Picking robot orchard operation area division method, device and equipment and medium

Abstract

The invention relates to a picking robot orchard operation area dividing method, device and equipment and a medium, and the method comprises the steps that D-H standard parameters corresponding to all joints in a mechanical arm are determined, and a homogeneous transformation matrix of all the joints in the mechanical arm is obtained according to the D-H standard parameters so as to determine the position and posture of an end effector of the mechanical arm; adopting a probability density function to determine a picking area of the mechanical arm according to the positions of the multiple end executors; determining a reachable area corresponding to each grape fruit string based on the grape fruit string coordinate corresponding to each grape fruit string and the picking area of the mechanical arm, and according to the overall distribution information of each grape fruit string and the reachable area corresponding to each grape fruit string, dividing the operation area of the picking robot in the target grape orchard, so as to complete the picking robot orchard operation area division of the picking robot. According to the invention, picking can be performed in a gathering area where grape fruits are distributed, and the precision of automatic picking of grapes is greatly improved.

Description

Technical Field

[0001] The present application relates to the field of automated picking, and in particular to a method for dividing an orchard operation area of ​​a picking robot, a corresponding device, an electronic device, and a computer-readable storage medium. Background Art

[0002] Fruit harvesting is an important part of grape planting management. Currently, fruit harvesting is still a labor-intensive and inefficient job. With the development of robotics technology, autonomous navigation harvesting robots have been widely used in the field of fruit picking, which can significantly improve work efficiency. Among the various technologies of harvesting robots, path planning is an important component. For orchard path planning tasks, dividing the work area is an important link.

[0003] The existing method of dividing the working area of ​​the fruit picking robot usually focuses on the global large-scale division. For orchards with uniform fruit distribution, this method can achieve better results. Under the same cultivation conditions, the grape fruits are usually more evenly distributed in the crown. Especially when high-density planting is adopted, the fruits may gather in certain areas, especially in the parts with sufficient light, such as the outside of the trunk and main branches, which causes the fruits in these positions to mature faster and have a relatively high yield. As the grape fruits are unevenly distributed in the grape orchard, the larger grape orchards need to pay more attention to the clustered areas where the grape fruits are distributed. Although the existing method of dividing the working area of ​​the picking robot can divide the grape orchard, there are problems such as a large number of divisions, low division efficiency and low division accuracy, and it is impossible to focus on the clustered areas where the grape fruits are distributed and accurately pick the grape fruits.

[0004] To sum up, the method of dividing the working area of ​​the picking robot in the prior art has the problems of a large number of divisions, low division efficiency and low division accuracy, and is unable to focus on the concentrated areas where grape fruits are distributed and to accurately pick the grape fruits. The applicant has made corresponding explorations in order to solve this problem. Summary of the invention

[0005] The purpose of the present application is to solve the above-mentioned problem and to provide a method for dividing the orchard operation area of ​​a picking robot, a corresponding device, an electronic device and a computer-readable storage medium.

[0006] In order to meet the various objectives of this application, this application adopts the following technical solutions:

[0007] A method for dividing an orchard operation area of ​​a picking robot proposed to meet one of the purposes of this application includes:

[0008] Acquire laser radar data and inertial measurement unit data in the information acquisition module of the picking robot, construct a two-dimensional grid map of the grape orchard according to the laser radar data and the inertial measurement unit data, and construct a grape orchard spatial model according to the spatial posture relationship between the information acquisition module and the picking module of the picking robot and the coordinate relationship between the grid map coordinate system of the two-dimensional grid map of the grape orchard and the grape orchard coordinate system;

[0009] Determine the grape fruit bunch point cloud data of each grape fruit bunch at different viewing angles collected by the structured light camera in the mechanical arm of the picking robot and its corresponding point cloud collection position, and use a preset iterative closest point algorithm to align the grape fruit bunch point cloud data at different viewing angles to determine the complete grape fruit bunch information at the same position;

[0010] Based on the point cloud collection position corresponding to each grape fruit bunch, the complete grape fruit bunch information at different positions is integrated in the grape orchard spatial model to determine the grape fruit bunch coordinates and overall distribution information corresponding to each grape fruit bunch under the two-dimensional grid map of the grape orchard;

[0011] Determine the DH standard parameters corresponding to each joint in the robotic arm, obtain the homogeneous transformation matrix of each joint in the robotic arm according to the DH standard parameters to determine the end effector position and posture of the robotic arm, and use a probability density function to determine the picking area of ​​the robotic arm according to multiple end effector positions;

[0012] Based on the grape fruit bunch coordinates corresponding to each grape fruit bunch and the picking area of ​​the robotic arm, the reachable area corresponding to each grape fruit bunch is determined; according to the overall distribution information of each grape fruit bunch and the reachable area corresponding to each grape fruit bunch, the operating area of ​​the picking robot in the target grape orchard is divided to complete the division of the picking robot's orchard operating area.

[0013] Optionally, the step of constructing a two-dimensional grid map of a grape orchard according to the laser radar data and the inertial measurement unit data includes:

[0014] Determining laser radar data and inertial measurement unit data in the information acquisition module of the picking robot;

[0015] Constructing a three-dimensional grape orchard map of the target grape orchard based on the laser radar data and the inertial measurement unit data using a preset simultaneous positioning and mapping algorithm;

[0016] The three-dimensional map of the grape orchard is projected onto a two-dimensional plane to construct a two-dimensional grid map of the grape orchard.

[0017] Optionally, before the step of determining the grape fruit bunch point cloud data of each grape fruit bunch at different viewing angles collected by the structured light camera in the mechanical arm of the picking robot and its corresponding point cloud collection position, and registering the grape fruit bunch point cloud data at different viewing angles using a preset iterative closest point algorithm to determine the complete grape fruit bunch information at the same position, the step includes:

[0018] The structured light camera in the mechanical arm of the picking robot is used to collect the viewing angle information of different positions in the target grape orchard, and each posture parameter of the mechanical arm is recorded, wherein the posture parameter includes the position coordinate and the rotation angle.

[0019] Optionally, determining DH standard parameters corresponding to each joint in the robotic arm, obtaining a homogeneous transformation matrix of each joint in the robotic arm according to the DH standard parameters to determine the end effector position and posture of the robotic arm, and using a probability density function to determine the picking area of ​​the robotic arm according to a plurality of the end effector positions, comprises:

[0020] Acquire the joint angle, joint offset, connecting rod length and connecting rod torsion angle corresponding to each joint in the mechanical arm of the picking robot, wherein the DH standard parameters include the joint angle, joint offset, connecting rod length and connecting rod torsion angle;

[0021] Determine the homogeneous transformation matrix corresponding to each joint according to the joint angle, joint offset, connecting rod length and connecting rod torsion angle corresponding to each joint, and multiply the homogeneous transformation matrices corresponding to each joint to determine the position and posture of the end effector of the robot arm;

[0022] Sampling the end effector position multiple times and recording the multiple end effector positions, and calculating and determining the probability density of each end effector position based on the multiple end effector positions using a preset probability density function;

[0023] Detect whether the probability density of the end effector position exceeds a preset probability density threshold. If so, use the position exceeding the preset probability density threshold as the picking area of ​​the robotic arm.

[0024] Optionally, the step of determining the reachable area corresponding to each grape fruit bunch based on the grape fruit bunch coordinates corresponding to each grape fruit bunch and the picking area of ​​the robotic arm includes:

[0025] A spherical area is obtained with the coordinates of each grape bunch in the target grape orchard as the center and the picking area of ​​the robot arm as the radius, and the spherical area is used as the reachable area corresponding to each grape bunch, wherein the spherical area is the spatial position where the picking robot can pick the grape bunch;

[0026] The spherical area of ​​each grape fruit bunch in the target grape orchard is projected onto a two-dimensional plane to determine a circular area corresponding to each grape fruit bunch, and the circular area is used as a reachable plane of each grape fruit bunch.

[0027] Optionally, the step of dividing the operation area of ​​the picking robot in the target grape orchard according to the overall distribution information of each grape bunch and the reachable area corresponding to each grape bunch includes:

[0028] Taking the first grape bunch close to the origin of the two-dimensional grid map of the grape orchard as the starting point and the diameter of the picking area of ​​the robot arm as the radius, a circular coverage area is determined, and the grape bunches in the circular coverage area are recorded from near to far according to the distance between each grape bunch and the origin of the map as grape bunch P0, grape bunch P1, grape bunch P2, grape bunch P3, and grape bunch P4;

[0029] Determine the reachable areas corresponding to the grape fruit bunch P1, the grape fruit bunch P2, the grape fruit bunch P3, and the grape fruit bunch P4 within the circular coverage area;

[0030] Determine whether the reachable area corresponding to the grape fruit bunch P1 and the reachable area corresponding to the grape fruit bunch P2 intersect; if the reachable areas corresponding to the grape fruit bunch P1 and the grape fruit bunch P2 do not intersect, use the reachable plane of the grape fruit bunch P1 as the operation area D1;

[0031] If the reachable area corresponding to the grape fruit bunch P1 intersects with the reachable area corresponding to the grape fruit bunch P2, the intersection is recorded as intersection T1, and it is further determined whether the intersection T1 intersects with the reachable area corresponding to the grape fruit bunch P3. If so, the intersection is recorded as intersection T2; if not, the intersection T1 is used as the operation area D1;

[0032] It is sequentially determined whether the reachable areas corresponding to the grape fruit bunches with later numbers and the reachable areas corresponding to the grape fruit bunches with earlier numbers intersect, until the grape fruit bunches P1, grape fruit bunches P2, grape fruit bunches P3, and grape fruit bunches P4 within the circular coverage area are determined;

[0033] The above steps are repeated until the reachable areas of all grape fruit bunches in the target grape orchard are determined, so as to determine the operating area of ​​the picking robot in the target grape orchard.

[0034] Optionally, the point cloud collection position representation refers to the specific spatial coordinates and related status information of each grape fruit bunch when collected in the grape orchard; the probability density function includes a Gaussian distribution model, and the simultaneous positioning and mapping algorithm includes a FASTL IO algorithm, a LIOSAM algorithm or an R3 live algorithm.

[0035] A device for dividing an orchard operation area of ​​a picking robot provided for another purpose of the present application includes:

[0036] A spatial model building module is configured to obtain laser radar data and inertial measurement unit data in an information acquisition module of a picking robot, build a two-dimensional grid map of a grape orchard according to the laser radar data and the inertial measurement unit data, and build a spatial model of a grape orchard according to a spatial posture relationship between the information acquisition module and the picking module of the picking robot and a coordinate relationship between a grid map coordinate system of the two-dimensional grid map of the grape orchard and a grape orchard coordinate system;

[0037] a fruit information determination module, configured to determine the grape fruit bunch point cloud data of each grape fruit bunch at different viewing angles collected by the structured light camera in the mechanical arm of the picking robot and its corresponding point cloud collection position, and to register the grape fruit bunch point cloud data at different viewing angles using a preset iterative closest point algorithm to determine the complete grape fruit bunch information at the same position;

[0038] The fruit distribution information determination module is configured to integrate the complete grape fruit bunch information at different positions in the grape orchard spatial model based on the point cloud collection position corresponding to each grape fruit bunch, so as to determine the grape fruit bunch coordinates and overall distribution information corresponding to each grape fruit bunch under the two-dimensional grid map of the grape orchard;

[0039] A picking area determination module is configured to determine the DH standard parameters corresponding to each joint in the robotic arm, obtain the homogeneous transformation matrix of each joint in the robotic arm according to the DH standard parameters to determine the end effector position and posture of the robotic arm, and use a probability density function to determine the picking area of ​​the robotic arm according to a plurality of the end effector positions;

[0040] The operating area division module is configured to determine the reachable area corresponding to each grape fruit bunch based on the grape fruit bunch coordinates corresponding to each grape fruit bunch and the picking area of ​​the robotic arm, and divide the operating area of ​​the picking robot in the target grape orchard according to the overall distribution information of each grape fruit bunch and the reachable area corresponding to each grape fruit bunch, so as to complete the picking robot orchard operating area division of the picking robot.

[0041] An electronic device provided to meet another purpose of the present application includes a central processing unit and a memory, wherein the central processing unit is used to call and run a computer program stored in the memory to execute the steps of the method for dividing an orchard operation area of ​​a picking robot described in the present application.

[0042] A computer-readable storage medium is provided to meet another purpose of the present application, which stores a computer program implemented according to the method for dividing the orchard operation area of ​​the picking robot in the form of computer-readable instructions. When the computer program is called and executed by a computer, the steps included in the corresponding method are executed.

[0043] Compared with the prior art, the present application aims at the method for dividing the working area of ​​the picking robot in the prior art, which has the problems of a large number of divisions, low division efficiency and low division accuracy, and cannot focus on the concentrated area where grapes are distributed and accurately pick grapes. The present application includes but is not limited to the following beneficial effects:

[0044] This application obtains fruit information through a structured light camera, and integrates the fruits through the established spatial model to obtain the distribution information and distribution coordinates of the fruits in the orchard. Finally, the orchard area is divided to obtain the operating area of ​​the picking robot. This application proposes a new idea for the division of operating areas of picking robots equipped with mechanical arms in orchards with uneven distribution of grape fruits. The number of operating areas obtained by using the orchard operating area division method of the picking robot of this application is far less than the operating area obtained by the traditional solution, and it can focus on the gathering areas where grape fruits are distributed for picking, greatly improving the accuracy and efficiency of automatic grape picking; significantly improving the overall operating efficiency of the picking robot, accurate positioning and detection, optimized path planning and efficient operation capabilities, can significantly improve the picking speed and quality of grapes, thereby improving the yield and economic benefits of the vineyard. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0046] Figure 1 This is a flow chart of a method for dividing an orchard operation area of ​​a picking robot in an embodiment of the present application;

[0047] Figure 2 It is a flowchart of the method for dividing the orchard operation area of ​​the picking robot in the embodiment of the present application;

[0048] Figure 3 This is a schematic diagram of the accessible area of ​​a grape fruit bunch in an embodiment of the present application;

[0049] Figure 4 This is a schematic diagram of numbering grape bunches within a circular coverage area in an embodiment of the present application;

[0050] Figure 5 This is a schematic diagram of recording grape fruit bunches P0, P1, P2, P3, and P4 in a circular coverage area in an embodiment of the present application;

[0051] Figure 6 This is a schematic diagram of the accessible area of ​​the fruit within the circular coverage area in the embodiment of the present application;

[0052] Figure 7 This is a schematic diagram of determining the operation area D1 in an embodiment of the present application;

[0053] Figure 8 This is a principle block diagram of a device for dividing an orchard operation area of ​​a picking robot in an embodiment of the present application;

[0054] Fig. 9 It is a schematic diagram of the structure of the computer device in the embodiment of the present application. DETAILED DESCRIPTION

[0055] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and cannot be interpreted as limiting the present application.

[0056] It will be understood by those skilled in the art that, unless expressly stated, the singular forms "one", "said", and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present application refers to the presence of the features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof. It should be understood that when we refer to an element as being "connected" or "coupled" to another element, it may be directly connected or coupled to the other element, or there may be an intermediate element. In addition, the "connection" or "coupling" used herein may include wireless connection or wireless coupling. The term "and / or" used herein includes all or any unit and all combinations of one or more associated listed items.

[0057] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those generally understood by those skilled in the art to which this application belongs. It should also be understood that terms such as those defined in general dictionaries should be understood to have meanings consistent with those in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless specifically defined as here.

[0058] It will be understood by those skilled in the art that the terms "client", "terminal" and "terminal device" as used herein include both devices with wireless signal receivers, which are devices with only wireless signal receivers without transmission capability, and devices with receiving and transmitting hardware, which are devices with receiving and transmitting hardware capable of two-way communication over a two-way communication link. Such devices may include: cellular or other communication devices such as personal computers, tablet computers, which have single-line displays or multi-line displays or cellular or other communication devices without multi-line displays; PCS (Personal Communications Services) which may combine voice, data processing, fax and / or data communication capabilities; PDA (Personal Digital Assistant) which may include a radio frequency receiver, pager, Internet / intranet access, web browser, notepad, calendar and / or GPS (Global Positioning System) receiver; conventional laptop and / or palmtop computers or other devices which have and / or include a radio frequency receiver. The "client", "terminal" and "terminal device" used herein may be portable, transportable, installed in a vehicle (air, sea and / or land), or adapted and / or configured to run locally, and / or in a distributed form, at any other location on the earth and / or in space. The "client", "terminal" and "terminal device" used herein may also be a communication terminal, an Internet terminal, a music / video playing terminal, such as a PDA, a MID (Mobile Internet Device) and / or a mobile phone with a music / video playing function, or may be a smart TV, a set-top box and other devices.

[0059] The hardware referred to by the names such as "server", "client", and "service node" in this application is essentially an electronic device with the equivalent capabilities of a personal computer. It is a hardware device with the necessary components revealed by the von Neumann principle, such as a central processing unit (including an arithmetic unit and a controller), a memory, an input device, and an output device. The computer program is stored in its memory, and the central processing unit calls the program stored in the external memory into the internal memory for execution, executes the instructions in the program, and interacts with the input and output devices to complete specific functions.

[0060] It should be pointed out that the concept of "server" referred to in this application can also be extended to the case of server clusters. According to the network deployment principle understood by those skilled in the art, the servers should be logically divided. In physical space, these servers can be independent of each other but can be called through interfaces, or integrated into a physical computer or a set of computer clusters. Those skilled in the art should understand this flexibility, and should not use it to restrict the implementation of the network deployment method of this application.

[0061] Unless expressly specified, one or more technical features of the present application can be deployed on a server for implementation and accessed by a client through a remote call to obtain an online service interface provided by the server, or can be directly deployed and run on a client for access.

[0062] The neural network models referenced or may be referenced in this application, unless expressly specified, can be deployed on a remote server and remotely called on the client, or can be deployed and directly called on a client with sufficient device capabilities. In some embodiments, when it runs on the client, its corresponding intelligence can be obtained through transfer learning to reduce the requirements for the client's hardware operating resources and avoid excessive occupation of the client's hardware operating resources.

[0063] Unless explicitly specified, the various data involved in this application can be stored remotely on a server or on a local terminal device, as long as it is suitable for being called by the technical solution of this application.

[0064] Those skilled in the art should be aware that, although the various methods of the present application are described based on the same concept and thus present commonality to each other, unless otherwise specified, these methods can be independently executed. Similarly, for each embodiment disclosed in the present application, they are all proposed based on the same inventive concept, therefore, concepts with the same expression, and concepts that are appropriately changed for convenience despite different expressions, should be understood as equivalent.

[0065] Unless the mutually exclusive relationship between the embodiments to be disclosed in this application is explicitly stated, the relevant technical features involved in each embodiment can be cross-combined to flexibly construct a new embodiment, as long as such combination does not deviate from the creative spirit of this application and can meet the needs of the prior art or solve certain deficiencies in the prior art. Those skilled in the art should be aware of this flexibility.

[0066] See also Figures 1 to 3 In one embodiment, the method for dividing the operation area of ​​an orchard of a harvesting robot of the present application includes:

[0067] Step S10, acquiring laser radar data and inertial measurement unit data in the information acquisition module of the picking robot, constructing a two-dimensional grid map of the grape orchard according to the laser radar data and the inertial measurement unit data, and constructing a grape orchard spatial model according to the spatial posture relationship between the information acquisition module and the picking module of the picking robot and the coordinate relationship between the grid map coordinate system of the two-dimensional grid map of the grape orchard and the grape orchard coordinate system;

[0068] The terminal device can obtain the laser radar data and the inertial measurement unit data in the information acquisition module of the picking robot, construct a two-dimensional grid map of the grape orchard according to the laser radar data and the inertial measurement unit data, and construct a grape orchard spatial model according to the spatial posture relationship between the information acquisition module and the picking module of the picking robot and the coordinate relationship between the grid map coordinate system of the two-dimensional grid map of the grape orchard and the grape orchard coordinate system;

[0069] In some embodiments, the information acquisition module includes a laser radar sensor, an IMU (inertial measurement unit) sensor, and a structured light camera, etc., which are used to sense the grape orchard environment, identify the position of grape fruit bunches and obstacles, etc. The picking module is the part responsible for performing the picking action, which includes a robotic arm, etc. The coordinates of the information acquisition module can be converted into the coordinates of the picking module through a posture transformation matrix to ensure that the grape fruit bunch position information collected by the information acquisition module can be correctly transmitted to the picking module to perform the action.

[0070] In some embodiments, in order to enable the picking robot to perceive the grape orchard environment and identify the position of the grape fruit bunch through the information acquisition module, and accurately convert this information into the operation instructions of the picking module, the picking process of the robot is ensured to be accurate and efficient. According to the laser radar data and the inertial measurement unit data, a two-dimensional grid map of the grape orchard is constructed, and according to the spatial posture relationship between the information acquisition module and the picking module of the picking robot and the coordinate relationship between the grid map coordinate system of the two-dimensional grid map of the grape orchard and the grape orchard coordinate system, a grape orchard space model is constructed; after determining the spatial posture relationship between the information acquisition module and the picking module, and the transformation relationship between the grid map coordinate system and the grape orchard coordinate system, a complete grape orchard space model can be established. The grape orchard space model can be realized by geometric modeling technology and simultaneous positioning and mapping algorithms. Simultaneous positioning and mapping algorithms can help the picking robot build a real-time map of the environment during movement and update its own position. During the picking process, the robot can use this spatial model to adjust its position and the motion trajectory of the robotic arm in real time to adapt to the complex environment of the grape orchard and the diverse distribution of grape fruit bunches. At the same time, the spatial model can also be used for path planning and obstacle avoidance, making the robot's movement in the grape orchard safer and more efficient.

[0071] In summary, the establishment of the above-mentioned grape orchard spatial model is based on the spatial posture relationship between the information acquisition module and the picking module, and the coordinate relationship between the grid map coordinate system of the grape orchard two-dimensional grid map and the grape orchard coordinate system, ensuring that the picking robot can work efficiently in the grape orchard.

[0072] In some embodiments, the step of constructing a two-dimensional grid map of a grape orchard based on the laser radar data and the inertial measurement unit data includes:

[0073] Step S101, determining laser radar data and inertial measurement unit data in the information acquisition module of the picking robot;

[0074] Step S102, using a preset simultaneous positioning and mapping algorithm to construct a three-dimensional map of the target grape orchard according to the laser radar data and the inertial measurement unit data;

[0075] Specifically, the simultaneous positioning and mapping algorithm includes a FASTLIO algorithm, a LIOSAM algorithm or an R3 live algorithm.

[0076] Step S103: projecting the three-dimensional map of the grape orchard onto a two-dimensional plane to construct a two-dimensional grid map of the grape orchard.

[0077] Step S20, determining the grape fruit bunch point cloud data of each grape fruit bunch at different viewing angles collected by the structured light camera in the mechanical arm of the picking robot and its corresponding point cloud collection position, and using a preset iterative closest point algorithm to align the grape fruit bunch point cloud data at different viewing angles to determine the complete grape fruit bunch information at the same position;

[0078] After constructing the spatial model of the grape orchard, the grape fruit bunch point cloud data of each grape fruit bunch at different viewing angles collected by the structured light camera in the mechanical arm of the picking robot and its corresponding point cloud collection position are determined, and the grape fruit bunch point cloud data at different viewing angles are aligned using a preset iterative closest point algorithm to determine the complete grape fruit bunch information at the same position, wherein the point cloud collection position representation refers to the specific spatial coordinates and related status information of each grape fruit bunch when collected in the grape orchard, and the complete grape fruit bunch information at the same position refers to processing all point cloud data of each grape fruit bunch at different viewing angles, and finally obtaining a detailed and complete grape fruit bunch point cloud model at that position.

[0079] In some embodiments, before the step of determining the grape fruit bunch point cloud data of each grape fruit bunch at different viewing angles collected by the structured light camera in the mechanical arm of the picking robot and its corresponding point cloud collection position, and registering the grape fruit bunch point cloud data at different viewing angles using a preset iterative closest point algorithm to determine the complete grape fruit bunch information at the same position, the step includes:

[0080] The structured light camera in the mechanical arm of the picking robot is used to collect the viewing angle information of different positions in the target grape orchard, and each posture parameter of the mechanical arm is recorded, wherein the posture parameter includes the position coordinate and the rotation angle.

[0081] In some embodiments, the structured light camera is a device that uses structured light technology to perform three-dimensional imaging. This camera usually projects a specific pattern of light (such as stripes, dots or other graphics) onto the surface of an object and uses the camera to capture the deformation of these light patterns, thereby obtaining the three-dimensional shape and depth information of each grape bunch in the grape orchard.

[0082] In some embodiments, the structured light camera carried by the mechanical arm of the picking robot is used to collect grape fruit cluster point cloud data corresponding to each grape fruit cluster in the grape orchard, and record the point cloud collection position of each grape fruit cluster, laying the foundation for subsequent data processing; the grape fruit cluster point cloud data under different perspectives are aligned using a preset iterative closest point (ICP) algorithm to determine the complete grape fruit cluster information at the same position, and the multi-perspective point cloud at the same position is aligned using the ICP algorithm to obtain the complete information of the same grape fruit cluster. This process helps identify and integrate information from different angles, thereby obtaining more accurate grape fruit cluster features.

[0083] Step S30, based on the point cloud collection position corresponding to each grape fruit bunch, integrating the complete grape fruit bunch information at different positions in the grape orchard spatial model to determine the grape fruit bunch coordinates and overall distribution information corresponding to each grape fruit bunch under the two-dimensional grid map of the grape orchard;

[0084] After determining the complete grape bunch information at the same location, based on the point cloud collection location corresponding to each grape bunch, the complete grape bunch information at different locations is integrated in the grape orchard spatial model to determine the grape bunch coordinates and overall distribution information corresponding to each grape bunch under the two-dimensional grid map of the grape orchard;

[0085] Specifically, the spatial model is used to integrate the grape bunch information at different locations in combination with the recorded point cloud collection locations. The purpose of this process is to obtain the distribution of each grape bunch in the entire grape orchard. By integrating the grape bunch information from different locations and forming an overall grape bunch distribution map, the distribution of grape bunches of different types or varieties in the grape orchard and the distribution of different individual grape bunches in various areas of the grape orchard can be determined.

[0086] As well as the specific coordinates of each grape bunch on the map, this grape bunch distribution map information is very important for the division of the working area of ​​the picking robot.

[0087] From the above steps, we can see that through the structured light camera carried by the robot arm, grape fruit bunch point cloud data collection, data registration and spatial integration, the grape fruit bunches in the grape orchard can be accurately identified and located. This provides the necessary information basis for the division of the working area of ​​the picking robot, so that it can work more efficiently and accurately when performing the division of the working area of ​​the picking robot. This method not only improves the working efficiency of the robot arm, but also enhances the feasibility of dividing the working area of ​​the picking robot in a complex environment.

[0088] Step S40, determining the DH standard parameters corresponding to each joint in the robotic arm, obtaining the homogeneous transformation matrix of each joint in the robotic arm according to the DH standard parameters to determine the end effector position and posture of the robotic arm, and using a probability density function to determine the picking area of ​​the robotic arm according to the multiple end effector positions;

[0089] After determining the grape fruit bunch coordinates and overall distribution information of each grape fruit bunch corresponding to each grape fruit bunch under the two-dimensional grid map of the grape orchard, determine the DH standard parameters corresponding to each joint in the robotic arm, obtain the homogeneous transformation matrix of each joint in the robotic arm according to the DH standard parameters, so as to determine the end effector position and posture of the robotic arm, and use a probability density function according to a plurality of the end effector positions to determine the picking area of ​​the robotic arm, wherein the probability density function includes a Gaussian distribution model or a Gaussian mixture model, etc.;

[0090] In some embodiments, the steps of determining the DH standard parameters corresponding to each joint in the robotic arm, obtaining the homogeneous transformation matrix of each joint in the robotic arm according to the DH standard parameters to determine the end effector position and posture of the robotic arm, and using a probability density function to determine the picking area of ​​the robotic arm according to a plurality of the end effector positions include:

[0091] Step S401, obtaining the joint angle, joint offset, connecting rod length and connecting rod torsion angle corresponding to each joint in the mechanical arm of the picking robot, wherein the DH (Denavit-Hartenberg) standard parameters include the joint angle, joint offset, connecting rod length and connecting rod torsion angle;

[0092] Step S402, determining the homogeneous transformation matrix corresponding to each joint according to the joint angle, joint offset, connecting rod length and connecting rod torsion angle corresponding to each joint, and multiplying the homogeneous transformation matrices corresponding to each joint to determine the position and posture of the end effector of the robot arm;

[0093] Step S403, sampling the end effector position multiple times and recording the multiple end effector positions, and using a preset probability density function to calculate and determine the probability density of each end effector position according to the multiple end effector positions;

[0094] Step S404, detecting whether the probability density of the end effector position exceeds a preset probability density threshold, and if so, taking the position exceeding the preset probability density threshold as the picking area of ​​the robot arm.

[0095] The probability density function includes a Gaussian distribution model or a Gaussian mixture model, etc., by obtaining the joint angle, joint offset, connecting rod length and connecting rod torsion angle corresponding to each joint in the mechanical arm of the picking robot, according to the joint angle, joint offset, connecting rod length and connecting rod torsion angle corresponding to each joint, to determine the homogeneous transformation matrix corresponding to each joint, and multiply the homogeneous transformation matrix corresponding to each joint to determine the end effector position and posture of the mechanical arm; the end effector position is sampled multiple times and multiple end effector positions are recorded, and the probability density of each end effector position is calculated and determined according to the multiple end effector positions using a preset probability density function; whether the probability density of the end effector position exceeds the preset probability density threshold, if it exceeds, the position exceeding the preset probability density threshold is used as the picking area of ​​the mechanical arm. Using the preset probability density function and calculating the probability density of the end effector position can effectively determine the picking area that exceeds the preset probability density threshold. This method helps to optimize the picking process, improve work efficiency, and reduce unnecessary operations. Ultimately, the selection of the picking area will make the system more intelligent and efficient under specific circumstances.

[0096] Step S50, determining the reachable area corresponding to each grape fruit bunch based on the grape fruit bunch coordinates corresponding to each grape fruit bunch and the picking area of ​​the robotic arm, dividing the operating area of ​​the picking robot in the target grape orchard according to the overall distribution information of each grape fruit bunch and the reachable area corresponding to each grape fruit bunch, so as to complete the division of the picking robot orchard operating area of ​​the picking robot.

[0097] After determining the picking area of ​​the robotic arm according to the positions of the multiple end effectors using a probability density function, determining the reachable area corresponding to each grape bunch based on the grape bunch coordinates corresponding to each grape bunch and the picking area of ​​the robotic arm, dividing the operating area of ​​the picking robot in the target grape orchard according to the overall distribution information of each grape bunch and the reachable area corresponding to each grape bunch, so as to complete the division of the picking robot orchard operating area of ​​the picking robot.

[0098] In some embodiments, Figure 3 Schematic diagram of the accessible area of ​​the grape fruit bunch in this embodiment, wherein: Figure 3 A1, A2 and A3 are accessible areas, and g1, g2 and g3 are grape bunches.

[0099] In some embodiments, the step of determining the reachable area corresponding to each grape bunch based on the grape bunch coordinates corresponding to each grape bunch and the picking area of ​​the robotic arm includes:

[0100] Step S501, taking the coordinates of each grape bunch in the target grape orchard as the center and the picking area of ​​the robot arm as the radius to obtain a spherical area, and taking the spherical area as the reachable area corresponding to each grape bunch, wherein the spherical area is the spatial position where the picking robot can pick the grape bunch;

[0101] Step S502: Project the spherical area of ​​each grape bunch in the target grape orchard onto a two-dimensional plane to determine a circular area corresponding to each grape bunch, and use the circular area as a reachable plane for each grape bunch.

[0102] Furthermore, the step of dividing the operation area of ​​the picking robot in the target grape orchard according to the overall distribution information of each grape bunch and the reachable area corresponding to each grape bunch includes:

[0103] Step S5001, taking the first grape bunch close to the origin of the two-dimensional grid map of the grape orchard as the starting point and the diameter of the picking area of ​​the robot arm as the radius, determining a circular coverage area, and recording the grape bunches in the circular coverage area from near to far according to the distance between each grape bunch and the origin of the map as grape bunch P0, grape bunch P1, grape bunch P2, grape bunch P3, and grape bunch P4;

[0104] Taking the first grape bunch close to the origin of the two-dimensional grid map of the grape orchard as the starting point and the diameter of the picking area of ​​the robot arm as the radius, a circular coverage area is determined, and the distance between each grape bunch and the origin of the map is used as a judgment basis, and the grape bunches in the circular coverage area are recorded from near to far according to the distance between each grape bunch and the origin of the map as grape bunch P0, grape bunch P1, grape bunch P2, grape bunch P3, and grape bunch P4;

[0105] Step S5002, determining the reachable areas corresponding to the grape fruit bunch P1, grape fruit bunch P2, grape fruit bunch P3, and grape fruit bunch P4 within the circular coverage area;

[0106] Step S5003, determining whether the reachable area corresponding to the grape fruit bunch P1 and the reachable area corresponding to the grape fruit bunch P2 intersect; if the reachable areas corresponding to the grape fruit bunch P1 and the grape fruit bunch P2 do not intersect, taking the reachable plane of the grape fruit bunch P1 as the operation area D1;

[0107] Step S5004: if the reachable area corresponding to the grape fruit bunch P1 and the reachable area corresponding to the grape fruit bunch P2 have an intersection, the intersection is recorded as intersection T1, and it is further determined whether the intersection T1 and the reachable area corresponding to the grape fruit bunch P3 have an intersection. If yes, the intersection is recorded as intersection T2; if no, the intersection T1 is used as the operation area D1;

[0108] Step S5005, sequentially determining whether the reachable areas corresponding to the grape fruit bunches with later numbers and the reachable areas corresponding to the grape fruit bunches with earlier numbers intersect, until the grape fruit bunches P1, P2, P3, and P4 within the circular coverage area are determined;

[0109] Step S5006, repeat the above steps until the reachable areas of all grape fruit bunches in the target grape orchard are determined, so as to determine the operating area of ​​the picking robot in the target grape orchard.

[0110] Specifically, the step of dividing the operation area of ​​the picking robot in the target grape orchard according to the overall distribution information of each grape fruit bunch and the reachable area corresponding to each grape fruit bunch specifically includes:

[0111] Step S1100: Taking the first grape bunch close to the origin of the grape orchard map as the starting point and the diameter of the picking area of ​​the above-mentioned robot arm as the radius, determine a circular coverage area, see Figure 4 and Figure 5 , the distance between each grape bunch and the origin of the map is used as the judgment basis, and the grape bunches in the circular coverage area are numbered as grape bunch P0, grape bunch P1, grape bunch P2, grape bunch P3, and grape bunch P4 according to the distance between each grape bunch and the origin of the map.

[0112] Step S1200, see Figure 6 , determine the reachable area of ​​each grape bunch within the circular coverage area;

[0113] Step S1300, determining whether the reachable areas of the grape fruit bunch P1 and the grape fruit bunch P2 intersect, if so, recording the intersection as intersection T1; if not, taking the reachable area of ​​the grape fruit bunch P1 as the operation area D1.

[0114] Step S1400: if the reachable area corresponding to the grape fruit bunch P1 and the reachable area corresponding to the grape fruit bunch P2 have an intersection, the intersection is recorded as intersection T1, and it is further determined whether the intersection T1 and the reachable area corresponding to the grape fruit bunch P3 have an intersection. If yes, the intersection is recorded as intersection T2; if no, the intersection T1 is used as the operation area D1;

[0115] Step S1500, please refer to Figure 7 , repeat the above steps, and sequentially determine whether the reachable areas corresponding to the grape fruit bunches with later numbers and the reachable areas corresponding to the grape fruit bunches with earlier numbers intersect, until the grape fruit bunches P1, grape fruit bunches P2, grape fruit bunches P3, and grape fruit bunches P4 within the circular coverage area are determined;

[0116] Step S1600, repeat the above steps until the reachable areas of all grape fruit bunches in the target grape orchard are determined, so as to determine the operating area of ​​the picking robot in the target grape orchard.

[0117] In some embodiments, Figure 4 This is a schematic diagram of numbering grape bunches within the circular coverage area in this embodiment; Figure 5 This is a schematic diagram of recording grape fruit bunches P0, P1, P2, P3, and P4 within the circular coverage area in this embodiment; Figure 6 Schematic diagram of the accessible area of ​​the fruit within the circular coverage area in this embodiment; Figure 7 Schematic diagram of determining the operation area D1 in this embodiment; wherein P0, P1, P2, P3, and P4 are grape fruit bunches in a grape orchard, and D1 is the operation area.

[0118] Compared with the prior art, the present application aims at the method for dividing the working area of ​​the picking robot in the prior art, which has the problems of a large number of divisions, low division efficiency and low division accuracy, and cannot focus on the concentrated area where grapes are distributed and accurately pick grapes. The present application includes but is not limited to the following beneficial effects:

[0119] This application obtains fruit information through a structured light camera, and integrates the fruits through the established spatial model to obtain the distribution information and distribution coordinates of the fruits in the orchard. Finally, the orchard area is divided to obtain the operating area of ​​the picking robot. This application proposes a new idea for the division of operating areas of picking robots equipped with mechanical arms in orchards with uneven distribution of grape fruits. The number of operating areas obtained by using the orchard operating area division method of the picking robot of this application is far less than the operating area obtained by the traditional solution, and it can focus on the gathering areas where grape fruits are distributed for picking, greatly improving the accuracy and efficiency of automatic grape picking; significantly improving the overall operating efficiency of the picking robot, accurate positioning and detection, optimized path planning and efficient operation capabilities, can significantly improve the picking speed and quality of grapes, thereby improving the yield and economic benefits of the vineyard.

[0120] See also Figure 8A picking robot orchard operation area division device is provided to meet one of the purposes of the present application, including a space model construction module 1100, a fruit information determination module 1200, a fruit distribution information determination module 1300, a picking area determination module 1400 and an operation area division module 1500. Among them, the spatial model construction module 1100 is configured to obtain the laser radar data and inertial measurement unit data in the information acquisition module of the picking robot, construct a two-dimensional grid map of the grape orchard according to the laser radar data and the inertial measurement unit data, and construct a grape orchard spatial model according to the spatial posture relationship between the information acquisition module and the picking module of the picking robot and the coordinate relationship between the grid map coordinate system of the two-dimensional grid map of the grape orchard and the grape orchard coordinate system; the fruit information determination module 1200 is configured to determine the grape fruit bunch point cloud data under different viewing angles of each grape fruit bunch collected by the structured light camera in the mechanical arm of the picking robot and its corresponding point cloud collection position, and use a preset iterative closest point algorithm to align the grape fruit bunch point cloud data under different viewing angles to determine the complete grape fruit bunch information at the same position; the fruit distribution information determination module 1300 is configured to, based on the point cloud collection position corresponding to each grape fruit bunch, for different grape fruit bunches in the grape orchard spatial model. The complete grape fruit bunch information of the position is integrated to determine the grape fruit bunch coordinates and overall distribution information of each grape fruit bunch corresponding to each grape fruit bunch under the two-dimensional grid map of the grape orchard; the picking area determination module 1400 is configured to determine the DH standard parameters corresponding to each joint in the robotic arm, obtain the homogeneous transformation matrix of each joint in the robotic arm according to the DH standard parameters, so as to determine the end effector position and posture of the robotic arm, and use the probability density function to determine the picking area of ​​the robotic arm according to the multiple end effector positions; the operation area division module 1500 is configured to determine the reachable area corresponding to each grape fruit bunch based on the grape fruit bunch coordinates corresponding to each grape fruit bunch and the picking area of ​​the robotic arm, and divide the operation area of ​​the picking robot in the target grape orchard according to the overall distribution information of each grape fruit bunch and the reachable area corresponding to each grape fruit bunch, so as to complete the picking robot orchard operation area division of the picking robot.

[0121] Based on any embodiment of this application, please refer to Fig. 9 Another embodiment of the present application further provides an electronic device, which can be implemented by a computer device, such as Fig. 9As shown, a schematic diagram of the internal structure of a computer device. The computer device includes a processor, a computer-readable storage medium, a memory, and a network interface connected via a system bus. Among them, the computer-readable storage medium of the computer device stores an operating system, a database, and computer-readable instructions. The database may store a control information sequence. When the computer-readable instructions are executed by the processor, the processor can implement a method for dividing the operation area of ​​an orchard for a picking robot. The processor of the computer device is used to provide computing and control capabilities to support the operation of the entire computer device. The memory of the computer device may store computer-readable instructions. When the computer-readable instructions are executed by the processor, the processor can execute the method for dividing the operation area of ​​an orchard for a picking robot of the present application. The network interface of the computer device is used to connect and communicate with a terminal. Those skilled in the art will understand that Fig. 9 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0122] In this embodiment, the processor is used to execute Figure 8 The memory stores the program code and various data required to execute the above modules or submodules. The network interface is used to transmit data between user terminals or servers. The memory in this embodiment stores the program code and data required to execute all modules / submodules in the orchard operation area division device of the picking robot of this application, and the server can call the program code and data of the server to execute the functions of all submodules.

[0123] The present application also provides a storage medium storing computer-readable instructions. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the method for dividing the orchard operation area of ​​the harvesting robot described in any embodiment of the present application.

[0124] The present application also provides a computer program product, including a computer program / instruction, which, when executed by one or more processors, implements the steps of the method for dividing an orchard operation area of ​​a harvesting robot described in any embodiment of the present application.

[0125] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments of the present application can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the aforementioned storage medium can be a computer-readable storage medium such as a disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0126] The above description is only a partial implementation method of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

[0127] To sum up, by adopting the method for dividing the orchard working areas of the harvesting robot of the present application, the number of working areas obtained is far less than the working areas obtained by the traditional scheme, and it is possible to focus on the concentrated areas where grape fruits are distributed for picking, which greatly improves the accuracy and efficiency of automated grape picking; significantly improves the overall operating efficiency of the harvesting robot, and the precise positioning and detection, optimized path planning and efficient operation capabilities can significantly improve the picking speed and quality of grapes, thereby increasing the yield and economic benefits of the vineyard.

Claims

1. A method for dividing the operation area of ​​an orchard by a picking robot, characterized in that: include: Acquire laser radar data and inertial measurement unit data in the information acquisition module of the picking robot, construct a two-dimensional grid map of the grape orchard according to the laser radar data and the inertial measurement unit data, and construct a grape orchard spatial model according to the spatial posture relationship between the information acquisition module and the picking module of the picking robot and the coordinate relationship between the grid map coordinate system of the two-dimensional grid map of the grape orchard and the grape orchard coordinate system; Determine the grape fruit bunch point cloud data of each grape fruit bunch at different viewing angles collected by the structured light camera in the mechanical arm of the picking robot and its corresponding point cloud collection position, and use a preset iterative closest point algorithm to align the grape fruit bunch point cloud data at different viewing angles to determine the complete grape fruit bunch information at the same position; Based on the point cloud collection position corresponding to each grape fruit bunch, the complete grape fruit bunch information at different positions is integrated in the grape orchard spatial model to determine the grape fruit bunch coordinates and overall distribution information corresponding to each grape fruit bunch under the two-dimensional grid map of the grape orchard; Determine the DH standard parameters corresponding to each joint in the robotic arm, obtain the homogeneous transformation matrix of each joint in the robotic arm according to the DH standard parameters to determine the end effector position and posture of the robotic arm, and use a probability density function to determine the picking area of ​​the robotic arm according to multiple end effector positions; Based on the grape fruit bunch coordinates corresponding to each grape fruit bunch and the picking area of ​​the robotic arm, the reachable area corresponding to each grape fruit bunch is determined; according to the overall distribution information of each grape fruit bunch and the reachable area corresponding to each grape fruit bunch, the operating area of ​​the picking robot in the target grape orchard is divided to complete the division of the picking robot's orchard operating area.

2. The method for dividing the orchard operation area of ​​the picking robot according to claim 1, characterized in that: The step of constructing a two-dimensional grid map of a grape orchard according to the laser radar data and the inertial measurement unit data comprises: Determining laser radar data and inertial measurement unit data in the information acquisition module of the picking robot; Constructing a three-dimensional grape orchard map of the target grape orchard based on the laser radar data and the inertial measurement unit data using a preset simultaneous positioning and mapping algorithm; The three-dimensional map of the grape orchard is projected onto a two-dimensional plane to construct a two-dimensional grid map of the grape orchard.

3. The method for dividing the orchard operation area of ​​the picking robot according to claim 1, characterized in that: Before the step of determining the grape fruit bunch point cloud data of each grape fruit bunch at different viewing angles collected by the structured light camera in the mechanical arm of the picking robot and its corresponding point cloud collection position, and registering the grape fruit bunch point cloud data at different viewing angles using a preset iterative closest point algorithm to determine the complete grape fruit bunch information at the same position, the method includes: The structured light camera in the mechanical arm of the picking robot is used to collect the viewing angle information of different positions in the target grape orchard, and each posture parameter of the mechanical arm is recorded, wherein the posture parameter includes the position coordinate and the rotation angle.

4. The method for dividing the orchard operation area of ​​the picking robot according to claim 1, characterized in that: The steps of determining the DH standard parameters corresponding to each joint in the robotic arm, obtaining the homogeneous transformation matrix of each joint in the robotic arm according to the DH standard parameters to determine the end effector position and posture of the robotic arm, and using a probability density function to determine the picking area of ​​the robotic arm according to a plurality of the end effector positions include: Acquire the joint angle, joint offset, connecting rod length and connecting rod torsion angle corresponding to each joint in the mechanical arm of the picking robot, wherein the DH standard parameters include the joint angle, joint offset, connecting rod length and connecting rod torsion angle; Determine the homogeneous transformation matrix corresponding to each joint according to the joint angle, joint offset, connecting rod length and connecting rod torsion angle corresponding to each joint, and multiply the homogeneous transformation matrices corresponding to each joint to determine the position and posture of the end effector of the robot arm; Sampling the end effector position multiple times and recording the multiple end effector positions, and calculating and determining the probability density of each end effector position based on the multiple end effector positions using a preset probability density function; Detect whether the probability density of the end effector position exceeds a preset probability density threshold. If so, use the position exceeding the preset probability density threshold as the picking area of ​​the robotic arm.

5. The method for dividing the orchard operation area of ​​the picking robot according to claim 1, characterized in that: The step of determining the reachable area corresponding to each grape fruit bunch based on the grape fruit bunch coordinates corresponding to each grape fruit bunch and the picking area of ​​the robotic arm comprises: A spherical area is obtained with the coordinates of each grape bunch in the target grape orchard as the center and the picking area of ​​the robot arm as the radius, and the spherical area is used as the reachable area corresponding to each grape bunch, wherein the spherical area is the spatial position where the picking robot can pick the grape bunch; The spherical area of ​​each grape fruit bunch in the target grape orchard is projected onto a two-dimensional plane to determine a circular area corresponding to each grape fruit bunch, and the circular area is used as a reachable plane of each grape fruit bunch.

6. The method for dividing the orchard operation area of ​​the picking robot according to claim 5, characterized in that: The step of dividing the operation area of ​​the picking robot in the target grape orchard according to the overall distribution information of each grape fruit bunch and the reachable area corresponding to each grape fruit bunch comprises: Taking the first grape bunch close to the origin of the two-dimensional grid map of the grape orchard as the starting point and the diameter of the picking area of ​​the robot arm as the radius, a circular coverage area is determined, and the grape bunches in the circular coverage area are recorded from near to far according to the distance between each grape bunch and the origin of the map as grape bunch P0, grape bunch P1, grape bunch P2, grape bunch P3, and grape bunch P4; Determine the reachable areas corresponding to the grape fruit bunch P1, the grape fruit bunch P2, the grape fruit bunch P3, and the grape fruit bunch P4 within the circular coverage area; Determine whether the reachable area corresponding to the grape fruit bunch P1 and the reachable area corresponding to the grape fruit bunch P2 intersect; if the reachable areas corresponding to the grape fruit bunch P1 and the grape fruit bunch P2 do not intersect, use the reachable plane of the grape fruit bunch P1 as the operation area D1; If the reachable area corresponding to the grape fruit bunch P1 intersects with the reachable area corresponding to the grape fruit bunch P2, the intersection is recorded as intersection T1, and it is further determined whether the intersection T1 intersects with the reachable area corresponding to the grape fruit bunch P3. If so, the intersection is recorded as intersection T2; if not, the intersection T1 is used as the operation area D1; It is sequentially determined whether the reachable areas corresponding to the grape fruit bunches with later numbers and the reachable areas corresponding to the grape fruit bunches with earlier numbers intersect, until the grape fruit bunches P1, grape fruit bunches P2, grape fruit bunches P3, and grape fruit bunches P4 within the circular coverage area are determined; The above steps are repeated until the reachable areas of all grape fruit bunches in the target grape orchard are determined, so as to determine the operating area of ​​the picking robot in the target grape orchard.

7. The method for dividing the orchard operation area of ​​a picking robot according to any one of claims 1 to 6, characterized in that: The point cloud collection position representation refers to the specific spatial coordinates and related state information of each grape bunch in the grape orchard when it is collected; the probability density function includes a Gaussian distribution model or a Gaussian mixture model, and the simultaneous positioning and mapping algorithm includes a FASTL IO algorithm, a LIOSAM algorithm or an R3 live algorithm.

8. A device for dividing the operation area of ​​an orchard by a picking robot, characterized in that: include: A spatial model building module is configured to obtain laser radar data and inertial measurement unit data in an information acquisition module of a picking robot, build a two-dimensional grid map of a grape orchard according to the laser radar data and the inertial measurement unit data, and build a spatial model of a grape orchard according to a spatial posture relationship between the information acquisition module and the picking module of the picking robot and a coordinate relationship between a grid map coordinate system of the two-dimensional grid map of the grape orchard and a grape orchard coordinate system; a fruit information determination module, configured to determine the grape fruit bunch point cloud data of each grape fruit bunch at different viewing angles collected by the structured light camera in the mechanical arm of the picking robot and its corresponding point cloud collection position, and to register the grape fruit bunch point cloud data at different viewing angles using a preset iterative closest point algorithm to determine the complete grape fruit bunch information at the same position; The fruit distribution information determination module is configured to integrate the complete grape fruit bunch information at different positions in the grape orchard spatial model based on the point cloud collection position corresponding to each grape fruit bunch, so as to determine the grape fruit bunch coordinates and overall distribution information corresponding to each grape fruit bunch under the two-dimensional grid map of the grape orchard; A picking area determination module is configured to determine the DH standard parameters corresponding to each joint in the robotic arm, obtain the homogeneous transformation matrix of each joint in the robotic arm according to the DH standard parameters to determine the end effector position and posture of the robotic arm, and use a probability density function to determine the picking area of ​​the robotic arm according to a plurality of the end effector positions; The operating area division module is configured to determine the reachable area corresponding to each grape fruit bunch based on the grape fruit bunch coordinates corresponding to each grape fruit bunch and the picking area of ​​the robotic arm, and divide the operating area of ​​the picking robot in the target grape orchard according to the overall distribution information of each grape fruit bunch and the reachable area corresponding to each grape fruit bunch, so as to complete the picking robot orchard operating area division of the picking robot.

9. An electronic device, comprising a central processing unit and a memory, characterized in that: The central processing unit is used to call and run the computer program stored in the memory to execute the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: It stores a computer program implemented according to the method described in any one of claims 1 to 7 in the form of computer-readable instructions, and when the computer program is called and executed by a computer, the steps included in the corresponding method are executed.

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