CN109556610B - Path planning method, controller and system - Google Patents
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Abstract
The disclosure provides a path planning method, a controller and a system. The path planning method based on the improved A algorithm comprises the following steps: planning paths of all the AGVs by using an improved A-x algorithm, and recording all the paths; wherein, the actual cost from the starting point to the current node in the heuristic function of the improved A-algorithm is equal to the cumulative sum of the path length converted from the moving distance from the starting point to the current node and the time consumption brought by the turning; counting the occurrence frequency of each section of path, and when an AGV passes through a certain section of path, gradually reducing the number of the AGV in advance planned on the corresponding path by 1 to obtain the number of the AGV actually planned on all the paths; taking the ratio of the actual planned AGV number of each section of path to the number of all paths, and evaluating the busyness degree of each section of path; accumulating the busy degree of each section of path with 1 to be used as a weight of the corresponding path, and further calculating all weighted paths; and planning the paths of all AGVs based on the weighted paths.
Description
Technical Field
The present disclosure relates to a path planning method, a controller and a system, and more particularly, to a path planning method, a controller and a system.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
With the development of social productivity and scientific technology and the improvement of labor cost, the traditional logistics mode of manpower and warehousing systems cannot meet the requirement of modern logistics, so that the production automation and the automation of the logistics system become the trend of social development, and an AGV (automatic Guided Vehicle) system combines the comprehensive application of computers, automatic control and other scientific technologies, and has great significance for improving the automatic production, reducing the production cost and promoting the social development. The path planning of the AGV is an important basis of an AGV system, and the evaluation indexes of the path planning method are as follows: the length of the shortest path, the shortest running time, the maximum utilization rate of the AGV and the like, and the classical algorithm of the path planning algorithm mainly comprises the following steps: dijkstra algorithm, a-algorithm, genetic algorithm, etc.; each algorithm has its own advantages and disadvantages; the Dijkstra algorithm can find the shortest path based on breadth first, but the time complexity is too high, and a plurality of unnecessary points are searched; in the process of searching the global optimal solution, the genetic algorithm is a random searching process, and when the number of nodes is increased, the searching time is longer and the premature phenomenon is easy to occur; the algorithm A is a heuristic search algorithm, can obtain a solution close to an optimal solution, and is high in solving speed and efficiency and widely used.
The inventor finds that the traditional A-x algorithm does not consider consumption at node turning and only carries out simple path planning, and does not have good prevention on path conflict.
Disclosure of Invention
According to one or more embodiments of the present disclosure, a path planning method is provided, in which, on one hand, a planned path is more reasonable after turning factors are added, so that an AGV makes fewer turns as much as possible; on the other hand, the busy degree of the path is improved through the weight path, part of path conflicts are solved, pressure is relieved for AGV dispatching in the later period, and therefore resource consumption is saved.
The disclosed path planning method includes:
planning paths of all the AGVs by using an improved A-x algorithm, and recording all the paths; wherein, the actual cost from the starting point to the current node in the heuristic function of the improved A-algorithm is equal to the cumulative sum of the path length converted from the moving distance from the starting point to the current node and the time consumption brought by the turning;
counting the occurrence frequency of each section of path, and when an AGV passes through a certain section of path, gradually reducing the number of the AGV in advance planned on the corresponding path by 1 to obtain the number of the AGV actually planned on all the paths;
taking the ratio of the actual planned AGV number of each section of path to the number of all paths, and evaluating the busyness degree of each section of path;
accumulating the busy degree of each section of path with 1 to be used as a weight of the corresponding path, and further calculating all weighted paths;
and planning the paths of all AGVs based on the weighted paths.
In one or more embodiments, the initial value of the number of pre-planned AGVs on all paths is the total number of AGVs that have traveled the corresponding path.
In one or more embodiments, the path length into which the time consumption incurred by the turn is translated is calculated as:
in the turning process, if the AGV starts to decelerate from a constant speed, decelerating to a first preset speed; then the vehicle runs to a stop position at a constant speed; stopping when the vehicle reaches a stopping position, then turning, and accelerating to a second preset speed; finally, driving at a constant speed according to a second preset speed; obtaining the actual consumed time of the turning according to the known relation among deceleration, acceleration, speed, time and distance;
if the turning section path is converted into an equal-length non-turning path (namely a straight path), the AGV advances at a constant speed to obtain the time consumed by the equal-length path during constant-speed running;
and obtaining the path length converted from the time consumption brought by the turning according to the difference value between the time actually consumed by the turning and the time consumed by the equal-speed running equal-length path and the known constant speed.
In one or more embodiments, the heuristic function that improves the a-algorithm is represented as:
the evaluation function for each point is equal to the sum of the actual cost from the starting point to the current node and the distance evaluation value from the current node to the end point.
According to one or more embodiments of the present disclosure, a path planning controller is provided, which, on one hand, adds turning factors to make the planned path more reasonable, so that the AGV makes fewer turns as much as possible; on the other hand, the busy degree of the path is improved through the weight path, part of path conflicts are solved, pressure is relieved for AGV dispatching in the later period, and therefore resource consumption is saved.
The disclosed path planning controller includes:
the path initial planning module is configured to plan paths of all AGVs by using an improved A-x algorithm and record all the paths; wherein, the actual cost from the starting point to the current node in the heuristic function of the improved A-algorithm is equal to the cumulative sum of the path length converted from the moving distance from the starting point to the current node and the time consumption brought by the turning;
the practical AGV number planning module is configured to count the number of appearing AGVs on each section of path, and when a certain section of path of the AGV path exists, the number of the AGVs pre-planned on the corresponding path is gradually reduced by 1 to obtain the number of the AGV actually planned on all the paths;
the path busy degree evaluation module is configured to evaluate the busy degree of each path by taking the ratio of the actual planned AGV number of each path to the number of all paths;
the weighted path calculation module is configured to accumulate the busy degree of each section of path and 1 to be used as a weight of the corresponding path, and further calculate all weighted paths;
a weighted path planning module configured to perform path planning for all AGVs based on the weighted all paths.
In one or more embodiments, in the actual AGV number planning module, the initial value of the number of AGVs pre-planned on all paths is the total number of AGVs appearing on the corresponding path.
In one or more embodiments, in the path initial planning module, the calculation process of the path length converted from the time consumption caused by the turning is as follows:
in the turning process, if the AGV starts to decelerate from a constant speed, decelerating to a first preset speed; then the vehicle runs to a stop position at a constant speed; stopping when the vehicle reaches a stopping position, then turning, and accelerating to a second preset speed; finally, driving at a constant speed according to a second preset speed; obtaining the actual consumed time of the turning according to the known relation among deceleration, acceleration, speed, time and distance;
if the turning section path is converted into an equal-length non-turning path (namely a straight path), the AGV advances at a constant speed to obtain the time consumed by the equal-length path during constant-speed running;
and obtaining the path length converted from the time consumption brought by the turning according to the difference value between the time actually consumed by the turning and the time consumed by the equal-speed running equal-length path and the known constant speed.
In one or more embodiments, in the initial path planning module, the heuristic function of the improved a-algorithm is expressed as:
the evaluation function for each point is equal to the sum of the actual cost from the starting point to the current node and the distance evaluation value from the current node to the end point.
According to one or more embodiments of the present disclosure, a path planning control system is provided, which, on one hand, adds turning factors to make a planned path more reasonable, so that an AGV makes fewer turns as much as possible; on the other hand, the busy degree of the path is improved through the weight path, part of path conflicts are solved, pressure is relieved for AGV dispatching in the later period, and therefore resource consumption is saved.
The path planning system comprises the path planning controller.
In one or more embodiments, the path planning controller is further coupled to a memory.
Compared with the prior art, the beneficial effect of this disclosure is:
(1) on the basis of the traditional A-x algorithm, firstly, the actual cost from the starting point to the current node is obtained by the sum of the path lengths converted from the moving distance from the starting point to the current node and the time consumption caused by turning, and all AGVs are initially planned, because turning has certain time consumption and the time is the evaluation standard of path planning, the planned path is more reasonable after turning factors are added, so that the AGVs are turned as much as possible, and resources are saved;
(2) the method comprises the steps of comparing the actual planning AGV quantity of each path with the quantity of all paths, evaluating the busy degree of each path, adding a weight path concept, wherein path conflict is definitely existed after the initial path planning is finished, the busy degree is represented by recording the times of the path planning, the possibility that the paths with high busy degree conflict is the largest, the busy degree of the paths is improved through the weight path, further partial path conflict is solved, pressure is relieved for AGV scheduling in the later period, and further resource consumption is saved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure and are not to limit the disclosure.
Fig. 1 is a flowchart of a path planning method according to the present disclosure.
FIG. 2 is a flow chart of a turn embodiment.
Fig. 3 is a schematic structural diagram of a path planning controller according to the present disclosure.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. 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 disclosure belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present disclosure. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
Term interpretation section:
the algorithm a (a-Star) is a direct search method which is most effective for solving the shortest path in the static road network, and is also an effective algorithm for solving a plurality of search problems. The closer the distance estimate is to the actual value in the algorithm, the faster the final search speed. The A-algorithm is a heuristic search algorithm, can obtain a solution close to an optimal solution, and is high in solving speed and high in efficiency.
The heuristic function of the A-algorithm adopts the following calculation formula:
F(n)＝G(n)+H(n)
wherein, F (n) is an evaluation function of the A-algorithm to each point, and comprises two parts of information, one is G (n), and the other is H (n);
g (n) is a value representing the actual cost from the origin to the current node n, i.e., the moving distance from the origin to the current node;
h (n) is an evaluation value of the distance from the current node n to the end point, that is, an evaluation value of the distance from the current node to the end point.
The improved a algorithm in this disclosure is based on the conventional a algorithm.
Fig. 1 is a flowchart of a path planning method according to the present disclosure.
As shown in fig. 1, a path planning method of the present disclosure includes:
s110: planning paths of all the AGVs by using an improved A-x algorithm, and recording all the paths; wherein, the actual cost from the starting point to the current node in the heuristic function of the improved A-algorithm is equal to the cumulative sum of the path length converted from the moving distance from the starting point to the current node and the time consumption brought by the turn.
Specifically, the heuristic function of the improved a-algorithm is expressed as:
evaluation function F for each point1(n) is equal to the actual cost G from the origin to the current node1(n) and the distance evaluation value H (n) from the current node to the end point.
The distance evaluation value h (n) from the current node to the end point, that is, an evaluation value of the distance from the current node to the end point.
Specifically, the calculation process of the path length into which the time consumption brought by the turning is converted is:
in the turning process, if the AGV starts to decelerate from a constant speed, decelerating to a first preset speed; then the vehicle runs to a stop position at a constant speed; stopping when the vehicle reaches a stopping position, then turning, and accelerating to a second preset speed; finally, driving at a constant speed according to a second preset speed; obtaining the actual consumed time of the turning according to the known relation among deceleration, acceleration, speed, time and distance;
if the turning section path is converted into an equal-length non-turning path (namely a straight path), the AGV advances at a constant speed to obtain the time consumed by the equal-length path during constant-speed running;
and obtaining the path length converted from the time consumption brought by the turning according to the difference value between the actual time consumed by the turning and the time consumed by the uniform-speed turning and the known uniform speed.
As shown in FIG. 2, each time a turn is made, the AGV needs to be at s from the node0The speed is reduced to 0.2m/s at 1 m; and the acceleration a can be calculated from the rotational speed of the motor as: a is 0.5m/s2；
So that the deceleration distance can be calculated according to the physical kinematics formula
v1 2-v2 2＝2ax
Obtaining the deceleration distance s10.24m, the time required is t10＝0.4s；
The time required for the movement to a turn to stop immediately is then t11＝0.2/0.2＝1s；
Time t consumed in making a turn12＝1s；
Then at a speed of 2m/s from 02Accelerating to 1 m/s; takes a time t13＝0.5s；
Acceleration distance of s2＝0.25m。
The process of the turning section is as follows:
the method comprises the steps of firstly starting deceleration from a constant speed to 0.2m/s, then driving at the constant speed to a stop position (the stop is more stable through experimental tests), then turning, accelerating to 1m/s, and further driving at the constant speed to consume the total time t1Can be calculated by the following formula:
t1＝t10+t11+t12+t13
obtaining: t is t1＝0.4+1+1+0.5＝2.9s；
If no speed reduction is performedTime t required for speech (i.e. straight-line travel)0Can be calculated by the following formula
t0＝(s0+s1+s2)/v1
Obtaining: t is t0＝(0.24+1+0.25)/1＝1.49s；
The time is increased by 1.51 s; the distance corresponding to the conversion to the original speed (1m/s) should be 1.51 m;
an improved actual cost function and evaluation function can be obtained:
Gt(n)＝G(n)+1.51m
F1(n)＝G1(n)+H(n)。
the above data and equations are obtained from the experimental environment and parameter settings, and are related to the experimental environment.
S120: counting the occurrence times of the AGVs on each section of the path, and when the AGV exists in a certain section of the path, gradually subtracting 1 from the number of the AGVs pre-planned on the corresponding path to obtain the number of the AGVs actually planned on all the paths.
Specifically, the initial value of the number of AGVs preplanned on all paths is the total number of AGVs.
Counting the occurrence times of each segment of path according to the recorded paths, and recording ni(ii) a N represents the number of all paths; i is 1,2, …, N.
M represents the set of AGV numbers on the partition path; m isiThe number of AGVs planned for each segment of the path during system operation is represented; if there is an AGV passing through the path, then this mi1 is subtracted.
S130: the actual planning AGV number of each section of path is compared with the number of all paths, and the busyness degree of each section of path is evaluated
S140: the busy degree of each path segment
Tired of 1Added as a corresponding path LiFurther calculates all weighted paths Li *。
Weighted path Li *；
S150: and planning the paths of all AGVs based on the weighted paths.
On the basis of the traditional A-x algorithm, firstly, the actual cost from the starting point to the current node is obtained by the sum of the path lengths converted from the moving distance from the starting point to the current node and the time consumption caused by turning, and all AGVs are initially planned, because turning has certain time consumption and the time is the evaluation standard of path planning, the planned path is more reasonable after turning factors are added, so that the AGVs are turned as much as possible, and resources are saved;
the method comprises the steps of comparing the actual planning AGV quantity of each path with the quantity of all paths, evaluating the busy degree of each path, adding a weight path concept, wherein path conflict is definitely existed after the initial path planning is finished, the busy degree is represented by recording the times of the path planning, the possibility that the paths with high busy degree conflict is the largest, the busy degree of the paths is improved through the weight path, further partial path conflict is solved, pressure is relieved for AGV scheduling in the later period, and further resource consumption is saved.
Fig. 3 is a schematic structural diagram of a path planning controller according to the present disclosure.
As shown in fig. 3, a path planning controller of the present disclosure includes:
(1) the path initial planning module is configured to plan paths of all AGVs by using an improved A-x algorithm and record all the paths; wherein, the actual cost from the starting point to the current node in the heuristic function of the improved A-algorithm is equal to the cumulative sum of the path length converted from the moving distance from the starting point to the current node and the time consumption brought by the turn.
Specifically, the heuristic function of the improved a-algorithm is expressed as:
evaluation function F for each point1(n) is equal to the actual cost G from the origin to the current node1(n) and the distance evaluation value H (n) from the current node to the end point.
The distance evaluation value h (n) from the current node to the end point, that is, an evaluation value of the distance from the current node to the end point.
Specifically, the calculation process of the path length into which the time consumption brought by the turning is converted is:
in the turning process, if the AGV starts to decelerate from a constant speed, decelerating to a first preset speed; then the vehicle runs to a stop position at a constant speed; then turning and accelerating to a second preset speed; finally, driving at a constant speed according to a second preset speed; obtaining the actual consumed time of the turning according to the known relation among deceleration, acceleration, speed, time and distance;
if the turning section path is converted into an equal-length non-turning path (namely a straight path), the AGV advances at a constant speed to obtain the time consumed by the equal-length path during constant-speed running;
and obtaining the path length converted from the time consumption brought by the turning according to the difference value between the actual time consumed by the turning and the time consumed by the uniform-speed turning and the known uniform speed.
As shown in FIG. 2, each time a turn is made, the AGV needs to be at s from the node0The speed is reduced to 0.2m/s at 1 m; and the acceleration a can be calculated from the rotational speed of the motor as: a is 0.5m/s2；
So that the deceleration distance can be calculated according to the physical kinematics formula
v1 2-v2 2＝2ax
Obtaining the deceleration distance s10.24m, the time required is t10＝0.4s；
The time required for the movement to a turn to stop immediately is then t11＝0.2/0.2＝1s；
Time t consumed in making a turn12＝1s；
Then at a speed of 2m/s from 02Accelerating to 1 m/s; takes a time t13＝0.5s；
Acceleration distance of s2＝0.25m。
The process of the turning section is as follows:
the method comprises the steps of firstly starting deceleration from a constant speed to 0.2m/s, then driving at the constant speed to a stop position (the stop is more stable through experimental tests), then turning, accelerating to 1m/s, and further driving at the constant speed to consume the total time t1Can be calculated by the following formula:
t1＝t10+t11+t12+t13
obtaining: t is t1＝0.4+1+1+0.5＝2.9s；
Time t required if deceleration is not performed (i.e., straight travel)0Can be calculated by the following formula
t0＝(s0+s1+s2)/v1
Obtaining: t is t0＝(0.24+1+0.25)/1＝1.49s；
The time is increased by 1.51 s; the distance corresponding to the conversion to the original speed (1m/s) should be 1.51 m;
an improved actual cost function and evaluation function can be obtained:
G1(n)＝G(n)+1.51m
F1(n)＝G1(n)+H(n)。
the above data and equations are obtained from the experimental environment and parameter settings, and are related to the experimental environment.
(2) And the actual AGV number planning module is configured to count the occurrence times of the AGVs on each section of the path, and when the AGV path exists in a certain section of the path, the number of the AGVs pre-planned on the corresponding path is gradually reduced by 1 to obtain the number of the AGVs actually planned on all the paths.
Specifically, the initial value of the number of pre-planned AGVs on all the paths is the total number of AGVs passing through the path.
Counting the occurrence times of each segment of path according to the recorded paths, and recording ni(ii) a N represents the number of all paths; i is 1,2, …, N.
M represents the set of AGV numbers on the partition path; m isiThe number of AGVs planned for each segment of the path during system operation is represented; if there is an AGV passing through the path, then this mi1 is subtracted.
(3) A path busy degree evaluation module configured to evaluate the busy degree of each path segment by comparing the actual planned AGV number of each path segment with the number of all paths
(4) A weighted path calculation module configured to calculate a busy level of each segment of the path
Added to 1 as the corresponding path LiFurther calculates all weighted paths Li *。
Weighted path Li *；
(5) A weighted path planning module configured to perform path planning for all AGVs based on the weighted all paths.
On the basis of the traditional A-x algorithm, firstly, the actual cost from the starting point to the current node is obtained by the sum of the path lengths converted from the moving distance from the starting point to the current node and the time consumption caused by turning, and all AGVs are initially planned, because turning has certain time consumption and the time is the evaluation standard of path planning, the planned path is more reasonable after turning factors are added, so that the AGVs are turned as much as possible, and resources are saved;
the method comprises the steps of comparing the actual planning AGV quantity of each path with the quantity of all paths, evaluating the busy degree of each path, adding a weight path concept, wherein path conflict is definitely existed after the initial path planning is finished, the busy degree is represented by recording the times of the path planning, the possibility that the paths with high busy degree conflict is the largest, the busy degree of the paths is improved through the weight path, further partial path conflict is solved, pressure is relieved for AGV scheduling in the later period, and further resource consumption is saved.
A path planning system of the present disclosure includes a path planning controller as shown in fig. 3.
In one or more embodiments, the path planning controller is further coupled to a memory.
The memory is used for storing the steps of the path planning method, and the path planning controller is used for calling the stored program in the memory and executing the steps of the path planning method.
As will be appreciated by one skilled in the art, embodiments of the present disclosure may be provided as a method, system, or computer program product. Accordingly, the present disclosure may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present disclosure may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present disclosure is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
Although the present disclosure has been described with reference to specific embodiments, it should be understood that the scope of the present disclosure is not limited thereto, and those skilled in the art will appreciate that various modifications and changes can be made without departing from the spirit and scope of the present disclosure.
Claims (7)
1. A method of path planning, comprising:
planning paths of all the AGVs by using an improved A-x algorithm, and recording all the paths; wherein, the actual cost from the starting point to the current node in the heuristic function of the improved A-algorithm is equal to the cumulative sum of the path length converted from the moving distance from the starting point to the current node and the time consumption brought by the turning;
counting the occurrence frequency of each section of path, and when an AGV passes through a certain section of path, gradually reducing the number of the AGV in advance planned on the corresponding path by 1 to obtain the number of the AGV actually planned on all the paths; the initial values of the number of the AGV in the pre-planning on all the paths are the total number of the AGV appearing on the corresponding paths; counting the occurrence times of each segment of path according to the recorded paths, and recording ni(ii) a N represents the number of all paths; 1,2, …, N; m represents the set of AGV quantity on each section of path; m isiThe number of AGVs planned for each segment of the path during system operation is represented; if there is an AGV passing through the path, then this mi1 is subtracted;
taking the ratio of the actual planned AGV number of each section of path to the number of all paths, and evaluating the busyness degree of each section of path;
accumulating the busy degree of each path segment with 1 as the weight of the corresponding path, and further calculating all weighted paths, and the weighted path Li *；
Planning the paths of all the AGVs based on all the weighted paths;
the calculation process of the path length converted from the time consumption brought by the turning is as follows:
in the turning process, if the AGV starts to decelerate from a constant speed, decelerating to a first preset speed; then the vehicle runs to a stop position at a constant speed; stopping when the vehicle reaches a stopping position, then turning, and accelerating to a second preset speed; finally, driving at a constant speed according to a second preset speed; obtaining the actual consumed time of the turning according to the known relation among deceleration, acceleration, speed, time and distance;
if the turning section path is converted into an equal-length non-turning path, the AGV advances at a constant speed to obtain the time consumed by the equal-length path during constant-speed driving;
obtaining the path length converted from the time consumption brought by turning according to the difference value between the actual time consumed by turning and the time consumed by uniform turning and the known uniform speed;
the heuristic function of the improved a-algorithm is expressed as:
the evaluation function for each point is equal to the sum of the actual cost from the starting point to the current node and the distance evaluation value from the current node to the end point.
2. A path planning controller, comprising:
the path initial planning module is configured to plan paths of all AGVs by using an improved A-x algorithm and record all the paths; wherein, the actual cost from the starting point to the current node in the heuristic function of the improved A-algorithm is equal to the cumulative sum of the path length converted from the moving distance from the starting point to the current node and the time consumption brought by the turning;
the practical AGV number planning module is configured to count the number of the AGVs on each section of the path, and when a certain section of the path exists, the number of the AGVs pre-planned on the corresponding path is gradually reduced by 1 to obtain the number of the AGVs actually planned on all the paths;
the path busy degree evaluation module is configured to evaluate the busy degree of each path by taking the ratio of the actual planned AGV number of each path to the number of all paths;
the weighted path calculation module is configured to accumulate the busy degree of each section of path and 1 to be used as a weight of the corresponding path, and further calculate all weighted paths;
a weighted path planning module configured to perform path planning for all AGVs based on the weighted all paths.
3. A path planning controller according to claim 2, wherein in the actual AGV number planning module, the initial value of the number of AGVs preplanned on all paths is the total number of AGVs present on the corresponding path.
4. A path planning controller according to claim 2, wherein in the path initial planning module, the calculation process of the path length converted from the time consumption caused by the turning is:
in the turning process, if the AGV starts to decelerate from a constant speed, decelerating to a first preset speed; then the vehicle runs to a stop position at a constant speed; stopping when the vehicle reaches a stopping position, then turning, and accelerating to a second preset speed; finally, driving at a constant speed according to a second preset speed; obtaining the actual consumed time of the turning according to the known relation among deceleration, acceleration, speed, time and distance;
if the turning section path is converted into an equal-length non-turning path, the AGV advances at a constant speed to obtain the time consumed by the equal-length path during constant-speed driving;
and obtaining the path length converted from the time consumption brought by the turning according to the difference value between the actual time consumed by the turning and the time consumed by the uniform-speed turning and the known uniform speed.
5. A path-planning controller according to claim 2, wherein in the initial path-planning module, the heuristic function of the modified a-algorithm is expressed as:
the evaluation function for each point is equal to the sum of the actual cost from the starting point to the current node and the distance evaluation value from the current node to the end point.
6. A path planning system comprising a path planning controller according to any of claims 2-5.
7. A path planning system according to claim 6 wherein the path planning controller is further coupled to the memory.
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| CN111693050B (en) * | 2020-05-25 | 2023-04-18 | 电子科技大学 | Indoor medium and large robot navigation method based on building information model |
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| CN113310492B (en) * | 2021-05-27 | 2023-04-14 | 青岛星华智能装备有限公司 | Single-steering-wheel AGV path planning method and system based on A-star algorithm |
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