Automatic pesticide spraying operation control method and system for orchard robot

Abstract

The invention relates to an orchard robot automatic pesticide spraying operation control method and system, and the method comprises the following steps: obtaining laser radar point cloud data in real time, carrying out the preprocessing, and constructing a local three-dimensional grid map with a vehicle coordinate system as the center; configuring length, width and height parameters of ROI regions on the left side and the right side of the vehicle, and generating corresponding ROI regions in the local three-dimensional grid map; detecting the number of grids occupied by the ROI areas on the left and right sides of the vehicle; when the number of the grids in the left ROI area and / or the right ROI area of the vehicle is smaller than a preset threshold value, a pesticide spraying opening in the corresponding side of the vehicle is controlled to stop spraying pesticide. By constructing the local three-dimensional grid map, setting the ROI area and analyzing the grid occupation condition of the ROI area around the orchard robot in the local three-dimensional grid map, accurate starting and stopping control of pesticide spraying is achieved, and the problems that in the prior art, an orchard machine is insufficient in control precision, poor in environmental adaptability and low in resource utilization efficiency are effectively solved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of agricultural robot control, and particularly relates to a method and system for automatically controlling pesticide spraying of an orchard robot. BACKGROUND

[0002] With the rapid development of agricultural intelligence, orchard robots have been widely used in plant protection spraying to replace manual backpack or towed pesticide spraying, thereby significantly reducing labor input and improving work efficiency.

[0003] However, the existing orchard robots mainly rely on preset work paths and fixed time point control strategies. Although this control method can achieve basic automatic work, it lacks real-time perception and dynamic adjustment capability for the actual work environment, which makes it difficult to meet the needs of precision agriculture in terms of pesticide spraying accuracy. In addition, due to insufficient control accuracy, it is difficult to accurately control the amount of pesticide used, which easily leads to resource waste and environmental pollution. SUMMARY

[0004] The present application aims to solve one of the above technical problems, and provides a method and system for automatically controlling pesticide spraying of an orchard robot. By constructing a local three-dimensional grid map, setting an ROI area, and analyzing the grid occupancy of the ROI area around the orchard robot in the local three-dimensional grid map, accurate start-stop control of pesticide spraying is achieved, effectively solving the problems of insufficient control accuracy, poor environmental adaptability, and low resource utilization efficiency of the existing orchard robot control.

[0005] To achieve the above-mentioned purposes, the technical solution adopted by the present application is as follows: A method for automatically controlling pesticide spraying of an orchard robot, comprising the following steps: real-time acquisition of laser radar point cloud data information, and preprocessing of the acquired point cloud data; constructing a local three-dimensional grid map centered on the vehicle coordinate system based on the preprocessed point cloud data; configuring the length, width, and height parameters of the ROI area on the left and right sides of the vehicle, and generating the corresponding ROI area in the local three-dimensional grid map; detecting the number of occupied grids in the ROI area on the left and right sides of the vehicle; when the number of grids in the left ROI area and / or the right ROI area is less than a predetermined threshold, calculating a pesticide stopping delay time based on the current vehicle speed and the distance between the pesticide spraying port and the vehicle coordinate origin, and controlling the pesticide spraying port on the corresponding side of the vehicle to stop spraying after waiting for the pesticide stopping delay time.

[0006] In some embodiments of the present application, the following steps are further included: judging a position of the vehicle based on a current pose or state of the vehicle in a map; the position includes a headland and an in-line; when the pesticide spraying ports on the left and right sides of the vehicle stop spraying, detecting in real time a position of the vehicle and a number of grids occupied by ROI regions on the left and right sides of the vehicle; when it is detected that the vehicle is in the in-line and the number of grids occupied by the ROI regions on the left and right sides of the vehicle are both greater than a predetermined threshold, controlling the pesticide spraying ports on the left and right sides of the vehicle to resume spraying.

[0007] In some embodiments of the present application, the method further comprises the following steps: when the pesticide spraying ports on the left and right sides of the vehicle stop spraying, detecting in real time a position of the vehicle and a number of grids occupied by ROI regions on the left and right sides of the vehicle; when it is detected that the vehicle is in the in-line and the number of grids occupied by the ROI regions on the left and right sides of the vehicle are both greater than a predetermined threshold, controlling the pesticide spraying ports on the left and right sides of the vehicle to resume spraying.

[0008] In some embodiments of the present application, before the pesticide spraying ports on the left and right sides of the vehicle resume spraying, the method further comprises the following steps: calculating a delay time for resuming spraying based on a current vehicle speed and a distance between the pesticide spraying ports and a coordinate origin of the vehicle, and after waiting for the delay time for resuming spraying, controlling the pesticide spraying ports on the left and right sides of the vehicle to resume spraying.

[0009] In some embodiments of the present application, the method of controlling the pesticide spraying ports on the corresponding side of the vehicle to stop spraying specifically comprises the following steps: judging a position of the vehicle based on a current pose or state of the vehicle in a map; the position includes a headland and an in-line; setting a global spraying stop flag and a local spraying stop flag for the vehicle; when the number of grids in the left ROI region and the right ROI region of the vehicle is less than a predetermined threshold, judging a position of the vehicle; if the vehicle is located at the headland, setting the global spraying stop flag to 1; if the vehicle is located in the in-line, setting the local spraying stop flags on both sides of the vehicle to 1; when the number of grids in the left ROI region or the right ROI region of the vehicle is less than a predetermined threshold, setting the local spraying stop flag on the corresponding side of the vehicle to 1; when the global spraying stop flag is 1 or the local spraying stop flag is 1, calculating a delay time for stopping spraying based on a current vehicle speed and a distance between the pesticide spraying ports and a coordinate origin of the vehicle, and after waiting for the delay time for stopping spraying, controlling the pesticide spraying ports on the corresponding side of the vehicle to stop spraying.

[0010] In some embodiments of the present application, the method further comprises the following steps: when the global spraying stop flag is 1, detecting in real time a position of the vehicle and a number of grids occupied by ROI regions on the left and right sides of the vehicle; 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 greater than a predetermined threshold, the global pesticide spraying stop flag position is set to 0, and the pesticide spraying ports on the left and right sides of the vehicle are controlled to resume pesticide spraying.

[0011] In some embodiments of the present application, the method further comprises the following steps: When the local pesticide spraying stop flag position 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 a predetermined threshold, the local pesticide spraying stop flag position 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.

[0012] In some embodiments of the present application, the pre-processing method for the obtained point cloud data information comprises the following steps: The obtained point cloud data is converted into PCL format point cloud coordinates based on a predetermined PCL library; the point cloud coordinates are converted from the laser radar coordinate system to the vehicle coordinate system cloud data through translation and rotation transformation, and intensity filtering, statistical filtering and effective area point cloud filtering are performed to obtain the pre-processed point cloud data.

[0013] In some embodiments of the present application, the method for constructing a local three-dimensional grid map comprises 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; A map storage buffer is applied based on the configured length L, width W and height H parameters and the resolution of the grid map; A conversion relationship and an inverse conversion relationship of the point cloud coordinates (X, Y, Z) to the grid coordinates (X I ,Y I ,Z I ) are constructed to convert the pre-processed point cloud data into grid point clouds; The occupancy probability of the grid point clouds is updated using a ray casting method, and based on a set threshold, it is determined whether the grid is occupied, unoccupied or unknown; When a frame of point cloud data is processed, the local grid map based on the frame of point cloud is constructed.

[0014] Some embodiments of the present application further provide an automatic pesticide spraying operation control system for an orchard robot, comprising: At least one processor; At least one memory for storing at least one program; When at least one program is executed by at least one processor, the at least one processor implements the above-mentioned automatic pesticide spraying operation control method of the orchard robot.

[0015] Some embodiments of the present invention further provide a storage medium storing a program executable by a processor. The program executable by the processor is used to implement the above-mentioned method for controlling the automatic spraying operation of the orchard robot when executed by the processor.

[0016] The beneficial effects of the present invention are: 1. By constructing a local three-dimensional grid map, setting a ROI area, and analyzing the grid occupancy of the ROI area around the orchard robot within the local three-dimensional grid map, the present invention can accurately determine the position of the orchard robot, realize precise start and stop control of pesticide spraying, thereby reducing pesticide waste and missed spraying, improving crop spraying coverage, and effectively solving the problems of insufficient control accuracy of orchard machines and low pesticide resource utilization efficiency in the existing technology; 2. The present invention uses laser radar as an environmental perception sensor to collect point cloud data of the orchard operating environment in real time, construct a local three-dimensional grid map, and realize dynamic perception and accurate modeling of the orchard operating environment, thereby improving the orchard robot's adaptability to environmental changes; 3. The present invention sets a global stop-dose flag and a local stop-dose flag, so that the orchard robot can adopt different control strategies in different positions; when the orchard robot is at the edge of the field, the global stop-dose flag is used to control the robot to stop spraying globally, avoiding starting spraying too early or stopping spraying too late, resulting in pesticide waste or insufficient coverage; when the orchard robot enters the operation row, the local stop-dose flag is used to 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), resulting in pesticide waste and environmental pollution.

[0017] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purposes and other advantages of the present invention can be realized and obtained by the structures indicated in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0019] Figure 1 This is a flow chart of a method for controlling automatic pesticide spraying operations of an orchard robot; Figure 2A schematic diagram of a vehicle coordinate system provided for an embodiment of the present application; Figure 3 A schematic diagram of a vehicle coordinate system and a local three-dimensional grid map provided for an embodiment of the present application; Figure 4 A schematic diagram of a vehicle coordinate system and a local occupancy grid map provided for an embodiment of the present application; Figure 5 A structural schematic diagram of an ROI region in a local three-dimensional grid map provided for an embodiment of the present application. DETAILED DESCRIPTION

[0020] In order to make the purposes, technical solutions and advantages of the present application clearer, the present application is described and explained below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and not intended to limit the present application. Based on the embodiments provided by the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the present application.

[0021] It should be noted that the terms used herein are only intended to describe specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, the singular form is intended to include the plural form unless the context clearly indicates otherwise, and it should also be understood that the terms "comprise" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device comprising a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to the process, method, product or device.

[0022] The embodiments in the present application and the features in the embodiments can be combined with each other without conflict.

[0023] With the development of agricultural intelligence, automatic pesticide spraying of orchard robots has become an important means to improve the efficiency of orchard management. At present, in the automatic pesticide spraying process of orchard robots, the following problems mainly exist: 1. Insufficient control accuracy. In the prior art, the pesticide spraying control of the orchard robot is mainly based on the preset operation path and fixed time point, which cannot be adjusted in real time according to the actual operation environment, resulting in low pesticide spraying accuracy.

[0024] 2. Inaccurate head control. In the head area, due to the lack of effective environmental perception means, the position of the head cannot be accurately judged, resulting in inaccurate timing of starting and ending of pesticide spraying, causing waste of pesticides or insufficient coverage.

[0025] 3. Inaccurate control within the row. In the row, the prior art cannot identify the distribution of crops, and still sprays in areas without crops, causing waste of pesticides.

[0026] 4. Poor environmental adaptability. The prior art has poor adaptability to environmental changes and cannot adjust in real time according to the actual working environment (such as crop distribution, obstacles, etc.).

[0027] 5. Low resource utilization efficiency: due to insufficient control accuracy, it is difficult to accurately control the amount of pesticide used, resulting in waste of resources and potential environmental pollution.

[0028] To solve the above problems, the present application provides a method for precise control of automatic pesticide spraying of orchard robots. Laser radar is used as an environmental perception sensor, and by constructing a local point cloud map and a grid map, real-time perception and judgment of the working environment are achieved, effectively solving the problems of insufficient control accuracy, inaccurate control at the end of the row, unintelligent control within the row, poor environmental adaptability, and low resource utilization efficiency. The present application provides a more accurate and efficient control solution for intelligent pesticide spraying in orchards.

[0029] The technical solutions of the present application will be described in detail below in conjunction with specific embodiments and the accompanying drawings.

[0030] As shown in the accompanying Figure 1 - accompanying Figure 5 Fig. 1 is a schematic diagram of an embodiment of the present application, which shows a method for controlling automatic pesticide spraying of an orchard robot.

[0031] S1: Real-time acquisition of laser radar point cloud data information, and preprocessing of the acquired point cloud data.

[0032] In some embodiments of the present application, the preprocessing method for the acquired point cloud data information includes the following steps.

[0033] The acquired point cloud data is converted into PCL format point cloud coordinates based on the driving, message middleware and predetermined PCL library corresponding to the use of laser radar.

[0034] The point cloud coordinates are converted from the laser radar (Lidar) coordinate system to the vehicle coordinate system cloud data through translation and rotation transformation, and intensity filtering, statistical filtering and effective area point cloud filtering are performed to remove invalid points, obtaining the preprocessed point cloud data.

[0035] S2: Construct a local three-dimensional grid map centered on the vehicle coordinate system based on the preprocessed point cloud data. The vehicle is an orchard robot.

[0036] In some embodiments of the present application, the method for constructing a local three-dimensional grid map includes the following steps.

[0037] According to the actual environment of the orchard, a local three-dimensional grid map range is configured with a vehicle coordinate system as the center.

[0038] 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 direction of the forward direction, and the distance in the Z axis direction is the height H of the local three-dimensional grid map. It should be noted that in actual application, the length L needs to include the front fruit trees to be detected in the local three-dimensional grid map, the width W needs to include the left and right fruit trees to be detected in the local three-dimensional grid map, and the height H needs to include the bottom and top of the fruit trees in the local three-dimensional grid map.

[0039] The construction process of the local three-dimensional grid map includes the following steps: A map storage buffer is applied based on the configured length L, width W, and height H parameters and the resolution of the grid map.

[0040] A conversion relationship and an inverse conversion relationship of the point cloud coordinates (X, Y, Z) to the grid coordinates (X I ,Y I ,Z I ) are constructed, and the preprocessed point cloud data is converted into a grid point cloud.

[0041] The occupancy probability of the grid point cloud is updated using a raycasting method, and based on a set threshold, it is determined whether the grid is occupied, unoccupied, or unknown.

[0042] When a frame of point cloud data is processed, the local grid map based on the frame of point cloud is constructed.

[0043] S3: Based on the actual needs of orchard spraying, the length, width, and height parameters of the ROI regions on the left and right sides of the vehicle in the local three-dimensional grid map are configured, and the corresponding ROI regions are generated in the local three-dimensional grid map. It should be noted that the ROI regions on the left and right sides of the vehicle are the actual spatial regions for evaluating whether spraying is needed, and in actual application, the length, width, and height parameters of the ROI regions can be set in combination with the height and width of the fruit trees.

[0044] S4: The number of occupied grids in the ROI regions on the left and right sides of the vehicle is detected. It should be noted that by analyzing the grid occupancy on both sides of the orchard robot, the detection of crop distribution is achieved, and it is beneficial for the judgment of the vehicle position, providing basic data for the precise start-stop control of spraying.

[0045] When the number of grids in the left ROI region and / or the right ROI region of the vehicle is less than a predetermined threshold, a stop spraying delay time is calculated based on the current vehicle speed and the distance between the spraying port of the vehicle and the coordinate origin of the vehicle, and after waiting for the stop spraying delay time, the spraying port on the corresponding side of the vehicle is controlled to stop spraying.

[0046] In some embodiments of the present application, the method of controlling the spraying port on the corresponding side of the vehicle to stop spraying specifically comprises the following steps.

[0047] The position of the vehicle is determined based on the number of grids occupied by the left and right ROI regions of the vehicle and the current pose or state of the vehicle in the global map. The position of the vehicle at least includes the head and the row.

[0048] A global spraying stop flag and a local spraying stop flag are set for the vehicle.

[0049] When the number of grids in the left ROI region and the right ROI region of the vehicle is less than a predetermined threshold, it is considered that there are no fruit trees in the left and right regions of the robot, which may be in the head or in the row of fruitless tree region. At this time, the current position of the vehicle is determined.

[0050] If the vehicle is located at the head, the global spraying stop flag is set to 1. If the vehicle is located in the row, the local spraying stop flags on both sides of the vehicle are set to 1.

[0051] When the number of grids in the left ROI region or the right ROI region of the vehicle is less than a predetermined threshold, it is considered that there are no fruit trees in a certain region in the row of the robot, and the local spraying stop flag on the corresponding side of the vehicle is set to 1.

[0052] When the global spraying stop flag is 1 or the local spraying stop flag is 1, a stop spraying delay time is calculated based on the current vehicle speed and the distance between the spraying port of the vehicle and the coordinate origin of the vehicle, and after waiting for the stop spraying delay time, the spraying port on the corresponding side of the vehicle is controlled to stop spraying.

[0053] In some embodiments of the present application, the following steps are further included: S5: The position of the vehicle is determined based on the number of grids occupied by the left and right ROI regions of the vehicle and the current pose or state of the vehicle in the global map. The position of the vehicle at least includes the head and the row.

[0054] When the spraying ports on both sides of the vehicle stop spraying, the current position of the vehicle and the number of grids occupied by the left and right ROI regions are detected in real time.

[0055] When it is detected that the vehicle is in the row and the number of grids occupied by the left and right ROI regions is greater than a predetermined threshold, a resume spraying delay time is calculated based on the current vehicle speed and the distance between the spraying port of the vehicle and the coordinate origin of the vehicle, and after waiting for the resume spraying delay time, the spraying ports on both sides of the vehicle are controlled to resume spraying.

[0056] In some embodiments of the present application, step S5 further comprises the following steps: When the left or right pesticide spraying port stops spraying, the number of grids occupied by the ROI region on the pesticide spraying stop side of the vehicle is detected in real time.

[0057] 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 a predetermined threshold, a pesticide resuming delay time is calculated based on the current vehicle speed and the distance between the pesticide spraying port and the coordinate origin of the vehicle, and after waiting for the pesticide resuming delay time, the pesticide spraying ports on the left and right sides of the vehicle are controlled to resume spraying.

[0058] In some embodiments of the present application, the following steps are further included: When the global pesticide spraying stop flag is 1, the number of grids occupied by the ROI region on the left and right sides of the vehicle is detected in real time.

[0059] When it is detected that the vehicle is in a row and the number of grids occupied by the ROI region on the left and right sides is greater than a 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 spraying.

[0060] In some embodiments of the present application, the following steps are further included: 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.

[0061] 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 a predetermined threshold, the local pesticide spraying stop flag corresponding to the side is set to 0, and the pesticide spraying ports on the left and right sides of the vehicle are controlled to resume spraying.

[0062] In the above illustrative embodiments, by setting the global pesticide spraying stop flag and the local pesticide spraying stop flag, the orchard robot can adopt different control strategies at different positions; when the orchard robot is at the end of the field, the global pesticide spraying stop flag is used to control the robot to globally stop spraying, so as to avoid early or late spraying, causing waste of pesticides or insufficient coverage; when the orchard robot enters the working row, the local pesticide spraying stop flag is used to control the left and right pesticide spraying ports to start and stop respectively, so as to avoid unnecessary spraying in areas without crops (such as missing plants or gaps), causing waste of pesticides and environmental pollution.

[0063] In actual application, by accurately identifying the distribution of crops and intelligently controlling the spraying area, the amount of pesticides used can be reduced by 20% to 40%, effectively reducing resource waste and environmental pollution. At the same time, due to more accurate control at the end of the field and in the row, the crop spraying coverage rate can be increased to more than 95%, significantly reducing the phenomenon of missed spraying and re-spraying.

[0064] Some embodiments of the present application further provide an automatic pesticide spraying operation control system for an orchard robot, comprising: at least one processor.

[0065] at least one memory for storing at least one program.

[0066] When the at least one program is executed by the at least one processor, the at least one processor implements the above-mentioned orchard robot automatic pesticide spraying operation control method.

[0067] Some embodiments of the present application further provide a storage medium having a processor-executable program stored therein, the processor-executable program, when executed by a processor, is used to implement the above-mentioned orchard robot automatic pesticide spraying operation control method.

[0068] Finally, it should be noted that: the embodiments in the specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts of each embodiment can be referred to.

[0069] The above embodiments are only used to illustrate the technical solutions of the present application but not to limit it; although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the specific embodiments of the present application can be modified or some technical features can be replaced by equivalent ones; without departing from the spirit of the technical scheme of the present application, they should be covered in the technical scheme range of the present application.

Claims

1. A method for controlling an orchard robot's automatic pesticide spraying operation, characterized in that: The following steps are involved: Acquire lidar point cloud data information in real time and pre-process the acquired point cloud data; Construct a local 3D grid map centered on the vehicle coordinate system based on the preprocessed point cloud data; Configuring the length, width, and height parameters of the ROI areas on the left and right sides of the vehicle, and generating corresponding ROI areas in the local three-dimensional grid map; Detect the number of grids occupied by the ROI areas on the left and right sides of the vehicle; When the number of grids in the ROI area on the left side and / or the ROI area on the right side of the vehicle is less than a predetermined threshold, the stop delay time is calculated based on the current vehicle speed and the distance between the vehicle spray port and the vehicle coordinate origin, and after waiting for the stop delay time, the spray port on the corresponding side of the vehicle is controlled to stop spraying.

2. The orchard robot automatic pesticide spraying control method according to claim 1 is characterized in that: Further comprising the steps of: Determine the vehicle's location based on its current position or state in the map; the location includes the end of the road and within the line; When the spray nozzles on both sides of the vehicle stop spraying, the current position of the vehicle and the number of grids occupied by the ROI areas on the left and right sides are detected in real time; When it is detected that the vehicle is in the row and the number of grids occupied by the ROI areas on the left and right sides is greater than the predetermined threshold, the spraying ports on the left and right sides of the vehicle are controlled to resume spraying.

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

4. The orchard robot automatic pesticide spraying control method according to claim 2 or 3, characterized in that: Controlling the spray nozzles on the left and right sides of the vehicle to resume spraying further includes the following steps: The delay time for resuming spraying is calculated based on the current vehicle speed and the distance between the vehicle's spray port and the vehicle's coordinate origin. After waiting for the delay time for resuming spraying, the spray ports on the left and right sides of the vehicle are controlled to resume spraying.

5. The orchard robot automatic pesticide spraying control method according to claim 1 is characterized in that: The method for controlling the spraying ports on the corresponding side of the vehicle to stop spraying specifically comprises the following steps: Determine the vehicle's location based on its current position or state in the map; the location includes the end of the road and within the line; Set the global drug suspension flag and local drug suspension flag for the vehicle; When the number of grids in the left ROI area and the right ROI area of ​​the vehicle is less than a predetermined threshold, the current position of the vehicle is determined; If the vehicle is at the end of the field, the global stop-drug flag is set to 1; if the vehicle is in the row, the local stop-drug flags on both sides of the vehicle are set to 1; When the number of grids in the left ROI area or the right ROI area of ​​the vehicle is less than a predetermined threshold, the local drug suspension flag position 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, the stop-spraying delay time is calculated based on the current vehicle speed and the distance between the vehicle spray port and the vehicle coordinate origin, and after waiting for the stop-spraying delay time, the spray port on the corresponding side of the vehicle is controlled to stop spraying.

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

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

8. The orchard robot automatic pesticide spraying control method according to claim 1, characterized in that: The method for preprocessing the acquired point cloud data information specifically includes the following steps: The acquired point cloud data is converted into point cloud coordinates in PCL format based on a predetermined PCL library; the point cloud coordinates are converted from the lidar coordinate system to vehicle coordinate system cloud data through translation and rotation transformation, and intensity filtering, statistical filtering and effective area point cloud filtering are performed to obtain preprocessed point cloud data.

9. The orchard robot automatic pesticide spraying control method according to claim 1 or 8, characterized in that: The method for constructing the local three-dimensional grid map specifically comprises the following steps: Configure a local 3D grid map range centered on the vehicle coordinate system; wherein the X-axis of the vehicle coordinate system points in the direction of travel, 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 of the direction of travel, 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 direction of travel, and the distance in the Z-axis direction is the height H of the local 3D grid map; Apply for a map storage buffer based on the configured length L, width W, and height H parameters and the resolution of the raster map; Construct the transformation relationship from point cloud coordinates to grid coordinates and the inverse transformation relationship, and convert the pre-processed point cloud data into a grid point cloud; Use ray casting to update the occupancy probability of the grid point cloud, and determine whether the grid is occupied, unoccupied, or unknown based on the set threshold; When the processing of a frame of point cloud data is completed, the local grid map based on the frame of point cloud is constructed.

10. An orchard robot automatic pesticide spraying control system, characterized in that: include: 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 automatic pesticide spraying operation control method of the orchard robot as described in any one of claims 1 to 9.

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