AGV and angle；Quick Response Code identification decoding unit 20 is right
The environmental information decoding data of Quick Response Code record.
Quick Response Code vision positioning unit 21 includes that embedded image processor 211 is (public based on Texas Instrument
Take charge of 6000 series DSP), digital camera 212 (OV9650) and CS annular light source 213；Embedded image
The super low-power consumption float-point DSP processor that processor 211 is promoted mainly for TI, local TI Floating-point DSP, standard configuration
Technical grade, the interface such as integrated uPP, EMIF, USB2.0；Digital camera 212 is that OmniVision is public
The 1300000 pixel camera heads of the COMS of department, image in 2 D code data to be collected are passed by COMS image
Sensor gathers, embedded image processor 211 read image in 2 D code data and be stored in outside storage
In device SDRAM, carrying out image procossing, result is transferred to motion control by information interactive module 3
Molding block 4.
Controller local area network (Controller Area Network) is also called CAN, be
The specification that generation nineteen ninety is just formulated, and standardized (ISO 11898-1) in 1993, by widely
Apply on various vehicles with electronic equipment, be more referred to as the STD bus in new-energy automobile.CAN
Bus is a serial bus, and it provides high safety grade and efficient real-time control, more for adjusting
The mechanism that examination and priority differentiate, under such mechanism, it is the most reliable that the transmission of internet message becomes
And it is efficient.In the present invention, physical layer uses standard CAN bus, and protocol layer uses puppy parc,
Node in each module composition CAN, relies on the transmission of CAN real-time high-efficiency, completes same
Step controls and information interworking.
Motion-control module 4 by PID controller 40, Sage-Husa adaptive Kalman filter 41,
Electric power management circuit 42, high-performance embedded processor DSP43 (2000 series DSP) and light-coupled isolation
Circuit 44 forms；The data of each sensor are carried out by Sage-Husa adaptive Kalman filter 41
Fusion treatment；PID controller 40 is according to the fusion of Sage-Husa adaptive Kalman filter 41
AGV is controlled by data；Electric power management circuit 42 uses to be opened LDO linear voltage regulator with DC-DC
The mode closing power supply combination designs, it is contemplated that input voltage and output voltage pressure reduction are relatively big, and load
The actual conditions that function is bigger, use the TPS54360 of Texas Instruments, and it is a wide input electricity
Pressure scope (4.5-60V), the voltage-releasing voltage stabilizer of High Output Current scope (follow current 3.5A), should
Voltage-stablizer uses current loop control, reduce further the noise in output voltage, reaches as high as 2.5MHz
Switching frequency, be highly suitable to be applied for requiring harsh industrial occasions, PS767D318 is doubleway output
Low pressure drop (LDO) voltage-stablizer, it can export 3.3V/1.8V two-way voltage, the precision of voltage regulation 2%, bear
Loading capability fully meets the demand of DSP, and it may be provided for reset signal to high-performance embedded
Processor DSP43 uses；High-performance embedded processor DSP43 uses the high property of Texas Instruments
Energy floating type microcontroller, on the one hand, it coordinates TI company with an independent floating type multiplier
The library file writing mathematical function based on bottom assembler language can quickly finish complicated algorithm, especially
It is to calculate data fusion and the floating-point function in motion control arithmetic and trigonometric function, on the other hand,
Its peripheral hardware is the abundantest, not only meets current demand, also further expands for system and leave
Space.
As in figure 2 it is shown, be the flowchart of the present invention, the present invention relates to adaptable Kalman filter
Technology, Quick Response Code vision location technology, Quick Response Code identification decoding technique and multi-stage data integration technology,
Quick Response Code vision location technology, Quick Response Code identification decoding technique and adaptive Kalman filtering technique are
The basis of the AGV composite guide system that the present invention provides, combination placement technology is the base at both
On plinth, carry out depth integration.AGV composite guide system utilize come from inertial guide module 1 and
The multi-sensor data of visual guidance module 2, and be filtered processing, wherein, every one-level merges all
Use Sage-Husa adaptive Kalman filter, it is characteristic of the invention that and utilize Multiple Information Sources mutual
Supplement mutually, constitute one and there is redundancy and navigation accuracy higher composite guide system.
As it is shown on figure 3, be the principle schematic of the present invention.
About Sage-Husa adaptive Kalman filter:
The movable information of AGV needs to rely on the measurement of multiple sensor, but the measurement data of various sensor
There is features, such as, when MEMS gyroscope 101 is in zero input state, MEMS gyroscope
The output signal of 101 is white noise and the slow superposition becoming random function, and the slow random function that becomes can be understood as
Static error, will not fluctuate in its short time, just can software correction by arithmetic programming.And in moving
Bias stabilization degree and angle random move about, and are difficult to use the main source that software correction is accumulated error,
Therefore need data anastomosing algorithm to utilize the data of other sensor that it is corrected.
Kalman filter algorithm is a kind of linear minimum-variance estimation, is passing of a kind of Discrete Linear filtering
Predication method, this algorithm has the following characteristics that
Algorithm is to use state space method design wave filter in time domain, and state equation uses dynamics side
Journey describes the dynamic rule of the amount of being estimated, and Kalman filter algorithm is not only suitable for stationary process, also
It is applicable to non-stationary process；Discrete Kalman filter algorithm can realize the most on the microprocessor.
As shown in Figure 4, for two counter circuits of Kalman filter algorithm, Kalman filter algorithm is wide
The general every field being applied in engineering, especially in terms of integrated navigation information fusion, this algorithm is public
It is considered optimal algorithm, Kalman filter substantially a kind of state estimator, utilize and measure
The internal state of output estimation dynamical system.Kalman filter algorithm can be divided into two counter circuits: shape
State estimates that loop and gain update loop, and it is independent calculating that gain updates loop, and state estimation is returned
The calculating on road needs to rely on gain and updates loop.
But during the conventional Kalman filter algorithm of application, it is desirable to the structural parameters of dynamical system are united with noise
Count characteristic it is known that the optimal estimation of state so could be obtained, but in actual applications, system mould
Shape parameter is inaccurate, there is the biggest error；System noise and measurement noise statistical property be unknown and time
Becoming, this makes conventional Kalman filter algorithm lose optimality, and estimated accuracy is substantially reduced, even
Filtering divergence can be caused.Therefore scholar is had to propose Sage-Husa Adaptive Kalman Filtering Algorithm,
This algorithm achieves also can online adaptive estimating system noise and measurement while carrying out state estimation
The function of noise statistics.
Consider that known discrete-time linear system model is:
In formula (1), XkState vector, Z is tieed up for n × 1kVector, Φ is measured for m × 1 dimensionk/k-1For
N × n ties up state Matrix of shifting of a step, HkMeasurement matrix, W is tieed up for m × nkSystem noise is tieed up for n × 1
Sound vector, VkFor m × 1 dimension measurement noise vector, WkAnd VkIt is two orthogonal Gauss white noises
Sound sequence, and meet:
In formula (2), qkAnd rkFor white Gaussian noise Mean Parameters, QkAnd RkFor variance matrix parameter,
When these four parameters are all unknown, it is possible to use Sage-Husa adaptable Kalman filter (SHAKF)
Algorithm is estimated the most in real time.
Sage-Husa Adaptive Kalman Filtering Algorithm process of solution within the single information fusion cycle
For:
State one-step prediction:
Innovation sequence updates:
Here " newly breath " refers to the difference of measuring value and predicted value, i.e. by the measurement in nearest moment
Value ZkAs carrying the fresh information about state to revise status predication value
State one-step prediction Square Error matrix updates:
Measurement noise is estimated:
Measurement noise is unknown, used here as waiting average conduct of weight temporalEstimate.
Filtering gain updates:
Kalman filter gain KkIt is to solve under the criterion of state estimation mean square error minimum, system
Gain matrix KkValue by initial Square Error matrix Po, state one-step prediction Square Error matrix
Pk/k-1And measurement noise matrixDetermine, and Pk/k-1Actually by system noise matrix Qk-1Determine.
If system noise parameter Qk-1Become big, then gain matrix KkCan diminish, represent status predication accuracy of measurement relatively
Height, it is less that this stylish breath value just utilizes.
Linear weighted function mean state is estimated:
State estimation Square Error matrix updates:
Square Error matrix PkThe important component part of wave filter, represent the precision of state estimation with
Reliability.
System noise is estimated:
This algorithm only need to give initial value XO、PO、QO, so that it may the shape in k moment is obtained by recurrence calculation
State is estimated.
The invention have the characteristics that: (1) constructs multisensor AGV composite guide system, will figure
The modes such as picture, Quick Response Code, inertial navigation organically combine, and every kind of single navigation system has respective uniqueness
Energy and limitation, combine several different triangular webs, just can utilize Multiple Information Sources,
Complementing each other, constituting one has redundance and the higher multifunction system of navigation accuracy.(2) based on
Attitude, position, the multi-stag information fusion structure of speed, one is divided into three grades of fusions: attitude information
Under fusion, the fusion of horizontal direction velocity information, the frame of reference, positional information merges, and this structure is real
It is to have employed distributing filtering on border, effectively reduces amount of calculation and the complexity of blending algorithm, again can
Improve the fault-tolerant ability of navigation system.(3) according to the actual requirements, Kalman filter algorithm is changed
Enter, have employed a kind of Sage-Husa self adaptation EKF filter (SHAEKF) algorithm, according to
Correlated condition updates system noise variance statistic characteristic, enhances the adaptivity of filtering algorithm.(4)
Quick Response Code has that information capacity is big, fault-tolerant ability strong and low cost and other advantages, is widely used in various
Commercial situations, present invention introduces QR Quick Response Code vision location technology, by the information such as the direction of motion and position
It is stored in Quick Response Code, identifies simple efficient, and accuracy is the highest, accurate realizing AGV
Automation efficiency can be significantly improved while location.
The present invention is not limited to above-mentioned preferred forms, and anyone makes under the enlightenment of the present invention
Structure changes, and every have same or like technical scheme with the present invention, each falls within the guarantor of the present invention
Within the scope of protecting.
Claims (8)
1. AGV composite guide system based on image Yu inertial technology, it is characterised in that include inertia
Guiding module, visual guidance module, information interactive module and motion-control module,
Described inertial guide module by multiple inertial sensor Intellisense AGV per time be engraved in vehicle sit
Position under mark system and movable information；
Described visual guidance module is moved to relevant position by AGV described in vision sensor Intellisense
Position under the frame of reference and environmental information during calibration point；
Described information interactive module realizes described inertial guide module, described visual guidance module and institute
State the transmission information of real-time high-efficiency between motion-control module；
Described motion-control module obtains described inertial guide module and institute by described information interactive module
State the data of each sensor of visual guidance module, and use Sage-Husa adaptable Kalman filter to calculate
Method carries out fusion treatment to the data of each sensor, carries out described AGV according to the fused data obtained
Control, revise the accumulated error of described inertial guide module.
2. the system as claimed in claim 1, it is characterised in that described inertial guide module is by inertia
The embedded microprocessor composition of measuring unit, angular encoder and technical grade,
Described Inertial Measurement Unit is by three axis MEMS gyro, mems accelerometer and three axle magnetometer groups
Becoming, described three axis MEMS gyro, described mems accelerometer and described three axle magnetometers rely on height
Resolution analog digital quantizer carries out digital-to-analogue conversion, and has temperature-compensating, dynamic compensation and acceleration
Degree calibration function；
Described angular encoder is a kind of incremental encoder based on transmission-type grating, and resolution ratio reaches
3600P/R, maximum permissible revolution 6000RPM, in conjunction with the input of described embedded microprocessor timer
Function obtains the rotary speed of motor, angle and direction；
Described embedded microprocessor is the ARM of STM32 series, has that peripheral hardware is abundant, chip integration
Advantage high and low in energy consumption.
3. the system as claimed in claim 1, it is characterised in that described movable information includes but do not limits
In acceleration and angular speed.
4. the system as claimed in claim 1, it is characterised in that described visual guidance module is by two dimension
Code identifies decoding unit and Quick Response Code vision positioning unit composition, and described Quick Response Code vision positioning unit exists
Described AGV moves to the center exporting Quick Response Code center and described AGV during the calibration point of relevant position
The position deviation of position and angle；The environment of Quick Response Code record is believed by described Quick Response Code identification decoding unit
Breath decoding data.
5. system as claimed in claim 4, it is characterised in that
Described Quick Response Code vision positioning unit includes embedded image processor, digital camera and CS annular
Light source；
Described embedded image processor is the super low-power consumption float-point DSP processor that TI promotes mainly, local TI
Floating-point DSP, standard configuration technical grade, integrated uPP, EMIF and USB2.0 interface；
Described digital camera is the 1300000 pixel camera heads of COMS, image in 2 D code data to be collected
Gathered by COMS imageing sensor, described embedded image processor read image in 2 D code data also
Being stored in external memory storage SDRAM, carry out image procossing, result is by the mutual mould of described information
Block is transferred to described motion-control module.
6. the system as claimed in claim 1, it is characterised in that described motion-control module is by PID
Controller, Sage-Husa adaptive Kalman filter, electric power management circuit, high-performance embedded
Processor DSP and optical coupling isolation circuit composition, described Sage-Husa adaptive Kalman filter pair
The data of each sensor carry out fusion treatment；Described PID controller is adaptive according to described Sage-Husa
Described AGV is controlled by the fused data answering Kalman filter.
7. the system as claimed in claim 1, it is characterised in that described information interactive module is CAN
Bus.
8. the system as claimed in claim 1, it is characterised in that described fusion treatment includes that attitude is believed
Breath merges, horizontal direction velocity information merges and under the frame of reference, positional information merges.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610237820.1A CN105928514A (en) | 2016-04-14 | 2016-04-14 | AGV composite guiding system based on image and inertia technology |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610237820.1A CN105928514A (en) | 2016-04-14 | 2016-04-14 | AGV composite guiding system based on image and inertia technology |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN105928514A true CN105928514A (en) | 2016-09-07 |
Family
ID=56839222
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610237820.1A Pending CN105928514A (en) | 2016-04-14 | 2016-04-14 | AGV composite guiding system based on image and inertia technology |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105928514A (en) |
Cited By (24)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106444750A (en) * | 2016-09-13 | 2017-02-22 | 哈尔滨工业大学深圳研究生院 | Two-dimensional code positioning-based intelligent warehousing mobile robot system |
| CN106595635A (en) * | 2016-11-30 | 2017-04-26 | 北京特种机械研究所 | AGV (automated guided vehicle) positioning method with combination of multiple positioning sensor data |
| CN106647765A (en) * | 2017-01-13 | 2017-05-10 | 深圳拓邦股份有限公司 | Planning platform based on mowing robot |
| CN106774335A (en) * | 2017-01-03 | 2017-05-31 | 南京航空航天大学 | Guiding device based on multi-vision visual and inertial navigation, terrestrial reference layout and guidance method |
| CN107179091A (en) * | 2017-06-27 | 2017-09-19 | 广东嘉腾机器人自动化有限公司 | A kind of AGV walkings vision positioning error correcting method |
| CN107272690A (en) * | 2017-07-07 | 2017-10-20 | 中国计量大学 | Inertial guide car air navigation aid and inertial guide car based on binocular stereo vision |
| WO2018064841A1 (en) * | 2016-10-09 | 2018-04-12 | 浙江国自机器人技术有限公司 | Inventory item management system, transport apparatus, and method for docking same with transported item |
| CN107976187A (en) * | 2017-11-07 | 2018-05-01 | 北京工商大学 | A kind of high-precision track reconstructing method and system in the interior of fusion IMU and visual sensor |
| CN108279026A (en) * | 2018-01-19 | 2018-07-13 | 浙江科钛机器人股份有限公司 | A kind of AGV inertial navigation systems and method based on T-type RFID beacons |
| WO2018145632A1 (en) * | 2017-02-10 | 2018-08-16 | 苏州瑞得恩光能科技有限公司 | Navigation system for solar panel cleaning robot and navigation method thereof |
| CN108733039A (en) * | 2017-04-18 | 2018-11-02 | 广东工业大学 | The method and apparatus of navigator fix in a kind of robot chamber |
| CN109002046A (en) * | 2018-09-21 | 2018-12-14 | 中国石油大学(北京) | A kind of Navigation System for Mobile Robot and air navigation aid |
| WO2019015385A1 (en) * | 2017-07-17 | 2019-01-24 | 纳恩博（北京）科技有限公司 | Abnormality recovery method, electronic device and storage medium |
| CN109300145A (en) * | 2018-08-20 | 2019-02-01 | 彭楷文 | NEW ADAPTIVE intelligence dazzle system |
| CN109489667A (en) * | 2018-11-16 | 2019-03-19 | 楚天智能机器人（长沙）有限公司 | A kind of improvement ant colony paths planning method based on weight matrix |
| CN109656246A (en) * | 2018-11-01 | 2019-04-19 | 广东电网有限责任公司 | The small vehicle drive circuit of AGV and its driving method for electrometric experiment room instrument check |
| CN109800828A (en) * | 2017-11-17 | 2019-05-24 | 比亚迪股份有限公司 | Vehicle positioning system and localization method based on two dimensional code |
| CN110030995A (en) * | 2019-04-04 | 2019-07-19 | 华南理工大学 | The intelligent carriage control method and system of blending image sensor and inertial sensor |
| CN110186459A (en) * | 2019-05-27 | 2019-08-30 | 深圳市海柔创新科技有限公司 | Air navigation aid, mobile vehicle and navigation system |
| CN110308729A (en) * | 2019-07-18 | 2019-10-08 | 石家庄辰宙智能装备有限公司 | The AGV combined navigation locating method of view-based access control model and IMU or odometer |
| CN110554702A (en) * | 2019-09-30 | 2019-12-10 | 重庆元韩汽车技术设计研究院有限公司 | Unmanned automobile based on inertial navigation |
| WO2020103297A1 (en) * | 2018-11-19 | 2020-05-28 | 炬星科技（深圳）有限公司 | Robot sensor arrangement system |
| CN113218403A (en) * | 2021-05-14 | 2021-08-06 | 哈尔滨工程大学 | AGV system of inertia vision combination formula location |
| CN116592876A (en) * | 2023-07-17 | 2023-08-15 | 北京元客方舟科技有限公司 | Positioning device and positioning method thereof |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060106533A1 (en) * | 2004-11-12 | 2006-05-18 | Mitsubishi Denki Kabushiki Kaisha | System for autonomous vehicle navigation with carrier phase DGPS and laser-scanner augmentation |
| CN103644904A (en) * | 2013-12-17 | 2014-03-19 | 上海电机学院 | Visual navigation method based on SIFT (scale invariant feature transform) algorithm |
| CN104848858A (en) * | 2015-06-01 | 2015-08-19 | 北京极智嘉科技有限公司 | Two-dimensional code and vision-inert combined navigation system and method for robot |
| CN105180931A (en) * | 2015-09-16 | 2015-12-23 | 成都四威高科技产业园有限公司 | Inertial navigation system for storage AGV |
| CN105486311A (en) * | 2015-12-24 | 2016-04-13 | 青岛海通机器人系统有限公司 | Indoor robot positioning navigation method and device |
-
2016
- 2016-04-14 CN CN201610237820.1A patent/CN105928514A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060106533A1 (en) * | 2004-11-12 | 2006-05-18 | Mitsubishi Denki Kabushiki Kaisha | System for autonomous vehicle navigation with carrier phase DGPS and laser-scanner augmentation |
| CN103644904A (en) * | 2013-12-17 | 2014-03-19 | 上海电机学院 | Visual navigation method based on SIFT (scale invariant feature transform) algorithm |
| CN104848858A (en) * | 2015-06-01 | 2015-08-19 | 北京极智嘉科技有限公司 | Two-dimensional code and vision-inert combined navigation system and method for robot |
| CN105180931A (en) * | 2015-09-16 | 2015-12-23 | 成都四威高科技产业园有限公司 | Inertial navigation system for storage AGV |
| CN105486311A (en) * | 2015-12-24 | 2016-04-13 | 青岛海通机器人系统有限公司 | Indoor robot positioning navigation method and device |
Non-Patent Citations (1)
| Title |
|---|
| 张欣: "多旋翼无人机的姿态与导航信息融合算法研究", 《中国博士学位论文全文数据库工程科技II辑》 * |
Cited By (37)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106444750A (en) * | 2016-09-13 | 2017-02-22 | 哈尔滨工业大学深圳研究生院 | Two-dimensional code positioning-based intelligent warehousing mobile robot system |
| WO2018064841A1 (en) * | 2016-10-09 | 2018-04-12 | 浙江国自机器人技术有限公司 | Inventory item management system, transport apparatus, and method for docking same with transported item |
| CN106595635B (en) * | 2016-11-30 | 2020-12-08 | 北京特种机械研究所 | AGV positioning method fusing data of multiple positioning sensors |
| CN106595635A (en) * | 2016-11-30 | 2017-04-26 | 北京特种机械研究所 | AGV (automated guided vehicle) positioning method with combination of multiple positioning sensor data |
| CN106774335A (en) * | 2017-01-03 | 2017-05-31 | 南京航空航天大学 | Guiding device based on multi-vision visual and inertial navigation, terrestrial reference layout and guidance method |
| CN106774335B (en) * | 2017-01-03 | 2020-01-21 | 南京航空航天大学 | Multi-view vision and inertial navigation based guiding device, landmark layout and guiding method |
| CN106647765A (en) * | 2017-01-13 | 2017-05-10 | 深圳拓邦股份有限公司 | Planning platform based on mowing robot |
| CN106647765B (en) * | 2017-01-13 | 2021-08-06 | 深圳拓邦股份有限公司 | Planning platform based on mowing robot |
| WO2018145632A1 (en) * | 2017-02-10 | 2018-08-16 | 苏州瑞得恩光能科技有限公司 | Navigation system for solar panel cleaning robot and navigation method thereof |
| CN108733039A (en) * | 2017-04-18 | 2018-11-02 | 广东工业大学 | The method and apparatus of navigator fix in a kind of robot chamber |
| CN107179091A (en) * | 2017-06-27 | 2017-09-19 | 广东嘉腾机器人自动化有限公司 | A kind of AGV walkings vision positioning error correcting method |
| CN107179091B (en) * | 2017-06-27 | 2019-09-20 | 广东嘉腾机器人自动化有限公司 | A kind of AGV walking vision positioning error correcting method |
| CN107272690B (en) * | 2017-07-07 | 2023-08-22 | 中国计量大学 | Inertial guided vehicle navigation method based on binocular stereoscopic vision and inertial guided vehicle |
| CN107272690A (en) * | 2017-07-07 | 2017-10-20 | 中国计量大学 | Inertial guide car air navigation aid and inertial guide car based on binocular stereo vision |
| WO2019015385A1 (en) * | 2017-07-17 | 2019-01-24 | 纳恩博（北京）科技有限公司 | Abnormality recovery method, electronic device and storage medium |
| CN107976187A (en) * | 2017-11-07 | 2018-05-01 | 北京工商大学 | A kind of high-precision track reconstructing method and system in the interior of fusion IMU and visual sensor |
| CN107976187B (en) * | 2017-11-07 | 2020-08-04 | 北京工商大学 | Indoor track reconstruction method and system integrating IMU and vision sensor |
| CN109800828B (en) * | 2017-11-17 | 2020-10-20 | 比亚迪股份有限公司 | Vehicle positioning system and positioning method based on two-dimensional code |
| CN109800828A (en) * | 2017-11-17 | 2019-05-24 | 比亚迪股份有限公司 | Vehicle positioning system and localization method based on two dimensional code |
| CN108279026A (en) * | 2018-01-19 | 2018-07-13 | 浙江科钛机器人股份有限公司 | A kind of AGV inertial navigation systems and method based on T-type RFID beacons |
| CN109300145B (en) * | 2018-08-20 | 2020-06-16 | 彭楷文 | Self-adaptive intelligent camouflage system |
| CN109300145A (en) * | 2018-08-20 | 2019-02-01 | 彭楷文 | NEW ADAPTIVE intelligence dazzle system |
| CN109002046A (en) * | 2018-09-21 | 2018-12-14 | 中国石油大学(北京) | A kind of Navigation System for Mobile Robot and air navigation aid |
| CN109002046B (en) * | 2018-09-21 | 2020-07-10 | 中国石油大学(北京) | Mobile robot navigation system and navigation method |
| CN109656246A (en) * | 2018-11-01 | 2019-04-19 | 广东电网有限责任公司 | The small vehicle drive circuit of AGV and its driving method for electrometric experiment room instrument check |
| CN109489667A (en) * | 2018-11-16 | 2019-03-19 | 楚天智能机器人（长沙）有限公司 | A kind of improvement ant colony paths planning method based on weight matrix |
| WO2020103297A1 (en) * | 2018-11-19 | 2020-05-28 | 炬星科技（深圳）有限公司 | Robot sensor arrangement system |
| CN110030995A (en) * | 2019-04-04 | 2019-07-19 | 华南理工大学 | The intelligent carriage control method and system of blending image sensor and inertial sensor |
| CN110030995B (en) * | 2019-04-04 | 2023-11-24 | 华南理工大学 | Intelligent trolley control method and system integrating image sensor and inertial sensor |
| CN110186459A (en) * | 2019-05-27 | 2019-08-30 | 深圳市海柔创新科技有限公司 | Air navigation aid, mobile vehicle and navigation system |
| CN110186459B (en) * | 2019-05-27 | 2021-06-29 | 深圳市海柔创新科技有限公司 | Navigation method, mobile carrier and navigation system |
| CN113390420A (en) * | 2019-05-27 | 2021-09-14 | 深圳市海柔创新科技有限公司 | Navigation method, mobile carrier and navigation system |
| CN110308729A (en) * | 2019-07-18 | 2019-10-08 | 石家庄辰宙智能装备有限公司 | The AGV combined navigation locating method of view-based access control model and IMU or odometer |
| CN110554702A (en) * | 2019-09-30 | 2019-12-10 | 重庆元韩汽车技术设计研究院有限公司 | Unmanned automobile based on inertial navigation |
| CN113218403A (en) * | 2021-05-14 | 2021-08-06 | 哈尔滨工程大学 | AGV system of inertia vision combination formula location |
| CN116592876A (en) * | 2023-07-17 | 2023-08-15 | 北京元客方舟科技有限公司 | Positioning device and positioning method thereof |
| CN116592876B (en) * | 2023-07-17 | 2023-10-03 | 北京元客方舟科技有限公司 | Positioning device and positioning method thereof |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105928514A (en) | AGV composite guiding system based on image and inertia technology | |
| Köseoğlu et al. | Design of an autonomous mobile robot based on ROS | |
| Marin et al. | Event-based localization in ackermann steering limited resource mobile robots | |
| Wang et al. | AGV navigation analysis based on multi-sensor data fusion | |
| CN112077839B (en) | Motion control method and device for mechanical arm | |
| Wang et al. | Research on logistics autonomous mobile robot system | |
| CN110763224A (en) | Navigation method and navigation system for automatic guided transport vehicle | |
| Zhou et al. | An EKF-based multiple data fusion for mobile robot indoor localization | |
| Ziebinski et al. | Challenges associated with sensors and data fusion for AGV-driven smart manufacturing | |
| Gao et al. | Gyro-net: IMU gyroscopes random errors compensation method based on deep learning | |
| Pandey et al. | Real time navigation strategies for webots using fuzzy controller | |
| Duleba et al. | Layer, Lie algebraic method of motion planning for nonholonomic systems | |
| Victorino et al. | Localization and map building using a sensor-based control strategy | |
| CN108344412A (en) | A kind of Navigation System for Mobile Robot and method | |
| Ekmanis | Self-calibration in differential drive dynamics/kinematics model | |
| Guo et al. | Model-Based Deep Learning for Low-Cost IMU Dead Reckoning of Wheeled Mobile Robot | |
| Gang et al. | Robust tightly coupled pose estimation based on monocular vision, inertia, and wheel speed | |
| Nguyen et al. | Dual rate Kalman filter considering delayed measurement and its application in visual servo | |
| CN115157249A (en) | Dynamic target tracking method and device and robot | |
| CN114061573A (en) | Ground unmanned vehicle formation positioning device and method | |
| Gobee et al. | Guided Vehicle based with Robot Operating System for Mapping and Navigation Task | |
| Eberhard et al. | Sensor data fusion for the localization and position control of one kind of omnidirectional mobile robots | |
| Wei et al. | Neural network based extended kalman filter for localization of mobile robots | |
| Ashokaraj et al. | Feature based robot navigation: using fuzzy logic and interval analysis | |
| Negirla et al. | Mobile Robot Platform for Studying Sensor Fusion Localization Algorithms |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WD01 | Invention patent application deemed withdrawn after publication | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20160907 |