Dust suppression robot control method and device

Abstract

The application relates to the technical field of industrial automation control, and discloses a dust suppression robot control method and device, the method comprising the following steps: acquiring point cloud data of a working environment, classifying to obtain space occupation data of obstacles and concentration distribution of a dust raising area; determining a dust raising core area according to the concentration distribution and generating an area boundary; determining an occlusion state value according to the overlapping area of an obstacle edge contour and the area boundary; if the occlusion state value is greater than a preset occlusion threshold value, acquiring robot motion constraint data, generating an obstacle avoidance path according to the motion constraint data and the space occupation data; calculating the obstacle avoidance time of robot travel along the obstacle avoidance path, if the obstacle avoidance time is greater than a coverage time threshold value, determining the offset position of the dust raising core area, updating the obstacle avoidance path to obtain a compensation path according to the offset position; and controlling the robot to travel along the compensation path to the offset position to perform a spraying task. The application can realize the collaborative optimization of obstacle avoidance and spraying operation of the dust suppression robot in a dynamic obstacle environment.

Description

Technical Field

[0001] This invention relates to the field of industrial automation control technology, and in particular to a dust suppression robot control method and device. Background Technology

[0002] Currently, dust pollution in industrial settings poses a direct threat to environmental quality and the health of workers. Dust suppression robots, which rely on industrial real-time control software, play a crucial role in pollution control as automated spraying equipment in complex working environments such as mines.

[0003] In existing technologies, dust suppression equipment often employs fixed spraying routes or manual remote control. When encountering temporarily piled materials or moving vehicles, it either stops and awaits manual intervention or, after detouring, cannot return to the original spraying area, resulting in gaps in dust coverage. While some solutions incorporate obstacle detection and path planning, obstacle avoidance decisions are disconnected from the spraying task. The industrial process control logic fails to unify the scheduling of obstacle avoidance behavior with the spraying target, causing the robot to often deviate from the dense dust area after avoiding obstacles, or to interrupt spraying due to frequent turning, failing to maintain the integrity of task execution in dynamic environments. When sensors detect obstacles with irregular outlines ahead, the system needs to determine the degree of impact of the obstacle on the current spraying task. For example, a pile of temporary materials may block the necessary path to a high-concentration dust area. Simply detouring will result in missed spraying of that area, while forcibly approaching may pose a collision risk. In such cases, path planning needs to simultaneously balance the space occupied by the obstacle, the coverage priority of the dust area, and the turning radius and acceleration capability of the robot chassis. Improper handling of any of these factors can lead to task interruption or a significant reduction in spraying effectiveness.

[0004] Existing technologies suffer from a lack of coordination between obstacle avoidance and spray coverage paths. Summary of the Invention

[0005] This invention provides a dust suppression robot control method and device to solve the problem of the lack of coordination between obstacle avoidance and spraying coverage paths.

[0006] In a first aspect, to solve the above-mentioned technical problems, the present invention provides a dust suppression robot control method, comprising: The point cloud data and wind direction and speed data of the robot's operating environment are acquired, and the point cloud data is classified and processed to obtain the space occupancy data of dynamic obstacles and the concentration distribution data of dust areas. The dust core area is determined based on the concentration distribution data, and the area boundary of the dust core area is generated. The edge contour of the dynamic obstacle is extracted based on the space occupancy data, and the occlusion state value is determined based on the overlap area between the edge contour and the region boundary. If the occlusion state value is greater than the preset occlusion threshold, then the motion constraint data of the robot is obtained, and a path search is performed based on the motion constraint data and the space occupancy data to generate an obstacle avoidance path; The obstacle avoidance time of the robot traveling along the obstacle avoidance path is calculated. The offset position of the dust core area is determined based on the obstacle avoidance time and the wind direction and wind speed data. The obstacle avoidance path is updated based on the offset position to obtain a compensation path. The robot is controlled to travel along the compensation path to the offset position to perform the spraying task.

[0007] In a second aspect, the present invention provides a dust suppression robot control device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method described above.

[0008] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described above.

[0009] Compared with the prior art, the present invention has the following beneficial effects: (1) The present invention determines the occlusion status value based on the overlapping area of ​​the edge contour of the obstacle and the boundary of the dust core area. It can quantitatively assess the impact of dynamic obstacles on dust suppression operations, avoid the problem of blank areas in dust coverage caused by obstacle occlusion in traditional methods, and improve the coverage integrity of dust suppression operations.

[0010] (2) This invention calculates the obstacle avoidance time of the robot traveling along the obstacle avoidance path. When the obstacle avoidance time is greater than the coverage time threshold, the offset position of the dust core area is determined according to the wind direction and wind speed data and the obstacle avoidance path is updated to obtain a compensation path. It can track the dust core area that has shifted due to wind force while bypassing obstacles, solves the problem of traditional obstacle avoidance methods ignoring the dynamic changes of the operation target, and improves the adaptability of dust suppression operations to complex dynamic environments.

[0011] (3) The present invention generates an obstacle avoidance path based on chassis motion constraint data, and extracts the curvature extreme points in the compensation path to generate steering and speed commands to control the robot's movement. This ensures that the generated path conforms to the robot's physical motion capabilities, avoids sudden stops and turns or exceeding the chassis motion limits, and improves the stability and safety of the dust suppression robot in complex work scenarios. Attached Figure Description

[0012] Figure 1 This is a schematic diagram of the dust suppression robot control method provided in the first embodiment of the present invention. Detailed Implementation

[0013] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0014] Reference Figure 1 The first embodiment of the present invention provides a dust suppression robot control method, including the following steps: S11, acquire point cloud data and wind direction and speed data of the robot's working environment, classify the point cloud data, and obtain space occupancy data of dynamic obstacles and concentration distribution data of dust area; S12, determine the dust core area based on the concentration distribution data, and generate the area boundary of the dust core area; S13, extract the edge contour of the dynamic obstacle based on the space occupancy data, and determine the occlusion state value based on the overlap area between the edge contour and the region boundary; S14, if the occlusion state value is greater than the preset occlusion threshold, then obtain the motion constraint data of the robot, perform path search based on the motion constraint data and the space occupancy data, and generate an obstacle avoidance path; S15, calculate the obstacle avoidance time of the robot traveling along the obstacle avoidance path, determine the offset position of the dust core area based on the obstacle avoidance time and the wind direction and wind speed data, update the obstacle avoidance path based on the offset position, and obtain a compensation path; S16, control the robot to travel along the compensation path to the offset position and perform the spraying task.

[0015] In step S11, point cloud data and wind direction and speed data of the robot's operating environment are acquired. The point cloud data is then classified to obtain spatial occupancy data of dynamic obstacles and concentration distribution data of dust areas, including: The point cloud data is filtered and simplified to obtain the target point cloud data; Based on the spatial distribution density between points in the target point cloud data, the points are classified to obtain multiple clusters; Extract the geometric features of the clusters, and classify the clusters into dynamic obstacles and dust areas based on the geometric features; The spatial coordinates and contour dimensions of the dynamic obstacle are extracted as spatial occupancy data, and the point cloud density of the dust area is extracted as the concentration distribution data.

[0016] In one implementation, this embodiment acquires point cloud data and wind direction and speed data of the robot's operating environment through vehicle-mounted sensors. The vehicle-mounted sensors include a lidar and an anemometer. The lidar is mounted on the top of the robot to collect three-dimensional spatial point cloud data of the operating environment; the anemometer is mounted on the outside of the robot to collect environmental wind speed and direction data. The initial point cloud data collected by the lidar contains a large number of spatial coordinate points, which contain a lot of redundant information. Direct processing would lead to excessive consumption of computational resources and affect the system's real-time performance. Therefore, the initial point cloud data is filtered and simplified to reduce the amount of data while retaining the main geometric features of the environment.

[0017] Specifically, the filtering and simplification process employs voxel filtering. The basic principle of voxel filtering is to divide the 3D space into several cubic grids with fixed side lengths. For all point cloud data within each grid, the coordinates of its geometric center or centroid are calculated, and this center point replaces all point cloud data within the grid, thereby reducing the total number of point clouds. The voxel grid size is set to 0.2 meters, meaning each cubic grid has a side length of 0.2 meters. For each non-empty grid, the mean coordinate of all points within that grid is calculated, and this mean point is used as the output point after filtering. After voxel filtering, the downsampled target point cloud data is output.

[0018] Furthermore, the target point cloud data is classified according to the spatial distribution density between points to obtain multiple clusters. Specifically, a density-based spatial clustering algorithm is used. The core idea of ​​this algorithm is that in densely distributed areas, points are close to each other and should be grouped into the same category; while in sparse areas, points are far apart and should be treated as different categories or noise. The search radius of the neighborhood of a core point is set to 0.5 meters, and the minimum threshold for the number of points within the neighborhood is 15. The algorithm traverses each point in the target point cloud, constructs a search sphere with the current point as the center and a radius of 0.5 meters, and counts the number of neighboring points contained within the sphere. If the number of neighboring points is greater than or equal to 15, the point is marked as a core point; if the number of neighboring points is less than 15 but within the neighborhood of a core point, the point is marked as a boundary point; if the number of neighboring points is less than 15 and does not belong to the neighborhood of any core point, the point is marked as a noise point. The algorithm groups all core points and all boundary points connected by the neighborhood relationships of the core points into the same cluster, and finally outputs multiple independent cluster sets.

[0019] It should be noted that after obtaining the cluster set, the geometric features of each cluster are extracted. These geometric features include the 3D bounding box volume and the intra-cluster point cloud density. The 3D bounding box volume refers to the space occupied by the smallest cuboid that can completely enclose all points in the cluster. It is calculated by extracting the maximum and minimum coordinate values ​​of the cluster in the horizontal, vertical, and height directions, calculating the size difference in these three directions, and multiplying them. The intra-cluster point cloud density is the ratio of the total number of points in the cluster to the 3D bounding box volume, i.e., the number of points per unit volume. Based on these geometric features, the clusters are distinguished into dynamic obstacles and dusty areas.

[0020] Specifically, an entity determination threshold is set: the volume of the 3D bounding box is greater than 1.5 cubic meters and the point cloud density within the cluster is higher than 50 points per cubic meter. If a cluster simultaneously satisfies the conditions of having a volume greater than 1.5 cubic meters and a point cloud density higher than 50 points per cubic meter, or if the extracted geometric features include planar or right-angled edge features, then the cluster is determined to correspond to a dynamic obstacle; if the volume of a cluster is greater than 1.5 cubic meters but the point cloud density is less than or equal to 50 points per cubic meter and it does not have the aforementioned rigid body geometric features, then it is determined to correspond to a dusty area.

[0021] It is worth further explaining that the spatial coordinates and outline dimensions of dynamic obstacles are extracted as space occupancy data. Specifically, for clusters identified as dynamic obstacles, the coordinate range of the circumscribed cuboid corresponding to the cluster in 3D space is extracted, including the minimum and maximum values ​​of the x-coordinate, y-coordinate, and height coordinates, as well as the length, width, and height dimensions of the circumscribed cuboid, as the space occupancy data of the dynamic obstacle. The point cloud density of dusty areas is extracted as concentration distribution data. Specifically, for clusters identified as dusty areas, the ratio of the total number of point clouds in the cluster to the volume of the 3D bounding box is calculated, and this ratio is used as the concentration distribution data for that spatial region, and stored in association with the spatial coordinate range of that region.

[0022] For example, in a mining operation scenario, the vehicle-mounted LiDAR of a dust suppression robot acquires initial point cloud data of dust generated by mining trucks, excavators, and blasting or loading / unloading operations in real time. The initial point cloud data contains approximately 5 million points. After voxel filtering (grid size 0.2 meters) and downsampling, approximately 800,000 target point cloud data points are obtained. After clustering, eight clusters are output. The 3D bounding box volume of one cluster is 25 cubic meters, the number of point clouds within the cluster is 2000, and the calculated point cloud density is 80 points per cubic meter. This meets the entity identification threshold of a volume greater than 1.5 cubic meters and a density greater than 50 points per cubic meter, thus identifying the cluster as a transport vehicle. The spatial coordinates of this vehicle are extracted as follows: x-coordinate 100 to 130 meters, y-coordinate 60 to 80 meters, and height coordinate 0 to 4.5 meters. The outline dimensions are 30 meters long, 20 meters wide, and 4.5 meters high, which are output as the spatial occupancy data of this dynamic obstacle.

[0023] Another cluster has a 3D bounding box volume of 40 cubic meters and 320 point clouds within it, with a calculated point cloud density of 8 points per cubic meter. Although the volume is greater than 1.5 cubic meters, the density is less than 50 points per cubic meter, indicating it is a dust area generated by loading and unloading operations. This area has a volume of 40 cubic meters and a total of 320 point clouds, with a calculated concentration distribution of 8 points per cubic meter. This concentration data is then correlated with spatial coordinates (70-90 meters x-axis, 20-40 meters y-axis, and 1-6 meters height). Yet another cluster has a 3D bounding box volume of 0.3 cubic meters, which does not meet the condition of a volume greater than 1.5 cubic meters, and is therefore identified as sensor noise or small particulate matter splashing from the particles.

[0024] In step S12, the dust core area is determined based on the concentration distribution data, and the boundary of the dust core area is generated, including: Filter the spatial coordinate points in the concentration distribution data whose concentration is greater than a preset concentration threshold, and delineate the dust core area based on the set of the spatial coordinate points. The spatial coordinate points of the dust core area are projected onto a two-dimensional top-view plane, and the projected points are statistically analyzed to generate a closed polygon surrounding the spatial coordinate points. The closed polygon is used as the boundary of the dust core area.

[0025] In one implementation, this embodiment requires first obtaining the dust concentration distribution data of the dust-affected area and its corresponding spatial coordinate range output in step S11. The concentration distribution data is organized in units of spatial voxels, each voxel containing the three-dimensional coordinates (horizontal, vertical, and height coordinates) of its center point and the corresponding point cloud concentration value. To determine the core operating area with the highest dust concentration, a preset concentration threshold is set, which is used to filter out the core sub-region with the highest concentration from the entire dust-affected area.

[0026] The preset concentration threshold is determined through statistical analysis of historical operational data, based on the effective spraying range of the dust suppression robot and the effectiveness requirements of dust suppression operations. Specifically, dust concentration distribution data and corresponding actual spraying effect data from multiple typical mining operation scenarios are collected. The average concentration value, maximum concentration value, and minimum spraying coverage concentration required to achieve effective dust suppression are statistically analyzed for each scenario. Statistical analysis shows that when the concentration value in a dusty area is in the top 30% to top 40% quantile, that area is the main source of dust diffusion, and spraying coverage of this area can suppress more than 80% of the dust in the entire dusty area. Therefore, the preset concentration threshold is set as the concentration value corresponding to the 35th quantile in the dust area concentration distribution data, arranged from high to low.

[0027] The process involves filtering spatial coordinates in the concentration distribution data where the concentration exceeds a preset threshold. Specifically, each voxel in the concentration distribution data is traversed, its concentration value is extracted, and compared to the preset threshold. If the concentration value is greater than the threshold, the center coordinates of that voxel are extracted and added to the dust core area point set; otherwise, the voxel is discarded. After the traversal is complete, all extracted coordinates form a set that spatially delineates the area where the dust concentration exceeds the threshold, i.e., the dust core area.

[0028] The spatial coordinates of the dust core area are projected onto a two-dimensional top-down plane. Specifically, height coordinates are ignored, and only the x and y coordinates are retained. The coordinates in three-dimensional space are mapped onto the horizontal plane to form a two-dimensional projection point set. The projection points are statistically analyzed to generate a closed polygon enclosing the spatial coordinates. Specifically, a convex hull algorithm is used to process the two-dimensional projection point set to find the smallest convex polygon that can enclose all projection points. The vertices of this polygon are the outermost points in the projection point set, and the edges are the lines connecting adjacent outermost points. This closed polygon serves as the boundary of the dust core area. The area enclosed by this boundary on the horizontal plane represents the coverage area of ​​the dust core area on the two-dimensional plane.

[0029] For example, following the mining operation scenario in step S11, the dust concentration distribution data output in step S11 covers a spatial range of 70 to 90 meters on the horizontal axis, 20 to 40 meters on the vertical axis, and 1 to 6 meters on the height axis. This area is divided into several voxel grids of 0.2 meters × 0.2 meters × 0.2 meters, with each voxel corresponding to a concentration value. The concentration values ​​of all voxels within this dust area are counted, resulting in approximately 150 voxels. All concentration values ​​are sorted from highest to lowest, and the concentration value corresponding to the 35th quantile is taken as the preset concentration threshold. After sorting and calculation, the concentration corresponding to the 35th quantile is 10 points per cubic meter; therefore, the preset concentration threshold is set to 10 points per cubic meter. All voxels within this range are traversed, and the center coordinates of voxels with a concentration greater than 10 are extracted, resulting in 50 coordinate points. These coordinate points constitute the point set of the core dust area. Ignoring the height coordinates, these 50 points are projected onto a two-dimensional top-down plane, resulting in 50 two-dimensional projection points. The convex hull algorithm is used to process these 50 projection points to generate a pentagonal closed polygon with the vertex coordinates of (73, 23), (88, 24), (89, 32), (82, 38), and (71, 35) in sequence. This pentagon is used as the boundary of the core dust area.

[0030] In step S13, the edge contour of the dynamic obstacle is extracted based on the space occupancy data, and the occlusion state value is determined based on the overlap area between the edge contour and the region boundary, including: Extract the vertex coordinates of the dynamic obstacle from the space occupancy data, project the vertex coordinates onto the horizontal plane, and connect the projected vertex coordinates to form the edge contour of the dynamic obstacle; Construct a target passage line from the robot's current position to the center point of the core dust area; If the edge contour and the region boundary intersect, the area of ​​the overlapping region between the edge contour and the region boundary is calculated, and the region occlusion weight is obtained based on the ratio of the area of ​​the overlapping region to the area of ​​the edge contour. If the edge contour and the target passage line intersect, then based on the coordinates of the intersection point, the interval of the target passage line separated by the obstacle contour is extracted, and the ratio of the line segment size of the interval to the overall size of the target passage line is calculated to obtain the path blocking weight. The final occlusion state value is obtained by weighted summing of the region occlusion weight and the path blocking weight.

[0031] In one implementation, this embodiment extracts the vertex coordinates of the dynamic obstacle from the space occupancy data output in step S11. The space occupancy data describes the spatial extent of the dynamic obstacle in the form of a three-dimensional bounding box (cube), containing the coordinate values ​​of the eight vertices of the cuboid in three-dimensional space. These eight vertex coordinates are projected onto a horizontal plane (i.e., a two-dimensional top-view plane), ignoring the height coordinate information and retaining only the horizontal and vertical coordinates, resulting in eight two-dimensional projection points. The projected vertex coordinates are then connected sequentially in a clockwise or counterclockwise order to form a closed two-dimensional rectangle, which represents the edge contour of the dynamic obstacle. The edge contour characterizes the spatial extent occupied by the dynamic obstacle on the horizontal plane.

[0032] Furthermore, a target passage line is constructed from the robot's current position to the center point of the dust core area. Specifically, the current coordinates of the dust suppression robot are obtained (in real time through the positioning system), and the coordinates of the center point of the dust core area (the geometric center of the region boundary polygon) determined in step S12 are obtained. On a two-dimensional plane, the robot's current position and the center point of the dust core area are connected to form a directed line segment. This line segment represents the theoretical shortest passage path for the robot to travel from its current position to the dust core area.

[0033] It should be noted that the determination of the occlusion state value comprehensively considers two dimensions of occlusion factors. First, the degree to which dynamic obstacles cover the core dust area itself (i.e., whether the obstacle directly intrudes into or obstructs the core dust area); second, the degree to which dynamic obstacles hinder the robot's path to the core dust area (i.e., whether the obstacle blocks the robot's movement). The occlusion of the two dimensions is weighted separately and then combined.

[0034] Specifically, it is determined whether the edge contour intersects with the region boundary generated in step S12. The region boundary is a closed polygon surrounding the dust core region, and the edge contour is a rectangle surrounding the dynamic obstacle. Line segment intersection detection is performed on each edge of the edge contour and each edge of the region boundary. If an intersection is detected between any two edges, it is determined that the edge contour and the region boundary have overlapping line segments or overlapping areas, indicating that the dynamic obstacle has spatially intruded into the dust core region. At this time, the area of ​​the overlapping area between the edge contour and the region boundary is calculated. Specifically, a polygon clipping algorithm is used, taking the edge contour rectangle and the region boundary polygon as input, calculating the intersection polygon of the two in a two-dimensional plane, and outputting the area of ​​this intersection polygon as the overlapping area. The overlapping area is divided by the total area of ​​the edge contour to obtain the region occlusion weight. This weight reflects the degree of coverage of the dust core region by the dynamic obstacle, with a value ranging from 0 to 1. The larger the value, the more severe the encroachment of the obstacle on the dust core region.

[0035] Further, it is determined whether there is an intersection between the edge contour and the target passage line. The target passage line (segment) is intersected with each edge of the edge contour. If an intersection is detected, it indicates that a dynamic obstacle is blocking the robot's straight-line path to the dust core area. Based on the coordinates of the intersection point, the section of the target passage line separated by the obstacle contour is extracted. Specifically, when the target passage line crosses the obstacle edge contour, the line is divided into three segments: the section in front of the obstacle (from the robot's current position to the first intersection point), the middle section cut off by the obstacle contour (between the two intersection points), and the section behind the obstacle (from the second intersection point to the center point of the dust core area). The middle section separated by the obstacle contour is extracted, the length of this segment is calculated, and this length is divided by the overall length of the target passage line to obtain the path blocking weight.

[0036] Specifically, the occlusion weight and the path blocking weight are weighted and summed to obtain the final occlusion state value. Specifically, the occlusion state value equals the product of a first preset coefficient and the regional occlusion weight, plus the product of a second preset coefficient and the path blocking weight. The sum of the first and second preset coefficients is 1. The regional occlusion weight is the area overlapping the boundary of the obstacle edge and the dust core area divided by the total area of ​​the obstacle edge contour. The path blocking weight is the length of the section where the target passage is truncated by the obstacle contour divided by the total length of the target passage. The first preset coefficient is set based on the fact that the regional occlusion weight directly reflects the degree of coverage of the dust core area and has a more direct impact on the spraying task; therefore, its weight should not be lower than the path blocking weight.

[0037] It should be noted that the first and second preset coefficients are set according to the current obstacle type. When the obstacle is a moving vehicle, they are set to 0.6 and 0.4 respectively. When the obstacle is a fixed pile of materials, they are set to 0.5 and 0.5 respectively. The sum of the two is 1. The two sets of coefficients are manually switched through the control terminal.

[0038] For example, assuming the robot is located at (0, 0), the center of the dust core area is located at (100, 0), the target passage line is the X-axis, and the dynamic obstacle edge contour is a rectangle from (0, -15) to (85, 15); the line intersects the rectangle at (0, 0) and (85, 0), the length of the truncated section is 85 meters, the total length of the line is 100 meters, and the path blocking weight is 85 / 100=0.85; the rectangle does not overlap with the boundary of the core area, the area occlusion weight is 0, the first preset coefficient is 0.6, the second preset coefficient is 0.4, and the calculated occlusion state value is 0.6×0+0.4×0.85=0.34.

[0039] In step S14, if the occlusion state value is greater than a preset occlusion threshold, the motion constraint data of the robot is acquired, and a path search is performed based on the motion constraint data and the space occupancy data to generate an obstacle avoidance path, including: When the occlusion state value is greater than the preset occlusion threshold, the chassis motion constraint data of the robot is obtained, and the chassis motion constraint data includes the maximum steering angle and the minimum turning radius; Starting from the robot's current position, the obstacle occupancy area is determined in the work space based on the space occupancy data. Random node sampling is performed outside the obstacle occupancy area to generate an initial sampling node set. The initial set of sampling nodes is filtered based on the chassis motion constraint data, and nodes that do not meet the maximum steering angle or the minimum turning radius between adjacent nodes are removed to obtain a valid set of sampling nodes. Collision detection is performed on each node in the set of valid sampling nodes and the area occupied by the obstacle. Nodes that collide are removed, and obstacle avoidance paths are generated based on the remaining nodes.

[0040] In one implementation, this embodiment compares the occlusion state value calculated in step S13 with a preset occlusion threshold. The preset occlusion threshold is determined by statistical analysis of historical operating data of the dust suppression robot in typical operating scenarios. Data on the correlation between obstacle occlusion levels and robot spray coverage are collected over multiple operating cycles. When the occlusion state value exceeds 0.25, the probability of collision during the robot's journey along its original straight path to the core dust-generating area increases significantly, while the spray coverage drops below an acceptable level. Therefore, the preset occlusion threshold is set to 0.25. Those skilled in the art will understand that this threshold can be adjusted within the range of 0.15 to 0.40 based on the robot's size, obstacle density, and dust area distribution characteristics in the specific operating scenario.

[0041] It should be noted that if the occlusion status value is less than or equal to the preset occlusion threshold, it is determined that the current obstacle has a low impact on the dust suppression operation, and the robot does not need to change its travel path, continuing to proceed along the current route to the core dust area to perform the spraying task. If the occlusion status value is greater than the preset occlusion threshold, it is determined that the current obstacle's coverage of the core dust area or its obstruction of the passageway has reached a level requiring intervention, triggering the obstacle avoidance path planning process.

[0042] Further, after triggering obstacle avoidance path planning, the robot's chassis motion constraint data is acquired. This data includes the robot's maximum steering angle and minimum turning radius. The maximum steering angle refers to the maximum angle the robot's front wheels or steering mechanism can deflect, limiting the upper limit of the rate of change of the robot's direction of travel. The minimum turning radius refers to the minimum circular radius the robot can achieve under the maximum steering angle condition, limiting the upper limit of the spatial curvature of the robot's trajectory. These maximum steering angle and minimum turning radius are directly provided by the robot chassis's factory technical parameters; in this embodiment, the maximum steering angle is taken as 35 degrees and the minimum turning radius as 12 meters. The chassis motion constraint data is used to ensure that the subsequently generated obstacle avoidance path is physically executable, avoiding the planning of paths beyond the robot's motion capabilities.

[0043] Simultaneously, starting from the robot's current position, the obstacle-occupied area is determined in the work space based on the dynamic obstacle space occupancy data output in step S11. The obstacle-occupied area is the projection range of the three-dimensional bounding box of the dynamic obstacle onto the horizontal plane, and the space within this range is marked as a non-passable area.

[0044] The obstacle avoidance path is generated by first determining the location and size of the obstacle-occupied area, and then selecting a path from the robot's current position to bypass the obstacle-occupied area and ultimately reach the dust core area. Specifically, based on the position of the obstacle-occupied area on the horizontal plane, it is determined whether the obstacle is located to the left or right of the robot's current direction of travel. If the obstacle is located slightly to the left of the travel direction, the robot chooses to bypass it from the right; if the obstacle is located slightly to the right of the travel direction, the robot chooses to bypass it from the left. After determining the bypass direction, starting from the robot's current position, the robot deflects a certain angle in the bypass direction and travels along the outer edge of the obstacle-occupied area until it bypasses the obstacle and readjusts its direction to travel towards the dust core area. During the path generation process, the required steering angle and turning radius for each segment of the path are calculated segment by segment. These parameters are compared with the chassis's maximum steering angle and minimum turning radius. If the required steering angle for a certain path segment exceeds the maximum steering angle or the required turning radius is less than the minimum turning radius, the curvature of that path segment is adjusted (e.g., increasing the turning radius or decreasing the steering angle) until all segments of the entire path satisfy the chassis motion constraints. The final generated path is the obstacle avoidance path.

[0045] For example, in a mining operation scenario, if the calculated occlusion value is 0.34, which is greater than the preset occlusion threshold of 0.25, the obstacle avoidance path planning process is triggered. The robot's current chassis motion constraint data is obtained: the maximum turning angle is 35 degrees, and the minimum turning radius is 12 meters. Starting from the robot's current position (40, 25), based on the space occupancy data of the mining truck ahead (a 3D bounding box 15 meters long and 8 meters wide), the horizontal projection range of the mining truck (horizontal coordinate 50 to 65 meters, vertical coordinate 30 to 38 meters) is marked as the obstacle occupancy area. Since the mining truck is located northeast of the robot's current position, it is determined to be slightly to the right ahead; therefore, the robot chooses to bypass the mining truck from its left side. Starting from the robot's current position (40, 25), the robot turns left at a certain angle based on its current orientation (pointing towards the core dust area) and travels to the outer left corner of the mining truck (48, 38). Then, it travels along the outer left boundary of the mining truck to the point ahead of the upper left corner (68, 38), and then turns right towards the core dust area. The required turning angle and turning radius for each segment of this path are calculated. Taking the segment from point A (40, 25) to point B (48, 38) as an example, the robot's heading angle at the starting point of this segment is 45 degrees (pointing towards the core dust area), and the heading angle at the ending point is 65 degrees (circling around the left boundary of the mining truck), with a heading angle change of 20 degrees. Taking the robot's current position A as the tangent point and the heading direction at point A as the tangent direction, draw an arc with a radius of 15 meters tangent to the heading direction at point B. The calculated curvature of this arc corresponds to a turning angle of 20 degrees, which is less than the maximum turning angle of 35 degrees. The turning radius is 15 meters, which is greater than the minimum turning radius of 12 meters, satisfying the constraints. The center of this 15-meter turning radius arc is located 15 meters perpendicular to the left of point A in the heading direction, with coordinates (40 + 15 × cos135°, 25 + 15 × sin135°), approximately (29.4, 35.6).

[0046] Furthermore, the movement from (48, 38) to (68, 38) is a straight line, satisfying the constraints; the turning angle required to adjust the direction from (68, 38) towards the core dust area is 25 degrees, which is less than the maximum turning angle of 35 degrees, and the turning radius is 14 meters, which is greater than the minimum turning radius of 12 meters, satisfying the constraints. Generate an obstacle avoidance path starting from the robot's current position (40, 25), passing through the first intermediate point (48, 38), the second intermediate point (68, 38), the third intermediate point (120, 42), and the fourth intermediate point (160, 45), and finally reaching the center point of the core dust area (180, 200). The first intermediate point (48, 38) is located outside the upper left corner of the mining truck, and the second intermediate point (68, 38) is located outside the front left corner of the mining truck. These two intermediate points are used to navigate around the mining truck. The third intermediate point (120, 42) and the fourth intermediate point (160, 45) are located in the open passage between the mining truck and the core dust area, and are used to guide the robot to travel safely to the core dust area. The turning angle and turning radius of each segment of the above path are checked. The required turning angle of each segment is less than the maximum turning angle of 35 degrees, and the required turning radius is greater than the minimum turning radius of 12 meters. The entire obstacle avoidance path meets the chassis motion constraints.

[0047] In step S15, the obstacle avoidance time of the robot traveling along the obstacle avoidance path is calculated. Based on the obstacle avoidance time and the wind direction and speed data, the offset position of the dust core area is determined. The obstacle avoidance path is updated based on the offset position to obtain a compensation path, including: Obtain the robot's rated travel speed, extract the path length of the obstacle avoidance path, and calculate the obstacle avoidance time of the robot traveling along the obstacle avoidance path based on the path length and the rated travel speed; Obtain the coverage time threshold of the dust core area. If the obstacle avoidance time is greater than the coverage time threshold, determine the offset direction and offset distance of the dust core area within the obstacle avoidance time based on the wind direction and wind speed data, and obtain the offset position. The target point is redefined based on the offset position, and the target point is used as the endpoint of the path search to update the obstacle avoidance path, thereby obtaining a compensated path.

[0048] In one implementation, the rated travel speed in this embodiment refers to the speed at which the robot travels smoothly according to the speed setting value of the chassis control system under normal operating conditions. The path length of the obstacle avoidance path generated in step S14 is extracted, and the path length is the total spatial trajectory length of the obstacle avoidance path from the starting point to the ending point. Dividing the path length by the rated travel speed yields the obstacle avoidance time required for the robot to travel along the obstacle avoidance path. The obstacle avoidance time characterizes the expected time required for the robot to reach the original dust core area after bypassing the obstacle from its current position.

[0049] Furthermore, the coverage time threshold of the core dust area is obtained. The coverage time threshold refers to the length of time from the current moment during which the dust in the core area, under the influence of ambient wind, spreads beyond the effective spraying range of the robot. This threshold characterizes the timeframe within which the robot must reach the target area and implement spraying; otherwise, the dust will spread uncontrollably. The coverage time threshold is calculated as the ratio of the robot's maximum spraying range to the lateral dust diffusion rate, where the lateral dust diffusion rate is 0.2 times the current wind speed. Those skilled in the art will understand that this threshold can be dynamically updated based on the dust concentration change rate and wind speed in the actual operating environment.

[0050] It should be noted that if the obstacle avoidance time is less than or equal to the coverage time threshold, it is determined that the robot can reach the target area before the dust spreads to an uncontrollable range by traveling along the current obstacle avoidance path, and no path adjustment is needed; the robot can continue to execute the current obstacle avoidance path. If the obstacle avoidance time is greater than the coverage time threshold, it is determined that the robot will miss the effective dust suppression time window if it continues to travel along the current obstacle avoidance path. In this case, it is necessary to adjust the target position based on the dynamic displacement of the dust core area under the action of wind.

[0051] It is worth further explaining that, according to the well-known principle of advection in fluid mechanics, the drift of the dust core on the horizontal plane is approximately a uniform translational motion following the ambient wind field. When the obstacle avoidance time exceeds the coverage time threshold, the offset direction and distance of the dust core area within that obstacle avoidance time are determined based on wind direction and speed data, thus obtaining the offset position. Specifically, the current ambient wind direction and speed data are obtained using a vehicle-mounted anemometer. The wind direction determines the offset direction of the dust core area, that is, the dust cloud moves along the wind direction under the action of wind force; the product of wind speed and obstacle avoidance time determines the offset distance of the dust core area, that is, the total distance the dust cloud moves along the wind direction within the obstacle avoidance time. By translating the current initial position of the dust core area along the wind direction by a distance equal to the wind speed multiplied by the obstacle avoidance time, the new position reached by the dust core area after the obstacle avoidance time ends can be obtained, and this new position is the offset position.

[0052] It should be noted that if the dust core area continues to spread under the influence of wind, its concentration distribution range will also continue to expand. Therefore, in addition to displacement on the horizontal plane, the spatial coverage of the offset position may also increase due to the spread, but the coordinates of the core point of the target position are determined according to the advection offset method described above. The target point is re-determined based on the offset position, and this target point is used as the endpoint of the path search to update the obstacle avoidance path and obtain the compensation path. Specifically, the original target point (i.e., the initial center position of the dust core area) used in step S14 when generating the obstacle avoidance path is replaced with the new coordinates corresponding to the offset position. Then, with the robot's current position as the starting point and the offset position as the endpoint, the obstacle avoidance path search is re-executed to generate a new path that goes around the obstacle from the current position and points to the offset position. This path is the compensation path.

[0053] Furthermore, after updating the obstacle avoidance path to obtain the compensated path, the process includes calculating the updated length of the compensated path and calculating the updated time consumption based on the updated length and rated travel speed. If the updated time consumption is still greater than the coverage time threshold, the offset position of the dust core area is re-determined based on the updated time consumption and wind direction and speed data. The obstacle avoidance path is then updated again based on the re-determined offset position until the updated time consumption is less than or equal to the coverage time threshold. Specifically, the compensated path points to the offset position of the dust core area after the obstacle avoidance time consumption ends. However, since the length of the compensated path is different from the original obstacle avoidance path, the time required for the robot to travel along the compensated path will also change accordingly. This time consumption may be different from the time length on which the original obstacle avoidance time consumption was calculated. Therefore, it is necessary to recalculate the time consumption based on the actual length of the compensated path. If the recalculated time consumption is still greater than the coverage time threshold, it indicates that the dust core area will further shift during the time the robot travels along the compensated path. In this case, it is necessary to re-predict the offset position based on the updated time consumption and update the path again. The iteration process continues until a path is generated that takes no more than the coverage time threshold. The iteration stops when the final generated path is output as the final compensation path.

[0054] In step S16, the robot is controlled to travel along the compensation path to the offset position to perform the spraying task, including: Extract the curvature extrema points in the compensation path; Chassis control commands are generated based on the curvature extreme points, and the chassis control commands include steering commands and speed commands. The robot is controlled to travel along the compensation path according to the chassis control commands; The robot's real-time position during its movement is obtained, and when the real-time position matches the offset position, the robot is controlled to perform a spraying task.

[0055] In one implementation, the compensation path in this embodiment consists of a series of discrete spatial coordinate points. These coordinate points are connected sequentially to form a continuous trajectory that starts from the robot's current position, bypasses dynamic obstacles, and ultimately points to the offset position of the core dust area. To facilitate the robot chassis in executing this path, key control nodes need to be extracted from the compensation path.

[0056] It's important to note that curvature extrema points refer to the locations on the path with the greatest curvature, i.e., the points where the path direction changes most abruptly. Specifically, after discretizing the compensation path into multiple spatial coordinate points, the curvature values ​​at each location on the path are calculated. The curvature value reflects the rate of change of the path's direction at that location; a larger value indicates a sharper curve. The curvature values ​​at each point are iterated along the path direction. When a point's curvature value is detected to be greater than the curvature values ​​of its immediate neighbors, and this peak value exceeds a preset curvature threshold, that point is extracted as a curvature extrema point. Curvature extrema points represent the locations of the most abrupt curves in the compensation path and are critical nodes that require careful attention when the robot chassis performs steering and speed control. The preset curvature threshold is determined in advance based on the robot's wheelbase and center of gravity height, with a value range of 0.2 to 0.5 per meter. The longer the wheelbase or the higher the center of gravity, the smaller the value should be. In actual use, the value is adjusted within ±20% of the calibrated value. The specific value is calibrated through actual vehicle turning tests. The robot is controlled to travel at the working speed, and the steering angle is gradually increased until the body roll angle reaches 5 degrees. The turning radius at this time is recorded, and 0.8 times the reciprocal of this radius is taken as the preset curvature threshold.

[0057] Furthermore, chassis control commands are generated based on the curvature extrema, including steering and speed commands. Specifically, the robot traverses the compensation path from the starting point to the ending point to determine the path length between the robot's current position and the next curvature extrema. Based on this path length and the robot's current speed, it is determined whether the robot is on a flat section or about to enter a curve. If the distance from the current position to the next curvature extrema is greater than a preset far-distance threshold, the robot is determined to be on a straight or flat section, and a speed command is generated to control the robot to travel at its rated speed or in an accelerated state to improve work efficiency. If the distance from the current position to the next curvature extrema is less than or equal to a preset near-distance threshold, the robot is determined to be about to enter a curve, and a steering command is generated to control the chassis steering mechanism to deflect at the corresponding angle. At the same time, a speed command is generated to control the robot to decelerate to ensure that the robot smoothly passes through the curve. After passing the curvature extreme point, a speed command is generated to control the robot to accelerate again and resume normal travel speed. The long-distance threshold is set to 5 meters, and the short-distance threshold is set to 2 meters. These two values ​​are based on the robot's rated travel speed and braking distance and are used to distinguish between straight-line travel and approaching a curve. Both can be manually adjusted at the control terminal. Specifically, the dust suppression robot's operating speed is usually 1 to 2 meters per second. 5 meters corresponds to a reaction window of about 3 to 5 seconds, and 2 meters corresponds to an execution window of about 1 to 2 seconds, which is in line with the conventional calibration range for path tracking control of industrial mobile robots.

[0058] Controlling the robot to travel along the compensation path according to chassis control commands involves sending generated steering and speed commands to the robot's chassis control system in real time. The chassis control system drives the steering mechanism to deflect to a specified angle based on the steering command and adjusts the speed of the drive motor based on the speed command, enabling the robot to travel along the compensation path. During travel, the steering and speed commands are continuously and dynamically updated based on the robot's real-time position and the current curvature extremum position until all curvature extremum points of the compensation path have been traversed.

[0059] The robot's real-time position during its movement is acquired. When the real-time position matches the offset position, the robot is controlled to perform the spraying task. Specifically, as the robot travels along the compensation path, its actual position coordinates in three-dimensional space are acquired in real time through a positioning system. The positioning system can employ real-time dynamic differential positioning technology, which provides centimeter-level positioning accuracy, ensuring the robot's position information is accurate and reliable. The real-time acquired robot position coordinates are compared in real time with the offset position coordinates determined in step S15 to calculate the spatial distance between the robot's current position and the offset position. When this spatial distance is less than a preset distance threshold, it is determined that the robot has reached the offset position of the dust core area. At this time, the control module issues a work start command to the spraying mechanism, activating the high-pressure water pump and atomizing nozzles, and the robot enters the dust core area to perform the dust suppression spraying task. The distance threshold is set to 0.5 meters, which corresponds to a comprehensive consideration of the centimeter-level positioning accuracy of the real-time dynamic differential positioning technology and the robot's body size, ensuring that the robot can start spraying in time when it reaches the target position without any omissions. This threshold can be adjusted within the range of 0.2 meters to 1.0 meters through the control terminal.

[0060] It should be noted that the spraying mechanism remains in standby mode and does not perform spraying operations until the robot reaches the offset position, in order to avoid wasting dust suppressant in non-target areas or affecting the working environment around obstacles. Once the robot reaches the offset position and begins spraying, the spraying mechanism continues to work until it has completely covered the core dust-generating area or receives a stop command.

[0061] In summary, this invention discloses a dust suppression robot control method, including acquiring point cloud data and wind direction and speed data of the robot's operating environment; classifying and processing the point cloud data to obtain spatial occupancy data of dynamic obstacles and concentration distribution data of dust areas; determining the core dust area based on the concentration distribution data and generating the area boundary; extracting the edge contours of dynamic obstacles based on the spatial occupancy data and determining the occupancy status value based on the overlap area between the edge contours and the area boundary; when the occupancy status value is greater than a preset occupancy threshold, acquiring the robot's motion constraint data and generating an obstacle avoidance path based on the motion constraint data and spatial occupancy data; calculating the obstacle avoidance time of the robot traveling along the obstacle avoidance path; if the obstacle avoidance time is greater than the coverage time threshold, determining the offset position of the core dust area based on the wind direction and speed data, updating the obstacle avoidance path based on the offset position to obtain a compensation path; and controlling the robot to travel along the compensation path to the offset position to perform the spraying task.

[0062] A second embodiment of the present invention provides a dust suppression robot control device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the method described above.

[0063] It should be noted that the dust suppression robot control device provided in this embodiment of the invention is used to execute all the process steps of the dust suppression robot control method in the above embodiment. The working principle and beneficial effects of the two are one-to-one, so they will not be described again.

[0064] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and 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 modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0065] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A dust suppression robot control method, characterized in that, include: The point cloud data and wind direction and speed data of the robot's operating environment are acquired, and the point cloud data is classified and processed to obtain the space occupancy data of dynamic obstacles and the concentration distribution data of dust areas. The dust core area is determined based on the concentration distribution data, and the area boundary of the dust core area is generated. The edge contour of the dynamic obstacle is extracted based on the space occupancy data, and the occlusion state value is determined based on the overlap area between the edge contour and the region boundary. If the occlusion state value is greater than the preset occlusion threshold, then the motion constraint data of the robot is obtained, and a path search is performed based on the motion constraint data and the space occupancy data to generate an obstacle avoidance path; The obstacle avoidance time of the robot traveling along the obstacle avoidance path is calculated. The offset position of the dust core area is determined based on the obstacle avoidance time and the wind direction and wind speed data. The obstacle avoidance path is updated based on the offset position to obtain a compensation path. The robot is controlled to travel along the compensation path to the offset position to perform the spraying task.

2. The dust suppression robot control method according to claim 1, characterized in that, The process of acquiring point cloud data and wind direction and speed data of the robot's operating environment, classifying the point cloud data, and obtaining spatial occupancy data of dynamic obstacles and concentration distribution data of dust areas includes: The point cloud data is filtered and simplified to obtain the target point cloud data; Based on the spatial distribution density between points in the target point cloud data, the points are classified to obtain multiple clusters; Extract the geometric features of the clusters, and classify the clusters into dynamic obstacles and dust areas based on the geometric features; The spatial coordinates and contour dimensions of the dynamic obstacle are extracted as spatial occupancy data, and the point cloud density of the dust area is extracted as the concentration distribution data.

3. The dust suppression robot control method according to claim 1, characterized in that, The step of determining the dust core area based on the concentration distribution data and generating the area boundary of the dust core area includes: Filter the spatial coordinate points in the concentration distribution data whose concentration is greater than a preset concentration threshold, and delineate the dust core area based on the set of the spatial coordinate points. The spatial coordinate points of the dust core area are projected onto a two-dimensional top-view plane, and the projected points are statistically analyzed to generate a closed polygon surrounding the spatial coordinate points. The closed polygon is used as the boundary of the dust core area.

4. The dust suppression robot control method according to claim 1, characterized in that, The step of extracting the edge contour of the dynamic obstacle based on the space occupancy data and determining the occlusion state value based on the overlap area between the edge contour and the region boundary includes: Extract the vertex coordinates of the dynamic obstacle from the space occupancy data, project the vertex coordinates onto the horizontal plane, and connect the projected vertex coordinates to form the edge contour of the dynamic obstacle; Construct a target passage line from the robot's current position to the center point of the core dust area; If the edge contour and the region boundary intersect, the area of ​​the overlapping region between the edge contour and the region boundary is calculated, and the region occlusion weight is obtained based on the ratio of the area of ​​the overlapping region to the area of ​​the edge contour. If the edge contour and the target passage line intersect, then based on the coordinates of the intersection point, the interval of the target passage line separated by the obstacle contour is extracted, and the ratio of the line segment size of the interval to the overall size of the target passage line is calculated to obtain the path blocking weight. The final occlusion state value is obtained by weighted summing of the region occlusion weight and the path blocking weight.

5. The dust suppression robot control method according to claim 1, characterized in that, If the occlusion state value is greater than a preset occlusion threshold, then the robot's motion constraint data is acquired, and a path search is performed based on the motion constraint data and the space occupancy data to generate an obstacle avoidance path, including: When the occlusion state value is greater than the preset occlusion threshold, the chassis motion constraint data of the robot is obtained, and the chassis motion constraint data includes the maximum steering angle and the minimum turning radius; Starting from the robot's current position, the obstacle occupancy area is determined in the work space based on the space occupancy data. Random node sampling is performed outside the obstacle occupancy area to generate an initial sampling node set. The initial set of sampling nodes is filtered based on the chassis motion constraint data, and nodes that do not meet the maximum steering angle or the minimum turning radius between adjacent nodes are removed to obtain a valid set of sampling nodes. Collision detection is performed on each node in the set of valid sampling nodes and the area occupied by the obstacle. Nodes that collide are removed, and obstacle avoidance paths are generated based on the remaining nodes.

6. The dust suppression robot control method according to claim 1, characterized in that, The calculation of the obstacle avoidance time for the robot to travel along the obstacle avoidance path, the determination of the offset position of the dust core area based on the obstacle avoidance time and the wind direction and speed data, and the updating of the obstacle avoidance path based on the offset position to obtain a compensation path include: Obtain the robot's rated travel speed, extract the path length of the obstacle avoidance path, and calculate the obstacle avoidance time of the robot traveling along the obstacle avoidance path based on the path length and the rated travel speed; Obtain the coverage time threshold of the dust core area. If the obstacle avoidance time is greater than the coverage time threshold, determine the offset direction and offset distance of the dust core area within the obstacle avoidance time based on the wind direction and wind speed data, and obtain the offset position. The target point is redefined based on the offset position, and the target point is used as the endpoint of the path search to update the obstacle avoidance path, thereby obtaining a compensated path.

7. The dust suppression robot control method according to claim 1, characterized in that, The process of controlling the robot to travel along the compensation path to the offset position and perform the spraying task includes: Extract the curvature extrema points in the compensation path; Chassis control commands are generated based on the curvature extreme points, and the chassis control commands include steering commands and speed commands. The robot is controlled to travel along the compensation path according to the chassis control commands; The robot's real-time position during its movement is obtained, and when the real-time position matches the offset position, the robot is controlled to perform a spraying task.

8. The dust suppression robot control method according to claim 6, characterized in that, After updating the obstacle avoidance path to obtain the compensation path, the process further includes: Calculate the updated length of the compensation path, and calculate the updated time based on the updated length and the rated driving speed; If the updated time is still greater than the coverage time threshold, the offset position of the dust core area is re-determined based on the updated time and the wind direction and speed data. The obstacle avoidance path is then updated again based on the re-determined offset position until the updated time is less than or equal to the coverage time threshold.

9. A dust suppression robot 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 program, it implements the method according to any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the method described in any one of claims 1 to 8.

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