CN114593712A - Indoor top construction shaking detection method based on vision and automatic control method - Google Patents
Indoor top construction shaking detection method based on vision and automatic control method Download PDFInfo
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- G05B11/01—Automatic controllers electric
- G05B11/36—Automatic controllers electric with provision for obtaining particular characteristics, e.g. proportional, integral, differential
- G05B11/42—Automatic controllers electric with provision for obtaining particular characteristics, e.g. proportional, integral, differential for obtaining a characteristic which is both proportional and time-dependent, e.g. P. I., P. I. D.
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Abstract
The invention discloses a method for detecting indoor top construction shaking based on vision, which comprises the following steps: calculating angle variation of the depth camera on an X axis, a Y axis and a Z axis by using a correction method vector to obtain first inclination angle variation; calculating angle variation of the six-axis sensor on an X axis, a Y axis and a Z axis to obtain second inclination angle variation; obtaining a three-axis angle change approximate quantity of a first inclination angle change quantity and a second inclination angle change quantity by using a longest common subsequence algorithm; comparing the triaxial angle approximation with a specified threshold value to determine whether the construction robot device shakes; also discloses an automatic control method for shaking. Through the three-axis angle variation of the depth camera and the angle variation of the six-axis sensor, the inclination angle of the construction robot is determined through the longest common subsequence algorithm, PID control is performed on the fastening pull rod, irregular shaking of the construction robot is eliminated, and the risk of dumping caused by excessive shaking in high-altitude construction is avoided.
Description
Technical Field
The invention relates to the technical field of intelligent construction of indoor robots, in particular to a method for detecting indoor top construction shaking based on vision and an automatic control method.
Background
Along with the intellectuality of building trade, the multiple construction robot has emerged, and the construction robot replaces artifically, can realize safely, high-efficient ground construction. The inner wall processing part comprises the steps of polishing the cement wall surface, removing burrs of the cement surface, and smearing putty and a putty layer on the cement wall surface, so that the wall surface is integrally smoother. Polishing the wall surface of the putty layer, removing seams of the putty layer and the like to enable the putty layer to be smoother; and spraying paint or brushing paint on the putty layer.
The indoor building construction area comprises ceiling construction with the height of 6-10m, the working module is lifted to the corresponding height by the construction robot equipment during construction, construction is completed by moving the construction robot equipment, the top of the construction robot equipment which is in contact with the ceiling can shake at the moment, and the shaking reason can come from the chassis or the friction caused by the unevenness of the top construction module and the ceiling.
The shaking of the top of the construction robot equipment can be divided into a direction parallel to the advancing direction of the equipment and a direction perpendicular to the advancing direction of the equipment. In the work progress, no matter horizontal rocking or vertical rocking can all produce adverse effect to the construction effect, consequently, eliminate effectively and rock, be the indoor work progress of current robot equipment a problem that awaits a moment to solve.
Disclosure of Invention
In the construction of indoor ceilings, the construction robot is affected by the construction environment, resulting in lateral and longitudinal shaking of the top end, which can adversely affect the construction.
Aiming at the problems, the indoor top construction shaking detection method and the automatic control method based on vision are provided, the three-axis angle change approximate quantity is obtained through the longest common subsequence algorithm by calculating the angle change quantity of the depth camera in the X axis, the Y axis and the Z axis and the angle change quantity of the six-axis sensor of the construction robot, the inclination angle of the construction robot is determined by utilizing the angle change approximate quantity, the PID control is carried out on the fastening pull rod by utilizing the inclination angle as an input parameter, the irregular shaking of the construction robot is eliminated, and the dumping risk caused by the overlarge shaking in the high-altitude construction is avoided.
In a first aspect, a method for detecting indoor roof construction sway based on vision comprises the steps of:
200, correcting the plane normal vector of the ceiling plate, acquiring a normal vector with the smallest included angle with the optical axis of the depth camera arranged at the top, and acquiring a corrected normal vector;
500, acquiring a triaxial angle change approximate quantity of the first inclination angle change quantity and the second inclination angle change quantity by using a longest common subsequence algorithm;
and 600, comparing the triaxial angle approximation quantity with a specified threshold value to determine whether the construction robot equipment shakes.
In a first possible implementation manner of the method for detecting indoor roof construction shaking based on vision according to the present invention, the step 100 includes:
and step 130, fitting a ceiling plane perpendicular to the optical axis of the depth camera by using the point cloud data after data filtering according to a ransac algorithm.
With reference to the first possible implementation manner and the second possible implementation manner of the present invention, in a second possible implementation manner, the step 110 includes:
and 113, deleting the outliers from the point cloud data.
With reference to the second possible implementation manner of the present invention, in a third possible implementation manner, the step 120 includes:
all points in each voxel are approximately represented by the center of gravity of all points in the voxel, step 122.
With reference to the third possible implementation manner of the present invention, in a fourth possible implementation manner, the step 400 includes:
and 440, performing data filtering on the corrected rotation angle by using a Kalman filtering algorithm to obtain a second inclination angle.
With reference to the fourth possible implementation manner of the present invention, in a fifth possible implementation manner, the step 500 includes:
and step 520, calculating the difference value between the current frame and the next frame of the second inclination angle of the six-axis sensor, and acquiring the second inclination angle variation.
With reference to the fifth possible implementation manner of the present invention, in a sixth possible implementation manner, the step 500 further includes:
calculating the three-axis angle change approximate quantity of the first track A and the second track B;
where δ is the length difference threshold and ε is the distance threshold.
With reference to the sixth possible implementation manner of the present invention, in a seventh possible implementation manner, the step 600 includes:
and step 620, if the angle change approximate quantities of the X axis, the Y axis and the Z axis are all larger than corresponding threshold values, judging that the construction robot equipment shakes.
In a second aspect, a method for automatically controlling indoor top construction shaking based on vision, which uses the method for detecting indoor top construction shaking based on vision of the first aspect to detect an inclination angle, comprises the following steps:
and 800, adjusting a fastening pull rod of the construction robot by using the first inclination angle and a PID algorithm according to the shaking degree of the construction robot so as to eliminate shaking.
In a first possible implementation manner of the automatic control method according to the second aspect, the step 800 includes:
and 820, performing PID control on the output power of the fastening pull rod.
According to the indoor top construction shaking detection method and the automatic control method based on the vision, the three-axis angle change approximate quantity is obtained through the longest common subsequence algorithm by calculating the angle change quantity of the depth camera in the X axis, the Y axis and the Z axis and the angle change quantity of the six-axis sensor of the construction robot, the inclination angle of the construction robot is determined by utilizing the angle change approximate quantity, the inclination angle is used as an input parameter, PID control is carried out on the fastening pull rod, irregular shaking of the construction robot is eliminated, and the dumping risk caused by overlarge shaking in high-altitude construction is avoided.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be 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 to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a schematic diagram of a first embodiment of a method for detecting indoor roof construction sloshing based on vision in the present invention;
FIG. 2 is a schematic diagram of a second embodiment of a method for detecting indoor roof construction sloshing based on vision according to the present invention;
FIG. 3 is a schematic diagram of a third embodiment of a method for detecting indoor roof construction sway based on vision in the present invention;
FIG. 4 is a schematic diagram of a fourth embodiment of a vision-based indoor roof construction sway detection method in accordance with the present invention;
FIG. 5 is a schematic diagram of a fifth embodiment of a method for detecting indoor roof construction sloshing based on vision in the present invention;
FIG. 6 is a schematic diagram of a sixth embodiment of a method for detecting indoor roof construction sway based on vision in the present invention;
FIG. 7 is a schematic diagram of a seventh embodiment of a method for detecting indoor roof construction sloshing based on vision in the present invention;
FIG. 8 is a schematic view of an eighth embodiment of a method for detecting indoor roof construction sway based on vision in the present invention;
FIG. 9 is a schematic diagram of a first embodiment of an automatic control method for indoor roof construction shaking based on vision according to the present invention;
FIG. 10 is a schematic diagram of a second embodiment of an automatic control method for indoor roof construction shaking based on vision according to the present invention;
Detailed Description
The technical solutions in the present invention will be described clearly and completely with reference to the accompanying drawings, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. Other embodiments, which can be derived by one of ordinary skill in the art from the embodiments given herein without any creative effort, shall fall within the protection scope of the present invention.
The noun interpretation:
RANSAC algorithm: RANSAC is an abbreviation for "RANdom SAmple Consensus". It can iteratively estimate the parameters of the mathematical model from a set of observed data sets comprising "outliers". It is an uncertain algorithm-it has a certain probability to get a reasonable result; the number of iterations must be increased in order to increase the probability.
In the construction process of indoor ceilings, the construction robot is influenced by the construction environment, so that the top end of the construction robot generates transverse shaking and longitudinal shaking, and the shaking can generate adverse effect on the construction effect.
Aiming at the problems, a method for detecting indoor top construction shaking based on vision and an automatic control method are provided.
In a first aspect, as shown in fig. 1, fig. 1 is a schematic view of a first embodiment of a vision-based indoor roof construction shake detection method according to the present invention, and the vision-based indoor roof construction shake detection method includes:
Preferably, as shown in fig. 2, fig. 2 is a schematic view of a second embodiment of a vision-based indoor roof construction shake detection method according to the present invention, and step 100 includes: step 110, acquiring a ceiling point cloud through a depth camera, and removing outliers in the ceiling point cloud; step 120, carrying out data filtering on the denoised ceiling point cloud; and step 130, fitting a ceiling plane perpendicular to the optical axis of the depth camera by using the point cloud data after data filtering according to a ransac algorithm.
Preferably, as shown in fig. 3, fig. 3 is a schematic view of a third embodiment of a vision-based indoor roof construction shake detection method according to the present invention, and step 110 includes: step 111, calculating the distance from each point in the point cloud data to a nearby point, and acquiring point cloud distance distribution; step 112, if the point cloud average distance of a certain point is greater than a specified threshold, determining that the point is an outlier; and 113, deleting the cluster points from the point cloud data.
Outlier removal based on statistics is used for the point cloud data, and miscellaneous points are removed to a certain extent. The principle is that the distance distribution condition from each point to the adjacent point in the input point cloud data is calculated to obtain the average distance from each point to all the adjacent points. Assuming that the result is a gaussian distribution whose shape is determined by the mean and standard deviation, the mean distance is outside a specified threshold range, defined as outliers, which are removed from the point cloud data set.
Preferably, as shown in fig. 4, fig. 4 is a schematic view of a fourth embodiment of a vision-based indoor roof construction shake detection method according to the present invention, and step 120 includes: step 121, constructing a three-dimensional voxel grid; all points in each voxel are approximately represented within that voxel by the centroid of all points within that voxel, step 122.
And carrying out point cloud data down-sampling on the denoised point cloud data, reducing the number of point clouds under the condition of keeping point cloud characteristics, and playing a role of smoothing the point clouds. By constructing a three-dimensional voxel grid and then approximately displaying other points in the voxel by using the gravity centers of all the points in the voxel in each voxel, all the points in the voxel are represented by using one gravity center point, the effect of filtering point cloud data is achieved, and the data volume of the point cloud data is greatly reduced.
Preferably, as shown in fig. 5, fig. 5 is a schematic diagram of a fifth embodiment of a method for detecting indoor roof construction shake based on vision in the present invention, and step 400 includes: step 410, integrating angular velocity data of a gyroscope in a six-axis sensor to obtain a three-axis rotation angle; step 420, integrating acceleration data in the six-axis sensor to obtain three-axis acceleration data; step 430, correcting the three-axis rotation angle by using the three-axis acceleration data; and 440, performing data filtering on the corrected rotation angle by using a Kalman filtering algorithm to obtain a second inclination angle.
The angular velocity is measured by a gyroscope in the six-axis accelerator, and the velocity is multiplied by the time to obtain the angle of the object rotating in a certain time period. The angle obtained by integral operation has errors, the errors are aggravated along with the accumulation of time, and at the moment, the attitude angle needs to be calculated by the aid of an accelerometer.
The acceleration is measured by using the accelerometer in the six-axis accelerator, the components of the acceleration on the X axis and the Y axis are calculated, and the inclination angle of the object relative to the horizontal plane can be calculated.
The rotation angle is obtained through the integration of the gyroscope, then the integration result of the gyroscope is corrected through the proportional and integral operation of the accelerometer, and finally the final angle is obtained through Kalman filtering, namely the second inclination angle calculated based on the six-axis sensor.
Preferably, as shown in fig. 6, fig. 6 is a schematic diagram of a sixth embodiment of a vision-based indoor roof construction shaking detection method in the present invention, and step 500 includes: step 510, calculating a difference value between a current frame and a next frame of a first inclination angle of the depth camera to obtain a first inclination angle variation; and step 520, calculating the difference value between the current frame and the next frame of the second inclination angle of the six-axis sensor, and acquiring the second inclination angle variation.
Preferably, as shown in fig. 7, fig. 7 is a schematic view of a seventh embodiment of a method for detecting indoor roof construction shake based on vision according to the present invention, and the step 500 further includes: step 530, acquiring a first track A of a first inclination angle variable quantity with the length of n; step 540, acquiring a first track B of a second inclination angle variable quantity with the length of m; step 550, using the longest common subsequence algorithm (LCSS) equation:
calculating the three-axis angle change approximate quantity of the first track A and the second track B;
where δ is the length difference threshold and ε is the distance threshold.
For the extracted single-frame normal vector, the 3-axis accelerometer and the 3-axis angular velocity of a six-axis sensor placed in a construction robot lifting module are combined for comprehensive processing. The first inclination angle detected by vision based on the depth camera may cause misjudgment due to the change of the inclination angle of the ceiling, because the construction module is in hard contact with the ceiling, and there is a movement margin between the construction module and the lifting module. The second tilt angle calculated by the single six-axis accelerator may also cause erroneous determination due to the change in the tilt angle of the ground.
Preferably, as shown in fig. 8, fig. 8 is a schematic view of an eighth embodiment of a vision-based indoor roof construction shaking detection method according to the present invention, and step 600 includes: step 610, comparing the angle change approximate quantities of the X axis, the Y axis and the Z axis with corresponding threshold values respectively; and step 620, if the angle change approximate quantities of the X axis, the Y axis and the Z axis are all larger than the corresponding threshold values, judging that the construction robot equipment shakes.
And obtaining segments with similar first inclination angle variable quantities of an X axis, a Y axis and a Z axis and second inclination angle variable quantities of the 6-axis sensor through a longest common subsequence algorithm (LCSS). When the variation of the data on the three axes meets the condition, the whole equipment generates shaking or uniform motion when the data is considered, and the uniform motion is eliminated according to the angular velocity.
In a second aspect, an automatic control method for indoor roof construction shaking is provided, which uses the method for detecting indoor roof construction shaking of the first aspect to detect an inclination angle, as shown in fig. 9, where fig. 9 is a schematic view of a first embodiment of the automatic control method for indoor roof construction shaking of the invention, and includes: step 700, obtaining an absolute value of the inclination angle, and eliminating the constant-speed running situation of the construction robot by using the absolute value of the inclination angle; and 800, adjusting a fastening pull rod of the construction robot by using the first inclination angle and a PID algorithm according to the shaking degree of the construction robot so as to eliminate shaking.
Preferably, as shown in fig. 10, fig. 10 is a schematic view of a second embodiment of an automatic control method for indoor roof construction shaking based on vision in the present invention; step 800 comprises: step 810, taking the first inclination angle as a PID control parameter; and 820, performing PID control on the output power of the fastening pull rod.
The motor power of the fastening pull rod is controlled through the preset proportional, integral and differential control parameters, and the included angle between the ceiling angle detected by the camera and the optical axis of the camera is used as a control parameter. When the included angle is increased, the power of a motor for controlling the fastening pull rod is increased.
According to the indoor top construction shaking detection method and the automatic control method based on the vision, the angular variation quantity of the depth camera in the X axis, the Y axis and the Z axis and the angular variation quantity of the six-axis sensor of the construction robot are calculated, the three-axis angular variation approximate quantity is obtained through the longest common subsequence algorithm, the inclination angle of the construction robot is determined by utilizing the angular variation approximate quantity, the inclination angle is used as an input parameter, PID control is carried out on the fastening pull rod, irregular shaking of the construction robot is eliminated, and the dumping risk caused by overlarge shaking in high-altitude construction is avoided.
The present invention is not limited to the above embodiments, and any modifications, equivalent substitutions, improvements, etc. within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A visual-based indoor top construction shaking detection method is characterized by comprising the following steps:
Step 100, fitting a constructed ceiling plane by using an image depth algorithm, and calculating a normal vector of the ceiling plane;
200, correcting the plane normal vector of the ceiling plate, acquiring a normal vector with the smallest included angle with the optical axis of the depth camera arranged at the top, and acquiring a corrected normal vector;
step 300, calculating angle variation of the depth camera on an X axis, a Y axis and a Z axis by using the corrected normal vector to obtain first inclination angle variation;
step 400, calculating angle variation of the six-axis sensor in an X axis, a Y axis and a Z axis to obtain second inclination angle variation;
500, obtaining a triaxial angle change approximation quantity of the first inclination angle change quantity and the second inclination angle change quantity by using a longest common subsequence algorithm;
and step 600, comparing the triaxial angle approximation quantity with a specified threshold value to determine whether the construction robot equipment shakes.
2. The vision-based indoor roof construction sloshing detection method of claim 1, wherein said step 100 comprises:
step 110, obtaining a ceiling point cloud through a depth camera, and removing outliers in the ceiling point cloud;
120, performing data filtering on the denoised ceiling point cloud;
And step 130, fitting a ceiling plane perpendicular to the optical axis of the depth camera by using the point cloud data after data filtering according to a ransac algorithm.
3. The vision-based indoor roof construction sloshing detection method of claim 2, wherein said step 110 comprises:
111, calculating the distance from each point in the point cloud data to a nearby point to obtain point cloud distance distribution;
step 112, if the point cloud average distance of a certain point is greater than a specified threshold, determining that the point is an outlier;
and 113, deleting the outliers from the point cloud data.
4. The vision-based indoor roof construction sloshing detection method of claim 3, wherein said step 120 comprises:
step 121, constructing a three-dimensional voxel grid;
all points in each voxel are approximately represented by the center of gravity of all points in the voxel, step 122.
5. The vision-based indoor roof construction sloshing detection method of claim 4, wherein said step 400 comprises:
step 410, integrating the angular velocity data of the gyroscope in the six-axis sensor to obtain a three-axis rotation angle;
step 420, integrating the acceleration data in the six-axis sensor to obtain three-axis acceleration data;
Step 430, correcting the triaxial rotation angle by using the triaxial acceleration data;
and 440, performing data filtering on the corrected rotation angle by using a Kalman filtering algorithm to obtain a second inclination angle.
6. The vision-based indoor roof construction sloshing detection method of claim 5, wherein said step 500 comprises:
step 510, calculating a difference value between a current frame and a next frame of a first inclination angle of the depth camera, and acquiring a first inclination angle variation;
and step 520, calculating the difference value between the current frame and the next frame of the second inclination angle of the six-axis sensor, and acquiring the second inclination angle variation.
7. The vision-based indoor roof construction sloshing detection method of claim 6, wherein said step 500 further comprises:
step 530, acquiring a first track A of a first inclination angle variable quantity with the length of n;
step 540, acquiring a first track B of a second inclination angle variable quantity with the length of m;
step 550, using the equation:
calculating the three-axis angle change approximate quantity of the first track A and the second track B;
where δ is the length difference threshold and ε is the distance threshold.
8. The vision-based indoor roof construction sloshing detection method of claim 7, wherein said step 600 comprises:
Step 610, comparing the angle change approximate quantities of the X axis, the Y axis and the Z axis with corresponding threshold values respectively;
and step 620, if the angle change approximate quantities of the X axis, the Y axis and the Z axis are all larger than corresponding threshold values, judging that the construction robot equipment shakes.
9. An indoor roof construction shaking automatic control method based on vision, which utilizes the indoor roof construction shaking detection method based on vision as claimed in any one of claims 1-8 to detect the inclination angle, comprising the steps of:
step 700, acquiring the absolute value of the inclination angle, and eliminating the situation of uniform speed operation of the construction robot by using the absolute value of the inclination angle;
and 800, adjusting a fastening pull rod of the construction robot by using the first inclination angle and a PID algorithm according to the shaking degree of the construction robot so as to eliminate shaking.
10. The vision-based automatic control method for indoor roof construction sway of claim 9, characterized in that said step 800 comprises:
step 810, taking the first inclination angle as a PID control parameter;
and 820, performing PID control on the output power of the fastening pull rod.
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| CN104167069A (en) * | 2014-07-21 | 2014-11-26 | 苏州昊枫环保科技有限公司 | Indoor monitoring system based on vibration and infrared induction detection |
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| CN110308727A (en) * | 2019-07-12 | 2019-10-08 | 沈阳城市学院 | A kind of control method for eliminating biped robot's upper body posture shaking |
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