CN110929418B - Method for synchronizing motion trail of multi-axis linkage railcar - Google Patents
Method for synchronizing motion trail of multi-axis linkage railcar Download PDFInfo
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Abstract
The application discloses a method for synchronizing motion trail of a multi-axis linkage railcar, which at least comprises the following steps: acquiring running mechanism parameters of a rail car running system, and establishing a mathematical model for the rail car running mechanism; dividing a track of the travelling of the railway vehicle into a plurality of blocks, wherein each block corresponds to an environment partition, completing the motion data acquisition of all the blocks, and modeling a weighted average value of a travelling mechanism of the railway vehicle on the basis of a mathematical model established in Step 1; on the basis of Step2 weighted average modeling, key point compensation is carried out on key points of the running track of the rail car through an S-shaped curve acceleration and deceleration algorithm, so that the fluency of a running mechanism of the rail car is ensured, and the fluency of the running track of the rail car is further ensured. According to the technical scheme, the motion track error of the rail car in the motion process due to the self, the track and the environmental factors is effectively reduced, the synchronism of the motion track of the rail car and the story line is improved, and the amusement body feeling is enhanced.
Description
Technical Field
The application relates to the technical field of rail cars, in particular to a method for synchronizing movement tracks of a multi-axis linkage rail car.
Background
Dark riding (dark) refers to a large indoor entertainment project that tourists take on a multi-degree-of-freedom dynamic railcar, and the tourists travel in a virtual and actual combined simulation environment along a given story line, so that the dark riding (dark) is the most attractive entertainment project in the current world. At present, large theme parks such as world wide film city, disco paradise, fang Te myth and the like are all developing dark riding projects, such as transformers in los angeles world wide film city, spiders on the Osaka world wide film city, the strongpoint of the spider knight, hong Kong disco, caribbean pirate of Shanghai disco and Ladies' scroll of Ningbo Fang Te, have great economic benefit and social influence.
The existing track of the dark riding track car at least comprises a track car rotating shaft, six-degree-of-freedom motion shafts and a track clamping and traveling double shaft, wherein the motion curves are represented by real-time linkage of nine motion shafts in total. In the whole experience process of nine movement axes of the dark riding track car, the movement axes need to be linked in real time at any position, namely, the information of each frame of preset movement codes of the track car at least comprises movement information and time information of all axes, the nine movement axes are linked in real time, the movement of a travelling mechanism cannot jump frames or leak frames, the accuracy and smoothness are ensured, particularly, some key experience points, such as a tourist on the track car, enter the screen area obliquely from the side surface, the content of the screen area at the moment is high-altitude falling, and all movement axes needing to walk, six degrees of freedom, rotate and the like are restored according to the preset movement code height, so that the tourist is immersed into a relatively stimulated quick falling scene. However, the control system for dark riding is very complex, and is particularly easy to be influenced by the movement track to generate asynchronous combination of virtual and real, so that the experience effect of tourists is influenced, and therefore, relatively high synchronism between the movement track of the rail car and a given story line is required.
In general, in the course of moving according to a given story line, a control system can output according to a given action file, but the fault tolerance of an actuating mechanism is poor, and in addition, the on-site operation generates larger deviation due to the operation of multiple degrees of freedom due to uncertain factors generated by the on-site environment.
The problem of unsynchronization of rail cars is mainly twofold:
(1) The design and production of the rail car are limited by cost, time, nonstandard design, complex system and other factors, so that the difference of the rail car is caused;
(2) The design and production of the track car walking track are subjected to nonstandard customization, installation environment, installation process and other factors, so that the walking track is different;
the influence of the variability of the rail cars, the variability of the rails, uncertain resistance, skidding and the like can cause the unsynchronized movement of the rail cars.
In addition, in the existing dark riding motion control technology, most debugging personnel divide a motion area into a plurality of areas by taking a screen image observation point as a reference point, six-degree-of-freedom motion codes of contents related to screen contents are operated after the motion area reaches the starting point of each area, and most areas of scenes do not use six-degree-of-freedom motion, and the operation precision is not required to be controlled, so that tourists cannot obtain omnibearing immersive feeling obtained by combined motion of walking, rotation and six degrees of freedom, and the experience effect of dark riding is greatly reduced.
Accordingly, those skilled in the art have been working to develop a method of synchronizing the motion profile of a multi-axis linkage railcar to improve the synchronicity of the railcar's motion profile with a given story line.
Disclosure of Invention
The application aims to provide a method for synchronizing movement tracks of a multi-axis linkage railcar so as to solve the problems in the background technology.
In order to solve the above problems, the present application provides a method for synchronizing motion trajectories of a multi-axis linkage railcar, at least comprising:
step1: acquiring running mechanism parameters of a rail car running system, and establishing a mathematical model for the rail car running mechanism;
step2: dividing a track for travelling the railcar into a plurality of blocks, wherein each block corresponds to an environmental partition, completing the operation of all the blocks, and modeling a weighted average value of a travelling mechanism of the railcar on the basis of a mathematical model established by Step 1;
step3: on the basis of Step2 weighted average modeling, key point compensation is carried out on key points of the track of the railway car through an S-shaped curve acceleration and deceleration algorithm, so that the fluency of a railway car running mechanism is ensured, and the fluency of a railway car running track is ensured.
Further, in Step1, the running mechanism of the rail car running system at least comprises a control PLC, a servo driver, a servo motor, a running speed reducer, a gripping running wheel, a guiding rail and an auxiliary element.
Further, according to the tourist process, the rail car running rail is at least divided into a passenger loading area, an experience area, a station entering area and a passenger unloading area; the experience area is at least divided into a plurality of curtain areas and machine model areas, and each curtain area and machine model area comprises a plurality of special effect lamplight and special effect equipment; the blocks in Step2 refer to the experience area blocks.
Further, in Step2, the weighted average model is built only for the rail clamping and walking biaxial movement of the rail car movement track, and the movement of the rail car six-degree-of-freedom movement axis and the rotation axis is not included.
Further, in Step2, the Step of modeling the weighted average at least includes:
step20: recording a weighted average value coefficient contained in the mathematical model established in Step1 as eta, setting the eta as 1, and writing the eta into all railcar control PLCs on site;
step21: initializing all the rail cars on site, prohibiting the six-degree-of-freedom motion shafts and the rotation shafts of all the rail cars from moving, and only keeping the motion of the walking shafts; the method comprises the steps of carrying out a first treatment on the surface of the
Step22: sending a departure command, wherein the rail vehicle moves along the rail until the rail vehicle moves to a passenger area, and waiting for a control command;
step23: sequentially sending the on-site railcars from a passenger area to a passenger area, then sending the railcars from the passenger area to an experience area, then entering a station entering area and the passenger area, ending a complete tour process, recording the initial position of the experience area, the experience position of a curtain area and the position information of key points, and recording the number M of the key points;
step24: repeating Step23 until enough stable data are obtained, and recording the number N of stable data groups;
step25: and (3) carrying out weighting treatment on the data obtained from Step23 to Step24, obtaining a weighted average value of the difference between the starting point and the starting point, dividing the weighted average value by the actual motion length of the walking shaft, finally obtaining a weighted average value coefficient eta, and substituting the weighted average value coefficient eta into the mathematical model of Step1 to obtain the track of the railway car for weighted average modeling.
Further, in Step25, the method for obtaining the weighted average coefficient η is as follows:
step250: observing all data, and directly removing the data group with obvious distribution gap between the initial position data and other data;
step251: the rest data sets are firstly removed from the second key point data, the rest data sets are grouped according to the data distribution areas, the rest data sets belong to most weighting 1 times, otherwise, each data set obtains own weighting coefficient;
step252: the weighted average coefficient of the difference between the starting point and the starting point is calculated by using the formula:
wherein M is i For the weighting coefficient of the i-th group data, N i L is the difference between the start point and the start point of the ith group data max The theoretical maximum motion value of walking according to the motion file is represented.
Furthermore, since the modeling track of weighted average modeling in Step2 only keeps the running of the traveling shaft of the railcar, and does not contain the running of the six-degree-of-freedom motion shaft and the rotating shaft of the railcar, and the information of each frame of the preset action code in the running process of the railcar contains the motion information of all shafts, namely the action of the traveling mechanism cannot skip frames or leak frames, and also ensures the accuracy and the smoothness, the multi-degree-of-freedom key points in the running process of the railcar need to be compensated on the basis of Step2 weighted average modeling, and the method at least comprises the following steps:
step30: completing a weighted average modeling method of the walking part in Step 2;
step31: taking the rail car with the closest average value as a reference vehicle of the key point compensation method, and recording the position information of the running track of the reference vehicle;
step32: listing all key point information to be compensated, then observing the running track of the railway vehicle, adjusting action codes which do not accord with the preset effect until the actual running track of the railway vehicle is within the tolerance range of the set track, recording the position information of all key points, and simultaneously recording the position information of the W-th frame before all key points; the method comprises the steps of carrying out a first treatment on the surface of the
Step33: writing all key points and the position information of the previous W frame into the PLC programs of all the rail cars;
step34: when the W frame position before a certain key point is reached in the running process of any railway vehicle, comparing the position information at the moment with the previously recorded reference vehicle position information to obtain a difference value;
step35: in the process of moving from the W-th frame before the key point to the key point, an S-shaped curve acceleration and deceleration algorithm is used for distributing the difference value to N frames, so that the fluency of a motion track is ensured, and then the difference value is overlapped into subsequent motion data in a carrier wave form, so that the synchronism of the motion track is ensured. .
Furthermore, the position information of the key points of the rail car is calibrated in a mode of reading the position information of the two-dimensional code and is transmitted to the control PLC.
Further, in Step35, the implementation method of the S-shaped curve acceleration/deceleration algorithm is as follows:
step350: in all the key point compensation methods, the previous W-th frame is a fixed frame number, and the detection error data in T seconds is advanced according to the interval time of each frame being Pms;
step350: using an S-curve acceleration and deceleration algorithm, the formula is as follows:
Y＝A+B/(1+e -ax+b )
wherein A, B, a, b is constant and represents translation and pull-up in the X and Y directions, respectively;
step352: the sum of the data of the final W frames is S, the data Y of the W frames is obtained i The sum S of the sum data is stored in the PLC; if the error between the position data of the W-th frame before a certain key point and the expected data is detected as delta, the final real-time compensation data is delta i ＝Y i Δ/S。
The present application also provides a computer-readable storage medium in which a program is stored that, when executed, performs the aforementioned method of compensating for a critical point of a track of a railcar.
By implementing the method for synchronizing the motion trail of the multi-axis linkage railcar, provided by the application, the method has the following technical effects:
(1) In the technical scheme, the running track of the travelling shaft of the rail car is subjected to weighted average modeling, errors of the rail car caused by factors such as design and production in the travelling deviation of the rail car are controlled within a reasonable range, and the influence of the rail car on running asynchronism is reduced;
(2) According to the technical scheme, the key points of the travelling mechanism are compensated, so that the synchronization of the six-degree-of-freedom motion shaft and the rotation shaft of the railcar is ensured, the difference generated by the field environment of the key points in the story line is controlled within a reasonable range, and the influence of unstable operation caused by environmental factors is further reduced;
(3) According to the technical scheme, the rail car running track is synchronized by a weighted average modeling and key point compensation method, the self-balancing error capability is achieved, and the industry requirements of dark riding are met;
(4) The track of the track car is synchronous based on the technical scheme, so that the coordination consistency of track car travel and story lines is effectively improved, and the activity impression of an experienter is enhanced;
(5) In the technical scheme, the key point compensation adopts the S-shaped curve acceleration and deceleration algorithm to control the difference value within a reasonable range, so that the smoothness and continuity of the running track of the railway car are effectively ensured, and the experience effect is enhanced.
Drawings
The conception, specific structure, and technical effects of the present application will be further described with reference to the accompanying drawings to fully understand the objects, features, and effects of the present application.
FIG. 1 is a schematic view of a dark ride track vehicle according to an embodiment of the present application;
FIG. 2 is a block diagram of the track car travel guidance system of FIG. 1;
FIG. 3 is a schematic diagram of a rail car running test in a running rail in accordance with an embodiment of the present application;
fig. 4 is a diagram showing a situation that a code reader reads a track car in an embodiment of the present application.
In the figure:
1. a rail car; 10. driven wheel; 11. a guide rail; 12. a trolley line; 13. a clamping wheel; 14. a clamping cylinder; 15. a driving wheel;
a: a boarding zone; b: a passenger area; c: a station entering area; d: experience area.
Detailed Description
The technical solutions of the embodiments of the present application will be clearly and completely described below in conjunction with the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The application provides a method for synchronizing motion trail of a multi-axis linkage railcar, which at least comprises the following steps:
step1: acquiring running mechanism parameters of a rail car movement system and constructing a mathematical model for a transmission, wherein the running mechanism of the rail car movement system at least comprises a control PLC, a servo driver, a servo motor, a running speed reducer, a clamping running wheel, a guide rail and an auxiliary element;
step2: according to the tourist process, dividing the rail car running track into at least a passenger loading area, an experience area, a station entering area and a passenger unloading area; the experience area is at least divided into a plurality of curtain areas and machine model areas, and each curtain area and machine model area comprises a plurality of special effect lamplight and special effect equipment; the running of the running shafts of all the blocks in the experience area is completed, and the weighted average modeling is carried out on the running mechanism of the rail car on the basis of the mathematical model established by Step 1;
step3: because the modeling track modeled by the weighted average value in Step2 only keeps the running of the running shaft of the railway vehicle and does not contain the running of the six-degree-of-freedom movement shaft and the rotating shaft of the railway vehicle, key point compensation is carried out on key points of the running track of the railway vehicle by an S-shaped curve acceleration and deceleration algorithm on the basis of Step2 weighted average value modeling so as to ensure the fluency of the running track of the railway vehicle.
Based on the above method, the following examples are used to describe the technical solution of the present application in detail.
The dark riding track car 1 is shown in fig. 1-2, and the track car 1 is provided with a walking guide system, wherein the walking guide system at least comprises a control PLC, a clamping wheel 13, a clamping cylinder 14, a driving wheel 15, a servo motor, a walking speed reducer, a driven wheel 10, a guide track 11, a trolley line 12 and other auxiliary elements, and the track car structure belongs to the existing structure and is not described in detail herein.
The mathematical model for the running mechanism of the movement system of the railway car can be expressed by the following formula:
P i ＝G(·)*A(x i ) (equation 1)
Wherein P is i The number of turns of the walking servo motor is represented, G (·) represents the parameters of the walking mechanism, and x i Walk axis information, a (x) i ) And a model for converting the walking data in the action file and the number of turns of the actual motor is shown.
In an actual dark riding track car system, a running system is provided with a control PLC, a clamping wheel 13, a servo motor and a speed reducer which are all standard devices, and a driven wheel 10 and a driving wheel 15 are non-standard devices, but the circumference difference among individuals does not exceed +/-5%; the length change of the whole track is negligible due to the change of the walking track along with time, so that the analysis can be performed by using a unified one-time equation model.
However, because of the limitation of the clamping walking scheme and the two-dimensional code position information tape mounting scheme, and the actual difference of products is considered, the average value of the system error proportionality coefficient is required to be increased to describe the input-output relationship of the rail car walking:
here, P i Indicating the movement circle number, x of the walking servo motor i Position data representing walking axes in an action file, x is 0.ltoreq.x i ≥10000，L max Representing the actual movement range of the walking axis of the action file, L max The unit is mm,10000 is the actual walking coefficient, S represents the circumference of the clamped walking wheel, S is mm, R represents the reduction ratio of the walking speed reducer, and eta represents the weighted average coefficient.
In the dark riding project, as shown in the walking route in fig. 3, according to the tourist tour process, the track can be divided into a boarding area A, a alighting area B, a boarding area C and an experience area D; the method aims at an experience area D; according to the actual dark riding project, the experience area D can be divided into a plurality of curtain areas and machine model areas, and each curtain area and machine model area comprises a plurality of special effect lights and special effect devices; for each moving position, the rail car has gesture data comprising real-time combined movement of walking, rotation and six degrees of freedom. The experience is quantified by dividing the experience area into 10000 equal parts, each action code data in each equal part, the movement track of the rail car and the corresponding environment are matched one by one, and the whole tour process of the project is completed.
In this scheme, based on the above-mentioned mathematical model of the railcar, in combination with the railcar operation information in fig. 3, the weighted average modeling method adopted includes the following steps:
step1, setting a coefficient eta representing a weighted average value in a formula (2) to be 1, and writing the coefficient eta into all railway car control PLCs on site;
step2, initializing the railcar, namely initializing internal information by a vehicle PLC, prohibiting the movement of a six-degree-of-freedom movement shaft and a rotation shaft of the railcar, and only keeping the movement of a walking shaft;
step3, sending a departure command, wherein the rail car moves along the rail until moving to a passenger area, and waiting for a control command;
step 4, the on-site railcars are sequentially sent from a passenger area B to a passenger area A, then sent from the passenger area A to enter an experience area D, then enter a station entering area C and a passenger area B, as shown in figure 3, a car 2 enters the passenger area B, a car 4 enters the station entering area C, and a car 5 and a car 7 are positioned in the experience area D; ending a complete tour process, recording the initial position of the experience area D, the experience position of the curtain area and other key point position information, and totalizing M key points;
step 5, repeating the step 4 until enough stable data are obtained, and totaling N groups of data;
and step 6, carrying out weighting treatment on the data obtained in the step, obtaining a weighted average value of the difference between the starting point and the starting point, dividing the weighted average value by the actual movement length of the walking axis, and finally obtaining a weighted average value coefficient, namely in the formula (2).
The specific method for calculating the weighted average coefficient comprises the following steps:
step1, observing all data, and directly removing a data group with obvious distribution differences between the initial position data and other data;
step2, the rest data sets are firstly removed from the second key point data, the rest data sets are grouped according to the data distribution areas, the rest data sets belong to most weighting 1 times, otherwise, each data set obtains own weighting coefficient;
step3, calculating the weighted average coefficient of the difference between the starting point and the starting point,
wherein M is i For the weighting coefficient of the i-th group data, N i L is the difference between the start point and the start point of the ith group data max The theoretical maximum motion value of walking according to the motion file is represented.
Because the modeling track of the weighted average modeling only keeps the running of the travelling shaft of the rail car, the running of the six-degree-of-freedom moving shaft and the rotating shaft of the rail car is forbidden, the information of each frame of the preset action code in the running process of the rail car contains the movement information of all shafts, namely the action of the travelling mechanism cannot skip frames or leak frames, the accuracy and the smoothness are ensured, and uncertainty factors of the rail, the rail car and other field devices and the experience points of the dark riding project are mainly concentrated on main key points such as a screen, a machine model, a lamplight show and the like, the key point compensation value omega is added in the weighted average model to ensure the experience effect of the key points, and the formula after the key point compensation is added is that
Where ω is a dynamic value.
The key point compensation method comprises the following steps:
step1, completing a weighted average modeling method of a walking part;
step2, listing all key points to be compensated, observing the running track of the rail car, finely adjusting action codes which do not accord with the preset effect until the actual running track of the rail car is within the tolerance range of the set track, recording the position information of all key points (as shown in fig. 4, recording the position information by using a code reader), and simultaneously recording the position information of an N frame (the required frame number can be selected according to the actual situation, and the time of each frame is 10 milliseconds) before all key points;
step3, writing all key points and the position information of the previous W frame into a PLC program of the rail car;
step 4, when the track car runs and reaches the position of the W frame before a certain key point, comparing the position information at the moment with the position information of the reference value recorded before to obtain a difference value;
and 5, in the process of moving from the W-th frame before the key point to the key point, using an S-shaped curve acceleration and deceleration algorithm to distribute the difference value to N frames, wherein W is more than N, ensuring the fluency of the motion trail, and then overlapping the motion trail into subsequent motion data in a carrier wave mode to ensure the synchronism of the motion trail.
The implementation of the S-shaped curve acceleration and deceleration algorithm comprises the following steps:
step1, in all the key point compensation methods, the previous N frame is a fixed frame number, such as 1000 frames, and error data are detected in 10ms in advance according to the interval time of each frame, namely 10 seconds;
step2, using an S-shaped curve acceleration and deceleration algorithm, wherein the formula is as follows:
Y＝A+B/(1+e -ax+b ) (equation 5)
Wherein A, B, a, b is constant and represents translation and pull-up in the X and Y directions, respectively;
in practical use, for example, the first 300 frames are acceleration process, the middle 400 frames are uniform speed process, the last 400 frames are deceleration process, assuming that the configured coefficient is fixed value, the sum of the data of the final 1000 frames is S, and the above N frames are Y i The sum S of the sum data is stored in the PLC;
step3, if the error between the position data of the N frame before a certain key point and the expected data is detected as delta, the final real-time compensation data is thatΔ i ＝Y i Δ/S。
Based on the method, the applicant analyzes actual data of a real scene, takes a dark riding project as an example, and analyzes a weighted average modeling and key point compensation method of the No. 6 rail car walking of the dark riding project.
The field conditions were as follows:
track: the total length is 212.4 meters, and the two-dimensional code, the wireless leaky wave and the sliding contact line are contained;
rail cars, namely 7 on-rail cars for 1 standby car, wherein each car comprises six degrees of freedom and 360-degree rotation and walking;
projection macro screen, 11 screens in total;
other equipment, a plurality of machine models, a plurality of special effect lights and a plurality of special effect equipment;
railcar structural parameters: the walking speed reduction ratio is 1:20, and the circumference of the clamping wheel is 628mm;
weighted average coefficient: 0.970651458.
table 1: actual data analysis table
In table 1, the reference data is the data of the reference vehicle in step2 of the key point compensation method, the original data is the measured data which is not processed by any method, the error 1 is the difference between the original data and the reference data, the weighted average modeling is the measured data obtained by modeling the weighted average of the original data, the error 2 is the difference between the weighted average modeling and the reference data, the key compensation point is the measured data obtained by modeling the weighted average and the key compensation point, and the error 3 is the difference between the key compensation point and the reference data.
As can be seen from the figure, the maximum value of error 1 is-5585 mm, the maximum value of error 2 is-622 mm, and the maximum value of error 3 is-13 mm. From the trend of the data, the error of weighted average modeling is in an increasing trend, and the self-balancing error capability is realized after the key point compensation method is added.
A method for compensating walking positioning of a dark riding rail car for a large theme amusement project can model a weighted average value and compensate key points of the rail car walking positioning with poor fault tolerance. The result of the project field test shows that the method using weighted average modeling can reduce the maximum error from 5585mm to 622mm, and the method using weighted average modeling and key point compensation can reduce the maximum error from 622mm to 13mm or less, and the method has self-balancing error capability, thereby meeting the industry requirement of dark riding.
It should be additionally noted that unless otherwise defined, technical or scientific terms used herein should be given the ordinary meaning as understood by one of ordinary skill in the art to which this application pertains. Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application herein. The application is intended to cover any adaptations or variations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the application pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
It is to be understood that the application is not limited to the constructions herein above described and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.
Claims (8)
1. The method for synchronizing the motion trail of the multi-axis linkage railcar is characterized by at least comprising the following steps:
step1: acquiring running mechanism parameters of a rail car running system, and establishing a mathematical model for the rail car running mechanism;
step2: dividing a track of the travelling of the railway vehicle into a plurality of blocks, wherein each block corresponds to an environmental partition, completing the motion data acquisition of all the blocks, and carrying out weighted average modeling on a travelling mechanism of the railway vehicle on the basis of a mathematical model established in Step1, wherein the implementation steps of the weighted average modeling at least comprise:
step20: recording a weighted average value coefficient contained in the mathematical model established in Step1 as eta, setting the eta as 1, and writing the eta into all railcar control PLCs on site;
step21: initializing all the rail cars on site, prohibiting the six-degree-of-freedom motion shafts and the rotation shafts of all the rail cars from moving, and only keeping the motion of the walking shafts;
step22: sending a departure command, wherein the rail vehicle moves along the rail until the rail vehicle moves to a passenger area, and waiting for a control command;
step23: sequentially sending the on-site railcars from a passenger area to a passenger area, then sending the railcars from the passenger area to an experience area, then entering a station entering area and the passenger area, ending a complete tour process, recording the initial position of the experience area, the experience position of a curtain area and the position information of key points, and recording the number M of the key points;
step24: repeating Step23 until enough stable data are obtained, and recording the number N of stable data groups;
step25: carrying out weighting treatment on the data obtained from Step23 to Step24, obtaining a weighted average value of the difference between the starting point and the starting point, dividing the weighted average value by the actual motion length of the travelling shaft, finally obtaining a weighted average value coefficient eta, and substituting the weighted average value coefficient eta into a mathematical model of Step1 to obtain a travelling mechanism of the rail car motion system for carrying out weighted average value modeling;
step3: on the basis of Step2 weighted average modeling, key point compensation is carried out on key points of a rail car running track through an S-shaped curve acceleration and deceleration algorithm so as to ensure the fluency of a rail car running mechanism, further ensure the fluency of the rail car running track, and improve the synchronism of the rail car running track and a story line, wherein the compensation is carried out on the multi-degree-of-freedom key points in the rail car running process, and the method at least comprises the following steps:
step30: completing a weighted average modeling method of the walking part in Step 2;
step31: taking the rail car with the closest average value as a reference vehicle of the key point compensation method, and recording the position information of the running track of the reference vehicle;
step32: listing all key point information to be compensated, then observing the running track of the railway vehicle, adjusting action codes which do not accord with the preset effect until the actual running track of the railway vehicle is within the tolerance range of the set track, recording the position information of all key points, and simultaneously recording the position information of the W-th frame before all key points;
step33: writing all key points and the position information of the previous W frame into the PLC programs of all the rail cars;
step34: when the W frame position before a certain key point is reached in the running process of any railway vehicle, comparing the position information at the moment with the previously recorded reference vehicle position information to obtain a difference value;
step35: in the process of moving from the W-th frame before the key point to the key point, an S-shaped curve acceleration and deceleration algorithm is used for distributing the difference value to N frames, wherein W is more than N, and then the difference value is overlapped into subsequent movement data in a carrier wave mode so as to ensure the fluency of a track of the track car, further ensure the fluency of the track car, and improve the synchronism of the track car movement track and a story line.
2. The method of synchronizing motion trajectories of a multi-axis linkage railcar according to claim 1, wherein in Step1, the running gear of the railcar motion system includes at least a control PLC, a servo driver, a servo motor, a running speed reducer, grip running wheels, a guide rail, and auxiliary elements.
3. The method for synchronizing motion trajectories of multi-axis linkage railcars according to claim 1, wherein, according to the process of tourist, the railcar motion trajectory is divided into at least a boarding zone, an experience zone, a boarding zone, a disembarking zone; the experience area is at least divided into a plurality of curtain areas and machine model areas, and each curtain area and machine model area comprises a plurality of special effect lamplight and special effect equipment; the blocks in Step2 refer to the experience area blocks.
4. The method for synchronizing motion trajectories of a multi-axis linkage railcar according to claim 1, wherein in Step2, the weighted average model is built for rail-clamped traveling biaxial motions of the railcar motion trajectories only, excluding motions of six-degree-of-freedom motion axes and rotation axes of the railcar.
5. The method for synchronizing motion trajectories of a multi-axis linkage railcar according to claim 1, wherein in Step25, the method for obtaining the weighted average coefficient η is:
step250: observing all data, and directly removing the data group with obvious distribution gap between the initial position data and other data;
step251: the rest data sets are firstly removed from the second key point data, the rest data sets are grouped according to the data distribution areas, the rest data sets belong to most weighting 1 times, otherwise, each data set obtains own weighting coefficient;
step252: the weighted average coefficient of the difference between the starting point and the starting point is calculated by using the formula:
wherein M is i For the weighting coefficient of the i-th group data, N i L is the difference between the start point and the start point of the ith group data max The theoretical maximum motion value of walking according to the motion file is represented.
6. The synchronization method of the movement track of the multi-axis linkage railcar according to claim 1, wherein the position information of the railcar key points is obtained by reading the position information of the two-dimensional codes and is transmitted to the control PLC.
7. The method for synchronizing motion trajectories of a multi-axis linkage railcar according to claim 1, wherein in Step35, the implementation method of the S-curve acceleration/deceleration algorithm is:
step350: in all the key point compensation methods, the previous W-th frame is a fixed frame number, and the detection error data in T seconds is advanced according to the interval time of each frame being Pms;
step350: using an S-curve acceleration and deceleration algorithm, the formula is as follows:
Y＝A+B/(1+e -ax+b )
wherein A, B, a, b is constant and represents translation and pull-up in the X and Y directions, respectively;
step352: the sum of the data of the final W frames is S, the data Y of the W frames is obtained i The sum S of the sum data is stored in the PLC; if the error between the position data of the W-th frame before a certain key point and the expected data is detected as delta, the final real-time compensation data is delta i ＝Y i Δ/S。
8. A computer-readable storage medium in which a program is stored, wherein the program, when executed, is operable to perform the method of synchronizing motion trajectories of a multi-axis linkage railcar according to any one of claims 1-7.
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