CN106055745A - Method of establishing linear CCD four-wheeler simulation model based on MATLAB - Google Patents

Method of establishing linear CCD four-wheeler simulation model based on MATLAB Download PDF

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CN106055745A
CN106055745A CN201610341045.4A CN201610341045A CN106055745A CN 106055745 A CN106055745 A CN 106055745A CN 201610341045 A CN201610341045 A CN 201610341045A CN 106055745 A CN106055745 A CN 106055745A
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carriage
linear ccd
wheel
racing track
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CN106055745B (en
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吴泽正
韩涛
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Zhejiang University ZJU
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Abstract

The present invention relates to a method of establishing a linear CCD four-wheeler simulation model based on MATLAB. The method comprises the following steps of establishing a racing track model; establishing a four-wheeler static model; establishing a linear CCD camera model based on the four-wheeler static model; determining three points in the racing track model to obtain initial pose information of a four-wheeler; establishing a four-wheeler kinematic model, and obtaining four-wheeler pose status at the next moment when the pose remains unchanged based on the four-wheeler static model; calculating a scanning range of a linear CCD camera based on the linear CCD camera model, and obtaining sampling values from pixel information of racing track pictures in the scanning range; setting a line extraction algorithm and a control algorithm; and setting a simulation process dynamic demonstration module, and dynamically displaying travel tracks of the four-wheeler, turning conditions of front wheels and the scanning range of the linear CCD camera of the four-wheeler in every sampling interval. On-line debugging anytime and anywhere can be achieved by the modeling method.

Description

A kind of method setting up linear CCD carriage phantom based on MATLAB
Technical field
The present invention relates to carriage phantom field, be specifically related to one and set up linear CCD carriage based on MATLAB The method of phantom.
Background technology
In intelligent vehicle contest, in order to the control algolithm of Intelligent Optimal car often to carry out experiment repeatedly, and in reality Racing track on the acquisition of error in intelligent vehicle driving process and intelligent vehicle attitude relatively difficult.Therefore, the ginseng of most of algorithms Number is adjusted to be gathered by examination repeatedly and realizes.This method exists loses time and the shortcoming of resource.
Prior art use LabView virtual instrument technique establish intelligent vehicle analogue system, but LabView Data process, analysis ability more weak, be unfavorable for transplanting and the realization of algorithm.Establish the real-time monitoring system of intelligent vehicle, energy Enough operation conditions monitoring intelligent vehicle in real time, but it is the use of more software and hardware resources, add unnecessary expenditures.Zhou Bin Et al. (http://www.eepw.com.cn/event/action/Freescale/data06.htm) develop based on Intelligent vehicle analogue system Plastid of LabVIEW virtual instrument technique.System establishes model respectively to racing track and racing car, makes User can designed, designed racing track and racing car the most easily, racing track is designed to various forthright, detour, slope Road, becomes various sizes, shape by racecar design, so that the suitability of system is more extensive.But this system at present can only For using the intelligent vehicle of photosensitive sensors Path Recognition scheme, CCD camera technology is not supported.Secondly, in emulation During, system simply calculates, not according to the kinematics model (car being reduced to a four-wheel rigid body process) of automobile Consider that it breaks away and the impact of road surface friction force.Finally, the problem that speed is also systems face is calculated.
Li Jiusheng et al. (foundation following line intelligent vehicle emulation platform based on MATLAB, Chinese science and technology paper is online) uses MATLAB software establishes the software emulation platform of hunting intelligent vehicle, takes full advantage of the control system workbox of MATLAB software It is capable of the feature of the algorithm of various complexity.But this system is not based on the model of linear CCD, does not accounts for environment Interference, and need to draw racing track in MATLAB, it is impossible to use the racing track that other are originated；For the data acquisition amount ratio analyzed Less.
Mostly the intelligent vehicle analogue system that presently, there are is to emulate for photographic head, not the intelligence to linear CCD type Can carry out online artificial by car, therefore can only debug in place, debugging can be limited to the problems such as time, place and light.
Summary of the invention
Present invention aims to the deficiencies in the prior art, it is provided that one sets up linear CCD four-wheel based on MATLAB The method of car phantom, this modeling method can realize debugging on line whenever and wherever possible.
Technical scheme provided by the present invention is: the method setting up linear CCD carriage phantom based on MATLAB, bag Include following steps:
Step one: set up racing track model, by importing the racing track top view of picture format, Set scale chi simultaneously；Described Scale be width be the picture of the number of pixels in racing track top view corresponding to racing track width, for actual size is converted For Pixel-level size；Set the developed width of racing track；
Step 2: set up carriage static models, described carriage static models include wheel and vehicle body, and front-wheel is used for Turning to, trailing wheel is used for providing power；Set vehicle body length, vehicle body width, wheel position, wheel length, wheel width, radius of wheel, trailing wheel Rotating speed；
Step 3: set up linear CCD photographic head model based on carriage static models, by fixing bar, linear CCD is taken the photograph As head is arranged at carriage top；Set fixing bar size, position, the subtended angle of linear CCD photographic head, linear CCD photographic head Prediction information and the sampling interval of CCD camera；
Step 4: in racing track model, determines three points initial posture information with acquisition carriage, including carriage Initial position, inceptive direction and initial front-wheel beat angle；
Step 5: set up carriage kinematics model, obtains keeping constant feelings in attitude based on carriage static models The carriage pose situation in condition next but one moment；
Step 6: based on linear CCD photographic head model, calculate the sweep limits of linear CCD photographic head, and from this scanning model Enclose acquisition sampled value in the Pixel Information of interior racing track picture；
Step 7: arrange and carry line algorithm, by carrying line algorithm, the sampled value that linear CCD photographic head obtains is processed, Obtain right boundary information and the disalignment information of racing track；
Step 8: arrange control algolithm, the off-centring information obtained by carrying line algorithm controls carriage next one row Enter state；
Step 9: arrange simulation process dynamic demonstration module, linear CCD carriage step one～step 8 obtained is imitated True mode imports simulation process dynamic demonstration module, the track that Dynamic Announce carriage travels, the angle situation of beating of front-wheel and often The sweep limits of one sampling interval carriage linear CCD photographic head.
In above-mentioned technical scheme, the racing track employing a kind of novelty imports mechanism, breaches other analogue systems and puts down Platform racing track needs the restriction drawn under designated software and the restriction to call format.Described modeling method can import often See that the racing track picture of picture format uses, greatly facilitate the operating process of emulation, improve ease for use.
Emulation data can also be acquired and analyze by above-mentioned modeling method, acquisition target and the four-wheel of present main flow Car host computer acquisition target has major part coincidence, therefore can be connected analogue system and master system easily, make this system Practicality increased.Further, it is also possible to control the initial posture information of carriage by the mode of mouse interactive controlling, including The initial position of carriage, inceptive direction and initial front-wheel beat angle, can test multiple in the case of the travel situations of carriage.
As improvement, in described step 6 introduce noise processed, described noise processed include introduce light intensity coefficient with Sampled value under simulation different illumination conditions；Introduce random coefficient with simulated environment noise and quality of hardware fluctuation, sampled value to be made The impact become；Introduce the trend that breadth coefficient successively decreases to both sides value from centre with analog linearity CCD camera sampled value.Introduce Reasonably simulate the interference of actual place and the stochastic variable of hardware capability fluctuation, closer to truth, make simulation parameter with Actual tuning parameter narrows the gap as far as possible.
As preferably, in described step one, racing track top view form is jpeg, bmp or png；Described racing track top view Being filled to white inside interior racing track, outside is filled to Lycoperdon polymorphum Vitt, and racing track edge uses black.
In described step 3, linear CCD camera model is:
w = 2 × t a n γ 2 × ( p - b ) 2 + ( r + h ) 2
In formula, p is the barycenter vertical dimension away from linear CCD photographic head ground based scanning line, and b is fixing between bar and barycenter Distance, r is radius of wheel, h be fixing bar height, γ be the subtended angle of linear CCD photographic head, w is linear CCD photographic head ground sweeping Retouch the width of line.
If the prediction information of given linear CCD photographic head, the subtended angle of linear CCD photographic head, fixing bar height, at the bottom of bar away from The distance at chassis center and radius of wheel, i.e. can determine that the sweep limits of linear CCD, is used for obtaining linear CCD photographic head Sampled value.
Carriage kinematics model in described step 5 is:
α = Δ t × ω × t a n θ L β = t a n - 1 ( L × t a n θ W × tan θ + 2 × L )
In formula, α is the angle that carriage turns in a sampling interval, and β is barycenter-hind axle-radius of turn auxiliary angle, θ be front-wheel beat angle；W is vehicle body width, and L is that vehicle body is long；Δ t is sampling interval duration；R is radius of wheel；ω is rear wheel rotation speed.
Described step 7 carries the one-dimensional fenestella filtering algorithm that line algorithm is asymmetric change sash length；
First as threshold value, sampled value is carried out binaryzation according to the average of sampled value, the most repeatedly utilize one-dimensional fenestella to filter Ripple algorithm filters the fluctuation in binaryzation result, then through judging whether to lose line, finally to the undulatory property analysis of result Obtain right boundary information and the disalignment information of racing track.
In described step 8, control algolithm is multiplied by for controlling according to putting forward the disalignment information that line algorithm obtains again The K of algorithmpCoefficient beats the input quantity at angle to control carriage next one travel condition as front-wheel next time.
Compared with the existing technology, beneficial effects of the present invention is embodied in:
(1) present invention uses the racing track of a kind of novelty to import mechanism, breaches other Simulation System Platform racing track needs The restriction drawn under designated software and the restriction to call format.The racing track picture that can import common picture format is carried out Use, greatly facilitate the operating process of emulation, improve ease for use.
(2) emulation data can be acquired and analyze by the modeling method of the present invention, acquisition target and present main flow Carriage host computer acquisition target has major part coincidence, therefore can be connected analogue system and master system easily, make this The practicality of system is increased.
(3) mode of present invention mouse interactive controlling controls the initial posture information of carriage, including at the beginning of carriage Beginning position, inceptive direction and initial front-wheel beat angle, can test multiple in the case of the travel situations of carriage.
(4) invention introduces and reasonably simulate the interference of actual place and the stochastic variable of hardware capability fluctuation, closer to In truth, simulation parameter and actual tuning parameter is made to narrow the gap as far as possible.
Accompanying drawing explanation
Fig. 1 is the flow chart setting up linear CCD carriage phantom in embodiment 1 based on MATLAB；
Fig. 2 is the racing track top view importing picture format in embodiment 1；
Fig. 3 is linear CCD camera illustraton of model in embodiment 1；
Fig. 4 is the initial pose hum pattern after carriage determines three points in embodiment 1；
Fig. 5 is carriage kinematics model figure in embodiment 1；
Fig. 6 is to simulate all moment obtained in embodiment 1 to show as the linear CCD sampled value of 3-dimensional form；
Fig. 7 is the schematic diagram of the one-dimensional fenestella filtering algorithm of asymmetric change sash length in embodiment 1；
The line algorithm that carries that Fig. 8 is embedded in embodiment 1 judges to lose the schematic diagram of the entropy computing of line；
Fig. 9 is the dynamic demonstration graph in embodiment 1 in carriage simulation process；
Figure 10 embodiment 2 imports the racing track top view of picture format；
Figure 11 is the dynamic demonstration graph in embodiment 2 in carriage simulation process；
Figure 12 is to simulate all moment obtained in embodiment 2 to show as the linear CCD sampled value of 3-dimensional form.
Detailed description of the invention
The present invention is further described with embodiment below in conjunction with the accompanying drawings.
Embodiment 1
The method setting up linear CCD carriage phantom based on MATLAB, concrete flow process as shown in Figure 1:
Step one: set up racing track model, by importing the racing track top view of picture format, such as Fig. 2, in racing track top view Being filled to white inside racing track, outside is filled to Lycoperdon polymorphum Vitt, and racing track edge uses black；Set scale chi simultaneously, scale is wide The picture of degree number of pixels corresponding to racing track width in racing track top view, for being scaled Pixel-level chi by actual size Very little；Set the developed width of racing track；
Step 2: set up carriage static models, carriage static models include wheel and vehicle body, and front-wheel is used for turning to, Trailing wheel is used for providing power；Set vehicle body length, vehicle body width, wheel position, wheel length, wheel width, radius of wheel, rear wheel rotation speed；
Step 3: set up linear CCD photographic head model based on carriage static models, by fixing bar, linear CCD is taken the photograph As head is arranged at carriage top；Set fixing bar size, position, the subtended angle of linear CCD photographic head, linear CCD photographic head Prediction information and the sampling interval of CCD camera, as it is shown on figure 3, linear CCD photographic head model is:
w = 2 × t a n γ 2 × ( p - b ) 2 + ( r + h ) 2
In formula, p is the barycenter vertical dimension away from linear CCD photographic head ground based scanning line, and b is fixing between bar and barycenter Distance, r is radius of wheel, h be fixing bar height, γ be the subtended angle of linear CCD photographic head, w is linear CCD photographic head ground sweeping Retouch the width of line.
Step 4: in racing track model, determines three points initial posture information with acquisition carriage, including carriage Initial position, inceptive direction and initial front-wheel beat angle；As shown in Figure 4, first in racing track top view, carriage is clicked with mouse Location point, be now obtained in that a position coordinates (x1,y1), put second location point (x the most again2,y2), (x2,y2) and (x1,y1) may determine that the inceptive direction of carriage altogether, then put the 3rd location point (x3,y3), (x3,y3) and (x1,y1) close Get up may determine that the initial steering wheel of carriage beats angle.Initial position, inceptive direction and initial steering wheel has been had just may be used after beating angle To determine next position and the attitude of carriage according to the carriage kinematics model in step 5.
Step 5: set up carriage kinematics model, obtains keeping constant feelings in attitude based on carriage static models The carriage pose situation in condition next but one moment；As it is shown in figure 5, carriage kinematics model is:
α = Δ t × ω × t a n θ L β = t a n - 1 ( L × t a n θ W × tan θ + 2 × L )
In formula, α is the angle that carriage turns in a sampling interval, and β is barycenter-hind axle-radius of turn auxiliary angle, θ be front-wheel beat angle.W is vehicle body width, and L is that vehicle body is long；Δ t is sampling interval duration；R is radius of wheel；ω is rear wheel rotation speed.
Step 6: based on linear CCD photographic head model, calculate the sweep limits of linear CCD photographic head, and from this scanning model Enclose acquisition sampled value in the Pixel Information of interior racing track picture；Introducing noise processed, described noise processed includes introducing light intensity Coefficient is with the sampled value under simulation different illumination conditions；Introduce random coefficient to fluctuate to adopting with simulated environment noise and quality of hardware The impact that sample value causes；Introduce the trend that breadth coefficient successively decreases to both sides value from centre with analog linearity CCD camera sampled value.
The simulation of ambient light and hardware condition fluctuation is as shown in Figure 6: the sampling of actual CCD is impossible highly desirable, the most in vain Color correspondence 255, black correspondence 0, therefore to the effect that simulation is actual, introduce light intensity coefficient and sampled value entirety is carried out on multiple Increase and reduction, introduce random coefficient, carry out adding a random sequence on the basis of preferable sampled value.After these process Find that random sequence fluctuation is more violent, carry out a step smoothing techniques the most again, i.e. from the beginning of the 3rd point, each point Numerical value is all the first two point and the meansigma methods of data own.Finding through actual experiment, the sampling of linear CCD is from central authorities to both sides Data have the trend of reduction, and therefore the sampled value after above-mentioned process has also been taken advantage of a quadratic function that Open Side Down, to realize Forcing down the effect of two side datas, after these process, CCD sampled value and actual CCD sampling that emulation obtains are the most close.
Step 7: arrange and carry line algorithm, by carrying line algorithm, the sampled value that linear CCD photographic head obtains is processed, Obtain right boundary information and the disalignment information of racing track；The described line algorithm that carries is the one-dimensional of asymmetric change sash length Fenestella filtering algorithm；
The principle of fenestella filtering algorithm is, when the both sides sash numeral of fenestella is all consistent, fenestella central region is all composed Value is the data of fenestella both sides sash.The fenestella filtering algorithm both sides sash length symmetry of measured length is immutable, therefore for district The noise of territory edge can not effectively filter out.And the fenestella filtering algorithm of elongated degree is in the feelings ensureing that fenestella sash total length is constant Under condition, can be with the length of dynamic mapping both sides fenestella, it is not required that symmetrical.The noise so gone out for edges of regions can effectively be filtered Remove.As it is shown in fig. 7, black represents 1, white represents 0, and wherein the place, edge of black region band exists the noise of white, if Using fenestella central region is 3, and fenestella sash often fans the measured length fenestella filtering algorithm of a length of 4, can cannot effectively filter out this The noise at place, because the sash numeral in left side is inconsistent.
If but use the fenestella filtering algorithm of elongated degree, then can realize a left side by the length of dynamic mapping fenestella sash Side all keeps consistent with right-hand side sash numeral, just can effectively filter out the noise of fenestella central region.After often successfully filtering, next The distance of right-hand side sash length can be moved in the position of secondary fenestella, and the left-hand side sash of new fenestella can not carry out solid colour and sentence Disconnected.
The embedded line algorithm that carries also has used the entropy computing for judging to lose line, for the CCD sampled value collected, Define an entropy and be used for evaluating the confusion degree of these group data.Entropy is the transition times of this group data digital, if certain group data Entropy is more than certain threshold value, and when substantially can be determined that these group data of collection, intelligent vehicle loses line phenomenon.As shown in Figure 8, first The entropy of group data is 13, and the entropy of second group of data is 1, it is seen that first group more chaotic than second group of data.
Step 8: arrange control algolithm, the off-centring information obtained by carrying line algorithm controls carriage next one row Enter state；Described control algolithm is multiplied by the K for control algolithm again according to putting forward the disalignment information that line algorithm obtainspSystem Number beats the input quantity at angle to control carriage next one travel condition as front-wheel next time.
Step 9: arrange simulation process dynamic demonstration module, linear CCD carriage step one～step 8 obtained is imitated True mode imports simulation process dynamic demonstration module, the track that Dynamic Announce carriage travels, the angle situation of beating of front-wheel and often The sweep limits of one sampling interval carriage linear CCD photographic head, as shown in Figure 9.Dynamic demonstration, can select to see in real time See, it is also possible to select direct observed result.Carriage stops after returning within certain distance of initial position voluntarily, then may be used So that the data collected in emulation are analyzed and process, revise initial parameter, put forward line algorithm and control algolithm etc..Can To select the picture in dynamic demonstration preserved and synthesizes cardon, it is simple to exchange with other members and sharing, more Intuitively, vividly.
Embodiment 2
Concrete operations are same as in Example 1, and difference is in step one that the racing track top view imported is different, such as Figure 10 Shown in；As shown in figure 11, all moment that simulation obtains show as the linear CCD sampled value of 3-dimensional form to final dynamic demonstration As shown in figure 12.Actual racing track is not easy to be designed to equidistant helix form, but is easy in software draw equidistant spiral shell Spin line, when carriage is along the racing track traveling of equidistant helix, because radius of curvature is continually changing, this makes it possible to be apparent from Find out that the intelligent vehicle curved Performance comparision of the mistake under which kind of radius of curvature is good, which kind of is poor, when it is possible that line ball Or the situation of out-of-bounds, can adjust control algolithm according to this targetedly.The data that obtain of emulation can be analyzed and Process, it is possible to obtain boundary information that CCD collects and side-play amount, observe trend and the situation of change of curve, to some data Point is adjusted.
For the control algolithm in embodiment, the extensibility of this modeling method is very big, and control algolithm is not limited to PID, Can be arbitrary form, as long as it is identical with the return value of plug-in to meet return value, carry line algorithm the most in like manner, the present invention In carry line and control algolithm can be specified by user oneself.

Claims (7)

1. the method setting up linear CCD carriage phantom based on MATLAB, comprises the steps:
Step one: set up racing track model, by importing the racing track top view of picture format, Set scale chi simultaneously；Described ratio Example chi be width be the picture of the number of pixels in racing track top view corresponding to racing track width, for actual size is scaled picture Element level size；Set the developed width of racing track；
Step 2: set up carriage static models, described carriage static models include wheel and vehicle body, and front-wheel is for turning To, trailing wheel is used for providing power；Set vehicle body length, vehicle body width, wheel position, wheel length, wheel width, radius of wheel, rear round Speed；
Step 3: set up linear CCD photographic head model based on carriage static models, by fixing bar by linear CCD photographic head It is arranged at carriage top；Set fixing bar size, position, the subtended angle of linear CCD photographic head, the prediction of linear CCD photographic head Information and the sampling interval of CCD camera；
Step 4: in racing track model, determines that three points are to obtain the initial posture information of carriage, initial including carriage Position, inceptive direction and initial front-wheel beat angle；
Step 5: set up carriage kinematics model, obtains in the case of attitude holding is constant based on carriage static models The carriage pose situation in next moment；
Step 6: based on linear CCD photographic head model, calculate the sweep limits of linear CCD photographic head, and in this sweep limits Racing track picture Pixel Information in obtain sampled value；
Step 7: arrange and carry line algorithm, by carrying line algorithm, the sampled value that linear CCD photographic head obtains is processed, obtain The right boundary information of racing track and disalignment information；
Step 8: arrange control algolithm, the off-centring information obtained by carrying line algorithm controls carriage next one traveling shape State；
Step 9: simulation process dynamic demonstration module is set, the linear CCD carriage that step one～step 8 are obtained emulation mould Type import simulation process dynamic demonstration module, Dynamic Announce carriage travel track, the angle situation of beating of front-wheel and each The sweep limits of sampling interval carriage linear CCD photographic head.
The method of linear CCD carriage analog simulation based on MATLAB the most according to claim 1 motion, its feature exists In, described step 6 introduces noise processed, described noise processed includes introducing light intensity coefficient with the different illumination bar of simulation Sampled value under part；Introduce the impact that sampled value is caused by random coefficient with simulated environment noise and quality of hardware fluctuation；Introduce The trend that breadth coefficient successively decreases to both sides value from centre with analog linearity CCD camera sampled value.
The method of linear CCD carriage analog simulation based on MATLAB the most according to claim 1 motion, its feature exists In, in described step one, racing track top view form is jpeg, bmp or png；Fill out inside the described racing track in racing track top view Filling for white, outside is filled to Lycoperdon polymorphum Vitt, and racing track edge uses black.
The method of linear CCD carriage analog simulation based on MATLAB the most according to claim 1 motion, its feature exists In, in described step 3, linear CCD camera model is:
w = 2 × t a n γ 2 × ( p - b ) 2 + ( r + h ) 2
In formula, p is the barycenter vertical dimension away from linear CCD photographic head ground based scanning line, b be fixing between bar and barycenter away from From, r is radius of wheel, h be fixing bar height, γ be the subtended angle of linear CCD photographic head, w is linear CCD photographic head ground based scanning The width of line.
The method of linear CCD carriage analog simulation based on MATLAB the most according to claim 1 motion, its feature exists In, the carriage kinematics model in described step 5 is:
α = Δ t × ω × t a n θ L β = t a n - 1 ( L × t a n θ W × tan θ + 2 × L )
In formula, α is the angle that carriage turns in a sampling interval, and β is barycenter-hind axle-radius of turn auxiliary angle, and θ is Front-wheel beat angle；W is vehicle body width, and L is that vehicle body is long；Δ t is sampling interval duration；R is radius of wheel；ω is rear wheel rotation speed.
The method of linear CCD carriage analog simulation based on MATLAB the most according to claim 1 motion, its feature exists In, described step 7 carries the one-dimensional fenestella filtering algorithm that line algorithm is asymmetric change sash length；
First as threshold value, sampled value is carried out binaryzation according to the average of sampled value, the most repeatedly utilize one-dimensional little window filtering to calculate Method filters the fluctuation in binaryzation result, then through judging whether to lose line to the undulatory property analysis of result, finally obtains The right boundary information of racing track and disalignment information.
The method of linear CCD carriage analog simulation based on MATLAB the most according to claim 1 motion, its feature exists In, in described step 8, control algolithm is multiplied by for control algolithm according to putting forward the disalignment information that line algorithm obtains again KpCoefficient beats the input quantity at angle to control carriage next one travel condition as front-wheel next time.
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CN111523814B (en) * 2020-04-26 2022-02-11 西南交通大学 Intelligent planning method for urban rail transit schedule and vehicle bottom application plan
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