Robot control method, device, system, robot and storage medium

Abstract

The application is suitable for the field of robot technology, and provides a control method, device and system of a robot, the robot and a storage medium. The method comprises: controlling the robot to move based on a planned path on a preset map; acquiring a first preset gas concentration of an environment where the robot is located, which is detected in real time by at least one first gas sensor; when the first preset gas concentration is greater than a first concentration threshold, controlling the robot to enter an adsorption mode; the adsorption mode is to adsorb a preset gas in air sucked by an air inlet device through a reaction unit. The embodiment of the application can continuously detect the first preset gas concentration of the environment where the robot is located during the movement of the robot, so that when the first preset gas concentration is high, the preset gas in the air is adsorbed through the reaction unit, the preset gas capture process is more flexible, and the preset gas capture efficiency of the robot is improved.

Description

Technical Field

[0001] This application relates to the field of robotics technology, and in particular to robot control methods, devices, systems, robots, and storage media. Background Technology

[0002] With accelerated industrialization and population growth, energy demand has surged, leading to a significant increase in fossil fuel consumption and consequently, a rise in greenhouse gas emissions. Direct air capture (DAC) is an engineered process for directly removing CO2 from the atmosphere. This process is technically challenging because the CO2 concentration in the atmosphere is only 0.04%, far lower than the concentration in common CO2 capture sources in energy production and industrial processes (such as flue gas). DAC technology, as a rare negative-emission carbon reduction method, has attracted widespread attention for its ability to balance unavoidable emissions by capturing and permanently storing CO2 from the atmosphere.

[0003] However, existing robots used to adsorb pre-set gases from the air are generally stationary and lack flexibility in the process of capturing the pre-set gases. Summary of the Invention

[0004] In view of this, embodiments of this application provide a robot control method, device, system, robot, and storage medium to solve the problem that the robots used to adsorb preset gases in the prior art are fixed, resulting in a lack of flexibility in the preset gas capture process.

[0005] A first aspect of this application provides a robot control method. The robot includes a robot body, the robot body being equipped with an air intake device, a reaction unit, and at least one first gas sensor. The control method includes:

[0006] Control the robot's movement based on the planned path on the preset map;

[0007] Obtain the first preset gas concentration in the robot's environment as detected in real time by at least one first gas sensor;

[0008] When the first preset gas concentration is greater than the first concentration threshold, the robot is controlled to enter the adsorption mode; the adsorption mode is to adsorb the preset gas in the air drawn in by the air intake device through the reaction unit.

[0009] A second aspect of this application provides a robot control device. The robot includes a robot body, which is equipped with an air intake device, a reaction unit, and at least one first gas sensor. The control device includes:

[0010] The first control module is used to control the robot's movement based on the planned path on the preset map;

[0011] The acquisition module is used to acquire the first preset gas concentration of the robot's environment as detected in real time by at least one first gas sensor;

[0012] The second control module is used to control the robot to enter the adsorption mode when the first preset gas concentration is greater than the first concentration threshold; the adsorption mode is to adsorb the preset gas in the air drawn in by the air intake device through the reaction unit.

[0013] A third aspect of this application provides an intelligent control device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the robot control method of the first aspect.

[0014] A fourth aspect of this application provides a robot, including: a robot body and an intelligent control device as described in the third aspect;

[0015] The intelligent control device is located on the robot body, which is also equipped with an air intake device, a reaction unit, and at least one first gas sensor.

[0016] The fifth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the robot control method of the first aspect.

[0017] The beneficial effects of the embodiments in this application compared with the prior art are:

[0018] The robot control method of the first aspect of this application can control the robot's movement based on a planned path on a preset map. It can also acquire a first preset gas concentration in the robot's environment, detected in real time by at least one first gas sensor. Therefore, when the first preset gas concentration is greater than a first concentration threshold, the robot can be controlled to enter an adsorption mode, whereby the reaction unit adsorbs the preset gas in the air drawn in by the air intake device. Because the robot of this application continuously detects the first preset gas concentration in its environment while moving, it can adsorb the preset gas in the air through the reaction unit when the first preset gas concentration is high. This makes the preset gas capture process more flexible and improves the robot's preset gas capture efficiency.

[0019] It is understood that the beneficial effects of the second to sixth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a schematic diagram of the hardware electrical connection relationship of the first type of robot provided in the embodiments of this application;

[0022] Figure 2 This is a flowchart of a robot control method provided in an embodiment of this application;

[0023] Figure 3 This is a schematic diagram of the hardware electrical connection relationship of the second type of robot provided in the embodiments of this application;

[0024] Figure 4 This is a flowchart illustrating the steps for controlling a robot's movement based on a planned path on a preset map, as provided in an embodiment of this application.

[0025] Figure 5 This is a schematic diagram of the hardware electrical connection relationship of the third type of robot provided in the embodiments of this application;

[0026] Figure 6 This is a schematic diagram of the structure of a robot control device provided in an embodiment of this application;

[0027] Figure 7 This is a schematic diagram of the structure of an intelligent control device provided in an embodiment of this application.

[0028] Figure label:

[0029] 1-Robot;

[0030] 10-Intelligent control equipment, 20-Air intake device, 30-Reaction chamber, 301-Reaction unit, 40-First gas sensor, 50-3D lidar sensor, 60-Solar photovoltaic power generation system, 70-Temperature sensor, 80-Humidity sensor, 90-Wind speed sensor, 100-Second gas sensor, 110-Third gas sensor, 120-Pressure sensor, 130-Vacuum system, 140-Heating system, 150-Operation panel.

[0031] 60 - Robot control device, 601 - First control module, 602 - Acquisition module, 603 - Second control module. Detailed Implementation

[0032] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0033] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0034] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0035] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0036] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0037] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0038] Research has found that accelerated industrialization and population growth have led to a surge in energy demand, resulting in a significant increase in fossil fuel consumption and consequently, a rise in greenhouse gas emissions. Since pre-industrial times, human activities have caused a global surface temperature rise of 1.1°C. Limiting the temperature rise to below 1.5°C is crucial for mitigating extreme impacts on resources, ecosystems, biodiversity, food security, and urban areas. Direct air capture balances unavoidable emissions by capturing and permanently storing CO2 from the atmosphere.

[0039] Once the CO2 concentration in the capture area decreases, the subsequent capture efficiency drops significantly. In urban environments, robots need to detect various obstacles and distinguish different types of terrain while avoiding pedestrians and vehicles. Furthermore, traditional DAC (Digital Deposition and Control) technologies are typically energy-intensive and expensive to operate. Therefore, developing an energy-efficient and cost-effective DAC system has become a research hotspot in this field. The utilization of clean and renewable energy sources, such as solar energy, is becoming increasingly important, especially given its location between the tropics and subtropics, where solar energy resources are abundant.

[0040] Currently, direct air capture still has the following problems in practical applications:

[0041] (1) Low CO2 capture efficiency in high-density areas

[0042] Traditional direct air carbon capture (DAC) devices are mostly stationary and lack the ability to adjust their location based on changes in CO2 concentration. This results in the inability to dynamically track and capture CO2 in areas with high but ineffective CO2 capture (such as during peak urban traffic hours or in areas with concentrated industrial emissions), thus missing opportunities for efficient carbon sequestration.

[0043] (2) Low purity of captured CO2

[0044] In existing DAC technology, residual air within the reaction unit during the temperature swing adsorption process dilutes the CO2 concentration in the captured gas, reducing CO2 purity. Furthermore, the desorption process requires air as a heat transfer medium, further increasing the difficulty of achieving high-purity CO2 capture, which is crucial for its utilization in subsequent industrial applications.

[0045] (3) Insufficient navigation and obstacle avoidance capabilities in complex environments

[0046] In urban and industrial environments, robots performing CO2 capture tasks need to navigate in complex environments filled with obstacles such as pedestrians, vehicles, steps, potholes, and slopes. Traditional map generation and navigation systems are labor-intensive, costly, and often fail to provide accurate, real-time information for safe and efficient navigation. This poses a significant challenge to the autonomous operation of CO2 capture robots, especially in congested areas such as shopping malls or busy city streets.

[0047] (4) High energy consumption and high operating costs

[0048] Current DAC systems require significant energy to operate, particularly for processes such as heating and vacuum pumping. This high energy consumption not only increases operating costs but also contradicts the goal of reducing greenhouse gas emissions. Reliance on traditional energy sources further exacerbates the carbon footprint of these systems, limiting their sustainability and economic viability.

[0049] (5) Poor environmental adaptability

[0050] Many existing DAC technologies are sensitive to environmental conditions such as temperature and humidity, and may not perform optimally under varying outdoor conditions, limiting their deployment and effectiveness in different geographical and climatic regions. This lack of adaptability hinders the widespread adoption of DAC systems and their contribution to global carbon reduction.

[0051] (6) High initial investment and maintenance costs

[0052] The construction of DAC facilities involves expensive materials and components, especially those capable of withstanding high temperatures and pressures. The complexity of the technology also leads to high maintenance requirements, increasing initial investment and long-term operating costs. This financial burden is a significant obstacle to the widespread adoption and large-scale application of DAC technology.

[0053] (7) Insufficient utilization of renewable energy and waste heat

[0054] Current DAC systems fail to fully utilize renewable energy sources such as solar power, and also fail to effectively recover waste heat generated during the capture process. This underutilization of available energy resources not only fails to optimize energy efficiency but also misses opportunities to further reduce environmental impact and operating costs.

[0055] The robot control method, device, system, robot, and storage medium provided in this application are intended to solve the above-mentioned technical problems of the prior art.

[0056] The technical solution of this application and how it solves the above-mentioned technical problems are described in detail below with specific embodiments. It should be noted that the following embodiments can be referenced, learned from, or combined with each other, and the same terms, similar features, and similar implementation steps in different embodiments will not be described again.

[0057] See Figure 1 As shown, this application embodiment provides a schematic diagram of the hardware electrical connection relationship of a first type of robot 1. Combined with... Figure 1As shown, the robot 1 includes a robot body and an intelligent control device 10. The intelligent control device 10 is located in the robot body, which also includes an air intake device 20, a reaction unit 301, and at least one first gas sensor 40.

[0058] in, Figure 1 As an example, only one first gas sensor 40 is shown. In practical applications, at least two first gas sensors 40 can be set. For example, there are four first gas sensors 40, which are spaced apart at four positions on the robot body to detect the preset gas concentration in four directions (north, south, east, and west) of the environment in which the robot body is located.

[0059] See Figure 1 As shown, the robot body may have a reaction chamber 30 connected to the air intake device 20, and the reaction unit 301 is disposed within the reaction chamber 30. The intelligent control device 10 is connected to the air intake device 20 to control the air intake or stop operation of the air intake device 20; the intelligent control device 10 is connected to the reaction chamber 30 to control the closing and opening of the reaction chamber 30; the intelligent control device 10 is connected to at least one first gas sensor 40 to obtain a preset gas concentration detected by the first gas sensor 40.

[0060] Optionally, carbon dioxide is used as the preset gas in this application embodiment. The principle is the same for other gases that need to be adsorbed to purify and filter the air and protect the environment. Correspondingly, the gas sensors in this application embodiment can all be carbon dioxide sensors, the preset gas concentration is carbon dioxide concentration, and the adsorbent in the reaction unit 301 is used to adsorb carbon dioxide.

[0061] See Figure 2 The flowchart shown is a control method for a robot 1 provided in this application embodiment. This robot 1 control method is applied to an intelligent control device 10, and the control method includes steps S201 to S203.

[0062] S201. Control robot 1 to move based on the planned path on the preset map.

[0063] Optionally, the preset map is a map stored in advance in the intelligent control device 10. By planning a path on the preset map, the robot 1 can achieve automatic navigation and movement.

[0064] S202. Obtain the first preset gas concentration in the environment where the robot 1 is located, which is detected in real time by at least one first gas sensor 40.

[0065] Specifically, the first gas sensor 40 can detect the first preset gas concentration in the environment where the robot 1 is located in real time. If the robot body is equipped with one first gas sensor 40, then the preset gas concentration detected by the first gas sensor 40 is the first preset gas concentration. If the robot body is equipped with at least two first gas sensors 40, then the average value of at least two preset gas concentrations detected by at least two first gas sensors 40 is taken as the first preset gas concentration.

[0066] In practical applications, the robot body can be equipped with four first gas sensors 40, which are oriented in four directions towards the environment in which the robot body is located.

[0067] S203. When the first preset gas concentration is greater than the first concentration threshold, control robot 1 to enter adsorption mode; the adsorption mode is to adsorb the preset gas in the air drawn in by air intake device 20 through reaction unit 301.

[0068] Specifically, if the first preset gas concentration is greater than the first concentration threshold, it indicates that the preset gas concentration in the environment is high, and the adsorption mode can be entered to adsorb the preset gas in the air drawn in by the air intake device 20 through the reaction unit 301.

[0069] Optionally, after controlling robot 1 to enter the adsorption mode, robot 1 can stop moving until the first preset gas concentration is not greater than the third concentration threshold, indicating that the preset gas concentration at this point is not very high, and robot 1 can continue to move along the planned path. While controlling robot 1 to enter the adsorption mode, robot 1 is continuously controlled to move along the planned path to achieve dynamic collection of the preset gas.

[0070] Based on the above technical solution, the control method of robot 1 in this embodiment can control robot 1 to move based on a planned path on a preset map. Simultaneously, it can acquire the first preset gas concentration in the environment where robot 1 is located, detected in real time by at least one first gas sensor 40. Therefore, when the first preset gas concentration is greater than a first concentration threshold, robot 1 can be controlled to enter an adsorption mode, and the preset gas in the air drawn in by the air intake device 20 can be adsorbed through the reaction unit 301. Since robot 1 in this embodiment continuously detects the first preset gas concentration in its environment while moving, it can adsorb the preset gas in the air when the first preset gas concentration is high, making the preset gas capture process more flexible and improving the preset gas capture efficiency of robot 1.

[0071] See Figure 3 As shown, this application embodiment provides a schematic diagram of the hardware electrical connection relationship of a second type of robot 1. See also... Figure 3As shown, the robot body is also equipped with multiple 3D LiDAR sensors 50. These multiple 3D LiDAR sensors 50 are used to detect the global environment surrounding the robot body.

[0072] in, Figure 3 As an example, only one 3D LiDAR sensor 50 is shown. In practical applications, four 3D LiDAR sensors 50 can be set up at four intervals on the robot body, which can detect the environment of the robot body in four directions. The 3D LiDAR sensor 50 can be a 3D LiDAR sensor.

[0073] Correspondingly, controlling the movement of robot 1 based on the planned path on the preset map includes: controlling the movement of robot 1 based on the initial path on the preset map; the preset map is a global environment map of the movement range of robot 1 represented by a three-dimensional occupancy grid.

[0074] The first point cloud data is obtained in real time through multiple 3D LiDAR sensors 50. The initial path is adjusted based on the first point cloud data to obtain the planned path.

[0075] Optionally, the 3D occupancy mesh represents the environment using a 3D mesh, dividing the entire area into blocks of the environment. The higher the precision, the more likely it is to be divided into 1-centimeter blocks, and if the precision is not high enough, it can be divided into 10-centimeter blocks. The entire environment is formed by multiple cubes.

[0076] This embodiment of the application selects a 3D LiDAR sensor 50 to achieve automatic generation of navigation maps. Compared with other sensors such as monocular cameras, binocular cameras, ToF cameras, and 2D laser scanners, the 3D LiDAR sensor 50 has advantages in providing high point density data. Although it also has the problem of sparse point clouds, its impact on capturing preset gas concentration information is negligible. In practice, four 3D LiDAR sensors 50 can be installed at the four corners of the robot 1 to provide 360-degree environmental coverage and ±15° vertical field of view.

[0077] In some embodiments, adjusting the initial path based on the first point cloud data to obtain a planned path includes:

[0078] Based on the first point cloud data and the preset inverse sensor model, the infeasible area around robot 1 is determined; the inverse sensor model is a three-dimensional occupancy grid that is divided into octtree grids.

[0079] The preset map is updated based on infeasible areas;

[0080] Based on the updated preset map, the initial path is adjusted to obtain the planned path.

[0081] Optionally, infeasible regions can be information representing positive obstacles, negative obstacles, impassable terrain, or unexplored areas. An octree is a data structure for efficiently representing 3D space. Each node represents a voxel, and if the voxel contains child voxels, the node is further subdivided. Octrees can dynamically adjust resolution, subdividing only where necessary, thus reducing memory requirements.

[0082] In this embodiment, the 3D LiDAR sensor 50 enables the robot 1 to avoid obstacles or impassable areas during movement, adjust the initial route, and obtain a planned path.

[0083] This application innovatively applies a navigation map to robot 1, which can achieve at least the following technical effects for a direct air carbon capture robot: 1. tracking and searching for high-concentration preset gases; 2. precise positioning; 3. effective obstacle avoidance during navigation; and 4. playing a key role in global planning. The positioning map can be directly compared with point cloud data to estimate the position of robot 1, while updating the preset map and storing information on whether robot 1 can safely pass through a certain location.

[0084] See Figure 4 As shown, this application embodiment provides a flowchart for controlling the movement of robot 1 based on a planned path on a preset map. For example... Figure 4 As shown, the robot 1 is controlled to move based on the planned path on the preset map, including the following steps: S401 to S405.

[0085] S401. Control robot 1 to move based on the initial path on the preset map.

[0086] In some embodiments, the preset map is obtained in advance by controlling the robot 1 to move, and during the movement of the robot 1, acquiring second point cloud data detected in real time by multiple 3D LiDAR sensors 50; merging all the second point cloud data to obtain the preset map.

[0087] The embodiments of this application can merge all point clouds into a global representation of the environment, use a three-dimensional occupancy mesh to represent the environment, recursively update the occupancy value through an inverse sensor model, and use an octet tree to implement the occupancy mesh to reduce memory requirements and ray tracing time.

[0088] In practical applications, this embodiment of the application can collect point cloud and odometry data from a 3D LiDAR sensor 50 via manual remote control of a carbon capture robot. The Cartographer SLAM algorithm is then used to estimate the pose of the carbon capture robot during each data acquisition by the 3D LiDAR sensor 50, enabling offline map construction. Cartographer is a graph-optimized LiDAR SLAM algorithm that supports both 2D and 3D LiDAR SLAM, is cross-platform compatible, and supports various sensor configurations including LiDAR, IMU, Odemetry, GPS, and Landmark. It is one of the most widely used LiDAR SLAM algorithms in practical applications.

[0089] In some embodiments, the initial path is obtained as follows:

[0090] At least one target area is identified on a preset map; the preset gas concentration in the target area is theoretically and / or practically greater than the preset gas concentration in other areas of the preset map.

[0091] Determine the initial path through at least one target region;

[0092] Wherein, at least one target area is determined on the preset map, including at least one of the following:

[0093] In response to a region selection operation on a preset map, the target region is determined;

[0094] During the movement of robot 1, a first preset gas concentration in the environment where robot 1 is located is obtained by real-time detection through at least one first gas sensor 40. When the first preset gas concentration is greater than a second concentration threshold, the area where robot 1 is currently located is determined as the target area.

[0095] The control method described in this application can be applied to Direct Air Capture Robots (DACR). By pre-determining the target area, the robot can strategically pass through areas with high CO2 concentrations during path planning. Therefore, this application combines mobile and autonomous path planning to dynamically track and capture CO2 in these high-density areas, providing a solution to enhance carbon sequestration capabilities for the places where it is most needed.

[0096] S402, Acquire the first point cloud data obtained in real time through multiple 3D LiDAR sensors 50.

[0097] This embodiment of the application can detect the environment around the robot 1 by using first point cloud data obtained in real time through multiple 3D LiDAR sensors 50. This embodiment of the application utilizes the 3D LiDAR sensors 50 to generate a detailed navigation map for the robot 1. The 3D LiDAR sensors 50 provide accurate and real-time spatial perception capabilities, enabling the robot 1 to safely traverse complex urban landscapes, avoid obstacles, and locate the optimal capture point, ensuring the safety and efficiency of the robot 1's operation and reducing the risk of collisions and system downtime. Simultaneously, it enables more accurate and effective carbon capture in cluttered environments.

[0098] S403. Based on the first point cloud data and the preset inverse sensor model, determine the infeasible area around robot 1.

[0099] The inverse sensor model of this application embodiment can represent the probability that a certain location in the environment is occupied given sensor measurement results. Over time, new sensor data is continuously received, and the occupancy value is recursively updated based on this data. Each time new data is received, the occupancy value is recalculated to reflect the latest environmental state.

[0100] This application embodiment is sensitive to rough or unpaved terrain. The navigation map needs to contain information on four main hazard types: positive obstacles, negative obstacles, impassable terrain, and unexplored areas. The algorithm processes these hazards separately through independent modules and generates a binary two-dimensional discrete drivability map. Finally, the maps generated by each sub-module are fused using a logical AND operation to obtain the overall drivability map.

[0101] Alternatively, infeasible areas may include the following:

[0102] Exploration Area: Based on the assumptions that the ground is roughly level and the slope is lower than the maximum slope that the robot can climb, and that the reachable ground must be connected to the surface that the robot travels on during the data acquisition phase, the ground surface is extracted from the 3D occupied grid. The normal vector of the point is calculated using the PCA method. The angle between the normal vector and the vertical vector is used to determine whether it is a candidate ground point. Then, the ground area is determined by Euclidean clustering algorithm and intersection with the trajectory. Finally, it is projected onto the xy plane to obtain a binary map and an elevation map is constructed.

[0103] Positive obstacles: Similar to but complementary to ground point detection, after calculating the surface normal, points with a normal angle greater than a threshold are candidate obstacles. Then, height filtering is used to eliminate obstacles that are too tall. Finally, Euclidean clustering algorithm is used to remove noise, and cells containing obstacle points are marked as impassable.

[0104] Impassable terrain: Accessibility analysis is performed at the point cloud level. The surface roughness is assessed by measuring the impact of terrain irregularities on the shape of the 3D "ring" of the 3D LiDAR sensor 50. The accessibility index of the points is calculated, a map is created and projected onto the points. The accessible area is determined by a dynamic thresholding method (considering that robot 1 only travels along accessible terrain during the mapping process). Then, a shape closure operation is applied to remove noise points.

[0105] Negative obstacles: The blind spots are filled by recursively expanding the map to make the elevation jump of negative obstacles visible. Then, the positive obstacle algorithm is used for detection. False obstacles are removed by Euclidean clustering algorithm and intersection with impassable areas.

[0106] Using mapping trajectories: By using the trajectory data covered by the robot, points around the trajectory are marked as passable to reduce misclassification of navigable points as dangerous. The generated passability map is then merged with a portion of the map using a logical "OR" operation.

[0107] S404. Update the preset map based on infeasible areas.

[0108] Optionally, infeasible areas are used to represent information such as positive obstacles, negative obstacles, impassable terrain, or unexplored areas. Infeasible areas can be added to the preset map to update the preset map.

[0109] S405. Based on the updated preset map, adjust the initial path to obtain the planned path.

[0110] As an example, the mobile experiment verification (quantitative verification) of Robot 1: A map of a teaching area was selected for verification because it is a realistic navigation scenario containing all the features of interest. Preset gas concentrations in different spaces within the area were sensed using gas sensors, and pixels were labeled to create a ground truth map. Classification metrics were then obtained through pixel-level comparisons, showing excellent performance. However, in motion planning, pure classification performance does not necessarily represent map quality.

[0111] Therefore, this application employs the Monte Carlo method for a more realistic evaluation. Two maps are inflated to account for the carbon-capturing robot's footprint. Then, 500 start-target pairs are randomly selected, and the A* programming algorithm is applied, with the results compared. Of the successful plans on the ground truth map, 92.30% were also successful on the automatically generated map; most failures were due to misclassification of the target location. Conversely, of the failed planning requests on the ground truth map, 99.00% were also rejected on the automatically generated map. These experimental scenarios included different ground types (e.g., cobblestones, grass, gravel, cement, asphalt), positive obstacles of varying sizes (e.g., pillars, trash cans, benches), and typical negative obstacles (e.g., sidewalk edges). For truly positive cases, the difference in path length between the two maps was calculated, showing an average over-length of only 0.21 meters.

[0112] It should be understood that the sequence number of each step 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.

[0113] This application embodiment enables the carbon capture robot to operate safely and efficiently in complex urban and industrial environments by using a 3D LiDAR sensor 50 and advanced navigation algorithms. This capability is crucial for the deployment of such systems in real-world environments, as they need to operate around crowds, vehicles, and various obstacles. Therefore, this application embodiment possesses advanced navigation and obstacle avoidance capabilities in complex environments, addressing the challenge of enhancing the autonomy and reliability of robot 1 under challenging conditions.

[0114] The robot 1 in this embodiment of the application employs a control method that enables mobility, allowing it to autonomously navigate to areas with high CO2 concentrations. This mobility allows the robot 1 to dynamically track CO2 sources, improving capture efficiency in variable environments. It can adapt to the instantaneous characteristics of emissions in urban and industrial environments, increasing the overall amount of CO2 captured and more effectively promoting carbon reduction efforts. Furthermore, it expands the application scope of carbon capture technology, making it suitable for a wider range of scenarios.

[0115] Existing carbon capture robots may encounter navigation and obstacle avoidance difficulties in complex environments such as urban and industrial areas. They may fail to accurately identify different types of terrain, obstacles, and restricted areas, resulting in inaccurate navigation maps and increased operational risks.

[0116] The control method of robot 1 according to the embodiments of this application uses a 3D LiDAR sensor 50 to generate a detailed navigation map, including information on positive obstacles, negative obstacles, impassable terrain, and unexplored areas. It can accurately identify ground, obstacles, and terrain roughness, enabling the robot to plan safer and more efficient routes. The control method of robot 1 according to the embodiments of this application has been experimentally verified in various scenarios, demonstrating strong robustness and accuracy, and significantly improving the robot's autonomous navigation capabilities.

[0117] See Figure 5 As shown in the figure, this application embodiment provides a schematic diagram of the hardware electrical connection relationship of a third type of robot 1.

[0118] See Figure 5 As shown, the robot body is also equipped with a solar photovoltaic power generation system 60, which includes at least one solar panel. Correspondingly, the control method also includes at least one of the following:

[0119] When at least one solar panel is detected to be faulty, a first fault information is generated, and the first fault information is displayed through robot 1 and / or sent to the back-end management system.

[0120] When the power of the solar photovoltaic power generation system 60 is detected to be less than the preset power, a first prompt message is generated, and the first prompt message is displayed through the robot 1 and / or sent to the background management system.

[0121] Optionally, the first fault information is used to indicate that the solar panel of the solar photovoltaic power generation system 60 has malfunctioned, and the first prompt information is used to indicate that the power provided by the solar photovoltaic power generation system 60 is insufficient.

[0122] The solar photovoltaic power generation system 60 of this application embodiment can provide clean, renewable energy, reducing dependence on traditional energy sources and lowering the system's carbon footprint. The intelligent control device 10 can monitor the power output of the solar photovoltaic power generation system 60 and effectively distribute electrical energy to the various systems of the robot 1 to achieve efficient energy utilization.

[0123] This application embodiment combines solar photovoltaic power generation with carbon capture, reducing dependence on traditional fossil fuels, aligning with the trend of energy transition, and helping to reduce the carbon footprint of carbon capture technology operation. Simultaneously, this application embodiment can generate initial fault information to remind maintenance when a solar panel malfunctions, and, in the event of insufficient solar energy, remind the user to connect an external power source or stop operation to generate solar power.

[0124] See Figure 5As shown, the robot body is also equipped with a temperature sensor 70 and a humidity sensor 80. Correspondingly, the control method also includes:

[0125] The temperature of the environment where robot 1 is located is obtained in real time through temperature sensor 70, and the humidity of the environment where robot 1 is located is obtained in real time through humidity sensor 80.

[0126] When the temperature exceeds the preset temperature and / or the humidity exceeds the preset humidity, control robot 1 to stop working.

[0127] The robot 1 in this embodiment can adjust its working state according to changes in the environment.

[0128] See Figure 5 As shown, the robot body is also equipped with a wind speed sensor 90, and the air intake device 20 is equipped with a fan. Correspondingly, the control method also includes:

[0129] The wind speed of the environment in which the robot 1 is located is detected in real time by the wind speed sensor 90; based on the wind speed, the rotation speed of the fan is adjusted.

[0130] The embodiments of this application can reduce the rotation speed of the fan when the wind speed is high, thereby partially relying on natural wind to achieve air intake and saving energy.

[0131] The embodiments of this application, through the scalability of the robot 1 design, can be adjusted according to different climates and operating parameters, making it suitable for a wide range of applications, from small-scale urban projects to large-scale industrial carbon capture programs. The efficient CO2 capture and utilization of the embodiments of this application can serve as a demonstration of practical environmental solutions, raising public awareness and encouraging participation in sustainable practices, promoting the development of a green economy, and facilitating the transformation of society towards a more sustainable and technologically advanced direction.

[0132] See Figure 5 As shown, the robot body has a reaction chamber 30 connected to the air intake device 20. A reaction unit 301 is disposed within the reaction chamber 30. A second gas sensor 100 and a third gas sensor 110 are respectively installed at the inlet and outlet of the reaction chamber 30. A pressure sensor 120 is also installed in the reaction chamber 30. The robot body also includes a vacuum system 130 and a heating system 140. Correspondingly, after controlling the robot 1 to enter the adsorption mode when the first preset gas concentration is greater than the first concentration threshold, the system further includes:

[0133] When the difference between the second preset gas concentration and the third preset gas concentration is less than the preset concentration value, the robot 1 is controlled to enter the vacuum mode. The second preset gas concentration is detected in real time by the second gas sensor 100, and the third preset gas concentration is detected in real time by the third gas sensor 110. The vacuum mode is when the inlet and outlet of the reaction chamber 30 are closed, the air in the reaction chamber 30 is extracted by the vacuum system 130.

[0134] When the pressure sensor 120 detects that the pressure inside the reaction chamber 30 has reached the preset pressure, the robot 1 is controlled to enter the desorption mode. The desorption mode is to heat the reaction chamber 30 by the heating system 140 so that the heating temperature inside the reaction chamber 30 is within the preset temperature range.

[0135] Optionally, the opening and closing of the inlet and outlet of the reaction chamber 30 can be achieved by setting solenoid valves.

[0136] This application embodiment can collect data through a sensor network, including but not limited to environmental parameters such as temperature, humidity, preset gas concentration and wind speed. The collected data is processed and analyzed in real time to optimize the operating parameters of robot 1.

[0137] The embodiments of this application can monitor the overall operating status of robot 1 in real time, including key components such as air intake device 20, reaction unit 301 and electric dehumidification device. The built-in diagnostic algorithm predicts and identifies potential faults, allowing for proactive maintenance and repair.

[0138] The intelligent control device 10 of this embodiment can be connected to the Internet to achieve remote operation and monitoring, facilitating remote maintenance and troubleshooting. It supports remote software upgrades and adjustments to control strategies. Simultaneously, the intelligent control device 10 of this embodiment can monitor operational safety, including overload protection, short-circuit protection, and fail-safe shutdown mechanisms. In the event of an abnormal situation, it automatically performs a safe shutdown or switches to a backup system. The intelligent control device 10 can also automatically adjust operating parameters according to environmental changes (including wind speed and temperature) to ensure efficient operation under various climatic and environmental conditions.

[0139] See Figure 5 As shown, the robot body also includes an operation panel 150, which includes a first display area, adsorption mode controls, vacuum mode controls, and desorption mode controls. The first display area is used to display a first preset gas concentration, a second preset gas concentration, a third preset gas concentration, detection pressure, and heating temperature. Correspondingly, the control method also includes at least one of the following:

[0140] In response to the selection operation of the adsorption mode control, control robot 1 to enter adsorption mode;

[0141] In response to the selection operation of the vacuum mode control, control robot 1 to enter vacuum mode;

[0142] In response to the selection operation of the desorption mode control, control robot 1 to enter the desorption mode.

[0143] This embodiment of the application allows for manual operation via the operation panel 150. The operator can select the adsorption mode control, vacuum mode control, or desorption mode control based on the information displayed in the first display area to perform corresponding processing. In other words, this embodiment provides a user-friendly interface, enabling the operator to easily view the status of the robot 1 and manually control and configure relevant parameters.

[0144] The adsorption mode in this application embodiment is a room temperature adsorption process, the vacuum mode is a vacuum extraction process, and the desorption mode is a temperature-switched desorption process. The specific processes of the three modes are as follows:

[0145] Room temperature adsorption process: The solenoid valves at the inlet and outlet of the reaction chamber 30 remain open, allowing the second gas sensor 100 and the third gas sensor 110 at the inlet and outlet to monitor the preset gas level in normal atmosphere (approximately 400 ppm, or parts per million). In a well-ventilated environment, ambient air enters the reaction unit 301 due to the action of a blower or natural wind. The preset gas in the air flows into the reaction unit 301 with the wind speed and is adsorbed by the adsorbent on the honeycomb carrier at room temperature. This process is called the room temperature adsorption process.

[0146] During the adsorption phase, the outlet (annular) third gas sensor 110 rapidly drops to approximately 0 ppm. As the signal from the third gas sensor 110 changes, it detects that the preset gas concentration has risen to near normal atmospheric levels, indicating that the adsorbent has reached its capacity to adsorb the preset gas, and no more adsorption sites are available. The third gas sensor 110 can then send a signal to the intelligent control device 10 to close the solenoid valve, initiating the next stage: the vacuum extraction process.

[0147] Vacuum extraction process: The solenoid valves at the inlet and outlet of the reaction chamber 30 are closed, and the remaining air in the reaction unit 301 is rapidly extracted and released into the atmosphere, bringing the internal pressure of the reaction chamber 30 close to a vacuum state. This application is designed to achieve this vacuum state in approximately 30 seconds.

[0148] During the vacuum extraction process, the pressure gauge inside the chamber rapidly drops from atmospheric pressure to approximately 0, approaching a vacuum state. The pressure gauge signal is converted, and the pressure sensor 120 on the pressure gauge sends a signal to the intelligent control device 10, temporarily shutting down the vacuum pump of the vacuum system 130. Then, the heating system 140 begins infrared heating of the reaction unit 301. The valve connecting the vacuum pump and the atmosphere closes, while the valve connecting the preset gas collection bag or bottle opens, entering the next stage: the temperature-controlled desorption process.

[0149] Temperature-dependent desorption process: The solenoid valves at the inlet and outlet of reaction chamber 30 remain closed, while heating system 140 heats reaction unit 301. The internal temperature of the chamber rapidly rises to the suitable temperature for the saturated adsorbent to carry out the preset gas desorption reaction, which is maintained by a thermostat between approximately 90°C and 100°C. While maintaining the suitable desorption temperature, the saturated adsorbent undergoes a desorption reaction, separating from the preset gas (this process involves a chemical change). Once the temperature reaches the suitable level for the desorption reaction, the thermometer signal from temperature sensor 70 is converted, and temperature sensor 70 sends a signal to intelligent control device 10 to restart the vacuum pump, allowing the desorbed preset gas to flow into the preset gas collection bag or bottle connected to the vacuum pump.

[0150] When the high-range preset gas concentration sensor at the interface between the vacuum pump and reaction unit 301 detects that the preset gas concentration rises from approximately 0 and then falls back to approximately 0, it indicates that the desorption process is complete and the preset gas collection is finished. The signal conversion at the interface of the third gas sensor 110 sends a signal to the intelligent control device 10 to stop the heating system 140 that maintains the chamber temperature and reopen the solenoid valves at the inlet and outlet of the reaction chamber 30, starting the next direct air carbon capture cycle (alternating cycle: room temperature adsorption process → vacuum extraction process → variable temperature desorption process).

[0151] The control method of robot 1 in this application embodiment can be applied to a direct air capture robot (DACR) for capturing preset gases from urban and industrial areas. The intelligent direct air capture robot employs a mobile autonomous design and is equipped with 3D LiDAR sensors for navigation and obstacle avoidance. The intelligent direct air capture robot can also utilize temperature vacuum oscillation adsorption technology to achieve high-purity capture of preset gases, with a daily capture volume of approximately 7 kg.

[0152] Meanwhile, the Direct Air Capture Robot (DACR) is equipped with an intelligent control device 10 to automate its operation, and, in conjunction with a solar photovoltaic power generation system 60 and other systems such as a waste heat recovery system, reduces reliance on traditional energy sources, supporting global carbon neutrality goals. This intelligent Direct Air Capture Robot (DACR) can utilize renewable energy to drive the direct air capture process and optimize the capture process through intelligent scheduling.

[0153] See Figure 6 As shown in the diagram, this application embodiment provides a schematic diagram of the structure of a robot control device 60. The robot 1 includes a robot body, which is equipped with an air intake device 20, a reaction unit 301, and at least one first gas sensor 40. The robot control device 60 includes: a first control module 601, an acquisition module 602, and a second control module 603.

[0154] The first control module 601 is used to control the movement of robot 1 based on the planned path on the preset map.

[0155] The acquisition module 602 is used to acquire the first preset gas concentration in the environment where the robot 1 is located, which is detected in real time by at least one first gas sensor 40.

[0156] The second control module 603 is used to control the robot 1 to enter the adsorption mode when the first preset gas concentration is greater than the first concentration threshold; the adsorption mode is to adsorb the preset gas in the air drawn in by the air intake device 20 through the reaction unit 301.

[0157] Optionally, the first control module 601 is further configured to control the movement of robot 1 based on an initial path on a preset map; the preset map is a global environment map representing the movement range of robot 1 using a three-dimensional occupancy grid; the initial path is adjusted based on the first point cloud data to obtain a planned path. Correspondingly, the acquisition module 602 is configured to acquire the first point cloud data detected in real time by multiple 3D LiDAR sensors 50.

[0158] Optionally, the first control module 601 is also used to determine the infeasible area around the robot 1 based on the first point cloud data and the preset inverse sensor model; the inverse sensor model is a three-dimensional occupancy grid implemented by using an octree mesh partitioning method; the preset map is updated based on the infeasible area; the initial path is adjusted based on the updated preset map to obtain the planned path.

[0159] Optionally, the acquisition module 602 is used to control the movement of the robot 1, and during the movement of the robot 1, it acquires the second point cloud data detected in real time by multiple 3D LiDAR sensors 50; the first control module 601 is used to merge all the second point cloud data to obtain a preset map.

[0160] Optionally, the first control module 601 is further configured to determine at least one target area on a preset map; the preset gas concentration of the target area is theoretically and / or actually greater than the preset gas concentration of other areas on the preset map; and based on the at least one target area, determine an initial path through the target area; wherein determining at least one target area on the preset map includes at least one of the following: determining the target area in response to an area selection operation on the preset map; during the movement of the robot 1, acquiring a first preset gas concentration of the environment where the robot 1 is located, detected in real time by at least one first gas sensor 40, and determining the area where the robot 1 is currently located as the target area when the first preset gas concentration is greater than a second concentration threshold.

[0161] Optionally, the second control module 603 is further configured to generate first fault information when at least one solar panel is detected to have malfunctioned, display the first fault information through the robot 1 and / or send the first fault information to the background management system; and generate first prompt information when the power of the solar photovoltaic power generation system 60 is detected to be less than a preset power, display the first prompt information through the robot 1 and / or send the first prompt information to the background management system.

[0162] Optionally, the acquisition module 602 is used to acquire the temperature of the environment where the robot 1 is located in real time via the temperature sensor 70, and to acquire the humidity of the environment where the robot 1 is located in real time via the humidity sensor 80. The second control module 603 is also used to control the robot 1 to stop working when the temperature is greater than a preset temperature and / or the humidity is greater than a preset humidity.

[0163] Optionally, the acquisition module 602 is used to acquire the wind speed of the environment in which the robot 1 is located, which is detected in real time by the wind speed sensor 90. The second control module 603 is also used to adjust the rotation speed of the fan based on the wind speed.

[0164] Optionally, the second control module 603 is further configured to control the robot 1 to enter a vacuum mode when the difference between the second preset gas concentration and the third preset gas concentration is less than a preset concentration value. The second preset gas concentration is detected in real time by the second gas sensor 100, and the third preset gas concentration is detected in real time by the third gas sensor 110. The vacuum mode is achieved by evacuating the air from the reaction chamber 30 through the vacuum system 130 when the inlet and outlet of the reaction chamber 30 are closed. When the pressure sensor 120 detects that the pressure inside the reaction chamber 30 reaches a preset pressure, the robot 1 is controlled to enter a desorption mode. The desorption mode is achieved by heating the reaction chamber 30 through the heating system 140 to keep the heating temperature inside the reaction chamber 30 within a preset temperature range.

[0165] Optionally, the second control module 603 is also configured to control the robot 1 to enter adsorption mode in response to a selection operation of the adsorption mode control; control the robot 1 to enter vacuum mode in response to a selection operation of the vacuum mode control; and control the robot 1 to enter desorption mode in response to a selection operation of the desorption mode control.

[0166] In applications, the modules in the robot's control device 60 can be software program modules, or they can be implemented through different logic circuits integrated in the processor, or they can be implemented through multiple distributed processors.

[0167] The apparatus in this application embodiment can execute the method provided in this application embodiment, and the implementation principle is similar. The actions performed by each module in the apparatus of each embodiment of this application correspond to the steps in the method of each embodiment of this application. For detailed functional descriptions of each module of the apparatus, please refer to the descriptions in the corresponding methods shown above, which will not be repeated here.

[0168] This application provides an intelligent control device 10, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the control method of the robot 1 of this application.

[0169] Traditional carbon capture devices may lack an efficient control system, leading to unstable operation, complex procedures, and low operability. This application embodiment enables automatic signal transmission and process control within the reaction unit 301 and the entire intelligent control device 10 via the intelligent control device 10 and the operation panel 150. This simplifies operation, enhances stability, and allows for easy monitoring and adjustment of the carbon capture process. The intelligent control device 10 of this application embodiment optimizes the scheduling of the entire capture process, ensuring efficient and stable system operation.

[0170] See Figure 7 As shown, this application provides a schematic diagram of the structure of an intelligent control device 10. Figure 7 As shown, the intelligent control device 10 of this embodiment includes: at least one processor 103 ( Figure 7 (Only one is shown) a processor, a memory 101, and a computer program 102 stored in the memory 101 and executable on at least one processor 103. When the processor 103 executes the computer program 102, it implements the steps in any of the above-described embodiments of the control methods for the robot 1.

[0171] The intelligent control device 10 can be a computing device such as a desktop computer, laptop, handheld computer, or cloud server. The intelligent control device 10 may include, but is not limited to, a processor 103 and a memory 101. Those skilled in the art will understand that... Figure 6This is merely an example of the intelligent control device 10 and does not constitute a limitation on the intelligent control device 10. It may include more or fewer components than shown, or combine certain components, or different components, such as input / output devices, network access devices, etc.

[0172] The processor 103 can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0173] In some embodiments, memory 101 may be an internal storage unit of the intelligent control device 10, such as a hard disk or memory of the intelligent control device 10. In other embodiments, memory 101 may be an external storage device of the intelligent control device 10, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the intelligent control device 10. Furthermore, memory 101 may include both internal storage units and external storage devices of the intelligent control device 10. Memory 101 is used to store operating systems, applications, bootloaders, data, and other programs, such as program code of computer programs. Memory 101 may also be used to temporarily store data that has been output or will be output.

[0174] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0175] This application provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the steps of the control method for robot 1 according to this application.

[0176] This application provides a computer program product that, when run on a mobile terminal, enables the mobile terminal to implement the steps described in the above-described method embodiments.

[0177] The robot 1 in this embodiment can be a direct air capture robot (DACR), which has the following advantages in practical applications:

[0178] (1) Direct carbon capture and climate change mitigation

[0179] The primary function of Direct Air Capture Robots (DACR) is to capture carbon dioxide (CO2) from the atmosphere, particularly in areas with high CO2 emissions such as cities and industrial zones. This helps reduce the overall concentration of greenhouse gases, thereby mitigating the effects of climate change and aligning with global efforts to achieve carbon neutrality.

[0180] (2) Resource recycling and utilization

[0181] Captured CO2 can be converted into useful resources, such as raw materials for the production of chemicals, fuels, or other industrial products. For example, CO2 can be used to synthesize carbonates or to produce synthetic fuels through processes such as Fischer-Tropsch synthesis, thereby transforming a substance that would otherwise be an environmental pollutant into economic value.

[0182] (3) Energy efficiency and sustainability

[0183] This application's embodiments achieve energy efficiency by utilizing solar photovoltaic power. This reduces dependence on traditional fossil fuels and lowers the carbon footprint of the carbon capture process itself. Simultaneously, energy efficiency is further improved through the integration of a waste heat recovery system, which captures and reuses heat that would otherwise be lost, promoting a more sustainable energy cycle.

[0184] (4) Mobility and Adaptive Operations

[0185] Unlike traditional stationary carbon capture devices, Direct Air Capture Robots (DACRs) are mobile, capable of autonomously navigating and tracking areas of high CO2 concentration. This adaptability makes carbon capture more effective and targeted, especially in dynamic environments where CO2 emissions vary over time and space. Using 3D LiDAR sensors and advanced mapping algorithms, the robots can navigate safely in complex urban and industrial environments, avoiding obstacles and ensuring efficient operation.

[0186] (5) Technological Innovation and Research

[0187] This application represents a significant advancement in carbon capture technology, incorporating innovative features such as a "horizontal dual-valve" design for the carbon capture unit. These designs improve the efficiency and purity of CO2 capture, expanding the application scope of direct air carbon capture technology. The system also provides a platform for further research and development in environmental engineering and renewable energy fields.

[0188] (6) Environmental monitoring and data collection

[0189] Robotic direct air capture (DACR) systems can be equipped with various sensors to monitor environmental parameters such as CO2 concentration, temperature, humidity, and wind speed. This data is invaluable for environmental research, urban planning, and policy development related to climate change mitigation. It provides real-time information on the effectiveness of carbon capture efforts and helps optimize system operation based on environmental conditions.

[0190] (7) Public awareness and education

[0191] Operating direct air capture robots (DACR) in public spaces can raise public awareness of the importance of carbon capture and the role of technology in addressing climate change. It can serve as an educational tool, showcasing practical solutions to environmental challenges and encouraging public participation and support for sustainable initiatives.

[0192] If the integrated units described above are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a device / terminal equipment, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0193] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0194] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0195] In the embodiments provided in this application, it should be understood that the disclosed apparatus / network devices and methods can be implemented in other ways. For example, the apparatus / network device embodiments described above are merely illustrative. For instance, the division of modules or units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0196] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0197] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for controlling a robot, characterized in that, The robot includes a robot body, which is equipped with an air intake device, a reaction unit, and at least one first gas sensor. The control method includes: The robot is controlled to move based on a planned path on a preset map. Obtain a first preset gas concentration in the environment where the robot is located, detected in real time by at least one of the first gas sensors; When the first preset gas concentration is greater than the first concentration threshold, the robot is controlled to enter the adsorption mode; the adsorption mode is to adsorb the preset gas in the air drawn in by the air intake device through the reaction unit.

2. The robot control method according to claim 1, characterized in that, The robot body is also equipped with multiple 3D LiDAR sensors, which are used to detect the global environment around the robot body. Controlling the robot's movement based on a planned path on a preset map includes: The robot is controlled to move based on an initial path on a preset map; the preset map is a global environment map of the robot's movement range represented by a three-dimensional occupancy grid. The first point cloud data is obtained by real-time detection through multiple 3D LiDAR sensors, and the initial path is adjusted based on the first point cloud data to obtain the planned path.

3. The robot control method according to claim 2, characterized in that, The planned path is obtained by adjusting the initial path based on the first point cloud data, including: Based on the first point cloud data and the preset inverse sensor model, the infeasible area around the robot is determined; the inverse sensor model is a three-dimensional occupancy grid implemented by using an octree mesh partitioning method. The preset map is updated based on the infeasible areas; Based on the updated preset map, the initial path is adjusted to obtain the planned path.

4. The robot control method according to claim 2, characterized in that, The preset map is obtained in advance in the following manner: Control the robot to move, and during the robot's movement, acquire second point cloud data detected in real time by multiple 3D LiDAR sensors; All the second point cloud data are merged to obtain the preset map.

5. The robot control method according to claim 2, characterized in that, The initial path is obtained in the following way: At least one target area is determined on the preset map; the preset gas concentration in the target area is theoretically and / or practically greater than the preset gas concentration in other areas of the preset map. Based on at least one of the target regions, the initial path passing through the target regions is determined; Wherein, determining at least one target area on the preset map includes at least one of the following: In response to a region selection operation on the preset map, the target region is determined; During the robot's movement, a first preset gas concentration in the robot's environment is obtained by real-time detection through at least one of the first gas sensors. When the first preset gas concentration is greater than a second concentration threshold, the area where the robot is currently located is determined as the target area.

6. The robot control method according to claim 1, characterized in that, The robot body is also equipped with a solar photovoltaic power generation system, which includes at least one solar panel. The control method further includes at least one of the following: When at least one solar panel malfunction is detected, a first fault information is generated, and the first fault information is displayed through the robot and / or sent to the background management system. When the power of the solar photovoltaic power generation system is detected to be less than the preset power, a first prompt message is generated, and the first prompt message is displayed through the robot and / or sent to the background management system.

7. The robot control method according to claim 1, characterized in that, The robot body is also equipped with a temperature sensor and a humidity sensor; The control method further includes: The temperature of the robot's environment is detected in real time by the temperature sensor, and the humidity of the robot's environment is detected in real time by the humidity sensor. When the temperature is greater than a preset temperature and / or the humidity is greater than a preset humidity, the robot is controlled to stop working.

8. The robot control method according to claim 1, characterized in that, The robot body is also equipped with a wind speed sensor, and the air intake device is equipped with a fan. The control method further includes: The wind speed in the robot's environment is detected in real time by the wind speed sensor. The rotation speed of the fan is adjusted based on the wind speed.

9. The robot control method according to claim 1, characterized in that, The robot body has a reaction chamber connected to the air intake device, the reaction unit is located in the reaction chamber, a second gas sensor and a third gas sensor are respectively provided at the inlet and outlet of the reaction chamber, the reaction chamber is provided with a pressure sensor, and the robot body is also provided with a vacuum system and a heating system. After controlling the robot to enter adsorption mode when the first preset gas concentration is greater than the first concentration threshold, the method further includes: When the difference between the second preset gas concentration and the third preset gas concentration is less than a preset concentration value, the robot is controlled to enter a vacuum mode. The second preset gas concentration is obtained in real time by the second gas sensor, and the third preset gas concentration is obtained in real time by the third gas sensor. The vacuum mode is achieved by extracting air from the reaction chamber through the vacuum system when the inlet and outlet of the reaction chamber are closed. When the pressure sensor detects that the pressure inside the reaction chamber reaches a preset pressure, the robot is controlled to enter the desorption mode; the desorption mode is to heat the reaction chamber through the heating system so that the heating temperature inside the reaction chamber is within a preset temperature range.

10. The robot control method according to claim 9, characterized in that, The robot body is also equipped with an operation panel, which includes a first display area, an adsorption mode control, a vacuum mode control, and a desorption mode control. The first display area is used to display a first preset gas concentration, a second preset gas concentration, a third preset gas concentration, a detection pressure, and a heating temperature. The control method further includes at least one of the following: In response to the selection operation of the adsorption mode control, the robot is controlled to enter the adsorption mode; In response to a selection operation on the vacuum mode control, the robot is controlled to enter vacuum mode; In response to the selection operation of the desorption mode control, the robot is controlled to enter the desorption mode.

11. A control device for a robot, characterized in that, The robot includes a robot body, which is equipped with an air intake device, a reaction unit, and at least one first gas sensor. The control device includes: The first control module is used to control the movement of the robot based on a planned path on a preset map; The acquisition module is used to acquire a first preset gas concentration in the environment where the robot is located, which is detected in real time by at least one of the first gas sensors; The second control module is used to control the robot to enter the adsorption mode when the first preset gas concentration is greater than the first concentration threshold; the adsorption mode is to adsorb the preset gas in the air drawn in by the air intake device through the reaction unit.

12. An intelligent control device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, when the processor executes the computer program, it implements the steps of the control method for the robot as claimed in any one of claims 1 to 10.

13. A robot, characterized in that, include: The robot body and the intelligent control device as described in claim 12; The intelligent control device is located on the robot body, which also includes an air intake device, a reaction unit, and at least one first gas sensor.

14. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the robot control method as described in any one of claims 1 to 10.

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