CN117901144B - AGV car washing robot intelligent control method and system - Google Patents
AGV car washing robot intelligent control method and system Download PDFInfo
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- CN117901144B CN117901144B CN202410309086.XA CN202410309086A CN117901144B CN 117901144 B CN117901144 B CN 117901144B CN 202410309086 A CN202410309086 A CN 202410309086A CN 117901144 B CN117901144 B CN 117901144B
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- 238000000034 method Methods 0.000 title claims abstract description 72
- 238000005406 washing Methods 0.000 title claims abstract description 65
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 134
- 238000004140 cleaning Methods 0.000 claims abstract description 127
- 239000007921 spray Substances 0.000 claims abstract description 106
- 238000004422 calculation algorithm Methods 0.000 claims description 27
- 230000008569 process Effects 0.000 claims description 14
- 230000000903 blocking effect Effects 0.000 claims description 10
- 230000006870 function Effects 0.000 claims description 10
- 238000004364 calculation method Methods 0.000 claims description 9
- 238000005507 spraying Methods 0.000 claims description 9
- 238000004590 computer program Methods 0.000 claims description 6
- 238000005457 optimization Methods 0.000 claims description 6
- 238000005070 sampling Methods 0.000 claims description 5
- 238000012216 screening Methods 0.000 claims description 4
- 230000001133 acceleration Effects 0.000 claims description 3
- 238000005265 energy consumption Methods 0.000 abstract description 3
- 230000000694 effects Effects 0.000 description 5
- 230000005484 gravity Effects 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 230000009295 sperm incapacitation Effects 0.000 description 4
- 238000001914 filtration Methods 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 238000004544 sputter deposition Methods 0.000 description 2
- 239000002699 waste material Substances 0.000 description 2
- 238000009825 accumulation Methods 0.000 description 1
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- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 239000012459 cleaning agent Substances 0.000 description 1
- 239000012141 concentrate Substances 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 239000003651 drinking water Substances 0.000 description 1
- 235000020188 drinking water Nutrition 0.000 description 1
- 230000003670 easy-to-clean Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- JEIPFZHSYJVQDO-UHFFFAOYSA-N iron(III) oxide Inorganic materials O=[Fe]O[Fe]=O JEIPFZHSYJVQDO-UHFFFAOYSA-N 0.000 description 1
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Classifications
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J11/00—Manipulators not otherwise provided for
- B25J11/008—Manipulators for service tasks
- B25J11/0085—Cleaning
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1656—Programme controls characterised by programming, planning systems for manipulators
- B25J9/1661—Programme controls characterised by programming, planning systems for manipulators characterised by task planning, object-oriented languages
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1679—Programme controls characterised by the tasks executed
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60S—SERVICING, CLEANING, REPAIRING, SUPPORTING, LIFTING, OR MANOEUVRING OF VEHICLES, NOT OTHERWISE PROVIDED FOR
- B60S3/00—Vehicle cleaning apparatus not integral with vehicles
- B60S3/04—Vehicle cleaning apparatus not integral with vehicles for exteriors of land vehicles
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- Engineering & Computer Science (AREA)
- Mechanical Engineering (AREA)
- Robotics (AREA)
- Cleaning By Liquid Or Steam (AREA)
Abstract
The invention relates to the technical field of intelligent control of automobile robots, in particular to an intelligent control method and system of an AGV car washing robot, wherein the method comprises the following steps: obtaining grids to be cleaned in a candidate set of grids to be cleaned, and obtaining movable grids in a movable set of an AGV car washing robot; acquiring the speed of water flow in the spray gun from each movable grid to other grids to be cleaned; acquiring an included angle between water flow in the spray gun and the horizontal plane from each movable grid to other grids to be cleaned; acquiring a single grid cleaning suitability index; acquiring a multi-grid cleaning efficiency index of each movable grid; acquiring a multi-grid cleaning smoothness index; acquiring a multi-grid cleaning suitability index; acquiring the number of grids at the positions of the spray guns in the candidate set of grids to be cleaned; and acquiring grid routes passing through all the optimal spray gun positions, and finishing the optimal movement track for cleaning the bottom of the automobile. The invention optimizes the position of the spray gun, and can more accurately finish cleaning operation with low energy consumption.
Description
Technical Field
The invention relates to the technical field of intelligent control of automobile robots, in particular to an intelligent control method and system of an AGV car washing robot.
Background
In recent years, with rapid progress and development of society and economy, the holding amount of automobiles is increased, the car washing product industry is promoted and developed strongly, and considering that a large amount of drinking water resources may be wasted in the traditional car washing service mode, time and labor are consumed, an AGV robot (Automated Guided Vehicle) is a robot capable of automatically guiding and moving, the guiding and moving of the robot are realized by utilizing a visual or magnetic navigation tracking tape, the intelligent car washing efficiency is further improved, and the intelligent car washing of the robot is realized.
In the actual car washing process, attention is required to wash the bottom of the car, otherwise sludge accumulation can be caused on the side of a wheel cavity of the car and the chassis, so that rust is easily caused in the wheel cavity due to moisture hiding, and even the wheel cavity is likely to loosen and perforate, so that the bottom of the car after washing is ensured to be clean. The bottom of the automobile is complex in structure and has various structures with complex geometric shapes, such as a suspension system, an exhaust pipe, a control arm and the like, when a traditional path planning algorithm such as an A algorithm is used for planning a spray gun path, the condition that the path planning is unreasonable can occur, the bottom of the automobile is easy to clean thoroughly and cleanly, and further, the bottom parts of the automobile are loose, so that risks can occur in the running process of the automobile.
Disclosure of Invention
In order to solve the technical problems, the invention aims to provide an intelligent control method and system for an AGV car washing robot, and the adopted technical scheme is as follows:
In a first aspect, an embodiment of the present invention provides an intelligent control method for an AGV car-washing robot, including the following steps:
Step S10, collecting three-dimensional position data of a spray gun head, spray gun diameter data, spray gun port water flow velocity data and three-dimensional point cloud data of an automobile bottom contour;
Step S20, dividing the space of the bottom of the automobile according to three-dimensional point cloud data of the contour of the bottom of the automobile to obtain grids to be cleaned and movable grids;
step S30, calculating the water flow arrival speed from each movable grid to each grid to be cleaned according to the spray gun head direction of the car washing robot, the water flow velocity data of the spray gun opening and the spray gun diameter data;
Calculating an included angle between water flow in the spray gun and the horizontal plane from the center of each movable grid to the center of each cleaning grid according to the space position data among the grids;
According to the three-dimensional point cloud data of the bottom contour of the automobile, acquiring the area of a grid curved surface of each grid and the projection area of each grid curved surface in the direction of an included angle between water flow in the spray gun and the horizontal plane from each movable grid center to each cleaning grid center by combining a differential geometry method;
Step S40, calculating a single grid cleaning suitability index when the spray gun sprays water to each grid to be cleaned at each movable grid according to the water flow arrival speed and the area and projection area of the grid curved surface; acquiring a multi-grid cleaning energy efficiency index of each movable grid according to the single-grid cleaning suitability index; acquiring a multi-grid cleaning smooth index of each movable grid according to Euclidean distance and Gaussian curvature between each movable grid and the center point of each grid to be cleaned; calculating a multi-grid cleaning suitability index of each movable grid according to the multi-grid cleaning energy efficiency index and the multi-grid cleaning smoothness index of each movable grid;
And S50, determining the positions of all the optimal spray guns of the car washing robot by taking the multi-grid cleaning suitability index as an objective function value of an optimization algorithm, and carrying out path planning on all the optimal spray gun positions through a path planning algorithm.
Further, the dividing the vehicle bottom space according to the three-dimensional point cloud data of the vehicle bottom outline to obtain each grid to be cleaned and each movable grid includes:
dividing three-dimensional point cloud data of an automobile bottom contour into grids with the same size by using a grid method, wherein each grid is a cube with the same volume, the side length of each grid is a preset side length, the grids with the automobile bottom contour point cloud inside are used as the automobile bottom contour grids, a set formed by the automobile bottom contour grids which are cleaned by a robot is marked as an intelligent cleaning grid screening set, a set formed by the automobile bottom contour grids which are not cleaned by the robot is marked as a to-be-cleaned grid candidate set, and the grids in the to-be-cleaned grid candidate set are marked as to-be-cleaned grids;
the method comprises the steps of acquiring grids with the same size by adopting the same dividing method as three-dimensional point cloud data of the outline of the bottom of a car in a space between the bottom of the car and the ground in a car washing room to form a movable set of AGV car washing robots, and marking each grid in the movable set of the AGV car washing robots as a movable grid.
Further, according to the spray gun head direction, the spray gun mouth water flow velocity data and the spray gun diameter data of the car washing robot, the water flow arrival speed from each movable grid to each grid to be washed is calculated, and the method comprises the following steps:
For each sampling moment, calculating the product of the initial speed of water flow in the spray gun at the last sampling moment when the water flow is sprayed from each movable grid to each grid to be cleaned and the diameter of the spray gun, calculating the sine value of the included angle between the connecting line between the center point of each movable grid and the center point of each grid to be cleaned and the vertical direction, calculating the sum value of the sine value and a preset coordination factor, and taking the ratio of the product to the sum value as the impact blocking coefficient when the water flow in the spray gun is sprayed from each movable grid to each grid to be cleaned;
the expression of the water flow arrival speed of the water flow in the spray gun from each movable grid to other grids to be cleaned is as follows:
In the method, in the process of the invention, Representing the impact resistance coefficient of the water flow from grid a to grid b,/>The value range is [0,90] for the included angle between the connecting line between the center point of the grid a and the center point of the grid b and the vertical direction; /(I)A coordination factor is preset; /(I)The time required for the water flow from grid a to grid b; /(I)Representing the velocity component of the water flow in the vertical direction when it starts from grid a and reaches grid b; /(I)The water flow arrival speed when the water flow starts from the grid a and reaches the grid b is shown; sin () is a sine function, cos () is a cosine function, g is gravitational acceleration,/>Representing the initial velocity of the water flow as it is ejected from grid a to grid b.
Further, the calculating the included angle between the water flow in the spray gun and the horizontal plane from the movable grid center to the cleaning grid center comprises the following steps:
Calculating the product of the initial velocity of the water flow in the spray gun when the water flow is sprayed from each movable grid to each grid to be cleaned and the sine value as a first product, calculating the sum value of the first product and a preset coordination factor as a first sum value, and taking the arctangent value of the ratio of the velocity component in the vertical direction of the water flow in the spray gun from each movable grid to each grid to be cleaned and the first sum value as the included angle between the water flow in the spray gun and the horizontal plane when the water flow reaches each grid to be cleaned from each movable grid.
Further, the acquiring the area of the grid curved surface of each grid and the projection area of each grid curved surface in the direction of the included angle between the water flow in the spray gun and the horizontal plane when the water flow flows from each movable grid center to each cleaning grid center comprises the following steps:
taking the bottom contour point cloud of the automobile in each grid as input of a Bessel curve fitting algorithm, outputting the bottom contour point cloud of the automobile in each grid as a curve fitted by the bottom contour point cloud of the automobile in each grid, and marking the curve as a grid curve;
And respectively calculating the area of the grid curved surface of each grid to be cleaned and the projection area of the grid curved surface of each grid to be cleaned in the direction of an included angle between the water flow in the spray gun and the horizontal plane when reaching each grid to be cleaned from each movable grid by using a differential geometric method.
Further, the acquiring the multi-grid cleaning energy efficiency index of each movable grid according to the single-grid cleaning suitability index comprises the following steps:
for each movable grid, calculating the average value of the single grid cleaning suitability index when water flows in the spray gun from the movable grid to other grids to be cleaned, and taking the average value as the multi-grid cleaning effectiveness index of each movable grid.
Further, the obtaining the multi-grid cleaning smoothness index of each movable grid according to the euclidean distance and the gaussian curvature between each movable grid and the center point of each grid to be cleaned comprises the following steps:
For each grid to be cleaned, taking an area formed by the grid to be cleaned and the nearest preset number of grids to be cleaned to the grid to be cleaned as a neighboring area of each grid to be cleaned;
Fitting the neighbor areas through a Bessel curved surface fitting algorithm to obtain neighbor area fitting curved surfaces of the grids to be cleaned, and calculating Gaussian curvatures of positions of the neighbor areas of the grids to be cleaned;
Calculating Euclidean distance between each movable grid and the center point of each grid to be cleaned, calculating the product of the Euclidean distance and the Gaussian curvature of the grid to be cleaned, calculating the reciprocal of the sum of the product and a preset coordination factor, and taking the average value of all the reciprocal of each movable grid as the multi-grid cleaning smooth index of each movable grid.
Further, the calculation formulas of the single grid cleaning suitability index and the multi-grid cleaning suitability index are as follows:
the calculation formula of the single grid cleaning suitability index is as follows:
In the method, in the process of the invention, Indicating the single grid cleaning suitability index of the spray gun when spraying water to grid b at grid a,/>, for cleaningRepresenting the area of the grid curved surface of grid b,/>Representing the projected area of the grid curved surface of grid b in the direction of the angle with the horizontal plane when the water flow in the spray gun goes from the center of grid a to the center of grid b,/>Represents the water flow arrival velocity when the water flow starts from grid a and reaches grid b,/>Representing the initial velocity of the water flow as it is ejected from grid a to grid b,/>Representing a preset coordination factor;
the calculation formula of the multi-grid cleaning suitability index is as follows:
In the method, in the process of the invention, Multi-grid cleaning suitability index indicating cleaning of vehicle bottom contour by grid a in movable set of AGV cleaning robot,/>Multi-grid cleaning smoothness index representing cleaning of the bottom contours of an automobile by a grid a in a movable set of an AGV cleaning robot,/>The multi-grid cleaning performance index when the AGV cleaning robot is movable and centralized to clean the bottom contour of the automobile is shown.
Further, the path planning algorithm performs path planning on all the optimal spray gun positions, including:
and taking the optimal spray gun position, the position of the automobile to be cleaned when entering the car washing room and the spray gun position farthest from the Euclidean distance of the position of the automobile to be cleaned when entering the car washing room as inputs of a path planning algorithm, wherein the output of the path planning algorithm is an optimal route of all the optimal spray gun positions.
In a second aspect, an embodiment of the present invention further provides an intelligent control system for an AGV car-washing robot, including a memory, a processor, and a computer program stored in the memory and running on the processor, where the processor implements the steps of any one of the methods described above when executing the computer program.
The invention has at least the following beneficial effects:
The invention provides an intelligent control method and system for an AGV car washing robot, wherein the cloud of the outline point at the bottom of an automobile is divided into a plurality of grids, an intelligent cleaning grid screening set and a grid candidate set to be cleaned, the operable space of the AGV robot is divided into a set formed by a plurality of grids, impact obstruction coefficients are constructed by analyzing the direction of a spray gun head and the water spraying rate, and the obstruction degree of water flow under different angles and different water spraying rates on subsequent water flow is reflected, so that the water flow velocity reaching the grid to be cleaned is calculated more accurately; constructing a single grid cleaning suitability index based on the water flow arrival speed when reaching the grids to be cleaned, and reflecting the suitability degree when cleaning one of the grids to be cleaned at the current position; constructing a multi-grid cleaning suitability index based on the single-grid cleaning suitability index, and reflecting the suitability degree of the plurality of grids to be cleaned when the grids to be cleaned are cleaned at the current position; the spray gun position is optimized based on the multi-grid cleaning suitability index, so that the obtained spray gun position suitable for cleaning the bottom of the automobile can more accurately finish cleaning operation with low energy consumption.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of steps of an intelligent control method for an AGV car washing robot according to one embodiment of the present invention;
FIG. 2 is a schematic diagram of a movable collection of AGV car washing robots;
fig. 3 is a schematic diagram of multi-grid cleaning suitability index acquisition.
Detailed Description
In order to further describe the technical means and effects adopted by the invention to achieve the preset aim, the following is a detailed description of specific implementation, structure, characteristics and effects of an intelligent control method and system for an AGV car washing robot according to the invention, which are provided by the invention, with reference to the accompanying drawings and the preferred embodiment. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The invention provides an intelligent control method and a system for an AGV car washing robot.
Referring to fig. 1, a flowchart of steps of an intelligent control method for an AGV car-washing robot according to an embodiment of the present invention is shown, where the method includes the following steps:
and S001, collecting data and preprocessing.
After a vehicle to be cleaned enters a carwash room, three-dimensional position data of a spray gun head of the carwash robot mechanical arm are acquired through a positioning system on the carwash robot mechanical arm, water flow speed data of a spray gun opening on the carwash robot mechanical arm are acquired through a flow speed sensor, an acquisition interval is T, a value 1s is acquired in the embodiment, the acquisition times are N, a value 600 is acquired in the embodiment, the bottom of the vehicle is scanned through a laser radar assembled on the carwash robot mechanical arm, three-dimensional point cloud data of the contour of the bottom of the vehicle are acquired, and diameter data of a spray gun on the carwash robot mechanical arm are acquired through a caliper.
In order to avoid the influence of the missing value on the subsequent steps, the embodiment uses a regression filling method to fill the missing value, and simultaneously uses Gaussian filtering to denoise the three-dimensional point cloud data of the automobile bottom contour to record the processed point cloud data as the three-dimensional point cloud data of the automobile bottom contour in order to avoid noise of the three-dimensional point cloud data of the automobile bottom contour caused by the conditions of vibration conduction and the like of the automobile. The regression filling method and the gaussian filtering are known techniques, and the specific process is not described in detail in this embodiment.
Step S002, dividing the outline point cloud of the bottom of the automobile into a plurality of grids and classifying the grids, analyzing the direction of a spray gun head and the water spray rate, constructing an impact blocking coefficient, calculating the water flow speed when reaching the grids, analyzing the suitability degree of one grid at the bottom of the automobile based on the water flow speed when reaching the grids, constructing a single grid cleaning suitability index, analyzing the suitability degree of a plurality of grids at the bottom of the automobile based on the single grid cleaning suitability index, constructing a plurality of grid cleaning suitability indexes, optimizing the spray gun position based on the plurality of grid cleaning suitability indexes, and obtaining a plurality of spray gun positions suitable for cleaning the bottom of the automobile.
In order to facilitate planning of a car washing path of the AGV car washing robot in a subsequent step and improve optimization efficiency of an algorithm, in the embodiment, the three-dimensional point cloud data of the outline of the bottom of the car after pretreatment is divided into grids with the same size by using a grid method, each grid is a cube with the same volume, the side length of each grid is recorded as H, and the value of the grid is 5cm in the embodiment; if the vehicle bottom contour point cloud exists in the grids, marking the grids as vehicle bottom contour grids, marking a set formed by the vehicle bottom contour grids which are cleaned by the robot as an intelligent cleaning grid screening set, marking a set formed by the vehicle bottom contour grids which are not cleaned by the robot as a grid candidate set to be cleaned, and marking the grids in the grid candidate set to be cleaned as the grids to be cleaned. The grid method is a well-known technique, and the specific process is not repeated in this embodiment.
The same processing is carried out according to the steps, the space between the bottom of the automobile and the ground is divided into a plurality of grids, the set formed by the grids is marked as an AGV car washing robot movable set, each grid in the AGV car washing robot movable set is marked as a movable grid, and a schematic diagram of the AGV car washing robot movable set is shown in fig. 2.
And constructing an impact obstruction coefficient based on the direction of the spray gun head and the water spraying rate, and calculating the water flow speed when reaching the grid to be cleaned. When the AGV car washing robot performs car washing tasks, water is sprayed to the car through a spray gun on a robot mechanical arm, cleaning agents and the like, and as the direction of a spray gun head is upward when the bottom of the car is cleaned, the water is affected by gravity when the spray gun head is upward sprayed, the angle of the spray gun head is different, the track change of the water affected by gravity is different, and the cleaning effect on the bottom of the car is further different; the greater the speed of the water flow reaching the bottom of the automobile, the greater the impact degree of the water flow on the bottom of the automobile, the greater the energy loss, the water flow can splash and possibly generate a certain blocking effect on the subsequent water flow, and at the moment, the higher energy loss is caused, and the water resource waste is caused.
Based on the above analysis, in this embodiment, an impact blocking coefficient is constructed based on the direction of the gun head and the water spraying rate of the gun, which reflects the degree to which the water in the gun head is affected by gravity, and calculates the water flow rate when reaching the grid, and for convenience of description, the present embodiment uses the case that the gun sprays water to the grid b in the candidate set of grids to be cleaned in the movable set of the AGV car washing robot, and the calculation formula of the impact blocking coefficient and the water flow arrival rate when reaching the grid is as follows:
Wherein, Represents the impact resistance coefficient of the water flow from grid a to grid b, d represents the lance diameter,/>Representing the initial velocity of the water flow from grid a to grid b at the previous sampling time,/>The value range is [0,90 ]/>, which is the included angle between the connecting line between the center point of the grid a and the center point of the grid b and the vertical directionThe coordination factor is used for avoiding incapacitation caused by zero denominator, and the value of the coordination factor is 0.1 in the embodiment; /(I)The time required for the water flow from grid a to grid b; /(I)Representing the velocity component of the water flow in the vertical direction when it starts from grid a and reaches grid b; /(I)The water flow arrival speed when the water flow starts from the grid a and arrives at the grid b is represented, sin () is a sine function, cos () is a cosine function, and g is a gravity acceleration, and the value in the embodiment is 9.8; /(I)Representing the initial velocity of the water flow as it is ejected from grid a to grid b.
The larger the diameter of the spray gun, i.eThe larger the initial velocity of the water flow ejected from the spray gun is, the greater the velocity of the water flow ejected from the spray gun isThe larger the water resource waste generated by sputtering during water spraying, namely the larger the sputtering amount, the stronger the blocking effect on the subsequent water flow, and the smaller the included angle between the connecting line between the grids a and b and the vertical direction, namely/>The smaller the spray gun is, the closer to the vertical direction is sprayed, the water flow is influenced by the gravity vertically downwards, the stronger the blocking effect on the subsequent water flow is, the larger the calculated impact blocking coefficient is, the smaller the component in the vertical direction is when the water flow reaches the bottom of the automobile chassis, and the calculated impact blocking coefficient isThe smaller the velocity is, vector synthesis is carried out on a horizontal velocity component and a vertical velocity component when the water flow reaches the automobile bottom grid b, so as to obtain the velocity/>, when the water flow reaches the automobile bottom grid b。
A single grid cleaning suitability index is constructed based on the water flow velocity at the time of reaching the grid. When the water flow in the spray gun is sprayed from the grid a to the grid b, the water flow speed is too low, and the bottom of the automobile may not be cleaned; the excessive water flow speed can cause a certain damage to the bottom of the automobile. In this embodiment, the single-grid cleaning suitability index is constructed based on the water flow rate at the time of reaching the grid, and reflects the degree to which the spray gun is suitably sprayed from the grid a to the grid b to clean the grid b.
Firstly, calculating an included angle between water flow in the spray gun and the horizontal plane when the water flow reaches the grid b from the grid a, wherein the calculation formula is as follows:
Wherein the method comprises the steps of Representing the angle from horizontal of the water flow in the lance from grid a to grid b, arctan () representing the arctan function,/>Representing the velocity component in the vertical direction of the water flow when it starts from grid a to grid b,/>Representing the initial velocity of the water flow as it is ejected from grid a to grid b,/>Is the included angle between the connecting line between the center point of the grid a and the center point of the grid b and the vertical direction,/>And the coordination factor is expressed and used for avoiding incapacitation caused by zero denominator, and the value of the coordination factor is 0.1 in the embodiment.
The method comprises the steps of taking the bottom contour point cloud of an automobile in each grid as input of a Bessel curve fitting algorithm, outputting the bottom contour point cloud of the automobile in each grid as a curve surface after fitting, marking the curve surface as a grid curve surface, and respectively calculating the area of the grid curve surface and the position of the grid curve surface in the Bessel curve fitting algorithm through a differential geometric methodThe projected area in the direction, wherein the Bessel surface fitting algorithm and the differential geometry method are known techniques, and the specific process is not described in detail in this embodiment. A single grid cleaning suitability index may be calculated as follows:
Wherein the method comprises the steps of Indicating the single grid cleaning suitability index of the spray gun when spraying water to grid b at grid a,/>, for cleaningRepresenting the area of the grid curved surface of grid b,/>Grid surface of grid b/>Projected area in the direction,/>Represents the water flow arrival velocity when the water flow starts from grid a and reaches grid b,/>Representing the initial velocity of the water flow as it is ejected from grid a to grid b,/>And the coordination factor is expressed and used for avoiding incapacitation caused by zero denominator, and the value of the coordination factor is 0.1 in the embodiment.
The larger the projected area of the grid curved surface in the direction of the water flow in the spray gun from grid a to grid b is, i.e. the larger the ratio of the grid curved surfaceThe larger the water flow in the spray gun is, the larger the coverage area of the water flow in the spray gun for cleaning the automobile bottom contour is, the better the effect of cleaning the automobile bottom contour is, and the smaller the difference between the speed of the water flow reaching the grid b and the initial speed starting from the grid a is, namely/>The smaller the energy loss, the smaller the energy consumption is, the more suitable the vehicle bottom contour is for cleaning, so the calculated single grid cleaning suitability index is larger.
A multi-grid cleaning suitability index is constructed based on the single-grid cleaning suitability index. In an actual car washing process, a car washing robot usually washes a region at the bottom of a car in one position, namely, a spray gun washes a plurality of grids in a candidate set of grids to be washed in one grid, while details at the bottom of the car are more, such as structures of an exhaust pipe, a fender and the like, grid distribution conditions of different regions may be different, smoothness of the spray gun during movement, water spraying rate of the spray gun and the like may be different, and in order to enable the bottom of the car to be washed cleaner, the AGV needs to movably concentrate a plurality of grids to be selected for washing all grids in the candidate set of grids to be washed. According to this embodiment, a multi-grid cleaning suitability index is constructed based on a single-grid cleaning suitability index, reflecting the suitability of a spray gun for cleaning a plurality of grids in a candidate set of grids to be cleaned in one grid, and the calculation formula of the multi-grid cleaning suitability index is as follows:
Wherein the method comprises the steps of Multi-grid cleaning efficacy index representing the cleaning of the bottom contours of an automobile by a grid a in a movable set of an AGV cleaning robot,/>Represents the number of the grids to be cleaned, which are cleaned when the AGV cleaning robot movably collects the grids a to clean the bottom outline of the automobile,/>, of the grids to be cleanedThe single grid cleaning suitability index is shown when the grid a in the movable set of the AGV car-washing robot cleans the ith grid in the grids to be cleaned in the bottom outline of the car.
The method comprises the steps of representing a multi-grid cleaning smooth index when a grid a in a movable set of an AGV car washing robot cleans an automobile bottom contour, marking an area formed by an ith grid and 3 grids closest to the ith grid in the automobile bottom contour to be cleaned as a neighboring area of the grid i by the grid a in the movable set of the AGV car washing robot, fitting the neighboring area through a Bessel surface fitting algorithm to obtain a neighboring area fitting surface of the grid i, calculating a Gaussian curvature of the position of the fitting surface of the grid i in the neighboring area, and marking the Gaussian curvature as/>，/>Representing Euclidean distance between grid a in movable collection of AGV car washing robot and center point of ith grid in grids to be washed of automobile bottom outline,/>The coordination factor is used for avoiding incapacitation caused by zero denominator, and the value of the coordination factor is 0.1 in the embodiment; the Bessel surface fitting algorithm is a well-known technique, and is not described herein in detail.
A multi-grid cleaning suitability index indicating when the AGV cleaning robot is movable and concentrated to clean the bottom contour of the automobile. Wherein, the multi-grid cleaning suitability index acquisition schematic diagram is shown in fig. 3.
The higher the single grid cleaning suitability index of the AGV vehicle cleaning robot when the grid a cleans a plurality of grids to be cleaned in the bottom outline of the vehicle, namelyThe larger indicates the more suitable for cleaning the vehicle underbody at grid a, while the smaller the gaussian curvature of grid i, i.e./>The smaller the curve surface fluctuation degree of the position of the grid i is, the more complex the local structure is, and the smaller the Euclidean distance between the grid i and the grid a is, namely/>The smaller the grid a shows that the smaller the energy that the spray gun needs to provide for water when cleaning the grid i, namely the more suitable the spray gun is for cleaning the bottom of an automobile, so the calculated multi-grid cleaning suitability index is larger.
The grids are selected based on a multi-grid cleaning fitness index. The multi-grid cleaning suitability index obtained through the steps reflects the suitability of the spray gun for cleaning a plurality of grids in a candidate set of grids to be cleaned in one grid.
Taking the value of the multi-grid cleaning suitability index as the objective function value of the optimization algorithm to obtain K grids which are suitable as the spray gun positions in a movable and centralized manner of the AGV car washing robot, wherein the value of K in the embodiment is 9, and the positions of the K grids are taken as the optimal spray gun positions; the optimization algorithm in this embodiment is a hawk optimization algorithm, and the specific process is a known technology, which is not described in detail in this embodiment.
And step S003, based on the optimal spray gun position grid, acquiring an optimal movement track of the AGV car washing robot when the bottom of the car is washed, and intelligently controlling the car washing robot.
The K best spray gun positions, the initial position of the car washing robot and the final position of the car washing robot obtained in the steps are used as inputs of a path planning algorithm, and the path planning algorithm in the embodiment is as followsAnd outputting an optimal route passing through all the optimal spray gun positions to finish the optimal movement track of the bottom operation of the cleaning automobile.
According to the method, the obtained optimal movement track is used as the input of the Bezier curve, the output is the smoothed optimal route, the obtained smoothed optimal route is sent to a robot cleaning system, and the system controls all joints of the mechanical arm of the AGV car washing robot respectively through the optimal route, so that action control in a cleaning process is realized, and cleaning efficiency and cleaning effect are improved. The bezier curve is a known technique, and the specific process of this embodiment is not described in detail.
Based on the same inventive concept as the above method, the embodiment of the invention also provides an intelligent control system of an AGV car-washing robot, which comprises a memory, a processor and a computer program stored in the memory and running on the processor, wherein the processor realizes the steps of any one of the above intelligent control methods of the AGV car-washing robot when executing the computer program.
It should be noted that: the sequence of the embodiments of the present invention is only for description, and does not represent the advantages and disadvantages of the embodiments. And the foregoing description has been directed to specific embodiments of this specification. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
The foregoing description of the preferred embodiments of the present invention is not intended to be limiting, but rather, any modifications, equivalents, improvements, etc. that fall within the principles of the present invention are intended to be included within the scope of the present invention.
Claims (7)
1. An intelligent control method of an AGV car washing robot is characterized by comprising the following steps:
Step S10, collecting three-dimensional position data of a spray gun head, spray gun diameter data, spray gun port water flow velocity data and three-dimensional point cloud data of an automobile bottom contour;
step S20, dividing the space of the bottom of the automobile according to three-dimensional point cloud data of the contour of the bottom of the automobile to obtain grids to be cleaned and movable grids; aiming at the space between the bottom of an automobile in a carwash room and the ground, the same dividing method as the three-dimensional point cloud data of the contour of the bottom of the automobile is adopted to obtain grids with the same size to form a movable set of AGV car washing robots, and each grid in the movable set of the AGV car washing robots is marked as a movable grid;
step S30, calculating the water flow arrival speed from each movable grid to each grid to be cleaned according to the spray gun head direction of the car washing robot, the water flow velocity data of the spray gun opening and the spray gun diameter data;
Calculating an included angle between water flow in the spray gun and the horizontal plane from the center of each movable grid to the center of each cleaning grid according to the space position data among the grids;
According to the three-dimensional point cloud data of the bottom contour of the automobile, acquiring the area of a grid curved surface of each grid and the projection area of each grid curved surface in the direction of an included angle between water flow in the spray gun and the horizontal plane from each movable grid center to each cleaning grid center by combining a differential geometry method;
Step S40, calculating a single grid cleaning suitability index when the spray gun sprays water to each grid to be cleaned at each movable grid according to the water flow arrival speed and the area and projection area of the grid curved surface; acquiring a multi-grid cleaning energy efficiency index of each movable grid according to the single-grid cleaning suitability index; acquiring a multi-grid cleaning smooth index of each movable grid according to Euclidean distance and Gaussian curvature between each movable grid and the center point of each grid to be cleaned; calculating a multi-grid cleaning suitability index of each movable grid according to the multi-grid cleaning energy efficiency index and the multi-grid cleaning smoothness index of each movable grid;
Step S50, determining the positions of all the optimal spray guns of the car washing robot by taking the multi-grid cleaning suitability index as an objective function value of an optimization algorithm, and carrying out path planning on all the optimal spray gun positions by a path planning algorithm;
The multi-grid cleaning energy efficiency index of each movable grid is obtained according to the single-grid cleaning suitability index, and the method comprises the following steps:
for each movable grid, calculating the average value of the single grid cleaning suitability index when water flow in the spray gun sprays water from the movable grid to other grids to be cleaned as the multi-grid cleaning efficiency index of each movable grid;
the method for obtaining the multi-grid cleaning smoothness index of each movable grid according to the Euclidean distance and Gaussian curvature between each movable grid and the center point of each grid to be cleaned comprises the following steps:
For each grid to be cleaned, taking an area formed by the grid to be cleaned and the nearest preset number of grids to be cleaned to the grid to be cleaned as a neighboring area of each grid to be cleaned;
Fitting the neighbor areas through a Bessel curved surface fitting algorithm to obtain neighbor area fitting curved surfaces of the grids to be cleaned, and calculating Gaussian curvatures of positions of the neighbor areas of the grids to be cleaned;
Calculating Euclidean distance between each movable grid and the central point of each grid to be cleaned, calculating the product of the Euclidean distance and Gaussian curvature of the grids to be cleaned, calculating the reciprocal of the sum of the product and a preset coordination factor, and taking the average value of all the reciprocal of each movable grid as a multi-grid cleaning smooth index of each movable grid;
The calculation formulas of the single grid cleaning suitability index and the multi-grid cleaning suitability index are as follows:
the calculation formula of the single grid cleaning suitability index is as follows:
In the method, in the process of the invention, Indicating the single grid cleaning suitability index of the spray gun when spraying water to grid b at grid a,/>, for cleaningRepresenting the area of the grid curved surface of grid b,/>Representing the projected area of the grid curved surface of grid b in the direction of the angle with the horizontal plane when the water flow in the spray gun goes from the center of grid a to the center of grid b,/>Represents the water flow arrival velocity when the water flow starts from grid a and reaches grid b,/>Representing the initial velocity of the water flow as it is ejected from grid a to grid b,/>Representing a preset coordination factor;
the calculation formula of the multi-grid cleaning suitability index is as follows:
In the method, in the process of the invention, Multi-grid cleaning suitability index indicating cleaning of vehicle bottom contour by grid a in movable set of AGV cleaning robot,/>Multi-grid cleaning smoothness index representing cleaning of the bottom contours of an automobile by a grid a in a movable set of an AGV cleaning robot,/>The multi-grid cleaning performance index when the AGV cleaning robot is movable and centralized to clean the bottom contour of the automobile is shown.
2. The intelligent control method of an AGV car-washing robot according to claim 1, wherein the dividing the space of the bottom of the car according to the three-dimensional point cloud data of the contour of the bottom of the car to obtain each grid to be washed comprises:
Dividing three-dimensional point cloud data of an automobile bottom contour into grids with the same size by using a grid method, wherein each grid is a cube with the same volume, the side length of each grid is a preset side length, the grids with the automobile bottom contour point cloud inside are used as the grids of the automobile bottom contour, a set formed by the grids of the automobile bottom contour through which a robot is cleaned is recorded as an intelligent cleaning grid screening set, a set formed by the grids of the automobile bottom contour through which the robot is not cleaned is recorded as a to-be-cleaned grid candidate set, and the grids in the to-be-cleaned grid candidate set are recorded as to-be-cleaned grids.
3. The intelligent control method of an AGV car-washing robot according to claim 1, wherein calculating the water flow arrival speed from each movable grid to each grid to be cleaned based on the gun head direction of the car-washing robot, the gun mouth water flow velocity data and the gun diameter data, comprises:
For each sampling moment, calculating the product of the initial speed of water flow in the spray gun at the last sampling moment when the water flow is sprayed from each movable grid to each grid to be cleaned and the diameter of the spray gun, calculating the sine value of the included angle between the connecting line between the center point of each movable grid and the center point of each grid to be cleaned and the vertical direction, calculating the sum value of the sine value and a preset coordination factor, and taking the ratio of the product to the sum value as the impact blocking coefficient when the water flow in the spray gun is sprayed from each movable grid to each grid to be cleaned;
the expression of the water flow arrival speed of the water flow in the spray gun from each movable grid to other grids to be cleaned is as follows:
In the method, in the process of the invention, Representing the impact resistance coefficient of the water flow from grid a to grid b,/>The value range is [0,90] for the included angle between the connecting line between the center point of the grid a and the center point of the grid b and the vertical direction; /(I)A coordination factor is preset; /(I)The time required for the water flow from grid a to grid b; /(I)Representing the velocity component of the water flow in the vertical direction when it starts from grid a and reaches grid b; /(I)The water flow arrival speed when the water flow starts from the grid a and reaches the grid b is shown; sin () is a sine function, cos () is a cosine function, g is gravitational acceleration,/>Representing the initial velocity of the water flow as it is ejected from grid a to grid b.
4. The intelligent control method of an AGV car-washing robot according to claim 3, wherein said calculating the angle between the water flow in the spray gun and the horizontal plane from each movable grid center to each cleaning grid center comprises:
Calculating the product of the initial velocity of the water flow in the spray gun when the water flow is sprayed from each movable grid to each grid to be cleaned and the sine value as a first product, calculating the sum value of the first product and a preset coordination factor as a first sum value, and taking the arctangent value of the ratio of the velocity component in the vertical direction of the water flow in the spray gun from each movable grid to each grid to be cleaned and the first sum value as the included angle between the water flow in the spray gun and the horizontal plane when the water flow reaches each grid to be cleaned from each movable grid.
5. The intelligent control method of an AGV car-washing robot according to claim 4, wherein the acquiring the area of the curved surface of each grid and the projected area of each curved surface of the grid in the direction of the included angle between the water flow in the spray gun and the horizontal plane from the center of each movable grid to the center of each cleaning grid comprises:
taking the bottom contour point cloud of the automobile in each grid as input of a Bessel curve fitting algorithm, outputting the bottom contour point cloud of the automobile in each grid as a curve fitted by the bottom contour point cloud of the automobile in each grid, and marking the curve as a grid curve;
And respectively calculating the area of the grid curved surface of each grid to be cleaned and the projection area of the grid curved surface of each grid to be cleaned in the direction of an included angle between the water flow in the spray gun and the horizontal plane when reaching each grid to be cleaned from each movable grid by using a differential geometric method.
6. The intelligent control method of an AGV car-washing robot according to claim 1, wherein said path planning for all the optimal gun positions by the path planning algorithm comprises:
and taking the optimal spray gun position, the position of the automobile to be cleaned when entering the car washing room and the spray gun position farthest from the Euclidean distance of the position of the automobile to be cleaned when entering the car washing room as inputs of a path planning algorithm, wherein the output of the path planning algorithm is an optimal route of all the optimal spray gun positions.
7. An intelligent control system for an AGV car-washing robot comprising a memory, a processor and a computer program stored in said memory and running on said processor, wherein said processor, when executing said computer program, implements the steps of the method according to any one of claims 1-6.
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