A cherry tomato vibration picking robot and method

Abstract

This invention discloses a cherry tomato vibrating harvesting robot and method. The robot includes a walking mechanism, a position adjustment mechanism, a vibrating harvesting mechanism, and an image recognition mechanism. The position adjustment mechanism includes a vertical adjustment mechanism and a horizontal adjustment mechanism. The horizontal adjustment direction of the horizontal adjustment mechanism is perpendicular to the straight walking direction of the walking mechanism. The vibrating harvesting mechanism includes a vibration drive motor, a transmission rod, and a vibrating rod. One end of the transmission rod is fixedly connected to the drive end of the vibration drive motor, and the other end is fixedly connected to the vibrating rod. Multiple sets of vibrating rods are provided, extending in different directions, with each set including multiple vibrating rods. A cotton sleeve is fitted onto the end of the vibrating rod furthest from the transmission rod. The image recognition mechanism includes a camera and an image processor. This invention can quickly complete the harvesting of cherry tomatoes, has high harvesting efficiency, and is suitable for large-scale planting.

Description

Technical Field

[0001] This invention relates to a cherry tomato harvesting device and method, specifically to a cherry tomato vibrating harvesting robot and method. Background Technology

[0002] Cherry tomatoes, a cultivated variety of the genus *Solanum* in the Solanaceae family, are annual or perennial vine-like herbaceous plants. Their fruit, cherry tomatoes, is used to treat thirst, loss of appetite, and constipation, and can also lower blood pressure and enhance the body's anti-cancer capabilities, making them highly valuable for consumption. In my country, the planting area of ​​winter-grown cherry tomatoes exceeds 500,000 mu (approximately 33,333 hectares), with western Guangdong province becoming one of the leading production areas, covering nearly 100,000 mu (approximately 6,667 hectares). In recent years, cherry tomato cultivation has shifted towards large-scale, standardized plant factory cultivation models, which presents challenges to achieving efficient harvesting.

[0003] Traditional cherry tomato harvesting is primarily done manually, which suffers from high labor intensity, low efficiency, and high labor costs. With the development of agricultural robotics, cherry tomato harvesting robots have been extensively researched. Existing cherry tomato harvesting robots mainly use multi-axis gripping robotic arms equipped with target detection and visual recognition algorithms to identify cherry tomatoes and harvest them individually. While these gripping robots can perform precise harvesting tasks, their long recognition time per fruit and the considerable harvesting time make them inefficient in large-scale, standardized cherry tomato plant factory environments, hindering the efficient and rapid harvesting of this short-harvest fruit in large-scale cultivation settings. Summary of the Invention

[0004] The purpose of this invention is to overcome the above-mentioned problems and provide a cherry tomato vibrating harvesting robot. This harvesting robot can quickly complete the harvesting of cherry tomatoes, has high harvesting efficiency, and is suitable for large-scale planting.

[0005] Another objective of this invention is to provide a method for harvesting cherry tomatoes using vibration.

[0006] The objective of this invention is achieved through the following technical solution:

[0007] A cherry tomato vibrating harvesting robot includes a walking mechanism and a position adjustment mechanism, a vibrating harvesting mechanism, and an image recognition mechanism disposed on the walking mechanism.

[0008] The position adjustment mechanism includes a vertical adjustment mechanism and a horizontal adjustment mechanism; the horizontal adjustment mechanism is connected to the drive end of the vertical adjustment mechanism; the horizontal adjustment direction of the horizontal adjustment mechanism is perpendicular to the straight-line walking direction of the walking mechanism.

[0009] The vibrating harvesting mechanism is connected to the drive end of the lateral adjustment mechanism. The vibrating harvesting mechanism includes a vibrating drive motor, a transmission rod, and a vibrating rod. One end of the transmission rod is fixedly connected to the drive end of the vibrating drive motor, and the other end of the transmission rod is fixedly connected to the vibrating rod. The vibrating rod is provided in multiple groups and extends along different directions. Each group of vibrating rods includes multiple vibrating rods.

[0010] The image recognition mechanism includes a camera and an image processor; the camera is electrically connected to the image processor, and the image processor is electrically connected to the controllers of the position adjustment mechanism and the vibration harvesting mechanism, respectively.

[0011] In a preferred embodiment of the present invention, the walking mechanism includes a vehicle body, a walking component, and a walking controller; a three-dimensional LiDAR is provided at the front end of the vehicle body for collecting point cloud information of the working environment; the walking controller is electrically connected to the three-dimensional LiDAR; the walking mechanism has the functions of establishing a navigation grid map, self-positioning, path planning, and motion execution.

[0012] In a preferred embodiment of the present invention, the vertical adjustment mechanism includes a vertical mounting frame, a vertical drive motor, and a vertical transmission assembly; the vertical mounting frame is vertically and fixedly mounted on the vehicle body of the traveling mechanism; the vertical drive motor is mounted on the vertical mounting frame; the vertical transmission assembly includes a vertical transmission frame, a vertical lead screw, and a vertical lead screw nut, the vertical lead screw nut being fixedly connected to the vertical transmission frame; the vertical transmission frame is connected to the lateral adjustment mechanism.

[0013] Furthermore, a vertical guide structure is provided between the vertical transmission frame and the vertical mounting frame. The vertical guide structure includes a vertical guide rail and a vertical slider. The vertical guide rail is vertically arranged on the vertical mounting frame, and the vertical slider is fixedly connected to the vertical transmission frame.

[0014] Furthermore, an upper limit switch and a lower limit switch are respectively provided above and below the vertical guide rail.

[0015] Furthermore, the lateral adjustment mechanism includes a lateral drive motor and a lateral transmission assembly; the lateral drive motor is mounted on the vertical transmission frame; the lateral transmission assembly includes a lateral transmission frame, a lateral lead screw, and a lateral lead screw nut, the lateral lead screw nut being fixedly connected to the lateral transmission frame; the lateral transmission frame is connected to the vibrating harvesting mechanism.

[0016] Furthermore, a transverse guide structure is provided between the transverse transmission frame and the vertical transmission frame. The transverse guide structure includes a transverse guide rail and a transverse slider. The transverse guide rail is transversely arranged on the vertical transmission frame, and the transverse slider is fixedly connected to the transverse transmission frame.

[0017] Furthermore, a left limiter and a right limiter are respectively provided on the left and right sides of the transverse guide rail.

[0018] Furthermore, the transverse transmission frame includes a lower hollow fixed plate, a middle hollow fixed plate, a middle diamond-shaped belt bearing, an upper hollow fixed plate, an upper diamond-shaped belt bearing, and a back connecting plate;

[0019] The middle slot of the lower hollow fixed plate is fixedly connected to the top of the vibration drive motor. The middle slot of the middle hollow fixed plate is equipped with a middle diamond-shaped bearing with a seat. The plum blossom coupling is installed in the middle diamond-shaped bearing with a seat. The middle slot of the upper hollow fixed plate is equipped with an upper diamond-shaped bearing with a seat. The back connecting plate is fixedly connected to the surface of the horizontal slider.

[0020] In a preferred embodiment of the present invention, a cotton sleeve is fitted onto the end of the vibrating rod away from the transmission rod to prevent the tomatoes from being damaged.

[0021] A method for harvesting cherry tomatoes using vibration, comprising the following steps:

[0022] The walking mechanism, equipped with a position adjustment mechanism and a vibrating harvesting mechanism, moves to the front of the tomatoes to be harvested;

[0023] The system begins by capturing images of the tomatoes to be harvested using a camera, then uploading the images to an image processor. The image processor analyzes the images to determine the optimal vibration position and calculates the best harvesting amplitude and frequency for the tomatoes.

[0024] The image processor sends the corresponding position adjustment command to the controller of the position adjustment mechanism, which then drives the vibrating harvesting mechanism to move to the optimal vibration position.

[0025] The image processor sends the optimal harvesting amplitude and frequency command to the controller of the vibrating harvesting mechanism. The vibrating harvesting mechanism then controls the vibrating rod to vibrate according to the harvesting amplitude and frequency, shaking the ripe tomatoes off the ground and completing the current tomato harvesting work.

[0026] In a preferred embodiment of the present invention, the image processor includes a target detection, recognition, and localization module, which comprises a target detection module and a detection coordinate output module. The target detection module uses a lightweight target detection benchmark model and is trained using cherry tomato image data under different environmental and lighting conditions to finally obtain the detection model. The target detection module uses mean average precision (mAP) as an evaluation index, expressed as:

[0027]

[0028] In the formula: Pr is the model precision evaluation index, Re is the model recall evaluation index, AP is the mean precision evaluation index, and mAP is the mean average precision evaluation index.

[0029] Furthermore, the detection coordinate output module first extracts BoundingBox data based on the output results of the target detection module, obtains the x and y coordinates of all fruits in the current image coordinate system, and then performs traversal constraints on the coordinates to obtain the optimal vertical working region in the image plane coordinate system. The range of the working region satisfies the following constraints:

[0030] Y area ∈[Y min Y max ];

[0031] In the formula Y min The minimum y-coordinate in the Bounding Box data, Y max This represents the maximum y-coordinate value in the Bounding Box data;

[0032] The coordinate output module sends corresponding instructions to the controller of the position adjustment mechanism, which in turn controls the vertical adjustment mechanism to make vertical adjustments, so that the vibrating harvesting mechanism can reciprocate within the optimal vertical working range.

[0033] Furthermore, the image processor also includes a depth localization optimization module. This module calculates the average depth information of the currently detected fruit using the coordinates output by the target detection and recognition localization module. Then, it iterates through the depth information of each fruit location to obtain the optimal depth working region in the depth coordinate system. The range of the working region satisfies the following constraints:

[0034] D area ∈[D min D max ];

[0035] In the formula D min D is the minimum depth in the depth data. max This represents the maximum depth value in the depth data.

[0036] The coordinate output module sends corresponding instructions to the controller of the position adjustment mechanism, which then controls the lateral adjustment mechanism to make lateral adjustments, so that the vibrating harvesting mechanism can reciprocate within the optimal lateral working range.

[0037] In the above method, the optimal vertical working area and the optimal depth working area are output based on the target positioning and depth information of the fruit of the plant and merged into a precise picking area. The precise picking area is then used for reciprocating operations that cover the spatial range to improve the picking effect. Finally, the autonomous navigation function is combined to improve the autonomous movement operation capability of the picking robot.

[0038] In a preferred embodiment of the present invention, the method for calculating the optimal harvesting amplitude and frequency of tomatoes is as follows:

[0039] Based on the characteristics of cherry tomato plants, a parameter optimization method was established under actual operating conditions. Frequency X1 and amplitude X2 were used as factors, and harvest rate Y1 and damage rate Y2 were used as responses for response surface optimization. The established regression equation is as follows:

[0040]

[0041] In the formula, a, b, c, d, e, f, g, h, i, j, k, and l are the coefficients of the regression equation;

[0042] To achieve the goal of maximizing the harvest rate and minimizing the breakage rate, the design seeks the optimal combination of amplitude and frequency, satisfying the following simultaneous formulas:

[0043]

[0044] In the formula, f1max is the regression equation corresponding to Y1, and f2min is the regression equation corresponding to Y2.

[0045] The above method introduces a response surface optimization parameter method. First, the frequency and amplitude of the vibration harvesting mechanism in the cherry tomato picking environment are used as factors, and the picking rate and breakage rate are used as responses to construct a response surface optimization regression equation. Then, the response optimizer is used to optimize the optimal combination solution of frequency and amplitude to achieve a higher picking rate and a lower breakage rate.

[0046] Compared with the prior art, the present invention has the following advantages:

[0047] The harvesting robot of this invention can quickly complete the harvesting of cherry tomatoes, with high harvesting efficiency, and is suitable for large-scale planting. Attached Figure Description

[0048] Figure 1 This is a three-dimensional structural diagram of the cherry tomato vibrating harvesting robot of the present invention.

[0049] Figure 2 This is a three-dimensional structural diagram of the transverse transmission frame of the transverse adjustment mechanism of the present invention.

[0050] Figure 3 This is a flowchart illustrating the operation of the cherry tomato vibrating harvesting robot of the present invention. Detailed Implementation

[0051] To enable those skilled in the art to fully understand the technical solutions of the present invention, the present invention will be further described below in conjunction with embodiments and accompanying drawings, but the embodiments of the present invention are not limited thereto.

[0052] See Figure 1 The cherry tomato vibrating harvesting robot of this embodiment includes a walking mechanism 1 and a position adjustment mechanism, a vibrating harvesting mechanism, and an image recognition mechanism disposed on the walking mechanism 1. The walking mechanism 1 includes a vehicle body, a walking component, and a walking controller. A three-dimensional LiDAR 2 is disposed at the front end of the vehicle body for collecting point cloud information of the working environment. The walking controller is electrically connected to the three-dimensional LiDAR 2. The walking mechanism 1 has the functions of establishing a navigation grid map, self-positioning, path planning, and motion execution. Other structures can refer to the prior art.

[0053] See Figure 1 The position adjustment mechanism includes a vertical adjustment mechanism and a horizontal adjustment mechanism; the horizontal adjustment mechanism is connected to the drive end of the vertical adjustment mechanism; the horizontal adjustment direction of the horizontal adjustment mechanism is perpendicular to the straight-line travel direction of the traveling mechanism 1; wherein, the vertical adjustment mechanism includes a vertical mounting frame 3, a vertical drive motor 4, and a vertical transmission assembly; the vertical mounting frame 3 is vertically fixedly mounted on the vehicle body of the traveling mechanism 1; the vertical drive motor 4 is mounted on the vertical mounting frame 3; the vertical transmission assembly includes a vertical transmission frame 5, a vertical lead screw 6, and a vertical lead screw nut, the vertical lead screw nut being fixedly connected to the vertical transmission frame 5; the vertical transmission frame 5 is connected to the horizontal adjustment mechanism.

[0054] Furthermore, a vertical guide structure is provided between the vertical transmission frame 5 and the vertical mounting frame 3. The vertical guide structure includes a vertical guide rail 7 and a vertical slider. The vertical guide rail 7 is vertically arranged on the vertical mounting frame 3, and the vertical slider is fixedly connected to the vertical transmission frame 5.

[0055] Furthermore, an upper limit switch 8 and a lower limit switch 9 are respectively provided above and below the vertical guide rail 7.

[0056] See Figure 1 The lateral adjustment mechanism includes a lateral drive motor 10 and a lateral transmission assembly; the lateral drive motor 10 is mounted on the vertical transmission frame 5; the lateral transmission assembly includes a lateral transmission frame 11, a lateral lead screw 12 and a lateral lead screw nut, the lateral lead screw nut being fixedly connected to the lateral transmission frame 11; the lateral transmission frame 11 is connected to the vibrating harvesting mechanism.

[0057] Furthermore, a transverse guide structure is provided between the transverse transmission frame 11 and the vertical transmission frame 5. The transverse guide structure includes a transverse guide rail 13 and a transverse slider. The transverse guide rail 13 is transversely arranged on the vertical transmission frame 5, and the transverse slider is fixedly connected to the transverse transmission frame 11.

[0058] Furthermore, a left limiter 14 and a right limiter 15 are respectively provided on the left and right sides of the transverse guide rail 13.

[0059] See Figure 2 The transverse transmission frame 11 includes a lower hollow fixed plate 11-1, a middle hollow fixed plate 11-2, a middle diamond-shaped bearing 11-3, an upper hollow fixed plate 11-4, an upper diamond-shaped bearing 11-5, and a back connecting plate 11-6. The middle slot of the lower hollow fixed plate 11-1 is fixedly connected to the top of the vibration drive motor 16. The middle slot of the middle hollow fixed plate 11-2 is equipped with the middle diamond-shaped bearing 11-3. The plum blossom coupling is installed in the middle diamond-shaped bearing 11-3. The middle slot of the upper hollow fixed plate 11-4 is equipped with the upper diamond-shaped bearing 11-5. The back connecting plate 11-6 is fixedly connected to the surface of the horizontal slider.

[0060] See Figure 1 The vibrating harvesting mechanism is connected to the drive end of the lateral adjustment mechanism. The vibrating harvesting mechanism includes a vibrating drive motor 16, a transmission rod 17, and a vibrating rod 18. One end of the transmission rod 17 is fixedly connected to the drive end of the vibrating drive motor 16, and the other end of the transmission rod 17 is fixedly connected to the vibrating rod 18. The vibrating rod 18 is provided in multiple groups and extends along different directions. Each group of vibrating rods 18 includes multiple vibrating rods 18.

[0061] Furthermore, a cotton sleeve 19 is fitted onto the end of the vibrating rod 18 away from the transmission rod 17 to prevent the tomatoes from being damaged.

[0062] See Figure 1 The image recognition mechanism includes a camera 20 and an image processor; the camera 20 is electrically connected to the image processor, and the image processor is electrically connected to the controllers of the position adjustment mechanism and the vibration picking mechanism respectively; the image processor includes a target detection, recognition and positioning module and a depth positioning optimization module; the specific structure can refer to the prior art.

[0063] See Figure 1 and Figure 3 The cherry tomato vibration harvesting method of this embodiment includes the following steps:

[0064] The walking mechanism 1, equipped with a position adjustment mechanism and a vibrating harvesting mechanism, moves to the front of the tomatoes to be harvested.

[0065] The camera 20 begins acquiring images of the tomatoes to be picked and uploads them to the image processor. The image processor then analyzes the images to determine the optimal vibration position. The specific operation is as follows:

[0066] The target detection, recognition, and localization module includes a target detection module and a detection coordinate output module. The target detection module uses a lightweight target detection benchmark model and is trained using cherry tomato image data under different environments and lighting conditions to finally obtain the detection model. The target detection module uses mean average precision (mAP) as the evaluation index, and the expression is:

[0067]

[0068] In the formula: Pr is the model precision evaluation index, Re is the model recall evaluation index, AP is the mean precision evaluation index, and mAP is the mean average precision evaluation index.

[0069] Furthermore, the detection coordinate output module first extracts BoundingBox data based on the output results of the target detection module, obtains the x and y coordinates of all fruits in the current image coordinate system, and then performs traversal constraints on the coordinates to obtain the optimal vertical working region in the image plane coordinate system. The range of the working region satisfies the following constraints:

[0070] Y area ∈[Y min Y max ];

[0071] In the formula Y min The minimum y-coordinate in the Bounding Box data, Y max This represents the maximum y-coordinate value in the Bounding Box data.

[0072] The coordinate output module sends corresponding instructions to the controller of the position adjustment mechanism, which in turn controls the vertical adjustment mechanism to make vertical adjustments, so that the vibrating harvesting mechanism can reciprocate within the optimal vertical working range.

[0073] Furthermore, the depth positioning optimization module calculates the average depth information of the currently detected fruit using the coordinates output by the target detection and recognition positioning module. Then, it iterates through the depth information of each fruit location to obtain the optimal depth working region in the depth coordinate system. The range of this working region satisfies the following constraints:

[0074] D area ∈[D min D max ];

[0075] In the formula D min D is the minimum depth in the depth data. max This represents the maximum depth value in the depth data.

[0076] The coordinate output module sends corresponding instructions to the controller of the position adjustment mechanism, which then controls the lateral adjustment mechanism to make lateral adjustments, so that the vibrating harvesting mechanism can reciprocate within the optimal lateral working range.

[0077] In the above method, the optimal vertical working area and the optimal depth working area are output based on the target positioning and depth information of the fruit of the plant and merged into a precise picking area. The precise picking area is then used for reciprocating operations that cover the spatial range to improve the picking effect. Finally, the autonomous navigation function is combined to improve the autonomous movement operation capability of the picking robot.

[0078] The optimal harvesting amplitude and frequency for tomatoes were calculated using the following method:

[0079] Based on the characteristics of cherry tomato plants, a parameter optimization method was established under actual operating conditions. Frequency X1 and amplitude X2 were used as factors, and harvest rate Y1 and damage rate Y2 were used as responses for response surface optimization. The established regression equation is as follows:

[0080]

[0081] In the formula, a, b, c, d, e, f, g, h, i, j, k, and l are the coefficients of the regression equation.

[0082] To achieve the goal of maximizing the harvest rate and minimizing the breakage rate, the design seeks the optimal combination of amplitude and frequency, satisfying the following simultaneous formulas:

[0083]

[0084] In the formula, f1max is the regression equation corresponding to Y1, and f2min is the regression equation corresponding to Y2.

[0085] The above method introduces a response surface optimization parameter method. First, the frequency and amplitude of the vibration harvesting mechanism in the cherry tomato picking environment are used as factors, and the picking rate and breakage rate are used as responses to construct a response surface optimization regression equation. Then, the response optimizer is used to optimize the optimal combination solution of frequency and amplitude to achieve a higher picking rate and a lower breakage rate.

[0086] The image processor sends the optimal harvesting amplitude and frequency command to the controller of the vibrating harvesting mechanism. The vibrating harvesting mechanism then controls the vibrating rod 18 to vibrate according to the harvesting amplitude and frequency, shaking the ripe tomatoes off the ground. The tomatoes are then picked up by other mechanisms or manually, completing the current tomato harvesting work.

[0087] The above are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above content. Any changes, modifications, substitutions, combinations, or simplifications made without departing from the spirit and principle of the present invention shall be considered equivalent substitutions and shall be included within the protection scope of the present invention.

Claims

1. A cherry tomato vibration picking robot, characterized by, It includes a walking mechanism and a position adjustment mechanism, a vibration harvesting mechanism, and an image recognition mechanism mounted on the walking mechanism; The position adjustment mechanism includes a vertical adjustment mechanism and a horizontal adjustment mechanism; the horizontal adjustment mechanism is connected to the drive end of the vertical adjustment mechanism; the horizontal adjustment direction of the horizontal adjustment mechanism is perpendicular to the straight-line walking direction of the walking mechanism. The vibrating harvesting mechanism is connected to the drive end of the lateral adjustment mechanism. The vibrating harvesting mechanism includes a vibrating drive motor, a transmission rod, and a vibrating rod. One end of the transmission rod is fixedly connected to the drive end of the vibrating drive motor, and the other end of the transmission rod is fixedly connected to the vibrating rod. The vibrating rod is provided in multiple groups and extends along different directions. Each group of vibrating rods includes multiple vibrating rods. A cotton sleeve is fitted on the end of the vibrating rod away from the transmission rod. The image recognition mechanism includes a camera and an image processor; the camera is electrically connected to the image processor, and the image processor is electrically connected to the controllers of the position adjustment mechanism and the vibration harvesting mechanism, respectively. The harvesting method of the cherry tomato vibrating harvesting robot includes the following steps: The walking mechanism, equipped with a position adjustment mechanism and a vibrating harvesting mechanism, moves to the front of the tomatoes to be harvested; The system captures images of tomatoes to be picked using a camera, uploads the images to an image processor, analyzes the images to determine the optimal vibration position, and calculates the optimal picking amplitude and frequency for the tomatoes. The image processor sends the corresponding position adjustment command to the controller of the position adjustment mechanism, which then drives the vibrating harvesting mechanism to move to the optimal vibration position. The image processor sends the optimal harvesting amplitude and frequency command to the controller of the vibrating harvesting mechanism. The vibrating harvesting mechanism then controls the vibrating rod to vibrate according to the harvesting amplitude and frequency, shaking the ripe tomatoes off the ground and completing the current tomato harvesting work. The image processor includes a target detection, recognition, and localization module, which comprises a target detection module and a detection coordinate output module. The target detection module uses a lightweight target detection benchmark model and is trained using cherry tomato image data under different environmental and lighting conditions to finally obtain the detection model. The target detection module uses mean average precision (mAP) as its evaluation metric, expressed as follows: ； In the formula: Pr is the model precision evaluation index, Re is the model recall evaluation index, AP is the mean precision evaluation index, and mAP is the mean average precision evaluation index. The detection coordinate output module first extracts Bounding Box data based on the output results of the target detection module, obtains the x and y coordinates of all fruits in the current image coordinate system, and then performs traversal constraints on the coordinates to obtain the optimal vertical working domain in the image plane coordinate system. The range of the working domain satisfies the following constraints: ； In the formula is the minimum value of the y coordinate in the Bounding Box data, is the maximum value of the y coordinate in the Bounding Box data; The coordinate output module sends corresponding instructions to the controller of the position adjustment mechanism, which in turn controls the vertical adjustment mechanism to make vertical adjustments, so that the vibrating harvesting mechanism can reciprocate within the optimal vertical working range. The image processor also includes a depth localization optimization module. This module calculates the average depth information of the currently detected fruit using the coordinates output by the target detection and recognition localization module. Then, it iterates through the depth information of each fruit location to obtain the optimal depth working region in the depth coordinate system. The range of the working region satisfies the following constraints: ； In the formula is a minimum value of depth in the depth data, is a maximum value of depth in the depth data; The coordinate output module sends corresponding instructions to the controller of the position adjustment mechanism, which then controls the lateral adjustment mechanism to make lateral adjustments, so that the vibrating harvesting mechanism can reciprocate within the optimal lateral working range.

2. The cherry tomato vibration picking robot according to claim 1, characterized in that, The vertical adjustment mechanism includes a vertical mounting frame, a vertical drive motor, and a vertical transmission assembly; the vertical mounting frame is vertically and fixedly mounted on the vehicle body of the traveling mechanism; the vertical drive motor is mounted on the vertical mounting frame; the vertical transmission assembly includes a vertical transmission frame, a vertical lead screw, and a vertical lead screw nut, the vertical lead screw nut being fixedly connected to the vertical transmission frame; the vertical transmission frame is connected to the lateral adjustment mechanism.

3. The cherry tomato vibration picking robot according to claim 2, characterized in that, A vertical guide structure is provided between the vertical transmission frame and the vertical mounting frame. The vertical guide structure includes a vertical guide rail and a vertical slider. The vertical guide rail is vertically mounted on the vertical mounting frame, and the vertical slider is fixedly connected to the vertical transmission frame. The vertical guide rail is equipped with an upper limit switch and a lower limit switch at the top and bottom, respectively.

4. The cherry tomato vibration picking robot according to claim 2, characterized in that, The lateral adjustment mechanism includes a lateral drive motor and a lateral transmission assembly; the lateral drive motor is mounted on the vertical transmission frame; the lateral transmission assembly includes a lateral transmission frame, a lateral lead screw, and a lateral lead screw nut, the lateral lead screw nut being fixedly connected to the lateral transmission frame; the lateral transmission frame is connected to the vibrating harvesting mechanism.

5. The cherry tomato vibration picking robot according to claim 4, characterized in that, A transverse guide structure is provided between the transverse transmission frame and the vertical transmission frame. The transverse guide structure includes a transverse guide rail and a transverse slider. The transverse guide rail is transversely arranged on the vertical transmission frame, and the transverse slider is fixedly connected to the transverse transmission frame. The left and right limiters are respectively provided on the left and right sides of the transverse guide rail.

6. The cherry tomato vibration picking robot according to claim 1, characterized in that, The method for calculating the optimal harvesting amplitude and frequency for tomatoes is as follows: Based on the characteristics of cherry tomato plants, a parameter optimization method was established under actual operating conditions. Frequency X1 and amplitude X2 were used as factors, and harvest rate Y1 and damage rate Y2 were used as responses for response surface optimization. The established regression equation is as follows: ； In the formula, a, b, c, d, e, f, g, h, i, j, k, and l are the coefficients of the regression equation; To achieve the goal of maximizing the harvest rate and minimizing the breakage rate, the design seeks the optimal combination of amplitude and frequency, satisfying the following simultaneous formulas: ； In the formula for The corresponding regression equation, for The corresponding regression equation.

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