CN110989582B - Multi-AGV automatic avoiding type intelligent scheduling method based on path pre-occupation - Google Patents
Multi-AGV automatic avoiding type intelligent scheduling method based on path pre-occupation Download PDFInfo
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Abstract
The invention discloses a path pre-occupation-based multi-AGV automatic avoidance intelligent scheduling method, which comprises the following steps: and automatically planning a motion path of the AGV, generating a real-time motion instruction of the AGV, and judging node states, preempting idle nodes and automatically avoiding occupied nodes in advance. The automatic avoiding device and the automatic avoiding method realize automatic avoiding during simultaneous operation in the region of the multiple AGVs, and improve the operation efficiency and the operation safety.
Description
Technical Field
The invention belongs to the technical fields of path planning, automatic obstacle avoidance and real-time scheduling in the fields of electronic engineering and software engineering, and particularly relates to an automatic multi-AGV avoiding intelligent scheduling method based on path pre-occupation.
Background
AGVs are important components of flexible manufacturing systems, and have indispensable important roles in the aspects of automation and intellectualization of the production process, especially in discrete manufacturing enterprises, the automatic instant transportation of a plurality of stations without fixed points among multiple lines is completed, the automation of manufacturing industry is realized, and the processing, the assembly and the like are performed. AGVs are important tools for improving production and manufacturing efficiency, are beneficial to realizing intelligent and modern manufacturing of factories, workshops, production lines and the like, and particularly have wide application in industrial technologies such as processing, transportation and the like. And the AGV dispatching system is used for completing management, path planning, task allocation, state monitoring and the like of a plurality of AGVs, and the performance of the dispatching system directly influences the transfer efficiency, thereby influencing the production efficiency, the cost and the like.
At present, because the production site has limited path capacity, but a plurality of AGVs need to operate simultaneously, the problems of insufficient path capacity, multiple vehicle conflicts, path intersection, loop deadlock and the like are easy to occur, and the requirements of the fields of precise manufacture, assembly and the like on the automatic, collision-free, deadlock-free and efficient operation of the plurality of AGVs cannot be met.
Disclosure of Invention
The invention solves the technical problems that: the method overcomes the defects of the prior art, provides an automatic multi-AGV avoidance intelligent scheduling method based on path pre-occupation, and solves the problems of insufficient path capacity, multi-vehicle conflict, path intersection and loop deadlock in the prior art.
The invention aims at realizing the following technical scheme: a multi-AGV automatic avoiding intelligent scheduling method based on path pre-occupation comprises the following steps: (1) According to the characteristics of the field environment and task requirements, planning and constructing running path nodes of the AGV, constructing a field map, and storing path information of the AGV in a database; (2) Initializing a port of a communication link of the AGV dispatching management system, reading path information in a database, and dynamically storing the path information in a system memory; (3) Through a communication link, a scheduling system broadcasts and transmits query information to a plurality of AGVs in a network link in an operation scene, and acquires the number, the current position and the state information of whether a task is being executed or not of the AGVs in the network link; (4) Task information in an upper system database is read at regular time, tasks which are not started to be executed are classified and ordered according to the emergency degree, the tasks are divided into special tasks, charging tasks and transferring tasks, and the emergency degree is gradually decreased; (5) Distributing the task to a proper AGV according to the task type and the current state information of the AGV; (6) After the allocation is completed, carrying out real-time path planning on the AGV until the task execution is completed; (7) After the task is executed, the state of the task in the database and the idle state of the current AGV are updated to prepare for the subsequent execution of a new task.
In the automatic avoidance type intelligent scheduling method of the multi-AGV based on the pre-occupied path, in the step (1), the AGV performs autonomous navigation in a visual navigation mode, and the method has the characteristics of line patrol, matrix code number identification and incapability of continuous positioning; according to the characteristics that a plurality of fixed barriers exist in the field environment and the self volume and the load volume of the AGV are large, the running route of the AGV is limited; setting acceleration and deceleration points, parking points, rotation points and station points in a field environment, setting different matrix codes at corresponding point positions, establishing a right-hand plane coordinate system, measuring or estimating coordinates corresponding to each matrix code, and constructing a field map; and storing path node numbers, namely matrix code numbers, adjacent reachable node numbers, distances between two nodes, node x coordinates and node y coordinates, in a database.
In the above-mentioned multi-AGV automatic avoiding intelligent scheduling method based on path pre-occupation, in step (2), the ports of the available communication links are automatically detected in the scheduling system, the selected ports can be defaulted or changed, parameters of baud rate, data bit and stop bit are set, after the setting is completed, the ports of the communication links are automatically opened, and the communication link between the scheduling system and the AGV is established; and reading path information in the database, dynamically storing the path information in a system memory, and virtually mapping the field environment in a dispatching system.
In the above-mentioned multi-AGV automatic avoidance intelligent scheduling method based on path pre-occupation, in step (3), the scheduling system only sends inquiry information to a plurality of AGVs belonging to the network link in the operation scene in a broadcasting mode, acquires the AGV number in the network link, the network address of the unique number, the current position of the AGV, whether to execute tasks, fault coding state information and the like, and records in the system; the scheduling system carries out directional communication with the AGV according to the network address of the unique number corresponding to the AGV, and data communication efficiency is improved.
In the multi-AGV automatic avoidance intelligent scheduling method based on path pre-occupation, in the step (4), an upper system of an MES system, an intelligent warehouse and a production line system can issue task demands to a database, and a scheduling system reads task information in the database every 500ms to realize seamless fusion with the upper system; the tasks are divided into special tasks, charging tasks and transferring tasks according to types; the special tasks comprise sudden stop of the executing task, sudden stop of the AGV executing the task, suspension of the executing task, suspension of the AGV executing the task, suspended task recovery and suspended AGV recovery movement; the tasks are sequentially in descending order according to the priority: sudden stop of a task being executed, sudden stop of an AGV (automatic guided vehicle) executing the task, suspension of the task being executed, suspension of the AGV executing the task, suspended task restoration and suspended AGV restoration movement, task charging and task transferring; and the scheduling system classifies and sorts the read tasks which are not started to be executed according to the emergency degree, and sorts the tasks according to the issuing sequence of the tasks if the priorities of the tasks are the same.
In the above-mentioned multi-AGV automatic avoidance intelligent scheduling method based on path pre-occupation, in step (5), allocating tasks to appropriate AGVs according to task types and current state information of the AGVs includes:
1) If no unexecuted task exists, continuing to execute the original task or stand by and waiting for a new task;
2) If the special task which is not executed exists, the task is distributed and processed according to the task priority as follows:
if the AGV number is specified in the special task: searching whether the AGV is executing the task, if so, sending an instruction corresponding to the special task, and after the AGV gives a reply message that the instruction is set successfully, correspondingly changing the executing state of the charging or transferring task which is being executed by the AGV, marking the executing state of the special task as completed, and feeding back the executing state to an upper database; otherwise, the execution state of the special task is marked as completed directly and is fed back to the upper database;
if only a task number is designated in the special task, searching the task number and the current state thereof from a database, if the task number exists and is not in an execution completion or discarding state, sending an instruction corresponding to the special task to an AGV executing the task number, after the AGV gives a reply message that the instruction is successfully set, correspondingly changing the execution state of the task number task, marking the execution state of the special task as completed, and feeding back the execution state to an upper database; otherwise, the execution state of the special task is marked as completed directly and is fed back to the upper database;
3) If only the automatic charging or transferring tasks are not executed, the tasks are distributed and processed according to the task priority as follows:
when the AGV number is specified in the task: if the AGV exists in the system and is in an idle state currently, a task is distributed to the AGV, the task state is marked as being executed, and the task state is fed back to an upper database; otherwise, the task waits for the allocation of the next unexecuted charging or transferring task;
when no AGV number is specified in the task: if no idle AGV exists, the task waits; otherwise, searching the AGV with the shortest path from the current position to the initial position specified in the task according to the current position of all the idle AGVs and the nearby principle, distributing the task to the AGVs, marking the task state as being executed, and feeding back to an upper database.
In the above-mentioned multi-AGV automatic avoidance intelligent scheduling method based on path pre-occupation, in step (6), in the undirected graph, the minimum weight path from the source position, i.e. the fixed point V0 to the rest points is found out as the optimal path, assuming the weight wi of each edge Ei; in the path planning process, the fixed point set V is divided into two groups, wherein one group is a vertex set S of the path with the minimum weight value calculated, and the other group is a vertex set U of the rest undetermined optimal paths; in the initial step, only one source point V0 is arranged in the S, and the minimum path of a weight is added into the S set every time a subsequent path is obtained until all vertexes are added into the S or the target position is found; in order to prevent the problems of insufficient path capacity, multiple-vehicle collision, path intersection and loop deadlock when multiple AGVs run simultaneously in a limited path capacity range, after the multiple tasks respectively select AGVs and plan an initial path, in order to ensure motion continuity, the motion instructions of the AGVs need to be planned in advance and prestored in the memory of the AGVs, the current position of the AGVs and the nodes to be arrived are marked as occupied, the nodes are marked as idle when the AGVs leave the nodes, and the nodes currently occupied by the non-self AGVs are excluded when other AGVs are planned in real-time paths; the scheduling system pre-judges the time of the AGVs reaching the intersection according to the real-time position and the movement speed of the AGVs, and selects the avoided AGVs according to the task priority; and for the AGVs which pass through a certain intersection in the same period, marking the node of the intersection as occupied by the AGVs which execute the high-priority task, then, in order to avoid the AGVs which need to pass through the point, re-planning paths which do not contain occupied nodes, if the re-planning result is that the paths are not reachable, waiting by the AGVs which execute the low-priority task until the occupied node state is changed into idle state, and re-planning the paths and the instructions until the task execution is completed.
In the above-mentioned multi-AGV automatic avoiding intelligent scheduling method based on path pre-occupation, in step (7), after task execution is completed, the AGV executing the task is marked as idle state, and the corresponding executed task in the database is marked as execution completion, so as to prevent the same task from being repeatedly executed; the idle AGV can perform a new task.
Compared with the prior art, the invention has the following beneficial effects:
(1) The invention pre-stores path nodes (working sites, acceleration and deceleration points, parking points and the like) planned in advance in the on-site running environment of the AGV in the database, establishes virtual mapping of the electronic map in the scheduling system to the on-site environment, can flexibly and variably set each path node according to actual needs, and improves the flexibility of arrangement and change of the assembly line such as automatic processing or assembly.
(2) The invention classifies and sorts the tasks according to the task types, the execution states and the priorities, is favorable for preferentially processing emergency tasks, and is convenient for users to manage AGV tasks and process AGV state information in real time.
(3) The invention adopts the path pre-occupation strategy under the high priority and time priority principle to realize the time-sharing use and automatic avoidance of multiple AGVs on the same road section, and prevent the problems of multiple-vehicle conflict, path intersection, loop deadlock and the like.
(4) According to the automatic intelligent control system, the automatic intelligent control is performed on the multiple AGVs, so that the operations such as material transportation, accurate positioning and the like on a production site are automatically completed, the time and difficulty of positioning and adjusting are greatly reduced, and the production efficiency and the automation level are effectively improved.
(5) According to the invention, through automatic avoiding control of multiple AGVs, the operation efficiency and the operation safety are effectively improved.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to designate like parts throughout the figures. In the drawings:
FIG. 1 is a flowchart of Dijkstra algorithm according to an embodiment of the present invention;
FIG. 2 is a flow chart of an automatic avoidance process for a multiple AGV according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art. It should be noted that, without conflict, the embodiments of the present invention and features of the embodiments may be combined with each other. The invention will be described in detail below with reference to the drawings in connection with embodiments.
FIG. 2 is a flow chart of an automatic avoidance process for a multiple AGV according to an embodiment of the present invention. Referring to fig. 2, the automatic avoiding type intelligent scheduling method for the multi-AGV based on path pre-occupation comprises the following steps: (1) And planning and constructing a running path node of the automatic guided vehicle AGV (Automated Guided Vehicle) according to the characteristics of the field environment and task requirements, and constructing a field map. An AGV, also called an unmanned carrier, is an unmanned carrier equipped with an automatic guide device, capable of automatically traveling along a predetermined guide path, and having safety protection and various transfer functions. The AGV can automatically transport goods or materials from a starting point to a destination, and the running path of the AGV can be flexibly changed according to the requirements of warehouse goods places, production process flows and the like. AGVs generally consist of a vehicle body, an electricity storage and charging device, a driving device, an automatic guiding device, an on-board controller, a safety protection device, a transfer device, etc. AGVs are of various types and forms. AGVs are often classified according to the navigation mode during automatic travel of the AGVs: electromagnetic induction guided AGVs, laser guided AGVs, vision guided AGVs, and the like. The AGV adopts a visual navigation mode to carry out autonomous navigation, and has the characteristics of line walking, identifiable matrix code numbering, incapability of continuous positioning and the like. The characteristics of the field environment and the task requirements are that according to the characteristics that a plurality of fixed barriers exist in the field environment and the self volume and the load volume of the AGV are large, the running route of the AGV is limited. And an acceleration and deceleration point, a parking point, a rotating point, a working point and the like are arranged in the field environment, so that the AGV is ensured not to interfere with a fixed barrier when in normal operation, and smoothly passes. And setting matrix codes at corresponding point positions, wherein the matrix codes are rectangular array codes with a right-hand coordinate system, and the matrix codes are non-negative integer numbers, so that the AGV can identify the matrix code numbers in the path during visual navigation. The pasting directions of all matrix codes in the path are the same, and the matrix codes at different positions in the same system are different in number. And establishing a right-hand plane coordinate system with the same direction as the matrix codes, measuring or estimating coordinates corresponding to each matrix code, and constructing a site map. In order to facilitate the flexibility of scene change, the AGV running path nodes are planned and constructed, a site map is constructed, and path information such as path node numbers (matrix code numbers), adjacent reachable node numbers, two-node distances, node x coordinates, y coordinates and the like is stored in an ACCESS database.
(2) And automatically detecting available hardware ports in the dispatching system, defaulting or changing selected ports, setting parameters such as baud rate, data bit, stop bit and the like, automatically opening communication ports after the setting is completed, and establishing a communication link between the dispatching system and the AGV. And reading path information in the database, dynamically storing the path information in a system memory, and virtually mapping the field environment in a dispatching system.
(3) In order to ensure that AGVs in different systems operate simultaneously and prevent data collision, a wireless communication network in the same system is set to be the same link, a scheduling system only sends inquiry information to a plurality of AGVs belonging to the network link in an operation scene in a broadcasting mode, acquires the AGV number in the network link, the network address of the unique number, the current position of the AGV, whether tasks are being executed, fault codes and other state information, and records the state information in the system. And the subsequent dispatching system carries out directional communication with the AGV according to the network address of the unique number corresponding to the AGV, so that the data communication efficiency is improved.
(4) And (3) periodically reading task information in an upper system database, classifying and sequencing tasks which are not started to be executed according to the emergency degree, and dividing the tasks into special tasks (emergency stop, pause and resume tasks), charging tasks and transferring tasks, wherein the emergency degree is gradually decreased. The upper systems such as the MES system, the intelligent warehouse system and the production line system can issue task demands to the database, and the scheduling system reads task information in the database every 500ms, so that seamless fusion with the upper systems is realized. Tasks are classified by type into special tasks (including sudden stop of the executing task, sudden stop of the AGV executing the task, pause of the executing task, pause of the AGV executing the task, paused task resume and paused AGV resume motion), charging tasks, transferring tasks. Wherein the tasks are sequentially in descending order of priority: scram, pause, resume, auto charge, transfer task. And the scheduling system classifies and sorts the read tasks which are not started to be executed according to the emergency degree, and sorts the tasks according to the issuing sequence of the tasks if the priorities of the tasks are the same.
(5) According to the information such as the task type and the current state of the AGV, the task is allocated to the proper AGV, if a special task exists, the task is required to be suddenly stopped (abandoned), suspended (temporarily stopped) or restored (restored to the action of the AGV with suspended movement), the task number or the number of the AGV appointed in the special task is searched from the task which is currently executed by the AGV, and the task is processed; if there is only a charging task or a transferring task, the task is allocated or waited according to whether the task designates a specific AGV and the state of the AGV.
The scheduling system judges and processes the task according to the task reading result, and the task is as follows:
1) And (3) no unexecuted task exists, the original task is continuously executed or stands by and waits for a new task.
2) If the special task is not executed, the task is allocated and processed according to the task priority as follows:
if the AGV number is specified in the special task: searching whether the AGV is executing the task, if so, sending an instruction corresponding to the special task, and after the AGV gives a reply message that the instruction is set successfully, correspondingly changing the executing state of the charging or transferring task which is being executed by the AGV, marking the executing state of the special task as completed, and feeding back the executing state to an upper database; otherwise, the execution state of the special task is marked as completed directly and is fed back to the upper database.
If the task number is only appointed in the special task, searching the task number and the current state thereof from a database, if the task number exists and is not in an execution completion or discard (scram) state, sending an instruction corresponding to the special task to an AGV executing the task number, after the AGV gives a reply message that the instruction is successfully set, correspondingly changing the execution state of the task number task, marking the execution state of the special task as completed, and feeding back the execution state to an upper database; otherwise, the execution state of the special task is marked as completed directly and is fed back to the upper database.
3) Only the automatic charging or transferring tasks which are not executed exist, the tasks are distributed and processed according to the task priority:
when the AGV number is specified in the task: if the AGV exists in the system and is in an idle state currently, a task is distributed to the AGV, the task state is marked as being executed, and the task state is fed back to an upper database; otherwise, the task waits for allocation of a next unexecuted charge or transfer task.
When no AGV number is specified in the task: if no idle AGV exists, the task waits; otherwise, searching the AGV with the shortest path from the current position to the initial position specified in the task according to the current position of all the idle AGVs and the nearby principle, distributing the task to the AGVs, marking the task state as being executed, and feeding back to an upper database.
(6) And when the AGVs with the assigned tasks are subjected to path planning, a Dijkstra algorithm is adopted, and the path planning process is as follows: as shown in fig. 1, in the undirected graph, assuming the weight wi of each edge Ei, a weight minimum path from the source position (fixed point) V0 to the remaining points is found as an optimal path. In the path planning process, the fixed point set V is divided into two groups, one group is a vertex set S of the path with the minimum weight value calculated, and the other group is a vertex set U of the rest undetermined optimal paths. And (3) only one source point V0 is arranged in the S at the beginning, and the minimum path of the weight is added into the S set every time a subsequent minimum path of the weight is obtained until all vertexes are added into the S or the target position is found. In order to prevent the problems of insufficient path capacity, multiple-vehicle collision, path intersection, loop deadlock and the like which are easily caused when multiple AGVs run simultaneously in a limited path capacity range, after the multiple tasks respectively select the AGVs and plan an initial path, in order to ensure motion continuity, the motion instructions of the AGVs need to be planned in advance and prestored in the memory of the AGVs, the current positions of the AGVs and the nodes to be arrived are marked as occupied, the current positions of the AGVs and the nodes to be arrived are marked as idle when the AGVs leave the nodes, and the nodes currently occupied by the non-AGVs are excluded when other AGVs are planned in real-time paths. As shown in FIG. 2, the scheduling system pre-judges the time of the AGVs reaching the intersection according to the real-time position, the moving speed and other factors of a plurality of AGVs, and selects the avoided AGVs according to the task priority. For the AGVs passing through a certain intersection in the same period, the AGVs executing the high-priority tasks mark the nodes of the intersection as occupied, other AGVs needing to pass through the point are used for avoiding the AGVs to carry out path planning without occupied nodes again, if the path is unreachable as a result of the re-planning, the AGVs executing the low-priority tasks wait until the occupied nodes are changed into idle states, and then path and instruction planning is carried out again. Until the task execution is completed.
(7) After the task is executed, the state of the task in the database and the idle state of the current AGV are updated to prepare for the subsequent execution of a new task. And marking the AGV executing the task as an idle state, marking the corresponding executed task in the database as execution completion, and preventing the same task from being repeatedly executed. The idle AGV can perform a new task. According to the method, the node state, the preemption of the idle node and the automatic avoidance of the occupied node are judged in advance, so that the automatic avoidance in the simultaneous operation in the multi-AGV area is realized, and the operation efficiency and the operation safety are improved.
According to the invention, the path nodes planned in advance according to the AGV field operation environment are stored in the database in advance, the virtual mapping of the electronic map in the scheduling system to the field environment is established, each path node can be flexibly and variably set according to actual needs, and the flexibility of arrangement and change of the assembly line such as automatic processing or assembly is improved. The tasks are classified and ordered according to the task types, the execution states and the priorities, so that the priority processing of emergency tasks is facilitated, and the management of AGV tasks and the real-time processing of AGV state information by users are facilitated. The method has the advantages that the path pre-occupation strategy under the high priority and time priority principle is adopted, the time-sharing use and automatic avoidance of multiple AGVs on the same road section are realized, the problems of multiple-vehicle conflict, path intersection, loop deadlock and the like are prevented, and the running efficiency and the running safety are effectively improved.
Although the present invention has been described in terms of the preferred embodiments, it is not intended to be limited to the embodiments, and any person skilled in the art can make any possible variations and modifications to the technical solution of the present invention by using the methods and technical matters disclosed above without departing from the spirit and scope of the present invention, so any simple modifications, equivalent variations and modifications to the embodiments described above according to the technical matters of the present invention are within the scope of the technical matters of the present invention.
Claims (4)
1. The automatic multi-AGV avoiding type intelligent scheduling method based on path pre-occupation is characterized by comprising the following steps of:
(1) According to the characteristics of the field environment and task requirements, planning and constructing running path nodes of the AGV, constructing a field map, and storing path information of the AGV in a database;
(2) Initializing a port of a communication link of the AGV dispatching management system, reading path information in a database, and dynamically storing the path information in a system memory;
(3) Through a communication link, a scheduling system broadcasts and transmits query information to a plurality of AGVs in a network link in an operation scene, and acquires the number, the current position and the state information of whether a task is being executed or not of the AGVs in the network link;
(4) Task information in an upper system database is read at regular time, tasks which are not started to be executed are classified and ordered according to the emergency degree, the tasks are divided into special tasks, charging tasks and transferring tasks, and the emergency degree is gradually decreased;
(5) Distributing the task to a proper AGV according to the task type and the current state information of the AGV;
(6) After the allocation is completed, carrying out real-time path planning on the AGV until the task execution is completed;
(7) After the task execution is completed, updating the state of the task in the database and the idle state of the current AGV to prepare for the subsequent execution of a new task;
in the step (1), the AGV performs autonomous navigation in a visual navigation mode, and has the characteristics of line walking, matrix code number identification and continuous positioning incapability; according to the characteristics of a plurality of fixed barriers, the self volume of the AGV and the load volume in the field environment, the running route of the AGV is limited; setting acceleration and deceleration points, parking points, rotation points and station points in a field environment, setting different matrix codes at corresponding point positions, establishing a right-hand plane coordinate system, measuring or estimating coordinates corresponding to each matrix code, and constructing a field map; storing path node numbers, namely matrix code numbers, adjacent reachable node numbers, distances between two nodes, node x coordinates and node y coordinates, in a database;
in the step (4), an upper system of an MES system, an intelligent warehouse and a production line system can issue task demands to a database, and a scheduling system reads task information in the database every 500ms to realize seamless fusion with the upper system;
the tasks are divided into special tasks, charging tasks and transferring tasks according to types; the special tasks comprise sudden stop of the executing task, sudden stop of the AGV executing the task, suspension of the executing task, suspension of the AGV executing the task, suspended task recovery and suspended AGV recovery movement; the tasks are sequentially in descending order according to the priority: sudden stop of a task being executed, sudden stop of an AGV (automatic guided vehicle) executing the task, suspension of the task being executed, suspension of the AGV executing the task, suspended task restoration and suspended AGV restoration movement, task charging and task transferring;
the scheduling system classifies and sorts the read tasks which are not started to be executed according to the emergency degree, and sorts the tasks according to the issuing sequence of the tasks if the priorities of the tasks are the same;
in step (5), assigning the task to the appropriate AGV according to the task type and the current status information of the AGV includes:
1) If no unexecuted task exists, continuing to execute the original task or stand by and waiting for a new task;
2) If the special task which is not executed exists, the task is distributed and processed according to the task priority as follows:
if the AGV number is specified in the special task: searching whether the AGV is executing the task, if so, sending an instruction corresponding to the special task, and after the AGV gives a reply message that the instruction is set successfully, correspondingly changing the executing state of the charging or transferring task which is being executed by the AGV, marking the executing state of the special task as completed, and feeding back the executing state to an upper database; otherwise, the execution state of the special task is marked as completed directly and is fed back to the upper database;
if only a task number is designated in the special task, searching the task number and the current state thereof from a database, if the task number exists and is not in an execution completion or discarding state, sending an instruction corresponding to the special task to an AGV executing the task number, after the AGV gives a reply message that the instruction is successfully set, correspondingly changing the execution state of the task number task, marking the execution state of the special task as completed, and feeding back the execution state to an upper database; otherwise, the execution state of the special task is marked as completed directly and is fed back to the upper database;
3) If only the automatic charging or transferring tasks are not executed, the tasks are distributed and processed according to the task priority as follows:
when the AGV number is specified in the task: if the AGV exists in the system and is in an idle state currently, a task is distributed to the AGV, the task state is marked as being executed, and the task state is fed back to an upper database; otherwise, the task waits for the allocation of the next unexecuted charging or transferring task;
when no AGV number is specified in the task: if no idle AGV exists, the task waits; otherwise, searching the AGV with the shortest path from the current position to the initial position specified in the task according to the current positions of all idle AGVs and the nearby principle, distributing the task to the AGVs, marking the task state as executing, and feeding back to an upper database;
in the step (6), in the undirected graph, assuming the weight wi of each edge Ei, finding a minimum weight path from the source position, namely the source point V0, to other points, and taking the minimum weight path as an optimal path; in the path planning process, the fixed point set V is divided into two groups, wherein one group is a vertex set S of the path with the minimum weight value calculated, and the other group is a vertex set U of the rest undetermined optimal paths; in the initial step, only one source point V0 is arranged in the S, and the minimum path of a weight is added into the S set every time a subsequent path is obtained until all vertexes are added into the S or the target position is found; in order to prevent the problems of insufficient path capacity, multiple-vehicle collision, path intersection and loop deadlock when multiple AGVs run simultaneously in a limited path capacity range, after the multiple tasks respectively select AGVs and plan an initial path, in order to ensure motion continuity, the motion instructions of the AGVs need to be planned in advance and prestored in the memory of the AGVs, the current position of the AGVs and the nodes to be arrived are marked as occupied, the nodes are marked as idle when the AGVs leave the nodes, and the nodes currently occupied by the non-self AGVs are excluded when other AGVs are planned in real-time paths;
the scheduling system pre-judges the time of the AGVs reaching the intersection according to the real-time position and the movement speed of the AGVs, and selects the avoided AGVs according to the task priority; and for the AGVs which pass through a certain intersection in the same period, marking the node of the intersection as occupied by the AGVs which execute the high-priority task, then, in order to avoid the AGVs which need to pass through the point, re-planning paths which do not contain occupied nodes, if the re-planning result is that the paths are not reachable, waiting by the AGVs which execute the low-priority task until the occupied node state is changed into idle state, and re-planning the paths and the instructions until the task execution is completed.
2. The path pre-occupation-based multi-AGV automatic avoidance intelligent scheduling method according to claim 1, wherein the method comprises the following steps: in the step (2), the ports of the available communication links are automatically detected in the dispatching system, the selected ports can be defaulted or changed, parameters of baud rate, data bits and stop bits are set, after the setting is completed, the ports of the communication links are automatically opened, and the communication links between the dispatching system and the AGV are established;
and reading path information in the database, dynamically storing the path information in a system memory, and virtually mapping the field environment in a dispatching system.
3. The path pre-occupation-based multi-AGV automatic avoidance intelligent scheduling method according to claim 1, wherein the method comprises the following steps: in the step (3), the scheduling system only sends inquiry information to a plurality of AGVs belonging to the network link in the operation scene in a broadcasting mode, acquires the AGV number in the network link, the network address of the unique number, the current position of the AGV, whether the AGV is executing tasks or not and state information of fault codes, and records the state information in the system;
the scheduling system carries out directional communication with the AGV according to the network address of the unique number corresponding to the AGV, and data communication efficiency is improved.
4. The path pre-occupation-based multi-AGV automatic avoidance intelligent scheduling method according to claim 1, wherein the method comprises the following steps: in the step (7), after the task is executed, marking the AGV executing the task as an idle state, marking the corresponding executed task in the database as the executed task, and preventing the same task from being repeatedly executed; the idle AGV can perform a new task.
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