CN115981257A - AGV cluster cooperative operation method and system for industrial intelligent manufacturing flexible production line - Google Patents
AGV cluster cooperative operation method and system for industrial intelligent manufacturing flexible production line Download PDFInfo
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Abstract
The invention discloses an AGV cluster cooperative operation method and system for an industrial intelligent manufacturing flexible production line, which relate to the technical field of AGV dolly scheduling, and are characterized in that an electromechanical equipment module is configured to obtain video data of an operation field in real time through video capture equipment; setting a data collection module to send collected field data to a remote dispatching center in real time; the remote dispatching center analyzes the operation state of each AGV through the field video data by using an artificial intelligent algorithm; generating scheduling decision schemes of all AGV trolleys in real time by using a neural network algorithm; a data processing module is arranged to operate an AGV trolley cooperation algorithm, and each AGV trolley is cooperatively scheduled according to a scheduling decision scheme; therefore, the problems of conflict of the distributed scheduling algorithm and non-optimal scheduling schemes are avoided.
Description
Technical Field
The invention belongs to the field of flexible production workshops, relates to an AGV cluster cooperative scheduling technology, and particularly relates to an AGV cluster cooperative operation method and system for an industrial intelligent manufacturing flexible production line.
Background
The AGV intelligent transfer robot is a small forklift system applied to a factory workshop for realizing automatic transfer and goods loading and unloading, and mainly comprises a forklift device and a software and hardware control system, wherein the forklift device mainly comprises a traveling assembly and an upper and lower movable arm assembly, the traveling assembly is responsible for driving the forklift device to travel and turn, and the upper and lower movable arm assembly is responsible for lifting (loading) and descending (unloading) goods. The software and hardware control system is mainly responsible for positioning tracking and motion trail control of the forklift device. The existing AGV intelligent robot is divided into a full-forward type stack-up AGV, a tray type stack-up AGV, a balance weight type stack-up AGV, a three-way rotary type stack-up AGV and the like, but has two characteristics of running and lifting up and down.
In the existing factory production workshop, the transmission or transition of parts, components and materials can be realized through AGV dollies, the cooperative operation of a plurality of AGV dollies (AGV clusters) is finally matched with a fixed mechanical arm (a dismounting robot) to realize flexible production supply, and the navigation and the line planning of the AGV dollies are the core of the AGV cluster cooperative operation, so that the maximum efficiency of the driving of each AGV dolly according to the optimal cooperative navigation path is ensured, and the AGV is protected from collision; however, most of the existing scheduling algorithms adopt a distributed scheduling algorithm, that is, each AGV trolley independently runs a route scheduling algorithm and determines an operation route by itself, but generally the scheduling algorithms which independently run often have the problem of conflict or non-optimal scheduling schemes.
Therefore, an AGV cluster cooperative operation method and system for an industrial intelligent manufacturing flexible production line are provided.
Disclosure of Invention
The present invention is directed to solving at least one of the problems of the prior art. The invention provides an AGV cluster cooperative operation method and system for an industrial intelligent manufacturing flexible production line, wherein the AGV cluster cooperative operation method and system for the industrial intelligent manufacturing flexible production line are used for taking charge of a scheduling scheme of cooperation and scheduling of each AGV in an AGV cluster through a uniform AGV command system; the remote dispatching center collects the global information of the job site, thereby avoiding the conflict of the distributed dispatching algorithm and the problem of non-optimal dispatching scheme
In order to achieve the above object, an embodiment according to a first aspect of the present invention provides an AGV cluster cooperative operation method for an industrial intelligent manufacturing flexible production line, including the following steps:
the method comprises the following steps: the electromechanical equipment module of the operation field acquires video data of the operation field in real time through the video capture equipment; sending the video data to a data collection module of an operation site;
step two: each AGV sends the position coordinate of the AGV to a data collection module of an operation field in real time;
step three: a data collection module of an operation site sends collected site data to a remote dispatching center in real time; the field data comprises video data of an operation field and position coordinates of each AGV trolley;
step four: the remote dispatching center analyzes the operation state of each AGV through the field video data;
step five: the remote dispatching center divides the production field into areas and distributes the number of AGV trolleys; generating scheduling decision schemes of all AGV trolleys in real time; sending the scheduling decision scheme to a data processing module of an operation field;
step six: a data processing module of the operation field runs an AGV car cooperative algorithm, and performs cooperative scheduling on each AGV car according to a scheduling decision scheme;
the electromechanical equipment module of the operation field consists of ground video capture equipment and aerial video capture equipment; specifically, the aerial equipment in an indoor production workshop is mainly deployed in a ceiling or bracket hanging mode, and the aerial equipment in an outdoor open-air operation production line is mainly deployed in an unmanned aerial vehicle mode; preferably, the electromechanical device module is responsible for driving the movement of the electromechanical device module;
the AGV comprises a AGV trolley, a lifting arm and a control system, wherein the AGV trolley is provided with a trolley software and hardware system which controls the driving of the AGV trolley and the action control of the lifting arm; meanwhile, a positioning device is configured to acquire the position coordinates of the AGV in real time;
the AGV comprises an intelligent and sensing system, a control system and a driving system, wherein the intelligent and sensing system is responsible for collecting AGV data, operation object data and partial environment data, analyzing and processing partial data and intelligently deciding; the AGV control system is responsible for controlling the operation of the AGV, so that the execution of specific mechanical operation is realized, the AGV unit modules are classified according to functional actions, and different AGV unit modules, different combinations of execution actions and AGV unit module operation arrangement of different time nodes form different operation effects;
the AGV trolley cluster is in communication connection with a data collection module of an operation field through a UDP communication protocol; the data collection module of the operation site is in communication connection with the remote dispatching center through a TCP/IP communication protocol;
the remote dispatching center analyzes the operation state of each AGV, namely identifies a scene through an image analysis algorithm, divides the scene according to the scene, performs algorithm processing and geometric processing on the divided image data, performs image identification by using a convolutional neural network algorithm, and identifies the operation state of the AGV; the AGV trolley operation state comprises the relative position in the image, the relative position of the loading and unloading target point position, the task completion condition of the loading and unloading target point position and the operation state of the AGV trolley; specifically, the task completion condition is obtained by calculation with reference to a set cargo task quantity image, and the running state includes running, loading and unloading; establishing a corresponding relation by combining position coordinates sent by a positioning system configured for the AGV and relative position data of each AGV trolley in the image;
the remote dispatching center divides the production line into N areas according to the actual material demand point position and the material type, and each area is provided with an AGV trolley cluster for material supply; the AGV trolley cluster number of each area is configured based on the production load and the handling capacity control of the material supply point of the production line;
the method for the remote dispatching center to generate the dispatching decision schemes of all AGV trolleys comprises the following steps: training an artificial neural network by applying a deep learning algorithm, and intelligently configuring throughput capacity based on input production tasks and loads to output an optimal decision; training an optimal AGV region configuration state by using historical data training region division and AGV configuration classification characteristics; designing an early warning model, taking AGV configuration data as characteristic data, and designing an algorithm model to intelligently pre-judge the change of the production progress;
the data processing module of the operation site carries out collaborative scheduling on the AGV in the following modes: each AGV in each AGV cluster has a unique number, the data processing module of the operation site sends a signal containing a scheduling decision scheme to all AGV trolleys, and when the AGV trolleys receive the signal sent by the data processing module, whether the serial number information of the AGV trolleys exists or not is judged firstly;
if yes, sending a specific operation instruction to the control system of the vehicle based on the numbered instruction set; if the number of the vehicle is not available, detecting specific AGV number information and instruction information, judging whether the future operation planning action of the vehicle is influenced, if so, re-formulating an action plan, wherein the action plan is an action subdivision plan in an instruction range issued by a data processing module on an operation site and comprises an emergency lane change, obstacle avoidance, braking, loading and unloading stopping action, and direction and position conversion; and a data processing module of the operation site divides driving lane lines including going and returning for the AGV cluster.
The AGV cluster cooperative operation system comprises an electromechanical equipment module, a data collection module, a remote dispatching center and a data processing module; the electromechanical equipment module, the data collection module and the data processing module are all configured on an operation site; the electromechanical equipment module, the data collection module, the remote dispatching center and the data processing module are connected in a wireless network mode;
the electromechanical equipment module is used for acquiring video data of an operation site in real time through video capture equipment; sending the video data to a data collection module of an operation site;
the data collection module sends collected field data to a remote dispatching center in real time; the field data comprises video data of an operation field and position coordinates of each AGV trolley;
the remote dispatching center analyzes the operation state of each AGV through the field video data; generating scheduling decision schemes of all AGV trolleys in real time; the scheduling decision scheme is sent to a data processing module of the operation field;
and the data processing module runs an AGV trolley cooperative algorithm and performs cooperative scheduling on each AGV trolley according to a scheduling decision scheme.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a scheduling scheme for a unified AGV commanding system to be responsible for the cooperation and scheduling of each AGV in an AGV cluster; collecting global information of an operation site by a remote dispatching center, training an artificial neural network by applying a deep learning algorithm, and intelligently configuring throughput capacity based on input production tasks and loads to output an optimal decision; training an optimal AGV region configuration state by using historical data training region division and AGV configuration classification characteristics; generating an optimal scheduling scheme for the AGV trolleys in the whole operation field according to the global information, operating an AGV trolley cooperation algorithm by using a data processing module of the operation field, and performing cooperative scheduling on each AGV trolley according to a scheduling decision scheme; therefore, the problems of conflict of the distributed scheduling algorithm and non-optimal scheduling schemes are avoided.
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FIG. 1 is a flow chart of the present invention;
fig. 2 is a schematic diagram of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
As shown in FIG. 1, the AGV cluster cooperative operation method for the industrial intelligent manufacturing flexible production line comprises the following steps:
the method comprises the following steps: an electromechanical equipment module of an operation site acquires video data of the operation site in real time through a video capturing device; sending the video data to a data collection module of an operation site;
step two: each AGV sends the position coordinate of the AGV to a data collection module of an operation field in real time;
step three: a data collection module of an operation site sends collected site data to a remote dispatching center in real time; the field data comprises video data of an operation field and position coordinates of each AGV trolley;
step four: the remote dispatching center analyzes the operation state of each AGV through the field video data;
step five: the remote dispatching center divides the production field into areas and distributes the number of AGV trolleys; generating scheduling decision schemes of all AGV trolleys in real time; sending the scheduling decision scheme to a data processing module of an operation field;
step six: a data processing module of the operation field runs an AGV car cooperative algorithm, and performs cooperative scheduling on each AGV car according to a scheduling decision scheme;
in this embodiment, the electromechanical device module of the operation site is composed of a ground video capture device and an aerial video capture device; specifically, the aerial equipment in an indoor production workshop is mainly deployed in a ceiling or bracket hanging mode, and the aerial equipment in an outdoor open-air operation production line is mainly deployed in an unmanned aerial vehicle mode; preferably, the electromechanical device module is responsible for driving the movement of the electromechanical device module;
in this embodiment, the AGV has a car software and hardware system, and the car software and hardware system controls the driving of the AGV and the motion control of the lifting arm; meanwhile, a positioning device is configured to acquire the position coordinates of the AGV in real time;
furthermore, the AGV comprises an intelligent and sensing system, a control system and a driving system, wherein the intelligent and sensing system is responsible for collecting AGV data, operation object data and partial environment data, analyzing and processing partial data and intelligently deciding; the AGV control system is responsible for controlling the operation of the AGV, so that the execution of specific mechanical operation is realized, the AGV unit modules are classified according to functional actions, and different AGV unit modules, different combinations of execution actions and AGV unit module operation arrangement of different time nodes form different operation effects;
in this embodiment, the AGV trolley cluster is in communication connection with a data collection module of the job site by using a UDP communication protocol; the data collection module of the operation site is in communication connection with the remote dispatching center through a TCP/IP communication protocol;
furthermore, the data collection module of the operation site sends the collected site data to the remote dispatching center in a way of compressing the site data to obtain light image data;
the remote dispatching center analyzes the operation state of each AGV, namely identifies a scene through an image analysis algorithm, divides the scene according to the scene, performs algorithm processing and geometric processing on the divided image data, performs image identification by using a convolutional neural network algorithm, and identifies the operation state of the AGV; the AGV trolley operation state comprises the relative position in the image, the relative position of the loading and unloading target point position, the task completion condition of the loading and unloading target point position and the operation state of the AGV trolley; specifically, the task completion condition is obtained by calculation with reference to a set cargo task quantity image, and the running state includes running, loading and unloading; establishing a corresponding relation by combining position coordinates sent by a positioning system configured by the AGV and relative position data of each AGV in the image;
in this embodiment, the remote dispatching center performs area division on site by dividing the production line into N areas according to the actual material demand point position and the material type, and each area is provided with an AGV trolley cluster for material supply; the AGV trolley cluster number of each area is configured based on the production load and the handling capacity control of the material supply point of the production line;
in this embodiment, the manner for the remote dispatching center to generate the dispatching decision schemes for all AGV carts is as follows: training an artificial neural network by applying a deep learning algorithm, and intelligently configuring throughput capacity based on input production tasks and loads to output an optimal decision; training an optimal AGV region configuration state by using historical data training region division and AGV configuration classification characteristics; designing an early warning model, taking AGV configuration data as characteristic data, and designing an algorithm model to intelligently pre-judge the change of the production progress;
in this embodiment, the way for the data processing module on the job site to cooperatively schedule the AGV carts is as follows: each AGV in each AGV cluster has a unique number, the data processing module of the operation site sends a signal containing a scheduling decision scheme to all AGV trolleys, and when the AGV trolleys receive the signal sent by the data processing module, whether the serial number information of the AGV trolleys exists or not is judged firstly;
specifically, if yes, a specific operation instruction is sent to the control system of the vehicle based on the numbered instruction set; if the number of the vehicle is not available, detecting specific AGV number information and instruction information, judging whether the future operation planning action of the vehicle is influenced, if so, re-formulating an action plan, wherein the action plan is an action subdivision plan in an instruction range issued by a data processing module on an operation site and comprises an emergency lane change, obstacle avoidance, braking, loading and unloading stopping action, and direction and position conversion; a data processing module of an operation site divides driving lane lines including going and returning for the AGV cluster; meanwhile, the data processing module is responsible for the density and the interval of the AGV trolleys in a specific line, and a plurality of AGV trolleys are scheduled based on the density to form a cluster; and the AGV trolley determines whether to start running or not based on interval data of the command system.
As shown in fig. 2, the AGV cluster cooperative operation system for the industrial intelligent manufacturing flexible production line includes an electromechanical device module, a data collection module, a remote dispatching center and a data processing module; the electromechanical equipment module, the data collection module and the data processing module are all configured on an operation site; the electromechanical equipment module, the data collection module, the remote dispatching center and the data processing module are connected in a wireless network mode;
the electromechanical equipment module is used for acquiring video data of an operation site in real time through video capture equipment; sending the video data to a data collection module of an operation site;
the data collection module sends collected field data to a remote dispatching center in real time; the field data comprises video data of an operation field and position coordinates of each AGV trolley;
the remote dispatching center analyzes the operation state of each AGV according to the field video data; generating scheduling decision schemes of all AGV trolleys in real time; the scheduling decision scheme is sent to a data processing module of the operation field;
and the data processing module runs an AGV trolley cooperative algorithm and performs cooperative scheduling on each AGV trolley according to a scheduling decision scheme.
Although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the scope of the present invention.
Claims (8)
1. An AGV cluster cooperative operation method for an industrial intelligent manufacturing flexible production line is characterized by comprising the following steps:
the method comprises the following steps: an electromechanical equipment module of an operation site acquires video data of the operation site in real time through a video capturing device; sending the video data to a data collection module of an operation site;
step two: each AGV sends the position coordinates of the AGV to a data collection module of an operation field in real time;
step three: a data collection module of an operation site sends collected site data to a remote dispatching center in real time; the field data comprises video data of an operation field and position coordinates of each AGV trolley;
step four: the remote dispatching center analyzes the operation state of each AGV through the field video data;
step five: the remote dispatching center divides the production field into areas and distributes the number of AGV trolleys; generating scheduling decision schemes of all AGV trolleys in real time; sending the scheduling decision scheme to a data processing module of an operation field;
step six: and operating an AGV car cooperative algorithm by a data processing module of the operation field, and performing cooperative scheduling on each AGV car according to a scheduling decision scheme.
2. The AGV cluster cooperative operation method for the industrial intelligent manufacturing flexible production line according to claim 1, wherein the AGV trolley cluster is in communication connection with a data collection module of an operation field through a UDP communication protocol; and the data collection module of the job site is in communication connection with the remote dispatching center by a TCP/IP communication protocol.
3. The AGV cluster cooperative operation method for the industrial intelligent manufacturing flexible production line according to claim 1, wherein the remote dispatching center analyzes the operation state of each AGV by identifying scenes through an image analysis algorithm, segmenting according to the scenes, performing algorithm processing and geometric processing on segmented image data, performing image identification by using a convolutional neural network algorithm, and identifying the operation state of the AGV; and establishing a corresponding relation by combining the position coordinates sent by a positioning system configured for the AGV and the relative position data of each AGV in the image.
4. The AGV cluster cooperative operation method for the industrial intelligent manufacturing flexible production line according to claim 1, wherein the operation state of the AGV comprises the relative position in the image, the relative position of the unloading target point position, the task completion condition of the unloading target point position and the operation state of the AGV.
5. The AGV cluster cooperative operation method for the industrial intelligent manufacturing flexible production line according to claim 1, wherein the remote dispatching center performs area division on site by dividing a production line into N areas according to actual material demand point position and material type, and each area is provided with a AGV trolley cluster for material supply; the AGV trolley cluster number of each area is configured based on the production load and the throughput capacity control of the material supply point of the production line.
6. The AGV cluster cooperative operation method for the industrial intelligent manufacturing flexible production line according to claim 1, wherein the manner of generating the scheduling decision schemes of all AGV carts by the remote scheduling center is as follows: and training an artificial neural network by applying a deep learning algorithm, and intelligently configuring throughput capacity based on input production tasks and loads so as to output an optimal decision.
7. The AGV cluster cooperative operation method for the industrial intelligent manufacturing flexible production line according to claim 1, wherein the data processing module of the operation site performs cooperative scheduling on the AGV in a manner that:
each AGV in each AGV cluster has a unique number, the data processing module sends a signal containing a scheduling decision scheme to all AGV trolleys, and when the AGV trolleys receive the signal sent by the data processing module, whether the serial number information of the AGV trolleys exists or not is judged;
if so, sending a specific operation instruction to the control system of the vehicle based on the numbered instruction set; if the number of the vehicle does not exist, specific AGV number information and instruction information are detected, whether the future operation planning action of the vehicle is influenced or not is judged, and if the future operation planning action of the vehicle is influenced, an action plan is re-formulated;
and a data processing module on the operation site divides a driving lane line for the AGV cluster.
8. The AGV cluster cooperative operation system for the industrial intelligent manufacturing flexible production line is characterized by comprising an electromechanical equipment module, a data collection module, a remote dispatching center and a data processing module; the electromechanical equipment module, the data collection module and the data processing module are all configured on an operation site; the electromechanical equipment module, the data collection module, the remote dispatching center and the data processing module are connected in a wireless network mode;
the electromechanical equipment module is used for acquiring video data of an operation site in real time through video capture equipment; sending the video data to a data collection module of an operation site;
the data collection module sends collected field data to a remote dispatching center in real time; the field data comprises video data of an operation field and position coordinates of each AGV trolley;
the remote dispatching center analyzes the operation state of each AGV according to the field video data; generating scheduling decision schemes of all AGV trolleys in real time; the scheduling decision scheme is sent to a data processing module of the operation field;
and the data processing module runs an AGV trolley cooperative algorithm and performs cooperative scheduling on each AGV trolley according to a scheduling decision scheme.
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