A wind pressure adaptive control method and device for a wind pressure-type wall-climbing inspection robot

Abstract

This invention provides a wind pressure adaptive control method and device for a wind pressure-type wall-climbing inspection robot, including a robot main control board and a pressure sensor. The robot main control board is mounted on the robot chassis, and a line laser surface reconstruction sensing head is mounted on the robot chassis via a probe mounting bracket. A battery and a pressure sensor are also mounted on the robot chassis. The robot main control board contains a wind pressure estimation algorithm based on analog data conversion and analysis, and an adaptive adjustment control algorithm for duct power under different surface conditions. This invention eliminates the voltage measurement error of the decoder in traditional pressure sensor measurements by introducing digital analog quantities, improving the accuracy of the initial input data when the robot control program calculates pressure. A program control algorithm based on different working condition models is designed, enabling the robot to automatically adjust the duct output power according to the algorithm after acquiring its own working conditions, thereby changing the wind pressure and achieving motion adaptation.

Description

Technical Field

[0001] This invention belongs to the field of robot automatic control technology, specifically relating to a wind pressure adaptive control method and device for a wind pressure-type wall-climbing detection robot. Background Technology

[0002] Aircraft skin is a streamlined outer surface formed by fixing alloys or composite materials to the aircraft frame. It often needs to withstand the test of complex environments such as severe weather, so it is easy to develop surface defects such as fatigue cracks and pits, which can affect the safety of aircraft flight. Regular damage inspection and maintenance of aircraft skin is required to promptly identify and deal with potential safety hazards and ensure safe flight.

[0003] Currently, aircraft skin damage detection devices are mainly divided into manual inspection and robotic inspection. Traditional manual methods for aircraft skin damage detection include visual inspection, ultrasonic inspection, penetrant testing, and eddy current testing. However, each of these traditional methods has its shortcomings, such as low work efficiency, high inspection costs, and cumbersome processes, causing many inconveniences to the inspection work. Taking ground crew visual inspection as an example, it is labor-intensive, time-consuming, heavily reliant on the work experience of maintenance personnel, and has a high rate of missed detections. Research on robotic inspection technology has yielded significant results in recent years. However, according to publicly available information, existing aircraft inspection robots often consist of a large base paired with robotic arms of various shapes. Although some inspection processes can be performed by hoisting inspection robots, they cannot move to the belly of the aircraft, nor can they reach the central area of ​​the aircraft's back, and they cannot be directly applied in some small, semi-enclosed spaces. In other words, these robots are difficult to apply directly when facing non-open, poorly accessible areas.

[0004] In response, to enable the widespread application of robots to the entire surface of aircraft, numerous universities and aerospace research institutes both domestically and internationally have developed various adsorption-based wall-climbing robots specifically designed for aircraft surfaces. Among these studies, wind pressure-based wall-climbing robots, which utilize wind pressure for active adsorption, are widely used. However, current research often employs robots that generate a constant wind pressure, making them highly susceptible to variations in surface conditions. This can lead to robot instability, hindered movement, and excessive energy consumption in maintaining wind pressure, hindering the adaptive adjustment of wind pressure to suit the various tilted and curved surfaces of aircraft.

[0005] Therefore, it is necessary to start with the adsorption principle of wind pressure-based wall-climbing robots, and to optimize the robot's control system algorithm, differentiating it from the current active constant wind pressure control system and passive open-loop control system. A self-feedback, adaptive, and adjustable wind pressure robot control system and device should be developed to improve the robot's motion stability during the detection process. This will eliminate the obstacle to movement caused by excessive wind pressure under different working conditions, save the robot's energy, ensure the robot's detection accuracy, and increase the detection range.

[0006] Based on this, a wind pressure adaptive control method and device for a wind pressure-type wall-climbing inspection robot are proposed. Summary of the Invention

[0007] The technical problem to be solved by this invention is to address the shortcomings of the prior art by providing a wind pressure adaptive control method and device for a wind pressure-type wall-climbing inspection robot. This method addresses the difficulties in controlling wind pressure, the obstruction of robot movement by wind pressure, and the excessive power consumption required to maintain high wind pressure during the movement of the wind pressure-type wall-climbing robot on different working surfaces. It fully considers the overall force situation of the robot when moving on different surfaces, and dynamically adjusts the duct wind pressure by calling the pressure sensor on the robot to obtain the robot's posture information in real time, thereby achieving wind pressure adaptation of the robot on different working surfaces and solving the problems mentioned in the background art.

[0008] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is: a wind pressure adaptive control device for a wind pressure climbing detection robot, including a robot main control board and a pressure sensor;

[0009] The robot's main control board is mounted on the robot chassis, and two ducts are symmetrically mounted on the robot chassis via duct mounting brackets.

[0010] The line laser surface reconstruction sensing head is mounted on the robot chassis via a probe mounting bracket. Four motor brackets are also mounted on the robot chassis via a chassis bracket, and a pressure sensor is installed between each motor bracket and the chassis.

[0011] Each of the motor brackets is equipped with a coded motor, which is connected to a Mecanum wheel via a hexagonal coupling.

[0012] The robot chassis is also equipped with a battery, which supplies power to the robot's main control board, line laser surface reconstruction sensor head, and coded motor.

[0013] As a further explanation of the present invention, the robot main control board is equipped with a wind pressure estimation algorithm based on analog data conversion and analysis. Specifically, the data measured by the pressure sensor is homogenized according to the analog precision, and the data after precision homogenization is collected to obtain the real-time pressure value of the pressure sensor under any analog precision. At the same time, an integral calculation algorithm is used to integrate the data simulation method of analog acquisition time frequency. By eliminating the phenomenon of precision decrease caused by the increase of data acquisition time interval, the accuracy of parameter data in the acquisition process is improved.

[0014] As a further explanation of the present invention, the wind pressure estimation algorithm is specifically implemented as follows:

[0015] The voltage of the pressure sensor under normal operating conditions is denoted as 0. Assume the range of the pressure sensor selected for the robot measuring device is K, and the voltage variation range during pressure measurement is 0 to U. Let the analog value read by the system at a certain moment be α, and the pressure sensor voltage reading at that moment be U. t The system's analog accuracy is δ, and the pressure sensor's pressure reading at that moment is β.

[0016] Refining the voltage of the pressure sensor, denoted as ζ, we have:

[0017]

[0018] At this point, the pressure sensor actually measures the following pressure:

[0019]

[0020] The algorithm reads the instantaneous pressure value from the pressure sensor as N1, then:

[0021]

[0022] Where U, K, and δ are constants, and the analog quantity α is obtained by integrating the following formula:

[0023]

[0024] In the formula, U0 is the instantaneous voltage at the moment of measurement, Ψ is the electric flux constant of the pressure sensor material, and k is the material resistance constant.

[0025] As a further explanation of the present invention, the robot main control board is also equipped with an adaptive adjustment control algorithm for duct power under different surface conditions. According to the robot's posture, the output power of the robot's duct is automatically adjusted to change the wind pressure. Specifically, the robot's control algorithm is divided according to the working posture, and according to the angle between the robot and the upward Z-axis in space (0-180°), it is divided into six working conditions: 0-30°, 30-60°, 60-90°, 90-120°, 120-150°, and 150-180°. The robot's duct power is modeled and analyzed for the six working conditions, and the duct output power is controlled according to the angle change law of different intervals, which improves the robot's motion stability, saves the robot's own energy, and reduces the obstruction of wind pressure on the robot's movement.

[0026] As a further explanation of the present invention, the implementation process of the duct power adaptive adjustment control algorithm is as follows:

[0027] The robot is set to obtain the angle between itself and the Z-axis of space at any given time via the IMU, which is θ. The six working condition intervals are θ. i ,i=0,1,2,3,4,5,

[0028] Let the starting point of the interval be θ i0 The analog value α read by the system is introduced, and the range of α is evenly divided according to the number of intervals, with the starting point of the interval denoted as α. i0 α 00 =0, the maximum value that the analog quantity can obtain is α. max Let the intensity range of the duct control signal under each operating condition be 0~Φ. i The corresponding power value refined according to analog quantity precision is Let the maximum power of the duct be P, and the output power of the duct at any given time be p. Then the following relationship holds:

[0029] The spatial angle determination intervals for the six working condition intervals are θ. i It satisfies the following conditions:

[0030] θ i ∈{θ i0 θ (i+1)0}

[0031] θ (i+1)0 =θ i0 +(i+1)×30°

[0032] The range of analog values ​​for the six intervals is α. i It satisfies the following conditions:

[0033]

[0034] α i ∈{αi0 α (i+1)0}

[0035]

[0036] Signal strength, refined to analog precision, power value is as follows:

[0037]

[0038] When the system determines that the read analog value α∈α i At that time, the power output is as follows:

[0039]

[0040] By linking the real-time pressure value and the real-time power output through the analog numerical value α, the robot's self-sensing, self-adaptation, and self-adjustment functions are realized.

[0041] As a further explanation of the present invention, the duct is specifically a unidirectional thrust duct. The axis of the unidirectional thrust duct is along the vertical direction. When in use, the DC brushless motors in the two ducts drive the blades to rotate. The blades cut the air to generate a flow field. The flow field generates a pressure difference above and below the blades. The pressure difference generates a force on the blades, which is used to enable the robot to adhere to the wall.

[0042] Compared with the prior art, the present invention has the following advantages:

[0043] 1. This invention eliminates the voltage measurement error of the decoder in traditional pressure sensor measurement by introducing digital analog quantities, thereby improving the accuracy of the initial input data when the robot control program performs pressure calculation. At the same time, by adopting an integral algorithm, the average calculation method in the time dimension is no longer used as the pressure sensor voltage measurement result. Instead, the pressure measurement result is converted into the instantaneous voltage measurement value under the time derivative, which greatly reduces the measurement error and enables continuous reading of real-time measurement results.

[0044] 2. The invention summarizes the common characteristics of different working conditions during robot movement, establishes six robot motion working condition models, and gives corresponding working condition judgment criteria, realizing the robot's self-awareness of its own working state; it designs program control algorithms based on different working condition models, so that after the robot obtains its own working condition, it can automatically adjust the output power of the duct according to the algorithm, thereby changing the wind pressure, and realizing motion adaptation. Attached Figure Description

[0045] Figure 1 This is an assembly diagram of the main body of the wind pressure crawling inspection robot in this invention;

[0046] Figure 2 This is a schematic diagram of the pressure sensor installation in this invention;

[0047] Figure 3 This is a diagram illustrating the robot's working conditions in an embodiment of the present invention.

[0048] Figure 4 This is a flowchart illustrating the complete adaptive control process of the robot in this invention.

[0049] Figure 5 This is a flowchart of the robot's adaptive and self-adjusting wind pressure process in the invention.

[0050] Explanation of reference numerals in the attached diagram: 1-Robot chassis; 2-Ductwork; 3-Robot main control board; 4-Line laser surface reconstruction sensor head; 5-Probe mounting bracket; 6-Data transmission line; 7-Ductwork mounting bracket; 8-Chassis bracket; 9-Motor bracket; 10-Codeable motor; 11-Battery; 12-Hexagonal coupling; 13-Pressure sensor. Detailed Implementation

[0051] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0052] like Figure 1-5 As shown, the present invention provides a technical solution: a wind pressure adaptive control device for a wind pressure climbing detection robot, characterized in that it includes a robot main control board 3 and a pressure sensor 13;

[0053] The robot main control board 3 is mounted on the robot chassis 1. Two ducts 2 are symmetrically mounted on the robot chassis 1 via duct mounting brackets 7. The duct 2 is specifically a unidirectional thrust duct with its axis along the vertical direction. In use, the DC brushless motors in the two ducts 2 drive the blades to rotate. The blades cut the air to generate a flow field. The flow field generates a pressure difference above and below the blades. The pressure difference generates a force on the blades, which is used to enable the robot to adhere to the wall surface.

[0054] The line laser surface reconstruction sensing head 4 is mounted on the robot chassis 1 via the probe mounting bracket 5, and is used to complete the robot's supporting functional operations.

[0055] The robot chassis 1 is also equipped with four motor brackets 9 via chassis support 8. Each motor bracket 9 is also equipped with a pressure sensor 13 between itself and the chassis 1. The pressure sensor 13 is specifically an MD-30-60 thin film pressure sensor.

[0056] Each of the motor brackets 9 is equipped with a coded motor 10, and the coded motor 10 is equipped with a Mecanum wheel via a hexagonal coupling 12. When the robot moves on the wall, the forward, backward and turning movements of the four Mecanum wheels are controlled. The suction force generated by the duct 2, which is perpendicular to the direction of the robot body, provides the positive pressure of the robot.

[0057] The robot chassis 1 is also equipped with a battery 11, which is used to supply power to the robot main control board 3, the line laser surface reconstruction sensing head 4 and the codeable motor 10.

[0058] The robot is also connected to a data transmission line 6, which enables the laser surface reconstruction sensing head 4 to transmit the measured data back to the computer in real time, thus serving as a functional structure for the robot itself.

[0059] The robot's main control board 3 is equipped with a wind pressure estimation algorithm based on analog data conversion and analysis. Specifically, it homogenizes the data measured by the pressure sensor according to the analog precision, collects the data after precision homogenization, obtains the real-time pressure value of the pressure sensor under any analog precision, and adopts an integral calculation algorithm to integrate the data simulation method of analog acquisition time frequency. By eliminating the phenomenon of precision decrease caused by the increase of data acquisition time interval, the accuracy of parameter data in the acquisition process is improved.

[0060] The specific implementation process of the wind pressure estimation algorithm is as follows:

[0061] The voltage of pressure sensor 13 under normal operating conditions is denoted as 0. Assume the range of the pressure sensor selected for the robot measuring device is K. When the sensor is measuring pressure, the voltage variation range is 0 to U. Let the analog value read by the system at a certain moment be α, and the voltage reading of the pressure sensor at that moment be U. t The system's analog accuracy is δ, and the pressure sensor's pressure reading at that moment is β.

[0062] Refining the voltage of the pressure sensor, denoted as ζ, we have:

[0063]

[0064] At this point, the pressure sensor actually measures the following pressure:

[0065]

[0066] The algorithm reads the instantaneous pressure value from the pressure sensor as N1, then:

[0067]

[0068] Where U, K, and δ are constants, and the analog quantity α is obtained by integrating the following formula:

[0069]

[0070] In the formula, U0 is the instantaneous voltage at the moment of measurement, Ψ is the electric flux constant of the pressure sensor material, and k is the material resistance constant.

[0071] The robot's main control board 3 also includes an adaptive power adjustment control algorithm for the duct under different surface conditions. Based on the robot's posture, it automatically adjusts the output power of the robot's duct, thereby changing the wind pressure. Specifically, the robot's control algorithm is divided according to its working posture, specifically into six working conditions: 0–30°, 30–60°, 60–90°, 90–120°, 120–150°, and 150–180°, based on the angle between the robot and the upward Z-axis (0–180°). Figure 2 As shown;

[0072] We conducted duct power modeling and analysis for six operating conditions, and controlled the duct output power according to the angle variation law of different intervals to improve the robot's motion stability, save the robot's own energy, and reduce the obstruction of wind pressure on the robot's motion.

[0073] The implementation process of the duct power adaptive adjustment control algorithm is as follows:

[0074] The robot is set to obtain the angle between the robot and the Z-axis in space at any given time via the IMU of the robot's main control board 3. The six working condition intervals are θ. i ,i=0,1,2,3,4,5,

[0075] Let the starting point of the interval be θ i0 The analog value α read by the system is introduced, and the range of α is evenly divided according to the number of intervals, with the starting point of the interval denoted as α. i0 α 00 =0, the maximum value that the analog quantity can obtain is α. max Let the intensity range of the duct control signal under each operating condition be 0 to Φi, and its corresponding power value refined according to analog quantity accuracy be... Let the maximum power of the duct be P, and the output power of the duct at any given time be p. Then the following relationship holds:

[0076] The spatial angle determination intervals for the six working condition intervals are θ. i It satisfies the following conditions:

[0077] θ i ∈{θ i0 θ (i+1)0}

[0078] θ (i+1)0=θ i0 +(i+1)×30°

[0079] The range of analog values ​​for the six intervals is α. i It satisfies the following conditions:

[0080]

[0081] α i ∈{α i0 α (i+1)0}

[0082]

[0083] Signal strength, refined to analog precision, power value is as follows:

[0084]

[0085] When the system determines that the read analog value α∈α i At that time, the power output is as follows:

[0086]

[0087] By linking the real-time pressure value and the real-time power output through the analog numerical value α, the robot's self-sensing, self-adaptation, and self-adjustment functions are realized.

[0088] The calculation process of the robot's algorithm output when the robot transitions from detection interval 3 to detection interval 4 is as follows:

[0089] The pressure sensor selected for the robot measuring device has a range of 0 to 200 N. When the robot is working and the sensor is measuring the pressure, the voltage variation range is 0 to 3.3 V. Suppose that at a certain moment the system reads an analog value of 714, the system's analog accuracy is 4096, the maximum power of the duct is 6 kW, and the signal strength is 0 to 1.

[0090] The algorithm reads the instantaneous pressure value from the pressure sensor as N1, then:

[0091]

[0092] At this point, since α = 714, α ∈ 620, 827,

[0093] The system automatically determines that the robot is in the i=4, i.e., the fourth working condition range, and adjusts the robot's duct operating power accordingly:

[0094]

[0095] Furthermore, to better illustrate the application scenarios of this invention, taking the motion in interval 1 as an example, it can be calculated from the aforementioned formula that: α∈0,207. At this time, the robot is approximately in planar motion, and the wind pressure generated by the duct will hinder the robot's motion. The corresponding power range p∈0,1kw is calculated, which belongs to the low-power operation state, saving robot energy and reducing motion resistance.

[0096] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0097] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for wind pressure adaptive control of a wind pressure-type wall-climbing inspection robot, characterized in that, The device includes a robot main control board (3) and a pressure sensor (13). The robot main control board (3) is mounted on the robot chassis (1), and two ducts (2) are symmetrically mounted on the robot chassis (1) via duct mounting brackets (7). The line laser surface reconstruction sensing head (4) is mounted on the robot chassis (1) via the probe mounting bracket (5). Four motor brackets (9) are also mounted on the robot chassis (1) via the chassis bracket (8). A pressure sensor (13) is also installed between each motor bracket (9) and the chassis (1). Each of the motor brackets (9) is equipped with a coded motor (10), which is fitted with a Mecanum wheel via a hexagonal coupling (12); The robot chassis (1) is also equipped with a battery (11) and a line laser surface reconstruction sensor head (4). The battery (11) is used to supply power to the robot main control board (3), the line laser surface reconstruction sensor head (4), the codeable motor (10), and the pressure sensor (13). The method is as follows: The robot main control board (3) is equipped with a wind pressure estimation algorithm based on analog data conversion and analysis. The data measured by the pressure sensor is homogenized according to the analog accuracy. The data after the accuracy is homogenized is collected to obtain the real-time pressure value of the pressure sensor under any analog accuracy. At the same time, an integral calculation algorithm is used to integrate the data simulation method of the analog acquisition time frequency. By eliminating the phenomenon of decreased accuracy caused by the increase of the data acquisition time interval, the accuracy of the parameter data in the acquisition process is improved. The specific implementation process of the wind pressure estimation algorithm is as follows: The voltage of the pressure sensor (13) under normal working conditions is recorded as 0 voltage. Let the range of the pressure sensor selected by the robot measuring device be K. When the sensor is working to measure pressure, the voltage variation range is 0~U. Let the analog value read by the system at a certain moment be α, the voltage reading of the pressure sensor at that moment be Ut, the analog accuracy of the system be δ, and the pressure reading of the pressure sensor at that moment be β: Refining the voltage of the pressure sensor, denoted as ζ, we have: ； At this point, the pressure sensor actually measures the following pressure: ； The algorithm reads the instantaneous pressure value from the pressure sensor as N1, then: ； in, For a constant value, the analog quantity α is obtained by integrating the following formula: ； Where, The instantaneous voltage at the moment of measurement. Let be the electric flux constant of the pressure sensor material, and k be the material resistance constant; The robot main control board (3) is also equipped with an adaptive adjustment control algorithm for duct power under different surface conditions. According to the robot's position, the output power of the robot's duct is automatically adjusted to change the wind pressure. Specifically, the robot's control algorithm is divided according to the working position. According to the angle between the robot and the upward Z-axis in space (0-180°), it is divided into six working conditions: 0~30°, 30~60°, 60~90°, 90~120°, 120~150°, and 150~180°. The robot's duct power is modeled and analyzed for the six working conditions. The duct output power is controlled according to the angle change law of different intervals to improve the robot's motion stability, save the robot's own energy, and reduce the wind pressure's obstruction to the robot's motion.

2. The method for wind pressure adaptive control device of wind pressure type wall climbing detection robot according to claim 1, characterized in that, The implementation process of the duct power adaptive adjustment control algorithm is as follows: The robot is set to obtain the angle between the robot and the Z-axis of space at any given time through the IMU of the robot's main control board (3). The six operating condition ranges are as follows: i i = 0, 1, 2, 3, 4, 5 The starting point of the interval is denoted as i0 The analog value α read by the system is introduced, and the range of α is evenly divided according to the number of intervals, with the starting point of the interval denoted as α. i0 α 00 =0, the maximum value that the analog quantity can obtain is α. max Let the intensity range of the duct control signal under each operating condition be 0~Φ. i The corresponding power value refined according to analog quantity precision is φ. i Let the maximum power of the duct be P, and the output power of the duct at any given time be p. Then the following relationship holds: The spatial angle determination intervals for the six working condition intervals are as follows: It satisfies the following conditions: ； ； The range of analog values ​​for the six intervals is as follows: It satisfies the following conditions: ； ； ； Signal strength, refined to analog precision, power value is as follows: ； When the system determines the read analog value ∈ At that time, the power output is as follows: ； Through analog numerical values By linking real-time pressure values ​​with real-time power output, the robot achieves self-sensing, self-adaptation, and self-adjustment functions.

3. The method for wind pressure adaptive control device of wind pressure type wall climbing detection robot according to claim 1, characterized in that, The duct (2) is specifically a unidirectional thrust duct. The axis of the unidirectional thrust duct is vertical. When in use, the DC brushless motors in the two ducts (2) drive the blades to rotate. The blades cut the air to generate a flow field. The flow field generates a pressure difference above and below the blades. The pressure difference generates a force on the blades to achieve the robot's adsorption on the wall.

4. The method for wind pressure adaptive control device of wind pressure type wall climbing detection robot according to claim 1, characterized in that, The pressure sensor (13) is specifically an MD-30-60 thin-film pressure sensor.

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