Motion planning method and system for mechanical arm of curtain wall cleaning robot

Abstract

The invention provides a curtain wall cleaning robot mechanical arm motion planning method and system, relates to the technical field of cleaning robot motion planning, and solves the problems of dynamic stability and accurate force application in high-altitude curtain wall cleaning by fusing multi-source sensing data and a self-adaptive control algorithm. A pollution identification model based on HSV color space weighted analysis can quantify the severity of different pollution types, and provides a reliable basis for differentiated cleaning strategies; a multi-joint dynamic wind pressure compensation mechanism is combined with wind speed change feedforward control, so that the trajectory tracking precision of the mechanical arm in a strong wind environment is improved; the closed-loop self-calibration system ensures the stability of long-term operation through real-time offset detection and parameter optimization. Therefore, balance of cleaning efficiency, curtain wall protection and energy consumption control is achieved, the system is more suitable for urban high-rise building dense areas and chemical pollution sensitive areas, and an innovative scheme is provided for reliable operation of intelligent cleaning equipment under high-altitude complex working conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of cleaning robot motion planning, and in particular to a curtain wall cleaning robot mechanical arm motion planning method and system. Background Art

[0002] With the large-scale application of curtain walls in high-rise buildings, automated cleaning robots are gradually replacing manual high-altitude operations. However, existing curtain wall cleaning robots face two major technical bottlenecks: First, in complex wind environments (such as strong side winds and instantaneous gusts caused by the urban canyon effect), the robot arm will deviate from its trajectory due to wind pressure disturbance, resulting in a decrease in cleaning coverage and even a risk of collision; second, traditional cleaning parameters are fixed and cannot dynamically adjust the brush pressure according to the type of pollution on the curtain wall surface (such as dust accumulation, oil stains, chemical corrosion) and severity, resulting in insufficient cleaning or excessive wear.

[0003] In the prior art, the publication number CN114795033A discloses a real-time planning and control method and system for indoor three-dimensional cleaning of a robotic arm, including a mobile chassis module, a robotic arm module and a visual module; the robotic arm module starts working after receiving the task instruction, and the visual module transmits the information of the cleaned object and the surrounding environment to the robotic arm module. If the robotic arm completes the preset cleaning path, the workflow ends and the task execution is completed; if the robotic arm does not complete the preset cleaning path, it continues to move under the preset path, and plans the robotic arm posture at the next moment according to the environmental information obtained from the visual module and the robotic arm state, and controls the robotic arm to reach the posture. Although it can constrain the position movement of the robotic arm, it is not suitable for high-altitude environments, and pollution identification mostly uses grayscale analysis, which cannot distinguish the types of pollutants, resulting in poor targeted cleaning strategies. In addition, the traditional closed-loop calibration system relies on regular shutdown detection, which cannot meet the needs of continuous operation, and seriously restricts cleaning efficiency and safety.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0005] The object of the present invention is to provide a motion planning method and system for a mechanical arm of a curtain wall cleaning robot to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions: The motion planning method of the mechanical arm of the curtain wall cleaning robot includes the following specific steps: S1: Collect wind pressure data on the surface of the robot arm and the surface image of the curtain wall, and generate a surface pollution index based on the surface image of the curtain wall; S2: Generate a dynamic compensation amount based on the wind pressure data on the surface of the robot arm, correct the preset initial motion path according to the dynamic compensation amount, and dynamically adjust the brush head pressure of the robot arm based on the surface pollution index of the curtain wall; S3: Repeat steps S1 and S2 to detect the offset of the robot arm during movement, and use a closed-loop verification mechanism to perform real-time calibration on the corrected movement path until the overall cleaning of the curtain wall is completed.

[0007] Preferably, the method for generating the surface pollution index according to the surface image of the curtain wall is: The collected surface image is converted into HSV color space, and the pollution value of each pixel is calculated. The calculation method is: ; In the formula , , , Respectively represent the pollution value, hue value, saturation and brightness of the pixel point. , , All represent model weights, all are greater than 0, and ; Then, the surface pollution index is calculated according to the pollution value of each pixel in the surface image. The calculation method is: ; In the formula Represents the surface contamination index, Represents the total number of pixels in the surface image, Indicates The pollution value of each pixel, subscript represents the index of the pixel, and .

[0008] Preferably, the wind pressure data includes a pressure difference value and a wind speed change rate, and the calculation method for generating a dynamic compensation amount based on the wind pressure data on the surface of the robot arm is: ; In the formula Indicates the robot arm The dynamic compensation amount of each joint, in radians, , The robot arm The wind pressure torque coefficient of each joint and the wind speed change sensitivity coefficient of the robot as a whole, Indicates The pressure difference value of each detection point, Indicates The effective detection area of ​​the detection points is Indicates The detection point to the robot arm The distance between the joints, represents the wind speed change rate, , Represent the indexes of detection points and joints respectively, Indicates the total number of detection points.

[0009] Preferably, the generation logic of the wind pressure torque coefficient is: The offset angles of each joint of the robot arm are measured under constant wind speed, and the least squares method is used for fitting. The fitting equation is expressed as: ; In the formula Indicates The robot arm is measuring The offset angle of the joint, subscript An index indicating the number of measurements, Indicates the total number of measurements, represents the first fluctuation coefficient, .

[0010] Preferably, the wind speed change sensitivity coefficient is calculated as follows: ; In the formula represents the second fluctuation coefficient, , , , Represent the inertia components of the robot arm in three directions respectively.

[0011] Preferably, the method for correcting the preset initial motion path according to the dynamic compensation amount is: Coordinate compensation for the trajectory points on the initial motion path: ; In the formula , Respectively represent The horizontal and vertical coordinates of the trajectory points, represents the third coefficient of fluctuation, , represents the dynamic compensation of the base joint of the robot arm, Represents the end attitude angle of the robot arm.

[0012] Preferably, the method for dynamically adjusting the brush head pressure of the robot mechanical arm based on the surface contamination index of the curtain wall is: When the surface contamination index meets When , the area is considered to be slightly polluted, and the brush head pressure is set to the initial pressure; When the surface contamination index meets When the area is considered to be heavily polluted, the brush head pressure is calculated as follows: ; In the formula represents the pollution threshold, , , Represent the brush head pressure and initial pressure respectively, , Both represent empirical coefficients, , .

[0013] Preferably, the method of using the closed-loop verification mechanism to perform real-time calibration on the corrected motion path is: Collect the actual motion path of the robot arm and calculate the offset based on the actual motion path. The calculation method is: ; In the formula Indicates the offset, represents the total number of trajectory points, , Respectively represent the actual motion path The horizontal and vertical coordinates of the trajectory points; When the offset meets the requirement for 3 times in a row When the wind pressure torque coefficient of each joint of the robot arm and the wind speed change sensitivity coefficient of the robot as a whole are updated, Indicates the preset offset threshold.

[0014] A curtain wall cleaning robot mechanical arm motion planning system, wherein the curtain wall cleaning robot mechanical arm motion planning system adopts the above-mentioned curtain wall cleaning robot mechanical arm motion planning method, specifically comprising: A data acquisition module, the data acquisition module is used to collect wind pressure data on the surface of the robot mechanical arm and the surface image of the curtain wall; A dynamic compensation module, which is used to generate dynamic compensation amount based on wind pressure data on the surface of the robot arm; A trajectory optimization module, which is used to correct the preset initial motion path and adjust the brush head pressure; An offset monitoring module is used to monitor and calibrate the corrected motion path in real time.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention innovatively solves the problems of dynamic stability and precise force application in high-altitude curtain wall cleaning by integrating multi-source sensor data and adaptive control algorithms. The pollution identification model based on HSV color space weighted analysis can accurately quantify the severity of different pollution types, providing a reliable basis for differentiated cleaning strategies; the multi-joint dynamic wind pressure compensation mechanism combined with wind speed change feedforward control significantly improves the trajectory tracking accuracy of the robot arm in strong wind environments; the closed-loop self-calibration system ensures long-term operation stability through real-time offset detection and parameter optimization. This solution achieves a balance between cleaning efficiency, curtain wall protection and energy consumption control, and is particularly suitable for urban high-rise building dense areas and chemical pollution sensitive areas, providing an innovative solution for the reliable operation of intelligent cleaning equipment under complex working conditions at high altitudes. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic diagram of the overall method flow of the present invention; Figure 2 It is a schematic diagram of the overall module structure of the present invention. DETAILED DESCRIPTION

[0017] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0018] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0019] Example: See also Figure 1~Figure 2 , the present invention provides a technical solution: The motion planning method of the mechanical arm of the curtain wall cleaning robot includes the following specific steps: S1: Collect wind pressure data on the surface of the robot arm, including pressure difference value and wind speed change rate, as well as the surface image of the curtain wall, and generate a surface pollution index based on the surface image of the curtain wall; The method for generating the surface pollution index based on the surface image of the curtain wall is: The collected surface image is converted into HSV color space, and the pollution value of each pixel is calculated. The calculation method is: ; In the formula , , , Respectively represent the pollution value, hue value, saturation and brightness of the pixel point. , , All represent model weights, all are greater than 0, and The model weight can be determined based on historical data regression analysis, and can also be adjusted according to user needs. For example, when the degree of oil stain pollution on the curtain wall is high, the weight can be appropriately increased. The value of is used to identify the hue (H).

[0020] Then, the surface pollution index is calculated according to the pollution value of each pixel in the surface image. The calculation method is: ; In the formula Represents the surface contamination index, Represents the total number of pixels in the surface image, Indicates The pollution value of each pixel, subscript represents the index of the pixel, and .

[0021] In this embodiment, the wind pressure data can be collected by arranging a pressure difference sensor and a top ultrasonic anemometer on the surface of the robot arm, and a pixel industrial camera is used to capture the surface image of the curtain wall.

[0022] In this step, compared with the traditional grayscale analysis method, the HSV three-channel weighted calculation can accurately distinguish between pollution types such as dust accumulation (high S value) and chemical corrosion (low V value), which can greatly improve the accuracy of pollution identification. In addition, the dynamic weight mechanism can also be optimized through historical data training to avoid subjective bias in manually setting weights, providing a reliable basis for subsequent cleaning parameter adjustments.

[0023] S2: Generate a dynamic compensation amount based on the wind pressure data on the surface of the robot arm, correct the preset initial motion path according to the dynamic compensation amount, and dynamically adjust the brush head pressure of the robot arm based on the surface pollution index of the curtain wall.

[0024] The calculation method for generating dynamic compensation based on the wind pressure data on the surface of the robot arm is: ; In the formula Indicates the robot arm The dynamic compensation amount of each joint, in radians, , The robot arm The wind pressure torque coefficient of each joint and the wind speed change sensitivity coefficient of the robot as a whole, Indicates The pressure difference value of each detection point, Indicates The effective detection area of ​​the detection points is Indicates The detection point to the robot arm The distance between the joints, represents the wind speed change rate, , Represent the indexes of detection points and joints respectively, Indicates the total number of detection points.

[0025] The generation logic of wind pressure torque coefficient is: The offset angles of each joint of the robot arm are measured under constant wind speed, and the least squares method is used for fitting. The fitting equation is expressed as: ; In the formula Indicates The robot arm is measuring The offset angle of the joint, subscript An index indicating the number of measurements, Indicates the total number of measurements, represents the first fluctuation coefficient, .

[0026] The calculation method of wind speed change sensitivity coefficient is: ; In the formula represents the second fluctuation coefficient, , , , They respectively represent the inertia components of the robot arm in three directions. These three inertia components can be calculated based on the inertia parameters of the robot arm, and can also be simulated through 3D software, such as the quality analysis module in SolidWorks.

[0027] In this embodiment, when calculating the initial wind pressure torque coefficient, it can be fitted through a wind tunnel calibration experiment, and can be subsequently updated through the pressure difference value and joint offset angle collected in actual work. By measuring the wind pressure torque coefficient of each joint of the robot arm separately, the impact of wind pressure on different joints can be better described. For example, at the base joint (q=1), the wind pressure has a greater impact, while at the end joint (q=6), the wind pressure has a greater impact. Furthermore, for manufacturing cost and response speed considerations, the robot is only equipped with an ultrasonic anemometer on the top, so the wind speed change sensitivity coefficient is considered for the robot as a whole.

[0028] The method for correcting the preset initial motion path according to the dynamic compensation amount is: Coordinate compensation for the trajectory points on the initial motion path: ; In the formula , Respectively represent The horizontal and vertical coordinates of the trajectory points, represents the third coefficient of fluctuation, , represents the dynamic compensation of the base joint of the robot arm, It indicates the end attitude angle of the robot arm and can be read by the robot's encoder.

[0029] In this embodiment, the base joint of the robot arm is used as a reference when performing coordinate compensation because the base joint has the greatest influence on the displacement of the end of the robot arm. Therefore, in usage scenarios such as curtain wall cleaning that do not require ultra-high precision, such compensation can increase the adjustment speed and response speed.

[0030] The method for dynamically adjusting the brush head pressure of the robot arm based on the surface contamination index of the curtain wall is: When the surface contamination index meets When , the area is considered to be slightly polluted, and the brush head pressure is set to the initial pressure; When the surface contamination index meets When the area is considered to be heavily polluted, the brush head pressure is calculated as follows: ; In the formula represents the pollution threshold, , , Represent the brush head pressure and initial pressure respectively, , Both represent empirical coefficients, , .

[0031] In this step, when compensating for the offset angles of multiple joints, the differentiated compensation strategy can not only reduce energy consumption but also improve the trajectory accuracy of the end compared to the traditional uniform compensation method. When performing coordinate compensation, the base joint of the robot arm that has the greatest impact on the displacement of the robot arm end is used as the reference. In the applicable scenario of curtain wall cleaning that does not require ultra-high precision, it can increase the adjustment speed and response speed. The pressure-contamination coupling model breaks through the traditional constant pressure cleaning mode, automatically increases the pressure in heavily polluted areas, and has higher cleaning efficiency than the uniform force solution.

[0032] S3: Repeat steps S1 and S2 to detect the offset of the robot arm during movement, and use a closed-loop verification mechanism to perform real-time calibration on the corrected movement path until the overall cleaning of the curtain wall is completed.

[0033] The method of real-time calibration of the corrected motion path using the closed-loop verification mechanism is: Collect the actual motion path of the robot arm and calculate the offset based on the actual motion path. The calculation method is: ; In the formula Indicates the offset, represents the total number of trajectory points, , Respectively represent the first The horizontal and vertical coordinates of the trajectory points; When the offset meets the requirement for 3 times in a row When the wind pressure torque coefficient of each joint of the robot arm and the wind speed change sensitivity coefficient of the robot as a whole are updated, Indicates the preset offset threshold.

[0034] In this step, a small amount of fluorescent agent can be added to the cleaning agent, and then a laser tracker can be used to collect the actual movement path of the robot arm. The closed-loop verification system can achieve fully automatic calibration and reduce human intervention.

[0035] The curtain wall cleaning robot mechanical arm motion planning system adopts the curtain wall cleaning robot mechanical arm motion planning method mentioned above, specifically comprising: A data acquisition module, which is used to collect wind pressure data on the surface of the robot arm and the surface image of the curtain wall; Dynamic compensation module, which is used to generate dynamic compensation amount based on wind pressure data on the surface of the robot arm; Trajectory optimization module, which is used to correct the preset initial motion path and adjust the brush head pressure; The offset monitoring module monitors and calibrates the corrected motion path in real time.

[0036] In summary, the present invention innovatively solves the problems of dynamic stability and precise force application in high-altitude curtain wall cleaning by integrating multi-source sensor data and adaptive control algorithms. The pollution identification model based on HSV color space weighted analysis can accurately quantify the severity of different pollution types, providing a reliable basis for differentiated cleaning strategies; the multi-joint dynamic wind pressure compensation mechanism combined with wind speed change feedforward control significantly improves the trajectory tracking accuracy of the robot arm in strong wind environments; the closed-loop self-calibration system ensures long-term operation stability through real-time offset detection and parameter optimization. This solution achieves a balance between cleaning efficiency, curtain wall protection and energy consumption control, and is particularly suitable for urban high-rise building dense areas and chemical pollution sensitive areas. It provides an innovative solution for the reliable operation of intelligent cleaning equipment under complex working conditions at high altitudes.

[0037] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0038] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0039] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0040] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A motion planning method for a curtain wall cleaning robot arm, characterized in that: The specific steps include: S1: Collect wind pressure data on the surface of the robot arm and the surface image of the curtain wall, and generate a surface pollution index based on the surface image of the curtain wall; S2: Generate a dynamic compensation amount based on the wind pressure data on the surface of the robot arm, correct the preset initial motion path according to the dynamic compensation amount, and dynamically adjust the brush head pressure of the robot arm based on the surface pollution index of the curtain wall; S3: Repeat steps S1 and S2 to detect the offset of the robot arm during movement, and use a closed-loop verification mechanism to perform real-time calibration on the corrected movement path until the overall cleaning of the curtain wall is completed.

2. The motion planning method for the mechanical arm of a curtain wall cleaning robot according to claim 1, characterized in that: The method for generating the surface pollution index based on the surface image of the curtain wall is: The collected surface image is converted into HSV color space, and the pollution value of each pixel is calculated. The calculation method is: ； In the formula , , , Respectively represent the pollution value, hue value, saturation and brightness of the pixel point. , , All represent model weights, all are greater than 0, and ; Then, the surface pollution index is calculated according to the pollution value of each pixel in the surface image. The calculation method is: ； In the formula Represents the surface contamination index, Represents the total number of pixels in the surface image, Indicates The pollution value of each pixel, subscript represents the index of the pixel, and .

3. The motion planning method for the mechanical arm of a curtain wall cleaning robot according to claim 1, characterized in that: The wind pressure data includes the pressure difference value and the wind speed change rate. The calculation method for generating the dynamic compensation amount based on the wind pressure data on the surface of the robot arm is: ； In the formula Indicates the robot arm The dynamic compensation amount of each joint, in radians, , The robot arm The wind pressure torque coefficient of each joint and the wind speed change sensitivity coefficient of the robot as a whole, Indicates The pressure difference value of each detection point, Indicates The effective detection area of ​​the detection points is Indicates The detection point to the robot arm The distance between the joints, represents the wind speed change rate, , Represent the indexes of detection points and joints respectively, Indicates the total number of detection points.

4. The motion planning method for the mechanical arm of a curtain wall cleaning robot according to claim 3 is characterized in that: The generation logic of the wind pressure torque coefficient is: The offset angles of each joint of the robot arm are measured under constant wind speed, and the least squares method is used for fitting. The fitting equation is expressed as: ； In the formula Indicates The robot arm is measuring The offset angle of the joint, subscript An index indicating the number of measurements, Indicates the total number of measurements, represents the first fluctuation coefficient, .

5. The motion planning method for the mechanical arm of a curtain wall cleaning robot according to claim 3, characterized in that: The calculation method of the wind speed change sensitivity coefficient is: ； In the formula represents the second fluctuation coefficient, , , , Represent the inertia components of the robot arm in three directions respectively.

6. The motion planning method for the mechanical arm of a curtain wall cleaning robot according to claim 3, characterized in that: The method for correcting the preset initial motion path according to the dynamic compensation amount is: Coordinate compensation for the trajectory points on the initial motion path: ； In the formula , Respectively represent The horizontal and vertical coordinates of the trajectory points, represents the third coefficient of fluctuation, , represents the dynamic compensation of the base joint of the robot arm, Represents the end attitude angle of the robot arm.

7. The motion planning method for the mechanical arm of a curtain wall cleaning robot according to claim 1, characterized in that: The method for dynamically adjusting the brush head pressure of the robot arm based on the surface contamination index of the curtain wall is: When the surface contamination index meets When , the area is considered to be slightly polluted, and the brush head pressure is set to the initial pressure; When the surface contamination index meets When the area is considered to be heavily polluted, the brush head pressure is calculated as follows: ； In the formula represents the pollution threshold, , , Represent the brush head pressure and initial pressure respectively, , Both represent empirical coefficients, , .

8. The motion planning method for the mechanical arm of a curtain wall cleaning robot according to claim 3, characterized in that: The method of real-time calibration of the corrected motion path using the closed-loop verification mechanism is: Collect the actual motion path of the robot arm and calculate the offset based on the actual motion path. The calculation method is: ； In the formula Indicates the offset, represents the total number of trajectory points, , Respectively represent the first The horizontal and vertical coordinates of the trajectory points; When the offset is met for 3 consecutive times When the wind pressure torque coefficient of each joint of the robot arm and the wind speed change sensitivity coefficient of the robot as a whole are updated, Indicates the preset offset threshold.

9. Curtain wall cleaning robot arm motion planning system, characterized by: The curtain wall cleaning robot mechanical arm motion planning system adopts the curtain wall cleaning robot mechanical arm motion planning method according to any one of claims 1 to 8, specifically comprising: A data acquisition module, the data acquisition module is used to collect wind pressure data on the surface of the robot mechanical arm and the surface image of the curtain wall; A dynamic compensation module, which is used to generate dynamic compensation amount based on wind pressure data on the surface of the robot arm; A trajectory optimization module, which is used to correct the preset initial motion path and adjust the brush head pressure; An offset monitoring module is used to monitor and calibrate the corrected motion path in real time.

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