A multi-module adaptive wall-climbing cleaning system and a control method thereof

Abstract

This invention discloses a multi-module adaptive wall-climbing cleaning system and its control method. The system includes: a sensing module integrated on a main frame, which acquires wall environment data in real time and inputs it into a control module; a control module, including an embedded processor platform and multiple communication interfaces, which identifies contaminated areas by processing the received wall environment data, generates path instructions using a gray wolf optimization algorithm, and generates cleaning and adsorption instructions based on the contaminated area identification results; a motion module, which moves and adjusts the system's posture based on the path instructions; a cleaning module, including a replaceable cleaning tool unit, which matches the corresponding cleaning tool to the cleaning tool unit based on the cleaning instructions and performs cleaning; and an adsorption module, which dynamically adjusts the adsorption force based on adsorption instructions and real-time acquired adsorption state data to attach the system to the wall surface to be cleaned. This invention enables efficient, safe, and precise cleaning of complex walls, improving the level of automation.

Description

Technical Field

[0001] This invention relates to the field of industrial equipment wall cleaning and maintenance technology, and in particular to a multi-module adaptive wall-climbing cleaning system and its control method. Background Technology

[0002] On the vertical or near-vertical surfaces of ship hulls, oil tank walls, bridge piers, chemical containers, and other structural surfaces, phenomena such as pollution deposition, corrosion, and attached organisms are common, seriously affecting the operating performance and safe lifespan of the equipment. Therefore, there is an urgent need for efficient, safe, and adaptable wall cleaning and maintenance.

[0003] Currently, traditional cleaning operations still primarily rely on workers operating at heights using scaffolding, suspended platforms, or ropes. This method is not only labor-intensive and inefficient, but also carries extremely high safety risks such as falls and tool drops. Furthermore, rising labor costs make it difficult to meet the large-scale, high-frequency cleaning demands of industrial settings. To address this, some industrial settings have introduced rudimentary wall-climbing robots. However, these devices can only perform basic wall movement and simple cleaning operations, exhibiting significant deficiencies in core performance and functional design, making them completely unsuitable for the complex and ever-changing industrial wall environments. First, their environmental adaptability is extremely poor. Existing wall-climbing robots lack the ability to perceive and dynamically identify the target wall material, surface curvature, type of contaminants (such as bioadhesion, grease, dust, rust, etc.), and adhesion status in real time. Cleaning parameters (such as cleaning intensity and speed) are mostly fixed settings, making it impossible to adjust the operation strategy according to the characteristics of the on-site environment. This results in the equipment either failing to remove stubborn contaminants due to insufficient cleaning intensity or causing wall damage due to excessive intensity when facing walls with different types of contamination or materials, making it difficult to achieve a balance between cleaning effectiveness and wall protection. At the same time, the simple attachment structure design of some equipment makes it prone to unstable posture and weak adhesion on vertical or curved walls, further restricting the reliability of operation.

[0004] Secondly, the intelligence of path planning is severely lacking. Most existing systems only support running along preset paths or require real-time manual control, lacking the ability to perform global analysis of contamination distribution and obstacle locations within the cleaning area, and thus failing to dynamically generate the optimal cleaning path. This leads to frequent problems such as path overlap and cleaning omissions during cleaning operations, not only reducing operational efficiency but also significantly increasing the burden on manual operations, failing to truly achieve automation of cleaning operations.

[0005] Third, their operational efficiency and adaptability are low. Existing wall-climbing robots are generally equipped with only a single cleaning module and lack the ability to quickly switch between cleaning modules, making it impossible to switch cleaning methods according to different types of contaminants. At the same time, they lack a dynamic adjustment mechanism for key parameters such as cleaning intensity and speed, resulting in poor cleaning performance when faced with stubborn stains, while easily causing damage to fragile wall surfaces, making it difficult to meet the dual requirements of cleaning quality and wall protection.

[0006] Fourth, the system integration and autonomous operation capabilities are weak. Existing technologies mostly adopt the mode of multi-robot collaborative operation or rely on the central scheduler to issue instructions. This not only results in complex system structure and high deployment costs, but also makes the overall operation prone to interruption due to communication delays and scheduling failures, thus limiting adaptability. Summary of the Invention

[0007] The main objective of this invention is to provide a multi-module adaptive wall-climbing cleaning system and its control method, which achieves efficient, safe, and precise cleaning of complex walls through multi-source sensing and intelligent planning, thereby improving the level of automation.

[0008] The technical solution adopted in this invention is: a multi-module adaptive wall-climbing cleaning system, comprising: a cleaning module, a motion module, a sensing module, an adsorption module, and a control module; wherein, The sensing module is used to acquire real-time environmental data of the wall surface to be cleaned and input it into the control module; The control module includes an embedded processor platform and a multi-channel communication interface. The embedded processor platform identifies polluted areas by processing received wall environment data, generates cleaning and adsorption instructions based on the identification results, and generates path instructions using the Grey Wolf optimization algorithm, which are then sent to the corresponding modules through the multi-channel communication interface. The motion module moves and adjusts the posture of the system on the wall surface to be cleaned based on the path instructions. The cleaning module includes a cleaning tool unit, which includes a variety of cleaning tools and matches the corresponding cleaning tool based on the cleaning command. The adsorption module is used to collect real-time adsorption status data of the wall surface to be cleaned. The adsorption force is adjusted according to the adsorption commands to attach the system to the wall surface.

[0009] According to the above technical solution, the sensing module includes a visual sensor and a position sensor for acquiring wall environment data; the sensing module transmits the wall environment data to the control module in real time via a CAN bus or RS-485 interface.

[0010] According to the above technical solution, the wall environment data includes pollution distribution data, obstacle distribution data, and wall characteristic data; wherein, The pollution distribution data includes pollution type, pollution density distribution, and pollution area boundary information; The obstacle distribution data includes the obstacle's location, size, and distribution characteristics; The wall characteristic data includes wall material, radius of curvature, and permanent magnet adsorption gap parameters.

[0011] According to the above technical solution, the embedded processor platform is an STM32F407VET6 processor platform, and the multi-channel communication interface includes a TF card interface, an Ethernet module, and multiple PWM channels.

[0012] According to the above technical solution, the control module uses the Grey Wolf optimization algorithm to generate path instructions, specifically including: determining multiple target points to be cleaned based on the identification results of the contaminated area; generating multiple path segments by taking the current position of the system as the starting point and combining the sequence of target points to be cleaned, wherein obstacle avoidance priority and path shortestness are the optimization objectives when generating each path segment; performing path search based on the pre-divided grid nodes of the wall to be cleaned, combining the searched segment paths into the cleaning path of the system, and generating path instructions based on the cleaning path.

[0013] According to the above technical solution, the motion module includes multiple sets of drive wheels and multiple omnidirectional wheels, wherein the drive wheels adjust the speed and direction of the system based on path commands.

[0014] According to the above technical solution, the various cleaning tools include one or more combinations of brushes, high-pressure nozzles, and sandblasting guns.

[0015] According to the above technical solution, the adsorption module adopts a composite permanent magnet structure.

[0016] According to the above technical solution, it also includes a host computer interface for remote monitoring and manual takeover, wherein the host computer interface is connected to the control module via an Ethernet module.

[0017] Another aspect of the present invention provides a control method for the above-mentioned multi-module adaptive wall-climbing cleaning system, comprising: The sensing module acquires real-time environmental data of the wall surface to be cleaned and inputs it into the control module. The embedded processor platform of the control module processes the wall environment data, identifies polluted areas, generates cleaning and adsorption instructions based on the identification results, and generates path instructions using the Grey Wolf optimization algorithm, which are then sent to the corresponding modules through a multi-channel communication interface. Based on the path instructions, the motion module is driven to move and adjust the posture of the system on the wall surface to be cleaned; the cleaning module is controlled to match the corresponding cleaning tools to the cleaning tool unit and perform cleaning based on the cleaning instructions; according to the adsorption instructions and the real-time collected adsorption state data, the adsorption force of the adsorption module is adjusted to stably attach the system to the wall surface to be cleaned.

[0018] The beneficial effects of this invention are as follows: the system's control module is based on an embedded processor to achieve a lightweight, integrated design, eliminating the need for remote communication and providing stronger real-time responsiveness; the Grey Wolf optimization algorithm is used to achieve multi-target path optimization in dynamic environments; the cleaning module supports rapid switching between multiple tools to accurately match contamination characteristics; and all modules work collaboratively based on real-time sensor data to comprehensively improve cleaning efficiency, accuracy, and safety.

[0019] Furthermore, the Grey Wolf optimization algorithm, through segmented planning, obstacle avoidance, and shortest path optimization, ensures comprehensive and redundant cleaning paths, significantly improving operational efficiency.

[0020] Furthermore, a host computer interface is set up to communicate with the control module via Ethernet, supporting remote monitoring and manual takeover, which not only ensures the continuity of automated operations but also responds to emergencies and improves the system's emergency response capabilities. Attached Figure Description

[0021] Figure 1 This is a structural diagram of the multi-module adaptive wall-climbing cleaning system according to an embodiment of the present invention; Figure 2 This is a flowchart of the control method for a multi-module adaptive wall-climbing cleaning system according to an embodiment of the present invention; Figure 3 This is a structural diagram of the multi-module adaptive wall-climbing cleaning device according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the hardware structure of the electronic device in the multi-module adaptive wall-climbing cleaning device according to an embodiment of the present invention.

[0022] Reference numerals: 101, Main unit; 201, Control box; 301, High-pressure water spray device; 403, Casters; 404, Anti-slip drive wheels; 405, Climbing body frame; 502, Brush-pressed magnetic track. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0024] Example 1 This embodiment provides a multi-module adaptive wall-climbing cleaning system, the structure of which is as follows: Figure 1 As shown, the system achieves intelligent and efficient cleaning through the collaborative work of multiple modules, specifically including a cleaning module (high-pressure water spray device 301), a motion module, a sensing module (host 101), an adsorption module, and a control module (control box 201) integrated on the main frame (climbing body frame 405); preferably, the main frame (climbing body frame 405) is made of lightweight aluminum alloy material, which has high strength and corrosion resistance.

[0025] Among them, the sensing module (host 101) serves as the system's sensing terminal, collecting wall environment data in real time and synchronously inputting it into the control module (control box 201) to provide basic support for subsequent decision-making.

[0026] Furthermore, the sensing module (host 101) adopts a combination design of vision sensor and position sensor. The vision sensor is responsible for capturing visual information related to pollution distribution, obstacle shape and wall characteristics, while the position sensor accurately locates the system's own position on the wall. The two work together to comprehensively acquire wall environmental data. At the same time, the data is transmitted to the control module (control box 201) in real time through the industrial-grade CAN bus or RS-485 interface to ensure the real-time performance and stability of data transmission and avoid the impact of data delay on operational decisions.

[0027] Furthermore, the wall environment data is finely categorized, specifically including pollution distribution data, obstacle distribution data, and wall characteristic data. The pollution distribution data covers pollution type, pollution density distribution, and pollution area boundary information, clearly defining the core characteristics of the cleaning target. The obstacle distribution data includes obstacle location, size, and distribution characteristics, providing obstacle avoidance basis for path planning. The wall characteristic data includes wall material, radius of curvature, and permanent magnet adsorption gap parameters, supporting the adaptation and adjustment of cleaning methods and adsorption forces. This detailed data comprehensively covers the key information required for cleaning operations, ensuring the accuracy of the control module (control box 201) decisions.

[0028] The control module (control box 201) includes a highly integrated embedded processor platform and multiple communication interfaces, constructing an efficient data processing and command distribution channel. On one hand, the platform performs real-time comprehensive analysis and processing of the wall environment data transmitted by the sensor module (host 101), accurately identifying the spatial range and pollution level of the contaminated area; on the other hand, based on the identification results, it dynamically generates appropriate cleaning commands (such as matching cleaning tool types and working parameters) and adsorption commands (such as adjusting adsorption force), while using the Grey Wolf optimization algorithm designed specifically for complex wall scenarios to generate optimal path commands, achieving precise matching and coordination between high-level decision-making and low-level execution needs.

[0029] Furthermore, the embedded processor platform is specifically an STM32F407VET6 processor platform, whose powerful computing power provides hardware support for the real-time processing of complex environment data; the multi-channel communication interface includes a TF card interface, an Ethernet module, and multiple PWM channels, which together constitute a stable and reliable control information transmission network.

[0030] Furthermore, the control module (control box 201) employs the Grey Wolf optimization algorithm to generate path instructions in the following ways: First, based on the identification results of the contaminated area, multiple target points to be cleaned and their priority sequences are determined through cluster analysis; second, taking the current system position as the path starting point, and combining the above target point sequence, the Grey Wolf optimization algorithm iteratively generates each segment of the locally optimal path using obstacle avoidance priority and path shortest as multi-objective optimization functions; finally, based on the pre-divided grid nodes of the wall to be cleaned, path search and smooth connection are performed, and the searched segment paths are combined into a complete and efficient cleaning path for the system, thereby generating the final executable path instructions, ensuring the high adaptability and strong robustness of path planning in complex wall environments.

[0031] The motion module completes the movement and attitude adjustment of the system on the wall according to the path instructions issued by the control module (control box 201).

[0032] Furthermore, the motion module adopts a combination structure of two sets of drive wheels (anti-slip drive wheels 404) and at least one omnidirectional wheel (omnidirectional wheel 403). The drive wheels (anti-slip drive wheels 404) serve as power output units, flexibly adjusting the system's moving speed and direction of travel based on path commands. The omnidirectional wheel (omnidirectional wheel 403) assists in adjusting the posture, ensuring the system's stability and maneuverability on vertical walls, curved surfaces, and obstacle distribution areas, adapting to complex wall shapes.

[0033] The cleaning module (high-pressure water spray device 301) is equipped with a replaceable cleaning tool unit with a standardized quick-change interface. Based on the cleaning instructions issued by the control module (control box 201), it quickly matches the cleaning tool corresponding to the type of pollution and performs the cleaning operation.

[0034] Furthermore, the cleaning tools include various types such as brushes, high-pressure nozzles, and sandblasting guns, which are suitable for pollutants of different properties such as dust, grease, and rust. This enables targeted cleaning of polluted areas and avoids the problem of poor cleaning results with a single tool.

[0035] The adsorption module dynamically adjusts the adsorption force according to adsorption commands and real-time collected adsorption state data to ensure stable adhesion of the system to the wall surface to be cleaned. Furthermore, the adsorption module employs a composite permanent magnet structure with controllable magnetic flux characteristics. This structure can flexibly adjust the adsorption force based on parameters such as the permanent magnet adsorption gap and radius of curvature in the wall surface characteristic data, ensuring firm adhesion to vertical or curved walls while preventing excessive adsorption force from damaging the wall surface or insufficient force from causing the system to detach.

[0036] Furthermore, the system is also equipped with a host computer interface for remote monitoring and manual takeover. This interface establishes a communication connection with the control module (control box 201) via Ethernet. Ethernet communication enables long-distance and stable data transmission, allowing operators to monitor the work progress and equipment operating status in real time. When encountering sudden obstacles, equipment malfunctions, or other emergencies, the manual takeover function can intervene in a timely manner to ensure the safety and continuity of the operation.

[0037] This embodiment also provides a control method for the above-mentioned multi-module adaptive wall-climbing cleaning system, the process of which is as follows: Figure 2 As shown, the specific steps include: First, the sensing module (host 101) acquires wall environment data in real time. The wall environment data includes pollution distribution data, obstacle distribution data and wall characteristic data, comprehensively capturing key information of the cleaning scene and providing a complete basis for subsequent decision-making. Next, the control module (control box 201) comprehensively analyzes and processes the collected wall environment data to accurately identify the range and type of pollution in the polluted area, and then generates appropriate cleaning and adsorption instructions. At the same time, the gray wolf optimization algorithm is used to generate the optimal path instructions while taking into account obstacle avoidance requirements and path efficiency, so as to achieve accurate matching between environmental information and execution instructions. Finally, the motion module is driven to move and adjust the posture of the system based on the path command to ensure that it travels along the optimal path; the cleaning module (high-pressure water spray device 301) is controlled to match the corresponding cleaning tools based on the cleaning command and perform targeted cleaning; at the same time, the adsorption force of the adsorption module is dynamically adjusted according to the adsorption command and the real-time collected adsorption status data to ensure that the system is stably attached to the wall surface to be cleaned. All modules work together to ensure that the cleaning operation is carried out efficiently and safely.

[0038] Example 2 Based on the multi-module adaptive wall-climbing cleaning system described in Embodiment 1, this embodiment provides a multi-module adaptive wall-climbing cleaning device, the structure of which is shown in Figure 3.

[0039] The device consists of a wall-climbing frame 405, a control box 201, a main unit 101, a high-pressure water spray device 301, a motion unit, and an adsorption unit. All components are assembled into one unit through standardized interfaces.

[0040] The climbing frame 405 is made of aluminum alloy, which has high strength and corrosion resistance. It serves as the installation base for various functional modules and has multiple standardized interfaces to support the quick disassembly and replacement of the main unit 101 and the high-pressure water spray device 301.

[0041] Control box 201: Installed on top of the climbing body frame 405, it is the core control center of the device. It has built-in electronic hardware modules and is responsible for data processing, instruction generation and linkage control of various modules.

[0042] Main unit 101: Mounted on the side of the wall-climbing body frame 405, it has a built-in sensing unit containing a vision sensor and a position sensor, which is used to collect information on the distribution of wall pollution, the shape of obstacles, wall characteristics and the position of the device itself in real time, and transmit the data to the control box 201.

[0043] High-pressure water spray device 301: Located at the front end of the wall-climbing body frame 405, it is the core component of the cleaning execution unit. Equipped with a quick-change connector structure, it can quickly load cleaning tools such as brushes and sandblasting guns, and can adjust the key operating parameters of the cleaning tools according to the instructions of the control box 201.

[0044] Motion unit: Composed of anti-slip drive wheels 404, casters 403 and brushed magnetic rails 502; anti-slip drive wheels 404 are symmetrically arranged on both sides of the wall-climbing body frame 405, and can adjust the movement speed and direction according to the path instructions; casters 403 assist in adjusting the posture of the device, and brushed magnetic rails 502 work together to ensure the stability and mobility of the wall movement.

[0045] Adsorption unit: Embedded in the bottom of the wall-climbing body frame 405, it adopts a composite permanent magnet structure and can dynamically adjust the adsorption force based on real-time adsorption status data to ensure that the device is stably attached to the wall surface to be cleaned.

[0046] This device achieves efficient and adaptive cleaning of complex wall environments through the coordinated operation of its various modules. The environmental data collected by the main unit 101 is processed and analyzed by the control box 201 to generate optimized cleaning paths and operating parameters, driving the high-pressure water spray device 301, the motion unit, and the adsorption unit to coordinate their actions and complete precise and efficient cleaning tasks.

[0047] In summary, this invention provides a multi-module adaptive wall-climbing cleaning system and its control method, which can achieve efficient, safe, and precise cleaning of complex walls and improve the level of automation.

[0048] It should be noted that, depending on the implementation needs, the various steps / components described in this application can be broken down into more steps / components, or two or more steps / components or parts of the operation of steps / components can be combined into new steps / components to achieve the purpose of this invention.

[0049] The order of the steps in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0050] It should be understood that those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. A multi-module adaptive wall-climbing cleaning system, characterized in that, include: The system comprises a cleaning module, a motion module, a sensing module, an adsorption module, and a control module, all integrated onto the main frame; among which, The sensing module is used to acquire real-time environmental data of the wall surface to be cleaned and input it into the control module; The control module includes an embedded processor platform and a multi-channel communication interface. The embedded processor platform identifies polluted areas by processing received wall environment data, generates cleaning and adsorption instructions based on the identification results, and generates path instructions using the Grey Wolf optimization algorithm, which are then sent to the corresponding modules through the multi-channel communication interface. The motion module moves and adjusts the posture of the system on the wall surface to be cleaned based on the path instructions. The cleaning module includes a cleaning tool unit, which includes a variety of cleaning tools and matches the corresponding cleaning tool based on the cleaning command. The adsorption module is used to collect adsorption status data of the wall surface to be cleaned in real time, and adjust the adsorption force in combination with the adsorption command to attach the system to the wall surface to be cleaned.

2. The multi-module adaptive wall-climbing cleaning system according to claim 1, characterized in that, The sensing module includes a visual sensor and a position sensor for acquiring wall environment data; the sensing module transmits the wall environment data to the control module in real time via a CAN bus or RS-485 interface.

3. The multi-module adaptive wall-climbing cleaning system according to claim 1, characterized in that, The wall environment data includes pollution distribution data, obstacle distribution data, and wall characteristic data; among which... The pollution distribution data includes pollution type, pollution density distribution, and pollution area boundary information; The obstacle distribution data includes the obstacle's location, size, and distribution characteristics; The wall characteristic data includes wall material, radius of curvature, and permanent magnet adsorption gap parameters.

4. The multi-module adaptive wall-climbing cleaning system according to claim 2, characterized in that, The embedded processor platform is an STM32F407VET6 processor platform, and the multi-channel communication interface includes a TF card interface, an Ethernet module, and multiple PWM channels.

5. The multi-module adaptive wall-climbing cleaning system according to claim 1, characterized in that, The control module uses the Grey Wolf optimization algorithm to generate path instructions, specifically including: determining multiple target points to be cleaned based on the identification results of the contaminated area; generating multiple path segments starting from the current position of the system and combining the sequence of target points to be cleaned, wherein obstacle avoidance priority and path shortestness are the optimization objectives when generating each path segment; performing path search based on the pre-divided grid nodes of the wall to be cleaned, combining the searched segment paths into the cleaning path of the system, and generating path instructions based on the cleaning path.

6. The multi-module adaptive wall-climbing cleaning system according to claim 1, characterized in that, The motion module includes multiple sets of drive wheels and multiple omnidirectional wheels, wherein the drive wheels adjust the speed and direction of the system based on path commands.

7. The multi-module adaptive wall-climbing cleaning system according to claim 1, characterized in that, The various cleaning tools include one or more combinations of brushes, high-pressure nozzles, and sandblasting guns.

8. The multi-module adaptive wall-climbing cleaning system according to claim 1, characterized in that, The adsorption module adopts a composite permanent magnet structure.

9. The multi-module adaptive wall-climbing cleaning system according to claim 1, characterized in that, It also includes a host computer interface for remote monitoring and manual takeover, which is connected to the control module via an Ethernet module.

10. A control method for the multi-module adaptive wall-climbing cleaning system of claim 1, characterized in that, include: The sensing module acquires real-time environmental data of the wall surface to be cleaned and inputs it into the control module. The embedded processor platform of the control module processes the wall environment data, identifies polluted areas, generates cleaning and adsorption instructions based on the identification results, and generates path instructions using the Grey Wolf optimization algorithm, which are then sent to the corresponding modules through a multi-channel communication interface. Based on the path instructions, the motion module is driven to move and adjust the posture of the system on the wall surface to be cleaned; the cleaning module is controlled to match the corresponding cleaning tools to the cleaning tool unit and perform cleaning based on the cleaning instructions; according to the adsorption instructions and the real-time collected adsorption state data, the adsorption force of the adsorption module is adjusted to stably attach the system to the wall surface to be cleaned.

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