CN112214024B - AGV task allocation method, logistics sorting method and system - Google Patents
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Abstract
An AGV task allocation method, a logistics sorting method and a system are provided, wherein the AGV with m idle states randomly distributed in a working space preferentially allocates m tasks with high priority by using a KM algorithm; the AGV carries out path planning, package loading and package delivery according to the matched tasks, and when the system detects that an idle AGV exists, the weight of the idle AGV and the weight of other non-idle AGVs are calculated; and if the weight of the idle AGV is greater than that of other non-idle AGVs, allocating the idle AGV to the (m + 1) th task, and if not, allocating tasks to all other AGVs which receive at most one task and have weights not greater than that of the idle AGV. The technology of the invention is applied to scenes with the equivalent number of tasks and AGV trolleys, and the utilization efficiency of the system is increased.
Description
Technical Field
The invention relates to an automatic logistics technology, in particular to an AGV task allocation method, a logistics sorting method and a logistics sorting system.
Background
Today, when automation and intelligent logistics are rapidly developed, a belt transmission system or an Automatic Guided Vehicle (AGV) is adopted to match with a small amount of manpower to complete picking operation, which is favored by more and more practitioners, and Multi-robot Task assignment (MRTA) is an unavoidable problem in an AGV sorting and scheduling system, particularly relates to a sorting scene of large-scale cluster scheduling. For various types of AGV picking systems, how to orchestrate task allocation is one of the main factors that affect the efficiency of the picking system.
As shown in fig. 1, the task list ti and the free robot list rj are two groups of nodes without self-connection, where ti is connected to rj to indicate that the robot rj can reach the working area of the task ti, and we need to allocate as many tasks as possible to the free robots, which can be summarized in a graph theory as solving the problem of maximum matching of a bipartite graph.
In a small area, low AGV job scenario, the bipartite graph shown in fig. 1 is likely to be a perfect graph, in other words, each AGV and each task are connected by wire segments. A widely used scheme can enable a task with high priority in a task queue to search the nearest idle AGV for carrying until the robot queue or the task queue is empty. In fig. 1, let t1 first search for the nearest free robot rk1, and then let t2 search for the nearest free robot rk2 among the remaining robots (k 1 ≠ k 2), and assign them in sequence. When fig. 1 is a perfect bipartite graph and does not consider the requirement of shortest total path, the above allocation is necessarily the maximum match.
With the expansion of the dispatching field, the increase of the number of the AGVs and the additional limitation on the reachable working areas of the AGVs, the maximum matching without right can be solved by using the Hungarian algorithm. The hungarian solution is a novel and simple solution to the assignment problem, the essence of which is to find new augmented paths to expand the number of matches on the basis of the matches already determined until the maximum match is found. The Hungarian algorithm can be used for allocating as many tasks as possible to the idle robots, but the shortest total path of the maximum matching cannot be guaranteed.
In a large-scale sorting and scheduling system, in order to save labor cost, task distribution is concentrated on a few sorting tables in a map, and at this time, not only the weight of each task executed by the AGVs needs to be comprehensively considered, but also the AGVs near each sorting table need to be guaranteed not to queue up (flow control) as much as possible, and the problem can be abstracted as a minimum cost and maximum flow problem. The most commonly used algorithms for solving the least cost maximum flow problem include Bellman-Ford algorithms, SPFA algorithms, modified Dijkstra algorithms, and the like.
1. In the actual operation process, tasks are usually issued in a batch mode, and the Hungarian algorithm is more suitable for a random process. Moreover, the Hungarian algorithm does not consider the flow control problem, so that systematic congestion is likely to occur, and the matching result obtained by the algorithm loses the optimal property.
2. The least-cost-maximum-flow algorithm is more suitable for the flow control of the path distribution network, and the path searching and adjusting in the task distribution stage can take the cost of unacceptable extra time.
3. Most systems schedule AGVs, and generally schedule empty AGVs to complete the current task to reduce the task completion time, but this also results in increased waiting time and travel distance of the AGVs as a whole.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides an AGV task allocation method, which is used for increasing the utilization efficiency of a system in a scene with the number of tasks equivalent to the number of AGV trolleys.
In order to solve the technical problem, the invention is solved by the following technical scheme:
an AGV task allocation method comprises the following steps:
step 1, AGV with m number of idle states randomly distributed in a working space;
step 2, distributing tasks m before the weight by using a KM algorithm;
3, the AGV plans a path, wraps the packages and delivers the packages according to the matched tasks;
step 4, when idle AGVs are detected, calculating weights of the idle AGVs and other non-idle AGVs;
and 5, distributing tasks to other non-idle AGVs which receive at most one task and have weights not larger than the idle AGV, and executing the step 3 by the AGV after distributing the tasks.
Optionally, when detecting that there is an idle AGV, calculating weights of the idle AGV and other non-idle AGVs:
taking the Manhattan distance between the idle AGV and the sorting table where the task package is located as the weight of the idle AGV;
the AGV weight in the non-idle state is: and the Manhattan distance weight of the non-idle AGV from the sorting table where the task package is located is added with the weight required by the AGV in the non-idle state in the processes of loading and delivering and unloading, or the weight required by the delivering and unloading process.
Optionally, taking the manhattan distance of the sorting table where the AGV is located from the task as a bipartite graph weight in the KM algorithm, and the calculating method is as shown in formula (1):
D ij ＝|x(A i )-x(P j )|+|y(A i )-y(P j )| (1)
wherein A is i Denotes the i-th AGV, P j Denotes the sorting table, x (A), at which the jth task package is located i ) Is the x coordinate, x (P), of the ith AGV j ) Wrap up the location for the jth taskX-coordinate of the sorting table of (A), y (A) i ) Is the y coordinate of the i AGV, y (P) j ) The y coordinate of the sorting table where the jth task package is located.
Optionally, the weight when the non-idle AGV does not reach the sorting table to acquire the task is calculated according to formula (2):
D ij (j′)＝|x(A i )-x(P j′ )|+|y(A i )-y(P j′ )|+D 0
+|x(P j′ )-x(T j′ )|+|y(P j′ )-y(T j′ )|+D 1
+|x(T j′ )-x(P j )|+|y(T j′ )-y(P j )| (2)
where j' represents the task that the non-idle AGV is performing, T j′ Represents the bin corresponding to task j', where A i Denotes the i-th AGV, P j′ Denotes the sorting table, x (A), in which the task j' is located i ) Is the x coordinate, x (P), of the ith AGV j′ ) For the x-coordinate, y (A) of the sorting table in which task j' is located i ) Is the y coordinate of the i AGV, y (P) j′ ) For the y-coordinate of the sorting table in which task j' is located, D 0 Indicates the extra weight, D, required by the wrapping process 1 Indicating the additional weight required for the delivery and drop process.
Optionally, when a non-idle AGV is in a delivery job, the weight is calculated by equation (3),
D ij (j′)＝|x(A i )-x(T j′ )|+|y(A i )-y(T j′ )|+D 1
+|x(T j′ )-x(P j )|+|y(T j′ )-y(P j )| (3)
wherein j' is a task of the AGV in the delivery operation, T j′ Represents the bin corresponding to the jth task, D 1 Indicating the additional weight required for the delivery drop process.
The invention also provides a logistics sorting method, setting m' as the total quantity of AGV, setting n as the total quantity of task, obtaining the quantity of task at regular time,
when the values of n and m are the same or the difference value is not greater than the threshold value, scheduling the AGVs to deliver the tasks by adopting a first task allocation method and the AGV task allocation method according to claims 1-5;
and when the difference value between the values of n and m is larger than the threshold value, adopting a second task allocation method, and only receiving one task by each AGV at a time.
Optionally, in the second task allocation method, each AGV receives only one task at a time, and after each AGV delivers a package, the position of the next sorting table is re-planned through calculation of the weight of each sorting table.
Optionally, the calculating the weight of each sorting table includes: the running distance of the AGV returning to each sorting table and the AGV queue condition of each sorting table;
and selecting the sorting table with the highest weight as the next stopping position of the AGV, and returning to the sorting table through path planning.
The invention also provides a logistics sorting system, which comprises a main control system, a plurality of AGVs, a plurality of sorting tables and a plurality of grid areas, wherein,
the main control system is used for receiving task information; scheduling and task allocation of the AGV; planning an AGV path;
the AGV is used for receiving a command of the master control system to realize movement;
the sorting table is used for sorting goods to the AGV and completing sorting by adopting manpower or mechanical arms;
and the grid area comprises grids and a driving channel between the grids.
The present invention also provides a computer apparatus comprising: a memory for executing a computer program for implementing the logistics sorting method as described above, and a processor for storing the computer program executable by the processor.
The invention has the beneficial effects that:
the method improves the traditional KM algorithm and fully considers the competitive advantage of the working AGV. In an extreme embodiment, where n = m +1 tasks are allocated to AGVs, the upper layer first preferentially allocates m tasks according to the KM algorithm. Therefore, whether the AGV that completes the task first is used to deploy the last task needs to be controlled more effectively according to whether the AGV that is closer to the last task and is about to complete the last task is more suitable for receiving the task.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a graph of the maximum match of a bipartite graph of the KM algorithm;
FIG. 2 is a grid diagram of AGV task scenarios;
FIG. 3 is a flow chart of an AGV task assignment method.
Detailed Description
The present invention will be described in further detail with reference to examples, which are illustrative of the present invention and are not to be construed as being limited thereto.
Example 1:
the present embodiment discloses a logistics sorting system, as shown in fig. 1, comprising a master control system, a plurality of AGVs, a plurality of sorting tables and a plurality of bay areas, wherein,
the main control system is used for receiving task information; scheduling and task allocation of the AGV; planning an AGV path;
the AGV is used for receiving a command of the main control system to realize movement;
the sorting table is used for sorting goods to the AGV and finishing the sorting by adopting manpower or an mechanical arm;
and the grid area comprises grids and a driving channel between the grids.
Based on the logistics sorting system, as shown in fig. 2, a task allocation method is disclosed, where m is set as the total number of AGVs, n is the total number of tasks (or packages), and q is the total number of sorting platforms (loading points). Meanwhile, the method also meets the condition that (1) at the initial moment, all AGVs in the system are in an idle state and are randomly distributed on a map; (2) The AGV can receive at most two tasks and sequentially complete the tasks according to the task receiving time sequence as the priority; (3) Abnormal states such as charging, faults, communication delay, signal instability and the like are not considered; and (4) the current limiting influence of the sorting table on the task distribution is not considered.
The method specifically comprises the following steps:
step 1, in an initial state, there are m AGV's in an idle state randomly distributed in a working space, there are n tasks, and n = m +1 is set, that is, the number of AGV's is equal to or equal to the number of tasks;
step 2, preferentially distributing m tasks with high priority by using a KM algorithm;
step 3, the AGV plans a path according to the current position and the sorting table where the bound task packages are located;
step 4, the AGV reaches a sorting table and is put with a package;
step 5, the AGV carries out second path planning according to the position of the wrapping target grid;
6, the AGV reaches a target opening and puts down a package;
step 7, when detecting that an idle AGV exists, calculating the weight of the idle AGV and other non-idle AGVs;
and 8, distributing tasks to other non-idle AGVs which receive at most one task and have weights not larger than the idle AGV, and executing the step 3 by the AGV after distributing the tasks.
Step 8 may further be understood as: and if the weight of the idle AGV is greater than that of other non-idle AGVs, allocating the idle AGV to the (m + 1) th task, if not, allocating tasks to all other AGVs which receive at most one task and the weight of which is not greater than that of the idle AGV, and executing the step 3 by the AGV after the tasks are allocated.
Examples are as follows: 6 tasks are to be distributed at the current time, and if at most one task and the weight is not more than 4 idle AGVs at the current time, task matching is carried out on 5 AGVs including the idle AGVs;
and when the idle AGV appears again, calculating the weight of the idle AGV and other non-idle AGVs which receive at most 1 task state, and performing a new round of task allocation on the 6 th AGV again.
The method for acquiring the AGV state in real time by the control system and calculating the weight of the idle AGV and other non-idle AGVs comprises the following steps:
taking the Manhattan distance between the idle AGV and the sorting table where the task package is located as the weight of the idle AGV;
the AGV weight under the non-idle state is: and the Manhattan distance weight of the non-idle AGV from the sorting table where the task package is located is added with the weight required by the AGV in the non-idle state in the processes of loading and delivering and unloading, or the weight required by the delivering and unloading process.
Specifically, the manhattan distance of the sorting table where the AGV distance task is located is used as a bipartite graph weight in the KM algorithm, and the calculation method is as shown in a formula (1):
D ij ＝|x(A i )-x(P j )|+|y(A i )-y(P j )| (1)
wherein A is i Denotes the ith AGV, P j Denotes the sorting table, x (A), at which the jth task package is located i ) Is the x coordinate, x (P), of the ith AGV j ) X coordinate, y (A) of sorting table where parcel is located for jth task i ) Is the y coordinate of the i AGV, y (P) j ) The y coordinate of the sorting table where the jth task package is located.
The manhattan distance of the idle AGV from the sorting table where the task package is located is taken as the weight of the idle AGV,
calculated by formula (1).
When the non-idle AGV does not reach the sorting table to obtain the task, calculating the weight according to a formula (2):
where j' represents the task that the non-idle AGV is performing, T j′ Represents the bin corresponding to task j', where A i Denotes the i-th AGV, P j′ Denotes the sorting table in which task j' is located, x (A) i ) Is the x coordinate, x (P), of the ith AGV j′ ) For the x-coordinate, y (A) of the sorting table in which task j' is located i ) Is the y coordinate of the i AGV, y (P) j′ ) For the y-coordinate of the sorting table in which task j' is located, D 0 Additional weight, D, required to represent the wrapping process 1 Indicating the additional weight required for the delivery and drop process.
When the non-idle AGV is in the process of delivering, the weight value is calculated by the formula (3),
D ij (j′)＝|x(A i )-x(T j′ )|+|y(A i )-y(T j′ )|+D 1
+|x(T j′ )-x(P j )|+|y(T j′ )-y(P j )| (3)
wherein j' is a task of the AGV in the delivery operation, T j′ Represents the bin corresponding to the jth task, D 1 Indicating the additional weight required for the delivery drop process.
For all tasks accepted at most one and having a weight not greater than
The connected AGVs (including the idle AGVs) distribute the tasks and continue to enter the next cycle under the condition that the tasks to be distributed are sufficient;
the method improves the traditional KM algorithm and fully considers the competitive advantage of the working AGV. In an extreme embodiment, where n = m +1 tasks are allocated to AGVs, the upper layer first preferentially allocates m tasks according to the KM algorithm. Then whether the last task is deployed using the AGV that completed the task first may need to accept the task based on whether there is a working AGV closer to the last task and about to complete the last task.
Example 2:
a logistics sorting method comprises the following steps: setting m as total number of AGV, n as total number of task, obtaining task number in fixed time,
when the values of n and m are the same or the difference value is not greater than the threshold value, scheduling the AGVs to deliver the tasks by adopting the first task allocation method and the AGV task allocation method in the embodiment 1;
and when the difference value between the values of n and m is larger than the threshold value, adopting a second task allocation method, and only receiving one task by each AGV at a time.
And the second task allocation method is an uninterrupted operation task allocation method, each AGV only receives one task at a time, and after each time the AGV delivers a package, the position of the next sorting table is re-planned through the weight calculation of each sorting table. Further comprising the steps of:
when a sorting task starts, placing a package on an AGV positioned on a sorting table through manpower or a mechanical arm, namely, the AGV acquires the task on the sorting table;
scanning the package identification code to obtain a task target position, and planning a path according to the current position of the AGV and the position D;
the AGV reaches a target position according to a planned route and puts down a package to complete a delivery task;
after the task delivery is completed, searching and selecting the optimal sorting table in all the sorting tables;
and returning to the sorting table through path planning.
After the AGV finishes the jth task, calculating the position of the AGV at the current task to the next sorting table P k And the time required by the task is acquired, the sorting platform with the minimum time is selected as the next sorting platform returned after the AGV delivers the task, which comprises,
calculating the current AGV returning to each sorting table P k The path time of (c); and to the sorting table P k And nearby waiting until the time required for acquiring the task.
The method for calculating the time required for waiting to acquire the tasks by arriving near each sorting table comprises the following steps: computingSorting table P before the current AGV reaches the time node of each sorting table k The AGV at the portal waits for the queue length and calculates the queue wait time.
The method for calculating the queue length comprises the step of acquiring a timestamp t for the current AGV to finish the jth task j Will be at the time stamp t j Other AGVs joining P that respectively satisfy the following conditions k In the ingress queue.
In all returning AGV, the target sorting platform is P k And AGV and P k Has a Manhattan distance of less than A i And P k AGV of distance of (a);
target cell and P in all delivery-executing AGVs k Is the closest among all sorting decks, and the distance of the AGV and the target bay plus the bay and P k Is less than the current AGV and P k The distance of AGV.
Wherein AGV (A) of number i is calculated by formula (4) i ) Returning to the sorting table P after the jth distribution task is finished k The time taken to accept the next assignment task is T jk ，
Wherein, the time required for starting the process that each AGV enters the sorting platform, scans the distribution target grid, leaves the sorting platform and is next AGV in the queue is set as t 1 Then l (A) i ，t j ，P k )t 1 Is the queue latency.
Wherein, t 0 The time to complete the discharge for each AGV,
is the average linear velocity of the AGV, l represents the queue length; t is t 2 Extra time required for each AGV to perform a motion reversing process; t is t j A timestamp indicating that the current AGV completed task j.
Calculating A by equation (5) i After the execution of task T j After that, the sorting table P of place selection is returned k (A i ,T j )：
For the specific embodiment under the completely random process, it can be assumed that the distribution of the packages at the sorting station is random and the proportion of the bin type is set according to the proportion of the zone heat at which the packages are sent, then the delivery bins for the packages are also random, in which case:
where L and W are the length and width of the work site, respectively, d is the spacing distance between two adjacent sorting tables (the distribution pattern of the sorting tables is according to the pattern shown in fig. 2), and m is the total number of AGVs. On the other hand, if the AGV returns to the vicinity of the sorting floor, the average queue length is a fixed value
And the following conditions are satisfied: (1) Every other on the map
There is a return AGV; (2) The number of the returned AGV is equal to that of the delivered AGV, and then the AGV has
The estimation formula of (c):
the average time required by an AGV to complete a task is obtained by substitution as follows:
the dispatching efficiency of the current logistics sorting system is
As can be seen from the above estimation and derivation process of efficiency, the smaller the size of the lot, the smaller the number ratio of AGVs and sorting decks within a suitable range (which is smaller than a certain value and does not work), the stronger the acceleration capability of AGVs, and the less congestion caused by the scheduling system (the larger λ), the higher the scheduling efficiency obtained by the task allocation algorithm.
The invention also provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the method as described above when executing the program. The electronic device can be an electronic reading device, a text error correction device and other electronic devices which can realize a text error correction function.
The invention also provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, carries out the steps of the method as described above.
In addition, it should be noted that the specific embodiments described in the present specification may differ in the shape of the components, the names of the components, and the like. All equivalent or simple changes of the structure, the characteristics and the principle of the invention which are described in the patent conception of the invention are included in the protection scope of the patent of the invention. Various modifications, additions and substitutions for the specific embodiments described may occur to those skilled in the art without departing from the scope of the invention as defined in the accompanying claims.
Claims (10)
1. An AGV task allocation method is characterized by comprising the following steps:
step 1, AGV with m number distributed randomly in working space are in idle state;
step 2, distributing tasks m before the weight by using a KM algorithm;
3, the AGV plans a path, wraps the packages and delivers the packages according to the matched tasks;
step 4, when detecting that an idle AGV exists, calculating weights of the idle AGV and other non-idle AGVs;
and 5, distributing tasks to other non-idle AGVs which receive at most one task and have weights not larger than the idle AGV, and executing the step 3 by the AGV after distributing the tasks.
2. The AGV task allocation method according to claim 1, wherein when an idle AGV is detected, the weights of the idle AGV and other non-idle AGVs are calculated:
taking the Manhattan distance between the idle AGV and the sorting table where the task package is located as the weight of the idle AGV;
the AGV weight under the non-idle state is: and the Manhattan distance weight of the non-idle AGV from the sorting table where the task package is located is added with the weight required by the AGV in the non-idle state in the processes of loading and delivering and unloading, or the weight required by the delivering and unloading process.
3. The method for distributing AGV tasks according to claim 2, wherein the Manhattan distance between the AGV and the sorting table where the tasks are located is used as the bipartite graph weight in the KM algorithm, and the calculation method is as shown in formula (1):
D ij ＝|x(A i )-x(P j )|+|y(A i )-y(P j )| (1)
wherein A is i Denotes the i-th AGV, P j Denotes the sorting station, x (A), at which the jth task parcel is located i ) Is the x coordinate, x (P), of the ith AGV j ) X coordinate, y (A) of sorting table where parcel is located for jth task i ) Y coordinate, y (P) of the ith AGV j ) For the jth taskThe y-coordinate of the sorting station where the package is located.
4. The method of claim 2, wherein the weight when the non-idle AGV does not reach the sorting table to obtain the AGV task is calculated according to formula (2):
where j' represents the task that the non-idle AGV is performing, T j′ Represents the bin corresponding to task j', where A i Denotes the i-th AGV, P j′ Denotes the sorting table in which task j' is located, x (A) i ) Is the x coordinate, x (P), of the ith AGV j′ ) For the x-coordinate, y (A) of the sorting table in which task j' is located i ) Is the y coordinate of the i AGV, y (P) j′ ) For the y-coordinate of the sorting table in which task j' is located, D 0 Additional weight, D, required to represent the wrapping process 1 Indicating the additional weight required for the delivery drop process.
5. The AGV task assigning method according to claim 2, wherein when the non-idle AGVs are in the delivery process, the weight is calculated according to formula (3),
D ij (j′)＝|x(A i )-x(T j′ )|+|y(A i )-y(T j′ )|+D 1 +|x(T j′ )-x(P j )|+|y(T j′ )-y(P j )| (3)
wherein j' is a task of the AGV in the delivery operation, T j′ Represents the corresponding bin of the j' th task, D 1 Indicating the additional weight required for the delivery drop process.
6. A logistics sorting method is characterized in that m is set as the total number of AGV, n is set as the total number of tasks, the number of tasks is obtained at regular time,
when the values of n and m are the same or the difference value is not greater than the threshold value, scheduling the AGVs to deliver the tasks by adopting a first task allocation method and the AGV task allocation method according to any one of claims 1 to 5;
and when the difference value of the n and the m is larger than the threshold value, adopting a second task allocation method, and enabling each AGV to receive only one task at a time.
7. The logistics sorting method of claim 6, wherein in the second task allocation method, each AGV receives only one task at a time, and after each time the AGV delivers a package, the next sorting table position is re-planned through the weight calculation of each sorting table.
8. The method as claimed in claim 7, wherein the calculating of the weight of each sorting platform comprises: the running distance of the AGV returning to each sorting table and the AGV queue condition of each sorting table;
and selecting the sorting table with the highest weight as the next stopping position of the AGV, and returning to the sorting table through path planning.
9. A logistics sorting system for implementing the logistics sorting method of any one of claims 6 to 8,
comprises a main control system, a plurality of AGVs, a plurality of sorting tables and a plurality of cell areas, wherein,
the master control system is used for receiving task information; scheduling and task allocation of the AGV; planning an AGV path;
the AGV is used for receiving a command of the main control system to realize movement;
the sorting table is used for sorting goods to the AGV and finishing the sorting by adopting manpower or an mechanical arm;
and the grid area comprises grids and a driving channel between the grids.
10. A computer device, comprising: memory and processor, characterized in that the processor is adapted to execute a computer program implementing the method of any of claims 6 to 8, the memory being adapted to store the computer program executable by the processor.
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