Flow control method for spraying robot based on fractal theory to realize wall distance prediction

Abstract

The application discloses a spraying robot flow control method for realizing wall distance prediction based on fractal theory, and comprises the following steps: S1, measuring distance history data of a spraying robot and a wall; S2, constructing a first-order accumulated sum sequence of the distance history data; S3, marking the history data points in a double logarithmic coordinate system, and obtaining a linear regression equation; S4, calculating a next sequence according to the linear regression equation, so as to inversely deduce a distance prediction value; S5, the spraying robot performs spraying operation at a current time and a current position according to the prediction value through calculation; and S6, repeating the above steps until all spraying tasks are completed. The fractal theory is adopted to realize wall distance prediction, and flow adjustment is performed in advance during spraying, so that the spraying robot flow control method can not only avoid the adjustment delay problem caused by distance data updating lag of the spraying robot, but also can adapt to the characteristics of range spraying, and good wall spraying effect is achieved.

Description

Technical Field

[0001] This invention relates to the field of indoor spraying robots, and specifically to a flow control method for spraying robots based on fractal theory to predict wall distances. Background Technology

[0002] Indoor painting robots are mechatronic devices that integrate intelligent navigation and automatic painting technologies. They achieve precise paint application on building interior walls through algorithmic control. Mobile painting robots offer a high degree of freedom in handling various complex tasks within building interiors, demonstrating strong adaptability and thus are highly regarded.

[0003] The painting robot can complete wall painting along a predetermined route without human intervention, reducing manpower by 66%, painting time by 60%, and painting costs by 35%, demonstrating a significant increase in efficiency. However, its painting technology still requires further improvement. Planning the robot's motion trajectory to ensure uniform coating distribution is a challenging research topic. A reasonable robot motion trajectory has a significant impact on the quality of the painted surface. During the painting process, the robot needs to maintain a stable flow rate along the wall surface and adjust the flow rate accordingly to the unevenness of the wall.

[0004] However, most spray painting robots on the market are designed based on ideally flat walls, resulting in insufficient real-time response capabilities for parameter adjustments. Specifically, during the distance measurement process of the spray painting robot, the sensor's detection speed is limited, causing a lag in distance data updates. Generally, the robot's moving speed is typically 0.5~1.5m / s, while the sensor detection frequency is limited (e.g., 10Hz). Therefore, when the nozzle has moved to a changing area, the sensor does not provide timely feedback, and the robot continues to spray at the preset distance. Thus, the insufficient real-time response capability of spray painting robots in "distance perception - parameter adjustment - path adaptation" is one of the core technical bottlenecks affecting spray painting results. Summary of the Invention

[0005] The technical problem this invention aims to solve is that the limited detection speed of sensors causes a lag in distance data updates, resulting in insufficient real-time response capability of the spraying robot in flow control, severely affecting the spraying effect. This invention provides a flow control method for a spraying robot based on fractal theory to predict wall distance. By using fractal theory to predict wall distance and adjusting the flow rate in advance during spraying, it not only avoids the adjustment delay caused by the lag in distance data updates but also adapts to the characteristics of range spraying, achieving better wall spraying results.

[0006] The technical solution adopted by this invention to solve its technical problem is: a flow control method for a spraying robot based on fractal theory to predict wall distance, comprising the following steps: S1, measuring historical distance data between the spraying robot and the wall; S2, constructing a first-order cumulative sequence of historical distance data; S3, marking historical data points in a logarithmic coordinate system and obtaining a linear regression equation; S4, calculating the next sequence based on the linear regression equation, thereby deducing the distance prediction value; S5, the spraying robot performs spraying operations at the current time and current position based on the prediction value through calculation; S6, repeating the above steps until all spraying tasks are completed.

[0007] The painting robot includes a spray head and a laser rangefinder, which work synchronously. That is, while the spray head is moving, the laser rangefinder continuously collects distance data between itself and the wall.

[0008] At the initial stage of the spraying operation During the specified time, the nozzle uses the default flow rate. Perform the spraying operation. and Determined based on the actual situation.

[0009] At the initial stage of the spraying operation Within a given time period, the average of all distance data measured by the laser rangefinder is taken and used as the default distance value. .

[0010] Step S1 refers to using a laser rangefinder to measure historical distance data between the spraying robot and the wall, filtering the collected data, and retaining only the data segments that reflect the characteristics of the wall, denoted as... ,in This represents the total amount of historical distance data that has been retained.

[0011] Step S2 refers to performing cumulative summation on the historical distance data to obtain a first-order cumulative sum. Data, including It is a measurement scale.

[0012] Step S3 refers to the process in a logarithmic coordinate system, where... The horizontal axis is... Using the vertical axis as the ordinate, historical data points are plotted, and a linear regression is performed on the curve using the least squares method to obtain the linear regression equation. The slope of the regression line is the fractal dimension. ,and axial intercept is , It is a constant.

[0013] Step S4 refers to the process based on the obtained... and The value can be used to calculate the next accumulated sum. From this, the next wall distance data can be deduced. This is the predicted distance value.

[0014] Step S5 refers to the prediction value. Flow rate calculation is performed, and then the spraying operation is carried out at the current time and current location. The flow rate used is calculated using the following formula: ,in and These are the default traffic value and the default distance value, which can be set according to the actual situation.

[0015] Step S6 refers to measuring the current distance data. Merging with historical data yields Then execute steps S2 to S5 to continue the spraying operation at the next moment and the next position until all spraying tasks are completed.

[0016] The beneficial effect of this invention is that fractal geometry can analyze widely existing disordered, irregular, and self-similar systems, making it highly suitable for predicting uneven wall surfaces. Therefore, based on the predicted wall distance, the problem of lagging distance data updates for the spraying robot can be avoided, allowing for real-time adjustment of the spraying flow rate. The spraying robot flow control method based on fractal theory for predicting wall distance uses historical distance data to determine the spraying flow rate at the current point, thus conforming to the characteristics of robot-controlled range spraying. Attached Figure Description

[0017] Figure 1 This is a flowchart of the flow control method for the spraying robot of the present invention.

[0018] Figure 2 These are wall distance data from embodiments of the present invention.

[0019] Figure 3 This is an embodiment of the present invention. - Line graph. Detailed Implementation

[0020] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, illustrating only the basic structure of the invention, and therefore only show the components relevant to the invention.

[0021] like Figure 1As shown, a flow control method for a spraying robot based on fractal theory to predict wall distance includes the following steps: S1, measuring historical distance data between the spraying robot and the wall; S2, constructing a first-order cumulative sequence of historical distance data; S3, marking historical data points in a log-log coordinate system and obtaining a linear regression equation; S4, calculating the next sequence based on the linear regression equation, thereby deducing the predicted distance value; S5, the spraying robot performs spraying operations at the current time and position based on the predicted value. Finally, the above steps are repeated until all spraying tasks are completed.

[0022] As can be seen, a significant feature of this invention is that it uses fractal theory to predict wall distances and then calculates and converts the predicted distance values ​​into spray flow rates, thereby overcoming the problem of delayed distance data updates and completing the wall spraying operation.

[0023] Example

[0024] This embodiment applies fractal theory to predict wall distances and then sprays the wall surface. The wall surface to be sprayed is 2m long and 3m high, and is uneven. Figure 2 As shown. In Figure 2 In the diagram, the shade of color indicates the distance to the wall measured by the laser rangefinder; the darker the color, the farther the distance. The maximum distance is 24mm, and the minimum distance is 16mm.

[0025] The spraying robot's nozzle moves at a speed of 0.3 m / s, from left to right and from top to bottom. (Time...) The default setting is for the initial phase, during which the nozzle uses the default flow rate. During the spraying process, distance data was measured using a laser rangefinder. The sampling frequency here is 100Hz, therefore Based on distance data Calculate the average value using the following formula ,get It is 20.5mm.

[0026] Then, for the distance data Perform cumulative summation to obtain Measurement scale In a double logarithmic coordinate system, with The horizontal axis is... Using the vertical axis as the ordinate, historical data points are plotted, and the curve is linearly regressed using the least squares method to obtain the linear regression equation, as shown below. Figure 3 As shown. According to Figure 3 The predicted value can be obtained from the regression line shown. By deducing the next wall distance data, The value is 22.3 mm, which is the predicted value.

[0027] After obtaining the predicted value, the painting robot performs the painting operation at the current position, calculated using the following formula: ,get for This refers to the flow rate used in the spraying process. During spraying, the spraying robot simultaneously measures the current distance and incorporates this data into its overall data set. Repeat the above steps to predict the flow rate for the next location, and continue the work until the entire wall surface is painted.

[0028] Based on the preferred embodiments of the present invention described above, those skilled in the art can make various changes and modifications without departing from the technical concept of the invention. The technical scope of this invention is not limited to the contents of the specification, but must be determined by the scope of the claims.

Claims

1. A method for flow control of a spraying robot based on fractal theory to predict wall distance, characterized in that, The steps include: S1, measuring historical distance data between the spraying robot and the wall; S2, constructing a first-order cumulative sequence of historical distance data; S3, marking the historical data points in a log-log coordinate system and obtaining the linear regression equation. S4. Calculate the next sequence based on the linear regression equation, and then deduce the distance prediction value; S5. The painting robot performs the painting operation at the current time and position based on the prediction value through calculation; S6. Repeat the above steps until all painting tasks are completed.

2. The flow control method for a spraying robot as described in claim 1, characterized in that, The painting robot includes a spray head and a laser rangefinder, which work synchronously. That is, while the spray head is moving, the laser rangefinder continuously collects distance data between itself and the wall.

3. The flow control method for a spraying robot as described in claim 1, characterized in that, At the initial stage of the spraying operation During the specified time, the nozzle uses the default flow rate. Perform the spraying operation. and Determined based on the actual situation.

4. The flow control method for a spraying robot as described in claim 1, characterized in that, At the initial stage of the spraying operation Within a given time period, the average of all distance data measured by the laser rangefinder is taken and used as the default distance value. .

5. The flow control method for a spraying robot as described in claim 1, characterized in that, Step S1 refers to using a laser rangefinder to measure historical distance data between the spraying robot and the wall, filtering the collected data, and retaining only the data segments that reflect the characteristics of the wall, denoted as... ,in This represents the total amount of historical distance data that has been retained.

6. The flow control method for a spraying robot as described in claim 1, characterized in that, Step S2 refers to performing cumulative summation on the historical distance data to obtain a first-order cumulative sum. Data, including It is a measurement scale.

7. The flow control method for a spraying robot as described in claim 1, characterized in that, Step S3 refers to the process in a logarithmic coordinate system, where... The horizontal axis is... Using the vertical axis as the ordinate, historical data points are plotted, and a linear regression is performed on the curve using the least squares method to obtain the linear regression equation. The slope of the regression line is the fractal dimension. ,and axial intercept is , It is a constant.

8. The flow control method for a spraying robot as described in claim 1, characterized in that, Step S4 refers to the process based on the obtained... and The value can be used to calculate the next accumulated sum. From this, the next wall distance data can be deduced. This is the predicted distance value.

9. The flow control method for a spraying robot as described in claim 1, characterized in that, Step S5 refers to the prediction value. Flow rate calculation is performed, and then the spraying operation is carried out at the current time and current location. The flow rate used is calculated using the following formula: ,in and These are the default traffic value and the default distance value, which can be set according to the actual situation.

10. The flow control method for a spraying robot as described in claim 1, characterized in that, Step S6 refers to measuring the current distance data. Merging with historical data yields Then execute steps S2 to S5 to continue the spraying operation at the next moment and the next position until all spraying tasks are completed.

Images