CN111721297A - Path planning method for multiple AGV of intelligent garage - Google Patents
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Abstract
The invention relates to a path planning method for multiple AGV of an intelligent garage, belonging to the technical field of intelligence. The method specifically comprises the following steps: 1) determining an AGV to be distributed; 2) determining a path plan according to the improved A-algorithm; 3) determining the occupied time of each edge of the path and generating a time window; 4) judging whether the time windows are overlapped or not, if so, delaying the time windows until the time windows are not overlapped; 5) determining time window registrations for all paths; 6) determining a time cost from the starting point to the target point; 7) and determining a path planning result. The method and the device can solve the problem of poor flexibility of the heavy AGV, reduce the turning times, avoid the conflict of multiple AGVs and realize the shortest time cost.
Description
Technical Field
The invention belongs to the technical field of intellectualization, and relates to a path planning method for multiple AGVs in an intelligent garage.
Background
An Automated Guided Vehicle (AGV) is an Automated handling device that can travel along a predetermined path without manual navigation. The battery is generally used as a power source, the common navigation modes comprise magnetic navigation and laser navigation, and the battery is mainly used for carrying out automatic carrying work on a transportation system or a production line. The AGV is used as a core part of automatic transportation operation, and promotes the development of logistics and industrial automation to a certain extent.
Applying AGVs to smart parking industry, a less reclaimed field, is also very challenging, but there are also opportunities. From the current parking state, the manual parking is time-consuming and labor-consuming, the reserved area of the parking space is large, and the space is wasted. After the AGV is adopted for parking, on the one hand, people are brought convenient and fast experience, on the other hand, the land utilization rate can be improved through different garage and parking space designs, more parking spaces are added in a limited space, and the AGV parking device has great economic value.
In the research of AGVs, the path planning and scheduling strategy is an important part. Path planning can be generally divided into two categories, static path planning and dynamic path planning. The path planning under the globally known condition is static path planning, and obstacles in the environment are all static. There are many solutions to static path planning, such as genetic algorithms, particle swarm algorithms, a-algorithms, etc. The application environment of dynamic path planning includes many dynamic obstacles, which can be solved by using fuzzy logic algorithm, dynamic window method, etc. The method solves the problems that the speed change of the heavy AGV has poor flexibility, large turning is as few as possible, and the straight line running distance is as long as possible, and is a difficult point for planning multiple AGV paths.
Disclosure of Invention
In view of this, the present invention provides a method for planning multiple AGVs in an intelligent garage. And calculating paths among the targets by using an improved A-star algorithm, adding artificial potential field optimization, combining turning cost and optimizing search efficiency, and finally solving the problem of conflict among multiple AGVs by using a time window.
In order to achieve the purpose, the invention provides the following technical scheme:
a path planning method for multiple AGVs in an intelligent garage comprises the following steps:
s1: after the task is received, distributing the task according to the priority of the task, and distributing the AGV according to the time cost;
s2: solving a path set according to an improved A-algorithm;
s3: calculating the occupation time of one edge in the path according to the path set;
s4: according to the occupied time of one edge, the time window of the node is obtained;
s5: according to the time window of the node, compared with the idle time window set, judging whether the time windows are overlapped or not, if not, registering the time node, and if so, delaying the time of the node and then registering the time node;
s6: repeating steps S3-S5 until the time nodes of all paths in the set of paths are registered;
s7: calculating the time cost from the starting point to the target point;
s8: and generating a path planning result and finishing the global path planning.
Optionally, in S2, the method for solving the path set according to the improved a-algorithm includes:
s21: establishing an Open list and a Close list, wherein the Open list and the Close list are both empty initially, points to be expanded and searched are placed in the Open list, the searched points are placed in the Close list, and a starting node s is placed in the Open list at the beginning;
s22: adding a starting point s into a Close list from an Open list, then searching points near the starting point s, adding all points which can be reached by s into the Open list, and if the points are obstacle points, ignoring the points;
s23: f (n) is a cost function of the A-algorithm, a point with the minimum f (n) in the Open list is searched, the point is moved out of the Open list and is placed in the Close list;
s24: checking the reachable points around the point with the minimum f (n), if the reachable points are not in the Open list, adding the reachable points, and setting the current node as a parent node; otherwise, f (n) of the nodes is calculated, if the new f (n) is smaller than the previous value, the f (n) value is updated and is set as a parent node;
s25: the search for the path is accomplished by repeatedly examining the target node and searching for intermediate nodes.
Optionally, in S2, the heuristic function of the modified a algorithm is selected as manhattan distance: the absolute value of the sum of the horizontal and vertical distances of the two points; the expression is as follows:
h(n)＝|x1-x2|+|y1-y2|
in the formula x1And x2Abscissa, y, representing the current position and end point on the map1And y2Representing the ordinate of the current position and the end point on the map.
Optionally, in S23, the modified a-algorithm optimizes the conventional a-algorithm by introducing the idea of artificial potential field, where the cost function of the modified a-algorithm is:
f(n)＝g(x)+h(n)+F(q)
wherein:
where x represents the current position of the AGV and is a vector, xobsRepresenting the position of the obstacle as a vector, KrepRepresents a repulsive potential field gain coefficient, and is a constant.
Optionally, in S23, the improved a-algorithm introduces optimization of the turn cost, and improves g (x) as follows:
g′(x)＝g(x)+αg(x)
wherein g' (x) represents the actual cost value from the starting node to the current node after improvement, g (x) represents the initial cost value from the starting node to the current node, alpha is the turning weight, and takes a specific value at the turning, otherwise 0.
Optionally, in S23, the improved a-algorithm optimizes the search efficiency, and increases the value of the weight β occupied by the heuristic function, where the cost function of the improved a-algorithm is:
f(n)＝g′(x)+βh(n)+F(q)
g′(x)＝g(x)+αg(x)
where β represents the weight occupied by the heuristic function.
Optionally, in S2, the obtained path set is Ri，z＝{ei，j，ej，k，...ek，zIs starting node eiThe target node is ez。
Optionally, in S3, the occupied time of each edge is calculated as the straight-driving and the turning-bending-2 part, and the turning-bending time is a constant toThe calculation formula of the straight travel time is as follows:
in the formula, LcThe length of the AGV body, the length between nodes and the speed of the AGV when the AGV runs at a constant speed are respectively calculated according to the length of the AGV body and the length of the node.
Optionally, in S4, setting an F-idle path node time window set, where T is a path node time window set that has been reserved for the current AGV, M is a current path node set to be allocated, N is a path combination of nodes to which a time window has been allocated, and a current path node set M that initializes a time window to be allocated is (R, N) null;
in the S4, the path set R is comparedi，zNode time window information for each edge within, from ei，jTo begin, AGV is at time
From node i into edge ei，jThen at time
Leave the edge ei，jThen AGV is on path ei，jUpper run time of
If no AGV currently occupies path ei，jIts time window is registered as
If there are other AGVs already engaged ei，jAnd releasing the road segment time as
Then its time window is
Optionally, in the S5, the time window
Comparing with the idle time window set F if the time window is
Belonging to an idle time window, then the time window
That is, the time window that the path can be assigned to the current AGV; time window for allocating this path in set F
Deleting the time window and adding the time window into the reserved time window set T; this path e is also aggregated in the AGV pathi，jDeleting and adding the path into the distributed path set N; if the idle time window node does not include
If the time period is not longer, then the system will
Delaying until an idle time window is found and allocating the idle time window to the current path; after the distribution is finished, handle ei，jAdding to the set N,
Is added to the collectionAnd (6) combining T.
The invention has the beneficial effects that:
1. the invention provides a multi-AGV path planning method for an intelligent garage, which determines the driving path of an AGV by using an improved A-x algorithm, enables the turning times of the finally obtained path to be as small as possible, and solves the problem of poor speed change flexibility of a heavy AGV.
2. The invention establishes a time window model, and avoids the problem of path conflict possibly occurring among multiple AGVs.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention may be realized and attained by the means of the instrumentalities and combinations particularly pointed out hereinafter.
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For the purposes of promoting a better understanding of the objects, aspects and advantages of the invention, reference will now be made to the following detailed description taken in conjunction with the accompanying drawings in which:
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a map simulation of a conventional A-x algorithm;
fig. 3 is a map simulation of the modified a-x algorithm.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention in a schematic way, and the features in the following embodiments and examples may be combined with each other without conflict.
Wherein the showings are for the purpose of illustrating the invention only and not for the purpose of limiting the same, and in which there is shown by way of illustration only and not in the drawings in which there is no intention to limit the invention thereto; to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product; it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The same or similar reference numerals in the drawings of the embodiments of the present invention correspond to the same or similar components; in the description of the present invention, it should be understood that if there is an orientation or positional relationship indicated by terms such as "upper", "lower", "left", "right", "front", "rear", etc., based on the orientation or positional relationship shown in the drawings, it is only for convenience of description and simplification of description, but it is not an indication or suggestion that the referred device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes, and are not to be construed as limiting the present invention, and the specific meaning of the terms may be understood by those skilled in the art according to specific situations.
The purpose of the invention is realized by the technical scheme, as shown in figure 1, the specific steps are as follows:
1) after the task is received, according to the time for receiving the task, the earliest received task has the highest priority, and an AGV is preferentially distributed to the earliest received task;
2) calculating the time cost of each AGV from the current node to the target node, in order to improve the efficiency, approximately representing the time cost by using the distance, selecting the Manhattan distance between 2 points as a reference basis, and selecting one distribution task with the minimum Manhattan distance in all the AGVs;
3) determining the path set as R by using the improved A-algorithm and the current node and the target node obtained in the step 2) as known conditionsi，z＝{ei，j，ej，k，...，ek，zIs starting node eiThe target node is ez；
4) Determining the occupation time of one edge in the path set by taking the path set obtained in the step 3) as a known condition;
5) determining the time window of the node by taking the occupied time of one edge obtained in the step 4) as a known condition;
6) comparing the time window of the node obtained in the step 5) with the idle time window set to judge whether the time windows are overlapped or not, if not, registering the time node, if so, delaying the time of the node until no overlap exists, and then registering the time node;
7) repeating the steps 4) to 6) until the time nodes of all paths in the path set are registered to obtain a final time window set;
8) calculating the time cost of the AGV from the starting point to the target point by taking the time window set obtained in the step 7) as a known condition;
9) and generating a path planning result and finishing the global path planning.
In step 3), the heuristic function of the improved a-algorithm is selected as manhattan distance: the absolute value of the sum of the horizontal and vertical distances of the two points. The expression is as follows:
h(n)＝|x1-x2|+|y1-y2|
in the formula x1And x2Abscissa, y, representing the current position and end point on the map1And y2Representing the ordinate of the current position and the end point on the map.
In step 3), the specific steps of solving the path set according to the improved a-algorithm are as follows:
31) establishing an Open list and a Close list, wherein the Open list and the Close list are both empty initially, points to be expanded and searched are placed in the Open list, the searched points are placed in the Close list, and a starting node s is placed in the Open list at the beginning;
32) adding a starting point s into a Close list from an Open list, then searching points near the starting point s, adding all points which can be reached by s into the Open list, and if the points are obstacle points, ignoring the points;
33) f (n) is the cost function of the A-algorithm, the point with the minimum f (n) in the Open list is searched, and the point is moved out of the Open list and is put into the Close list.
34) Checking the reachable points around the point with the minimum f (n), if the reachable points are not in the Open list, adding the reachable points, and setting the current node as a parent node; otherwise, f (n) of the nodes is calculated, and if the new f (n) is smaller than the previous value, the f (n) value is updated and is set as a parent node.
35) The search for the path is accomplished by repeatedly examining the target node and searching for intermediate nodes.
In step 33), the improved a-algorithm performs artificial potential field optimization, turning cost optimization and search efficiency optimization on the conventional a-algorithm. The cost function of the improved a-algorithm at this time is:
f(n)＝g′(x)+βh(n)+F(q)
wherein:
g′(x)＝g(x)+αg(x)
β represents the weight occupied by the heuristic function, g' (x) is the actual cost value from the starting node to the current node after improvement, g (x) is the initial cost value from the starting node to the current node, α is the turn weight, the concrete value is taken at the turn, otherwise, the value is 0, x represents the current position of the AGV, and is a vector, xobsRepresenting the position of the obstacle as a vector, KrepRepresents a repulsive potential field gain coefficient, and is a constant.
As shown in fig. 2 and 3, the improved a-algorithm makes fewer turns in the planned path than the conventional a-algorithm.
Calculating the occupied time of each edge in the step 4) and dividing the occupied time into a straight line driving part and a rotating bending part 2, wherein the rotating bending time is a constant toThe calculation formula of the straight travel time is as follows:
in the formula, LcThe length of the AGV body, the length between nodes and the speed of the AGV when the AGV runs at a constant speed are respectively calculated according to the length of the AGV body and the length of the node.
In the step 5), the step of mixing the raw materials,according to the obtained occupation time of one edge, the specific content of the time window of the node is determined as follows: setting F as an idle path node time window set, T as a path node time window set reserved for the current AGV, M as a current path node set waiting for allocation, N as a path combination of the allocated time window nodes, and initializing the current path node set waiting for allocation, wherein the current path node set M is equal to (R, N) and is null. First from the set of paths Ri，zIn which a node is selected, e.g. from the edge ei，jTo begin, AGV is at time
From node i into edge ei，jThen at time
Leave the edge ei，jThen AGV is on path ei，jUpper run time of
If no AGV currently occupies path ei，jIts time window is registered as
If there are other AGVs already engaged ei，jAnd releasing the road segment time as
Then its time window is
In step 6), the specific steps of judging whether the time windows are overlapped and registering the time windows are as follows: time window
This is compared to the set of free time windows F, which is the time window that the path can be assigned to the current AGV if it belongs to a free time window. Time window for allocating this path in set F
Deleted and added into the reserved time window set T. This path e is also aggregated in the AGV pathi，jDeleting and adding the path into the already allocated path set N. If the idle time window node does not include
If the time period is not longer, then the system will
And delaying until a free time window is found, which is assigned to the current path. After the distribution is finished, handle ei，jAdding to the set N,
Join the set T.
Finally, the above embodiments are only intended to illustrate the technical solutions of the present invention and not to limit the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all of them should be covered by the claims of the present invention.
Claims (10)
1. A path planning method for multiple AGVs in an intelligent garage is characterized by comprising the following steps: the method comprises the following steps:
s1: after the task is received, distributing the task according to the priority of the task, and distributing the AGV according to the time cost;
s2: solving a path set according to an improved A-algorithm;
s3: calculating the occupation time of one edge in the path according to the path set;
s4: according to the occupied time of one edge, the time window of the node is obtained;
s5: according to the time window of the node, compared with the idle time window set, judging whether the time windows are overlapped or not, if not, registering the time node, and if so, delaying the time of the node and then registering the time node;
s6: repeating steps S3-S5 until the time nodes of all paths in the set of paths are registered;
s7: calculating the time cost from the starting point to the target point;
s8: and generating a path planning result and finishing the global path planning.
2. The method of claim 1, wherein the method comprises: in S2, the method for solving the path set according to the modified a-algorithm includes the following steps:
s21: establishing an Open list and a Close list, wherein the Open list and the Close list are both empty initially, points to be expanded and searched are placed in the Open list, the searched points are placed in the Close list, and a starting node s is placed in the Open list at the beginning;
s22: adding a starting point s into a Close list from an Open list, then searching points near the starting point s, adding all points which can be reached by s into the Open list, and if the points are obstacle points, ignoring the points;
s23: f (n) is a cost function of the A-algorithm, a point with the minimum f (n) in the Open list is searched, the point is moved out of the Open list and is placed in the Close list;
s24: checking the reachable points around the point with the minimum f (n), if the reachable points are not in the Open list, adding the reachable points, and setting the current node as a parent node; otherwise, f (n) of the nodes is calculated, if the new f (n) is smaller than the previous value, the f (n) value is updated and is set as a parent node;
s25: the search for the path is accomplished by repeatedly examining the target node and searching for intermediate nodes.
3. The method of claim 2, wherein the method comprises: in S2, the heuristic function of the modified a algorithm is selected as manhattan distance: the absolute value of the sum of the horizontal and vertical distances of the two points; the expression is as follows:
h(n)＝|x1-x2|+|y1-y2|
in the formula x1And x2Abscissa, y, representing the current position and end point on the map1And y2Representing the ordinate of the current position and the end point on the map.
4. The method of claim 3, wherein the method comprises: in S23, the modified a-algorithm optimizes the conventional a-algorithm by introducing the idea of artificial potential field, and the cost function of the modified a-algorithm is:
f(n)＝g(x)+h(n)+F(q)
wherein:
where x represents the current position of the AGV and is a vector, xobsRepresenting the position of the obstacle as a vector, KrepRepresents a repulsive potential field gain coefficient, and is a constant.
5. The method of claim 4, wherein the method comprises: in S23, the improved a-algorithm introduces optimization of the turn cost, and improves g (x) as follows:
g′(x)＝g(x)+αg(x)
wherein g' (x) represents the actual cost value from the starting node to the current node after improvement, g (x) represents the initial cost value from the starting node to the current node, alpha is the turning weight, and takes a specific value at the turning, otherwise 0.
6. The method of claim 5, wherein the method comprises: in S23, the improved a-algorithm optimizes the search efficiency and increases the value of the weight β occupied by the heuristic function, and the cost function of the improved a-algorithm is:
f(n)＝g′(x)+βh(n)+F(q)
g′(x)＝g(x)+αg(x)
where β represents the weight occupied by the heuristic function.
7. The method of claim 6, wherein the method comprises: in S2, the obtained route set is Ri，z＝{ei，j，ej，k，...，ek，zIs starting node eiThe target node is ez。
8. The method of claim 7, wherein the method comprises: in S3, the occupied time of each edge is calculated as the straight running and the turning and bending 2 portion, and the turning and bending time is constant toThe calculation formula of the straight travel time is as follows:
in the formula, LcThe length of the AGV body, the length between nodes and the speed of the AGV when the AGV runs at a constant speed are respectively calculated according to the length of the AGV body and the length of the node.
9. The method of claim 8, wherein the method comprises: in S4, setting an F-free path node time window set, where T is a path node time window set already reserved for the current AGV, M is a current path node set waiting for allocation, N is a path combination of nodes already allocated with a time window, and the current path node set M waiting for initializing an allocation time window is (R, N) null;
in the S4, the path set R is comparedi，zNode time window information for each edge within, from ei，jTo begin, AGV is at time
From node i into edge ei，jThen at time
Leave the edge ei，jThen AGV is on path ei，jUpper run time of
If no AGV currently occupies path ei，jIts time window is registered as
If there are other AGVs already engaged ei，jAnd releasing the road segment time as
Then its time window is
10. The method of claim 9, wherein the method comprises: in said S5, a time window is set
Comparing with the idle time window set F if the time window is
Belonging to an idle time window, then the time window
That is, the time window that the path can be assigned to the current AGV; time window for allocating this path in set F
Deleting the time window and adding the time window into the reserved time window set T; will also be in the AGV Path setThis path ei，jDeleting and adding the path into the distributed path set N; if the idle time window node does not include
If the time period is not longer, then the system will
Delaying until an idle time window is found and allocating the idle time window to the current path; after the distribution is finished, handle ei，jAdding to the set N,
Join the set T.
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