CN115860293A - Warehousing control method and device based on warehousing goods space and path planning - Google Patents
Warehousing control method and device based on warehousing goods space and path planning Download PDFInfo
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Abstract
The invention relates to a warehousing control method and device based on warehousing goods space and path planning, belongs to the technical field of intelligent transportation, and solves the problem that solving of the problem of optimization of paths and shelves in the existing warehousing in a linear superposition mode is easy to fall into local optimization. The method comprises the following steps: coding the zero bulk cargo according to the historical warehousing batches, and calculating the similarity among the goods by using the codes generated by the historical warehousing batches; combining scattered goods into goods groups according to the similarity, wherein each goods group in a to-be-warehoused state is regarded as a goods shelf unit, and the goods shelf units correspond to the goods shelf positions one by one; using improvement A * The method calculates the storage path of AGV and improvesA * The algorithm comprises improving a heuristic function and adding a turning correction cost parameter; and storing the warehousing path corresponding to each shelf position, and randomly putting the goods group into the shelf position based on the warehousing path. Using improvement A * The algorithm generates a warehousing optimized transportation scheme considering the goods position placing factors.
Description
Technical Field
The invention relates to the technical field of intelligent transportation, in particular to a warehousing control method and device based on warehousing goods space and path planning.
Background
The arrival of the intelligent era urges the logistics industry to develop towards the intelligent direction, and the warehousing operation is used as a key link of logistics and directly influences the whole logistics level and the operation efficiency. An Automated Guided Vehicle (AGV), which is a core component of an intelligent transportation system, has good research value and application prospect in commercial and military applications, and attracts the attention of a large number of researchers.
The effective planning of the paths which cannot be opened when the AGVs work in the intelligent storage, particularly the planning of the paths of the AGV, needs to consider the constraints of time, distance and the like, and avoids the conflict between the AGVs and the obstacles. Multiple AGV path planning has become a key research topic in the field.
The optimization of the intelligent warehouse is generally divided into two parts of shelf optimization and path optimization. The goods shelf optimization optimizes the goods placement position according to the relation between the goods and the goods shelf, and the path optimization mainly searches the optimal path planning of the AGV. At present, most of intelligent warehousing optimization only independently researches the two parts, and the problems can be solved only in a linear superposition mode in practical warehousing application, so that the problem solving is easy to fall into local optimization.
Disclosure of Invention
In view of the foregoing analysis, embodiments of the present invention are directed to providing a warehousing control method and apparatus based on warehousing goods space and path planning, so as to solve the problem that solving the problem is prone to falling into local optimization when solving the path and shelf optimization problem in the existing warehousing application in a linear superposition manner.
In one aspect, an embodiment of the present invention provides a warehousing control method based on warehousing goods space and path planning, including: coding the zero-weight bulk goods according to the historical warehousing batches, and calculating the similarity among the goods by using the codes generated by the historical warehousing batches; combining the scattered goods into goods groups according to the similarity, wherein each goods group in a to-be-warehoused state is regarded as a shelf unit, and the shelf units correspond to shelf positions one by one; calculating a warehousing path of the AGV by using an improved A algorithm, wherein the improved A algorithm comprises an improved heuristic function and a turning correction cost parameter added into the improved heuristic function; and saving a warehousing path corresponding to each shelf location and randomly placing the group of items into the shelf location based on the warehousing path.
The beneficial effects of the above technical scheme are as follows: the A-algorithm has higher path searching efficiency and accuracy, and combines reasonable goods position placement to provide assistance for planning of warehousing paths, and a dynamic updating algorithm is used for generating an optimized transportation scheme considering goods position placement factors.
Based on further improvement of the method, calculating the warehousing path of the AGV by using an improved a-x algorithm includes: improving the estimated path distance from the current node to the target node in an A-algorithm by adopting the weighted Manhattan distance; judging whether the AGV turns when running in the estimated path from the current node to the target node or not according to the position relations of the current node, the father node, the child node and the target node; and determining whether a turning modification cost parameter is introduced into the heuristic function according to whether the AGV turns.
Based on a further improvement of the method, the method for improving the estimated path distance from the current node to the target node by using the weighted manhattan distance A comprises the following steps: h (n) of the a algorithm is modified with the following weighted manhattan distance:
h * (n)＝λ 1 |x n -x end |+λ 2 |y n -y end |
wherein, the proper lambda is selected 1 And λ 2 Value improves path search efficiency in A-algorithm path planning, (x) n ,y n ) Is the current node, and (x) end ,y end ) Is the target node.
Based on a further improvement of the above method, determining whether to introduce a turn modification cost parameter in the heuristic function according to whether the AGV is turning comprises: and correcting the heuristic function according to whether the AGV turns to:
m 1 ＝(x n -x n-1 )(y n+1 -y n )
m 2 ＝(y n -y n-1 )(x n+1 -x n )
wherein (x) n-1 ,y n-1 ) Is a parent node, (x) n+1 ,y n+1 ) Is a child node, when m 1 And m 2 When the AGV runs in the straight running state, the AGV runs in the straight running state; and when m is 1 And m 2 When the AGV direction is not equal to the direction of the target object,
ensuring that the AGV preferentially selects straight and->
Based on a further improvement of the above method, randomly placing the group of items into the shelf location based on the warehousing path comprises: dispersedly placing the shelf units with high similarity, so that the AGV finds the goods at any station; and placing the high-frequency warehousing goods at the place where the paths do not coincide.
Based on the further improvement of the method, the coding processing is carried out on the zero bulk cargo according to the historical warehousing batches, and the similarity among the goods is calculated by utilizing the codes generated by the historical warehousing batches comprises the following steps: processing the data of the stored goods to remove irrelevant data and extract the goods number and the goods batch; identifying whether the goods number is put in storage in the batch or not by 1 and 0, wherein each goods is a vector formed by dimensions of the batch number; and calculating the similarity between the vectors by the following cosine similarity formula:
and taking an absolute value obtained by subtracting 1 from the similarity among the vectors as the similarity among the goods, wherein the smaller the absolute value is, the higher the similarity among the goods is, x is the vector of one kind of goods, y is the vector of another kind of goods, and x is i For a shipment of goods in the ith lot, y i For another shipment of goods in lot i, n is the dimensions of the vectors x and y.
Based on the further improvement of the method, the step of calculating the warehousing path of the AGV by using an improved A-star algorithm comprises the following steps: modeling an AGV working environment by adopting a grid map method, inputting a starting node and a target node of the AGV, and respectively creating an Openlist chain table for storing nodes to be detected and a Closelist chain table for storing detected nodes; putting the coordinates of the starting node S of the AGV into the Openlist linked list; traversing all nodes in the Openlist linked list, searching the node with the minimum improved cost function value, and setting the node as the current node; moving the current node into the Closelist linked list; detecting four adjacent domain nodes of the current node, ignoring nodes or barrier nodes in the Closelist table, and then judging whether the remaining adjacent domain nodes are in the Openlist linked list; determining whether a value of g (n) calculated when the AGV passes through the current node is smaller when the remaining neighborhood nodes are in the Openlist linked list, wherein,if the g (n) value is smaller, setting the father node of the remaining neighborhood nodes as the current node, and recalculating the g (n) value and the f * (n) value, wherein f * (n) is the estimated path distance from the start node S to the target node M through the current node n; g (n) is the actual path distance from the starting node S to the current node n; and judging whether a target node M of the AGV is found, wherein when the target node M is found, a father node to an initial node S of the AGV are sequentially searched from the target node M of the AGV, and the obtained path is an AGV driving path based on an improved A-x algorithm.
Based on further improvement of the method, when the target node M of the AGV is not found, whether the Openlist linked list is an empty linked list or not is continuously judged, wherein when the Openlist linked list is not an empty linked list, the following steps are returned: and traversing all nodes in the Openlist linked list until the Openlist linked list is an empty linked list.
Based on further improvement of the method, when the remaining neighborhood nodes are not in the Openlist linked list, adding the remaining neighborhood nodes into the Openlist linked list; setting the current node as the father node thereof, and respectively calculating f of each remaining neighborhood node * The values of (n), g (n) and h * (n) value, wherein h * (n) is the estimated path distance from the current node n to the target node M; and jumping to the following steps: and judging whether the target node M of the AGV is found.
In one aspect, an embodiment of the present invention provides a warehousing control device based on warehousing goods space and path planning, including: the similarity acquisition module is used for coding the zero bulk cargo according to the historical warehousing batches and calculating the similarity among the goods by using the codes generated by the historical warehousing batches; the goods group generating module is used for combining the scattered goods into goods groups according to the similarity, wherein each goods group in a to-be-warehoused state is regarded as a goods shelf unit, and the goods shelf units correspond to the goods shelf positions one by one; path computation module for using improvement A * Calculating the warehousing path of the AGV by an algorithm, and improving A * The algorithm comprises an improved heuristic function and a turning correction cost parameter added in the improved heuristic function; and the goods group setting module is used for storing the warehousing path corresponding to each shelf position and randomly putting the goods groups into the shelf positions based on the warehousing path.
Compared with the prior art, the invention can realize at least one of the following beneficial effects:
1. the algorithm A has higher route searching efficiency and accuracy, reasonable goods position arrangement is combined, assistance is provided for planning the warehousing route, and an optimized transportation scheme considering goods position arrangement factors is generated by using a dynamic updating algorithm.
2. After the goods space planning and the path planning are considered in a collaborative mode, the optimal path function analysis is combined, the goods shelves with high similarity are dispersed and released, the goods can be found by the AGV at any station quickly, high-frequency goods are placed at the place where the paths do not coincide, therefore, the AGV blocking caused by the coincidence of the paths can be avoided when a plurality of tasks are carried out, and the warehouse goods entering efficiency is improved on the whole.
3. Improved cost estimation function f * (n) for the cost estimation function f (n) before improvement, the heuristic function h (n) is mainly improved, and the weighted Manhattan distance is used as the heuristic function, so that the distance estimation cost is closer to the shortest distance, and the number of algorithm traversal nodes is reduced. In addition, a turning correction cost parameter is introduced into the heuristic function, so that the turning times of the path are reduced.
In the invention, the technical schemes can be combined with each other to realize more preferable combination schemes. Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and drawings.
Drawings
The drawings are only for purposes of illustrating particular embodiments and are not to be construed as limiting the invention, wherein like reference numerals are used to designate like parts throughout.
Fig. 1 is a flowchart of an warehousing control method based on warehousing goods space and path planning according to an embodiment of the invention;
FIG. 2 is a flow diagram of a good similarity algorithm according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of node locations according to an embodiment of the present invention;
FIG. 4 is a comparison of three paths according to an embodiment of the present invention;
FIG. 5 is a flow chart of AGV path planning based on the modified A-algorithm according to an embodiment of the present invention; and
fig. 6 is a block diagram of an warehousing control device based on warehousing goods space and path planning according to an embodiment of the invention.
Detailed Description
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate preferred embodiments of the invention and together with the description, serve to explain the principles of the invention and not to limit the scope of the invention.
Referring to fig. 1, an embodiment of the present invention discloses a warehousing control method based on warehousing goods space and path planning, including: in step S102, coding the zero-weight bulk goods according to the historical warehousing batches, and calculating the similarity among the goods by using codes generated by the historical warehousing batches; in step S104, the scattered goods are combined into goods groups according to the similarity, wherein each goods group in a to-be-warehoused state is regarded as a shelf unit, and the shelf units correspond to shelf positions one by one; in step S106, improvement A is utilized * Calculating the warehousing path of the AGV by an algorithm, and improving A * The algorithm comprises an improved heuristic function and a turning correction cost parameter added in the improved heuristic function; and in step S108, the warehousing path corresponding to each shelf position is saved, and the goods group is randomly put into the shelf position based on the warehousing path.
Compared with the prior art, in the warehousing control method based on warehousing goods space and path planning provided by the embodiment, the algorithm A has higher path search efficiency and accuracy, reasonable goods space placement is combined to provide assistance for the planning of warehousing paths, and a dynamic update algorithm is used to generate an optimized transportation scheme considering goods space placement factors.
Hereinafter, each step of the warehousing control method based on warehousing goods space and path planning according to the embodiment of the invention will be described in detail with reference to fig. 1.
In step S102, the zero bulks are encoded according to the historical warehousing batches, and the similarity between the goods is calculated by using the codes generated by the historical warehousing batches. Specifically, the encoding processing of the zero bulk cargo according to the historical warehousing batches, and the calculating of the similarity among the goods by using the codes generated by the historical warehousing batches comprises the following steps: processing the data of the stored goods to remove irrelevant data and extract the goods number and the goods batch; identifying whether the goods number is put in storage in the batch or not by 1 and 0, wherein each goods is a vector formed by dimensions of the batch number; and calculating the similarity between vectors by the following cosine similarity formula:
and taking the absolute value of the similarity between the vectors after subtracting 1 as the similarity between the goods, wherein the smaller the absolute value is, the higher the similarity between the goods is, x is the vector of one kind of goods, y is the vector of another kind of goods, and x is i For a shipment of goods in the ith lot, y i For another item to be shipped in the ith lot, n is the dimension of the vectors x and y, x, y being 0 or 1,0 indicates no shipment, and 1 indicates shipment.
In step S104, the scattered goods are grouped into goods groups according to the similarity, wherein each goods group in the to-be-warehoused state is regarded as a shelf unit, and the shelf units are in one-to-one correspondence with the shelf positions. Specifically, the grouping of the scattered goods into the goods group according to the similarity includes: obtaining an upper triangular matrix of the goods similar to the goods based on the similarity between the goods; and calculating a group of the goods according to the value of the upper triangular matrix.
In step S106, improvement A is utilized * Calculating the warehousing path of the AGV by an algorithm, and improving A * The algorithm includes improving a heuristic function and adding a turn correction cost parameter to the improved heuristic function. In particular, with improvement A * The method for calculating the warehousing path of the AGV by the algorithm comprises the following steps: improvement of A by weighted Manhattan distance * An estimated path distance from a current node to a target node in the algorithm; judging whether the AGV turns when running in the estimated path from the current node to the target node or not according to the position relations of the current node, the father node, the child node and the target node; and determining whether to introduce a turning modification cost parameter into the heuristic function according to whether the AGV turns.
Refining the estimated path distance from the current node to the target node in the a algorithm using the weighted manhattan distance comprises refining h (n) of the a algorithm using the weighted manhattan distance:
h * (n)＝λ 1 |x n -x end |+λ 2 |y n -y end |，
wherein, the proper lambda is selected 1 And λ 2 Value improves path search efficiency in A-algorithm path planning, (x) n ,y n ) Is the current node, and (x) end ,y end ) Is a target node.
Determining whether to introduce a turning modification cost parameter into the heuristic function according to whether the AGV turns comprises the following steps: and correcting the heuristic function according to whether the AGV turns:
m 1 ＝(x n -x n-1 )(y n+1 -y n )
m 2 ＝(y n -y n-1 )(x n+1 -x n )
wherein (x) n-1 ,y n-1 ) Is a parent node, (x) n+1 ,y n+1 ) Is a child node, when m 1 And m 2 When equal, theThe AGV is in a straight running state; and when m is 1 And m 2 When the AGV direction is not equal to the direction of the target object,
ensuring that the AGV preferentially selects straight and->
The step of calculating the warehousing path of the AGV by using the improved A-star algorithm comprises the following steps: a grid map method is adopted to model the AGV working environment, a starting node and a target node of the AGV are input, and an Openlist chain table used for storing the nodes to be detected and a Closelist chain table used for storing the detected nodes are respectively established. And putting the coordinates of the starting node S of the AGV into an Openlist linked list. And traversing all nodes in the Openlist linked list, searching the node with the minimum improved cost function value, and setting the node as the current node. The current node is moved into the Closelist linked list. Detecting four adjacent domain nodes of the current node, ignoring nodes or barrier nodes in a Closelist table, and then judging whether the remaining adjacent domain nodes are in an Openlist linked list. When the remaining neighborhood nodes are not in the Openlist linked list, adding the remaining neighborhood nodes into the Openlist linked list; setting the current node as its father node, and calculating f of each remaining neighborhood node * The values of (n), g (n) and h * (n) value, wherein h * (n) is the estimated path distance from the current node n to the target node M; and jumping to the following steps: and judging whether the target node M of the AGV is found. When the remaining neighborhood nodes are in the Openlist linked list, judging whether the g (n) value calculated by the AGV through the current node is smaller, if so, setting the father node of the remaining neighborhood nodes as the current node, and recalculating the g (n) value and the f (n) value * (n) value, wherein f * (n) is the estimated path distance from the start node S to the target node M through the current node n; g (n) is the actual path distance from the starting node S to the current node n; and judging whether a target node M of the AGV is found or not, wherein when the target node M of the AGV is found, the target node M of the AGV followsAnd searching the parent node to the starting node S of the AGV, wherein the obtained path is the AGV driving path based on the improved A-star algorithm. When the target node M of the AGV is not found, continuously judging whether the Openlist linked list is an empty linked list or not, wherein when the Openlist linked list is not the empty linked list, returning to the following steps: and traversing all nodes in the Openlist linked list until the Openlist linked list is an empty linked list.
In step S108, the warehousing path corresponding to each shelf position is saved, and the group of the goods is randomly placed in the shelf position based on the warehousing path. Specifically, randomly placing groups of items into shelf locations based on a warehousing route includes: the shelf units with high similarity are placed in a scattered mode, so that the AGV finds the goods at any station; and placing the high-frequency warehousing goods at the place where the paths do not coincide.
Referring to fig. 6, an embodiment of the present invention discloses a warehousing control device based on warehousing goods space and path planning, including: the similarity obtaining module 602 is configured to perform coding processing on the zero bulk cargo according to the historical warehousing batches, and calculate similarity between the goods by using codes generated by the historical warehousing batches; the goods group generating module 604 combines scattered goods into goods groups according to the similarity, wherein each goods group in a to-be-warehoused state is regarded as a shelf unit, and the shelf units correspond to shelf positions one by one; path computation Module 606 for utilizing improvement A * Calculating the warehousing path of the AGV by an algorithm, and improving A * The algorithm comprises an improved heuristic function and a turning correction cost parameter added in the improved heuristic function; and an item group setting module 608 for saving the warehousing path corresponding to each shelf position and randomly putting the item group into the shelf position based on the warehousing path.
Hereinafter, a warehousing control method based on warehousing goods space and route planning according to an embodiment of the present invention will be described in detail by way of specific examples with reference to fig. 2 to 5.
The specific examples of the present application can be realized by the following technical means:
analyzing the mutual influence of the path planning of goods, goods shelves and AGV vehicles based on the inspiration of the co-evolution ideaThe relation provides a shelf planning and path planning coevolution algorithm to realize shelf planning and AGV experience planning coevolution, and the coevolution algorithm is based on A * The invention discloses an improved algorithm (A-Star) which is a most effective direct searching method for solving the shortest path in a static road network and is also an effective algorithm for solving a plurality of searching problems).
Specifically, A * The algorithm has higher route searching efficiency and accuracy, and provides assistance for planning the warehousing route by combining reasonable goods position placement, and an optimized transportation scheme considering goods position placement factors is generated by using a dynamic updating algorithm.
The collaborative optimization algorithm firstly carries out coding processing on the zero bulk cargo according to the historical warehousing batch; the similarity between goods is calculated by utilizing codes generated by the goods batches, and therefore scattered goods are combined to generate goods groups, each goods group is in a to-be-warehoused state and is regarded as a goods shelf unit, and the goods shelf units correspond to the goods positions one by one. Secondly, before the stock item group is put in storage, a corresponding storage path is calculated for each goods position, and the storage path corresponding to the shelf position is recorded and stored. Based on the calculation results, the algorithm enters a collaborative optimization module, namely the order of putting the goods groups into the goods positions is free. The randomly placed scheme forms a goods space factor in a collaborative optimization process, different placing schemes of goods groups can generate different transportation routes when a warehousing task arrives, the uncertain transportation routes are path factors in the optimization process, and finally, improvement A is utilized * The method comprises the steps of processing a plurality of AGV path planning algorithms by the algorithms, improving the total station efficiency of the warehouse logistics system by using an improved heuristic function and turning correction cost parameters, calculating the value of a fitness function under different ex-warehouse schemes, searching the optimal scheme in the process of continuous iteration, and calculating the total station efficiency of the warehouse logistics system by using the improved heuristic function and the turning correction cost parametersAnd determining a warehousing path while determining warehousing and placing of the goods groups.
1. Collaborative optimization mathematical model
Through coupling analysis of the relation of each part in the intelligent storage link, a mathematical model for collaborative optimization of the goods location is provided. The model is different from the traditional intelligent storage optimization algorithm in that the path planning and the shelf optimization are integrated, and the relation between the path planning and the shelf optimization is expressed by a mathematical formula. The specific variables and variable constraints are described as follows, f (x) is a total objective function of the cooperative optimization, f path Total time spent ex warehouse for all tasks, f other For overhead of other parts of the algorithm than AGV vehicle path planning, α and β are influence coefficients, respectively, and α + β =1.N is the total number of AGV vehicles, N i The current AGV vehicle number is i =1,2 \ 8230N, and N, i belongs to N. M is the total number of warehousing tasks, M i And numbering the current warehousing task, wherein i = l,2 \8230, M, i belongs to N. G is the total number of goods. g i For the current goods number, i =1,2 \8230G, i epsilon N. (i, j) represents the current coordinate point position, i, j ∈ N, i<＝1，j<= j.s is the total number of shelves, μ (0)<μ<1) A penalty factor for returning the vehicle from the terminal point to the starting point. Specific variables and variable constraints are described as follows, f (x) is a total objective function of collaborative optimization (wherein x represents any node), f path Total time spent ex warehouse for all tasks, f other For overhead of other parts of the algorithm than AGV vehicle path planning, α and β are influence coefficients, respectively, and α + β =1.N is the total number of AGV vehicles, N i The current AGV vehicle number is i =1,2 \ 8230N, and N, i belongs to N. M is the total number of warehousing tasks, M i And numbering the current warehousing task, wherein i = l,2 \8230, M, i belongs to N. G is the total number of goods. g is a radical of formula i For the current goods number, i =1,2 \ 8230g, G, i e N. (i, j) represents the position of the current coordinate point, i, j belongs to N, i<＝1，j<= j.s is total number of shelves, μ (0)<μ<1) A penalty factor for returning the vehicle from the terminal point to the starting point.
Specifically, the overall objective of the collaborative optimization herein is expressed as equation 1:
f(x)＝αf path +βf other α + β =1 formula 1
Wherein, alpha and beta are influence coefficients, alpha coefficient is the weight of the shortest path, the path searched by the attention algorithm is shortest, beta system is the weight of the extra cost, and the attention is paid to collision, conflict, turning and other extra time consumption, namely the attention is paid to that the transportation paths selected by a plurality of AGVs are not overlapped as much as possible so as to avoid conflict and the like.
When M > Q, i.e. the number of tasks is large and the total number of vehicles is large, it takes a certain time for the vehicle to return to the starting point from the end point, see formula 2:
when M < = Q, namely the number of vehicles is greater than the number of tasks, one AGV vehicle is arranged in each task, and the formula 3 is shown:
where μ is the penalty factor for the AGV returning from the end point to the start point, f m,q The vehicle takes time to run for a certain dispatch, wherein (i, j) is the target point, f c Gamma is a dynamic parameter which is the waiting time of the vehicle after the vehicle is blocked, and the dynamic parameter gamma and the estimated path distance f from the starting node to the target node through the current node n * (n)/v is proportional, v is the AGV speed, f i Is the theoretical time, f, required for the AGV to reach point i j Is the theoretical time, f, required for the AGV to reach point j I-i Is the waiting time, f, required for the AGV to encounter an obstacle in the process of reaching the point i J-j The waiting time required for the AGV to encounter the obstacle in the process of reaching the point j is shown in the formula 4.
f m,q ＝γ(f i +f j +f I-i +f J-j )+f c Equation 4
f other Mainly comprises two parts, one is the calculation time f of the correlation degree between the articles gi,gj For sorting of articles, and the other is the consumption time f of the shelf optimization algorithm S Here i +1<G, see official
Formula 5:
the implementation idea of the cooperative optimization algorithm for the goods level planning and the AGV path planning provided herein will be described in detail in three parts, namely, a goods similarity calculation method, a multiple AGV path planning algorithm, a goods level planning algorithm, and an AGV path planning cooperative optimization algorithm. And the goods similarity algorithm and the multi-AGV path planning algorithm provide support for the final goods space path collaborative optimization algorithm.
2. Goods similarity calculation method
The method processes the data of the stored goods which are actually in operation and maintenance, eliminates irrelevant data such as operation time, goods names and the like, extracts data such as goods numbers (namely unique marks of the goods), batches (the same batch of the goods can be understood as being delivered to a warehouse and put into the warehouse in the same time) and the like for data analysis, and marks whether the goods with the numbers are delivered to the warehouse in the batch by 0 and 1, wherein each goods is a vector consisting of dimensions of the number of the batch. The similarity between the vectors is calculated by a cosine similarity calculation method, and the obtained product similarity is specifically, the specific flow of the product similarity calculation method based on the cosine similarity is shown in fig. 2.
Firstly, calculating the maximum warehousing batch number according to the warehousing data information. And recording warehousing information of the goods to form a vector table. For example, the vector value of the goods No. 2 is (1, 0,1, 0), 1 represents warehousing, 0 represents warehousing-free, and sequentially represents warehousing times, (1, 0,1, 0) represents warehousing in 1, 3, 4 batches respectively, and so on for other goods. On the basis, the total times of goods delivery are accumulated, and the delivery frequency is calculated. The cosine value between the two goods is calculated based on cosine equation 6. Since the cosine of the good and its own sum is 1, it represents the two goods most similar. Therefore, the smaller the difference between the cosine value of the goods and 1, the higher the similarity is proved, the new value represents the similarity between two goods, and finally an upper triangular matrix similar to the goods is obtained. Based on the values of this matrix, a set of items can be calculated. For example, if the triangular matrices of both commodity groups are (1, 0,1, 0), then in equation 6, n =5, x1=1, x2=0, x3=1, x4=1, x5=0; y1=1, y2=0, y3=1, y4=1, y5=0:
3. Based on the improvement A * Algorithmic AGV path planning
Based on tradition A * The AGV path planning of the algorithm has the advantages of high path searching efficiency, simplicity in implementation and the like in the path searching process, but the path searching efficiency of the AGV is greatly influenced by the heuristic function, and the method aims at the traditional A * The heuristic functions in the algorithm give the following improvements.
(1) Improvement of heuristic function
When A is * When the heuristic function h (n) of the algorithm adopts a Manhattan distance calculation mode, the closer the value of h (n) is to the shortest path distance from the current node to the target node, the higher the algorithm search efficiency is, but in an actual situation, the situation that the difference between h (n) and the shortest distance from the current node to the target node is larger often occurs. During the actual AGV operation, use (AL) x ,AL y ) Represents the current node (x) n ,y n ) To the target node (x) end ,y end ) Actual path distance, A * When the algorithm searches a path, | x appears in h (n) n -x end |<AL x Or | y n -y end |<AL y In this case, h (n) is much shorter than the actual path distance, A * The number of nodes traversed by the algorithm search path is more, thereby reducing A * The algorithm searches for path efficiency.
For tradition A * The algorithm generally has the problem of more traversal nodes in AGV path planning, and the method adopts the weighting Manhattan distance to A * H (n) of the algorithmThe improvement is that h (n) after the improvement is:
h * (n)＝λ 1 |x n -x end |+λ 2 |y n -y end equation 7
Selecting proper lambda 1 And λ 2 The value can be increased to a certain extent * Path search efficiency in algorithmic path planning, but for improvement using weighted Manhattan distance as heuristic function A * The problem that the path drawn by a calculation method still has more path turning times is solved, and a turning correction cost parameter is introduced into a heuristic function by taking a weighted Manhattan distance as a heuristic function, and the specific method is as follows:
by the current node (x) n ,y n ) Parent node (x) n-1 ,y n-1 ) Child node (x) n+1 ,y n+1 ) And a target node (x) end ,y end ) The positional relationship of (2) is determined by using equation 8 to determine whether the AGV is turning.
m 1 ＝(x n -x n-1 )(y n+1 -y n )
m 2 ＝(y n -y n-1 )(x n+1 -x n ) Equation 8
If the result m calculated by equation 8 1 And m 2 If the two are equal, the AGV is in a straight-driving state, otherwise the AGV turns, as shown in FIG. 3.
According to the judgment result, a heuristic function h is performed * (n) correction:
wherein the content of the first and second substances,
ensuring that the AGV preferentially selects straight and->
Improved cost estimationCounting function f * (n) represents as shown in equation 10:
f * (n)＝g(n)+h * (n) formula 10
Improved cost estimation function f * (n) for the cost estimation function f (n) before improvement, the heuristic function h (n) is mainly improved, and the weighted Manhattan distance is used as the heuristic function, so that the distance estimation cost is closer to the shortest distance, and the number of algorithm traversal nodes is reduced. In addition, a turning correction cost parameter is introduced into the heuristic function, so that the turning times of the path are reduced.
(2) Cost parameter for turn correction
Most AGV path planning algorithms generally take the path length as an evaluation index, but the time cost of increasing path turning caused by excessive path turning times is rarely considered. As shown in fig. 4, the AGV has three paths from the start node a to the destination node B, which are path 1, path 2 and path 3, and the manhattan distances of the three paths are equal and the shortest distance, but there are multiple path turning points in path 2 and path 3, and path 1 has only one turning point compared with path 2 and path 3, so that path 1 has the shortest path distance and better time. Therefore, it is necessary to consider the turn cost in the AGV path planning process, which is specifically embodied in that: when a single AGV executes a task, the distance of a general path is short, if the turn times of the AGV driving path are too many, the execution time of the AGV task is obviously improved, and frequent steering can increase the energy consumption of the AGV to a certain extent.
Herein by h in equation 9 * (n) introducing a turn correction cost parameter into the calculation
Therefore, the cost of turning the AGV path is increased, so that the turning times of the planned path are reduced, and the method specifically comprises the following steps: />
Is selected according to the influence h * The magnitude of the (n) value, thereby affecting the cost estimation function f * Of value (n)Size. If the turning correction cost parameter is selected too much, the AGV may generate a bypassing phenomenon; if the turning correction cost parameter is selected to be too small, the correction function cannot be usually realized; if a proper turning correction cost parameter is selected, the AGV can be guaranteed to be in a straight line preferentially, and the bypassing phenomenon of the AGV is avoided.
(3) Based on the improvement A * AGV path planning design of algorithm
This text is based on the improvement A * The single AGV path planning flow of the algorithm is shown in fig. 5.
Wherein: the start node S of the AGV has the coordinate of (x) start ,y start ) (ii) a The coordinate of the target node M is (x) end ,y end )；f * 、g、h * Are respectively f in the formula 9 * (n)、g(n)、h * (n)。
The method comprises the following specific steps:
step 1: modeling the AGV working environment by adopting a grid map method, inputting a starting node S and a target node M of the AGV, and respectively creating an Openlist chain table and a Closelist chain table to store a node to be detected and a detected node;
step 2: starting AGV with the coordinate (x) of node S start ,y start ) Putting the data into an Openlist linked list;
and step 3: traversing all nodes in Openlist chain table, and searching f * (n) a node having the smallest value and setting it as a current node;
and 4, step 4: moving the current node into a Closelist linked list;
and 5: detecting four adjacent domain nodes (south-east-west-north) of the current node, ignoring the nodes already in the Closelist table or the barrier nodes, then judging whether the remaining adjacent domain nodes are in the Openlist linked list, and if the remaining adjacent domain nodes are not in the Openlist linked list, turning to step 6; otherwise, turning to step 7;
step 6: adding the remaining neighborhood nodes into the Openlist linked list, setting the current node as the father node of the current node, and respectively calculating f * The values of (n), g (n) and h * (n) and then go to step 8;
and 7: judging g (n) calculated by AGV passing through current nodeIf the value is smaller, the parent node of the node is set as the current node if the value of g (n) is smaller, and the values of g (n) and f are recalculated * (n) a value;
and 8: judging whether a target node M of the AGV is found, and if the target node M is found, turning to the step 9; otherwise, turning to the step 10;
and step 9: sequentially searching a father node from a target node M of the AGV to an initial node S of the AGV, wherein the obtained path is based on the improvement A * An AGV driving path of the algorithm is calculated, and step 11 is carried out;
step 10: judging whether the Openlist linked list is an empty linked list, and if the Openlist linked list is the empty linked list, turning to the step 11; if not, turning to step 3;
step 11: the algorithm ends.
The invention aims to overcome the problems in the prior art and provides a warehousing control method for warehouse goods level planning and path planning, the collaborative optimization algorithm is improved based on an A-algorithm, the invention is characterized in that after the goods level planning and the path planning are considered collaboratively, the goods shelves with high similarity are scattered and released by combining with the analysis of an optimal path function, the AGV can find goods at any station quickly, and high-frequency goods are placed at the place where the paths do not coincide, so that the AGV blockage caused by the coincidence of the paths can be avoided when multiple tasks are carried out, and the warehouse goods entering and exiting efficiency is improved integrally.
Those skilled in the art will appreciate that all or part of the flow of the method implementing the above embodiments may be implemented by a computer program, which is stored in a computer readable storage medium, to instruct related hardware. The computer readable storage medium is a magnetic disk, an optical disk, a read-only memory or a random access memory, etc.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.
Claims (10)
1. A warehousing control method based on warehousing goods space and path planning is characterized by comprising the following steps:
coding the zero bulk cargo according to the historical warehousing batches, and calculating the similarity among the goods by using the codes generated by the historical warehousing batches;
combining the scattered goods into goods groups according to the similarity, wherein each goods group in a to-be-warehoused state is regarded as a shelf unit, and the shelf units correspond to shelf positions one by one;
using improvement A * Calculating the warehousing path of the AGV by an algorithm, and improving A * The algorithm comprises an improved heuristic function and a turning correction cost parameter added in the improved heuristic function; and
and storing the warehousing path corresponding to each shelf position, and randomly putting the goods group into the shelf position based on the warehousing path.
2. Warehousing control method based on warehousing goods space and path planning as claimed in claim 1, characterized by using improvement a * The step of calculating the warehousing path of the AGV by the algorithm comprises the following steps:
improvement of A by weighted Manhattan distance * An estimated path distance from a current node to a target node in the algorithm;
judging whether the AGV turns when running in the estimated path from the current node to the target node or not according to the position relations of the current node, the father node, the child node and the target node; and
and determining whether a turning modification cost parameter is introduced into the heuristic function according to whether the AGV turns.
3. The warehousing control method based on warehousing goods space and path planning of claim 2, characterized by using weighted manhattan distance improvement A * The estimated path distance from the current node to the target node in the algorithm comprises:
using the following weighted manhattan distanceTo A * H (n) of the algorithm is improved:
h * (n)＝λ 1 |x n -x end |+λ 2 |y n -y end |
wherein, the proper lambda is selected 1 And λ 2 Value increase A * Path search efficiency in algorithmic path planning, (x) n ,y n ) Is the current node, and (x) end ,y end ) Is the target node.
4. The warehousing control method based on warehousing goods space and path planning of claim 3, wherein determining whether to introduce a turn modification cost parameter in the heuristic function according to whether the AGV turns comprises:
and correcting the heuristic function according to whether the AGV turns to:
m 1 ＝(x n -x n-1 )(y n+1 -y n )
m 2 ＝(y n -y n-1 )(x n+1 -x n )
wherein (x) n-1 ,y n-1 ) Is a parent node, (x) n+1 ,y n+1 ) Is a child node, when m 1 And m 2 When the AGV runs in the straight running state, the AGV runs in the straight running state; and when m is 1 And m 2 When the AGV direction is not equal to the direction of the target object,
ensuring that the AGV preferentially selects straight and->
5. The warehousing control method based on warehousing goods space and path planning of claim 2, wherein randomly placing the group of goods into the shelf location based on the warehousing path comprises:
dispersedly placing the shelf units with high similarity, so that the AGV finds the goods at any station; and
and placing the high-frequency warehousing goods at the place with the non-coincident paths.
6. The warehousing control method based on warehousing goods space and path planning as claimed in claim 2, wherein coding the zero-bulk goods according to the historical warehousing batches, and calculating the similarity between the goods by using the codes generated by the historical warehousing batches comprises:
processing the data of the stored goods to remove irrelevant data and extract the goods number and the goods batch;
identifying whether the goods number is put in storage in the batch or not by 1 and 0, wherein each goods is a vector formed by dimensions of the batch number; and
calculating the similarity between the vectors by the following cosine similarity formula:
and taking an absolute value obtained by subtracting 1 from the similarity among the vectors as the similarity among the goods, wherein the smaller the absolute value is, the higher the similarity among the goods is, x is the vector of one kind of goods, y is the vector of another kind of goods, and x is i For a shipment of goods in the ith lot, y i For another shipment of goods in lot i, n is the dimensions of the vectors x and y.
7. The warehousing control method based on warehousing goods space and path planning of claim 4, wherein calculating the warehousing path of the AGV using the improved a algorithm comprises:
modeling an AGV working environment by adopting a grid map method, inputting a start node and a target node of the AGV, and respectively creating an Openlist chain table for storing nodes to be detected and a Closelist chain table for storing detected nodes;
putting the coordinates of the starting node S of the AGV into the Openlist linked list;
traversing all nodes in the Openlist linked list, searching the node with the minimum improved cost function value, and setting the node as the current node;
moving the current node into the Closelist linked list;
detecting four adjacent domain nodes of the current node, ignoring nodes or barrier nodes in the Closelist table, and then judging whether the remaining adjacent domain nodes are in the Openlist linked list;
when the remaining neighborhood nodes are in the Openlist linked list, judging whether a g (n) value calculated when the AGV passes through the current node is smaller, if so, setting the father node of the remaining neighborhood nodes as the current node, and recalculating the g (n) value and the f value * (n) value, wherein f * (n) is the estimated path distance from the start node S to the target node M through the current node n; g (n) is the actual path distance from the starting node S to the current node n; and
and judging whether a target node M of the AGV is found, wherein when the target node M is found, a father node to an initial node S of the AGV are sequentially searched from the target node M of the AGV, and the obtained path is an AGV driving path based on an improved A-x algorithm.
8. The warehousing control method based on warehousing goods space and path planning of claim 7 characterized in that when the target node M of the AGV is not found, it is continuously judged whether the Openlist linked list is an empty linked list, wherein,
when the Openlist linked list is not an empty linked list, returning to the following steps: and traversing all nodes in the Openlist linked list until the Openlist linked list is an empty linked list.
9. The warehousing control method based on warehousing goods space and path planning of claim 8, characterized by adding the remaining neighborhood nodes to the Openlist chain table when the remaining neighborhood nodes are not in the Openlist chain table;
setting the current node as the father node of the current node, and respectively calculating f of each remaining neighborhood node * The values of (n), g (n) and h * (n) value, wherein h * (n) is the estimated path distance from the current node n to the target node M; and
skipping to the following steps: and judging whether the target node M of the AGV is found.
10. The utility model provides a warehouse entry control device based on storage goods position and route planning which characterized in that includes:
the similarity acquisition module is used for coding the zero bulk cargo according to the historical warehousing batches and calculating the similarity among the goods by using the codes generated by the historical warehousing batches;
the goods group generating module is used for combining the scattered goods into goods groups according to the similarity, wherein each goods group in a to-be-warehoused state is regarded as a goods shelf unit, and the goods shelf units correspond to the goods shelf positions one by one;
path calculation module for using improvement A * Calculating the warehousing path of the AGV by an algorithm, and improving A * The algorithm comprises an improved heuristic function and a turning correction cost parameter added in the improved heuristic function; and
and the goods group setting module is used for storing the warehousing path corresponding to each shelf position and randomly putting the goods groups into the shelf positions based on the warehousing path.
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