CN115484656A - AGV efficient data acquisition path planning method - Google Patents
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- CN115484656A CN115484656A CN202211002270.7A CN202211002270A CN115484656A CN 115484656 A CN115484656 A CN 115484656A CN 202211002270 A CN202211002270 A CN 202211002270A CN 115484656 A CN115484656 A CN 115484656A
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Abstract
The invention discloses a method for planning a high-efficiency data acquisition path of an AGV (automatic guided vehicle), which ensures that complete sensor data is collected within time constraint, and utilizes an improved maximum clique problem algorithm to construct a moving path of the AGV, so that a vehicle can carry out data communication with a plurality of sensors in a communication area, and the AGV is not used for carrying out data collection of one sensor, thereby solving the phenomenon of data efficiency reduction caused by an island problem, reducing the road strength distance of the AGV, reducing the time for data collection and improving the efficiency of data collection.
Description
Technical Field
The invention relates to the technical field of AGV data collection, in particular to a method for planning a high-efficiency data collection path of an AGV.
Background
With the continuous development of automation technology, the appearance of automation ports is increasing. It is also increasingly common to collect container attached sensor data at ports using AGVs. However, the site of the port is wide and large, the sensor nodes are distributed discretely, and a phenomenon that a single sensor cannot transmit data to other sensors or only a plurality of sensors can only transmit data in an area block occurs, which is called as an islanding phenomenon. This islanding can cause the AGV to perform data collection with the following two results: 1. if the AGV collects complete port sensor data, the operating time of the AGV becomes too long; 2. if the runtime of the AGV is specified, the AGV may not be able to collect the complete port sensor data. How to efficiently collect data, collecting the complete data in an optimal time, is an important point. Because the AGVs have a certain transfer radius, the sensors on the container also have a certain propagation radius, and the path planning of the AGVs is especially important when the speed of the AGVs is constant.
Therefore, it is a problem worth studying to provide an AGV efficient data acquisition path planning method for constructing a moving path of an AGV by using an improved maximum clique problem algorithm.
Disclosure of Invention
The invention aims to provide an AGV high-efficiency data acquisition path planning method for constructing a moving path of the AGV by using an improved maximum clique problem algorithm.
The purpose of the invention is realized as follows:
an AGV efficient data acquisition path planning method comprises the following steps: step 1: utilizing hierarchical clustering to cluster the segmented network according to connectivity so as to know the connectivity condition of the network; step 2: searching an optimal mobile node in each clustered network by using an improved maximum clique problem algorithm, and constructing a data acquisition path of the AGV: the method comprises the following specific steps: step 2.1, calculating the overlapping level of the coverage area and selecting potential mobile nodes; step 2.2, judging whether a plurality of mobile nodes exist in a unified cluster before one square point exists in each cluster, if so, removing the potential mobile nodes and returning to the step 2.1 to select the potential mobile nodes, if not, planning the shortest path of the AGV, and judging whether the path meets the time constraint, and step 2.3, if the path in the step 2.2 meets the time constraint, completing the traversal of the sensor nodes, ending the program, and if the path does not meet the time constraint, returning to the step 2.2 for removing the potential mobile nodes; and 2.4, returning to the step 2.1 to select the potential mobile node if the sensor node is not traversed in the step 2.3.
The specific operation of the searching method for the maximum clique problem in step 2 is as follows, the communication radius of the sensor nodes is set as R, if the communication areas of any two sensor nodes are overlapped, the distance between the two sensor nodes must be less than or equal to 2R, the two sensor nodes are defined as having an overlapping area relationship, and the sensor nodes having the overlapping area relationship are connected to form an undirected graph of the sensor network; an undirected graph of the sensor network consists of five sensor nodes s _1, s _2, s _3, s _4and s _ 5; the searching method of the maximum group problem marks the overlapping levels of all the overlapping areas, the searching method of the maximum group problem finds the overlapping area with the largest overlapping level, the higher the coverage level of the communication overlapping area is, the higher the priority of the communication overlapping area is selected as the access area of the mobile sink;
solving for a node s using expression (1) 1 ，s 2 ，s 3 The intersection point k between 1 ，k 2 ，k 3 ，k 4 ，k 5 ，k 7 . Expression (2) judges whether or not the intersection points exist as the node s 1 ，s 2 ，s 3 Is less than the 2R intersection point. Knowing by calculation that there is no intersection with node s 1 ，s 2 ，s 3 Are all less than 2R, determine node s 1 ，s 2 And s 3 No overlapping region with the overlapping level of 3 is formed between the two, and the searching method of the maximum clique problem can be simplified into the searching method of the maximum overlapping region in the selected region.
The specific operation of the step 1 is as follows: randomly deploying points in a Euclidean space, constructing a complete graph G = (V, E) of n points, wherein the weight of each edge is the Euclidean distance of the two points, and the Euclidean distance is the similarity of the two nodes; inputting a threshold value, removing the edges with the minimum spanning tree distance or the weight value larger than the minimum spanning tree distance, and determining the rest connected branches as the desired clusters; and clustering the topology segmentation network by using a hierarchical clustering algorithm based on a minimum spanning tree, wherein the threshold value during clustering is the communication radius of the sensor node.
The mathematical modeling of the maximum clique problem in step 2 is described as follows:
the travel business model formula is as follows:
the formula for ensuring that the constructed movement track is smaller than the set maximum movement distance is as follows:
the hamilton loop formula is:
d ij Denotes the distance, X, from sensor i to sensor j ij For decision variables, only one Hamiltonian loop, X, exists in all given paths ij =1 denotes (i, j) in hamiltonian loop, otherwise not, P denotes the set of sensors accessed { P } 1 ,p 2 ,p 3 ,...,p n V is the set of all humidity sensor nodes, L max Is the set maximum travel track distance of the AGV.
After finding the optimal AGV moving area point by using a maximum group algorithm, constructing the shortest AGV moving path track by using a traveler model algorithm;
definition 1, i.e. maximum clique problem: given an undirected graph G = (V, E) where V is a non-empty set, called a set of vertices; e is in VA collection of unordered tuples of elements is called an edge set. If it is not
And (i, j) belongs to E for any two vertexes i, j belongs to U, and U is called a complete subgraph of G; when the number of edges contained in the complete subgraph is the most, the maximum overlapping area must exist in the complete subgraph, and the complete subgraph is called as the maximum clique; the maximum clique problem has many equivalent forms as an integer programming problem or a continuous non-convex optimization problem; the simplest expression is as follows:
maximization
And (3) constraint:
x i ∈{0,1},i＝1,2,....,n
in the present application, the complement of undirected graph G = (V, E) is
Herein, the
And weight w i ＝1。
The invention has the beneficial effects that: the method ensures that complete sensor data are collected within time constraint, and utilizes the improved maximum clique problem algorithm to construct the moving path of the AGV, so that the vehicle can carry out data communication with a plurality of sensors in a communication area, the AGV is not used for carrying out data collection of one sensor, the phenomenon of data efficiency reduction caused by island problems is solved, the road strength distance of the AGV is reduced, the data collection time is reduced, and the data collection efficiency is improved.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a diagram of a travel trajectory of an AGV, the AGV collecting data according to the trajectory;
FIG. 3 is an exemplary diagram of the maximum clique problem search method of the present invention, where a represents a data propagation diagram of an original sensor node, and b is a diagram after conversion by a maximum clique problem algorithm;
fig. 4 is a schematic diagram of two "false overlap regions" in the wireless sensor network according to the present invention.
Detailed Description
The invention is further illustrated by the following examples in conjunction with the drawings.
As shown in fig. 1 to 4, a method for planning an efficient data collection path of an AGV includes the following steps:
step 1: utilizing hierarchical clustering to cluster the segmented network according to connectivity so as to know the connectivity condition of the network; the specific operation is as follows: randomly deploying points in Euclidean space, constructing a complete graph G = (V, E) of n points, wherein the weight of each edge is the Euclidean distance of the two points, namely the similarity of the two nodes; inputting a threshold, removing the minimum spanning tree distance or the edge with the weight value larger than the minimum spanning tree distance, and determining the rest connected branches as the desired cluster; and clustering the topology segmentation network by using a hierarchical clustering algorithm based on a minimum spanning tree, wherein the threshold value during clustering is the communication radius of the sensor node.
And 2, step: searching an optimal mobile node in each clustered network by using an improved maximum clique problem algorithm, and constructing a data acquisition path of the AGV: the method comprises the following specific steps: step 2.1, calculating the overlapping level of the coverage area and selecting potential mobile nodes; step 2.2, judging whether a plurality of mobile nodes exist in a unified cluster before one square point exists in each cluster, if so, removing the potential mobile nodes and returning to the step 2.1 to select the potential mobile nodes, if not, planning the shortest path of the AGV and judging whether the path meets the time constraint, and step 2.3, if the path in the step 2.2 meets the time constraint, finishing the traversal of the sensor nodes and ending the program, and if the path does not meet the time constraint, returning to the step 2.2 to remove the potential mobile nodes; and 2.4, returning to the step 2.1 for selecting the potential mobile node if the sensor node is not traversed in the step 2.3.
The specific operation of the searching method for the maximum clique problem in step 2 is as follows, the communication radius of the sensor nodes is set as R, if the communication areas of any two sensor nodes are overlapped, the distance between the two sensor nodes must be less than or equal to 2R, the two sensor nodes are defined as having an overlapping area relationship, and the sensor nodes having the overlapping area relationship are connected to form an undirected graph of the sensor network; an undirected graph of the sensor network consists of five sensor nodes s _1, s _2, s _3, s _4and s _ 5; the searching method of the maximum group problem marks the overlapping levels of all overlapping areas, finds the overlapping area with the largest overlapping level, and the higher the coverage level of the communication overlapping area is, the higher the priority of the access area of the communication overlapping area selected as the mobile sink is;
solving for a node s using expression (1) 1 ，s 2 ，s 3 The intersection point k between 1 ，k 2 ，k 3 ，k 4 ，k 5 ，k 7 . Expression (2) judges whether or not the intersection points exist as the node s 1 ，s 2 ，s 3 Is less than the 2R intersection point. Knowing by calculation that there is no intersection with node s 1 ，s 2 ，s 3 Are all less than 2R, determine node s 1 ，s 2 And s 3 No overlapping region with the overlapping level of 3 is formed between the two, and the searching method of the maximum clique problem can be simplified into the searching method of the maximum overlapping region in the selected region.
The mathematical modeling of the maximum clique problem in step 2 is described as follows:
the travel business model formula is as follows:
the formula for ensuring that the constructed movement track is smaller than the set maximum movement distance is as follows:
the hamilton loop formula is:
d ij Denotes the distance, X, from sensor i to sensor j ij For decision variables, only one Hamiltonian loop, X, exists in all given paths ij =1 denotes (i, j) in hamiltonian loop, otherwise not, P denotes the set of sensors accessed { P } 1 ,p 2 ,p 3 ,...,p n V is the set of all humidity sensor nodes, L max Is the set maximum travel track distance of the AGV.
After finding the optimal AGV moving area point by using a maximum group algorithm, constructing the shortest AGV moving path track by using a traveler model algorithm;
definition 1, maximum clique problem: given an undirected graph G = (V, E) where V is a non-empty set, called a set of vertices; e is a set of unordered doublets of elements in V, called an edge set. If it is not
And for any two vertexes i, j belongs to U and has (i, j) belongs to E, then U is called as a complete subgraph of G; when the number of edges contained in the complete subgraph is the most, the maximum overlapping area must exist in the complete subgraph, and the complete subgraph is called as the maximum clique; there are many equivalent forms of the maximum clique problem as an integer programming problem or a continuous non-convex optimization problem; the simplest expression is as follows:
maximization
And (3) constraint:
x i ∈{0,1},i＝1,2,....,n
in the application, the complement of undirected graph G = (V, E) is
Herein, the
And weight w i ＝1。
The maximum clique problem is a graph theory phenomenon, and there are many methods for solving the problem, and the false overlap region is a false maximum clique problem search phenomenon which occurs when the search scheme of the maximum clique problem is utilized.
The AGV is used for collecting container information in ports, manual collection of the container information is replaced, and the AGV efficient data collection path planning method is increasingly popularized. And then searching the AGV in each cluster by using a search algorithm of the maximum group problem, finding the optimal moving destination point, wherein the optimal moving destination point can prevent the AGV from accessing the sensors one by one to carry out data, and the sensor node data in the whole cluster can be collected by reaching the optimal moving destination point, so that the path length of the AGV is reduced, the data collection efficiency of the AGV is improved, and the AGV can more efficiently collect the sensor data information on the container at the port.
The method ensures that complete sensor data is collected within time constraint, and utilizes the improved maximum clique algorithm to construct the moving path of the AGV, so that the vehicle can carry out data communication with a plurality of sensors in a communication area, and the AGV is not used for carrying out data collection of one sensor, thereby solving the phenomenon of data efficiency reduction caused by the island problem, reducing the road strength distance of the AGV, reducing the time of data collection and improving the efficiency of data collection.
Claims (4)
1. An AGV efficient data acquisition path planning method is characterized by comprising the following steps: the method comprises the following steps: step 1: utilizing hierarchical clustering to cluster the segmented network according to connectivity so as to know the connectivity condition of the network; step 2: searching an optimal mobile node in each clustered network by using an improved maximum clique problem algorithm, and constructing a data acquisition path of the AGV: the method comprises the following specific steps: step 2.1, calculating the overlapping grade of the coverage area and selecting potential mobile nodes; step 2.2, judging whether a plurality of mobile nodes exist in a unified cluster before one square point exists in each cluster, if so, removing the potential mobile nodes and returning to the step 2.1 to select the potential mobile nodes, if not, planning the shortest path of the AGV and judging whether the path meets the time constraint, and step 2.3, if the path in the step 2.2 meets the time constraint, finishing the traversal of the sensor nodes and ending the program, and if the path does not meet the time constraint, returning to the step 2.2 to remove the potential mobile nodes; and 2.4, returning to the step 2.1 to select the potential mobile node if the sensor node is not traversed in the step 2.3.
2. The AGV efficient data collection path planning method according to claim 1, further comprising: the specific operation of the method for searching the maximum clique problem in step 2 is as follows, the communication radius of the sensor nodes is set to be R, if the communication areas of any two sensor nodes are overlapped, the distance between the two sensor nodes must be less than or equal to 2R, the two sensor nodes are defined to have an overlapping area relationship, and the sensor nodes with the overlapping area relationship are connected to form an undirected graph of the sensor network; an undirected graph of the sensor network consists of five sensor nodes s _1, s _2, s _3, s _4and s _ 5; the searching method of the maximum group problem marks the overlapping levels of all the overlapping areas, the searching method of the maximum group problem finds the overlapping area with the largest overlapping level, the higher the coverage level of the communication overlapping area is, the higher the priority of the communication overlapping area is selected as the access area of the mobile sink;
solving for a node s using expression (1) 1 ，s 2 ，s 3 The intersection point k between 1 ，k 2 ，k 3 ，k 4 ，k 5 ，k 7 (ii) a Expression (2) judges whether or not the intersection points exist as the node s 1 ，s 2 ，s 3 Is less than 2 R The intersection point of (a); knowing by calculation that there is no intersection with node s 1 ，s 2 ，s 3 Are all less than 2 R Determining a node s 1 ，s 2 And s 3 No overlapping region with the overlapping level of 3 is formed between the two, and the searching method of the maximum clique problem can be simplified into the searching method of the maximum overlapping region in the selected region.
3. The method for efficient data collection path planning for an AGV of claim 1, further comprising: the specific operation of the step 1 is as follows: randomly deploying points in Euclidean space, constructing a complete graph G = (V, E) of n points, wherein the weight of each edge is the Euclidean distance of the two points, namely the similarity of the two nodes; inputting a threshold value, removing the edges with the minimum spanning tree distance or the weight value larger than the minimum spanning tree distance, and determining the rest connected branches as the desired clusters; and clustering the topology segmentation network by using a hierarchical clustering algorithm based on a minimum spanning tree, wherein the threshold value during clustering is the communication radius of the sensor node.
4. The AGV efficient data collection path planning method according to claim 1, further comprising: the mathematical modeling of the maximum clique problem in step 2 is described as follows:
the travel business model formula is as follows:
the formula for ensuring that the constructed movement track is smaller than the set maximum movement distance is as follows:
the hamilton loop formula is:
d ij Denotes the distance, X, from sensor i to sensor j ij For decision variables, only one Hamiltonian loop, X, exists in all given paths ij =1 denotes (i, j) in hamiltonian loop, otherwise not, P denotes the set of sensors accessed { P } 1 ,p 2 ,p 3 ,…,p n V is the set of all humidity sensor nodes, L max Setting the maximum moving track distance of the AGV;
after finding the optimal AGV moving area point by using a maximum group algorithm, constructing the shortest AGV moving path track by using a traveler model algorithm;
definition 1, i.e. maximum clique problem: given an undirected graph G = (V, E) where V is a non-empty set, called a set of vertices; e is a set of unordered dyads formed by elements in V, called an edge set; if it is not
And (i, j) belongs to E for any two vertexes i, j belongs to U, and U is called a complete subgraph of G; when the number of edges contained in the complete subgraph is maximum, the maximum overlapping area must exist in the complete subgraph, and the complete subgraph is called as a maximum clique; the maximum clique problem is many as an integer programming problem or a continuous non-convex optimization problem, etcA valence form; the simplest expression is as follows:
maximization
And (3) constraint:
x i ∈{0,1},i＝1,2,....,n
in the present application, the complement of undirected graph G = (V, E) is
Herein, the
And weight w i ＝1。
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| CN115951691A (en) * | 2023-03-13 | 2023-04-11 | 江西丹巴赫机器人股份有限公司 | Trajectory planning method and system for shuttle vehicle of dense warehouse under 5G communication |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115951691A (en) * | 2023-03-13 | 2023-04-11 | 江西丹巴赫机器人股份有限公司 | Trajectory planning method and system for shuttle vehicle of dense warehouse under 5G communication |
| CN115951691B (en) * | 2023-03-13 | 2023-05-30 | 江西丹巴赫机器人股份有限公司 | Shuttle track planning method and system for dense library under 5G communication |
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