CN117389341B - Speed control system for robot module transmission - Google Patents
Speed control system for robot module transmission Download PDFInfo
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- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D13/00—Control of linear speed; Control of angular speed; Control of acceleration or deceleration, e.g. of a prime mover
- G05D13/62—Control of linear speed; Control of angular speed; Control of acceleration or deceleration, e.g. of a prime mover characterised by the use of electric means, e.g. use of a tachometric dynamo, use of a transducer converting an electric value into a displacement
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Abstract
The invention discloses a speed control system for robot module transmission, which is characterized in that the speed of a robot module is regulated based on requirements by a user, an actual speed of the robot module is regulated to be gradually close to a target speed value by a control regulation module, an acquired robot module motion video is analyzed frame by frame, each frame of guide rail image, each frame of gear image and each frame of belt image of the robot module corresponding to the current monitoring time range are analyzed, and a speed regulation evaluation index of the robot module in the current monitoring time range is obtained, so that the states of a guide rail, a gear and a belt in the current monitoring time range of the robot module are reflected, the speed of the robot module is optimally regulated based on the matching and comparison results of the speed regulation evaluation index, and the intelligent degree and the service life of the robot module are improved based on the requirements of the user.
Description
Technical Field
The invention relates to the technical field of speed control of robot modules, in particular to a speed control system for robot module transmission.
Background
The robot module transmission refers to a mechanical transmission device used for transmitting power and motion in a robot system. The transmission device can convert electric energy into mechanical energy, and power is transmitted to each part of the robot through gears and belts so as to realize movement and operation of the robot.
In the process of robot module transmission, the speed of the robot module needs to be controlled, but the speed control system in the prior art has the following defects in the use process:
the states of the gears, the belts and the guide rails of the robot module cannot be analyzed in the process of adjusting the speed based on the user requirements, so that the speed is optimally adjusted, and the service life of the robot module is reduced;
the speed cannot be secondarily regulated according to the temperature change condition of the motor in the working process of the robot, so that the intelligent degree is low;
for this purpose, a speed control system for the robot module drive is proposed.
Disclosure of Invention
In view of this, the present invention provides a speed control system for driving a robot module, which can analyze the states of gears, belts and guide rails of the robot module, so as to optimize and adjust the speed, so as to solve the problems set forth in the above-mentioned background art.
The aim of the invention can be achieved by the following technical scheme: the system comprises a speed setting module, a state analysis module, a self-adaptive compensation module and a control and adjustment module;
the speed setting module sets an expected speed value of the robot module of the corresponding model based on the requirement, namely a target speed value which needs to be reached by the robot module, generates an initial adjusting signal and sends the initial adjusting signal to the control adjusting module, and the control adjusting module adjusts the actual speed of the robot module to gradually approach the target speed value;
the state analysis module monitors and analyzes the influence parameters of the robot module in the process of adjusting the actual speed to obtain a speed adjustment evaluation index SDW;
the method comprises the following steps:
setting a monitoring time range in an adjustment process, acquiring a motion video of a robot module in the current monitoring time range, and analyzing the acquired motion video of the robot module frame by frame to obtain each frame of reference image of the robot module corresponding to the current time range; the reference image comprises a guide rail image, a gear image and a belt image of the robot module;
extracting each frame of reference image in the current monitoring time range corresponding to the robot module, and preprocessing;
detecting the abrasion areas in the extracted guide rail images of each frame by utilizing an image processing technology to obtain the abrasion areas in the guide rail images of each frame in the current monitoring time range corresponding to the robot module, and integrating the abrasion areas corresponding to the obtained guide rail images of each frame according to the positions to obtain the abrasion areas of the guide rails in the current monitoring time range corresponding to the robot module;
extracting features of all the abrasion areas of the integrated guide rail, calculating the area size of each abrasion area of the guide rail by using pixel count, and marking as MJi; wherein i=1, 2, l; l is the total number of worn areas in the current monitoring time range;
meanwhile, the difference between the gray scale of each abrasion area and the difference between the gray scale of each abrasion area are compared, so that an abrasion depth value MYi is obtained;
substituting each group of wear area values MJi and wear depth values MYi of the robot module corresponding to the current guide rail into a formulaObtaining a wear influence value MSi of each group of wear areas; wherein a1 and a2 are the impact weight factors of each set of wear area values MJi and wear depth values MYi, respectively;
comparing each group of abrasion influence values MSi of the robot module corresponding to the current guide rail with a set corresponding threshold value, and marking the group of abrasion influence values as abrasion high values if the group of abrasion influence values are larger than the corresponding threshold value; counting the number of the high abrasion values to obtain a high number GZ;
calculating the difference value between each group of wear high values and the corresponding threshold value, and then calculating the average value between each group of obtained difference values to obtain a high average value GT;
extracting the maximum value in each group of abrasion influence values MSi as the abrasion peak value of the guide rail in the current monitoring time range and marking the abrasion peak value as GP;
substituting each group of abrasion area values MJi of the robot module corresponding to the current guide rail into a formulaCalculating to obtain a wear total value GR; therein MJi Threshold value Representing a corresponding threshold value set by the wear-affecting value MSi; gfi is an impact weight factor for each set of wear area values MJi based on wear position;
extracting allowable high-value number GZ of current model robot module from database Allow for Allows for high average GT Allow for Allowable wear peak GP Allow for Total allowable wear value GR Allow for ；
Based on the comparison result of the number L of the current wear areas of the robot module, substituting the parameters into a formula1) Or (2), specifically:calculating to obtain a guide rail influence evaluation index PGY of the robot module in the current monitoring time range; wherein jk1, jk2, jk3 and jk4 are the impact weight factors of the high value number GZ, the high average GT, the wear peak GP and the wear total value GR respectively;
extracting tooth profile information from each frame of gear image of the current robot module; aligning gear images of successive frames to a set reference coordinate system for comparison;
comparing tooth profiles of the aligned gear images by utilizing point-to-point matching;
calculating a distance change value between adjacent teeth, specifically:
distance change value = current frame tooth distance-original frame tooth distance; the tooth distance of the current frame is the profile tooth distance of the gear after comparison, and the tooth distance of the original frame is the profile tooth distance of the original gear after comparison;
carrying out average value calculation on each group of distance variation values to obtain a pitch estimated value CJ of the corresponding gear;
calculating an angle change value between adjacent teeth, specifically:
angle change value = current frame tooth angle-original frame tooth angle; the current frame tooth angle is the profile tooth angle of the gear after comparison, and the original frame tooth angle is the profile tooth angle of the original gear after comparison;
carrying out average value calculation on each group of angle change values to obtain tooth angle estimated values CT of corresponding gears;
calculating the area change value between adjacent teeth, specifically:
area change value = current frame gear tooth profile area-original frame gear tooth profile area; the tooth profile area of the current frame gear is the profile area of the gear after comparison, and the tooth profile area of the original frame gear is the profile area of the original gear after comparison;
carrying out average value calculation on each group of area change values to obtain tooth surface estimated values CE of the corresponding gears;
extracting reference pitch estimation CJ of corresponding gear of current model robot module from database Reference to Reference tooth angle estimation CT Reference to Reference tooth surface estimate CE Reference to ；
Substituting the pitch estimation CJ, the tooth angle estimation CT and the tooth surface estimation CE of the corresponding gear of the current robot module into a formulaCalculating to obtain a gear abrasion value CH of the corresponding gear; wherein ty1, ty2 and ty3 are the influence weight factors of the pitch estimation CJ, the tooth angle estimation CT and the tooth surface estimation CE respectively;
extracting weight coefficients of all gears in the current model robot module from a database, multiplying the gear abrasion value CH of the corresponding gear with the corresponding weight coefficient to obtain a gear influence value, accumulating the gear influence values of all gears in the current robot module to obtain a gear influence evaluation index PTM of the robot module in the current monitoring time range;
dividing and extracting belt areas in each frame of belt image of the current robot module;
tracking the position change of the same characteristic point among different frames by using a target tracking algorithm based on a SIFT method, and analyzing the position change of the characteristic point, namely calculating the displacement change condition of the characteristic point of the belt in continuous frames to obtain each group of displacement change values;
carrying out average value calculation on each group of displacement variation values to obtain a variation average value PA;
calculating displacement variation values of each group by using a standard deviation formula to obtain a variation estimated value PB;
extracting the maximum value in each group of displacement variation values as a variation peak value PC;
extracting reference change mean value PA of current model robot module belt from database Reference to Reference variation estimation PB Reference to Reference variation peak value PC Reference to ；
Substituting the above parameters into the formulaCalculating to obtain a belt use evaluation index PBD of the robot module within the current monitoring time range; wherein er1, er2 and er3 are the influence weight factors of the change mean value PA, the change estimated value PB and the change peak value PC, respectively;
substituting the guide rail influence evaluation index PGY, the gear influence evaluation index PTM and the belt use evaluation index PBD of the robot module in the current monitoring time range into a formulaObtaining a speed adjustment evaluation index SDW of the robot module; wherein uy1, uy2 and uy3 are the influence weighting factors of the guide rail influence evaluation index PGY, the gear influence evaluation index PTM and the belt usage evaluation index PBD, respectively;
matching the speed adjustment evaluation index SDW of the robot module with a plurality of set value ranges, wherein each value range corresponds to one speed adjustment range; obtaining a speed adjusting range in the current monitoring time range of the robot module, comparing the speed adjusting range with an expected speed value set by a user, and if the expected speed value is higher than the speed adjusting range, taking the highest speed value in the speed adjusting range as a marking speed and generating an optimized adjusting signal to be sent to a control adjusting module; if the desired speed value is at or below the speed adjustment range, no signal is generated;
the self-adaptive compensation module analyzes the motor temperature change parameters in a preset time period of the robot module to obtain an optimized adjustment evaluation index YHT;
the method comprises the following steps:
acquiring motor temperature values at different moments in a preset monitoring time period of the robot module by using a temperature sensor;
calculating the average value of the temperature values of the motors at different moments to obtain the average value of the motors; extracting the maximum value in the temperature values of each group of motors as a peak value; calculating the temperature values of each group of motors by using a standard deviation formula to obtain a computer variable value;
acquiring a preset temperature model in a database, respectively matching a machine mean value, a machine peak value and a machine variable value in a current preset monitoring time period with a plurality of corresponding value ranges to obtain a bottom circle area, a top circle area and a vertical distance between two circles of the model, and constructing the model based on the obtained values;
grooving is carried out on the bottom surface of a preset temperature model based on the constructed model, so that a temperature combination model in the current preset monitoring time period is obtained;
calculating a volume value TY of the construction model, and carrying out difference calculation between the volume value TY of the construction model and a preset temperature model to obtain a volume difference TJ;
calculating the distance between the top circle edge point of the constructed model and the preset top circle edge point of the model, and marking the distance as a distance difference value (TR);
calculating the distance between the edge point of the base circle of the constructed model and the edge point of the preset base circle of the model, and marking the distance as a distance difference value II TU;
performing difference calculation on a preset model machine variable value and a constructed model machine variable value to obtain a distance difference value of three TP;
substituting the above parameters into the formulaCalculating to obtain an optimized adjustment evaluation index YHT of the robot module in the current preset monitoring time period; wherein mn1, mn2, mn3, mn4 and mn5 are respectively the volume value TY, the volume difference TJ, the distance difference one TR, the distance difference two TU and the influence weight factors of the distance difference three TP;
the temperature change can influence the performance and the precision of the motor, compensation or self-adaptive adjustment is needed according to the temperature change, and stable speed control is realized by monitoring and analyzing the temperature change of the robot module in the working process.
Matching the optimal adjustment evaluation index YHT of the robot module with a plurality of set value ranges, wherein each value range corresponds to a speed adjustment interval; obtaining a speed regulation interval within the current monitoring time range of the robot module, generating a stage regulation signal and sending the stage regulation signal to a control regulation module;
and the control and adjustment module receives the corresponding adjustment signal, correspondingly adjusts the speed of the robot module, and adjusts the speed based on the speed value of the current robot module when receiving the phase adjustment signal.
Meanwhile, in the process, the load change of the robot module is monitored and analyzed to obtain a power output evaluation index GVS;
the method comprises the following steps:
the method comprises the steps that a pressure sensor is used for obtaining the load change condition of a robot module in a preset monitoring time period and substituting a line graph for representation;
the method comprises the steps of obtaining load values at different moments in a preset monitoring time period, and calculating an average value to obtain a load average value XQ;
drawing numerical value points in the line graph corresponding to load magnitude values at different moments in a preset monitoring time period, and connecting adjacent numerical value points to obtain a load change line;
calculating the slope of each load change line and the included angle between each load change line and the horizontal line; when the included angle between the load change line and the horizontal line is an acute angle, marking the slope of the load change line as a descending slope; when the included angle between the load change line and the horizontal is an obtuse angle, marking the slope of the load change line as an ascending slope; summing all the values of the falling slopes to obtain a falling estimated value, summing all the values of the rising slopes and taking absolute values to obtain a rising estimated value, and calculating the ratio of the rising estimated value to the falling estimated value to obtain a load variable value XT;
obtaining a load peak value XG with the largest value in each group of load values as a current preset monitoring time period;
extracting a reference load mean value XQ of a robot module of a corresponding model from a database Reference to Reference load variation value XT Reference to Reference load peak XG Reference to ；
Substituting the above parameters into the formulaCalculating to obtain a power output evaluation index GVS of the robot module in the current preset monitoring time period; wherein pw1, pw2, and pw3 are load averages X, respectivelyQ, load variation XT, and load peak XG;
comparing the power output evaluation index GVs within the current preset monitoring time period with a corresponding threshold range, if the power output evaluation index GVs is higher than the corresponding threshold range, matching the power output evaluation index GVs with the corresponding value range, setting an increment corresponding to one power output in each value range respectively, obtaining an output power adjustment increment, and adjusting by a control adjustment module;
if the power output evaluation index GVS is lower than the corresponding threshold range, the power output evaluation index GVS is matched with the corresponding value range, the reduction amount of each value range corresponding to one power output is set, the output power adjustment reduction amount is obtained, and the adjustment is carried out through the control adjustment module.
Compared with the prior art, the invention has the beneficial effects that:
according to the invention, the speed of the robot module is adjusted based on the requirement, the actual speed of the robot module is controlled to be gradually close to a target speed value, a monitoring time range in the adjustment process is set, a motion video of the robot module in the current monitoring time range is acquired, the acquired motion video of the robot module is analyzed frame by frame, each frame of guide rail image, each frame of gear image and each frame of belt image in the corresponding current monitoring time range of the robot module are analyzed, the speed adjustment evaluation index of the robot module in the current monitoring time range is obtained, the guide rail, the gears and the belt states of the robot module in the current monitoring time range are reflected, the speed of the robot module is optimally adjusted based on the requirement of the user, and the intelligent degree and the service life of the robot module are improved;
according to the invention, the optimal adjustment evaluation index of the robot module in the current preset monitoring time period is obtained by analyzing the temperature value of the motor in the preset monitoring time period in the working process of the robot module, so that the temperature change condition of the current robot module motor is reflected, the optimal adjustment evaluation index of the robot module is matched with a plurality of set value ranges, and each value range corresponds to a speed adjustment interval; and obtaining a speed regulation interval within the current monitoring time range of the robot module, and correspondingly regulating the speed of the robot module based on the obtained speed regulation interval, so that stable speed control is realized.
Drawings
Further details, features and advantages of the present application are disclosed in the following description of exemplary embodiments, with reference to the following drawings, wherein:
FIG. 1 is a schematic block diagram of the present invention;
FIG. 2 is a temperature combination model diagram of the present invention;
fig. 3 is a load change line graph of the present invention.
Detailed Description
Several embodiments of the present application will be described in more detail below with reference to the accompanying drawings in order to enable those skilled in the art to practice the present application. This application may be embodied in many different forms and objects and should not be limited to the embodiments set forth herein. These embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art. The embodiments are not limiting of the present application.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and/or the present specification and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
Referring to fig. 1-3, a speed control system for driving a robot module includes a speed setting module, a state analysis module, an adaptive compensation module, and a control adjustment module;
the speed setting module sets an expected speed value of the robot module of the corresponding model based on the requirement, namely a target speed value which needs to be reached by the robot module, generates an initial adjusting signal and sends the initial adjusting signal to the control adjusting module, and the control adjusting module adjusts the actual speed of the robot module to gradually approach the target speed value;
the state analysis module monitors and analyzes the influence parameters of the robot module in the process of adjusting the actual speed to obtain a speed adjustment evaluation index SDW;
the method comprises the following steps:
setting a monitoring time range in an adjustment process, acquiring a motion video of a robot module in the current monitoring time range, and analyzing the acquired motion video of the robot module frame by frame to obtain each frame of reference image of the robot module corresponding to the current time range; the reference image comprises a guide rail image, a gear image and a belt image of the robot module;
extracting each frame of reference image in the current monitoring time range corresponding to the robot module, and preprocessing;
detecting the abrasion areas in the extracted guide rail images of each frame by utilizing an image processing technology to obtain the abrasion areas in the guide rail images of each frame in the current monitoring time range corresponding to the robot module, and integrating the abrasion areas corresponding to the obtained guide rail images of each frame according to the positions to obtain the abrasion areas of the guide rails in the current monitoring time range corresponding to the robot module;
extracting features of all the abrasion areas of the integrated guide rail, calculating the area size of each abrasion area of the guide rail by using pixel count, and marking as MJi; wherein i=1, 2, l; l is the total number of worn areas in the current monitoring time range;
meanwhile, the difference between the gray scale of each abrasion area and the difference between the gray scale of each abrasion area are compared, so that an abrasion depth value MYi is obtained;
substituting each group of wear area values MJi and wear depth values MYi of the robot module corresponding to the current guide rail into a formulaObtaining each group of wear areasThe wear influence value MSi; wherein a1 and a2 are the impact weight factors of each set of wear area values MJi and wear depth values MYi, respectively;
comparing each group of abrasion influence values MSi of the robot module corresponding to the current guide rail with a set corresponding threshold value, and marking the group of abrasion influence values as abrasion high values if the group of abrasion influence values are larger than the corresponding threshold value; counting the number of the high abrasion values to obtain a high number GZ;
calculating the difference value between each group of wear high values and the corresponding threshold value, and then calculating the average value between each group of obtained difference values to obtain a high average value GT;
extracting the maximum value in each group of abrasion influence values MSi as the abrasion peak value of the guide rail in the current monitoring time range and marking the abrasion peak value as GP;
substituting each group of abrasion area values MJi of the robot module corresponding to the current guide rail into a formulaCalculating to obtain a wear total value GR; therein MJi Threshold value Representing a corresponding threshold value set by the wear-affecting value MSi; gfi is an impact weight factor for each set of wear area values MJi based on wear position;
extracting allowable high-value number GZ of current model robot module from database Allow for Allows for high average GT Allow for Allowable wear peak GP Allow for Total allowable wear value GR Allow for ；
Based on the comparison result of the number L of the current wear areas of the robot module, substituting the parameters into the formula (1) or (2), specifically:calculating to obtain a guide rail influence evaluation index PGY of the robot module in the current monitoring time range; wherein jk1, jk2, jk3 and jk4 are the impact weight factors of the high value number GZ, the high average GT, the wear peak GP and the wear total value GR respectively;
extracting tooth profile information from each frame of gear image of the current robot module; aligning gear images of successive frames to a set reference coordinate system for comparison;
comparing tooth profiles of the aligned gear images by utilizing point-to-point matching;
calculating a distance change value between adjacent teeth, specifically:
distance change value = current frame tooth distance-original frame tooth distance; the tooth distance of the current frame is the profile tooth distance of the gear after comparison, and the tooth distance of the original frame is the profile tooth distance of the original gear after comparison;
carrying out average value calculation on each group of distance variation values to obtain a pitch estimated value CJ of the corresponding gear;
calculating an angle change value between adjacent teeth, specifically:
angle change value = current frame tooth angle-original frame tooth angle; the current frame tooth angle is the profile tooth angle of the gear after comparison, and the original frame tooth angle is the profile tooth angle of the original gear after comparison;
carrying out average value calculation on each group of angle change values to obtain tooth angle estimated values CT of corresponding gears;
calculating the area change value between adjacent teeth, specifically:
area change value = current frame gear tooth profile area-original frame gear tooth profile area; the tooth profile area of the current frame gear is the profile area of the gear after comparison, and the tooth profile area of the original frame gear is the profile area of the original gear after comparison;
carrying out average value calculation on each group of area change values to obtain tooth surface estimated values CE of the corresponding gears;
extracting reference pitch estimation CJ of corresponding gear of current model robot module from database Reference to Reference tooth angle estimation CT Reference to Reference tooth surface estimate CE Reference to ；
Substituting the pitch estimation CJ, the tooth angle estimation CT and the tooth surface estimation CE of the corresponding gear of the current robot module into a formulaCalculating to obtain a gear abrasion value CH of the corresponding gear; wherein ty1, ty2, and ty3 are teeth respectivelyAn influence weight factor of the pitch estimate CJ, the tooth angle estimate CT and the tooth surface estimate CE;
extracting weight coefficients of all gears in the current model robot module from a database, multiplying the gear abrasion value CH of the corresponding gear with the corresponding weight coefficient to obtain a gear influence value, accumulating the gear influence values of all gears in the current robot module to obtain a gear influence evaluation index PTM of the robot module in the current monitoring time range;
dividing and extracting belt areas in each frame of belt image of the current robot module;
tracking the position change of the same characteristic point among different frames by using a target tracking algorithm based on a SIFT method, and analyzing the position change of the characteristic point, namely calculating the displacement change condition of the characteristic point of the belt in continuous frames to obtain each group of displacement change values;
carrying out average value calculation on each group of displacement variation values to obtain a variation average value PA;
calculating displacement variation values of each group by using a standard deviation formula to obtain a variation estimated value PB;
extracting the maximum value in each group of displacement variation values as a variation peak value PC;
extracting reference change mean value PA of current model robot module belt from database Reference to Reference variation estimation PB Reference to Reference variation peak value PC Reference to ；
Substituting the above parameters into the formulaCalculating to obtain a belt use evaluation index PBD of the robot module within the current monitoring time range; wherein er1, er2 and er3 are the influence weight factors of the change mean value PA, the change estimated value PB and the change peak value PC, respectively;
substituting the guide rail influence evaluation index PGY, the gear influence evaluation index PTM and the belt use evaluation index PBD of the robot module in the current monitoring time range into a formulaObtaining a speed adjustment evaluation index SDW of the robot module; wherein uy1, uy2 and uy3 are the influence weighting factors of the guide rail influence evaluation index PGY, the gear influence evaluation index PTM and the belt usage evaluation index PBD, respectively;
matching the speed adjustment evaluation index SDW of the robot module with a plurality of set value ranges, wherein each value range corresponds to one speed adjustment range; obtaining a speed adjusting range in the current monitoring time range of the robot module, comparing the speed adjusting range with an expected speed value set by a user, and if the expected speed value is higher than the speed adjusting range, taking the highest speed value in the speed adjusting range as a marking speed and generating an optimized adjusting signal to be sent to a control adjusting module; if the desired speed value is at or below the speed adjustment range, no signal is generated;
the self-adaptive compensation module analyzes the motor temperature change parameters in a preset time period of the robot module to obtain an optimized adjustment evaluation index YHT;
the method comprises the following steps:
acquiring motor temperature values at different moments in a preset monitoring time period of the robot module by using a temperature sensor;
calculating the average value of the temperature values of the motors at different moments to obtain the average value of the motors; extracting the maximum value in the temperature values of each group of motors as a peak value; calculating the temperature values of each group of motors by using a standard deviation formula to obtain a computer variable value;
acquiring a preset temperature model in a database, respectively matching a machine mean value, a machine peak value and a machine variable value in a current preset monitoring time period with a plurality of corresponding value ranges to obtain a bottom circle area, a top circle area and a vertical distance between two circles of the model, and constructing the model based on the obtained values;
grooving is carried out on the bottom surface of a preset temperature model based on the constructed model, so that a temperature combination model in the current preset monitoring time period is obtained;
calculating a volume value TY of the construction model, and carrying out difference calculation between the volume value TY of the construction model and a preset temperature model to obtain a volume difference TJ;
calculating the distance between the top circle edge point of the constructed model and the preset top circle edge point of the model, and marking the distance as a distance difference value (TR);
calculating the distance between the edge point of the base circle of the constructed model and the edge point of the preset base circle of the model, and marking the distance as a distance difference value II TU;
performing difference calculation on a preset model machine variable value and a constructed model machine variable value to obtain a distance difference value of three TP;
substituting the above parameters into the formulaCalculating to obtain an optimized adjustment evaluation index YHT of the robot module in the current preset monitoring time period; wherein mn1, mn2, mn3, mn4 and mn5 are respectively the volume value TY, the volume difference TJ, the distance difference one TR, the distance difference two TU and the influence weight factors of the distance difference three TP;
it should be noted that, temperature change can influence the performance and the precision of motor, need compensate or self-adaptation adjustment according to temperature change, through monitoring analysis to the temperature change of robot module in the course of the work, realized stable speed control.
Matching the optimal adjustment evaluation index YHT of the robot module with a plurality of set value ranges, wherein each value range corresponds to a speed adjustment interval; obtaining a speed regulation interval within the current monitoring time range of the robot module, generating a stage regulation signal and sending the stage regulation signal to a control regulation module;
and the control and adjustment module receives the corresponding adjustment signal, correspondingly adjusts the speed of the robot module, and adjusts the speed based on the speed value of the current robot module when receiving the phase adjustment signal.
Meanwhile, monitoring and analyzing the load change of the robot module to obtain a power output evaluation index GVS;
the method comprises the following steps:
the method comprises the steps that a pressure sensor is used for obtaining the load change condition of a robot module in a preset monitoring time period and substituting a line graph for representation;
the method comprises the steps of obtaining load values at different moments in a preset monitoring time period, and calculating an average value to obtain a load average value XQ;
drawing numerical value points in the line graph corresponding to load magnitude values at different moments in a preset monitoring time period, and connecting adjacent numerical value points to obtain a load change line;
calculating the slope of each load change line and the included angle between each load change line and the horizontal line; when the included angle between the load change line and the horizontal line is an acute angle, marking the slope of the load change line as a descending slope; when the included angle between the load change line and the horizontal is an obtuse angle, marking the slope of the load change line as an ascending slope; summing all the values of the falling slopes to obtain a falling estimated value, summing all the values of the rising slopes and taking absolute values to obtain a rising estimated value, and calculating the ratio of the rising estimated value to the falling estimated value to obtain a load variable value XT;
obtaining a load peak value XG with the largest value in each group of load values as a current preset monitoring time period;
extracting a reference load mean value XQ of a robot module of a corresponding model from a database Reference to Reference load variation value XT Reference to Reference load peak XG Reference to ；
Substituting the above parameters into the formulaCalculating to obtain a power output evaluation index GVS of the robot module in the current preset monitoring time period; wherein pw1, pw2, and pw3 are the influence weight factors of the load mean XQ, the load variable XT, and the load peak XG, respectively;
comparing the power output evaluation index GVs within the current preset monitoring time period with a corresponding threshold range, if the power output evaluation index GVs is higher than the corresponding threshold range, matching the power output evaluation index GVs with the corresponding value range, setting an increment corresponding to one power output in each value range respectively, obtaining an output power adjustment increment, and adjusting by a control adjustment module;
if the power output evaluation index GVS is lower than the corresponding threshold range, matching the power output evaluation index GVS with the corresponding value range, setting a reduction amount corresponding to one power output of each value range respectively, obtaining an output power adjustment reduction amount, and adjusting through a control adjustment module;
the method also comprises the following steps of: and the system is used for storing the corresponding threshold value of each parameter of the robot module of the corresponding model and a preset model.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (7)
1. A speed control system for a robot module drive, comprising:
the speed setting module is used for setting expected speed values of the robot modules of the corresponding models based on requirements by a user, generating initial adjustment signals and sending the initial adjustment signals to the control adjustment module;
the state analysis module is used for monitoring and analyzing the influence parameters of the robot module in the process of adjusting the actual speed to obtain a speed adjustment evaluation index SDW, and specifically comprises the following steps:
s1: setting a monitoring time range in an adjustment process, acquiring a motion video of a robot module in the current monitoring time range, and analyzing the acquired motion video of the robot module frame by frame to obtain each frame of reference image of the robot module corresponding to the current time range; the reference image comprises a guide rail image, a gear image and a belt image of the robot module;
s2: extracting each frame of reference image in the current monitoring time range corresponding to the robot module, and preprocessing;
s3: analyzing a guide rail image, a gear image and a belt image of the robot module respectively to obtain a guide rail influence evaluation index PGY, a gear influence evaluation index PTM and a belt use evaluation index PBD;
s4: substituting the guide rail influence evaluation index PGY, the gear influence evaluation index PTM and the belt use evaluation index PBD of the robot module in the current monitoring time range into a formulaObtaining a speed adjustment evaluation index SDW of the robot module; wherein uy1, uy2 and uy3 are the influence weighting factors of the guide rail influence evaluation index PGY, the gear influence evaluation index PTM and the belt usage evaluation index PBD, respectively;
s5: matching the speed adjustment evaluation index SDW of the robot module with a plurality of set value ranges, wherein each value range corresponds to one speed adjustment range; obtaining a speed adjusting range in the current monitoring time range of the robot module, comparing the speed adjusting range with an expected speed value set by a user, and if the expected speed value is higher than the speed adjusting range, taking the highest speed value in the speed adjusting range as a marking speed and generating an optimized adjusting signal to be sent to a control adjusting module;
the self-adaptive compensation module is used for analyzing the temperature change parameters of the motor in a preset time period of the robot module to obtain an optimized adjustment evaluation index;
the control and adjustment module is used for receiving the corresponding adjustment signals and correspondingly adjusting the speed of the robot module; and meanwhile, monitoring and analyzing the load change of the robot module to obtain a power output evaluation index.
2. The system according to claim 1, wherein the specific steps of obtaining the guide rail impact evaluation index PGY are:
201: setting a monitoring time range in an adjustment process, acquiring a motion video of a robot module in the current monitoring time range, and analyzing the acquired motion video of the robot module frame by frame to obtain each frame of reference image of the robot module corresponding to the current time range; the reference image comprises a guide rail image, a gear image and a belt image of the robot module;
202: detecting the abrasion areas in the extracted guide rail images of each frame by utilizing an image processing technology to obtain the abrasion areas in the guide rail images of each frame in the current monitoring time range corresponding to the robot module, and integrating the abrasion areas corresponding to the obtained guide rail images of each frame according to the positions to obtain the abrasion areas of the guide rails in the current monitoring time range corresponding to the robot module;
203: extracting features of all the abrasion areas of the integrated guide rail, calculating the area size of each abrasion area of the guide rail by using pixel count, and marking as MJi; wherein i=1, 2, l; l is the total number of worn areas in the current monitoring time range; meanwhile, the difference between the gray scale of each abrasion area and the difference between the gray scale of each abrasion area are compared, so that an abrasion depth value MYi is obtained;
204: substituting each group of wear area values MJi and wear depth values MYi of the robot module corresponding to the current guide rail into a formulaObtaining a wear influence value MSi of each group of wear areas; wherein a1 and a2 are the impact weight factors of each set of wear area values MJi and wear depth values MYi, respectively; comparing each group of abrasion influence values MSi of the robot module corresponding to the current guide rail with a set corresponding threshold value, and marking the group of abrasion influence values as abrasion high values if the group of abrasion influence values are larger than the corresponding threshold value; counting the number of the high abrasion values to obtain a high number GZ; calculating the difference value between each group of wear high values and the corresponding threshold value, and then calculating the average value between each group of obtained difference values to obtain a high average value GT; extracting the maximum value in each group of abrasion influence values MSi as the abrasion peak value of the guide rail in the current monitoring time range and marking the abrasion peak value as GP;
substituting each group of abrasion area values MJi of the robot module corresponding to the current guide rail into a formulaCalculating to obtain a wear total value GR; therein MJi Threshold value Representing a corresponding threshold value set by the wear-affecting value MSi; gfi is an impact weight factor for each set of wear area values MJi based on wear position;
205: extracting allowable high-value number GZ of current model robot module from database Allow for Allows for high average GT Allow for Allowable wear peak GP Allow for Total allowable wear value GR Allow for ；
Based on the comparison result of the number L of the current wear areas of the robot module, substituting the parameters into the formula (1) or (2), specifically:
calculating to obtain a guide rail influence evaluation index PGY of the robot module in the current monitoring time range; wherein jk1, jk2, jk3 and jk4 are the impact weight factors of the high value number GZ, the high mean GT, the wear peak GP and the wear total value GR, respectively.
3. The system according to claim 2, wherein the specific steps of obtaining the gear impact assessment index PTM are:
301: extracting tooth profile information from each frame of gear image of the current robot module; aligning gear images of successive frames to a set reference coordinate system for comparison; comparing tooth profiles of the aligned gear images by utilizing point-to-point matching;
302: calculating a distance change value between adjacent teeth, specifically:
distance change value = current frame tooth distance-original frame tooth distance; the tooth distance of the current frame is the profile tooth distance of the gear after comparison, and the tooth distance of the original frame is the profile tooth distance of the original gear after comparison;
carrying out average value calculation on each group of distance variation values to obtain a pitch estimated value CJ of the corresponding gear;
calculating an angle change value between adjacent teeth, specifically:
angle change value = current frame tooth angle-original frame tooth angle; the current frame tooth angle is the profile tooth angle of the gear after comparison, and the original frame tooth angle is the profile tooth angle of the original gear after comparison;
carrying out average value calculation on each group of angle change values to obtain tooth angle estimated values CT of corresponding gears;
calculating the area change value between adjacent teeth, specifically:
area change value = current frame gear tooth profile area-original frame gear tooth profile area; the tooth profile area of the current frame gear is the profile area of the gear after comparison, and the tooth profile area of the original frame gear is the profile area of the original gear after comparison;
carrying out average value calculation on each group of area change values to obtain tooth surface estimated values CE of the corresponding gears;
303: extracting reference pitch estimation CJ of corresponding gear of current model robot module from database Reference to Reference tooth angle estimation CT Reference to Reference tooth surface estimate CE Reference to The method comprises the steps of carrying out a first treatment on the surface of the Substituting the pitch estimation CJ, the tooth angle estimation CT and the tooth surface estimation CE of the corresponding gear of the current robot module into a formulaCalculating to obtain a gear abrasion value CH of the corresponding gear; wherein ty1, ty2 and ty3 are the influence weight factors of the pitch estimation CJ, the tooth angle estimation CT and the tooth surface estimation CE respectively;
and extracting weight coefficients of all gears in the current model robot module from the database, multiplying the gear abrasion value CH of the corresponding gear with the corresponding weight coefficient to obtain a gear influence value, and accumulating the gear influence values of all gears in the current robot module to obtain a gear influence evaluation index PTM of the robot module in the current monitoring time range.
4. A speed control system for a robot module drive according to claim 3, wherein the specific steps of obtaining the belt usage evaluation index PBD are:
401: dividing and extracting belt areas in each frame of belt image of the current robot module; tracking the position change of the same characteristic point among different frames by using a target tracking algorithm based on a SIFT method, and analyzing the position change of the characteristic point, namely calculating the displacement change condition of the characteristic point of the belt in continuous frames to obtain each group of displacement change values; carrying out average value calculation on each group of displacement variation values to obtain a variation average value PA; calculating displacement variation values of each group by using a standard deviation formula to obtain a variation estimated value PB; extracting the maximum value in each group of displacement variation values as a variation peak value PC;
402: extracting reference change mean value PA of current model robot module belt from database Reference to Reference variation estimation PB Reference to Reference variation peak value PC Reference to The method comprises the steps of carrying out a first treatment on the surface of the Substituting the above parameters into the formulaCalculating to obtain a belt use evaluation index PBD of the robot module within the current monitoring time range; wherein er1, er2 and er3 are the influence weight factors of the change mean PA, the change estimate PB and the change peak PC, respectively.
5. The system of claim 4, wherein the step of obtaining the optimized adjustment assessment index is:
501: acquiring motor temperature values at different moments in a preset monitoring time period of the robot module by using a temperature sensor; calculating the average value of the temperature values of the motors at different moments to obtain the average value of the motors; extracting the maximum value in the temperature values of each group of motors as a peak value; calculating the temperature values of each group of motors by using a standard deviation formula to obtain a computer variable value;
502: acquiring a preset temperature model in a database, respectively matching a machine mean value, a machine peak value and a machine variable value in a current preset monitoring time period with a plurality of corresponding value ranges to obtain a bottom circle area, a top circle area and a vertical distance between two circles of the model, and constructing the model based on the obtained values;
503: grooving is carried out on the bottom surface of a preset temperature model based on the constructed model, so that a temperature combination model in the current preset monitoring time period is obtained;
504: calculating the volume value of the constructed model, and carrying out difference calculation between the volume value of the constructed model and the volume value of the preset temperature model to obtain a volume difference value;
calculating the distance between the top circle edge point of the constructed model and the preset top circle edge point of the model, and marking the distance as a distance difference value I;
calculating the distance between the edge point of the base circle of the constructed model and the edge point of the preset base circle of the model, and marking the distance as a second distance difference value;
performing difference calculation on a preset model machine variable value and a constructed model machine variable value to obtain a distance difference value III;
505: comprehensively analyzing the parameters, and optimally adjusting and evaluating indexes of the robot module in the current preset monitoring time period; matching the optimal adjustment evaluation index YHT of the robot module with a plurality of set value ranges, wherein each value range corresponds to a speed adjustment interval; and obtaining a speed regulation interval within the current monitoring time range of the robot module, generating a stage regulation signal and sending the stage regulation signal to the control regulation module.
6. The system of claim 5, wherein the power output evaluation index is obtained by:
601: the method comprises the steps that a pressure sensor is used for obtaining the load change condition of a robot module in a preset monitoring time period and substituting a line graph for representation;
602: drawing numerical value points in the line graph corresponding to load magnitude values at different moments in a preset monitoring time period, and connecting adjacent numerical value points to obtain a load change line; calculating the slope of each load change line and the included angle between each load change line and the horizontal line; when the included angle between the load change line and the horizontal line is an acute angle, marking the slope of the load change line as a descending slope; when the included angle between the load change line and the horizontal is an obtuse angle, marking the slope of the load change line as an ascending slope; summing all the values of the falling slopes to obtain a falling estimated value, summing all the values of the rising slopes and taking absolute values to obtain a rising estimated value, and calculating the ratio of the rising estimated value to the falling estimated value to obtain a load variable value;
603: the method comprises the steps of obtaining load values at different moments in a preset monitoring time period, and calculating an average value to obtain a load average value; obtaining the load peak value with the largest value from each group of load values as the load peak value in the current preset monitoring time period;
604: and comprehensively analyzing the parameters to obtain the power output evaluation index of the robot module in the current preset monitoring time period.
7. The system according to claim 6, wherein the control adjustment module adjusts the output power of the robot module, specifically:
comparing the power output evaluation index in the current preset monitoring time period with a corresponding threshold range, if the power output evaluation index is higher than the corresponding threshold range, matching the power output evaluation index with the corresponding value range, setting an increment corresponding to one power output of each value range respectively, obtaining an output power adjustment increment, and adjusting by a control adjustment module;
if the power output evaluation index is lower than the corresponding threshold range, matching the power output evaluation index with the corresponding value range, setting a reduction amount of power output corresponding to each value range respectively, obtaining an output power adjustment reduction amount, and adjusting through a control adjustment module.
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