CN108873737B - Automatic sorting control and decision-making system based on M-HSTPN model - Google Patents
Automatic sorting control and decision-making system based on M-HSTPN model Download PDFInfo
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Abstract
The invention discloses an automatic sorting control and decision-making system based on an M-HSTPN model, which adopts a three-layer structure of a decision-making layer, an event layer and a physical layer and comprises a Python-based path planning decision-making subsystem, an automatic sorting process event subsystem based on HSTPNSim and a V-REP-based physical simulation environment subsystem. The system can perform modeling simulation on 5 types of mixed characteristics of discrete events, continuous processes, time delay characteristics, random phenomena and decision problems in the automatic sorting process. And (3) providing a design method and a design rule of the M-HSTPN model to realize modular modeling. By designing interfaces of HSTPNSim software and Python and V-REP software, more complex decision problems and more accurate physical modeling problems can be processed by means of strong scientific calculation and environmental modeling capabilities.
Description
Technical Field
The invention relates to the technical field of modeling of control and decision-making systems, in particular to an automatic sorting control and decision-making system based on an M-HSTPN model.
Background
The automatic sorting system is used for taking goods, generating obstacles, planning and deciding paths, judging AGV moving and unloading ports, waiting for taking goods and the like, and comprises 5 types of mixed characteristics of discrete events, a continuous process, a time delay characteristic, a random phenomenon and a decision problem, so that the automatic sorting system cannot be only used as a dynamic system of the discrete events, but needs to be regarded as a type of mixed system, and a corresponding mixed system modeling method is adopted to model each link in the system. Traditional advanced Petri Net and hybrid Petri Net models can only describe some of the hybrid properties in an automated sorting system. Traditional modeling platforms of automatic sorting systems, such as graphic modeling simulation software oriented to objects like Arena, Flexsim and Witness, focus on the construction of sorting system environments, and cannot effectively model, analyze, optimize and control various links in the automatic sorting systems. And Petri network modeling software, such as CPN tools, can realize the modeling of part of links in an automatic sorting system, but cannot process the complex path planning decision problem and the accurate physical modeling problem.
Disclosure of Invention
The invention provides an automatic sorting control and decision-making system based on an M-HSTPN model, which can effectively describe discrete events, a continuous process, time delay characteristics, random phenomena and a decision-making process in the automatic sorting system, and process complex path planning decision-making problems and more accurate physical modeling problems through a data interface.
The invention provides an automatic sorting control and decision-making system based on an M-HSTPN model, which comprises: the system comprises an automatic sorting process event subsystem, a path planning decision subsystem, a physical simulation environment subsystem, a first data interface and a second data interface;
the automatic sorting process event subsystem comprises a modeling module for describing the forward process and the return process of the automatic sorting process based on an HSTPN model;
the route planning decision subsystem is used for planning the route of the automatic sorting process in the HSTPN model;
the physical simulation environment subsystem is used for building a physical simulation environment of the automatic sorting process and carrying out analog simulation on the automatic sorting process in the HSTPN model; the establishment of the physical simulation environment comprises the simulation of the physical environment of the AGV, a feasible path, a node, a discharge port, a goods taking area and a piece supplying sliding groove;
the first data interface is used for transmitting decision variables to the path planning decision subsystem by the automatic sorting process event subsystem and acquiring a path planning result of the path planning decision subsystem;
and the second data interface is used for sending an instruction to the physical environment simulation subsystem by the automatic sorting process event subsystem so as to control the motion process of the AGV in the physical simulation environment and feeding back the motion data of the AGV to the automatic sorting process event subsystem in real time.
In the automatic sorting control and decision-making system based on the M-HSTPN model, the M-HSTPN model provides a three-layer architecture model comprising a decision layer, an event layer and a physical layer on the basis of the HSTPN model, wherein the decision layer is used for realizing decision processes such as calculation, judgment, optimization and planning, and a decision result influences an evolution path of the event layer model; the event layer describes the causal relationship of each link based on an HSTPN model; the physical layer describes the evolution of the physical process, and the operation result of the physical process can influence the enabling of the library in the event layer.
In the automatic sorting control and decision-making system based on the M-HSTPN model, the forward process comprises the following steps: a goods taking link, a trip obstacle generating link, a trip path planning decision link, a trip AGV moving link and a goods unloading port judging link; the backhaul includes: the method comprises a return barrier generation link, a return path planning decision link, a return AGV moving link, a goods taking port judgment link and a goods waiting and taking link.
In the automatic sorting control and decision system based on the M-HSTPN model, the path planning decision subsystem is realized based on Python language, and a path planning result is given by means of the scientific computing capacity of Python to complete a decision task.
In the automatic sorting control and decision-making system based on the M-HSTPN model, the physical simulation environment subsystem adopts the API of the V-REP virtual robot simulation platform and the distributed control architecture to build a physical simulation environment of the automatic sorting process, and realizes the movement process of the AGV in the physical simulation environment according to the instruction of the event subsystem of the automatic sorting process.
In the automatic sorting control and decision-making system based on the M-HSTPN model, the first data interface is defined in a decision-making base.
In the automatic sorting control and decision system based on the M-HSTPN model, the second data interface is defined in the continuous warehouse.
In the automatic sorting control and decision system based on M-HSTPN model of the invention, the automatic sorting process event subsystem further comprises: and the output module is used for carrying out data display on the operation result of the HSTPN model and outputting the operation result of the HSTPN model to the matrix module and the output module for storing the operation result of the HSTPN model and outputting the operation result to the file module.
In the automatic sorting control and decision-making system based on the M-HSTPN model, the automatic sorting process event subsystem divides the automatic sorting process into a plurality of links, wherein each link starts with a transition and ends with a transition, and all the links are connected by a discrete library.
In the automatic sorting control and decision system based on the M-HSTPN model, the path planning decision subsystem and the automatic sorting process event subsystem adopt synchronous communication; the automatic sorting process event subsystem and the physical simulation environment subsystem adopt synchronous communication.
The automatic sorting control and decision-making system based on the M-HSTPN model adopts a three-layer structure of a decision-making layer, an event layer and a physical layer, and comprises a Python-based path planning decision-making subsystem, an automatic sorting process event subsystem of a hybrid system modeling simulation platform (HSTPNSim software) based on the HSTPN model and a V-REP-based physical simulation environment subsystem. The system can model and simulate 5 types of mixed characteristics of dispersion, continuity, time delay, randomness and decision in the automatic sorting control and decision system based on the M-HSTPN model. And (3) providing a design method and a design rule of the M-HSTPN model to realize modular modeling. By designing interfaces of a hybrid system modeling simulation platform based on an HSTPN model and Python and V-REP software, a synchronous communication mode is adopted, and by means of strong scientific calculation and environment modeling capacity, more complex decision problems and more accurate physical modeling problems can be processed.
The automatic sorting control and decision system can be applied to modeling, dynamic and static characteristic analysis, rhythm optimization, AGV dispatching, system control and performance analysis and evaluation of a grid type automatic sorting system. The three-layer framework of the decision layer, the event layer and the physical layer can be applied to modeling simulation of hybrid systems such as an information physical fusion system, a flexible manufacturing system, a logistics system, the national defense industry and the like. The design method and the design rule of the M-HSTPN model can be used for guiding the design of the system model based on the M-HSTPN.
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FIG. 1 is a block diagram of an automatic sorting control and decision-making system based on M-HSTPN model according to the present invention;
FIG. 2a is a schematic diagram of a discrete library in the HSTPN model of the present invention;
FIG. 2b is a schematic representation of a continuum in the HSTPN model of the present invention;
FIG. 2c is a schematic diagram of the delay library in the HSTPN model of the present invention;
FIG. 2d is a schematic diagram of a random library in the HSTPN model of the present invention;
FIG. 2e is a schematic representation of the decision library in the HSTPN model of the present invention;
FIG. 3 is a schematic diagram of a three-layer architecture of the M-HSTPN model of the present invention, namely "decision layer-event layer-physical layer";
FIG. 4 is a schematic diagram of the present invention describing various links in the automatic sorting process based on HSTPN model;
FIG. 5 is a schematic diagram of a physical simulation environment of an automated sorting process set up by the present invention;
FIG. 6 is a schematic diagram of the event layer model of the automatic sorting control and decision system based on M-HSTPN model interacting with decision results and AGV movement data according to the present invention.
Detailed Description
The invention provides an automatic sorting control and decision-making system based on an M-HSTPN model, which can effectively describe discrete events, a continuous process, time delay characteristics, random phenomena and a decision-making process in the automatic sorting system, and process complex path planning decision-making problems and more accurate physical modeling problems through a data interface.
The M-HSTPN model is an improved Hybrid random time delay Petri Net (M-HSTPN) model, and is a three-layer framework including a decision layer, an event layer and a physical layer, as shown in fig. 3, wherein the decision layer is used for processing decision algorithms such as calculation, judgment, optimization and planning, and the decision result influences the evolution path of the event layer model; the event layer describes the cause and effect relationship of each event, and the activation of the library can trigger the evolution of a physical process in the physical layer; the method comprises the following steps that an evolution model of a physical process in a physical layer description system is operated, the operation result of the evolution model can influence the enabling of an event layer, wherein the event layer is established based on an HSTPN model, the HSTPN model is a Hybrid Stochastic time delay Petri network model (HSTPN), and the HSTPN model is defined as a quintuple:
HSTPN＝(SG,T,F,Q0,TH)
wherein SG ═ SD,SC,ST,SS,SJ) Denotes a finite set of libraries, SDRepresents a collection of discrete libraries, SCRepresenting a set of contiguous bins, STRepresenting a time delay library set, SSRepresenting a random pool, SJRepresenting a set of decision libraries;
t represents a finite set of transitions; f represents a directed arc set connecting the library and the transition; q0Representing an initial set of promiscuous states; TH denotes a library enabled threshold or set of parameters. The HSTPN model includes 5 types of libraries including a discrete library site, a continuous library site, a time delay library site, a random library site and a decision library site. The 5 miscellaneous characteristics of the miscellaneous system, including discrete characteristic, continuous characteristic, time delay characteristic, random characteristic and decision characteristic, are respectively defined in 5 kinds of libraries. Schematic representations of each library are shown in FIGS. 2a to 2 e.
Fig. 1 shows an automatic sorting control and decision system based on M-HSTPN according to the present invention, which includes: a path planning decision subsystem 1, an automatic sorting process event subsystem 2, a physical simulation environment subsystem 3, a first data interface 4 and a second data interface 5.
The automatic sorting process event subsystem 2 builds an HSTPN model of the automatic sorting process based on a hybrid system modeling simulation platform (HSTPNSim software) of the HSTPN model. The hybrid system modeling simulation platform of the HSTPN model is disclosed in the Chinese patent application with the publication number CN 107229789A. The automated sorting process event subsystem 2 comprises: the modeling module, the output-to-matrix module and the output-to-file module. The modeling module describes the outbound and inbound trips of the automated sorting process based on the HSTPN model. As shown in fig. 4, in specific implementation, the forward process includes: the method comprises a goods taking link 6, a trip obstacle generating link 7, a trip path planning decision link 8, a trip AGV moving link 9 and a goods unloading port judging link 10. The backhaul includes: a return obstacle generating link 11, a return path planning decision link 12, a return AGV moving link 13, a goods taking opening judging link 14 and a goods waiting and taking link 15. And outputting the data to a matrix module for displaying the running result of the HSTPN model. And outputting the result to a file module for storing the operation result of the HSTPN model.
The path planning decision-making subsystem 1 is used for designing and operating complex decision-making algorithms such as calculation, judgment, optimization and planning in path planning, and further planning paths in an automatic sorting process in the HSTPN model. In specific implementation, the path planning decision subsystem 1 is implemented based on Python language, and a path planning result is given by means of the scientific computing power of Python to complete a decision task.
The physical simulation environment subsystem 3 is used for building a physical simulation environment of the automatic sorting process and performing simulation on the automatic sorting process in the HSTPN model; the physical simulation environment is built by simulating the physical environment of the AGV, the feasible path, the node, the unloading port, the goods taking area and the goods supply sliding groove. In specific implementation, the physical simulation environment subsystem 3 adopts the API of the V-REP virtual robot simulation platform and the distributed control framework to build a physical simulation environment of the automatic sorting process, and realizes the motion process of the AGV in the physical simulation environment according to the instruction of the automatic sorting process event subsystem 2.
And the first data interface 4 is used for transmitting the decision variables to the path planning decision subsystem 1 by the automatic sorting process event subsystem 2 and acquiring the path planning result of the path planning decision subsystem 1. And the interaction of the automatic sorting process event subsystem 2 and the path planning decision subsystem 1 is realized. In practical implementation, the first data interface 4 is defined in a decision base.
And the second data interface 5 is used for sending an instruction to the physical environment simulation subsystem 3 by the automatic sorting process event subsystem 2 so as to control the motion process of the AGV in the physical simulation environment and feeding back the motion data of the AGV to the automatic sorting process event subsystem 2 in real time. And the interaction between the automatic sorting process event subsystem 2 and the physical environment simulation subsystem 3 is realized. In practice, the second data interface 5 is defined in a continuous library.
The path planning decision subsystem 1 and the automatic sorting process event subsystem 2 realize synchronous communication through a first data interface 4; the automated sorting process event subsystem 2 and the physical simulation environment subsystem 3 communicate synchronously via a second data interface 5.
The design refines the functions of each layer, reduces the coupling degree of the system and improves the expandability of the system.
As shown in fig. 4, the automated sorting process event subsystem 2 divides the automated sorting process into a plurality of links, each beginning with a transition and ending with a transition, with each link connected by a discrete bin. The automatic sorting process includes 5 types of hash features, each defined in a 5-type library. The meanings of the libraries and transitions in the HSTPN model during sorting are shown in tables 1 and 2, respectively.
Table 1 the library contains sense tables.
Table 2 transition meaning table.
Physical simulation Environment subsystem 3 a schematic diagram of the physical simulation environment for the automated sorting process is shown in FIG. 5, and includes AGV16, feasible path 17, nodes 18, discharge port 19, pick area 20, and supply chute 21. An automated sorting system in unmanned warehouses uses grid sorting maps. The delivery chute 21 on the left side of the map releases packages to be sorted of different masses at a certain frequency. The 3 x 3 discharge openings 19 (numbered I-IX) are distributed on the right side of the map, with different discharge openings representing different delivery addresses. The solid lines represent the possible path 17 from the feed chute 21 to the discharge opening 19. Multiple AGVs 16 are allowed to operate simultaneously, the AGV16 waits at the pick area 20 (enclosed by the dashed lines) for packages to be sorted and transports them to the discharge opening 19. The intersection of the feasible paths 17 is referred to as node 18. When the AGV16 moves to a new node, it needs to determine whether the target discharge slot has been reached and determine the direction and mode of movement. For a single AGV, other AGVs present on the feasible path are considered obstacles.
As shown in fig. 6, when the decision-making base is activated, a decision-making action is initiated, and at this time, the path planning decision-making subsystem 1 starts to operate based on the path planning algorithm of Python, and at this time, the HSTPN model of the event subsystem 2 in the automatic sorting process is suspended, and the decision-making base is kept in an activated state; when the operation of the path planning algorithm is finished, a decision result is given through the first data interface 4, the decision base of the event subsystem 2 in the automatic sorting process is enabled at the moment, and the HSTPN model enables corresponding transition to occur according to the decision result, so that decision behaviors are realized.
Similarly, after the continuous library is activated, the motion mode and the motion direction of the AGV are transmitted to the AGV in the physical simulation environment subsystem 3 through the second data interface 5, the AGV moves according to the instruction, at the moment, the HSTPN model of the automatic sorting process event subsystem 2 is hung, the continuous library is kept in an activated state, and the AGV transmits the motion parameters to the automatic sorting process event subsystem 2 in real time; when the AGV moves to a new node, the move process ends, at which point the model continues to run as enabled by the continuous library in the HSTPN model of the automated sorting process event subsystem 2.
The M-HSTPN model design method is mainly a process of establishing a hybrid system model based on M-HSTPN, namely an intermediate process from the proposal of a system modeling problem to the design of the M-HSTPN model, and the modeling method mainly comprises 5 steps:
the method comprises the following steps: physical-logical description of the problem: and analyzing the system modeling problem, including modeling purposes, conditions and modeling processes.
Step two: and (3) link decomposition: combining with the actual system operation process, giving the sequence of each link by adopting a system operation flow chart, wherein the sequence determines the backbone logic of the M-HSTPN;
step three: characteristic decomposition: considering the discrete, continuous, time delay, random and conflict (decision) mixed characteristics in each link in the step two; designing a subnet by combining with an HSTPN typical structure, wherein the process determines the topological details of each link; modeling mixed characteristics and setting and calling variables in a link;
step four: and (3) link integration: each link begins with a transition and ends with a transition, and the end of the old state and the start of the new state are represented by the discrete library in the middle.
Step five: model checking and debugging, and data display.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the scope of the present invention, which is defined by the appended claims.
Claims (9)
1. An automatic sorting control and decision-making system based on an M-HSTPN model is characterized by comprising: the system comprises an automatic sorting process event subsystem, a path planning decision subsystem, a physical simulation environment subsystem, a first data interface and a second data interface;
the automatic sorting process event subsystem comprises a modeling module for describing the forward process and the return process of the automatic sorting process based on an HSTPN model;
the route planning decision subsystem is used for planning the route of the automatic sorting process in the HSTPN model;
the physical simulation environment subsystem is used for building a physical simulation environment of the automatic sorting process and carrying out analog simulation on the automatic sorting process in the HSTPN model; the establishment of the physical simulation environment comprises the simulation of the physical environment of the AGV, a feasible path, a node, a discharge port, a goods taking area and a piece supplying sliding groove;
the first data interface is used for transmitting decision variables to the path planning decision subsystem by the automatic sorting process event subsystem and acquiring a path planning result of the path planning decision subsystem;
the second data interface is used for sending an instruction to the physical environment simulation subsystem by the automatic sorting process event subsystem so as to control the motion process of the AGV in the physical simulation environment and feeding back the motion data of the AGV to the automatic sorting process event subsystem in real time;
the M-HSTPN model is a three-layer architecture model comprising a decision layer, an event layer and a physical layer on the basis of the HSTPN model, wherein the decision layer is used for realizing calculation, judgment, optimization and planning of a decision process, and a decision result influences an evolution path of the event layer model; the event layer describes the causal relationship of each link based on an HSTPN model; the physical layer describes the evolution of the physical process, and the operation result of the physical process can influence the enabling of the library in the event layer.
2. The M-HSTPN model-based automated sorting control and decision system of claim 1, wherein the fronthaul comprises: a goods taking link, a trip obstacle generating link, a trip path planning decision link, a trip AGV moving link and a goods unloading port judging link; the backhaul includes: the method comprises a return barrier generation link, a return path planning decision link, a return AGV moving link, a goods taking port judgment link and a goods waiting and taking link.
3. The M-HSTPN model-based automatic sorting control and decision making system according to claim 1, wherein the path planning decision making subsystem is implemented based on Python language, and gives a path planning result by means of Python's scientific computing power to complete a decision making task.
4. The automatic sorting control and decision-making system based on the M-HSTPN model as claimed in claim 1, wherein the physical simulation environment subsystem adopts the API of the V-REP virtual robot simulation platform and the distributed control architecture to build the physical simulation environment of the automatic sorting process, and realizes the motion process of the AGV in the physical simulation environment according to the instruction of the automatic sorting process event subsystem.
5. The M-HSTPN model-based automated sorting control and decision-making system according to claim 1, wherein the first data interface is defined in a decision repository.
6. The M-HSTPN model-based automated sorting control and decision system of claim 1, wherein the second data interface is defined in a continuum library.
7. The M-HSTPN model based automated sorting control and decision system of claim 1, wherein the automated sorting process event subsystem further comprises: and the output module is used for carrying out data display on the operation result of the HSTPN model and outputting the operation result of the HSTPN model to the matrix module and the output module for storing the operation result of the HSTPN model and outputting the operation result to the file module.
8. The M-HSTPN model based automated sorting control and decision making system according to claim 1, wherein the automated sorting process event subsystem divides the automated sorting process into a plurality of links, each link beginning with a transition and ending with a transition, each link connected by a discrete pool.
9. The M-HSTPN model-based automated sorting control and decision system according to claim 1, wherein the path planning decision subsystem and the automated sorting process event subsystem employ synchronous communication; the automatic sorting process event subsystem and the physical simulation environment subsystem adopt synchronous communication.
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