CN116736859A - Static path planning method and system applied to storage robot - Google Patents
Static path planning method and system applied to storage robot Download PDFInfo
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Abstract
The invention provides a static path planning method and system applied to a warehouse robot, and belongs to the technical field of robot path planning in a static environment. In order to solve the problems that the traditional A-Star algorithm has longer path length, excessive inflection points and excessive access nodes and can reduce the working efficiency of the robot. The invention introduces a focusing search direction and a path reprocessing method based on the traditional A-Star algorithm, writes an improved A-Star algorithm based on the search direction and the path reprocessing, removes two types of unnecessary nodes, plans an optimal path for the storage robot, effectively reduces turning times, shortens the path length, can meet real-time performance, and effectively improves the working efficiency of the robot.
Description
Technical Field
The invention relates to the technical field of path planning in a static environment of a robot, in particular to a static path planning method and system applied to a storage robot.
Background
Path planning is one of the core contents of autonomous non-collision actions of robots, and often consists of a map and a search algorithm executed on the map. Particularly for warehouse robots, the path planning problem is one of the core problems in the robot field, and can be classified into static path planning and dynamic path planning according to the known degree of the robot to the space environment.
In static path planning, in order to quickly find an optimal or shortest path from a start point to an end point on a map in a static storage environment, researchers have proposed classical algorithms such as Dijkstra algorithm, ant colony algorithm and a-x algorithm. The A algorithm belongs to a heuristic search algorithm, compared with the Dijkstra algorithm, a heuristic function is added, and end point information is introduced in the path search process for guiding. However, the conventional A-Star algorithm has the problems of long path length, excessive inflection points and excessive access nodes, and the problem of reduced working efficiency of the robot.
Disclosure of Invention
The invention aims to solve the technical problems that:
in order to solve the problems that the traditional A-Star algorithm has longer path length, excessive inflection points and excessive access nodes and can reduce the working efficiency of the robot.
The invention adopts the technical scheme for solving the technical problems:
the invention provides a static path planning method applied to a storage robot, which comprises the following steps:
step one, acquiring and inputting a starting point, a target point and map information, and adding the starting point into an open list;
step two, removing adjacent nodes which are inconsistent with the direction of the target node, occupied by the barrier and in the closed list, and calling other adjacent nodes except the nodes as adjacent reachable nodes;
step three, obtaining a path, comprising the following steps,
(1) Judging whether the adjacent reachable node obtained in the step two is in an open list or not, if yes, recording a node with the minimum g (n) value in the adjacent nodes, and taking the current node as a father node of the adjacent node with the minimum g (n) value; if not, the adjacent reachable nodes are put into an open list, whether the open list is empty or not is judged, if the open list is empty, the route cannot be found, planning is finished, if the open list is not empty, whether the adjacent nodes are destination points or target nodes is judged, if the adjacent nodes are destination points, the route is saved as a final route, and circulation is finished; if the adjacent node is not the end point, the current node is used as a father node;
(2) Moving the current node obtained in the step (1) into a closed list;
(3) Calculating f (n) values of adjacent reachable nodes, taking f (n) as an evaluation function value, and selecting a node with the minimum f (n) value as a next current node;
(4) Repeating the steps II, the steps III (1) - (3) to the step III (1), wherein the open list is empty, so that a path can not be found, or the adjacent node in the step III (1) is used as an end point, and a final path is obtained;
and step four, carrying out path reprocessing on the acquired paths, including removing two types of unnecessary nodes.
Further, in the second step, the method of determining whether the directions of the neighboring node and the target node are consistent is to connect the center of the current node with the centers of the target point and the node to be searched, calculate the included angle θ, and set the coordinates of the current node as (x c ,y c ) The coordinates of the node to be searched are (x c1 ,y c1 ) The target node coordinates are (x G ,y G ) The calculation formula of the included angle theta is shown in (1):
when the included angle is smaller than or equal to 90 degrees, judging that the direction of the node relative to the current node and the target point is the same, adding the node into an open list, and calculating the value of a cost function of the node; when the included angle is larger than 90 degrees, the opposite direction of the node relative to the current node and the target point is judged to be opposite, and the node is abandoned.
Further, in the second step, the method of determining that the direction of the node to be expanded is consistent with the direction of the target point is that the starting point is connected with the target point, the current node is a straight line l perpendicular to the line segment, the straight line l divides the plane into two parts, namely a part containing the target point and a part not containing the target point, if the node to be expanded is located at the part containing the target point, the direction of the node is the same as the direction of the target point, otherwise, the direction of the node is opposite to the direction of the target point.
Further, in the fourth step, the method for determining whether the node is the first type node is to calculate whether the slopes of the path before the node and the path after the node are the same, and set the coordinates of the path point C to be determined as (x) C ,y C ) Its previous path point P 1 Is (x) p1 ,y p1 ) The latter path point P 2 Is (x) p2 ,y p2 ) The slope K of the two paths 1 And K 2 As shown in formulas (2) and (3), respectively:
if K 1 And K is equal to 2 If the path points are the same, determining the path points as the first type nodes, removing the path points, and if K 1 And K is equal to 2 Otherwise, the node is reserved.
Further, in the fourth step, the method of judging whether the second class node is that the starting point is connected with the target point to obtain a straight line, if the straight line does not pass through the obstacle, the path point between the starting point and the target point is judged to be the second class node, the second class node is removed, and the straight line is reserved as a final path; if the straight line passes through the obstacle, connecting the starting point with the penultimate path point to obtain the straight line, if the straight line does not pass through the obstacle, judging the path point between the starting point and the penultimate path point as a second class node, removing the second class node, connecting the penultimate node with the target point, and determining a final path; if the straight line passes through an obstacle, the start point is connected with the third last path point, and the like until the start point and the end point are connected without the obstacle.
The system is provided with a program module corresponding to the steps, and the steps in the storage robot static path planning method are executed during running.
A computer readable storage medium storing a computer program configured to implement, when invoked by a processor, the steps of a static path planning method applied to a warehouse robot.
Compared with the prior art, the invention has the beneficial effects that:
the invention discloses a static path planning method and a static path planning system for a storage robot, which are characterized in that a focusing search direction and a path reprocessing method are introduced on the basis of a traditional A-Star algorithm, an improved A-Star algorithm based on the search direction and the path reprocessing is compiled, unnecessary nodes are removed, an optimal path is planned for the storage robot, turning times are effectively reduced, the path length is further reduced, and the working efficiency of the robot is improved;
compared with the four algorithms in comparison, the static path planning method and system for the warehouse robot can show that the improved A-Star algorithm is stable, the number of turning points in the path and the path length have the greatest influence on the working efficiency of the robot, the improved A-Star algorithm provided by the invention has the optimal performance under the two indexes, the number of turning points is reduced by 50% -83.3%, and the path length is shortened by 2.9% at most; in terms of calculation time, the improved A-Star algorithm provided by the invention is generally longer than the traditional A-Star algorithm in terms of the calculation amount increased for shortening the path length and reducing the number of turning points, but the increased numerical value is smaller, so that the real-time performance can be met, and the aim of improving the working efficiency of the robot is achieved, and comprehensive consideration is taken into account, so that the performance of the algorithm provided by the invention is optimal.
Drawings
FIG. 1 is a flow chart of a static path planning method applied to a warehouse robot in an embodiment of the invention;
FIG. 2 is a schematic diagram illustrating a determination of a node to be searched according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a method for diameter reprocessing according to an embodiment of the present invention;
FIG. 4 is a path comparison chart of algorithm 1, algorithm 2, a traditional A-Star algorithm and an improved A-Star algorithm in an obstacle dense map environment, an obstacle moderate map environment and an obstacle sparse map environment in the embodiment of the invention;
fig. 5 is a transverse comparison chart of performance of algorithm 1, algorithm 2, a conventional a-Star algorithm and an improved a-Star algorithm in an obstacle dense map environment, an obstacle moderate map environment and an obstacle sparse map environment in an embodiment of the present invention.
Detailed Description
In the description of the present invention, it should be noted that the terms "first," "second," and "third" mentioned in the embodiments of the present invention are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first", "a second", or a third "may explicitly or implicitly include one or more such feature.
In order that the above objects, features and advantages of the invention will be readily understood, a more particular description of the invention will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings.
The specific embodiment I is as follows: referring to fig. 1 to 5, the invention provides a static path planning method applied to a storage robot, which comprises the following steps:
step one, acquiring and inputting a starting point, a target point and map information, and adding the starting point into an open list;
step two, removing adjacent nodes which are inconsistent with the direction of the target node, occupied by the barrier and in the closed list, and calling other adjacent nodes except the nodes as adjacent reachable nodes;
the method for determining whether the directions of the adjacent nodes and the target nodes are consistent is thatThe center of the current node is connected with the centers of the target point and the node to be searched, the included angle theta is calculated, and the coordinates of the current node are set as (x c ,y c ) The coordinates of the nodes to be searched are as followsThe target node coordinates are (x G ,y G ) The calculation formula of the included angle theta is shown in (1):
when the included angle is smaller than or equal to 90 degrees, judging that the direction of the node relative to the current node and the target point is the same, adding the node into an open list, and calculating the value of a cost function of the node; when the included angle is larger than 90 degrees, the opposite direction of the node relative to the current node and the target point is judged, the node is abandoned, and the calculation formula of the cost function is shown as (2):
f(n)＝h(n)+g(n) (2)
wherein g (n) is an observation evaluation value from a starting point to a node n, h (n) is an estimation evaluation value from the node n to an end point, and f (n) is an estimation evaluation value from the starting point to the end point through the node n;
step three, obtaining a path, comprising the following steps,
(1) Judging whether the adjacent reachable node obtained in the step two is in an open list or not, if yes, recording a node with the minimum g (n) value in the adjacent nodes, and taking the current node as a father node of the adjacent node with the minimum g (n) value; if not, the adjacent reachable nodes are put into an open list, whether the open list is empty or not is judged, if the open list is empty, the route cannot be found, planning is finished, if the open list is not empty, whether the adjacent nodes are destination points or target nodes is judged, if the adjacent nodes are destination points, the route is saved as a final route, and circulation is finished; if the adjacent node is not the end point, the current node is used as a father node;
(2) Moving the current node obtained in the step (1) into a closed list;
(3) Calculating f (n) values of adjacent reachable nodes, taking f (n) as an evaluation function value, and selecting a node with the minimum f (n) value as a next current node;
(4) Repeating the steps II, the steps III (1) - (3) to the step III (1), wherein the open list is empty, so that a path can not be found, or the adjacent node in the step III (1) is used as an end point, and a final path is obtained;
step four, after the path planning is completed, unnecessary nodes need to be removed;
the method for removing the unnecessary nodes can be realized by judging whether the unnecessary nodes are a first type node and a second type node and removing the two types of nodes, wherein the first type node has no influence on a path no matter whether the first type node is removed or the second type node is reserved, the robot cannot collide with an obstacle after the second type node is removed, the path length is shortened, and the turning times are reduced;
the method for judging whether the node is the first type node is that whether the slopes of the path before the node and the path after the node are the same is calculated, and the coordinates of the path point C to be judged are set as (x) C ,y C ) Its previous path point P 1 Is (x) p1 ,y p1 ) The latter path point P 2 Is (x) p2 ,y p2 ) The slope K of the two paths 1 And K 2 As shown in formulas (3) and (4), respectively:
if K 1 And K is equal to 2 If the path points are the same, determining the path points as the first type nodes, removing the path points, and if K 1 And K is equal to 2 If the nodes are different, reserving the nodes;
the method for judging whether the second class node is the second class node comprises the steps of firstly connecting a starting point with a target point to obtain a straight line, judging that a path point between the starting point and the target point is the second class node if the straight line does not pass through an obstacle, removing the second class node, and reserving the straight line as a final path; if the straight line passes through the obstacle, connecting the starting point with the penultimate path point to obtain the straight line, if the straight line does not pass through the obstacle, judging the path point between the starting point and the penultimate path point as a second class node, removing the second class node, connecting the penultimate node with the target point, and determining a final path; if the straight line passes through the obstacle, connecting the starting point with the third last path point, and the like until the starting point and the end point are connected without the obstacle;
after the second type of nodes are removed, the reprocessing of the path is completed, and the turning times of the path are effectively reduced, so that the path length is shortened.
Preferably, in the second step, the method for determining whether the direction of the node to be searched is consistent with the direction of the target point is: the starting point and the target point are connected, the current node is a straight line I which is perpendicular to the line segment, the plane is divided into two parts, namely a part containing the target point and a part not containing the target point, if the node to be expanded is positioned at the part containing the target point, the direction of the node is the same as the direction of the target point, otherwise, the direction of the node is opposite to the direction of the target point.
In determining whether the direction of the neighboring node in the second step is consistent with the direction of the target node, for example,
a schematic diagram for judging the nodes to be searched is shown in fig. 2, and it is assumed that no obstacle is included in the adjacent 8 nodes. Point S is the starting point, point G is the target point, and point C is the current node, where at point C 6 When judging, calculating a straight line CC 6 And the angle theta between the straight line CG 6 Angle theta 6 Greater than 90 °, point C 6 Discarding; at point C 7 When judging, calculating a straight line CC 7 And the angle theta between the straight line CG 7 Angle theta 7 Less than 90 DEG, point C 7 Add to the open list. And so on, the node which finally joins the open list is point C 3 、C 5 、C 7 And C 8 。
In determining whether the path point is an unnecessary node in the fourth step, for example,
an example of a path reprocessing method is shown in fig. 3, in which a path [ ACFDEB ] is a path obtained by primary planning, and in which the slopes of a path [ AC ] in the previous segment and a path [ CF ] in the next segment of the point C are different, so that the point C is reserved, and similarly, the points D and E are reserved. The slope of the previous segment of path [ CF ] of the point F is the same as that of the next segment of path [ FD ], so that the path point F is a first type node, the first type node is removed, and the reserved path is [ ACDEB ]. On the basis of the path, continuously judging the path point, firstly connecting the path point A with the path point B, connecting the path point A with the path point E when a connecting line passes through an obstacle, continuously connecting the path point A with the path point D when the connecting line still passes through the obstacle, judging the path point C as a second type node when the connecting line does not pass through the obstacle, and removing the path point C; and then the point D is connected with the target point B, the obstacle is not passed, the point E is judged to be the second class node, the second class node is removed, and the finally reserved path is [ ADB ].
Examples
Because the algorithm performance can be influenced by the density of the obstacles in the map, three different map environments are designed, the ratio of the obstacles is changed by changing the size of the map under the condition that the area of the obstacles is the same, the minimum size map is 20m multiplied by 20m, the minimum size map is the dense obstacle environment, the 30m multiplied by 30m map is the moderate obstacle environment, the 50m multiplied by 50m map is the sparse obstacle environment, and the configuration is shown in table 1:
table 1 different map environment configurations
| Numbering device | Map size/m 2 | Obstacle duty cycle | Origin coordinates | Endpoint coordinates |
| 1 | 20×20 | 7.0％ | (3,2) | (18,18) |
| 2 | 30×30 | 3.1％ | (3,1) | (27,29) |
| 3 | 50×50 | 1.1％ | (3,1) | (48,49) |
The invention carries out path planning on the improved A-Star algorithm based on the search direction and the path reprocessing and other three comparison algorithms in the map environment constructed in the foregoing, the obtained path is shown in figure 4,
the comparison algorithm 1 firstly improves the evaluation function so that the weight of the evaluation function can be dynamically adjusted according to map information, and the improved evaluation function is shown as a formula (5):
f(n)＝a*g(n)+b*h(n) (5)
wherein the method comprises the steps of
b＝[c/(1-c)]*[(d iE +d iS )/d SE ] (6)
dist 1 ＝|x i -x E | (8)
dist 2 ＝|y i -y E | (9)
Wherein d is iE D is the distance from the current node to the target point iS D is the distance from the current node to the starting point SE Is the distance from the origin to the target point, (x) i ,y i ) Is the coordinates of the current node, (x E ,y E ) Dist, the coordinates of the target node 1 Dist is the absolute value of the difference between the current node and the target node 2 Taking the absolute value of the difference between the current node and the longitudinal coordinate of the target point as the value of the parameter a is 0.8, and taking the value of the parameter c is 0.4;
the search is performed for 16 neighboring nodes in order to find a better path.
The comparison algorithm 2 connects the start point and the end point of the path first, and if the straight line AB does not pass through an obstacle, the straight line AB is determined to be a final path; if the straight line passes through the obstacle, determining the intersection point of the obstacle and the straight line AB, determining the starting point and the end point of the local path, planning the local path by adopting an improved A-Star algorithm, and finally combining a plurality of sections of local paths with the straight line AB to obtain a complete path.
The comparison algorithm 2 introduces beta as a parameter of h (n) in the evaluation function of the A-Star algorithm, and increases the expansion direction of the current node to the adjacent node. The improved evaluation function is shown in the formula (10):
f(n)＝g(n)+β·h(n)(10)
wherein, the value of the selected parameter beta is 1, and the direction number of the expansion to the adjacent node is 16.
Analysis of the algorithm performance under the above circumstances, respectively, showed that:
(1) Obstacle dense map
The data of the number of turns, the path length, the number of access nodes and the calculation time obtained by the 4 algorithms under the obstacle dense map and the evaluation index value are shown in table 2,
TABLE 2 comparison of different algorithm performances under obstacle dense map
In the aspect of inflection point number, the inflection point number of the improved A-Star algorithm provided by the invention is minimum in the map environment, and compared with the traditional A-Star algorithm, the inflection point number is reduced by 50%, and compared with the comparison algorithm 2, the inflection point number is reduced by 60%. The method is mainly because the improved A-Star algorithm provided herein removes the first type nodes and the second type nodes when the path obtained at first time is reprocessed, and the redundant turning points are removed because the comparison algorithm 1 also considers the problem of the number of the turning points in the path, and the number of the turning points of the planned path in the current map is the same as the number of the improved algorithm provided by the study.
In terms of path length, the path length planned by the improved A-Star algorithm provided by the invention is shortest in the current map environment, and is reduced by 2.9% compared with the traditional A-Star algorithm, 0.5% compared with the comparison algorithm 1 and 1.3% compared with the comparison algorithm 2. This is because, according to the principle of shortest straight line between two points, when the inflection point in the path is removed by the improved a-Star algorithm, the path length is correspondingly shortened. Although redundant turning points are removed by the comparison algorithm 1, the path which is planned for the first time is longer, the final path is longer than the algorithm provided by the invention, the straight line path used by the comparison algorithm 2 is shortest when no obstacle is encountered, but the path which is spent when the obstacle is bypassed is more, and the complete path is longer.
In terms of the number of access nodes, the number of access nodes of the improved A-Star algorithm provided by the invention is reduced by 22.2 percent and 1.5 percent compared with the number of access nodes of the traditional A-Star algorithm and the comparison algorithm 1 respectively, and is inferior to the comparison algorithm 2. This is mainly because the improved a-Star algorithm provided by the invention adopts a method of focusing the search direction, so that the number of search nodes is reduced, while the comparison algorithm 2 only uses the improved a-Star algorithm in a local path needing to bypass the obstacle, and does not need to search nodes in a path without encountering the obstacle. However, the initial goal of reducing the number of searching nodes is to reduce the calculation time, while the calculation time of the comparison algorithm 2 is very dependent on the number of obstacles, when the number of obstacles is large, the time required for bypassing the obstacles is obviously increased, and the method is not suitable for the map environment provided by the invention.
In terms of calculation time, the improved A-Star algorithm provided by the invention is the same as the comparison algorithm 1 in calculation time and is less than the comparison algorithm 2, and compared with the traditional A-Star algorithm, the improved A-Star algorithm is increased by 50%, but the actual increase time is 0.001s due to smaller numerical base. Although the improved A-Star algorithm provided by the invention reduces the number of access nodes and saves the calculation time, the path reprocessing also takes time to calculate, so the time is longer. The fact proves that the path planned by the improved A-Star algorithm provided by the invention has excellent performance in the aspects of path length and total inflection point number, so that the increased calculation time is in the range of meeting the real-time requirement, the improvement of efficiency brought by reducing the path length and the number of inflection points to the robot is comprehensively considered, and the improvement is necessary for the path reprocessing.
(2) Moderate map of obstacle
The data of the number of turns, the path length, the number of access nodes and the calculation time obtained by the 4 algorithms under the moderate obstacle map and the evaluation index value are shown in table 3,
table 3 comparison of different algorithm performances under moderate obstacle map
In the aspect of inflection point number, after the first type node and the second type node are removed, the inflection point number of the improved A-Star algorithm provided by the invention is still minimum under the current map, which is reduced by 50% compared with the traditional A-Star algorithm and by 83.33% compared with the comparison algorithm 2. Since the redundant points in the path are processed by the comparison algorithm 1, the improved A-Star algorithm proposed by the research in the current map is the same as the inflection point number.
In the aspect of path length, under a moderate map of the obstacle, compared with the traditional A-Star algorithm, the improved A-Star algorithm provided by the invention has the advantages that compared with the traditional A-Star algorithm, the compared algorithm 2 is respectively shortened by 2.0 percent and 1.5 percent, compared with the compared algorithm 1, the improved A-Star algorithm is increased by 0.15 percent, the increased length is less, and the efficiency of the robot for completing the task is higher by combining the calculation time.
In terms of access node number, under the moderate map of the obstacle, the access node number of the improved A-Star algorithm provided by the invention is reduced by 16.0% and 3.0% compared with the access node number of the traditional A-Star algorithm and the comparison algorithm 1 respectively, and the number of the access node is only more than that of the comparison algorithm 2, but the algorithm provided herein has great advantages in calculation time.
In terms of calculation time, the improved A-Star algorithm provided by the invention is only longer than the traditional A-Star algorithm, the numerical difference is smaller and is 0.001s, and compared with the comparison algorithm 1 and the comparison algorithm 2, the required calculation time is reduced by 50% and 98% respectively.
(3) Sparse map of obstacle
The data of the number of turns, the path length, the number of access nodes and the calculation time obtained by the 4 algorithms under the sparse map of the obstacle and the evaluation index value are shown in table 4,
TABLE 4 comparison of different algorithm performances under sparse map of obstacle
In the aspect of inflection point number, under the sparse map of the obstacle, the improved A-Star algorithm provided by the invention has the same inflection point number as that in the path planned by the comparison algorithm 1, and is least, compared with the traditional A-Star algorithm and the comparison algorithm 2, 75% and 80% of the inflection point number are respectively reduced.
In terms of path length, the improved A-Star algorithm provided by the invention has the shortest path length in the current map, and compared with the traditional A-Star algorithm, the comparison algorithm 1 and the comparison algorithm 2 are respectively reduced by 1.3%,0.2% and 0.1%.
In terms of access node number, compared with the traditional A-Star algorithm and the comparison algorithm 1, the access node number of the improved A-Star algorithm provided by the invention is reduced by 16.8% and 1.8% respectively. The number of access nodes of the A-Star algorithm is obviously reduced when the comparison algorithm 2 bypasses the obstacle due to the fact that the straight line connecting the starting point and the ending point of the path passes through the edge position of the obstacle, and the number of access nodes of the A-Star algorithm is more than that of the comparison algorithm 2 due to the fact that the map is larger in size, but the time required for calculation is still far smaller than that of the comparison algorithm 2.
In terms of calculation time, the time required by the improved A-Star algorithm provided by the invention is reduced by 7.7% and 93.6% compared with that required by the comparison algorithm 1 and the comparison algorithm 2 respectively, and the improved A-Star algorithm is only longer than the traditional A-Star algorithm.
(4) Algorithm performance transverse comparison under different map environments
A bar graph of the number of turns, path length, number of access nodes and lateral comparison of computation time for different algorithms under different map environments is given in figure 5,
in the aspect of inflection point number, as can be known from 5 (a), the inflection point number of the planned path in three different maps of the improved A-Star algorithm provided by the invention is minimum, and the performance is stable.
As can be seen from fig. 5 (b), the improved a-Star algorithm proposed by the present invention is only slightly longer than the comparative algorithm 1 in the moderate obstacle map, and the path length is shortest in the other two maps, and becomes longer as the map scale increases.
As to the number of access nodes, as can be seen from fig. 5 (c), the number of access nodes required for the improved a-Star algorithm proposed by the present invention is inferior to the comparison algorithm 2 in three maps. As the map scale becomes larger, the number of access nodes of the improved A-Star algorithm, the traditional A-Star algorithm and the comparison algorithm 1 provided by the invention is gradually increased, and the number of access nodes of the comparison algorithm 2 only uses the A-Star algorithm at the part which bypasses the obstacle and depends on the number of times of bypassing the obstacle and the position of the intersection point of the straight line connecting the starting point and the end point and the obstacle.
In terms of calculation time, as can be seen from fig. 5 (d), the calculation time required by the conventional a-Star algorithm is the shortest in three different maps, the calculation time required by the comparison algorithm 1 is slightly longer than that required by the improved algorithm proposed herein, the difference between the two calculation times is not great, and the calculation time is the longest of the comparison algorithm 2.
In summary, the four algorithms compared by the invention have stable performance in different map environments, the number of inflection points in the path and the path length have the greatest influence on the working efficiency of the robot, and the improved A-Star algorithm provided by the invention has the optimal performance under the two indexes, wherein the number of inflection points is reduced by 50% -83.3%, and the path length is shortened by 2.9% at most. In terms of calculation time, the increased calculation amount for shortening the path length and reducing the number of turns makes the improved A-Star algorithm generally longer than the traditional A-Star algorithm, but the increased value is smaller, so that the real-time performance can be met. Therefore, the algorithm performance proposed herein is optimal in view of comprehensive consideration with the aim of improving the working efficiency of the robot.
And a specific embodiment II: referring to fig. 1 to 5, the present invention provides a static path planning system for a storage robot, where the system has a program module corresponding to the above steps, and the steps in the static path planning method for a storage robot are executed during operation. Other combinations and connection relationships of this embodiment are the same as those of the first embodiment.
And a third specific embodiment: as shown in connection with fig. 1-5, a computer readable storage medium stores a computer program configured to implement steps of a static path planning method applied to a warehouse robot when called by a processor. Other combinations and connection relationships of this embodiment are the same as those of the second embodiment.
Although the present disclosure is disclosed above, the scope of the present disclosure is not limited thereto. Various changes and modifications may be made by one skilled in the art without departing from the spirit and scope of the disclosure, and such changes and modifications would be within the scope of the disclosure.
Claims (7)
1. The static path planning method applied to the warehousing robot is characterized by comprising the following steps of:
step one, acquiring and inputting a starting point, a target point and map information, and adding the starting point into an open list;
step two, removing adjacent nodes which are inconsistent with the direction of the target node, occupied by the barrier and in the closed list, and calling other adjacent nodes except the nodes as adjacent reachable nodes;
step three, obtaining a path, comprising the following steps,
(1) Judging whether the adjacent reachable node obtained in the step two is in an open list or not, if yes, recording a node with the minimum g (n) value in the adjacent nodes, and taking the current node as a father node of the adjacent node with the minimum g (n) value; if not, the adjacent reachable nodes are put into an open list, whether the open list is empty or not is judged, if the open list is empty, the route cannot be found, planning is finished, if the open list is not empty, whether the adjacent nodes are destination points or target nodes is judged, if the adjacent nodes are destination points, the route is saved as a final route, and circulation is finished; if the adjacent node is not the end point, the current node is used as a father node;
(2) Moving the current node obtained in the step (1) into a closed list;
(3) Calculating f (n) values of adjacent reachable nodes, taking f (n) as an evaluation function value, and selecting a node with the minimum f (n) value as a next current node;
(4) Repeating the steps II, the steps III (1) - (3) to the step III (1), wherein the open list is empty, so that a path can not be found, or the adjacent node in the step III (1) is used as an end point, and a final path is obtained;
and step four, carrying out path reprocessing on the acquired paths, including removing two types of unnecessary nodes.
2. The static path planning method applied to the warehousing robot according to claim 1, wherein the method comprises the following steps: in the second step, the method for determining whether the directions of the neighboring node and the target node are consistent is to connect the center of the current node with the centers of the target point and the node to be searched, calculate the included angle θ, and set the coordinates of the current node as (x) c ,y c ) The coordinates of the nodes to be searched are as followsThe target node coordinates are (x G ,y G ) The calculation formula of the included angle theta is shown in (1):
when the included angle is smaller than or equal to 90 degrees, judging that the direction of the node relative to the current node and the target point is the same, adding the node into an open list, and calculating the value of a cost function of the node; when the included angle is larger than 90 degrees, the opposite direction of the node relative to the current node and the target point is judged to be opposite, and the node is abandoned.
3. The static path planning method applied to the warehousing robot according to claim 2, wherein the method comprises the following steps: in the second step, the method for determining that the direction of the node to be expanded is consistent with the direction of the target point is that the starting point is connected with the target point, the current node is taken as a straight line l which is perpendicular to the line segment, the plane is divided into two parts, namely a part containing the target point and a part not containing the target point, if the node to be expanded is positioned at the part containing the target point, the direction of the node is the same as the direction of the target point, otherwise, the direction of the node is opposite to the direction of the target point.
4. A method for planning a static path for a warehousing robot according to claim 3, wherein: in the fourth step, the method for determining whether the node is the first type node is that whether the slopes of the path before the node and the path after the node are the same is calculated, and the coordinates of the path point C to be determined are set as (x) C ,y C ) Its previous path point P 1 Is (x) p1 ,y p1 ) The latter path point P 2 Is (x) p2 ,y p2 ) The slope K of the two paths 1 And K 2 As shown in formulas (2) and (3), respectively:
if K 1 And K is equal to 2 If the path points are the same, determining the path points as the first type nodes, removing the path points, and if K 1 And K is equal to 2 Otherwise, the node is reserved.
5. The method for planning a static path of a warehousing robot according to claim 4, wherein: in the fourth step, the method of judging whether the node is the second class node is that firstly, the starting point is connected with the target point to obtain a straight line, if the straight line does not pass through the obstacle, the path point between the starting point and the target point is judged to be the second class node, the second class node is removed, and the straight line is reserved as a final path; if the straight line passes through the obstacle, connecting the starting point with the penultimate path point to obtain the straight line, if the straight line does not pass through the obstacle, judging the path point between the starting point and the penultimate path point as a second class node, removing the second class node, connecting the penultimate node with the target point, and determining a final path; if the straight line passes through an obstacle, the start point is connected with the third last path point, and the like until the start point and the end point are connected without the obstacle.
6. Be applied to storage robot static path planning system, its characterized in that: the system has program modules corresponding to the steps of any of the preceding claims 1-5, which, when run, perform the steps described above as applied in the warehouse robot static path planning method.
7. A computer-readable storage medium, characterized by: the computer readable storage medium stores a computer program configured to implement the steps of any one of claims 1-5 applied to a warehouse robot static path planning method when invoked by a processor.
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