Automatic and rapid picking method for nut green peel fruits in hilly areas

Abstract

The invention provides an automatic quick picking method for nut green peel fruits in hilly areas, and belongs to the technical field of nut green peel fruit picking. The method comprises the following steps: arranging a picking trolley, driving the picking trolley to the front end of a nut tree, then detecting whether a bottom plate of the trolley is in a horizontal position or not, and if the bottom plate is not in the horizontal position, starting the picking trolley; adjusting to a horizontal position through a lifting device; performing initial imaging positioning on the nut green peel fruits by using a laser radar; constructing a nut green peel fruit picking three-dimensional space model; the picking sequence of all nuts is calculated according to the picking three-dimensional space model, and then a manipulator is controlled to pick the nuts one by one; after the nuts on one side are picked, the trolley moves to one side beside the nut tree until the whole nut is picked, and the nut can be picked from the east, west, south and north directions. The picking process is fully automatic, the picking effect is high, the probability of missing picking is very low, and accurate and rapid picking is achieved.

Description

Technical Field

[0001] The invention relates to the technical field of picking green-skinned nuts, and in particular to an automatic and fast picking method for green-skinned nuts in hilly areas. Background Art

[0002] Macadamia nuts are generally harvested manually or by mechanical vibration. Manual harvesting is when the fruit is ripe, using fruit hooks to peel off the involucres one by one. When using mechanical harvesting, the fruit trees are sprayed with ethephon two weeks before harvesting to accelerate ripening, and then the branches are vibrated mechanically to make the ripe fruits fall. The use of ethephon will cause the leaves to fall off early, which will affect the growth of the fruit trees and the yield of the following year. At present, most of the methods used in China are mechanical vibration, or manipulators are used for assisted harvesting. However, the manipulator picking does not have the function of automatically locating and identifying the green-skinned fruits of nuts, so the picking efficiency is low, the speed is slow, and the effect is not good.

[0003] Since the color of nuts and green fruits is very similar to the color of leaves, it is difficult to identify them when using image recognition. Since the colors of fruits and leaves are similar, the difficulty of identification is increased, and the position of nuts and green fruits cannot be accurately identified, which makes it very difficult to use manipulators to pick them, and the efficiency is low. It is necessary to build a more accurate recognition method to achieve more efficient control of the manipulator to continue picking. Therefore, an automatic and fast picking method for nuts and green fruits in hilly areas is designed. Summary of the invention

[0004] The purpose of the present invention is to provide a method for automatically and quickly picking green-skinned nuts in hilly areas, so as to solve the technical problems of low picking efficiency and easy missing of green-skinned nuts in existing nut picking methods.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0006] An automatic and fast picking method for green-skinned nuts in hilly areas, the method comprising the following steps:

[0007] Step 1: Install a balance detection device on the bottom plate of the crawler wheel trolley, and install a lifting device at the bottom end of the bottom plate of the trolley;

[0008] Step 2: Install the laser radar detection device, the manipulator picking device and the camera on the bottom plate of the trolley

[0009] Step 3: Drive the picking trolley to the front of the nut tree, and then check whether the bottom plate of the trolley is in a horizontal position. If it is not in a horizontal position, adjust it to a horizontal position through the lifting device;

[0010] Step 4: Use laser radar to initially image and locate the green-skinned fruit of the nut;

[0011] Step 5: Construct a three-dimensional spatial model for picking green-skinned nuts;

[0012] Step 6: Calculate the order of picking all nuts according to the three-dimensional picking space model, and then control the robot to pick them one by one;

[0013] Step 7: After picking the nuts on one side, move the cart to the side next to the nut tree and repeat steps 3-6 until the whole tree of nuts is picked. One nut can be picked from the four directions of east, west, south and north.

[0014] Furthermore, in step 1, the trolley with crawler wheels can realize automatic climbing in hilly areas. Before picking nuts, a balance detection device is used to detect the horizontal level of the bottom plate of the trolley. The lifting device is configured as four liftable screw rotating rods. The control device is connected to the balance detection device, and one or two of the screw rotating rods are raised or lowered according to the horizontal offset.

[0015] Furthermore, in step 2, a laser radar detection device is arranged at the front end of the trolley, the laser radar detection device includes a laser radar and a longitudinal and transverse moving device, the longitudinal and transverse moving device includes a transverse slide bar and a longitudinal slide bar, the transverse slide bar and the longitudinal slide bar are cross-arranged, a moving wheel device is arranged on the laser radar, the moving wheel device can switch and move between the transverse slide bar and the longitudinal slide bar, and a timing movement control device is arranged on the moving wheel device, the timing movement control device is used to control the moving speed, moving position and staying time of the moving wheel device, the timing movement control device is respectively connected to the laser radar and the external harvester, and is used to receive the movement control signal and notify the laser radar to scan.

[0016] Furthermore, the specific process of step 4 is:

[0017] Step 4.1: The laser radar is installed on the picker and brought to the nut tree. The laser radar first obtains the initial three-dimensional image of the nut range;

[0018] Step 4.2: Frame the initial 3D image and then mark several feature points as identification points;

[0019] Step 4.3: Construct a three-dimensional coordinate system for the framed area, set the identification center point, and then mark the coordinates of all points of the nut tree;

[0020] Step 4.4: The laser radar moves on the longitudinal and lateral moving devices, pausing after each point of movement. The laser radar scans to obtain a directional stereo image, and then obtains all the three-dimensional images and summarizes them to obtain a three-dimensional image set of the nut tree in different directions;

[0021] Step 4.5: All three-dimensional image sets are fused to obtain three-dimensional coordinate data sets of nut trees in different directions;

[0022] Step 4.6: Then find out the positions of all nut shapes from the nut tree three-dimensional coordinate data set, and output the three-dimensional position points of the nuts to achieve imaging positioning of the nuts.

[0023] Furthermore, the specific process of step 5 is:

[0024] Step 5.1: constructing a three-dimensional spatial position model of the green-skinned fruit of the nut according to the position of the green-skinned fruit of the nut;

[0025] Step 5.2: Install a distance sensor on the picking robot to measure the distance between the robot and the front object in real time;

[0026] Step 5.3: Construct a picking three-dimensional spatial model according to the fixed position of the picking manipulator, the position of the nut collection box, and the three-dimensional spatial position model of the nut green peel fruit;

[0027] Step 5.4: Use a camera to observe the robot status, nut collection box status and picking environment status data in real time in the picking three-dimensional space model;

[0028] Step 5.5: According to the state and position of the robot, calculate the real-time position of the next nut to be picked to facilitate the robot to pick it.

[0029] Furthermore, the specific process of step 6 is: since in the picking three-dimensional spatial model, the position coordinates of all nuts are known, and then the position coordinates of the nut collection box are also known, then the distances of all nuts from the nut collection box can be calculated, each nut is labeled, and then all the distances are stored in an array, and then the array data is sorted from small to large, and the output array sorting is the picking order, and the nuts are found according to the output distance table below, and the path of the robot is planned in advance.

[0030] Furthermore, the specific process of step 4.1 is as follows: when the laser radar obtains the initial three-dimensional image, the picker uses the laser radar for height recognition, and then determines the height of the nut green-skinned fruit tree, the picker aligns the center of the longitudinal and transverse moving device with the middle position of the nut green-skinned fruit tree, the laser radar stops at the intersection of the transverse slide bar and the longitudinal slide bar of the longitudinal and transverse moving device, the laser radar starts to scan the initial three-dimensional stereo image data of the nut green-skinned fruit tree, and uses the acquired initial three-dimensional stereo image as the nut green-skinned fruit positioning position map.

[0031] Furthermore, the specific process of step 4.2 is: when framing the initial three-dimensional image, use a square frame to frame it, and then the four sides of the square frame are close to the edge of the nut green-skinned fruit tree, and then obtain several edge points and three internal midpoints from the three-dimensional image of the nut green-skinned fruit tree as feature recognition points. The feature recognition points are used as reference points for the subsequent fusion of several images. During the fusion, the points in each three-dimensional image that are the same or similar to the feature recognition points are used as overlapping points to fit together, so that the corresponding coordinate points can be guaranteed to correspond when all three-dimensional images are fused.

[0032] Furthermore, the specific process of step 4.3 is: establish a three-dimensional coordinate system, then take the lower left corner of the framed three-dimensional image as the origin, label each point on the three-dimensional image with corresponding three-dimensional coordinate data, and take the internal center point of the front-end X-axis and Y-axis planes as the identification center point, and the identification center point is also used as the reference coincidence point for image fusion.

[0033] Furthermore, the specific process of step 4.4 is as follows: the optical radar first starts to move from the intersection of the horizontal slider and the vertical slider, and first moves to the upper part of the vertical slider. After each movement to a fixed position, it pauses, and then notifies the laser radar to scan a three-dimensional stereo image of the nut green-skinned fruit tree. After scanning, it moves to a fixed position online, and then scans until it moves to the top and then returns to the intersection of the horizontal slider and the vertical slider, and then moves down and scans to obtain several three-dimensional stereo images of vertical movement. The laser radar returns to the intersection of the horizontal slider and the vertical slider, and then moves and scans to both sides in turn to obtain several three-dimensional stereo images of horizontal movement. The several three-dimensional stereo images of vertical movement and the several three-dimensional stereo images of horizontal movement are summarized to obtain a three-dimensional stereo image set.

[0034] The present invention has the following beneficial effects due to the adoption of the above technical solution:

[0035] (1) The present invention realizes imaging of green nuts in different directions by moving the position of the laser radar, and recognizes the shape of nuts at different angles. It can effectively avoid the inability to recognize nuts due to obstruction by leaves or branches in front, and realize more effective and accurate imaging and positioning. At the same time, after recognition, the obstructed leaves or branches are deleted through image fusion, so that the nuts behind can be directly exposed, and the obstructions are automatically filtered out. At the same time, the position of the nuts will not change, making it easier to locate when using a robot to pick them.

[0036] (2) The present invention first uses a laser radar to perform multi-directional positioning of the green-skinned nuts, thereby avoiding the trouble caused by the green color of the leaves on image recognition, and constructs the movement space of the picking robot and the position space of the green-skinned nuts into a three-dimensional structure space, thereby collecting the distance between the storage point and each nut, and realizing the robot control program required to calculate each nut while picking, thereby saving data processing time and improving picking efficiency.

[0037] (3) The picking process of the present invention is fully automated, the picking effect is high, the probability of missed picking is very low, and accurate and fast picking is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 is a flow chart of the automatic picking method of the present invention;

[0039] Figure 2 This is a flow chart of the initial imaging and positioning method of nuts and green-skinned fruits by laser radar of the present invention;

[0040] Figure 3 It is a flow chart of the method for constructing a three-dimensional spatial model for picking green-skinned nuts of the present invention. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and preferred embodiments. However, it should be noted that many details listed in the specification are only for the purpose of enabling the reader to have a thorough understanding of one or more aspects of the present invention, and these aspects of the present invention can be implemented even without these specific details.

[0042] like Figure 1 As shown, a method for automatically and quickly picking green-skinned nuts in hilly areas comprises the following steps:

[0043] Step 1: Install a balance detection device on the bottom plate of the crawler wheel trolley, and install a lifting device at the bottom of the bottom plate of the trolley. The crawler wheel trolley can realize automatic climbing in hilly areas. Before picking nuts, the balance detection device is used to detect the level of the bottom plate of the trolley. The lifting device is set as four lifting screw rotating rods. The control device is connected to the balance detection device, and one or two of the screw rotating rods are raised or lowered according to the horizontal deviation.

[0044] Step 2: Install the laser radar detection device, the manipulator picking device and the camera on the bottom plate of the trolley. The laser radar detection device is set at the front end of the trolley. The laser radar detection device includes a laser radar and a longitudinal and transverse moving device. The longitudinal and transverse moving device includes a transverse slide bar and a longitudinal slide bar. The transverse slide bar and the longitudinal slide bar are cross-arranged. The laser radar is provided with a moving wheel device, which can switch and move between the transverse slide bar and the longitudinal slide bar. At the same time, the moving wheel device is provided with a timing movement control device, which is used to control the moving speed, moving position and staying time of the moving wheel device. The timing movement control device is connected to the laser radar and the external picking machine respectively, and is used to receive the movement control signal and notify the laser radar to scan.

[0045] The moving wheel device is set as a square bottom plate, and four rotatable pulleys are set on the four sides of the square bottom plate. When longitudinal movement is required, the pulleys on both sides of the longitudinal direction rotate to the back of the transverse slide bar for sliding, while the two pulleys at the upper and lower ends are retracted. When transverse movement is required, the opposite is true, realizing longitudinal and transverse movement. The laser radar is a 192-line laser radar.

[0046] Step 3: Drive the picking cart to the front of the nut tree, and then check whether the bottom plate of the cart is in a horizontal position. If it is not in a horizontal position, adjust it to a horizontal position through the lifting device.

[0047] Step 4: Use LiDAR to initially image and locate the green-skinned fruit of the nut.

[0048] Step 4.1: The laser radar is installed on the picker and comes to the nut tree. The laser radar first obtains the initial three-dimensional image of the nut range. When the laser radar obtains the initial three-dimensional image, the picker uses the laser radar for height recognition, and then determines the height of the nut green-skin fruit tree. The picker aligns the center of the longitudinal and transverse moving device with the middle position of the nut green-skin fruit tree. The laser radar stops at the intersection of the transverse slide bar and the longitudinal slide bar of the longitudinal and transverse moving device. The laser radar starts to scan the initial three-dimensional stereo image data of the nut green-skin fruit tree, and uses the obtained initial three-dimensional stereo image as the nut green-skin fruit positioning position map.

[0049] When picking is needed, you can go to the direction in front of the nut tree, identify and locate it first, and then move the whole machine and use the robot to move and pick the fruits. Basically, to pick the fruits from a nut tree, you only need to identify the four directions of east, west, south and north, and then pick them four times respectively to complete the positioning of the nuts on a tree. After identifying one side, pick one side first.

[0050] Step 4.2: Frame the initial three-dimensional image, and then mark several feature points as identification points. When framing the initial three-dimensional image, use a square frame to frame it, and then the four sides of the square frame are close to the edge of the nut green-skinned fruit tree, and then obtain several edge points and three internal midpoints from the three-dimensional image of the nut green-skinned fruit tree as feature identification points. The feature identification points serve as reference points for the subsequent fusion of several images. During fusion, the points in each three-dimensional image that are the same or similar to the feature identification points are used as coincidence points to fit together, so that the corresponding coordinate points can be guaranteed to correspond when all three-dimensional images are fused. The feature identification points become the secondary calibration of the coordinate annotations of the subsequent image fusion to avoid too large deviations in the coordinate positions between images.

[0051] Step 4.3: Construct a three-dimensional coordinate system for the framed area, set the recognition center point, and then annotate the coordinates of all points on the nut tree. Establish a three-dimensional coordinate system, then use the lower left corner of the framed three-dimensional image as the origin, annotate the three-dimensional coordinate data for each point on the three-dimensional image, and use the internal center point of the frontmost X-axis and Y-axis planes as the recognition center point. The recognition center point is also used as the reference coincidence point for image fusion. The recognition center point is used as the secondary positioning and verification of the recognition, making the annotation of all images more accurate.

[0052] Step 4.4: The laser radar moves on the longitudinal and transverse moving devices, and pauses after each point is moved. The laser radar scans to obtain a directional stereo image, and then obtains all three-dimensional images to summarize to obtain a three-dimensional stereo image set of the nut tree in different directions. The laser radar first starts to move from the intersection of the transverse slider and the longitudinal slider, and first moves to the upper half of the longitudinal slider. After each fixed position is moved, it pauses, and then notifies the laser radar to scan a three-dimensional image of the nut green peel fruit tree. After scanning, it moves to a fixed position online, and then scans until it moves to the top and then returns to the intersection of the transverse slider and the longitudinal slider, and then moves down and scans to obtain several three-dimensional stereo images of vertical movement. The laser radar returns to the intersection of the transverse slider and the longitudinal slider, and then moves and scans to both sides in turn to obtain several three-dimensional stereo images of horizontal movement. The several three-dimensional stereo images of vertical movement and the several three-dimensional stereo images of horizontal movement are summarized to obtain a three-dimensional stereo image set.

[0053] When moving, generally after moving 3-5cm, scanning is performed to capture a three-dimensional image, first moving upward, then downward, then to the left, and then to the right.

[0054] Step 4.5: All three-dimensional stereo image sets are fused to obtain three-dimensional coordinate data sets of nut trees in different directions. Each image in the three-dimensional stereo image set is fitted and fused into the initial three-dimensional image. When fusion is performed, it is first used as several layers of fused images, and each image does not affect each other where it is occluded. Then, the three-dimensional coordinate data set is annotated for each layer of image. This fusion does not involve coordinate data fusion, but only superposition as layer fusion, which is a fusion of length and width directions, so that the three-dimensional coordinate data pairs of all images correspond to each other and there will not be too much deviation.

[0055] Step 4.6: Then find the positions of all nut shapes from the nut tree three-dimensional coordinate data set, and output the three-dimensional position points of the nuts to achieve imaging positioning of the nuts. Identify the nut green peel fruit model image in each layer of the three-dimensional image, and then collect the position coordinates of the nut green peel fruit model image, summarize the position coordinates of the nut green peel fruit model image in all layers of images to obtain the position set coordinates of the nut shape, and then compare each layer of image with the position set coordinates. When the image of a layer overlaps with the position set coordinates and the position is not the nut green peel fruit model image, delete the image of the layer area, and process all layers of images in turn to remove the leaves and branches that block the nuts, and then completely fuse all images twice to obtain the position points of the nut model image, so as to filter out the blocked leaves or branches, and locate the nuts more accurately.

[0056] By fusing coordinate data and image pixels, the occluded parts are deleted and the occluded objects are filtered out. This is the "perspective" effect, which can better locate and identify the green-skinned nuts and avoid missed identification.

[0057] This method moves the position of the laser radar to image the green-skinned nuts in different directions, and recognizes the shape of the nuts at different angles. It can effectively avoid the inability to recognize the nuts due to the obstruction of leaves or branches in front, and realize more effective and accurate imaging and positioning. After recognition, the obstructed leaves or branches are deleted through image fusion, so that the nuts behind can be directly exposed, and the obstructions can be automatically filtered out. At the same time, the position of the nuts will not change, making it easier to locate when using a robot to pick them.

[0058] Step 5: Construct a three-dimensional spatial model for picking green nuts.

[0059] Step 5.1: Construct a three-dimensional spatial position model of nuts and green peel fruits according to the positions of nuts and green peel fruits. Construct a three-dimensional position space according to the positions of each nut and green peel fruit, and mark the three-dimensional position coordinates of the nuts in the three-dimensional space. Then, summarize the three-dimensional position coordinates of all nuts and green peel fruits to obtain a three-dimensional spatial position model of nuts and green peel fruits.

[0060] Step 5.2: Install distance sensors on the picking manipulator to measure the distance between the manipulator and the front object in real time. There are three distance sensors installed on the picking manipulator. One distance sensor is installed at the front end of the picking machine to detect the position of the object in front of the front end of the manipulator in real time. The other two sensors are installed on both sides of the picking manipulator and are tilted to detect the position of the object at the front end of both sides of the picking manipulator.

[0061] Step 5.3: Construct a three-dimensional picking space model based on the fixed position of the picking manipulator, the position of the nut collection box, and the three-dimensional space position model of the nut green peel fruit. Take the rear end of the nut collection box as the rear edge, and then take the lowest installation position of the picking manipulator as the bottom edge. Take the horizontal plane of the highest nut position in the three-dimensional space position model of the nut green peel fruit as the top of the space, and take the vertical plane at the front end of the three-dimensional space position model of the nut green peel fruit as the front end. The three-dimensional space box structure is formed according to the rear edge, bottom edge, top and front end, and then mark the position of the three-dimensional space box structure where the picking manipulator is located during the movement process to form a three-dimensional picking space model.

[0062] Step 5.4: Use the camera to observe the robot status, nut collection box status and picking environment status data in the picking three-dimensional space model in real time. The camera is used to collect whether the nut collection box is full, and then notify the relevant personnel to replace the nut collection box. At the same time, the camera detects whether there are people in the action range of the picking robot through images to prevent the robot from accidentally injuring people. The camera is based on the position of the picking robot in real time. When the picking robot comes to the nut string, it uses image recognition to identify the position of the nut string, and calibrates the position of the picking robot to improve the efficiency of picking. At the same time, when the picking robot passes through the leaves and pushes the original leaves away, the camera recognizes in real time whether there are nuts inside that have not been located and identified, and realizes supplementary identification during the picking process to avoid missing nuts.

[0063] Step 5.5: Calculate the real-time position of the next nut to be picked based on the state and position of the manipulator to facilitate the manipulator to pick. The camera determines whether the picking manipulator is in an open or closed state, and identifies whether the nut picking is successful. If the picking is not successful, the position just picked is picked again. At the same time, the straight-line distance between each nut and the nut collection box in the picking three-dimensional space model is calculated, and then the distances are sorted to obtain the order of picking nuts. When the picking manipulator picks nuts and puts them back into the nut collection box, the radar distance detection transmits the position of the next nut to be picked to the manipulator control device, and generates the control rotation dimension of the manipulator at the same time, realizing real-time positioning of the nuts and improving the efficiency of picking.

[0064] By placing all the nuts in a set space, the order and path of picking can be better planned to avoid missing unpicked nuts during the picking process. The camera and distance sensor are used in conjunction to make nut picking more efficient. At the same time, secondary internal supplementary recognition can be achieved, so that fruits that are tightly blocked by leaves can also be recognized for a second time, resulting in better picking effects.

[0065] Step 6: Calculate the order of picking all nuts according to the three-dimensional picking space model, and then control the robot to pick them one by one. Since the position coordinates of all nuts are known in the three-dimensional picking space model, and the position coordinates of the nut collection box are also known, the distances of all nuts to the nut collection box can be calculated, and each nut is labeled, and then all distances are stored in an array, and then the array data is sorted from small to large. The output array sorting is the picking order. According to the output distance table below, the nuts are found and the path of the robot is planned in advance.

[0066] Step 7: After picking the nuts on one side, move the cart to the side next to the nut tree and repeat steps 3-6 until the whole tree of nuts is picked. One nut can be picked from the four directions of east, west, south and north.

[0067] The method realizes automated picking in hilly areas without the need for human intervention, and has higher picking efficiency and better results.

[0068] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for automatically and quickly picking green-skinned nuts in hilly areas, characterized in that: The method comprises the following steps: Step 1: Install a balance detection device on the bottom plate of the crawler wheel trolley, and install a lifting device at the bottom end of the bottom plate of the trolley; Step 2: Install a laser radar detection device, a manipulator picking device and a camera on the bottom plate of the trolley; Step 3: Drive the picking trolley to the front of the nut tree, and then check whether the bottom plate of the trolley is in a horizontal position. If it is not in a horizontal position, adjust it to a horizontal position through the lifting device; Step 4: Use laser radar to initially image and locate the green-skinned fruit of the nut; Step 5: Construct a three-dimensional spatial model for picking green nut peels; Step 6: Calculate the order of picking all nuts according to the three-dimensional picking space model, and then control the robot to pick them one by one; Step 7: After picking the nuts on one side, move the cart to the side next to the nut tree and repeat steps 3-6 until the whole tree of nuts is picked. One nut can be picked from the four directions of east, west, south and north.

2. The method for automatically and quickly picking green-skinned nuts in hilly areas according to claim 1, characterized in that: In step 1, the trolley with crawler wheels can automatically climb hilly areas. Before picking nuts, a balance detection device is used to detect the horizontal level of the bottom plate of the trolley. The lifting device is configured as four liftable screw rotating rods. The control device is connected to the balance detection device, and one or two of the screw rotating rods are raised or lowered according to the horizontal deviation.

3. The method for automatically and quickly picking green-skinned nuts in hilly areas according to claim 1, characterized in that: In step 2, a laser radar detection device is arranged at the front end of the trolley. The laser radar detection device includes a laser radar and longitudinal and transverse moving devices. The longitudinal and transverse moving devices include a transverse slide bar and a longitudinal slide bar. The transverse slide bar and the longitudinal slide bar are cross-arranged. A moving wheel device is arranged on the laser radar. The moving wheel device can switch and move between the transverse slide bar and the longitudinal slide bar. At the same time, a timing movement control device is arranged on the moving wheel device. The timing movement control device is used to control the moving speed, moving position and staying time of the moving wheel device. The timing movement control device is respectively connected to the laser radar and an external harvester, and is used to receive movement control signals and notify the laser radar to scan.

4. The method for automatically and quickly picking green-skinned nuts in hilly areas according to claim 1, characterized in that: The specific process of step 4 is: Step 4.1: The laser radar is installed on the picker and brought to the nut tree. The laser radar first obtains the initial three-dimensional image of the nut range; Step 4.2: Frame the initial 3D image and then mark several feature points as identification points; Step 4.3: Construct a three-dimensional coordinate system for the framed area, set the identification center point, and then mark the coordinates of all points of the nut tree; Step 4.4: The laser radar moves on the longitudinal and lateral moving devices, pausing after each point of movement. The laser radar scans to obtain a directional stereo image, and then obtains all the three-dimensional images and summarizes them to obtain a three-dimensional image set of the nut tree in different directions; Step 4.5: All three-dimensional image sets are fused to obtain three-dimensional coordinate data sets of nut trees in different directions; Step 4.6: Then find out the positions of all nut shapes from the nut tree three-dimensional coordinate data set, and output the three-dimensional position points of the nuts to achieve imaging positioning of the nuts.

5. The method for automatically and quickly picking green-skinned nuts in hilly areas according to claim 1, characterized in that: The specific process of step 5 is: Step 5.1: constructing a three-dimensional spatial position model of the green-skinned fruit of the nut according to the position of the green-skinned fruit of the nut; Step 5.2: Install a distance sensor on the picking robot to measure the distance between the robot and the front object in real time; Step 5.3: Construct a picking three-dimensional spatial model according to the fixed position of the picking manipulator, the position of the nut collection box, and the three-dimensional spatial position model of the nut green peel fruit; Step 5.4: Use a camera to observe the robot status, nut collection box status and picking environment status data in real time in the picking three-dimensional space model; Step 5.5: Calculate the real-time location of the next nut to be picked based on the state and position of the robot to facilitate picking by the robot.

6. The method for automatically and quickly picking green-skinned nuts in hilly areas according to claim 1, characterized in that: The specific process of step 6 is: since in the picking three-dimensional spatial model, the position coordinates of all nuts are known, and the position coordinates of the nut collection box are also known, the distances of all nuts from the nut collection box can be calculated, each nut is labeled, and then all distances are stored in an array, and then the array data is sorted from small to large, and the output array sorting is the picking order. The nuts are found according to the output distance table below, and the path of the robot is planned in advance.

7. The method for automatically and quickly picking green-skinned nuts in hilly areas according to claim 1, characterized in that: The specific process of step 4.1 is as follows: when the laser radar obtains the initial three-dimensional image, the picker uses the laser radar for height recognition, and then determines the height of the nut green-skin fruit tree. The picker aligns the center of the longitudinal and transverse moving devices with the middle position of the nut green-skin fruit tree. The laser radar stops at the intersection of the transverse slide bar and the longitudinal slide bar of the longitudinal and transverse moving devices. The laser radar starts to scan the initial three-dimensional stereo image data of the nut green-skin fruit tree, and uses the acquired initial three-dimensional stereo image as the nut green-skin fruit positioning position map.

8. The method for automatically and quickly picking green-skinned nuts in hilly areas according to claim 1, characterized in that: The specific process of step 4.2 is: when framing the initial three-dimensional image, use a square frame to frame it, and then the four sides of the square frame are close to the edge of the nut green-skinned fruit tree, and then obtain several edge points and three internal midpoints from the three-dimensional image of the nut green-skinned fruit tree as feature recognition points. The feature recognition points are used as reference points for the subsequent fusion of several images. During the fusion, the points in each three-dimensional image that are the same or similar to the feature recognition points are used as coincidence points to fit together, so that the corresponding coordinate points can be guaranteed to correspond when all three-dimensional images are fused.

9. The method for automatically and quickly picking green-skinned nuts in hilly areas according to claim 1, characterized in that: The specific process of step 4.3 is as follows: establish a three-dimensional coordinate system, then take the lower left corner of the framed three-dimensional image as the origin, annotate each point on the three-dimensional image with corresponding three-dimensional coordinate data, and take the internal center point of the front-end X-axis and Y-axis planes as the recognition center point, which is also used as the reference coincidence point for image fusion.

10. The method for automatically and quickly picking green-skinned nuts in hilly areas according to claim 1, characterized in that: The specific process of step 4.4 is as follows: the optical radar starts to move from the intersection of the horizontal slider and the vertical slider, and moves to the upper part of the vertical slider first. After each fixed position is moved, it pauses, and then notifies the laser radar to scan a three-dimensional stereo image of the nut green-skin fruit tree. After scanning, it moves to a fixed position online, and then scans until it moves to the top and then returns to the intersection of the horizontal slider and the vertical slider, and then moves down and scans to obtain several three-dimensional stereo images of vertical movement. The laser radar returns to the intersection of the horizontal slider and the vertical slider, and then moves and scans to both sides in turn to obtain several three-dimensional stereo images of horizontal movement. The several three-dimensional stereo images of vertical movement and the several three-dimensional stereo images of horizontal movement are summarized to obtain a three-dimensional stereo image set.

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