CN113510711B - Industrial robot action execution monitoring and regulating method based on artificial intelligence and cloud monitoring and regulating platform - Google Patents
Industrial robot action execution monitoring and regulating method based on artificial intelligence and cloud monitoring and regulating platform Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1656—Programme controls characterised by programming, planning systems for manipulators
- B25J9/1664—Programme controls characterised by programming, planning systems for manipulators characterised by motion, path, trajectory planning
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J13/00—Controls for manipulators
- B25J13/08—Controls for manipulators by means of sensing devices, e.g. viewing or touching devices
- B25J13/085—Force or torque sensors
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J13/00—Controls for manipulators
- B25J13/08—Controls for manipulators by means of sensing devices, e.g. viewing or touching devices
- B25J13/087—Controls for manipulators by means of sensing devices, e.g. viewing or touching devices for sensing other physical parameters, e.g. electrical or chemical properties
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J15/00—Gripping heads and other end effectors
- B25J15/08—Gripping heads and other end effectors having finger members
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J18/00—Arms
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1679—Programme controls characterised by the tasks executed
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1679—Programme controls characterised by the tasks executed
- B25J9/1689—Teleoperation
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
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Abstract
The invention discloses an industrial robot action execution monitoring and regulating method based on artificial intelligence and a cloud monitoring and regulating platform. The method for monitoring and regulating the motion execution of the industrial robot based on artificial intelligence comprises the following steps: counting the number of robots corresponding to the operation area; acquiring basic operation information corresponding to each robot in the working area; acquiring basic information corresponding to an object to be clamped of each robot; acquiring current corresponding clamping basic information of each robot in the operation area; collecting operation track information corresponding to each robot in the operation area; analyzing the operation information corresponding to each robot in the operation area; according to the analysis result, remotely regulating and controlling the robot to be regulated and controlled; the problem that the accuracy of robot operation action execution cannot be effectively guaranteed by an existing industrial robot action execution monitoring and regulating method is solved, and then the regulating and controlling effect on the actions of the robots is effectively improved.
Description
Technical Field
The invention belongs to the technical field of robot action execution monitoring, and relates to an industrial robot action execution monitoring and regulating method and a cloud monitoring and regulating platform based on artificial intelligence.
Background
With the continuous development of science and technology, robots are gradually used by many manufacturing industries to replace traditional manual operation in heavy and highly-repeatable flow operation, and in order to guarantee the corresponding operation quality and operation efficiency of the robots, the action execution conditions in the operation process of the robots need to be monitored and regulated;
the existing monitoring and regulating method for the motion execution of the industrial robot mainly focuses on monitoring and regulating fault information corresponding to the motion execution of the robot, and the accuracy of the motion execution in the specific operation process of the robot is not analyzed, so that the existing monitoring and regulating method for the motion execution of the industrial robot also has certain disadvantages.
Disclosure of Invention
In view of the above, in order to solve the problems in the background art, an industrial robot motion execution monitoring and controlling method and a cloud monitoring and controlling platform based on artificial intelligence are provided, so that real-time monitoring and accurate control of the motion of the industrial robot are realized;
the purpose of the invention can be realized by the following technical scheme:
the invention provides an industrial robot action execution monitoring and regulating method based on artificial intelligence, which comprises the following steps:
s1, counting the number of robots: the robot number statistics is used for counting the number of robots corresponding to the working area, numbering the counted robots according to a preset sequence, and sequentially marking the robots as 1,2,. J.. I,. N;
s2, acquiring basic operation information of the robot: the basic operation information of the robot is obtained and used for obtaining basic operation information corresponding to each robot in the working area, wherein the basic operation information of the robot comprises the position of the working area of the robot and standard operation track information, and then a basic operation information set J of each robot is constructed w (J w 1,J w 2,...J w i,...J w n)，J w i represents w basic operation information corresponding to the ith robot in the operation area, w represents basic operation information of the robot, w = a1, a2, a1 and a2 respectively represent the position of the operation area of the robot and standard operation track information;
s3, the robot clamps and takes the basic information acquisition of the object: the robot clamping object basic information acquisition is used for acquiring basic information corresponding to each robot to-be-clamped object, and further acquiring the basic information corresponding to each robot to-be-clamped object;
s4, acquiring basic information of robot clamping: the robot clamping basic information is used for acquiring clamping basic information currently corresponding to each robot in the operation area;
s5, collecting robot operation track information: the robot operation track information acquisition is used for acquiring operation track information corresponding to each robot in the operation area so as to acquire an operation track corresponding to each robot in the operation area;
s6, robot operation information analysis: the robot operation information analysis is used for analyzing the clamping basic information and the operation track information corresponding to each robot in the operation area, and acquiring the number of the robots to be regulated and controlled and the parameters to be regulated and controlled corresponding to each robot to be regulated and controlled;
s7, remote operation regulation: and regulating and controlling the parameters to be regulated and controlled corresponding to the robots to be regulated and controlled according to the number of the robots to be regulated and the parameters to be regulated and controlled corresponding to the robots to be regulated and controlled and the standard values corresponding to the parameters to be regulated and controlled.
Further, the basic information of the object to be gripped by each robot in the working area includes size information corresponding to the object to be gripped by each robot, weight corresponding to the object to be gripped by each robot, and position corresponding to the object to be gripped by each robot, so as to construct a basic information set W of the object to be gripped by each robot z (W z 1,W z 2,...W z i,...W z n)，W z i represents the z-th basic information corresponding to the ith robot to be used for clamping the object in the working area, z represents the basic information of the object to be clamped by the robot, and z = b1, b2, b3, b1, b2 and b3 represent the size information, weight and position corresponding to the object to be clamped by the robot respectively.
Further, the robot clamping basic information includes the clamping force currently corresponding to the robot in the working area, the arm clamping angle currently corresponding to the robot and the opening distance of the clamping jaw currently corresponding to the robot, and according to the obtained clamping basic information corresponding to each robot in the working area, a clamping basic information set P of each robot is constructed e (P e 1,P e 2,...P e i,...P e n)，P e i represents the e-th clamping basic information corresponding to the ith robot in the working area, e represents the clamping basic information of the robot, and e = c1, c2, c3, c1, c2 and c3 respectively represent the clamping force and the arm corresponding to the robot at presentA gripping angle and a jaw opening distance.
Further, the specific collecting process of the robot operation track information is as follows: arranging target detection points in each robot clamping area of the operation area, collecting operation processes of the robots according to a preset collection time interval by using cameras corresponding to the operation area, further obtaining operation images corresponding to the robots in each collection time period of the operation area, extracting positions corresponding to the target detection points in the operation images of the robots in each collection time period of the operation area, converting the positions corresponding to the target detection points in the operation images of the robots in each collection time period of the operation area into a coordinate form, further obtaining position coordinates corresponding to the target detection points of the robots in each collection time period of the operation area, introducing the position coordinates corresponding to the target detection points of the robots in each collection time period of the operation area into a third-party platform, further generating operation tracks corresponding to the robots in the operation area through a third-party website, and recording the operation tracks as actual operation tracks of the robots.
Further, the robot clamping basic information analysis is used for analyzing clamping force corresponding to each robot, acquiring a clamping object basic information set of each robot, further acquiring a weight corresponding to an object to be clamped by each robot, further acquiring a standard clamping force corresponding to the object to be clamped by each robot according to the weight corresponding to the object to be clamped by each robot, acquiring a clamping basic information set of each robot, further acquiring a clamping force currently corresponding to each robot in the operation area, comparing the clamping force currently corresponding to each robot with the standard clamping force corresponding to the object to be clamped by each robot, and further counting the clamping force deviation influence coefficient of each robot.
Further, the robot presss from both sides and gets basic information analysis and be used for pressing from both sides the arm clamp that corresponds to each robot in this operation area and get the angle and carry out the analysis, press from both sides the basic information set according to each robot, and then acquire the arm clamp that each robot currently corresponds in this operation area and get the angle, press from both sides the object basic information set according to the robot, acquire each robot and treat to press from both sides the position that the clamp object corresponds, treat according to each robot and press from both sides the position that the clamp object corresponds, acquire each robot and treat to press from both sides the standard arm clamp that the clamp object position corresponds and get the angle, press from both sides the arm clamp that angle that each robot currently corresponds and each get each robot and treat to press from both sides the standard arm clamp that the clamp object position corresponds and get the angle and compare, and then statistics each robot arm clamp gets the angle deviation influence coefficient.
Further, the robot clamping basic information analysis is used for analyzing the opening distance of each robot clamping jaw in the operation area, obtaining the current corresponding opening distance of each robot clamping jaw in the operation area and the corresponding size information of each robot clamping object in the operation area, obtaining the thickness corresponding to each robot clamping object in the operation area according to the corresponding size information of each robot clamping object in the operation area, further obtaining the standard clamping jaw opening distance corresponding to the thickness of each robot clamping object in the operation area, comparing the current corresponding clamping jaw opening distance of each robot in the operation area with the standard clamping jaw opening distance corresponding to the thickness of each robot clamping object in the operation area, and further counting the influence coefficient of the opening distance deviation of each robot clamping jaw.
The robot working track information analysis is used for analyzing the working tracks corresponding to the robots in the working area, acquiring the actual working tracks and the standard working track information corresponding to the robots in the working area, further acquiring the position coordinates corresponding to the target detection points of the robots in each acquisition time period of the working area according to the actual working tracks corresponding to the robots in the working area, simultaneously acquiring the standard position coordinates corresponding to the target detection points of the robots in each acquisition time period of the working area according to the standard working track information corresponding to the robots in the working area, comparing the actual working tracks corresponding to the robots in the working area with the standard working tracks corresponding to the robots in the working area, and further counting the deviation influence coefficient of the working tracks of the robots.
Further, the robot operation information analysis is used for performing comprehensive analysis on the clamping basic information and the operation track information corresponding to each robot in the operation area, further counting the comprehensive influence coefficient of the action execution deviation of each robot, and acquiring the number of the robots to be regulated and controlled corresponding to the operation area, the position of the operation area corresponding to each robot to be regulated and controlled, and the parameters to be regulated and controlled of each robot to be slowly regulated and controlled according to the counted comprehensive influence coefficient of the action execution deviation of each robot.
The second aspect of the invention provides a cloud monitoring, regulating and controlling platform which comprises a processor, a machine-readable storage medium and a network interface, wherein the machine-readable storage medium, the network interface and the processor are connected through a bus system, the network interface is used for being in communication connection with at least one industrial robot action execution monitoring terminal, the machine-readable storage medium is used for storing programs, instructions or codes, and the processor is used for executing the programs, the instructions or the codes in the machine-readable storage medium so as to execute the artificial intelligence-based industrial robot action execution monitoring, regulating and controlling method.
The invention has the beneficial effects that:
(1) According to the method for monitoring and regulating the motion of the industrial robot based on artificial intelligence, the basic information of the object clamped by the robot, the clamping basic information and the operation track information are collected and analyzed, so that the problems that the precision of the robot operation motion execution cannot be effectively guaranteed and the robot operation execution efficiency cannot be effectively guaranteed by the existing method for monitoring and regulating the motion of the industrial robot are solved, the regulation and control effect on the motion of the robot is effectively improved, the operation quality of the robot is greatly improved, and the operation production progress of the robot is effectively guaranteed.
(2) According to the invention, the three-dimensional laser scanner and the pressure sensor are utilized to collect the basic information of the object clamped by each robot, so that the collection efficiency of the basic information of the object clamped by each robot and the accuracy of the collection result are greatly improved, and the reference and the authenticity of the subsequent operation information analysis result of each robot are effectively ensured.
(3) According to the invention, by remotely regulating and controlling each robot to be regulated and controlled, the response efficiency of the regulating and controlling signal of each robot to be regulated and controlled is effectively ensured, the regulating and controlling efficiency of each robot to be regulated and controlled is greatly improved, and meanwhile, the intellectualization and the precision of the regulation and control of each robot to be regulated and controlled are also greatly improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a diagram of the steps of the method of the present invention.
Detailed Description
While the foregoing is directed to embodiments of the present invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof, and the scope thereof is determined by the claims that follow.
Referring to fig. 1, an industrial robot motion execution monitoring and controlling method based on artificial intelligence includes the following steps:
s1, counting the number of robots: the robot number statistics is used for counting the number of robots corresponding to the working area, numbering the counted robots according to a preset sequence, and sequentially marking the robots as 1,2,. J.. I,. N;
s2, acquiring basic operation information of the robot: the basic operation information of the robot is obtained and used for obtaining basic operation information corresponding to each robot in the working area, wherein the basic operation information of the robot comprises the position of the working area of the robot and standard operation track information, and then a basic operation information set J of each robot is constructed w (J w 1,J w 2,...J w i,...J w n)，J w i represents the w-th basic operation information corresponding to the ith robot in the operation area, and w represents the basic operation information of the robotW = a1, a2, a1 and a2 respectively represent the robot working area position and the standard working trajectory information;
s3, the robot clamps and takes the basic information acquisition of the object: the robot clamping object basic information acquisition is used for acquiring basic information corresponding to the object to be clamped of each robot, and further acquiring the basic information corresponding to the object to be clamped of each robot;
specifically, the basic information of the object to be gripped by each robot in the working area includes size information corresponding to the object to be gripped by each robot, a weight corresponding to the object to be gripped by each robot, and a position corresponding to the object to be gripped by each robot, so as to construct a basic information set W of the object to be gripped by each robot z (W z 1,W z 2,...W z i,...W z n)，W z i represents the z-th basic information corresponding to the ith robot to be used for clamping the object in the working area, z represents the basic information corresponding to the robot to be used for clamping the object, and z = b1, b2, b3, b1, b2 and b3 respectively represent the size information, weight and position corresponding to the robot to be used for clamping the object.
Wherein, operation object basic information obtains including a plurality of basic information acquisition units, and then utilizes the three-dimensional laser scanner in the basic information acquisition unit to wait to press from both sides that each robot of this operation area corresponds and get the object and scan the shooting, and then obtains that each robot of this operation going region waits to press from both sides the size information that the object corresponds, utilizes pressure sensor among the basic information acquisition unit to obtain each robot and waits to press from both sides the weight that the object corresponds of getting, utilizes proximity sensor to obtain each robot simultaneously and waits to press from both sides the object corresponding position of getting.
The size information corresponding to the object to be clamped of each robot comprises length, width and thickness.
According to the embodiment of the invention, the three-dimensional laser scanner and the pressure sensor are utilized to collect the basic information of the object clamped by each robot, so that the collection efficiency of the basic information of the object clamped by each robot and the accuracy of the collection result are greatly improved, and the referential and authenticity of the subsequent operation information analysis result of each robot are effectively ensured.
S4, acquiring basic information of robot clamping: the machine clamping basic information is used for acquiring clamping basic information currently corresponding to each robot in the operation area;
specifically, the robot clamping basic information includes a clamping force currently corresponding to the robot in the working area, a currently corresponding arm clamping angle and a currently corresponding jaw opening distance, and according to the obtained clamping basic information corresponding to each robot in the working area, a clamping basic information set P of each robot is constructed e (P e 1,P e 2,...P e i,...P e n)，P e i represents the e-th clamping basic information corresponding to the ith robot in the working area, e represents the clamping basic information of the robot, and e = c1, c2, c3, c1, c2 and c3 respectively represent the current clamping force, the arm clamping angle and the jaw opening distance corresponding to the robot.
S5, collecting robot operation track information: the robot operation track information acquisition is used for acquiring operation track information corresponding to each robot in the operation area so as to acquire an operation track corresponding to each robot in the operation area;
specifically, the specific acquisition process of the robot operation track information is as follows: arranging target detection points in each robot clamping area of the operation area, collecting operation processes of the robots according to a preset collection time interval by using cameras corresponding to the operation area, further obtaining operation images corresponding to the robots in each collection time period of the operation area, extracting positions corresponding to the target detection points in the operation images of the robots in each collection time period of the operation area, converting the positions corresponding to the target detection points in the operation images of the robots in each collection time period of the operation area into a coordinate form, further obtaining position coordinates corresponding to the target detection points of the robots in each collection time period of the operation area, introducing the position coordinates corresponding to the target detection points of the robots in each collection time period of the operation area into a third-party platform, further generating operation tracks corresponding to the robots in the operation area through a third-party website, and recording the operation tracks as actual operation tracks of the robots.
S6, robot operation information analysis: the robot operation information analysis is used for analyzing the clamping basic information and the operation track information corresponding to each robot in the operation area, and acquiring the number of the robots to be regulated and controlled and the parameters to be regulated and controlled corresponding to each robot to be regulated and controlled;
specifically, the robot clamping basic information analysis is used for analyzing clamping force corresponding to each robot, acquiring that each robot clamps an object basic information set, further acquiring a weight corresponding to each robot to be clamped, according to the weight corresponding to each robot to be clamped, further acquiring a standard clamping force corresponding to each robot to be clamped, acquiring that each robot clamps a basic information set, further acquiring a clamping force currently corresponding to each robot in the operation area, comparing the clamping force currently corresponding to each robot with the standard clamping force corresponding to each robot to be clamped, and further counting the clamping force deviation influence coefficient of each robot.
Wherein, the calculation formula of the clamping force deviation influence coefficient of each robot is
α d C1, representing the clamping force deviation influence coefficient corresponding to the d robot in the working area d C1, representing the current corresponding clamping force of the ith robot in the operation area d standard And d represents the standard clamping force corresponding to the object to be clamped of each robot in the d-th robot in the working area, and d =1,2.
Specifically, the robot clamping basic information analysis is used for analyzing the arm clamping angles corresponding to the robots in the operation area, the basic information set is clamped according to the robots, then the arm clamping angles corresponding to the robots in the operation area at present are obtained, the object basic information set is clamped according to the robots, the positions corresponding to the objects to be clamped by the robots are obtained, the standard arm clamping angles corresponding to the positions where the objects to be clamped by the robots are located are obtained according to the positions corresponding to the objects to be clamped by the robots, the arm clamping angles corresponding to the robots at present are compared with the standard arm clamping angles corresponding to the positions where the objects to be clamped by the robots are located, and then the influence coefficient of the arm clamping angle deviation of each robot is counted.
Wherein, the calculation formula of the impact coefficient of the clamping angle deviation of each robot arm is
β d Representing the influence coefficient of the arm clamping angle deviation corresponding to the d-th robot in the working area, c2 d The current corresponding arm clamping angle of the d-th robot in the working area is shown, c2 d standard of And the standard arm clamping angle corresponding to the position of the object to be clamped of the d-th robot in the working area is shown.
Specifically, the robot clamping basic information analysis is used for analyzing the clamping jaw opening distance of each robot in the operation area, obtaining the clamping jaw opening distance currently corresponding to each robot in the operation area and the size information corresponding to the object to be clamped of each robot in the operation area, obtaining the thickness corresponding to the object to be clamped of each robot in the operation area according to the size information corresponding to the object to be clamped of each robot in the operation area, further obtaining the standard clamping jaw opening distance corresponding to the thickness of the object to be clamped of each robot in the operation area, comparing the clamping jaw opening distance currently corresponding to each robot in the operation area with the standard clamping jaw opening distance corresponding to the thickness of the object to be clamped of each robot in the operation area, and further counting the influence coefficient of the clamping jaw opening distance deviation of each robot.
Wherein, the calculation formula of the influence coefficient of the deviation of the opening distance of each robot clamping jaw is
δ d C3, a deviation influence coefficient corresponding to the d-th robot gripper opening distance in the working area d Indicates the current corresponding jaw opening distance of the d-th robot in the working area, c3 d standard And the standard clamping jaw opening distance corresponding to the thickness of the object to be clamped of the d-th robot in the working area is shown.
Specifically, the robot work track information analysis is used for analyzing work tracks corresponding to the robots in the work area, acquiring actual work tracks and standard work track information corresponding to the robots in the work area, further acquiring position coordinates corresponding to the target detection points of the robots in each acquisition time period of the work area according to the actual work tracks corresponding to the robots in the work area, simultaneously acquiring standard position coordinates corresponding to the target detection points of the robots in each acquisition time period of the work area according to the standard work track information corresponding to the robots in the work area, comparing the actual work tracks corresponding to the robots in the work area with the standard work tracks corresponding to the robots in the work area, and further counting the work track deviation influence coefficients of the robots.
Wherein the calculation formula of the operation track deviation influence coefficient of each robot is
γ d A deviation influence coefficient (x) indicating the operation locus of the d-th robot in the operation area d t ,y d t ) The position coordinates corresponding to the d-th robot target detection point in the t-th acquisition time period of the operation area are shown, (x) d standard t ,y d standard t ) And the standard position coordinates corresponding to the d-th robot target detection point in the t-th collection time period of the operation area are represented, t represents the collection time period, and t =1,2,. J,. M.
Specifically, the robot operation information analysis is used for performing comprehensive analysis on the clamping basic information and the operation track information corresponding to each robot in the operation area, further counting the comprehensive influence coefficient of the motion execution deviation of each robot, and acquiring the number of the robots to be controlled in the operation area, the position of the operation area corresponding to each robot to be controlled, and the parameters to be controlled of each robot to be controlled slowly according to the counted comprehensive influence coefficient of the motion execution deviation of each robot.
Wherein, the specific analysis process is as follows: according to the statistical clamping force deviation influence coefficient of each robot, the clamping angle deviation influence coefficient of the robot arm and the opening distance deviation influence coefficient of each robot clamping jaw, the comprehensive clamping information deviation influence coefficient of each robot is further statistical, further according to the statistical clamping information comprehensive clamping influence coefficient of each robot and the working track deviation influence coefficient of each robot, the comprehensive action execution deviation influence coefficient of each robot is statistical, the statistical action execution deviation comprehensive influence coefficient of each robot is compared with the action execution deviation influence coefficient corresponding to the preset dimensional regulation and control early warning, if the comprehensive action execution deviation influence coefficient corresponding to a certain robot is larger than the action execution deviation influence coefficient corresponding to the preset regulation and control early warning, the robot is marked as a robot needing to be regulated, the number corresponding to the robot needing to be regulated in the working area is further statistical, and the parameter needing to be regulated and controlled corresponding to each robot needing to be regulated is obtained according to the clamping basic information and the working track information corresponding to each robot needing to be regulated.
The parameters to be regulated comprise clamping angles, clamping force and the like.
Wherein, the calculation formula of the comprehensive deviation influence coefficient of the clamping basic information of each robot is
φ d And a comprehensive deviation influence coefficient corresponding to the d-th robot gripping basic information of the working area.
Wherein, the calculation formula of the comprehensive influence coefficient of the action execution deviation of each robot is
λ d And the comprehensive influence coefficient of the motion execution deviation corresponding to the d-th robot in the working area is shown.
According to the embodiment of the invention, by acquiring and analyzing the basic information of the object clamped by the robot, the basic information of clamping and the operation track information, the problems that the operation execution precision of the robot cannot be effectively guaranteed and the operation execution efficiency of the robot cannot be effectively guaranteed by the existing monitoring and regulating method for the operation execution of the industrial robot are solved, the regulation and control effect on the operation of the robot is effectively improved, the operation quality of the robot is greatly improved, and the operation production progress of the robot is effectively guaranteed.
S7, remote operation regulation: and regulating and controlling the parameters to be regulated and controlled corresponding to the robots to be regulated and controlled according to the number of the robots to be regulated and the parameters to be regulated and controlled corresponding to the robots to be regulated and controlled and the standard values corresponding to the parameters to be regulated and controlled.
Specifically, the position of each robot working area to be regulated and controlled and the parameters to be regulated and controlled corresponding to each robot to be regulated and controlled are obtained, and regulation and control are performed according to the standard values corresponding to the parameters to be regulated and controlled of each robot to be regulated and controlled.
According to the embodiment of the invention, the response efficiency of the regulation and control signal of each robot to be regulated and controlled is effectively ensured by remotely regulating and controlling each robot to be regulated and controlled, so that the regulation and control efficiency of each robot to be regulated and controlled is greatly improved, and the intellectualization and the precision of the regulation and control of each robot to be regulated and controlled are also greatly improved.
The invention provides a cloud monitoring, regulating and controlling platform which comprises a processor, a machine-readable storage medium and a network interface, wherein the machine-readable storage medium, the network interface and the processor are connected through a bus system, the network interface is used for being in communication connection with at least one industrial robot action execution monitoring terminal, the machine-readable storage medium is used for storing programs, instructions or codes, and the processor is used for executing the programs, the instructions or the codes in the machine-readable storage medium so as to execute the artificial intelligence-based industrial robot action execution monitoring, regulating and controlling method.
The foregoing is merely exemplary and illustrative of the principles of the present invention and various modifications, additions and substitutions of the specific embodiments described herein may be made by those skilled in the art without departing from the principles of the present invention or exceeding the scope of the claims set forth herein.
Claims (9)
1. An industrial robot action execution monitoring and regulating method based on artificial intelligence is characterized in that: the method comprises the following steps:
s1, counting the number of robots: the number statistics of the robots is used for counting the number of the robots corresponding to the operation area, numbering the counted robots according to a preset sequence, and marking the counted robots as 1,2,. I,. N in sequence;
s2, acquiring basic operation information of the robot: the basic operation information of the robot is obtained and used for obtaining basic operation information corresponding to each robot in the working area, wherein the basic operation information of the robot comprises the position of the working area of the robot and standard operation track information, and then a basic operation information set J of each robot is constructed w (J w 1,J w 2,...J w i,...J w n)，J w i represents w basic work information corresponding to the ith robot in the work area, w represents basic work information of the robot, w = a1, a2, a1 and a2 represent the position of the work area of the robot and standard work track information respectively;
s3, the robot clamps and takes the basic information acquisition of the object: the robot clamping object basic information acquisition is used for acquiring basic information corresponding to the object to be clamped of each robot, and further acquiring the basic information corresponding to the object to be clamped of each robot;
s4, acquiring basic information of robot clamping: the robot clamping basic information is used for acquiring clamping basic information currently corresponding to each robot in the operation area;
s5, collecting robot operation track information: the robot operation track information acquisition is used for acquiring operation track information corresponding to each robot in the operation area so as to acquire an operation track corresponding to each robot in the operation area;
s6, robot operation information analysis: the robot operation information analysis is used for analyzing the clamping basic information and the operation track information corresponding to each robot in the operation area, and acquiring the number of the robots to be regulated and controlled and the parameters to be regulated and controlled corresponding to each robot to be regulated and controlled;
s7, remote operation regulation: regulating and controlling the parameters to be regulated and controlled corresponding to the robots to be regulated and controlled according to the number of the robots to be regulated and the parameters to be regulated and controlled corresponding to the robots to be regulated and controlled and the standard values corresponding to the parameters to be regulated and controlled;
the robot operation information analysis is used for carrying out comprehensive analysis on the clamping basic information and the operation track information corresponding to each robot in the operation area, further counting the comprehensive influence coefficient of the action execution deviation of each robot, and acquiring the number of the robots to be regulated and controlled, the positions of the operation areas corresponding to the robots to be regulated and controlled and the parameters of the robots to be regulated and controlled slowly according to the counted comprehensive influence coefficient of the action execution deviation of each robot.
2. The method for monitoring and regulating the motion execution of the industrial robot based on the artificial intelligence as claimed in claim 1, wherein: the basic information of the object to be clamped of each robot in the operation area comprises the corresponding size information of the object to be clamped of each robot, the corresponding weight of the object to be clamped of each robot and the corresponding position of the object to be clamped of each robot, and then a basic information set W of the object to be clamped of each robot is constructed z (W z 1,W z 2,...W z i,...W z n)，W z i represents the z-th basic information corresponding to the ith robot to be used for clamping the object in the working area, z represents the basic information of the object to be clamped by the robot, and z = b1, b2, b3, b1, b2 and b3 represent the size information, weight and position corresponding to the object to be clamped by the robot respectively.
3. The method for monitoring and regulating the motion execution of the industrial robot based on the artificial intelligence as claimed in claim 1, wherein: the robot clamping basic information comprises the clamping force currently corresponding to the robot in the operation area, the arm clamping angle currently corresponding to the robot in the operation area and the opening distance of the clamping jaw currently corresponding to the robot in the operation area, and according to the obtained clamping basic information corresponding to each robot in the operation area, a clamping basic information set P of each robot is constructed e (P e 1,P e 2,...P e i,...P e n)，P e i represents the e-th gripping basic information corresponding to the ith robot in the working area, e represents the gripping basic information of the robot, and e = c1, c2, c3, c1, c2And c3 respectively represents the current corresponding clamping force, the current corresponding clamping angle of the machine arm and the current corresponding opening distance of the clamping jaw of the robot.
4. The method for monitoring and regulating the motion execution of the industrial robot based on the artificial intelligence as claimed in claim 1, wherein: the specific collecting process of the robot operation track information is as follows: arranging target detection points in each robot clamping area of the operation area, collecting operation processes of the robots according to a preset collection time interval by using cameras corresponding to the operation area, further obtaining operation images corresponding to the robots in each collection time period of the operation area, extracting positions corresponding to the target detection points in the operation images of the robots in each collection time period of the operation area, converting the positions corresponding to the target detection points in the operation images of the robots in each collection time period of the operation area into a coordinate form, further obtaining position coordinates corresponding to the target detection points of the robots in each collection time period of the operation area, introducing the position coordinates corresponding to the target detection points of the robots in each collection time period of the operation area into a third-party platform, further generating operation tracks corresponding to the robots in the operation area through a third-party website, and recording the operation tracks as actual operation tracks of the robots.
5. The method for monitoring and regulating the motion execution of the industrial robot based on the artificial intelligence as claimed in claim 1, wherein: the robot presss from both sides and gets basic information analysis and be used for pressing from both sides the dynamics of getting that each robot corresponds and analyze, it gets each robot and presss from both sides the thing basic information set to get, and then it waits to press from both sides the weight that the thing corresponds to get each robot, and then it waits to press from both sides the standard clamp dynamics that the thing corresponds to get each robot, it gets each robot and presss from both sides the basic information set to get, and then it gets the dynamics of pressing from both sides that each robot that this operation area each robot corresponds at present to get, it gets the dynamics of pressing from both sides to compare each robot current corresponding clamp dynamics and each robot waits to press from both sides the standard clamp dynamics that the thing corresponds to get the dynamics, and then statistics each robot presss from both sides dynamics of pressing from both sides and gets coefficient of influence.
6. The method for monitoring and regulating the motion execution of the industrial robot based on the artificial intelligence as claimed in claim 1, wherein: the robot clamping basic information analysis is used for analyzing arm clamping angles corresponding to all robots in the operation area, a basic information set is clamped according to all robots, then arm clamping angles corresponding to all robots in the operation area at present are obtained, an object basic information set is clamped according to the robots, positions corresponding to objects to be clamped of all the robots are obtained, standard arm clamping angles corresponding to positions where the objects to be clamped of all the robots are located are obtained according to positions where the objects to be clamped of all the robots are located, the arm clamping angles corresponding to all the robots at present are compared with the standard arm clamping angles corresponding to the positions where the objects to be clamped of all the robots are located, and then the influence coefficient of clamping angle deviation of all the robots is counted.
7. The method for monitoring and regulating the motion execution of the industrial robot based on the artificial intelligence as claimed in claim 1, wherein: the robot clamping basic information analysis is used for analyzing the clamping jaw opening distance of each robot in the operation area, the clamping jaw opening distance corresponding to each robot in the operation area at present and the size information corresponding to the object to be clamped by each robot in the operation area are obtained, the thickness corresponding to the object to be clamped by each robot in the operation area is obtained according to the size information corresponding to the object to be clamped by each robot in the operation area, the standard clamping jaw opening distance corresponding to the thickness of the object to be clamped by each robot in the operation area is further obtained, the clamping jaw opening distance corresponding to each robot in the operation area at present is compared with the standard clamping jaw opening distance corresponding to the thickness of the object to be clamped by each robot in the operation area, and then the influence coefficient of the deviation of the clamping jaw opening distance of each robot is counted.
8. The method for monitoring and regulating the motion execution of the industrial robot based on the artificial intelligence as claimed in claim 1, wherein: the robot operation track information analysis is used for analyzing operation tracks corresponding to the robots in the operation area, acquiring actual operation tracks and standard operation track information corresponding to the robots in the operation area, acquiring position coordinates corresponding to the target detection points of the robots in each acquisition time period of the operation area according to the actual operation tracks corresponding to the robots in the operation area, acquiring standard position coordinates corresponding to the target detection points of the robots in each acquisition time period of the operation area according to the standard operation track information corresponding to the robots in the operation area, comparing the actual operation tracks corresponding to the robots in the operation area with the standard operation tracks corresponding to the robots in the operation area, and counting deviation influence coefficients of the operation tracks of the robots.
9. The utility model provides a cloud monitoring regulation and control platform which characterized in that: the cloud monitoring, regulating and controlling platform comprises a processor, a machine readable storage medium and a network interface, wherein the machine readable storage medium, the network interface and the processor are connected through a bus system, the network interface is used for being in communication connection with at least one industrial robot action execution monitoring terminal, the machine readable storage medium is used for storing programs, instructions or codes, and the processor is used for executing the programs, the instructions or the codes in the machine readable storage medium so as to execute the artificial intelligence based industrial robot action execution monitoring, regulating and controlling method in any one of claims 1-8.
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Denomination of invention: A method for monitoring and controlling the execution of industrial robots based on artificial intelligence and a cloud monitoring and control platform Granted publication date: 20221216 Pledgee: Bank of China Limited Xiamen Haicang sub branch Pledgor: Xiamen Fengyuan robot Co.,Ltd. Registration number: Y2024110000024 |