CN114347032A - Control method and system for composite AGV robot - Google Patents
Control method and system for composite AGV robot Download PDFInfo
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Abstract
The invention discloses a control method and a system of a composite AGV robot, wherein the method comprises the following steps: according to the first intelligent detection device, first operation material information of the first compound robot is obtained; obtaining a loading composite detection model; inputting the first operation material information into the loading composite detection model to generate first loading detection data; acquiring path data and detection data of a first preset guide planning path to obtain a first driving data group, wherein the first driving data group comprises a first path data set and a first detection data set; and carrying out secondary planning on the first driving data group based on the first loading detection data to obtain a second driving data group, and controlling the first compound robot. The technical problems that in the prior art, when an AGV composite robot is in operation control, due to the fact that the number of functional units is large, adaptability cooperative control is weak along with the change of materials, and the intelligent degree is low are solved.
Description
Technical Field
The invention relates to the field related to intelligent control, in particular to a control method and a control system for a composite AGV robot.
Background
The AGV is an automatic guided transport vehicle, and based on continuous development of intelligent technology and improvement of manufacturing requirements in a factory in recent years, as indispensable transport equipment, continuous research is carried out on the use performance of the AGV robot applied to a complex environment. Compared with a robot with a single function, the AGV composite robot can better adapt to the complex environments of different production lines in the logistics industry, can perform multiple functions such as data management, storage sorting and automatic handling in an automatic factory, and better meets the requirements of the industry.
However, in the prior art, the AGV composite robot has the technical problems that when the operation is controlled, due to the fact that the number of functional units is large, the adaptive cooperative control is weak along with the change of materials, and the intelligent degree is low.
Disclosure of Invention
Aiming at the defects in the prior art, the embodiment of the application aims to solve the technical problems that in the prior art, when an AGV composite robot is in operation control, due to the fact that the number of functional units is large, the adaptability cooperative control is weak along with the change of materials, and the intelligent degree is low, and the technical effects that the material characteristic analysis and detection are carried out through a composite detection model, the driving data are optimized according to the cooperative loading detection result output by the composite detection model, and the intelligent control is achieved are achieved.
In one aspect, an embodiment of the present application provides a control method for a composite AGV robot, where the method is applied to a control system of the composite AGV robot, the system is in communication connection with a first intelligent detection device, and the method includes: according to the first intelligent detection device, first operation material information of a first compound robot is obtained, wherein the first operation material information comprises material attribute information and material geometric information; acquiring a loading composite detection model according to the first composite robot; inputting the first operation material information into the loading composite detection model, and acquiring first output information according to the loading composite detection model; generating first loading detection data according to the first output information; acquiring first driving data groups by acquiring path data and detection data of a first preset guide planning path, wherein the first driving data groups comprise first path data sets and first detection data sets, and the first path data sets and the first detection data sets are distributed in a time sequence and are in one-to-one correspondence; performing secondary planning on the first driving data group based on the first loading detection data to obtain a second driving data group; and controlling the first compound robot according to the second driving data group.
In another aspect, the present application further provides a control system for a composite AGV robot, the system including: the first obtaining unit is used for obtaining first operation material information of the first composite robot according to the first intelligent detection device, wherein the first operation material information comprises material attribute information and material geometric information; a second obtaining unit for obtaining a loading composite detection model according to the first composite robot; the first input unit is used for inputting the first operation material information into the loading composite detection model and obtaining first output information according to the loading composite detection model; a first generating unit configured to generate first loading detection data according to the first output information; a third obtaining unit, configured to obtain a first driving data group by acquiring path data and detection data of a first preset guidance planning path, where the first driving data group includes a first path data set and a first detection data set, and the first path data set and the first detection data set are distributed in a time series and are in one-to-one correspondence; a fourth obtaining unit, configured to perform quadratic programming on the first driving data group based on the first loading detection data to obtain a second driving data group; a first control unit for controlling the first compound robot according to the second driving data set.
In a third aspect, an embodiment of the present application provides a control system for a composite AGV robot, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method according to any one of the first aspect when executing the program.
One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:
the method comprises the steps of analyzing geometric information and material attribute information of a first compound robot according to a first intelligent detection device to obtain first operation material information, constructing a loading compound detection model according to equipment information of the first compound robot, inputting the operation material information obtained through detection into the loading compound model, outputting a compound detection result according to the loading compound detection model to generate first loading detection data, further analyzing a path and a detection area based on historical preset path planning to obtain a first driving data group of the first compound robot, obtaining a second driving data group according to the first loading detection data for the first driving planning, controlling the first compound robot based on the second driving data group, and analyzing and detecting material characteristics through the compound detection model, therefore, the driving data is optimized according to the cooperative loading detection result output by the composite detection model, and the technical effect of intelligent control is further achieved.
The foregoing description is only an overview of the technical solutions of the present application, and the present application can be implemented according to the content of the description in order to make the technical means of the present application more clearly understood, and the following detailed description of the present application is given in order to make the above and other objects, features, and advantages of the present application more clearly understandable.
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Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
fig. 1 is a schematic flowchart illustrating a control method of a composite AGV robot according to an embodiment of the present disclosure;
FIG. 2 is a schematic diagram illustrating a process of obtaining a loading composite detection model according to an exemplary embodiment of the present disclosure;
FIG. 3 is a schematic diagram illustrating a process of obtaining a third driving data set according to an exemplary embodiment of the present disclosure;
FIG. 4 is a schematic diagram of a control system of a composite AGV robot according to an embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of an exemplary electronic device according to an embodiment of the present application.
Detailed Description
The embodiment of the application provides a control method and a control system for a composite AGV robot, and solves the technical problems that in the prior art, when the AGV composite robot is in operation control, due to the fact that the number of functional units is large, the adaptive cooperative control is weak along with the change of materials, and the intelligent degree is low, the technical problems that the composite detection model is used for analyzing and detecting the characteristics of the materials are solved, the drive data are optimized according to the cooperative loading detection result output by the composite detection model, and the technical effect of intelligent control is achieved.
Hereinafter, example embodiments according to the present application will be described in detail with reference to the accompanying drawings. It should be apparent that the described embodiments are merely some embodiments of the present application and not all embodiments of the present application, and it should be understood that the present application is not limited to the example embodiments described herein.
Summary of the application
The composite robot can replace manual work to safely complete dangerous work in severe environment, improves the labor conditions of workers, and accordingly avoids negligence and loss caused by manual operation fatigue. However, for different characteristics of materials, cooperative processing of functional units of the AGV robot is not accurate enough, and adaptability to changes of attributes of the materials is weak, so that the AGV robot control method can be used for assembling and carrying the composite robot to output interactive information according to the characteristics of the materials, and further control pertinence and intelligence are improved.
In view of the above technical problems, the technical solution provided by the present application has the following general idea:
the application provides a control method and a system of a composite AGV robot, wherein the method comprises the following steps: analyzing geometric information and material attribute information of a first compound robot according to a first intelligent detection device in system communication connection to obtain first operation material information, constructing a loading compound detection model according to equipment information of the first compound robot, inputting the operation material information obtained through detection into the loading compound model, outputting a compound detection result according to the loading compound detection model to generate first loading detection data, further analyzing a path and a detection area based on a historical preset path plan to obtain a first driving data group of the first compound robot, obtaining a second driving data group according to the first loading detection data for the first driving plan, controlling the first compound robot based on the second driving data group, and analyzing and detecting material characteristics through the compound detection model, therefore, the driving data is optimized according to the cooperative loading detection result output by the composite detection model, and the technical effect of intelligent control is further achieved.
For better understanding of the above technical solutions, the following detailed descriptions will be provided in conjunction with the drawings and the detailed description of the embodiments.
Example one
As shown in fig. 1, an embodiment of the present application provides a control method for a composite AGV robot, where the method is applied to a control system of a composite AGV robot, and the system is communicatively connected to a first data acquisition device, and the method includes:
step S100: according to the first intelligent detection device, first operation material information of a first compound robot is obtained, wherein the first operation material information comprises material attribute information and material geometric information;
specifically, the first intelligent detection device is a device composed of a machine vision detection unit, a data processing unit and a data transmission unit, the machine vision detection unit is used for collecting images of operation materials of the composite robot, the collected images are subjected to feature recognition analysis based on the data processing unit, and accordingly recognized and analyzed data are transmitted to the system according to the data transmission unit. The first compound robot is a compound robot controlled in real time, and the first compound robot has different types and models and different data controlled by a central control processor of the first compound robot.
Furthermore, based on the first operation material information obtained by the first intelligent detection device, material attribute identification and material geometric data identification are carried out, namely, the material attribute is identified, so that the first compound robot can conveniently carry out mechanical arm positioning and parameter setting on the material; the loading of the first compound robot can be identified by identifying the geometric data, so that the control can be accurately performed.
Step S200: acquiring a loading composite detection model according to the first composite robot;
further, as shown in fig. 2, the obtaining a loading composite detection model according to the first composite robot further includes, in step S200 of this embodiment of the present application:
step S210: acquiring first mechanical arm operation information according to the first compound robot;
step S220: performing data model training according to the first mechanical arm operation information to obtain a first mechanical arm control model, wherein the first mechanical arm control model is obtained by training a training material characteristic set and a mechanical arm control parameter set to convergence;
step S230: obtaining a first carrying and loading detection model;
step S240: and establishing the loading composite detection model based on the connection of the first mechanical arm control model and the first carrying loading detection model, wherein the data interaction between the first mechanical arm control model and the first carrying loading detection model is realized.
Specifically, the first compound robot is subjected to equipment analysis, a cooperative mechanical arm and a cooperative work table are determined, and model training is further performed according to the loading and bearing work of the first compound robot, so that the loading compound model is obtained. The loading composite model comprises a first mechanical arm control model and a first carrying loading detection model, and information can be interacted between the first mechanical arm control model and the first carrying loading detection model.
Data acquisition is carried out through the operation of the mechanical arm of the first composite robot, so that historical operation material characteristics and control parameters are obtained, the control parameters of the mechanical arm are configured according to information of material packages when the composite robot carries out logistics sorting in the logistics industry, and therefore, multiple groups of data learning is carried out by taking a historical material characteristic set and a mechanical arm control parameter set in historical operation data as training data sets, so that logic models can be trained according to double-layer data sets, the models capable of carrying out self-adaptive control on the mechanical arm according to material attributes and geometric information are output, and the identification accuracy can be improved on the basis of mathematical models.
The first carrying and loading detection model is used for carrying out data acquisition and analysis according to the load of the first composite robot, carrying out data migration based on the data identified in the first mechanical arm control model, and realizing robot load identification according to migration data, such as space data and quality data of load-bearing objects. The data interaction is realized to construct a composite model based on the first mechanical arm control model and the first carrying loading detection model, and the model identification efficiency is improved.
Step S300: inputting the first operation material information into the loading composite detection model, and acquiring first output information according to the loading composite detection model;
step S400: generating first loading detection data according to the first output information;
specifically speaking, will first operation material information input in loading the compound detection model, because load the compound detection model and carry out data interaction through two data models and obtain, just there is the priority in the control task of first compound robot, thereby load the compound detection model and contrast the priority, will first operation material information input in the first arm control model, the mechanical control model can carry out the discernment of arm control data according to the material characteristics of real-time input, thereby obtain the data of output, such as positioning data, moment data, displacement data. Furthermore, the execution state of the data output by the first robot arm control model is used as a trigger for the first carrying and loading detection model to carry out carrying material updating detection, so that first output information output by the first carrying and loading detection model is obtained, wherein the first output information is information output by the loading composite detection model, the first carrying detection data is generated, namely the first carrying detection data is data obtained by carrying out space division on the basis of the three-dimensional space structure of the first composite robot in cooperation with detection data of a bearing operation platform on the basis of the first carrying detection data, and effective operation control for materials is further realized.
Step S500: acquiring first driving data groups by acquiring path data and detection data of a first preset guide planning path, wherein the first driving data groups comprise first path data sets and first detection data sets, and the first path data sets and the first detection data sets are distributed in a time sequence and are in one-to-one correspondence;
specifically, the first preset guidance planning path is a path for loading and carrying based on a working line of a logistics factory, and due to the path planning and route setting of the multi-joint robot in the execution process, the robot is prevented from colliding in the carrying process. The first path data set is preset path data of the first warehousing robot in positioning for automatic guiding operation, and data which ensures that the direction does not deviate, such as displacement, front wheel and rear wheel steering, weighing and moving speed and the like, are ensured; the first detection data set is the detection area distribution condition of the first composite robot based on the sensing device in the carrying path, such as the size, the area, the angle and the like of the distribution areas of the warning area and the reminding area, so that safe operation and obstacle avoidance identification can be effectively guaranteed.
By collecting the path data and the detection data of the first preset guide planning path, the identification points in the preset guide path can be analyzed, the path and the operation function module are positioned, the original drive data planning of the first composite robot is realized, and the drive adaptability adjustment can be realized according to different bearing material characteristics.
Step S600: performing secondary planning on the first driving data group based on the first loading detection data to obtain a second driving data group;
step S700: and controlling the first compound robot according to the second driving data group.
Specifically, the first loading detection data is data output by the loading composite detection model, so that the accuracy and the real-time performance are high; the first driving data set is an original driving data set of the first composite robot; and performing secondary planning on the first driving data group based on the first loading detection data so as to obtain the second driving data group, for example, performing secondary planning on detection distribution area data in the first driving data group by taking the result space overflow geometric data of the first loading detection data as an example, so as to prevent the composite robot from carrying a fault. And controlling the first composite robot according to the second driving data group, so that the material characteristic analysis and detection are realized through a composite detection model, the driving data are optimized according to the cooperative loading detection result output by the composite detection model, and the technical effect of intelligent control is realized.
Further, the obtaining a first carrying, loading and detecting model according to the first compound robot further includes, in step S230 of this embodiment of the present application:
step S231: obtaining first bearing space data of the first composite robot;
step S232: constructing a three-dimensional space model based on the first bearing space data, wherein the three-dimensional space model is used for carrying out bearing space detection on the first composite robot;
step S233: and obtaining the first carrying and loading detection model on the basis of the three-dimensional space model.
Specifically, the process of constructing the first transport loading detection model is as follows:
by analyzing the model of the first composite robot and constructing a three-dimensional space model by taking the bearing platform of the first composite robot as a plane, namely, the spatial data is constructed in the three-dimensional space on the basis of the side data of the bearing table of the first composite robot, so as to obtain a three-dimensional space model, because the collision detection of the first compound robot is based on the initial collision during the operation, the bearing space data of the first compound robot is analyzed according to the three-dimensional space model, and comparing the three-dimensional space model constructed by the first composite robot with real-time bearing space detection, and therefore, the first carrying and loading detection model is constructed, and the first carrying and loading detection model can carry out loading space overflow detection according to real-time input data so as to output the first loading detection data.
Further, according to the output information of the first carrying and loading detection model, the robot bearing space data is determined, and the operation of the first mechanical arm control model is stopped and restrained by judging the bearing limit weight of the first composite robot, so that the data interaction and accurate identification of the first carrying and loading detection model and the first mechanical arm control model are achieved.
Further, the step S300 of inputting the first operation material information into the loading composite detection model and obtaining first output information according to the loading composite detection model in the embodiment of the present application further includes:
step S310: inputting the first operation material information into the loading composite detection model, and acquiring a first loading and unloading control parameter according to the first mechanical arm control model in the loading composite detection model;
step S320: executing material loading operation according to the first loading and unloading control parameter to obtain first loading output information;
step S330: and inputting the first loading output information into the first carrying and loading detection model for spatial analysis to obtain the first loading detection data.
Specifically, the first operating material information is input into the loading composite detection model, and according to the first mechanical arm control model of the loading composite detection model, mechanical arm control data is identified for material characteristics input in real time, so as to obtain the first loading and unloading control parameters, such as positioning data, torque data, displacement data, and the like. Furthermore, the first loading and unloading control parameter outputted by the first robot arm control model is used as a trigger for carrying out carrying material updating detection on the first carrying and loading detection model, so as to obtain first output information outputted by the first carrying and loading detection model, wherein the first output information is information outputted by the loading composite detection model, the first loading detection data is generated, namely the first composite robot arm is operated, the first composite robot arm is cooperated with detection data of a bearing operation platform, according to the comparison data result after carrying out space overflow detection on the basis of the three-dimensional space structure of the first composite robot, for example, due to the geometrical characteristics of materials during space bearing, the quasi-three-dimensional space constructed by the platform has overflow, so that secondary planning of a path and a detection area is carried out through overflow output, therefore, based on the model information interaction, the first loading detection data is output as a subsequent data updating condition, and the operation safety is improved.
Further, after performing secondary planning on the first driving data group based on the first loading detection data and obtaining a second driving data group, step S600 in this embodiment of the present application further includes:
step S610 a: generating first safety auxiliary data according to the second driving data group;
step S620 a: connecting a safety auxiliary module of the first compound robot according to a first connecting instruction;
step S630 a: obtaining a configuration auxiliary partition for the security auxiliary module;
step S640 a: and updating the data of the configuration auxiliary partition of the safety auxiliary module of the first composite robot according to the first safety auxiliary data.
Specifically, the second driving data group is optimized data obtained after secondary planning is performed on the first driving data group based on first loading detection data, and includes a second path data set and a second detection data set; the first safety auxiliary data is corresponding early warning adjustment data generated according to the optimized second driving data group; the safety auxiliary module is a module which avoids the fault of the robot or the passing of the personnel in the composite robot, such as an alarm sound, an alarm vision and an emergency stop.
After the second driving data group path optimization and the detection area optimization, the early warning module of the first composite robot is adaptively adjusted, so that the first composite robot can adaptively adjust the early warning data during operation, and a matched result is achieved. Therefore, all the configuration auxiliary partitions of the safety auxiliary module are determined, the correlation between each partition and material bearing is analyzed and marked, the related auxiliary partition is determined to serve as an adjustment basis, the marked partition of the first composite robot in the safety auxiliary module is adaptively updated according to the first safety auxiliary data, and the operation safety early warning accurate control is guaranteed through adjustment of the adaptive module.
Further, step S600 in this embodiment of the present application further includes step S650:
step S651: when the first compound robot triggers early warning information, receiving first early warning information;
step S652: determining a first safety auxiliary partition according to the first early warning information;
step S653: determining whether to trigger a first switching feature based on the first security auxiliary partition;
step S654: and if the first switching characteristic is triggered, switching a first replacement robot to execute the transferring operation according to the first switching instruction.
Specifically, when the first compound robot triggers the warning information, it indicates that the current working state of the first compound robot is in an abnormal state, and in order to ensure normal operation of the production line, the first replacement robot performs a transfer operation on the first compound robot by adopting a trigger switching instruction.
The first early warning information is an early warning signal triggered by the first composite robot; the first safety auxiliary partition is an early warning block corresponding to the analyzed first early warning information, so that block positioning is carried out according to the first early warning information, and fault content is determined; the first switching feature is a switching feature preset in advance, for example, if the fault content is recovered to be normal, the operation time is long or the early warning is complicated, and if the first safety auxiliary partition triggers the first switching feature, the transfer operation is performed. And if the first switching characteristic is not triggered by the safety auxiliary partition, obtaining a first adjustment prompt to perform self-control adjustment.
When the first safety auxiliary partition triggers a switching feature, namely a marked partition, the switching condition is met, standby state query is carried out based on a first query instruction, adaptive robot screening is carried out according to material features, for example, robots of different models are allocated to carry out data file migration, so that the first replacement robot is moved, the second driving data group can be migrated, the first replacement robot is moved and configured, operation flexibility is improved, and efficiency and automation are guaranteed.
Further, as shown in fig. 3, step S600 in the embodiment of the present application further includes:
step S610 b: obtaining a first planning change degree according to the first loading detection data, wherein the first planning change degree is the change complexity of quadratic planning;
step S620 b: analyzing the guide path change nodes of the first composite robot according to the first planning change degree, and determining multi-type path nodes;
step S630 b: dividing detection areas of the first composite robot at various path nodes according to the various path nodes to generate various node detection areas;
step S640 b: and optimizing the second driving data group according to the multi-class node detection areas to obtain a third driving data group.
Specifically, a first planning change degree is obtained by performing secondary planning change complexity analysis on the first loading detection data, wherein the change complexity analysis can determine the first planning change degree in multiple dimensions through data planning associated quantity, a data change threshold value and computer computation complexity. The multi-type path nodes are main change nodes of the guiding path, including deceleration nodes, acceleration nodes, turning nodes and the like, and are classified based on different path nodes, for example, speed change nodes, turning change nodes, stopping operation nodes and the like, so that detection area division can be performed on the first composite robot according to different types of path nodes, detection data in the second driving data group are optimized according to multi-type node detection areas generated after division, the third driving data group is obtained, and control accuracy of the composite robot in operation is improved.
Furthermore, each class division of the multi-class node detection areas is different, for example, when an acceleration node and a deceleration node change linearly, the coverage area and the coverage layer of the detection areas are optimized, so that robot collision caused by speed change is prevented; or the area radius, the angle increase and the like of the detection area of the composite robot are optimized when the composite robot turns to the node, so that the control accuracy of the composite robot in terms of collision prevention and safety is further improved.
Compared with the prior art, the invention has the following beneficial effects:
1. the method comprises the steps of analyzing geometric information and material attribute information of a first compound robot according to a first intelligent detection device to obtain first operation material information, constructing a loading compound detection model according to equipment information of the first compound robot, inputting the operation material information obtained through detection into the loading compound model, outputting a compound detection result according to the loading compound detection model to generate first loading detection data, further analyzing a path and a detection area based on historical preset path planning to obtain a first driving data group of the first compound robot, obtaining a second driving data group according to the first loading detection data for the first driving planning, controlling the first compound robot based on the second driving data group, and analyzing and detecting material characteristics through the compound detection model, therefore, the driving data is optimized according to the cooperative loading detection result output by the composite detection model, and the technical effect of intelligent control is further achieved.
2. The hot compress characteristic extraction and the intersection of the hot compress parts are obtained according to two dimensions of life and work of the user, so that the obtained first intersection recommendation information is used as a first level in the first recommendation information to be output, the output hierarchy of the first recommendation information is improved, and effective information is provided for user interaction decision.
2. The method is characterized in that the method comprises the steps of determining a first safety auxiliary data, carrying out analysis marking on the first safety auxiliary data, and carrying out safety early warning on the first composite robot in the safety auxiliary module.
Example two
Based on the same inventive concept as the control method of the composite AGV robot in the foregoing embodiment, the present invention further provides a control system of the composite AGV robot, as shown in fig. 4, the system includes:
the first obtaining unit 11 is configured to obtain first operation material information of the first composite robot according to a first intelligent detection device, where the first operation material information includes material attribute information and material geometric information;
a second obtaining unit 12, where the second obtaining unit 12 is configured to obtain a loading composite detection model according to the first composite robot;
the first input unit 13 is configured to input the first operation material information into the loading composite detection model, and obtain first output information according to the loading composite detection model;
a first generating unit 14, wherein the first generating unit 14 is configured to generate first loading detection data according to the first output information;
a third obtaining unit 15, where the third obtaining unit 15 is configured to obtain a first driving data set by acquiring path data and detection data of a first preset guidance planning path, where the first driving data set includes a first path data set and a first detection data set, and the first path data set and the first detection data set are distributed in a time series and are in one-to-one correspondence;
a fourth obtaining unit 16, where the fourth obtaining unit 16 is configured to perform quadratic programming on the first driving data group based on the first loading detection data to obtain a second driving data group;
a first control unit 17, wherein the first control unit 17 is configured to control the first compound robot according to the second driving data set.
Further, the system further comprises:
a fifth obtaining unit configured to obtain first arm operation information according to the first compound robot;
a sixth obtaining unit, configured to perform data model training according to the first robot operation information to obtain a first robot control model, where the first robot control model is obtained by training a training material feature set and a robot control parameter set to converge;
a seventh obtaining unit configured to obtain a first carrying loading detection model;
the first construction unit is used for constructing the loading composite detection model based on the connection of the first mechanical arm control model with the first carrying loading detection model, wherein the data interaction of the first mechanical arm control model and the first carrying loading detection model is realized.
Further, the system further comprises:
an eighth obtaining unit, configured to obtain first bearing space data of the first composite robot;
a second construction unit, configured to construct a three-dimensional space model based on the first bearer space data, where the three-dimensional space model is used to perform bearer space detection on the first composite robot;
a ninth obtaining unit configured to obtain the first carrying/loading detection model based on the three-dimensional space model.
Further, the system further comprises:
a tenth obtaining unit, configured to input the first operation material information into the loading composite detection model, and obtain a first loading and unloading control parameter according to the first robot arm control model in the loading composite detection model;
an eleventh obtaining unit configured to obtain first loading output information based on performing a material loading operation according to the first loading and unloading control parameter;
a twelfth obtaining unit, configured to obtain a first migration node according to the first data analysis result;
and the second input unit is used for inputting the first loading output information into the first carrying and loading detection model for spatial analysis to obtain the first loading detection data.
Further, the system further comprises:
a second generation unit configured to generate first safety assistance data from the second drive data group;
a first connection unit for connecting a safety assistance module of the first compound robot according to a first connection instruction;
a thirteenth obtaining unit, configured to obtain a configuration auxiliary partition of the security auxiliary module;
a first updating unit, configured to perform data updating on the configuration auxiliary partition of the safety auxiliary module of the first composite robot according to the first safety auxiliary data.
Further, the system further comprises:
the first triggering unit is used for receiving first early warning information when the first composite robot triggers the early warning information;
the first determining unit is used for determining a first safety auxiliary partition according to the first early warning information;
a first determination unit to determine whether to trigger a first switching feature based on the first security auxiliary partition;
and the first switching unit is used for switching the first replacement robot to execute the transferring operation according to the first switching instruction if the first switching characteristic is triggered.
Further, the system further comprises:
a fourteenth obtaining unit, configured to obtain a first planning change degree according to the first loading detection data, where the first planning change degree is a change complexity of quadratic planning;
a second determining unit, configured to analyze the guide path change node of the first composite robot according to the first planning change degree, and determine a plurality of types of path nodes;
a third generating unit, configured to divide detection areas of the first composite robot at various types of path nodes according to the various types of path nodes, and generate various types of node detection areas;
a fifteenth obtaining unit, configured to optimize the second driving data group according to the multi-class node detection regions, and obtain a third driving data group.
Various changes and specific examples of the control method of the composite AGV robot in the first embodiment of fig. 1 are also applicable to the control system of the composite AGV robot in this embodiment, and through the foregoing detailed description of the control method of the composite AGV robot, those skilled in the art can clearly know the implementation method of the control system of the composite AGV robot in this embodiment, so for the brevity of the description, detailed description is omitted here.
EXAMPLE III
The electronic device of the embodiment of the present application is described below with reference to fig. 5.
Fig. 5 illustrates a schematic structural diagram of an electronic device according to an embodiment of the present application.
Based on the inventive concept of the control method of a composite AGV robot in the previous embodiment, the present invention further provides a control system of a composite AGV robot, on which a computer program is stored, which when executed by a processor implements the steps of any one of the methods of the control system of a composite AGV robot described above.
Where in fig. 5 a bus architecture (represented by bus 300), bus 300 may include any number of interconnected buses and bridges, bus 300 linking together various circuits including one or more processors, represented by processor 302, and memory, represented by memory 304. The bus 300 may also link together various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. A bus interface 305 provides an interface between the bus 300 and the receiver 301 and transmitter 303. The receiver 301 and the transmitter 303 may be the same element, i.e., a transceiver, providing a means for communicating with various other systems over a transmission medium. The processor 302 is responsible for managing the bus 300 and general processing, and the memory 304 may be used for storing data used by the processor 302 in performing operations.
The embodiment of the application provides a control method of a composite AGV robot, the method is applied to a control system of the composite AGV robot, the system is in communication connection with a first intelligent detection device, and the method comprises the following steps: according to the first intelligent detection device, first operation material information of a first compound robot is obtained, wherein the first operation material information comprises material attribute information and material geometric information; acquiring a loading composite detection model according to the first composite robot; inputting the first operation material information into the loading composite detection model, and acquiring first output information according to the loading composite detection model; generating first loading detection data according to the first output information; acquiring first driving data groups by acquiring path data and detection data of a first preset guide planning path, wherein the first driving data groups comprise first path data sets and first detection data sets, and the first path data sets and the first detection data sets are distributed in a time sequence and are in one-to-one correspondence; performing secondary planning on the first driving data group based on the first loading detection data to obtain a second driving data group; and controlling the first compound robot according to the second driving data group. The technical problems that in the prior art, when an AGV composite robot is in operation control, due to the fact that the number of functional units is large, adaptability cooperative control is weak along with the change of materials, and the intelligent degree is low are solved, the characteristic analysis and detection of the materials through a composite detection model are achieved, driving data are optimized according to cooperative loading detection results output by the composite detection model, and then the technical effect of intelligent control is achieved.
Those of ordinary skill in the art will understand that: the various numbers of the first, second, etc. mentioned in this application are only used for the convenience of description and are not used to limit the scope of the embodiments of this application, nor to indicate the order of precedence. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "at least one" means one or more. At least two means two or more. "at least one," "any," or similar expressions refer to any combination of these items, including any combination of singular or plural items. For example, at least one (one ) of a, b, or c, may represent: a, b, c, a-b, a-c, b-c, or a-b-c, wherein a, b, c may be single or multiple.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the application to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable system. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website site, computer, server, or data center to another website site, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device including one or more available media integrated servers, data centers, and the like. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
The various illustrative logical units and circuits described in this application may be implemented or operated upon by general purpose processors, digital signal processors, Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other programmable logic systems, discrete gate or transistor logic, discrete hardware components, or any combination thereof. A general-purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing systems, e.g., a digital signal processor and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a digital signal processor core, or any other similar configuration.
The steps of a method or algorithm described in the embodiments herein may be embodied directly in hardware, in a software element executed by a processor, or in a combination of the two. The software elements may be stored in RA memory, flash memory, RO memory, EPRO memory, EEPRO memory, registers, hard disk, removable disk, CD-RO, or any other form of storage medium known in the art. For example, a storage medium may be coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC, which may be disposed in a terminal. In the alternative, the processor and the storage medium may reside in different components within the terminal. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Although the present application has been described in conjunction with specific features and embodiments thereof, it will be evident that various modifications and combinations can be made thereto without departing from the spirit and scope of the application. Accordingly, the specification and figures are merely exemplary of the application as defined in the appended claims and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of the application. It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the present application and its equivalent technology, it is intended that the present application include such modifications and variations.
Claims (10)
1. A control method of a composite AGV robot is applied to a control system of the composite AGV robot, the system is in communication connection with a first intelligent detection device, and the method comprises the following steps:
according to the first intelligent detection device, first operation material information of a first compound robot is obtained, wherein the first operation material information comprises material attribute information and material geometric information;
acquiring a loading composite detection model according to the first composite robot;
inputting the first operation material information into the loading composite detection model, and acquiring first output information according to the loading composite detection model;
generating first loading detection data according to the first output information;
acquiring first driving data groups by acquiring path data and detection data of a first preset guide planning path, wherein the first driving data groups comprise first path data sets and first detection data sets, and the first path data sets and the first detection data sets are distributed in a time sequence and are in one-to-one correspondence;
performing secondary planning on the first driving data group based on the first loading detection data to obtain a second driving data group;
and controlling the first compound robot according to the second driving data group.
2. The method of claim 1, wherein the obtaining a loaded composite inspection model from the first composite robot, the method further comprises:
acquiring first mechanical arm operation information according to the first compound robot;
performing data model training according to the first mechanical arm operation information to obtain a first mechanical arm control model, wherein the first mechanical arm control model is obtained by training a training material characteristic set and a mechanical arm control parameter set to convergence;
obtaining a first carrying and loading detection model;
and establishing the loading composite detection model based on the connection of the first mechanical arm control model and the first carrying loading detection model, wherein the data interaction between the first mechanical arm control model and the first carrying loading detection model is realized.
3. The method of claim 2, wherein said obtaining a first carrier loading inspection model from said first compound robot, said method further comprises:
obtaining first bearing space data of the first composite robot;
constructing a three-dimensional space model based on the first bearing space data, wherein the three-dimensional space model is used for carrying out bearing space detection on the first composite robot;
and obtaining the first carrying and loading detection model on the basis of the three-dimensional space model.
4. The method of claim 3, wherein said inputting said first work material information into said load composite inspection model, obtaining a first output information based on said load composite inspection model, said method further comprising:
inputting the first operation material information into the loading composite detection model, and acquiring a first loading and unloading control parameter according to the first mechanical arm control model in the loading composite detection model;
executing material loading operation according to the first loading and unloading control parameter to obtain first loading output information;
and inputting the first loading output information into the first carrying and loading detection model for spatial analysis to obtain the first loading detection data.
5. The method of claim 1, wherein the quadratic programming the first drive data set based on the first load sense data, after obtaining a second drive data set, the method further comprises:
generating first safety auxiliary data according to the second driving data group;
connecting a safety auxiliary module of the first compound robot according to a first connecting instruction;
obtaining a configuration auxiliary partition for the security auxiliary module;
and updating the data of the configuration auxiliary partition of the safety auxiliary module of the first composite robot according to the first safety auxiliary data.
6. The method of claim 5, wherein the method further comprises:
when the first compound robot triggers early warning information, receiving first early warning information;
determining a first safety auxiliary partition according to the first early warning information;
determining whether to trigger a first switching feature based on the first security auxiliary partition;
and if the first switching characteristic is triggered, switching a first replacement robot to execute the transferring operation according to the first switching instruction.
7. The method of claim 1, wherein the method further comprises:
obtaining a first planning change degree according to the first loading detection data, wherein the first planning change degree is the change complexity of quadratic planning;
analyzing the guide path change nodes of the first composite robot according to the first planning change degree, and determining multi-type path nodes;
dividing detection areas of the first composite robot at various path nodes according to the various path nodes to generate various node detection areas;
and optimizing the second driving data group according to the multi-class node detection areas to obtain a third driving data group.
8. A control system for a composite AGV robot, the system comprising:
the first obtaining unit is used for obtaining first operation material information of the first composite robot according to the first intelligent detection device, wherein the first operation material information comprises material attribute information and material geometric information;
a second obtaining unit for obtaining a loading composite detection model according to the first composite robot;
the first input unit is used for inputting the first operation material information into the loading composite detection model and obtaining first output information according to the loading composite detection model;
a first generating unit configured to generate first loading detection data according to the first output information;
a third obtaining unit, configured to obtain a first driving data group by acquiring path data and detection data of a first preset guidance planning path, where the first driving data group includes a first path data set and a first detection data set, and the first path data set and the first detection data set are distributed in a time series and are in one-to-one correspondence;
a fourth obtaining unit, configured to perform quadratic programming on the first driving data group based on the first loading detection data to obtain a second driving data group;
a first control unit for controlling the first compound robot according to the second driving data set.
9. An electronic device, comprising: a processor coupled to a memory for storing a program, wherein the program, when executed by the processor, causes a system to perform the steps of the method of any of claims 1 to 7 when executed.
10. A computer-readable storage medium, characterized in that the storage medium has stored thereon a computer program which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
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