Autonomous pollination device and method for Amomum villosum

Abstract

The invention relates to an autonomous pollination device and method for Amomum villosum, and the device comprises a mechanical dog which is arranged at the bottom of the pollination device and is used for controlling the moving direction and the moving route of the device; the docking station and the laser radar are arranged above the mechanical dog, the laser radar can be used for detecting obstacles, and the docking station can be used for expanding the mechanical arm; the mechanical arm is mounted on the docking station and is used for receiving the pollination signal and executing a pollination instruction; the tail end executor is connected to the tail end of the mechanical arm and used for being aligned with a target flower; the depth camera is connected to the end effector and used for collecting environment information and collecting an image of Amomum villosum; and the flexible clamping jaw is connected to the front of the depth camera and is used for pollinating the Amomum villosum detected by the depth camera. Efficient and accurate pollination is realized, the technical problems that pollen is difficult to fall on stigmas and self-pollination cannot be realized due to the fact that the floral organ structure of the amomum villosum is slightly higher than anther than the special stigmas are solved, and the pollination success rate is increased.

Description

Technical Field

[0001] The present invention relates to the technical field of artificial assisted pollination, and more specifically, to an autonomous pollination device and method for Amomum villosum. Background Art

[0002] Due to the special structure of the floral organs of Amomum villosum, the anthers of the stamens are under the stigma of the pistil, and the pistil's medicine column is embedded in the grooves of the two medicine chambers. The stigma is slightly higher than the anthers, and it is difficult for pollen to fall on the stigma, so it cannot be self-pollinated. The auxiliary pollination schemes in the prior art mainly include: 1. Artificial assisted pollination: artificially completing assisted pollination by artificially adopting the methods of "push-pull method" and "powdering method", but this method has high pollination cost, high operating technical difficulty, large workload, and requires professional training. The style is easy to fall off, and it is necessary to complete the pollination meticulously. It is difficult to avoid stepping on the runners and breaking the young bamboo shoots during the process. 2. Air mechanical assisted pollination: using equipment such as fans to generate airflow, blowing pollen to the surrounding flowers to complete pollination, but this method is very poor in the uniformity of pollination for the moist and runner growth environment of Amomum villosum, which will lead to poor pollination effect. 3. Insect induced pollination: Utilize the natural pollination behavior of insects, increase the frequency of insect activity by increasing suitable nectar sources or field management to complete pollination, but the inflorescence of the sandflower grows on the stolons, which are easily shaded by branches and leaves, which will bring great difficulties to the pollination of flowers. 4. Beehive placement: Place beehives in the flower fields, cultivate male bees that have a unique attraction to the sandflower from an early age, and use bee colonies to collect pollen and complete pollination, but the process of breeding male bees is difficult and the cycle is long, so it cannot be put into large-scale agricultural use.

[0003] Therefore, in order to solve the problems existing in the prior art, the present invention provides an autonomous pollination device and method for Ampelopsis ternata. For the three periods of blooming of Ampelopsis ternata flowers, the blooming period with the largest number of flowers and the longest duration is selected from the three periods of "early flowering period", "full flowering period" and "late flowering period" to carry out auxiliary pollination, thereby realizing intelligent and autonomous, accurate and rapid pollination of Ampelopsis ternata. Summary of the invention

[0004] The present invention aims to overcome at least one defect (shortcoming) of the above-mentioned prior art and provide an autonomous pollination device and method for Ampelopsis ternata, which is used to solve the problems that Ampelopsis ternata cannot self-pollinate and that the artificial pollination operation technology in the prior art is difficult, the workload is large, the pollination effect is poor, and the pollination is difficult.

[0005] The technical solution adopted by the present invention is an autonomous pollination device for Amaryllis sinensis, and the device comprises:

[0006] A mechanical dog, which is set at the bottom of the pollination device, is used to control the moving direction and route of the device;

[0007] The expansion dock and the laser radar are arranged above the robot dog. The laser radar can be used to detect obstacles, and the expansion dock can be used to expand the robot arm;

[0008] A robotic arm, mounted on the expansion dock, is used to receive pollination signals and execute pollination instructions;

[0009] The end effector, which is attached to the end of the robotic arm and is used to aim at the target flower;

[0010] A depth camera, connected to the end effector, is used to collect environmental information and collect images of sand flowers;

[0011] A flexible clamp is connected in front of the depth camera and is used to pollinate the Ampelopsis ternata detected by the depth camera.

[0012] In the present application, by being equipped with laser radar, depth camera and other equipment, the surrounding environment can be scanned, autonomous navigation can be achieved, obstacles can be avoided, the location of the target plant can be identified and each plant that needs to be pollinated can be accurately reached according to the planned path, and then the pollen in the anthers of the stamens under the stigma of the pistil is transferred to the stigma through a robotic arm, thereby achieving efficient and accurate pollination, solving the technical problem that due to the special structure of the floral organ of Amomum villosum, the stigma is slightly higher than the anther, and the pollen is difficult to fall on the stigma, so self-pollination cannot be achieved, the success rate of pollination is improved, omissions or unevenness that may occur in traditional artificial pollination are avoided, human intervention and labor costs are reduced, and the robot has strong adaptability and is easy to be put into large-scale agricultural production.

[0013] Preferably, the flexible clamp includes a gripper and a turntable, wherein the turntable and the gripper are arranged on the same bearing, a transmission bearing is arranged at the center of the turntable for controlling the rotation of the turntable, and the turntable is located above the gripper; goose down is arranged on the outer ring of the turntable and inside the gripper, and as the turntable rotates, pollen adheres to the surface of the goose down, and at the same time, the gripper moves the pollen upward along the pistil until the goose down on the turntable delivers the pollen to the top of the pistil, thereby completing pollination.

[0014] The flexible clamps provided in the present application can complete an embracing grip on the stigma in the pistil, and then the turntable is rotated to allow the pollen under the stigma of the pistil to adhere to the goose down of the turntable. Then, driven by the robotic arm, the turntable with the pollen attached is gently swept across the top of the stigma in the stamen using the "push-pull method", thereby completing autonomous pollination, filling the technical gap in the pollination of Amomum villosum by robotic arms, and effectively increasing the yield of Amomum villosum.

[0015] On the other hand, the present application also provides a method for autonomous pollination of Amomum villosum, the method comprising the following steps:

[0016] S1: Start the robot dog and initialize it, then start the depth camera to collect environmental information in real time for path planning;

[0017] S2: When the robot dog moves along the planned route, it uses the depth camera to collect images in real time, and uses the target detection algorithm to detect and locate the target flower;

[0018] S3: When the robot dog reaches a certain range of the detected target flower, the robot arm installed on the robot dog is activated and the target flower is re-positioned using the depth camera, so as to obtain accurate position information of the flower relative to the robot arm;

[0019] S4: When the robot arm approaches the flower, the joint angle adjustment of the robot arm is calculated according to the real-time position of the flower and the current position of the end effector, so as to fine-tune the position and posture of the robot arm, thereby finally confirming the position of the flower;

[0020] S5: Identify and locate the position of the style, then use the robotic arm to control the transmission bearing to rotate at a constant speed to drive the turntable to make the pollen adhere to the goose down on the turntable, and use the flexible claws to grasp the style and send the pollen on the turntable to the top of the style to complete pollination.

[0021] In the present application, a depth camera is used to collect real-time information about the surrounding environment, and the robot dog's route is planned based on the environmental information. A target detection algorithm is also used to effectively detect the target flower and position it so that the robot dog can accurately reach within a certain range of the target flower. The robot arm is activated to accurately position the target flower and the target pistil again, thereby achieving precise pollination operations, ensuring the effective transfer of pollen, and improving the success rate of pollination, thereby avoiding omissions or unevenness that may occur in traditional artificial pollination, reducing human intervention, and reducing dependence on natural pollinators (such as bees and other insects) through automated pollination. This helps maintain crop growth and yield when the number of pollinators is insufficient or the ecological environment is disturbed, and is suitable for large-scale agricultural production.

[0022] Preferably, the step S1 includes:

[0023] S11: Use the built-in inertial measurement unit and odometer to determine the initial position coordinates, set the initial velocity to zero, and use the inertial measurement unit to measure the acceleration and angular velocity of the robot dog on the x, y, and z axes;

[0024] S12: Using an improved A* algorithm to perform path planning, the robot dog uses the terrain information obtained by the depth camera and then calculates a path to the approximate area of ​​flower distribution determined by the pre-scan in combination with its own position;

[0025] S13: The robot dog moves along the planned path, and updates its own position coordinates at regular time intervals according to the inertial measurement unit and odometer data during the movement, while continuously monitoring the environment using a depth camera. If an obstacle is found, the robot dog immediately stops moving, replans the route to bypass the obstacle, and then continues to move forward.

[0026] In the present application, the robot dog performs path planning through the improved A* algorithm, and can realize autonomous navigation in a complex flower field environment, avoid obstacles, and find the optimal path, thereby achieving accurate and efficient autonomous pollination.

[0027] Preferably, step S2 includes:

[0028] S21: Detect the target flower using the improved YOLOv5 algorithm;

[0029] S22: Calculate the distance between the target flower and the depth camera using a three-dimensional reconstruction algorithm, then combine the camera's intrinsic parameter matrix and the pixel coordinates of the target flower on the image plane to calculate the coordinates of the target flower in the camera coordinate system, and then convert the coordinates of the target flower in the camera coordinate system into coordinates in the world coordinate system where the robot dog is located through the pre-calibrated rotation matrix and translation vector of the camera relative to the robot dog.

[0030] The improved YOLOv5 algorithm can accurately detect target flowers in complex environments with a high recognition rate and low false detection rate. In addition, through the depth camera and three-dimensional reconstruction technology, accurate distance measurement of the target flowers can be achieved, so that the spatial coordinates of each flower in the world coordinate system of the robot dog can be accurately calculated, allowing the pollination device to perform precise operations on the flowers.

[0031] Further preferably, the step S3 includes:

[0032] S31: After receiving the activation signal, the robotic arm performs an initialization operation through the joint angle sensor carried by the robotic arm to measure the initial joint angle vectors of all joints of the robotic arm;

[0033] S32: using the depth camera to acquire an image of the target flower, and then matching the feature point set of the target flower on the camera image plane with the pre-stored standard feature point module of Amaryllis japonica based on the target positioning algorithm of feature point matching, and then calculating the matching error. When the matching error is less than a preset value, it is considered that the positioning is successful, otherwise, re-matching is performed;

[0034] S33: After successful positioning, the coordinates of the target flower in the camera coordinate system are calculated according to the camera's intrinsic parameter matrix and depth information, and then the coordinates of the target flower in the camera coordinate system are converted to the robotic arm base coordinate system through the pre-calibrated camera relative to the robotic arm base coordinate transformation matrix, thereby obtaining the precise position information of the target flower relative to the robotic arm base.

[0035] After the robot dog reaches a certain position range of the target flower, it activates the robot arm through an activation signal to prepare to perform the pollination task, and uses the depth camera again to more accurately locate the target flower. By converting the coordinates of the target flower in the camera coordinate system to the robot arm base coordinate system, more accurate target flower position information is obtained, thereby further improving the accuracy of autonomous pollination and improving the pollination effect.

[0036] Preferably, in step S4, the calculation formula for the joint angle adjustment amount is:

[0037]

[0038] Where f is the kinematic function of the robot arm, which represents the relationship between the joint angle and the position of the end effector; is the current joint angle vector of the robot arm at the kth frame image; (X bk (k), Y bk (k), Z bk (k)) is the real-time position of the target flower in the coordinates of the robot arm base.

[0039] Further preferably, the step S4 further includes: calculating the distance between the end effector and the target flower, comparing it with the preset precise operating distance, if it is less than the preset precise operating distance, stopping fine-tuning to complete the final confirmation of the flower position; otherwise, continuing fine-tuning until the calculated distance between the end effector of the robot arm and the target flower is less than the preset precise operating distance, wherein,

[0040] The calculation formula of the distance between the end effector and the target flower is:

[0041]

[0042] Among them, (X ek , Y ek , Z ek ) represents the coordinates of the current position of the end effector in the robot base coordinate system; (X bk (k), Y bk (k), Z bk (k)) represents the real-time position of the target flower in the coordinates of the robot base.

[0043] When the robotic arm approaches the flower, the above formula is used to calculate the joint angle adjustment amount of the current position of the robotic arm end effector relative to the real-time position of the target flower, and the position and posture of the robotic arm can be further fine-tuned until the distance between the robotic arm end effector and the target flower is less than the preset precise operating distance, thereby ensuring that the robotic arm end effector can obtain pollen from the anthers of the stamens and accurately release the pollen onto the stigma above the anthers, thereby achieving precise pollination operations, ensuring that the pollen is effectively transferred to the style, improving the pollination success rate, and thereby improving the yield and quality of Amomum villosum.

[0044] Preferably, in step S5, identifying and locating the style position includes:

[0045] S51: pre-processing the image containing the style obtained by the depth camera, and then using an image analysis algorithm based on edge detection and shape analysis to identify unique morphological features of the style;

[0046] S52: Calculating the central axis of the style according to the detected style edge line segment;

[0047] S53: Determine the position coordinates of the style in the three-dimensional space according to the central axis of the style and the depth information of the depth camera.

[0048] After determining the location of the target flower, in order to accurately release the pollen into the style, the style also needs to be identified and located, so that the pollen attached to the turntable goose down can be gently released onto the style using the "push-pull method" as the turntable rotates, thereby achieving autonomous pollination of the Amomum villosum flower.

[0049] Further preferably, in step S5, the use of flexible clamps to clamp the pistil includes: measuring the current clamp pressure in real time through a pressure sensor, and presetting a target closing pressure of the clamp, adjusting the clamp drive signal according to the error between the current clamp pressure and the target closing pressure during the process of clamping the pistil, and continuously updating the error until the current clamp pressure is close to the target closing pressure, thereby ensuring that the clamp can firmly clamp the pistil without causing damage to it.

[0050] In the present application, by setting a pressure sensor to control the pressure of the clamp, it can be effectively ensured that the clamp can firmly grasp the pistil without causing damage to it, so that the flexible clamp can be used to implement an anthropomorphic "push-pull" pollination technique and realize intelligent and autonomous pollination of the Ampelopsis ternata.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] The present application provides an autonomous pollination device and method for Amomum villosum to realize intelligent pollination of Amomum villosum, which solves the technical problem that the stigma of Amomum villosum is slightly higher than the anther due to its special floral structure, making it difficult for pollen to fall on the stigma, thus preventing self-pollination. At the same time, it also solves the problems of high labor cost, high operating technical difficulty, poor pollination effect and inability to be put into large-scale agricultural use in the existing assisted pollination technology, realizes intelligent and autonomous pollination of Amomum villosum, improves the pollination success rate, and thereby improves the yield and quality of Amomum villosum. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 This is a schematic diagram of the device structure provided in this embodiment.

[0054] Figure 2 This is a schematic diagram of the mechanical arm structure of the device provided in this embodiment.

[0055] Figure 3 It is a schematic diagram of the structure of the Amomum villosum described in this embodiment.

[0056] Description of the drawings: 1. Robot dog; 2. Docking station; 3. LiDAR; 4. Robot arm base; 5. Main robot arm; 6. First hinge; 7. Intermediate robot arm; 8. Second hinge; 9. Front robot arm; 10. Third hinge; 11. End effector; 12. Depth camera; 13. Flexible gripper; 14. Grip; 15. Turntable; 16. Goose down; 17. Bearing; 18. Rotating bearing; 19. Pollen sac (upper part of pseudo-synodic column); 20. Labellum of Yangchunhua; 21. Top of style (lobes of the appendage of the connective); 22. Style (pseudo-synodic column); DETAILED DESCRIPTION

[0057] The drawings of the present invention are only for illustrative purposes and should not be construed as limiting the present invention. In order to better illustrate the following embodiments, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of the actual product; it is understandable to those skilled in the art that some well-known structures and their descriptions in the drawings may be omitted.

[0058] Example 1

[0059] like Figure 1 As shown, this embodiment provides an autonomous pollination device for Amaryllis odoratus, the device comprising:

[0060] A mechanical dog 1 is arranged at the bottom of the pollination device and is used to control the moving direction and action route of the device;

[0061] The expansion dock 2 and the laser radar 3 are arranged above the robot dog 1. The laser radar 3 can be used to detect obstacles, and the expansion dock 2 can be used to expand the robot arm;

[0062] A robotic arm, installed on the expansion dock, for receiving pollination signals and executing pollination instructions; wherein the robotic arm is installed on the expansion dock 2 via a robotic arm base 4, and the robotic arm base 4 is sequentially connected with a main robotic arm 5, a first hinge 6, an intermediate robotic arm 7, a second hinge 8, a front robotic arm 9, a third hinge 10 and an end effector 11;

[0063] The end effector 11 is connected to the end of the robot arm and is used to aim at the target flower;

[0064] The depth camera 12 is connected to the end effector 11 and is used to collect environmental information and collect images of the sand flower;

[0065] The flexible clamp 13 is connected in front of the depth camera 12 and is used for pollinating the Amaryllis japonica detected by the depth camera 12 .

[0066] In this application, by equipping with laser radar 3, depth camera 12 and other equipment, the surrounding environment can be scanned, autonomous navigation can be achieved, obstacles can be avoided, the location of the target plant can be identified, and each plant that needs to be pollinated can be accurately reached according to the planned path, such as Figure 3 As shown, Figure 3 It is the structural schematic diagram of Amomum villosum, and it can be seen from the figure that the flower organ structure of Amomum villosum is relatively special, and the position of its pollen sac 19 is lower than style 22, so pollen is difficult to fall on the stigma and is difficult to complete self-pollination. Therefore, in the present application, it can be realized by mechanical arm that the pollen in the anther of the stamen under the stigma of the pistil is transferred to the stigma, so as to achieve efficient and accurate pollination, solve the technical problem that the flower organ structure of Amomum villosum is slightly higher than the anther than the special stigma, and pollen is difficult to fall on the stigma, and thus can not self-pollinate, improve the pollination success rate, and also avoid the omission or uneven situation that may occur in traditional artificial pollination, reduce human intervention and labor cost, strong adaptability, and easy to put into large-scale agricultural production.

[0067] Preferably, if Figure 2 As shown, the flexible clamp 13 includes a gripper 14 and a turntable 15, wherein the turntable 15 and the gripper 14 are arranged on the same bearing 17, a transmission bearing 18 is arranged at the center of the turntable 15 for controlling the rotation of the turntable 15, and the turntable 15 is located above the gripper; goose down 16 is arranged on the outer ring of the turntable 15 and inside the gripper 14, as the turntable 15 rotates, pollen adheres to the surface of the goose down 16, and at the same time, the gripper 14 moves the pollen upward along the style until the goose down 16 of the turntable delivers the pollen to the top of the style, thereby completing pollination.

[0068] The flexible clamping claw 13 provided in the present application can be used for Figure 3The stigma in the stamen shown is completed in an embracing manner, and then the turntable 15 is rotated to allow the pollen on the pollen sac 19 under the stigma of the pistil to adhere to the goose down 16 of the turntable 15, and then, driven by the robotic arm, the turntable 15 with the pollen attached is gently swept across the top of the stigma in the stamen using the "push-pull method", thereby completing autonomous pollination, filling the technical gap in the pollination of Amomum villosum by robotic arms, and effectively increasing the yield of Amomum villosum.

[0069] Example 2

[0070] This embodiment also provides a method for autonomous pollination of Amomum villosum, the method comprising the following steps:

[0071] Step S1: Start the robot dog and initialize it, then start the depth camera, and use the depth camera to collect environmental information in real time for path planning; wherein, in this embodiment, the depth camera is RealSense D435i.

[0072] Specifically, step S1 includes:

[0073] Step S11: Use the built-in inertial measurement unit (IMU) and odometer to determine the initial position coordinates (X0, Y0, Z0), set the initial velocity to zero, and use the inertial measurement unit to measure the acceleration a of the robot dog on the x, y, and z axes. x 、a y 、a z and angular velocity ω x ,ω y ,ω z ;

[0074] Step S12: using an improved A* algorithm to perform path planning, the robot dog uses the terrain information obtained by the depth camera and the laser radar, and then calculates the path to the approximate area of ​​flower distribution determined by the pre-scan in combination with its own position;

[0075] Specifically, the robot dog determines the terrain type through image analysis by presetting characteristic thresholds of different terrains, and then calculates the path to the approximate area of ​​flower distribution determined by the pre-scan in combination with its own position;

[0076] Assume the current position of the robot dog is (X n , Y n , Z n ), the center position of the target area is (X g , Y g , Z g), the target area is the approximate area of ​​flower distribution determined by the pre-scan. In the A* algorithm, the evaluation function of the node is f(n)=g(n)+h(n), where the actual cost g(n) from the starting node to the current node n is calculated taking into account the terrain cost C t (n), for example, the base value of the moving cost of ordinary ground is set to 1, the cost of soft land is set to 3, the cost of uneven area is set to 5, and the distance cost C d (n) and other costs of the robot dog's movement plus the turning cost C s (n) to express it, we can get:

[0077]

[0078] The distance cost can be calculated using the Euclidean distance formula:

[0079]

[0080] Where (X n-1 , Y n-1 , Z n-1 ) is the coordinate of the previous node before reaching node n.

[0081] The heuristic estimated cost from the current node n to the target node h(n) combines the distance and flower density information. The flower density ρ is obtained by scanning the surrounding small area in real time with a depth camera to estimate the number of flowers and the ratio of the area. Then, we can get:

[0082]

[0083] Among them, k is the weight coefficient, which is used to adjust the impact of flower density on the heuristic cost. The specific value can be determined according to the actual situation. For example, the optimal value can be determined by experimentally comparing the path planning effects under different values.

[0084] During the search process, the node with the smallest evaluation function value is selected for expansion, that is, As the next node to be searched, the path to the approximate area of ​​flower distribution determined by the pre-scan is calculated.

[0085] Step S13: The robot dog 1 moves along the planned path and updates its own position coordinates according to the inertial measurement unit and odometer data at regular time intervals during the movement.

[0086] Specifically, during the movement, the time interval can be set to 0.5 seconds, and then the position coordinates are updated according to the IMU and odometer data. The position update formula is as follows:

[0087] v x (t+Δt)=v x(t)+a x Δt

[0088] v y (t+Δt)=v y (t)+a y Δt

[0089] v z (t+Δt)=v z (t)+a z Δt

[0090] At the same time, the environment is continuously monitored using a depth camera. If an obstacle is found, the vehicle immediately stops moving and replans the route to bypass the obstacle before continuing to move forward. The obstacle is an object detected by an image recognition algorithm that matches a preset obstacle model.

[0091] Therefore, in this embodiment, the robot dog performs path planning through the improved A* algorithm, can realize autonomous navigation in a complex flower field environment, avoid obstacles, and find the optimal path, thereby achieving accurate and efficient autonomous pollination.

[0092] Preferably, in step S2, the depth camera is used to collect images in real time while the robot dog moves along the planned route, and the target flower is detected and located by a target detection algorithm;

[0093] Specifically, step S2 includes:

[0094] Step S21: The target flowers are detected using the improved YOLOv5 algorithm. When training the YOLOv5 algorithm, the network structure is optimized according to the morphological characteristics of the Amaryllis odoratus, and the attention layer for key features such as the style and pollen sac is increased. The YOLOv5 algorithm is improved through data enhancement techniques (such as random illumination changes, angle rotation, etc.), such as adding an attention mechanism, thereby improving the generalization ability of the model and making the detection rate of the target flowers higher.

[0095] Step S22: Use the three-dimensional reconstruction algorithm to calculate the distance between the target flower and the depth camera, and then combine the camera's intrinsic parameter matrix and the pixel coordinates of the target flower on the image plane to calculate the coordinates of the target flower in the camera coordinate system. Then, use the pre-calibrated rotation matrix and translation vector of the camera relative to the robot dog to convert the coordinates of the target flower in the camera coordinate system into the coordinates in the world coordinate system where the robot dog is located.

[0096] Specifically, when the target flower is detected, the depth camera emits infrared light and sets the camera baseline length to b. The camera baseline length represents the distance between two infrared light emitting points or receiving points, which is a known parameter of the camera. The angles formed by the two infrared light receiving points and the object are α and β, respectively. These two parameters are measured by the optical system and sensor inside the camera.

[0097] Then, the distance between the object and the camera, that is, the depth value, is calculated according to the 3D reconstruction algorithm. The calculation formula is:

[0098]

[0099] Where, f is the focal length of the camera, (x l ,y l ) and (x r ,y r ) are the pixel coordinates of the image of the object at the left and right infrared light receiving points, which are obtained through the camera image sensor.

[0100] Assume that the center of the camera image plane is the coordinate origin (0, 0), the horizontal direction of the image plane is the x-axis, and the vertical direction is the y-axis. Then, the camera intrinsic parameter matrix k is obtained by camera calibration. The form of the intrinsic parameter matrix k is:

[0101]

[0102] Among them, f x and f y are the focal lengths in the x and y directions, c x and c y are the coordinates of the center of the image plane.

[0103] Assume that the pixel coordinates of the target flower on the image plane are (u, v), and combined with the depth value d calculated by the depth camera, we can get the pixel coordinates of the target flower in the camera coordinate system (X c , Y c , Z c ) is calculated by:

[0104]

[0105] Then, the coordinates are transformed to the world coordinate system where the robot dog is located (X w , Y w , Z w), where the rotation matrix R is a 3×3 matrix, which represents the rotation relationship of the camera in the robot dog coordinate system and is obtained by pre-calibration; the translation vector t is a 3×1 vector, which represents the translation relationship of the camera in the robot dog coordinate system and is also a calibration parameter. The calculation formula for converting the coordinates of the target flower in the camera coordinate system to the coordinates in the world coordinate system where the robot dog is located is:

[0106]

[0107] Therefore, in this embodiment, the improved YOLOv5 algorithm can accurately detect the target flowers in a complex environment with a high recognition rate and a low false detection rate. In addition, through the depth camera and three-dimensional reconstruction technology, accurate distance measurement of the target flowers can be achieved, so that the spatial coordinates of each flower in the world coordinate system where the robot dog is located can be accurately calculated, so that the pollination device can perform precise operations on the flowers.

[0108] Preferably, in step S3, when the robot dog reaches within a certain range of the detected target flower, the robot arm installed on the robot dog is activated and the target flower is re-positioned using the depth camera, thereby obtaining accurate position information of the flower relative to the robot arm; wherein, in this embodiment, the certain range of the target flower is set as a circular area with a radius of 1 meter with the target flower as the center according to the working distance and accuracy of the robot arm, and when the robot dog reaches within the circular area with a radius of 1 meter from the center of the target flower, it triggers a signal to activate the pollination robot arm.

[0109] Specifically, step S3 includes:

[0110] Step S31: After receiving the activation signal, the robotic arm performs an initialization operation through the joint angle sensor carried by the robotic arm to measure the initial joint angle vectors of all joints of the robotic arm;

[0111] Assume that the robot arm has n joints in total, and the initial angle of the i-th joint measured by the joint angle sensor is θ i0 (i=1,2,…,n), then the initial joint angle vector of the robot arm is

[0112] Step S32: Use the depth camera to obtain the image of the target flower, and then match the feature point set of the target flower on the camera image plane with the pre-stored standard feature point module of Amaryllis japonica based on the target positioning algorithm of feature point matching. Suppose the feature point set of the target flower on the camera image plane is P = {p1, p2, ..., p m}, each feature point p j With coordinates (u j , v j), j = 1, 2, ..., m; then by comparing with the pre-stored Yangchun sand flower standard feature point template T = {t1, t2, ..., t m} for matching, where the coordinates of the feature points of the standard template are (u j ′,v j ′);

[0113] Then the matching error is calculated, and the formula for calculating the matching error is:

[0114]

[0115] In this embodiment, the threshold e is set according to the actual positioning accuracy requirement. threshold Set to 5 pixels, when e is less than the set threshold e threshold If the error is less than 5 pixels, it means that the target flower is successfully located. Otherwise, the matching is repeated and the error is recalculated for judgment.

[0116] Step S33: After successful positioning, the coordinates of the target flower in the camera coordinate system are calculated based on the camera's intrinsic parameter matrix and depth information, where the depth information is the target flower feature point p j The corresponding depth value d j ;

[0117] For feature point p j , its coordinates in the camera coordinate system (X cj , Y cj , Z cj ) is calculated by:

[0118] Z cj =d j

[0119]

[0120] Among them, c x , c y , f x , f y are the parameters in the camera intrinsic parameter matrix k.

[0121] Then, the coordinates of the target flower in the camera coordinate system are transformed into the coordinate system of the robot base through the pre-calibrated coordinate transformation matrix M of the camera relative to the robot base, wherein the coordinate transformation matrix M is a 4×4 matrix containing rotation and translation information, which is obtained through calibration;

[0122] Then let the transformed coordinates be (X bj , Y bj , Z bj), the precise position information of the target flower relative to the base of the robotic arm can be obtained by combining the above parameters, where the calculation formula is:

[0123]

[0124] Therefore, after the robotic dog in this embodiment reaches a certain position range of the target flower, it activates the robotic arm through an activation signal to prepare to perform the pollination task, and uses the depth camera again to more accurately locate the target flower. By converting the coordinates of the target flower in the camera coordinate system to the robotic arm base coordinate system, more accurate target flower position information is obtained, thereby further improving the accuracy of autonomous pollination and improving the pollination effect.

[0125] Step S4: When the robot arm approaches the flower, it moves toward the target flower at a speed of 0.1 m / s, while continuously using the depth camera and lidar for real-time monitoring. The depth camera acquires images at a frame rate of f=30 frames per second, and the target monitoring algorithm is used to determine the position of the target flower in each frame.

[0126] Suppose the coordinates of the center of the target flower detected in the k-th frame image on the image plane are (u k , v k ), calculate the real-time position (X) of the target flower in the coordinate system of the robot base according to the camera calibration parameters and the previously calculated depth information of the target flower in the coordinate system of the robot base. bk (k), Y bk (k), Z bk (k)), the calculation method is consistent with the above-mentioned coordinate conversion formula for converting the coordinates of the target flower in the camera coordinate system to the coordinate system of the robot base, and will not be repeated here.

[0127] Then, the joint angle adjustment of the robot arm is calculated based on the real-time position of the flower and the current position of the end effector, so as to fine-tune the position and posture of the robot arm and finally confirm the position of the flower.

[0128] Specifically, the coordinates of the current position of the end effector of the robot arm in the robot arm base coordinate system are (X ek , Y ek , Z ek ), the coordinates are calculated by the joint angles and kinematic model of the robot; then the kinematic model of the robot is assumed to be in, is the position coordinate of the end effector of the robot arm in the base coordinate system; is the robot joint angle vector; f is the robot kinematic function, which represents the relationship between the joint angle and the end effector position; then the kinematic model is inverted to obtain Then the calculation formula of the joint angle adjustment amount is obtained as follows:

[0129]

[0130] Where f is the kinematic function of the robot arm, which represents the relationship between the joint angle and the position of the end effector; is the current joint angle vector of the robotic arm at the kth frame image.

[0131] Preferably, the step S4 further includes: in the process of the robot arm controlling the movement of each joint to achieve fine adjustment of the position and posture according to the calculated joint angle adjustment amount, the distance between the end effector and the target flower is calculated in real time, and the distance is compared with the preset precise operation distance. In this embodiment, the preset precise operation distance d is calculated. precision Set to 10 cm. If it is less than the preset precise operating distance, stop fine-tuning, complete the final confirmation of the flower position, and prepare for pollination; otherwise, continue fine-tuning until the calculated distance between the end effector of the robot arm and the target flower is less than the preset precise operating distance.

[0132] The calculation formula of the distance between the end effector and the target flower is:

[0133]

[0134] Among them, (X ek , Y ek , Z ek ) represents the coordinates of the current position of the end effector in the robot base coordinate system; (X bk (k), Y bk (k), Z bk (k)) represents the real-time position of the target flower in the coordinates of the robot base.

[0135] In the process of the robotic arm approaching the flower in this embodiment, the joint angle adjustment amount of the current position of the robotic arm end effector relative to the real-time position of the target flower is calculated by the above formula, and the position and posture of the robotic arm can be further fine-tuned until the distance between the robotic arm end effector and the target flower is less than the preset precise operating distance, thereby ensuring that the robotic arm end effector can obtain pollen from the anthers of the stamens and can accurately release pollen onto the stigma above the anthers, thereby achieving precise pollination operations, ensuring that pollen is effectively transferred to the style, improving the pollination success rate, and thereby improving the yield and quality of Amomum villosum.

[0136] Step S5: Identify and locate the position of the style, then use the robotic arm to control the transmission bearing to rotate at a constant speed to drive the turntable to make the pollen adhere to the goose down on the turntable, and use the flexible claws to clamp the style and send the pollen on the turntable to the top of the style to complete pollination.

[0137] Specifically, the following steps are included:

[0138] Step S51: After the robot arm completes the final confirmation of the flower position, the depth camera is used to perform secondary positioning and identification on the style of the target flower in a static position. After the depth camera obtains the image containing the style, it performs image preprocessing, including graying, filtering (such as Gaussian filtering to remove noise), and other operations. The preprocessed image is set to I.

[0139] Then, an image analysis algorithm based on edge detection and shape analysis is used to identify the unique morphological features of the style. Edge detection is performed on image I, for example, using the Canny edge detection algorithm to obtain an edge image E. In edge image E, straight line segments are detected by a shape analysis algorithm, such as Hough transform. Combined with the prior knowledge of the shape of the style of Amomum villosum, the style is usually slender, and constraints such as the length of the straight line segment and the angle range can be set to detect possible style edge segments. Suppose the set of detected style edge segments is L = {l1, l2, ..., l q}, for each line segment l i , whose endpoint coordinates on the image plane are (x i1 ,y i1 ) and (x i2 ,y i2 ).

[0140] Step S52: Calculate the central axis of the style according to the detected style edge line segments; assume that the parametric equation of the central axis of the style is y=ax+b, which is for a two-dimensional image. If three-dimensional space is considered, it can be expanded to a plane equation and the like. Then, the least squares method is used to fit the edge line segment set L to determine the parameters a and b. The goal of the least squares method is to minimize the error function Among them, d(l i , y = ax + b) represents line segment l i The distance to the straight line y=ax+b, calculate the partial derivatives of e(a, b) with respect to a and b respectively and set them to zero, solve the system of simultaneous equations to obtain the values ​​of a and b, and thus determine the equation of the central axis of the style.

[0141] Step S53: Determine the position coordinates of the style in three-dimensional space based on the central axis of the style and the depth information of the depth camera. Select a number of points on the central axis equation (for example, select p points at equal intervals), and set the coordinates of the selected points on the image plane to (u i , v i), i = 1, 2, ..., p; the coordinates of these points in the camera coordinate system are calculated through camera calibration and depth information (X ci , Y ci , Z ci ), the coordinate transformation formula is the same as that in the previous step and will not be repeated here.

[0142] Finally, by averaging or weighted averaging these coordinates, the final position coordinates of the style are obtained to ensure the accuracy of the pollination operation. For example, in this embodiment, the weighted average method is used, and the weight W is set i It is related to the distance from the point to the center of the image. The closer the distance to the center of the image, the greater the weight. The weight function can be set according to the specific situation, such as:

[0143]

[0144] Where D is the length of the image diagonal, the final position coordinates of the style are:

[0145]

[0146] Therefore, after determining the position of the target flower, the pollen can be accurately released into the pistil. The pollen attached to the goose down on the turntable can be gently released onto the pistil using the "push-pull method" as the turntable rotates, thus achieving autonomous pollination of the Amomum villosum flower.

[0147] Further preferably, in step S5, the use of a flexible clamp to clamp the pistil includes: after the robot arm receives the clamping instruction, the flexible clamp of the end effector is controlled to start moving. The driving system of the flexible clamp is composed of a motor (such as a DC motor) and a transmission mechanism (such as a gear transmission, a connecting rod mechanism, etc.), and the rotation of the motor is converted into the opening and closing movement of the clamp through the transmission mechanism. Assume that the input voltage of the motor is U, the speed of the motor is n, and the torque of the motor is T. There is a certain relationship between them, which can be determined according to the motor characteristic curve, and can generally be approximately expressed as T=k T n, where k T is the torque coefficient, obtained through experimental tests.

[0148] The opening and closing angle α of the clamp is related to the rotation angle θ of the motor through the transmission ratio i of the transmission mechanism, that is, α=iθ. The closing force F of the clamp is related to the opening and closing angle α of the clamp, the structural parameters of the clamp (such as the clamp length L, the clamp width W, etc.) and the elastic modulus E of the clamp material. The relationship can be established through mechanical analysis. For example, a simple approximation is F=k F (α0-α), where α0 is the angle when the jaws are fully opened; k F It is a coefficient related to the structure and material of the gripper and is determined through experiments and mechanical calculations.

[0149] Then the current gripper pressure P is measured in real time by the pressure sensor current , and preset the target closing pressure P of the clamping jaws target In the process of clamping the style, the control system determines the error between the current clamping pressure and the target closing pressure according to e1 = P target -P current To adjust the driving signal of the gripper (i.e. the input voltage U of the motor), a simple proportional control algorithm is used, and the driving signal adjustment amount △U=K p e1, where K p is the proportional coefficient, which is determined according to the characteristics of the gripper drive system and experiments.

[0150] Thus, the error is continuously updated during the control process until the current clamp pressure approaches the target closing pressure, ensuring that the clamp can firmly grasp the style without damaging it. target =15N (determined according to the bearing capacity of the style and the requirements for grip stability), initial P target =0.5L, if K p =0.5V / NV / N, then the first adjustment value △U = 0.5×(1.5-0.5) = 0.5v. After adjusting the motor input voltage, measure P again. current , repeat the above process until the clamping jaw pressure reaches the target value.

[0151] Then, the pollen is transferred, and the robot arm controls the rotating bearing to drive the turntable. The rotating bearing is driven by a high-precision servo motor. The control of the servo motor is based on the feedback signal of its internal encoder to achieve precise position and speed control. Set the target speed n in the control system target =5 rpm, and use the encoder to monitor the actual motor speed n in real time actual .

[0152] Assume the control voltage of the motor is U m , a simple proportional integral (PI) control algorithm is used to adjust the motor speed and control the voltage U m =K p ×(n target -n actual )+K i ×∫(n target -n actual )dt, where K p is the proportionality coefficient; K i is the integral coefficient, which is determined according to the motor characteristics and experiments. For example, in this embodiment, the speed response curve of the motor under different control voltages is obtained through experimental testing, and K is determined according to the characteristics such as the slope of the curve and the steady-state error. p and K i If the value of K p =0.2V / (rev / min), When n actual =4 rpm, U m =0.2×(5-4)+0.05×∫(5-4)dt=0.2+0.05t, where t is time. As time increases, the integral term gradually increases, causing the motor speed to approach the target speed faster.

[0153] Then, the goose down covering the turntable collects pollen in the pollen sac through its softness and adsorption. As the turntable rotates, the pollen adheres to the surface of the goose down. In order to ensure the pollen collection effect, the material and density of the goose down can be optimized. Assume that the adsorption force F of the goose down is a It is related to factors such as the fiber length l, fiber diameter d, and density ρ of goose down (an empirical formula can be established through experiments, such as F a =k a l 2 ρd, where k a is the adsorption coefficient, which is determined through experiments. According to the characteristics of pollen and collection requirements, appropriate goose down parameters are selected to make F a Can effectively absorb pollen.

[0154] The pollen is transported upward to the style above the pollen sac. During the rotation of the turntable, the gripper slightly moves the pollen upward along the style at a slow and uniform speed. In this embodiment, the target speed is set to v target = 3 mm / s. The position and speed of the gripper are precisely controlled by the robotic arm, and the robotic arm control system generates a control signal according to the preset movement trajectory (such as straight rise along the central axis of the style) and speed parameters.

[0155] Use the encoder (or other position sensor) at the joint of the robot arm to obtain the current position information of the gripper. Let the current position of the gripper be x current , by comparing with the target position (determined according to the style length and pollination position requirements, such as 5 mm below the top of the style as the target position x target ) to compare and calculate the position error e x =x target -x current At the same time, a visual sensor (such as a small camera mounted on a robotic arm) or a distance sensor (such as an ultrasonic sensor) is used to monitor the relative position of the gripper and the style in real time to obtain the distance h between the gripper and the style surface in the vertical direction.

[0156] Assume that the driving signal of the control system to the gripper is U g , according to the speed error e v =v target -v current (where v currentThe current speed of the gripper is measured or calculated by the sensor) to adjust the gripper's drive signal. A similar proportional control algorithm is used. The drive signal adjustment amount △U g =K pv ×e v (where K pv is the speed control proportional coefficient, which is determined according to the gripper drive system characteristics and experiments). For example, if K pv =0.3V / (mm / s), when v current =0.2 mm / s, △U g =0.3×(3-2)=0.3V, adjust the drive signal to make the gripper speed approach the target speed. At the same time, fine-tune the gripper position according to the change of distance h. If h deviates from the preset ideal distance (such as h ideal =2 mm), the gripper position is corrected by adjusting the joint angle of the robotic arm to ensure that the pollen is transported upward smoothly until the goose down of the turntable delivers the pollen to the top of the style.

[0157] Then the pollen needs to be released to complete pollination. The pre-set position sensor or visual recognition determines that the turntable moves to the top of the style and controls the turntable to rotate in the opposite direction. Set the motor reversal time t reverse = 1 second. During the reversal process, the motor speed change is controlled by a trapezoidal speed curve. Assume that the initial motor speed is n start = 0 rpm, the acceleration phase time is t acc = 0.3 seconds, the speed at the end of the acceleration phase is n max =10 rpm, the deceleration time is t dec = 0.3 seconds, then the uniform speed stage time t const =t reverse -t acc -t dec = 0.4 seconds;

[0158] During the acceleration phase, the calculation formula for the motor speed n(t) is:

[0159] In the uniform speed stage n(t) = n max (t acc <t≤t acc +t const );

[0160] In the deceleration phase

[0161] Through such speed control, the motor gradually increases to a higher value during the reversal process, and then reverse At the end of the time, it gradually drops to zero, achieving the effect of slight shaking at the top of the style, ensuring that the pollen is effectively released and accurately reaches the top of the style.

[0162] After the pollination operation is completed, the pollination robot arm returns to its initial state according to the preset reset path. The preset reset path can be that the robot arm joints move back to the initial position in sequence according to a specific angle sequence. Suppose the robot arm joint angle vector is The initial joint angle vector is During the resetting process, each joint moves according to a certain speed curve (such as a linear speed curve or an exponential speed curve, etc., which is selected according to the requirements of the smoothness of the robot movement). For example, if a linear speed curve is used, the angle change formula of joint i is θ i (t) = θ i0 +v θi (tt start ), where v θi is the resetting speed of joint i, t start It is the reset start time until all joint angles return to their initial values, ready for the next pollination task.

[0163] After pollination is completed, the robot dog detects the surrounding environment again through the depth camera. The depth camera obtains point cloud data of the surrounding environment and processes and analyzes the data. First, the data is filtered (such as statistical filtering to remove outliers, voxel filtering to reduce data density, etc.) to obtain more accurate environmental point cloud information.

[0164] Then, the target flower is searched for in the point cloud data through the target detection algorithm (such as the target detection network based on deep learning or the traditional target detection algorithm based on geometric features). Suppose the point cloud set of the detected target flower is C = {c1, c2, ..., c q}, each point c j With three-dimensional coordinates (x j ,y j , z j ), calculate the center position of the point cloud set

[0165]

[0166] According to the center position of the target flower and the current position of the robot dog (obtained through the robot dog's own positioning system, set it as (X d , Y d , Z d ))Distance

[0167]

[0168] and the working range of the robot dog (set as R work , determined by the mechanical structure and movement ability of the robot dog) to evaluate whether there is a suitable next pollination target. cd ≤R work, it is considered that there is a suitable target, and the robot dog moves to the vicinity of the target flower and repeats the above target flower identification and subsequent pollination steps; if d cd >R work , it performs corresponding operations according to the preset instructions, such as returning to the charging area after completing the pollination task of the entire flower field (achieved through the pre-set charging area position coordinates and path planning algorithm) or waiting for new instructions (communicating with the external control system to obtain new instructions).

[0169] Therefore, in this embodiment, the surrounding environment is collected in real time by using a depth camera, and the route of the robot dog is planned according to the environmental information. The target detection algorithm is also used to effectively detect the target flower and position it so that the robot dog can accurately reach a certain range of the target flower, and activate the robotic arm to accurately position the target flower and the target pistil again, thereby achieving precise pollination operations, ensuring the effective transfer of pollen, and improving the success rate of pollination, thereby avoiding omissions or unevenness that may occur in traditional artificial pollination, reducing human intervention, and reducing dependence on natural pollinators (such as bees and other insects) through automated pollination. This helps to maintain crop growth and yield when the number of pollinators is insufficient or the ecological environment is disturbed, and is suitable for large-scale agricultural production.

[0170] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the technical solution of the present invention, and are not intended to limit the specific implementation methods of the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the claims of the present invention shall be included in the protection scope of the claims of the present invention.

Claims

1. An autonomous pollination device for Amaryllis odoratus, characterized in that: The device comprises: A mechanical dog, which is set at the bottom of the pollination device, is used to control the moving direction and route of the device; The expansion dock and the laser radar are arranged above the robot dog. The laser radar can be used to detect obstacles, and the expansion dock can be used to expand the robot arm; A robotic arm, mounted on the expansion dock, is used to receive pollination signals and execute pollination instructions; The end effector, which is attached to the end of the robotic arm and is used to aim at the target flower; A depth camera, connected to the end effector, is used to collect environmental information and collect images of sand flowers; A flexible clamp is connected in front of the depth camera and is used to pollinate the Ampelopsis ternata detected by the depth camera.

2. The autonomous pollination device for Amomum villosum according to claim 1, characterized in that: The flexible clamp includes a gripper and a turntable, wherein the turntable and the gripper are arranged on the same bearing, a transmission bearing is arranged at the center of the turntable for controlling the rotation of the turntable, and the turntable is located above the gripper; goose down is arranged on the outer ring of the turntable and inside the gripper, and as the turntable rotates, pollen adheres to the surface of the goose down, and at the same time, the gripper moves the pollen upward along the pistil until the goose down on the turntable delivers the pollen to the top of the pistil, thereby completing pollination.

3. A self-pollination method for Amomum villosum, characterized in that: The method comprises the following steps: S1: Start the robot dog and initialize it, then start the depth camera to collect environmental information in real time for path planning; S2: When the robot dog moves along the planned route, it uses the depth camera to collect images in real time, and uses the target detection algorithm to detect and locate the target flower; S3: When the robot dog reaches a certain range of the detected target flower, the robot arm installed on the robot dog is activated and the target flower is re-positioned using the depth camera, so as to obtain accurate position information of the flower relative to the robot arm; S4: When the robot arm approaches the flower, the joint angle adjustment of the robot arm is calculated according to the real-time position of the flower and the current position of the end effector, so as to fine-tune the position and posture of the robot arm, thereby finally confirming the position of the flower; S5: Identify and locate the position of the style, then use the robotic arm to control the transmission bearing to rotate at a constant speed to drive the turntable to make the pollen adhere to the goose down on the turntable, and use the flexible claws to grasp the style and send the pollen on the turntable to the top of the style to complete pollination.

4. The autonomous pollination method for Amomum villosum according to claim 3, characterized in that: The step S1 includes the following steps: S11: Use the built-in inertial measurement unit and odometer to determine the initial position coordinates, set the initial velocity to zero, and use the inertial measurement unit to measure the acceleration and angular velocity of the robot dog on the x, y, and z axes; S12: Using an improved A* algorithm to perform path planning, the robot dog uses the terrain information obtained by the depth camera and then calculates a path to the approximate area of ​​flower distribution determined by the pre-scan in combination with its own position; S13: The robot dog moves along the planned path, and updates its own position coordinates at regular time intervals according to the inertial measurement unit and odometer data during the movement, while continuously monitoring the environment using a depth camera. If an obstacle is found, the robot dog immediately stops moving, replans the route to bypass the obstacle, and then continues to move forward.

5. The autonomous pollination method for Amomum villosum according to claim 3, characterized in that: The step S2 includes: S21: Detect the target flower using the improved YOLOv5 algorithm; S22: Calculate the distance between the target flower and the depth camera using a three-dimensional reconstruction algorithm, then combine the camera's intrinsic parameter matrix and the pixel coordinates of the target flower on the image plane to calculate the coordinates of the target flower in the camera coordinate system, and then convert the coordinates of the target flower in the camera coordinate system into coordinates in the world coordinate system where the robot dog is located through the pre-calibrated rotation matrix and translation vector of the camera relative to the robot dog.

6. The autonomous pollination method for Amomum villosum according to claim 3, characterized in that: The step S3 includes: S31: After receiving the activation signal, the robotic arm performs an initialization operation through the joint angle sensor carried by the robotic arm to measure the initial joint angle vectors of all joints of the robotic arm; S32: using the depth camera to acquire an image of the target flower, and then matching the feature point set of the target flower on the camera image plane with the pre-stored standard feature point module of Amaryllis japonica based on the target positioning algorithm of feature point matching, and then calculating the matching error. When the matching error is less than a preset value, it is considered that the positioning is successful, otherwise, re-matching is performed; S33: After successful positioning, the coordinates of the target flower in the camera coordinate system are calculated according to the camera's intrinsic parameter matrix and depth information, and then the coordinates of the target flower in the camera coordinate system are converted to the robotic arm base coordinate system through the pre-calibrated camera relative to the robotic arm base coordinate transformation matrix, thereby obtaining the precise position information of the target flower relative to the robotic arm base.

7. The autonomous pollination method for Amomum villosum according to claim 3, characterized in that: In step S4, the calculation formula of the joint angle adjustment amount is: Where f is the kinematic function of the robot arm, which represents the relationship between the joint angle and the position of the end effector; is the current joint angle vector of the robot arm at the kth frame image; (X bk (k), Y bk (k), Z bk (k)) is the real-time position of the target flower in the coordinates of the robot arm base.

8. The autonomous pollination method for Amomum villosum according to claim 7, characterized in that: The step S4 also includes: calculating the distance between the end effector and the target flower, comparing it with the preset precise operating distance, if it is less than the preset precise operating distance, stopping fine-tuning to complete the final confirmation of the flower position; otherwise, continuing fine-tuning until the calculated distance between the end effector and the target flower is less than the preset precise operating distance, wherein, The calculation formula of the distance between the end effector and the target flower is: Among them, (X ek , Y ek , Z ek ) represents the coordinates of the current position of the end effector in the robot base coordinate system; (X bk (k), Y bk (k), Z bk (k)) represents the real-time position of the target flower in the coordinates of the robot base.

9. The autonomous pollination method for Amomum villosum according to claim 3, characterized in that: In step S5, identifying and locating the style position includes: S51: pre-processing the image containing the style obtained by the depth camera, and then using an image analysis algorithm based on edge detection and shape analysis to identify unique morphological features of the style; S52: Calculating the central axis of the style according to the detected style edge line segment; S53: Determine the position coordinates of the style in the three-dimensional space according to the central axis of the style and the depth information of the depth camera.

10. The autonomous pollination method for Amomum villosum according to claim 9, characterized in that: In step S5, the method of clamping the pistil with a flexible clamp includes: measuring the current clamp pressure in real time through a pressure sensor, and presetting a target closing pressure of the clamp, adjusting the clamp drive signal according to the error between the current clamp pressure and the target closing pressure during the process of clamping the pistil, and continuously updating the error until the current clamp pressure approaches the target closing pressure, thereby ensuring that the clamp can firmly clamp the pistil without causing damage to it.

Images