CN112286211A - Environment modeling and AGV path planning method for irregular layout workshop - Google Patents
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Abstract
The invention provides an environment modeling and AGV path planning method for an irregular layout workshop. The construction of the operation environment model establishes an overall structure model of the irregular layout workshop through a topological modeling method, and establishes a description model of a complex operation area through a grid modeling method. The planning of the AGV path comprises the following steps: the method comprises the steps of establishing a first AGV movement path, a second AGV movement path and a third AGV movement path, wherein the third AGV movement path is established according to an improved genetic algorithm, and the improved genetic algorithm comprises the steps of designing a new fitness function and introducing a simulated annealing population selection method. According to the method, the operation environment modeling of the irregular layout workshop is realized by adopting a topological modeling and grid modeling method, and the accurate and detailed description of the complex operation environment can be realized, so that the accuracy of AGV path planning is improved.
Description
Technical Field
The invention relates to the technical field of AGV path planning, in particular to an environment modeling and AGV path planning method for an irregular layout workshop.
Background
With the continuous development of manufacturing industry, the automation of production line logistics becomes one of the important directions for enterprise transformation and upgrade. An agv (automated Guided vehicle), i.e., an automated Guided vehicle or an automated Guided vehicle, is a vehicle that can travel along a predetermined guide path and has safety protection and various transfer functions. Generally speaking, after manufacturing shop or logistics workshop establish, need plan the walking route of AGV, only after the walking route planning of AGV, just can ensure AGV's walking speed, avoid AGV turn to the wearing and tearing that the number of times is too much or the angle is too big to cause at the in-process of walking, just can avoid the running stability difference of AGV automobile body and the goods that cause drop.
At present, most of existing AGV path planning in a workshop is a single modeling mode, for example, a grid modeling method, which can accurately and meticulously describe a complex operation environment, has high path planning accuracy, but has high calculation complexity, and the computation amount increases exponentially along with the increase of the number of grids, thereby greatly increasing the difficulty and cost of modeling. For example, the topological modeling method regards path intersections and operation point positions as nodes, regards a driving path as an edge, describes the path and the direction of the AGV through an ordered node set, is compact in structure and suitable for a multi-node association scene, but is low in accuracy of AGV path planning for a complex workshop.
Meanwhile, the existing AGV path planning method is improved from the traditional genetic algorithm, the traditional genetic algorithm is usually carried out from the aspects of coding, initial population setting, fitness function design, genetic operation and 2 control parameters, wherein the traditional fitness function does not combine the actual motion process of the AGV to consider the smoothness of the path, in the actual motion process, if the AGV turns to too many times, the motion time is increased, the rapid abrasion of the mechanical structure of the vehicle is caused, and the accumulated error of the motion is increased along with the increase of the turning times.
Therefore, there is a need for an improvement to existing methods for AGV path planning for a plant.
Disclosure of Invention
The invention aims to provide an environment modeling and AGV path planning method for an irregular layout workshop.
The technical scheme for realizing the purpose of the invention is as follows: a method for environment modeling of an irregular layout workshop and AGV path planning comprises the steps of building a working environment model and planning an AGV path.
The establishment of the operation environment model comprises the following steps:
s1, establishing an overall structure model of the irregular layout workshop through a topological modeling method, wherein the overall structure model comprises a simple operation area and a complex operation area;
s2, naming each path intersection point and operation point in the simple operation area as a first topological node, and naming the complex operation area as a second topological node;
and S3, establishing a description model of the complex operation area through a grid modeling method.
The AGV path planning method comprises the following steps:
s100, establishing a first AGV movement path in a simple operation area;
s200, establishing a second AGV movement path between the complex operation area and the complex operation area;
s300, establishing a third AGV movement path in the complex operation area according to an improved genetic algorithm, wherein the improved genetic algorithm comprises designing a new fitness function, and introducing a simulated annealing population selection method.
Further, the expression of the new fitness function is as follows:
Further, the above
The expression of (a) is:
。
wherein the content of the first and second substances,
is a grid
And a grid
The expression is:
；
in a preferred embodiment of the present invention, the improved genetic algorithm further comprises coding, initial population setting, genetic manipulation, control parameter setting.
Furthermore, the simulated annealing population selection method introduces a simulated annealing population selection method based on the solid annealing principle.
Further, the grid modeling method comprises the following steps:
carrying out grid decomposition on the complex operation area to form a grid map;
marking a cataract obstacle area and an obstacle-free area on a grid map;
setting the grid attribute value of the obstacle area to be 1, and forming a non-passing area; the pass area is formed by setting the grid attribute value of the obstacle-free area to 0.
Further, an expansion region is provided between the obstacle region and the obstacle-free region, the grid attribute value of the expansion region is set to 1, and the expansion region and the obstacle region are combined to form a non-traffic region.
Compared with the prior art, the invention has the beneficial effects that:
1. the method for modeling the operating environment of the irregular layout workshop by adopting the topological modeling and the grid modeling can realize accurate and detailed description of the complex operating environment so as to improve the accuracy of AGV path planning. Meanwhile, when the AGV path is planned, a description model established by the grid modeling method is improved through a traditional genetic algorithm, the generated AGV path is optimized through introducing a concept of path smoothness, and the AGV steering times and steering angles are reduced on the basis of avoiding increasing the movement time, so that the rapid abrasion caused by a vehicle mechanical structure is avoided.
2. In the grid modeling method, the expansion area is arranged between the obstacle area and the obstacle-free area, and the expansion area is defined as the non-passing area, so that the damage and falling of the AGV and articles carried by the AGV due to the fact that the AGV touches or collides with the obstacle area in the walking process can be avoided, and the potential safety hazard caused by the damage of the obstacle area can also be avoided.
Drawings
In order to more clearly illustrate the technical solution of the embodiment of the present invention, the drawings used in the description of the embodiment will be briefly introduced below. It should be apparent that the drawings in the following description are only for illustrating the embodiments of the present invention or technical solutions in the prior art more clearly, and that other drawings can be obtained by those skilled in the art without any inventive work.
FIG. 1 is a flowchart of a method for environment modeling and AGV path planning for an irregularly-arranged plant in an embodiment of the present invention;
FIG. 2 is a schematic diagram of a layout of an irregular layout plant and AGV paths in an embodiment of the present invention;
FIG. 3 is a grid map of a model depicting a complex work area in an embodiment of the present invention;
FIG. 4 is a schematic view of a first AGV path of travel and a second AGV path of travel according to an embodiment of the present invention;
FIG. 5 is a flow chart of simulated annealing population selection for an improved genetic algorithm in an embodiment of the present invention;
FIG. 6 is a flowchart illustrating the calculation of an optimal path for a third AGV movement path in accordance with an embodiment of the present invention;
FIG. 7 is a diagrammatic view of a third AGV path of the AGV paths of the present invention.
Detailed Description
The invention will be further described with reference to specific embodiments, and the advantages and features of the invention will become apparent as the description proceeds. These examples are illustrative only and do not limit the scope of the present invention in any way. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention, and that such changes and modifications may be made without departing from the spirit and scope of the invention.
In the description of the present embodiments, it is to be understood that the terms "center", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. indicate orientations or positional relationships based on those shown in the drawings, and are only for convenience of describing the present invention and simplifying the description, but do not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention.
Furthermore, the terms "first," "second," "third," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicit to a number of indicated technical features. Thus, a feature defined as "first," "second," etc. may explicitly or implicitly include one or more of that feature. In the description of the invention, the meaning of "a plurality" is two or more unless otherwise specified.
The embodiment of the disclosure provides an environment modeling and AGV path planning method for a workshop with an irregular layout, which includes the establishment of a working environment model and the planning of an AGV path, as shown in FIG. 1.
As shown in fig. 1, the establishment of the work environment model includes the following steps:
and S1, establishing an overall structure model of the irregular layout workshop through a topological modeling method, wherein the overall structure model comprises a simple operation area and a complex operation area.
And S2, naming each path intersection point and operation point in the simple operation area as a first topological node, and naming the complex operation area as a second topological node.
Specifically, because there are fewer operation points and path intersections in the simple operation area, the planning of the AGV path in this area is simpler, and each operation point and path intersection in the simple operation area can be set as the first topology node. Meanwhile, since the operation area of the complex operation area is relatively complex, the whole complex operation area is regarded as one operation node. When the AGV path is planned, a straight path is established between each first topological node and each second topological node, so that the AGV path planning setting is simplified.
S3, establishing a description model of the complex operation area through a grid modeling method, wherein the grid modeling method is as follows:
and S301, performing grid decomposition on the complex operation area to form a grid map.
Specifically, the grid dimension length in the description model is specified as the maximum value of the outer dimension of the AGV, and as shown in fig. 3, the size of the created grid map is 20 × 20.
S302, marking an obstacle area (shown as a black grid in the figure 3) and an obstacle-free area (shown as a white grid in the figure 3) in the grid map;
s303, setting the grid attribute value of the obstacle area to be 1, and forming a non-passing area; the pass area is formed by setting the grid attribute value of the obstacle-free area to 0.
Further, in order that the AGV cannot be regarded as a particle in an actual environment and does not pass through an obstacle boundary when the AGV travels, an expansion region (shown by a gray grid in fig. 3) is provided between the obstacle region and the obstacle-free region, the grid attribute value of the expansion region is set to 1, and the expansion region and the obstacle region are combined to form a non-passage region.
After the operating environment model is established, the route of the AGV walking in the irregular layout workshop is planned, and in the embodiment, as shown in fig. 1, the planning of the AGV route includes the following steps:
s100, establishing a first AGV moving path in the simple operation area, specifically, the first AGV moving path in the simple operation area is a simple straight path. And during path planning, setting a first AGV movement path between each path intersection point and each operation point, namely each first topology node according to operation requirements.
S200, establishing a second AGV movement path between the complex operation areas, specifically, the second AGV movement path established between the complex operation areas is also a simple straight path. During path planning, according to operation requirements, a second AGV motion path is established between a first topology node (path intersection or operation point) closest to the complex operation area in the simple operation area and a second topology node of each complex operation area.
S300, establishing a third AGV movement path in the complex operation area according to an improved genetic algorithm, wherein the improved genetic algorithm comprises designing a new fitness function, and introducing a simulated annealing population selection method.
Specifically, the conventional genetic algorithm is a calculation model simulating a biological evolution process, which searches for an optimal solution by simulating a natural evolution process. The genetic algorithm takes all individuals in a population as objects, and utilizes a randomization principle to efficiently search a coded parameter space. The five aspects of encoding, initial population setting, fitness function design, genetic operation and control parameter setting are the most basic contents of the traditional genetic algorithm.
The invention designs the fitness function of the traditional genetic algorithm to obtain a new fitness function, and selects an initial population by applying a simulated annealing population selection method to form an improved genetic algorithm.
In the AGV path planning, the length of the AGV path generally needs to be optimized, and in the conventional genetic algorithm, the fitness function expression is as follows:
，
the number of grids passed by the AGV, and D is the path length passed by the AGV.
Specifically, the expression of D is:
by passing
The fitness for measuring population individuals can eliminate individuals with large path length, but the traditional fitness function has the problem that smoothness of the AGV path is not considered in combination with the actual motion process of the AGV. In the actual moving process, if the AGV turns too many times, not only the moving time is increased, but also the mechanical structure of the vehicle is quickly worn, and the accumulated error of the movement is increased along with the increase of the turning times, so that the smoothness (the smoothness in a non-mathematical sense) of the generated AGV path needs to be considered.
Specifically, according to the geometric relationship of the triangle, the distance relationship between the adjacent three points can represent the smoothness of the path, so the distance expression between the three adjacent grids describing the model in the complex working area in the invention is as follows:
expression:
，
is a grid
And a grid
The distance between them.
Expression:
，
is a grid
And a grid
The distance between them;
expression:
，
is a grid
And a grid
The distance between them.
Expression:
，
is the difference in distance between three adjacent grids. According to the geometric relationship of the triangle when
The smaller the size of the product is,
、
、
the bigger the included angle between the three grids is, the smoother the path of the AGV is, the smaller the corner amplitude of the AGV is, the smaller the abrasion is, and the balance of the AGV is also improved. Therefore, the fitness function expression for adding smoothness is:
。
comprehensively considering the length (D) and smoothness of the path (D)
) The expression of the improved new fitness function is as follows:
。
further, because the traditional genetic algorithm is easy to fall into the defect of local optimization, in the improved genetic algorithm, the simulated annealing population selection method selects individuals in the initial population by introducing the simulated annealing algorithm.
Specifically, referring to fig. 5, the simulated annealing population selection method includes the following steps:
inputting a grid map of a complex operation area into an improved genetic algorithm;
selecting the initialized population individuals and calculating the fitness function value
；
The disturbance generates a new value X, and the fitness function value is calculated
；
Computing
Judgment is made
Size;
when in use
When it is time, accept the new value X, make
(ii) a When the iteration times are reached and the termination condition is met, ending the operation; when the iteration times are reached but the termination condition is not met, slowly cooling to ensure that
Returning to generate new X and recalculating fitness function value
(ii) a When the iteration times are not reached, returning to generate new X, and recalculating the fitness function value
。
When in use
According to the sudden jump characteristic of the simulated annealing probability, the method leads
Judging whether P is greater than a value between 0 and 1 randomly generated by the program, if so, accepting a new value X to make P be greater than the value
And continuing outer circulation; if the value is less than or equal to the value, directly judging whether the program reaches the iteration times, and continuing the outer loop.
According to an embodiment of the present invention, fig. 2 shows an irregular layout workshop, and fig. 3 and 5 show an AGV path planning method:
first, an operating environment model is established.
Specifically, S1, establishing an overall structure model through a topological modeling method, wherein the overall structure model comprises a simple operation area and a complex operation area (Z1, Z2).
And S2, naming the path cross points (C1 and C2) and the operation points (a fixture library and a charging pile) in the simple operation area as first topology nodes (C1, C2, the fixture library and the charging pile), and naming the complex operation area as second topology nodes.
S3, establishing a description model (Z1, Z2) of the complex operation area through a grid modeling method, as shown in FIG. 4.
Secondly, planning the traveling path of the AGV in the workshop with the irregular layout.
Specifically, S100, the establishment of the first AGV movement path in the simple work area, where the first AGV movement path in the simple work area is a simple straight-going path, as shown in fig. 4, the first AGV movement path is a charging pile → C1 → fixture warehouse → C1 → C2.
S200, establishing a second AGV moving path between the complex operation area and the complex operation area, wherein the second AGV moving path is also a simple straight path, and as shown in the figure 4, the second AGV moving paths are C2 → Z1 and C2 → Z2 paths.
Specifically, the traveling of the AGVs in the first AGV movement path and the second AGV movement path is realized through simple movement control, the AGVs acquire moving mileage information and steering angle information through odometers, and the odometer information is derived from the relative pose change recorded by the vehicle body driving wheel photoelectric encoder and the inertia measurement unit. On the basis, the laser radar of the AGV can measure the distance between the AGV and a workshop fixed reference object (such as a laser reflection strip attached to a wall) in real time, so that the real-time position and attitude information of the AGV is calculated, and further the fixed path driving is realized.
And S300, establishing a third AGV movement path in the complex operation area according to an improved genetic algorithm, wherein the third AGV movement path is shown in FIG. 7.
Specifically, the third AGV motion path is calculated by applying an improved genetic algorithm, the improvement of the genetic algorithm includes redesigning a fitness function, and performing population selection by using a simulated annealing population selection method, and specifically includes obtaining an optimal path through judgment steps of a grid map and grid codes (grid maps established by a grid modeling method in a complex operation area and grid codes are obtained), initializing a population, performing population selection by simulated annealing, performing cross operation, performing mutation operation, and performing iteration times, the optimal path calculation flowchart is shown in fig. 6, and the optimal path obtained by applying the improved genetic algorithm is shown in fig. 7.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Furthermore, it should be understood that although the present description refers to embodiments, not every embodiment may contain only a single embodiment, and such description is for clarity only, and those skilled in the art should integrate the description, and the embodiments may be combined as appropriate to form other embodiments understood by those skilled in the art.
Claims (7)
1. An environment modeling and AGV path planning method for an irregular layout workshop is characterized by comprising the following steps: establishing an operation environment model and planning an AGV path;
the establishment of the operation environment model comprises the following steps:
s1, establishing an overall structure model of the irregular layout workshop through a topological modeling method, wherein the overall structure model comprises a simple operation area and a complex operation area;
s2, naming each path intersection point and operation point in the simple operation area as a first topological node, and naming the complex operation area as a second topological node;
s3, establishing a description model of the complex operation area through a grid modeling method;
the AGV path planning method comprises the following steps:
s100, establishing a first AGV movement path in the simple operation area;
s200, establishing a second AGV movement path between the complex operation area and the complex operation area;
and S300, establishing a third AGV movement path in the complex operation area according to an improved genetic algorithm, wherein the improved genetic algorithm comprises designing a new fitness function and introducing a simulated annealing population selection method.
2. The environmental modeling and AGV path planning method of claim 1, wherein: the expression of the new fitness function is:
3. The environmental modeling and AGV path planning method of claim 2, wherein: the above-mentioned
The expression of (a) is:
，
wherein the content of the first and second substances,
is a grid
And a grid
The expression is:
；
4. the environmental modeling and AGV path planning method according to any one of claims 1 to 3, wherein: the improved genetic algorithm further comprises coding, initial population setting, genetic operation and control parameter setting.
5. The environmental modeling and AGV path planning method of claim 4, wherein: the simulated annealing population selection method introduces a simulated annealing population selection method based on the solid annealing principle.
6. The environmental modeling and AGV path planning method according to any one of claims 1 to 3 and 5, wherein: the grid modeling method comprises the following steps:
carrying out grid decomposition on the complex operation area to form a grid map;
marking the barrier area and the barrier-free area of the grid map;
setting the grid attribute value of the obstacle area to be 1, and forming a non-passing area; and setting the grid attribute value of the barrier-free area to be 0 to form a passing area.
7. The environmental modeling and AGV path planning method of claim 6, wherein: an expansion region is provided between the obstacle region and the obstacle-free region, a grid attribute value of the expansion region is set to 1, and the expansion region and the obstacle region are combined to form a non-passing region.
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