A method and system for controlling automatic pesticide spraying operations using orchard robots

Abstract

This invention relates to a method and system for controlling automated pesticide spraying operations using an orchard robot. The method includes the following steps: acquiring real-time lidar point cloud data, preprocessing it, and constructing a local three-dimensional grid map centered on the vehicle's coordinate system; configuring the length, width, and height parameters of the Regions of Interest (ROIs) on the left and right sides of the vehicle, generating corresponding ROIs in the local three-dimensional grid map; detecting the number of grids occupied by the ROIs on the left and right sides of the vehicle; and controlling the corresponding spray nozzles on the vehicle to stop spraying when the number of grids in the ROIs on the left and / or right sides of the vehicle is less than a predetermined threshold. This invention achieves precise start and stop control of pesticide spraying by constructing a local three-dimensional grid map, setting ROIs, and analyzing the grid occupancy of the ROIs surrounding the orchard robot within the local three-dimensional grid map. This effectively solves the problems of insufficient control precision, poor environmental adaptability, and low resource utilization efficiency of orchard robots in existing technologies.

Description

Technical Field

[0001] This invention belongs to the field of agricultural robot control technology, and in particular relates to a method and system for controlling automatic spraying operations of orchard robots. Background Technology

[0002] With the rapid development of intelligent agriculture, orchard robots have been widely used in plant protection spraying to replace manual spraying operations, thereby significantly reducing labor input and improving operational efficiency.

[0003] However, existing orchard robots mainly rely on preset work paths and fixed-time control strategies. While this control method can achieve basic automation, it lacks the ability to perceive and dynamically adjust to the actual working environment in real time. This results in spraying precision that cannot meet the needs of precision agriculture. Furthermore, the insufficient control precision makes it difficult to accurately control the amount of pesticides used, which can easily lead to resource waste and environmental pollution. Summary of the Invention

[0004] The purpose of this invention is to solve one of the above-mentioned technical problems by providing an automatic spraying operation control method and system for orchard robots. By constructing a local three-dimensional grid map, setting ROI regions, and analyzing the grid occupancy of the ROI regions around the orchard robot in the local three-dimensional grid map, precise start and stop control of spraying is achieved, effectively solving the problems of insufficient control precision, poor environmental adaptability, and low resource utilization efficiency of orchard robots in the prior art.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] A method for controlling the automatic spraying operation of an orchard robot includes the following steps:

[0007] Real-time acquisition of lidar point cloud data information, and preprocessing of the acquired point cloud data;

[0008] A local 3D raster map centered on the vehicle coordinate system is constructed based on the preprocessed point cloud data.

[0009] Configure the length, width, and height parameters of the ROI areas on the left and right sides of the vehicle, and generate the corresponding ROI areas in the local 3D raster map;

[0010] Detect the number of grid cells occupied by the ROI areas on the left and right sides of the vehicle;

[0011] When the number of grids in the left and / or right ROI regions of the vehicle is less than a predetermined threshold, the stopping delay time is calculated based on the current vehicle speed and the distance between the vehicle's spray nozzle and the vehicle's coordinate origin. After waiting for the stopping delay time, the spray nozzle on the corresponding side of the vehicle is controlled to stop spraying.

[0012] In some embodiments of the present invention, the following steps are further included:

[0013] The vehicle's location is determined based on its current pose or state on the map; the location includes the vehicle's head and its row.

[0014] When both the left and right spray nozzles of the vehicle stop spraying, the vehicle's current position and the number of grids occupied by the ROI areas on the left and right sides are detected in real time.

[0015] When the vehicle is detected to be in the row and the number of grids occupied by the ROI areas on both the left and right sides is greater than the predetermined threshold, the spray nozzles on the left and right sides of the vehicle are controlled to resume spraying.

[0016] In some embodiments of the present invention, the following steps are further included:

[0017] When the left or right spray nozzles stop spraying, the number of grid cells occupied by the ROI area on the side where the vehicle stops spraying is detected in real time.

[0018] When the number of grid cells occupied by the ROI area on the side of the vehicle where the pesticide is stopped is greater than a predetermined threshold, the spray nozzles on both the left and right sides of the vehicle are controlled to resume spraying.

[0019] In some embodiments of the present invention, controlling the spray nozzles on the left and right sides of the vehicle to resume spraying further includes the following steps:

[0020] The delay time for resuming pesticide application is calculated based on the current vehicle speed and the distance between the vehicle's spray nozzle and the vehicle's coordinate origin. After waiting for the delay time, the spray nozzles on both sides of the vehicle are controlled to resume spraying.

[0021] In some embodiments of the present invention, the method for controlling the corresponding spray nozzle on the vehicle to stop spraying specifically includes the following steps:

[0022] The vehicle's location is determined based on its current pose or state on the map; the location includes the vehicle's head and its row.

[0023] Set up global and local drug stop signs for the vehicle;

[0024] When the number of grids in the left and right ROI regions of the vehicle is less than a predetermined threshold, the current position of the vehicle is determined.

[0025] If the vehicle is at the edge of the field, set the global stop-treatment sign to 1; if the vehicle is in the row, set the local stop-treatment signs on both sides of the vehicle to 1.

[0026] When the number of grids in the left or right ROI area of ​​the vehicle is less than a predetermined threshold, the local drug stop flag position on the corresponding side of the vehicle will be set to 1.

[0027] When the global stop-spray flag is 1 or the local stop-spray flag is 1, the stop-spray delay time is calculated based on the current vehicle speed and the distance between the vehicle's spray nozzle and the vehicle's coordinate origin. After waiting for the stop-spray delay time, the corresponding spray nozzle on the vehicle is controlled to stop spraying.

[0028] In some embodiments of the present invention, the following steps are further included:

[0029] When the global drug stop flag is 1, the vehicle's current position and the number of grids occupied by the ROI areas on the left and right sides are detected in real time.

[0030] When a vehicle is detected to be in the row and the number of grid cells occupied by the ROI areas on both the left and right sides is greater than a predetermined threshold, the global stop spray flag is set to 0, and the spray nozzles on the left and right sides of the vehicle are controlled to resume spraying.

[0031] In some embodiments of the present invention, the following steps are further included:

[0032] When the local drug stop flag is 1, the number of grid cells occupied by the ROI area on the drug stop side of the vehicle is detected in real time.

[0033] When it is detected that the number of grid cells occupied by the ROI area on the side where the vehicle is stopped from spraying exceeds a predetermined threshold, the local stop spraying flag on the corresponding side is set to 0, and the spraying nozzles on both sides of the vehicle are controlled to resume spraying.

[0034] In some embodiments of the present invention, the method for preprocessing the acquired point cloud data specifically includes the following steps:

[0035] Based on a pre-defined PCL library, the acquired point cloud data is converted into PCL format point cloud coordinates; through translation and rotation transformations, the point cloud coordinates are converted from the lidar coordinate system to the vehicle coordinate system cloud data, and intensity filtering, statistical filtering, and effective area point cloud filtering are performed to obtain pre-processed point cloud data.

[0036] In some embodiments of the present invention, the method for constructing a local three-dimensional raster map specifically includes the following steps:

[0037] Configure a local 3D grid map range centered on the vehicle coordinate system; wherein, the X-axis of the vehicle coordinate system points to the forward direction, and the distance in the X-axis direction is the length L of the local 3D grid map; the Y-axis points to the left in the forward direction, and the distance in the Y-axis direction is the width W of the local 3D grid map; the Z-axis points upward in the forward direction, and the distance in the Z-axis direction is the height H of the local 3D grid map.

[0038] The map storage buffer is requested based on the configured length L, width W, and height H parameters and the resolution of the raster map;

[0039] Construct point cloud coordinates (X,Y,Z) to raster coordinates (X I ,Y I Z I The transformation relationship and inverse transformation relationship are used to convert the preprocessed point cloud data into raster point cloud;

[0040] The occupancy probability of the raster point cloud is updated using a ray casting method, and the raster is determined to be occupied, unoccupied, or unknown based on a set threshold.

[0041] Once a frame of point cloud data has been processed, a local raster map based on that frame of point cloud data is constructed.

[0042] Some embodiments of the present invention further provide an automatic spraying control system for orchard robots, comprising:

[0043] At least one processor;

[0044] At least one memory for storing at least one program;

[0045] When at least one program is executed by at least one processor, the at least one processor implements the above-described method for controlling the automatic spraying operation of an orchard robot.

[0046] Some embodiments of the present invention further provide a storage medium storing a processor-executable program, which, when executed by a processor, is used to implement the above-described orchard robot automatic spraying operation control method.

[0047] The beneficial effects of this invention are as follows:

[0048] 1. This invention constructs a local three-dimensional grid map, sets ROI regions, and analyzes the grid occupancy of the ROI regions around the orchard robot in the local three-dimensional grid map. This enables accurate determination of the orchard robot's location, achieving precise start and stop control for spraying pesticides. This reduces pesticide waste and missed spraying, improves crop spraying coverage, and effectively solves the problems of insufficient control precision of orchard robots and low pesticide resource utilization efficiency in the existing technology.

[0049] 2. This invention uses lidar as an environmental perception sensor to collect point cloud data of the orchard working environment in real time, construct a local three-dimensional grid map, realize dynamic perception and accurate modeling of the orchard working environment, and improve the adaptability of the orchard robot to environmental changes.

[0050] 3. This invention enables the orchard robot to adopt different control strategies in different locations by setting global and local stop-spray flags. When the orchard robot is at the edge of the field, the global stop-spray flag controls the robot to stop spraying globally, avoiding premature start or late stop of spraying, which would result in pesticide waste or insufficient coverage. When the orchard robot enters the work row, the local stop-spray flags control the start and stop of spraying on the left and right sides respectively, avoiding unnecessary spraying in areas without crops (such as missing plants or gaps), which would result in pesticide waste and environmental pollution.

[0051] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1 A flowchart of a control method for automatic pesticide spraying operations using an orchard robot;

[0054] Figure 2 A schematic diagram of the vehicle coordinate system provided in an embodiment of the present invention;

[0055] Figure 3 This is a schematic diagram of the vehicle coordinate system and local three-dimensional grid map provided in an embodiment of the present invention;

[0056] Figure 4 This is a schematic diagram of a vehicle coordinate system partially occupied by a grid map provided in an embodiment of the present invention;

[0057] Figure 5 This is a schematic diagram of the structure of the ROI region within a local three-dimensional raster map provided in an embodiment of the present invention. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of this application clearer, the application is described and illustrated below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application. All other embodiments obtained by those skilled in the art based on the embodiments provided in this application without inventive effort are within the scope of protection of this application.

[0059] It should be noted that the terminology used herein is for the purpose of describing particular implementations only and is not intended to limit the exemplary implementations according to this application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. Furthermore, it should be understood that the terms “comprising” and “having”, and any variations thereof, are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such process, method, product, or apparatus.

[0060] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0061] With the development of intelligent agriculture, automated spraying operations using orchard robots have become an important means of improving orchard management efficiency. Currently, the main problems encountered by orchard robots in automated spraying operations are as follows:

[0062] 1. Insufficient control precision. In existing technologies, the spraying control of orchard robots is mainly based on preset operation paths and fixed time points, which cannot be adjusted in real time according to the actual operation environment, resulting in low spraying precision.

[0063] 2. Inaccurate field edge control. In the field edge area, due to the lack of effective environmental sensing methods, it is impossible to accurately determine the field edge location, resulting in inaccurate timing of pesticide application start and end, leading to pesticide waste or insufficient coverage.

[0064] 3. Lack of intelligent control within the rows. Current technology cannot identify crop distribution within the operating rows, resulting in pesticide waste as pesticides are applied to areas without crops.

[0065] 4. Poor environmental adaptability. Existing technologies are poorly adaptable to environmental changes and cannot be adjusted in real time according to the actual operating environment (such as crop distribution, obstacles, etc.).

[0066] 5. Low resource utilization efficiency: Due to insufficient control precision, it is difficult to accurately control the amount of pesticides used, which not only wastes resources but may also cause environmental pollution.

[0067] To address the aforementioned problems, this invention proposes a precise control method for automated pesticide spraying operations using orchard robots. Employing lidar as an environmental perception sensor, and constructing local point cloud maps and grid maps, real-time perception and judgment of the operating environment are achieved. This effectively solves problems in existing technologies such as insufficient control precision, inaccurate field control, unintelligent row control, poor environmental adaptability, and low resource utilization efficiency, providing a more precise and efficient control solution for intelligent pesticide spraying operations in orchards.

[0068] The technical solution of the present invention will be described in detail below with reference to specific embodiments and accompanying drawings.

[0069] As attached Figure 1 - Appendix Figure 5 As shown in the schematic embodiment of the automatic spraying operation control method for orchard robots of the present invention, the control method includes the following steps.

[0070] S1: Acquire LiDAR point cloud data in real time and preprocess the acquired point cloud data.

[0071] In some embodiments of the present invention, the method for preprocessing the acquired point cloud data information specifically includes the following steps.

[0072] The point cloud data acquired is converted into PCL format point cloud coordinates using the corresponding LiDAR driver, message middleware, and predefined PCL library.

[0073] The point cloud coordinates are converted from the LiDAR coordinate system to the vehicle coordinate system through translation and rotation transformations. Intensity filtering, statistical filtering, and effective area point cloud filtering are then performed to remove invalid points, resulting in preprocessed point cloud data.

[0074] S2: Construct a local 3D raster map centered on the vehicle coordinate system based on the preprocessed point cloud data. The vehicle in this map refers to the orchard robot.

[0075] In some embodiments of the present invention, the method for constructing a local three-dimensional raster map specifically includes the following steps.

[0076] Based on the actual environment of the orchard, configure a local three-dimensional raster map centered on the vehicle coordinate system.

[0077] In this system, the X-axis of the vehicle coordinate system points in the forward direction, and the distance along the X-axis is the length L of the local 3D grid map. The Y-axis points to the left of the forward direction, and the distance along the Y-axis is the width W of the local 3D grid map. The Z-axis points upward in the forward direction, and the distance along the Z-axis is the height H of the local 3D grid map. It should be noted that in practical applications, the length L needs to include the fruit tree to be detected in front within the local 3D grid map, the width W needs to include the fruit trees to be detected to the left and right within the local 3D grid map, and the height H needs to include the bottom and top of the fruit trees within the local 3D grid map.

[0078] The process of constructing a local 3D raster map includes the following steps:

[0079] The map storage buffer is requested based on the configured length L, width W, and height H parameters, as well as the resolution of the raster map.

[0080] Construct point cloud coordinates (X,Y,Z) to raster coordinates (X I ,Y I Z I The transformation relationship and inverse transformation relationship are used to convert the preprocessed point cloud data into raster point cloud.

[0081] The occupancy probability of the raster point cloud is updated using the raycasting method. Based on a set threshold, the raster is determined to be occupied, unoccupied, or unknown.

[0082] Once a frame of point cloud data has been processed, a local raster map based on that frame of point cloud data is constructed.

[0083] S3: Based on the actual needs of orchard spraying, configure the length, width, and height parameters of the ROI areas on both sides of the vehicle in the local 3D raster map, and generate the corresponding ROI areas in the local 3D raster map. It should be noted that the ROI areas on both sides of the vehicle are the actual spatial areas for assessing whether spraying is necessary. In practical applications, the length, width, and height parameters of the ROI areas can be set in conjunction with the height and width of the fruit trees.

[0084] S4: Detects the number of grid cells occupied by the ROI areas on both sides of the vehicle. It should be noted that by analyzing the grid cell occupancy on both sides of the orchard robot, the crop distribution can be detected, which is helpful for determining the vehicle's position and provides basic data for precise start-stop control of pesticide spraying.

[0085] When the number of grids in the left and / or right ROI regions of the vehicle is less than a predetermined threshold, the stopping delay time is calculated based on the current vehicle speed and the distance between the vehicle's spray nozzle and the vehicle's coordinate origin. After waiting for the stopping delay time, the spray nozzle on the corresponding side of the vehicle is controlled to stop spraying.

[0086] In some embodiments of the present invention, the method for controlling the corresponding spray nozzle on the vehicle to stop spraying specifically includes the following steps.

[0087] The vehicle's location is determined based on the number of grid cells occupied by the ROI regions on its left and right sides and the vehicle's current pose or state on the global map. The vehicle's location includes at least the edge of the terrain and within its row.

[0088] Set up global and local drug stop signs for the vehicle.

[0089] When the number of grids in both the left and right ROI regions of the vehicle is less than a predetermined threshold, it is assumed that there are no fruit trees in the left and right regions of the robot. This could be at the edge of the field or in a row where there are no fruit trees. At this point, the current position of the vehicle is determined.

[0090] If the vehicle is at the edge of the field, set the global stop-treatment sign to 1. If the vehicle is within the row, set the local stop-treatment signs on both sides of the vehicle to 1.

[0091] When the number of grids in the left or right ROI region of the vehicle is less than a predetermined threshold, it is assumed that there are no fruit trees in a certain area of ​​the row, and the local stop-drug flag position on the corresponding side of the vehicle is set to 1.

[0092] When the global stop-spray flag is 1 or the local stop-spray flag is 1, the stop-spray delay time is calculated based on the current vehicle speed and the distance between the vehicle's spray nozzle and the vehicle's coordinate origin. After waiting for the stop-spray delay time, the corresponding spray nozzle on the vehicle is controlled to stop spraying.

[0093] In some embodiments of the present invention, the following steps are further included:

[0094] S5: Determine the vehicle's location based on the number of grid cells occupied by the ROI regions on the left and right sides of the vehicle and the vehicle's current pose or state in the global map. The vehicle's location includes at least the edge of the terrain and within the same row.

[0095] When both spray nozzles on the left and right sides of the vehicle stop spraying, the vehicle's current position and the number of grid cells occupied by the ROI areas on the left and right sides are detected in real time.

[0096] When a vehicle is detected to be in the row and the number of grid cells occupied by the ROI regions on both the left and right sides is greater than a predetermined threshold, the delay time for resuming spraying is calculated based on the current vehicle speed and the distance between the vehicle's spray nozzle and the vehicle's coordinate origin. After waiting for the delay time to resume spraying, the spray nozzles on the left and right sides of the vehicle are controlled to resume spraying.

[0097] In some embodiments of the present invention, step S5 further includes the following steps:

[0098] When the left or right spray nozzles stop spraying, the number of grid cells occupied by the ROI area on the side where the vehicle stops spraying is detected in real time.

[0099] When the number of grid cells occupied by the ROI area on the side where the vehicle is not spraying is detected to be greater than the predetermined threshold, the delay time for resuming spraying is calculated based on the current vehicle speed and the distance between the vehicle's spray nozzle and the vehicle's coordinate origin. After waiting for the delay time to resume spraying, the spray nozzles on both sides of the vehicle are controlled to resume spraying.

[0100] In some embodiments of the present invention, the following steps are further included:

[0101] When the global drug stop flag is 1, the vehicle's current position and the number of grid cells occupied by the ROI areas on the left and right sides are detected in real time.

[0102] When a vehicle is detected to be in the row and the number of grid cells occupied by the ROI areas on both the left and right sides is greater than a predetermined threshold, the global stop spray flag is set to 0, and the spray nozzles on the left and right sides of the vehicle are controlled to resume spraying.

[0103] In some embodiments of the present invention, the following steps are further included:

[0104] When the local drug-stopping flag is 1, the number of grid cells occupied by the ROI area on the drug-stopping side of the vehicle is detected in real time.

[0105] When it is detected that the number of grid cells occupied by the ROI area on the side where the vehicle is stopped from spraying exceeds a predetermined threshold, the local stop spraying flag on the corresponding side is set to 0, and the spraying nozzles on both sides of the vehicle are controlled to resume spraying.

[0106] In the above illustrative embodiments, by setting global stop-spray flags and local stop-spray flags, the orchard robot can adopt different control strategies in different locations. When the orchard robot is at the edge of the field, the global stop-spray flag controls the robot to stop spraying globally, avoiding spraying too early or too late, which would result in pesticide waste or insufficient coverage. When the orchard robot enters the work row, the local stop-spray flags control the start and stop of spraying on the left and right sides respectively, avoiding unnecessary spraying in areas without crops (such as missing plants or gaps), which would result in pesticide waste and environmental pollution.

[0107] In practical applications, by accurately identifying crop distribution and intelligently controlling spraying areas, pesticide use can be reduced by 20% to 40%, effectively reducing resource waste and environmental pollution. Simultaneously, due to more precise control at the field edge and within rows, crop spraying coverage can be increased to over 95%, significantly reducing missed and duplicate spraying.

[0108] Some embodiments of the present invention further provide an automatic spraying control system for orchard robots, comprising:

[0109] At least one processor.

[0110] At least one memory for storing at least one program.

[0111] When at least one program is executed by at least one processor, the at least one processor implements the above-described method for controlling the automatic spraying operation of an orchard robot.

[0112] Some embodiments of the present invention further provide a storage medium storing a processor-executable program, which, when executed by a processor, is used to implement the above-described orchard robot automatic spraying operation control method.

[0113] Finally, it should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0114] The above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them; although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications can still be made to the specific implementation of the present invention or equivalent substitutions can be made to some technical features without departing from the spirit of the technical solutions of the present invention, and all such modifications and substitutions should be covered within the scope of the technical solutions claimed in the present invention.

Claims

1. An orchard robot automatic pesticide spraying operation control method, characterized by, The method comprises the following steps: Real-time acquisition of laser radar point cloud data information, and preprocessing of the acquired point cloud data; Construction of a local three-dimensional grid map centered on a vehicle coordinate system based on the preprocessed point cloud data; Configuration of the length, width, and height parameters of the ROI regions on the left and right sides of the vehicle, and generation of the corresponding ROI regions in the local three-dimensional grid map; Detection of the number of occupied grids in the ROI regions on the left and right sides of the vehicle; When the number of grids in the left ROI region and / or the right ROI region is less than a predetermined threshold, a delay time for stopping spraying is calculated based on the current vehicle speed and the distance between the spraying port and the vehicle coordinate origin, and after waiting for the delay time, the spraying port on the corresponding side of the vehicle is controlled to stop spraying; The method of controlling the spraying port on the corresponding side of the vehicle to stop spraying specifically comprises the following steps: Determination of the position of the vehicle based on the current pose or state of the vehicle in the map; the position includes the head and the row; Setting a global stop spraying flag and a local stop spraying flag for the vehicle; When the number of grids in the left ROI region and the right ROI region is less than a predetermined threshold, the current position of the vehicle is determined; If the vehicle is at the head, the global stop spraying flag is set to 1; if the vehicle is in the row, the local stop spraying flags on both sides of the vehicle are set to 1; When the number of grids in the left ROI region or the right ROI region is less than a predetermined threshold, the local stop spraying flag on the corresponding side of the vehicle is set to 1; When the global stop spraying flag is 1 or the local stop spraying flag is 1, a delay time for stopping spraying is calculated based on the current vehicle speed and the distance between the spraying port and the vehicle coordinate origin, and after waiting for the delay time, the spraying port on the corresponding side of the vehicle is controlled to stop spraying.

2. The orchard robot automatic pesticide spraying operation control method according to claim 1, characterized in that, Further comprising the following steps: Determination of the position of the vehicle based on the current pose or state of the vehicle in the map; the position includes the head and the row; When the spraying ports on both sides of the vehicle stop spraying, the current position of the vehicle and the number of occupied grids in the ROI regions on both sides are detected in real time; When it is detected that the vehicle is in the row and the number of occupied grids in the ROI regions on both sides is greater than the predetermined threshold, the spraying ports on both sides of the vehicle are controlled to resume spraying.

3. The orchard robot automatic pesticide spraying operation control method according to claim 1, characterized in that, Further comprising the following steps: When the left or right spraying port stops spraying, the number of occupied grids in the ROI region on the side where the vehicle stops spraying is detected in real time; When it is detected that the number of occupied grids in the ROI region on the side where the vehicle stops spraying is greater than the predetermined threshold, the spraying ports on both sides of the vehicle are controlled to resume spraying.

4. The orchard robot automatic pesticide spraying operation control method according to claim 2 or 3, characterized by, Before controlling the spraying ports on both sides of the vehicle to resume spraying, further comprising the following steps: Calculation of a delay time for resuming spraying based on the current vehicle speed and the distance between the spraying port and the vehicle coordinate origin, and after waiting for the delay time, the spraying ports on both sides of the vehicle are controlled to resume spraying.

5. The orchard robot automatic pesticide spraying operation control method according to claim 1, characterized by, Further comprising the following steps: When the global stop spraying flag is 1, the current position of the vehicle and the number of occupied grids in the ROI regions on both sides are detected in real time; When it is detected that the vehicle is in the line and the number of grids occupied by the ROI regions on the left and right sides are both greater than the predetermined threshold, the global pesticide spraying stop flag is set to 0, and the pesticide spraying ports on the left and right sides of the vehicle are controlled to resume pesticide spraying.

6. The orchard robot automatic pesticide spraying operation control method according to claim 1 or 5, characterized by, Further comprising the following steps: When the local pesticide spraying stop flag is 1, the number of grids occupied by the ROI region on the pesticide spraying stop side of the vehicle is detected in real time; When it is detected that the number of grids occupied by the ROI region on the pesticide spraying stop side of the vehicle is greater than the predetermined threshold, the local pesticide spraying stop flag on the corresponding side is set to 0, and the pesticide spraying ports on the left and right sides of the vehicle are controlled to resume pesticide spraying.

7. The orchard robot automatic pesticide spraying operation control method according to claim 1, characterized by, The method for preprocessing the obtained point cloud data specifically includes the following steps: Based on the predetermined PCL library, the obtained point cloud data is converted into point cloud coordinates in PCL format; through translation and rotation transformation, the point cloud coordinates are converted from the laser radar coordinate system to the vehicle coordinate system cloud data, and intensity filtering, statistical filtering and effective area point cloud filtering are performed to obtain the preprocessed point cloud data.

8. The orchard robot automatic pesticide spraying operation control method according to claim 1 or 7, characterized by, The method for constructing the local three-dimensional grid map specifically includes the following steps: A local three-dimensional grid map range centered on the vehicle coordinate system is configured; wherein the X-axis of the vehicle coordinate system points to the forward direction, and the distance in the X-axis direction is the length L of the local three-dimensional grid map; the Y-axis points to the left of the forward direction, and the distance in the Y-axis direction is the width W of the local three-dimensional grid map; the Z-axis points to the upper side of the forward direction, and the distance in the Z-axis direction is the height H of the local three-dimensional grid map; Based on the configured length L, width W and height H parameters and the resolution of the grid map, a map storage buffer is applied; A conversion relationship and an inverse conversion relationship of the point cloud coordinates to the grid coordinates are constructed, and the preprocessed point cloud data is converted into grid point clouds; The occupancy probability of the grid point clouds is updated using the ray casting method, and based on the set threshold, it is judged that the grid is occupied, unoccupied or unknown; When a frame of point cloud data processing is completed, the local grid map construction based on the frame of point cloud is completed.

9. An orchard robot automatic pesticide spraying operation control system characterized by comprising: Comprise: At least one processor; At least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the orchard robot automatic pesticide spraying operation control method according to any one of claims 1-8.

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