A method, system and device for evaluating the grasping effect of a mechanical arm for picking camellia oleifera

Abstract

The present invention discloses a method, system and device for evaluating the grasping effect of a mechanical arm for oil-tea camellia picking, and the present invention relates to the technical field of stable grasping of picking robots. The method includes the following steps: obtaining the physical parameters of the mechanical arm structure and the characteristic parameters in the previous picking process, and calculating the actual grasping force accordingly. Constructing a finite element analysis model and calibrating it based on the above data to obtain the predicted value of the relative slip distance and the predicted value of the actual grasping force under the current working condition. Subsequently, establishing a relational expression between the branch deflection, physiological parameters and the actual grasping force, fitting the fitting parameters by the least square method, and calculating the branch deflection under the current working condition. Setting the branch deflection threshold range, collecting the ambient temperature, dynamically adjusting the adaptive deflection threshold, and evaluating the effect according to the comparison result between the actual grasping force and the deflection threshold range. This method ensures the fruit picking efficiency on the premise of not damaging the oil-tea camellia trees.

Description

Technical Field

[0001] The present invention relates to the technical field of stable grasping of a picking robot, and in particular to a grasping effect evaluation method, system and equipment for an oil-tea camellia picking robot arm. Background Art

[0002] In modern agricultural production, camellia oil is an important cash crop, and the development of its planting and harvesting technology is directly related to farmers' income and the sustainable development of agriculture. Traditional camellia oil harvesting methods rely mainly on manual labor, which has many problems such as high labor intensity, low picking efficiency, and high fruit damage rate. Furthermore, the harvesting method uses a rigid clamp to clamp around the trunk of the camellia oil tree, which causes severe damage to the tree during vibration harvesting and may cause missed harvests due to the growth characteristics of the camellia oil tree.

[0003] With the advancement of science and technology, mechanized picking has gradually become an effective means to solve the above problems. The emergence of oil-tea picking robotic arms has undoubtedly provided new possibilities for improving picking efficiency. However, the differences in the structural characteristics and physiological parameters of oil-tea branches make the gripping effect of the robotic arms face many challenges. When designing existing oil-tea picking robotic arms, there is often a lack of in-depth research on the characteristics of oil-tea branches, resulting in improper clamping force settings, which may cause damage to the fruit or breakage of the branches. Especially during the picking process, factors such as the contact force between the robotic arm and the branches, the slippage, and the deflection changes of the branches all have an important impact on the picking effect.

[0004] However, the growth and attachment of fruit to its branches is complex, especially during the harvest season, when fruit maturity varies. The physiological state of the branches and changes in environmental conditions can affect harvesting efficiency. Therefore, efficiently and accurately harvesting fruit from oil-tea camellia branches has become a critical issue in agricultural mechanization.

[0005] Prior art publication CN116673943A discloses a method for constructing a stable grasping evaluation model for a harvesting robot and related equipment. The method includes: obtaining the minimum damage stress and critical gripping force of the target object; determining a gripping force factor based on the minimum damage stress and critical gripping force of the target object; and determining the stable grasping evaluation model based on the gripping force factor, a target object characteristic factor, and a gripping incentive factor. However, this approach relies on static parameters such as the minimum damage stress and critical gripping force, and fails to fully consider the effects of dynamic environmental changes, such as temperature and humidity, on grasping force. Furthermore, the method fails to deeply analyze the biomechanical properties of the target object, particularly the impact of different physiological states, such as the state of the branch, on grasping effectiveness. This may result in the evaluation model's limited applicability to different environments or species. In actual harvesting, these dynamic factors can significantly affect grasping effectiveness, thus reducing the accuracy and effectiveness of the evaluation results.

[0006] The above information disclosed in the background art section is only used to enhance the understanding of the background of the present disclosure, and thus it may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0007] The purpose of the present invention is to provide a method, system and device for evaluating the grasping effect of a camellia oleifera picking robotic arm, so as to solve the problems raised in the above background art.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] A method for evaluating the grasping effect of a camellia oleifera picking robotic arm, the specific steps include:

[0010] S1: Obtain the physical parameters of the robotic arm structure of the camellia oleifera picking robotic arm, as well as the clamping force setting value, clamping characteristic parameters, average humidity on the surface of the clamping part, relative sliding distance between the robotic arm and the camellia oleifera branch, and camellia oleifera branch deflection and physiological parameters during previous picking processes, and calculate the actual grasping force of the robotic arm during previous picking processes based on these.

[0011] S2: Construct a finite element analysis model for obtaining the relative sliding distance, calibrate the finite element analysis model based on the data in step S1, input the physical parameters of the robotic arm structure, clamping force setting value, clamping characteristic parameters and average humidity on the surface of the clamping part under the current working condition of the camellia oleifera picking robotic arm into the finite element analysis model to obtain the predicted value of the relative sliding distance, and calculate the predicted value of the actual grasping force under the current working condition.

[0012] S3: Construct a relational expression based on the branch deflection, physiological parameters and actual grasping force data during previous picking processes, and determine the fitting parameters in the relational expression by least squares fitting. Input the physiological parameters and predicted value of the actual grasping force of the camellia oleifera branch under the current working condition into the relational expression to obtain the camellia oleifera branch deflection under the current working condition.

[0013] S4: Set the branch deflection threshold range, collect the environmental temperature at the location of the camellia oleifera tree, dynamically adjust to obtain the adaptive branch deflection threshold range based on the environmental temperature, compare the branch deflection under the current working condition with the adaptive branch deflection threshold range, and issue a corresponding effect evaluation according to the comparison result.

[0014] Further, the clamping characteristic parameters include the clamping angle between the robotic arm and the camellia oleifera branch, the contact area, and the friction coefficient between the claw toes and the tree trunk contact surface. The physical parameters of the robotic arm structure include the number of inner and outer claw toes, the length and width of the inner and outer claw toes, the claw toe damping coefficient, and the equivalent stiffness of the robotic arm claw toes.

[0015] Based on the parameters, the actual gripping force of the robotic arm during the previous picking process is calculated. The actual gripping force is calculated based on the following formula:

[0016] F ZA =F J -F HY +F DB

[0017] Where, F ZA is the actual gripping force, F J is the static friction force, F HY is the dynamic slip force, F DB is the dynamic compensation force;

[0018] The static friction force is determined by the clamping force setting value and the contact surface friction coefficient, the dynamic slip force represents the force loss caused by the vibration of the tea tree branches and the slip of the claws of the tea tree picking robot arm, and the dynamic compensation force represents the compensation force provided by the stiffness and damping characteristics of the claws of the tea tree picking robot arm.

[0019] Furthermore, the static friction force F J The calculation is based on the formula:

[0020] F J =μ z *F N

[0021] Where μ z is the friction coefficient between the claw and the trunk, F N is the effective pressure of the force applied by the claw toe distributed to the contact surface;

[0022] The friction coefficient μ between the contact surface of the claw and the trunk is z The specific formula is:

[0023]

[0024] Where μ c is the intrinsic friction coefficient of the contact surface between the claw and the trunk, is the surface texture correction factor;

[0025] The effective pressure F N It is converted from the force applied by the oil-tea camellia picking robot arm. The specific calculation formula is as follows:

[0026]

[0027] Where, F S The force applied by the oil-tea camellia picking robot arm is θ gripis the clamping angle between the robotic arm and the oil-tea camellia branch, and A is the contact area between the robotic arm and the oil-tea camellia branch. The number of claw toes, as well as their length and width, determine the total contact area. The formula for determining the contact area A between the robotic arm and the oil-tea camellia branch is as follows:

[0028] A = n in *(L in *W in ) + n out *(L out *W out )

[0029] In the formula, n in and n out are the numbers of inner and outer claw toes of the oil-tea camellia picking robotic arm respectively, L in and W in are the length and width of the inner claw toes respectively, L out and W out are the length and width of the outer claw toes respectively.

[0030] Further, the formula for calculating the dynamic slip force is as follows:

[0031]

[0032] In the formula, m is the equivalent mass of the oil-tea camellia branch, ω is the vibration angular frequency applied by the oil-tea camellia picking robotic arm, f z is the vibration amplitude, δ zz is the correction factor for flexible energy absorption. The correction factor δ zz for flexible energy absorption is characterized by the relative slip distance between the oil-tea camellia picking robotic arm and the oil-tea camellia branch. The specific formula is as follows:

[0033]

[0034] In the formula, Δx is the relative slip distance between the oil-tea camellia picking robotic arm and the oil-tea camellia branch, and k is the equivalent stiffness of the claw toes of the oil-tea camellia picking robotic arm.

[0035] Further, the formula for calculating the dynamic compensation force is as follows:

[0036] F DB = (k * L toe ) * f z + C z * v

[0037] In the formula, L toe is the equivalent length of the claw toes, C z is the damping coefficient of the claw toes, and v is the vibration speed of the oil-tea camellia picking robotic arm.

[0038] Further, the physiological parameters include the diameter and the corresponding elastic modulus of the oil-tea camellia branch;

[0039] Construct the relational expressions of branch deflection, physiological parameters, and actual grasping force. The relational expression between branch deflection and actual grasping force is specifically as follows:

[0040]

[0041] In the formula, d is the diameter of the oil-tea camellia branch, E is the corresponding elastic modulus of the oil-tea camellia branch, Y is the branch deflection, β and ε are fitting coefficients, where the fitting coefficients β and ε are determined by the least squares method. Specifically, the value range of coefficient β is 168±20, and the value range of coefficient ε is 3.102±0.421.

[0042] Furthermore, an adaptive branch deflection threshold range is dynamically adjusted based on the environmental temperature. The specific logic of the dynamic adjustment is as follows: Set a reference temperature. If the reference temperature is greater than or equal to the environmental temperature, the upper limit of the branch deflection threshold is dynamically decreased. If the reference temperature is less than the environmental temperature, the upper limit of the branch deflection threshold is dynamically increased. The formula based on which the specific adjustment logic is:

[0043]

[0044] In the formula, yz′ max is the upper limit of the threshold of the adaptive branch deflection threshold range, yz max is the initial value of the upper limit of the threshold of the set branch deflection threshold range, T S and T0 are the environmental temperature and the reference temperature respectively, and γ is the temperature correction constant;

[0045] Compare the branch deflection corresponding to the actual grasping force of the to-be-evaluated oil-tea camellia picking robotic arm obtained with the adaptive branch deflection threshold range. According to the comparison result, issue a corresponding effect evaluation. The specific logic of issuing a corresponding effect evaluation according to the comparison result is as follows:

[0046] When the branch deflection Y′ corresponding to the actual grasping force of the to-be-evaluated oil-tea camellia picking robotic arm ∈ [yz min , yz′ max , it is determined that the grasping effect of the oil-tea camellia picking robotic arm is excellent;

[0047] When the branch deflection corresponding to the actual grasping force of the to-be-evaluated oil-tea camellia picking robotic arm , it is determined that the grasping effect of the oil-tea camellia picking robotic arm is poor.

[0048] The present invention also provides a grasping effect evaluation system for an oil-tea camellia picking robotic arm. The grasping effect evaluation system for an oil-tea camellia picking robotic arm is used to execute the above-mentioned grasping effect evaluation method for an oil-tea camellia picking robotic arm, and includes:

[0049] The historical data acquisition module is used to obtain the physical parameters of the robotic arm structure of the oil-tea camellia picking robotic arm, as well as the clamping force setting values, clamping characteristic parameters, average humidity on the surface of the clamping part, relative sliding distance between the robotic arm and the oil-tea camellia branches, and the deflection and physiological parameters of the oil-tea camellia branches during previous picking processes, and calculate the actual grasping force of the robotic arm during previous picking processes based on these data;

[0050] The prediction model construction module is used to construct a finite element analysis model for obtaining the relative sliding distance, calibrate the finite element analysis model based on the data in step S1, input the physical parameters of the robotic arm structure, clamping force setting values, clamping characteristic parameters, and average humidity on the surface of the clamping part under the current working conditions of the oil-tea camellia picking robotic arm into the finite element analysis model to obtain the predicted value of the relative sliding distance, and calculate the predicted value of the actual grasping force under the current working conditions;

[0051] The branch deflection correlation module is used to construct a relationship expression based on the branch deflection, physiological parameters, and actual grasping force data during previous picking processes, determine the fitting parameters in the relationship expression by least squares fitting, and input the physiological parameters and predicted value of the actual grasping force of the oil-tea camellia branches under the current working conditions into the relationship expression to obtain the deflection of the oil-tea camellia branches under the current working conditions;

[0052] The grasping effect evaluation module is used to set a threshold range for the branch deflection, collect the environmental temperature at the location of the oil-tea camellia tree, dynamically adjust to obtain an adaptive branch deflection threshold range based on the environmental temperature, compare the branch deflection under the current working conditions with the adaptive branch deflection threshold range, and issue a corresponding effect evaluation according to the comparison result.

[0053] The present invention also provides a grasping effect evaluation device for an oil-tea camellia picking robotic arm, including one or more processors; a storage device for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the above-mentioned grasping effect evaluation method for an oil-tea camellia picking robotic arm.

[0054] Compared with the prior art, the beneficial effects of the present invention are:

[0055] Using finite element analysis, it is possible to accurately evaluate the relative slip distance between the robotic arm and the oil-tea camellia branches under different clamping forces. This scientific calculation method provides important data support for the design of the robotic arm, enabling it to optimize the grasping force and effectively avoid fruit damage and branch breakage. Secondly, through the dynamic analysis of the branch deflection changes and considering environmental factors such as temperature and humidity, the threshold of the branch deflection can be adjusted adaptively. This mechanism ensures that the robotic arm can maintain the best grasping effect under different environmental conditions, reducing the risk of damage to the oil-tea camellia trees and improving the picking quality at the same time. In addition, by fitting the relationship expression using the least squares method, it is possible to accurately predict the relationship between the grasping force and the branch deflection, providing a strong basis for further optimizing the performance of the robotic arm. It not only improves the working efficiency of oil-tea camellia picking but also promotes the mechanization process of the oil-tea camellia industry, providing strong support for the sustainable development of agricultural production. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is a schematic diagram of the overall method flow of the present invention;

[0057] Figure 2 It is a statistical dot-line graph of the elastic modulus of branches of different oil-tea camellia varieties;

[0058] Figure 3 It is a comparison curve graph of the predicted value - test value of the branch deflection of the Huashuo variety;

[0059] Figure 4 It is a comparison curve graph of the predicted value - test value of the branch deflection of the Huajin variety;

[0060] Figure 5 It is a comparison curve graph of the predicted value - test value of the branch deflection of the Huaxin variety;

[0061] Figure 6 It is a comparison curve graph of the predicted value - test value of the branch deflection of the Xianglin variety;

[0062] Figure 7 It is a schematic diagram of the structure of the oil-tea camellia picking robotic arm;

[0063] Figure 8 It is a schematic diagram of the overall system structure of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0064] To make the objectives, technical solutions, and advantages of the present invention more clear and understandable, the present invention will be further described in detail below in conjunction with specific embodiments.

[0065] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in this invention should have the ordinary meanings understood by those with ordinary skills in the field to which this invention belongs. The "first", "second" and similar terms used in this invention do not denote any order, quantity or importance, but are only used to distinguish different components. Words such as "comprising" or "including" mean that the elements or objects appearing before this word cover the elements or objects listed after this word and their equivalents, without excluding other elements or objects. Words such as "connected" or "linked" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right", etc. are only used to represent relative position relationships. When the absolute position of the object being described changes, the relative position relationship may also change accordingly.

[0066] Embodiment:

[0067] Please refer to Figures 1-7 , this invention provides a technical solution:

[0068] A method for evaluating the grasping effect of an oil-tea camellia picking robotic arm, the specific steps include:

[0069] Step 1: Obtain the physical parameters of the robotic arm structure of the oil-tea camellia picking robotic arm, as well as the clamping force setting values, clamping characteristic parameters, average humidity of the surface of the clamping part, relative sliding distance between the robotic arm and the oil-tea camellia branches, and the deflection and physiological parameters of the oil-tea camellia branches during previous picking processes, and calculate the actual grasping force of the robotic arm during previous picking processes based on these.

[0070] The physiological parameters include the diameter of the oil-tea camellia branches and the corresponding elastic modulus; multiple measurements are carried out at the clamping part of the oil-tea camellia branches to ensure the representativeness of the samples, record the diameter of each measurement, and take its average value as the diameter of the oil-tea camellia branches. The elastic modulus E corresponding to the oil-tea camellia branches is obtained through a static bending test.

[0071] The average humidity of the surface of the clamping part specifically refers to the average humidity of the entire contact surface between the oil-tea camellia picking robotic arm and the oil-tea camellia branches.

[0072] The relative sliding distance between the robotic arm and the oil-tea camellia branches is specifically determined through a clamping test. Clamp the oil-tea camellia branches with the robotic arm, apply a set clamping force, use a displacement sensor, such as a laser displacement sensor or a displacement gauge, to measure the displacement of the contact point between the claw toes of the robotic arm and the branches, record the clamping force setting value, and then calculate the relative sliding distance based on the moving distance of the branches.

[0073] The deflection of the oil-tea camellia branches can be measured using a displacement sensor or a micrometer to measure the deflection of the branches under the action of the clamping force, and record the deflection changes at the midpoint of the branches when different clamping forces are applied.

[0074] The clamping feature parameters include the clamping angle between the robotic arm and the oil-tea camellia branch, the contact area, and the friction coefficient of the contact surface between the claw toes and the tree trunk. The physical parameters of the robotic arm structure include the number of inner and outer claw toes, the length and width of the inner and outer claw toes, the damping coefficient of the claw toes, and the equivalent stiffness of the robotic arm claw toes.

[0075] Among them, in actual operation, the clamping angle between the claw toes and the branch is directly measured through the contact method between the robotic arm and the oil-tea camellia branch. This can be measured with high precision using a laser rangefinder or an angle sensor.

[0076] The contact area between the robotic arm and the oil-tea camellia branch can be characterized by the number of inner and outer claw toes of the oil-tea picking robotic arm and their corresponding lengths and widths.

[0077] The friction coefficient of the contact surface between the claw toes and the tree trunk is measured by conducting a friction experiment on a known surface, such as the tree trunk surface, to measure the required frictional force and normal force, and then calculating the friction coefficient. Or by referring to relevant literature to obtain friction coefficient data under similar material and surface conditions, or by characterizing it through the intrinsic friction coefficient and surface texture correction factor of the contact surface between the claw toes and the tree trunk.

[0078] Parameters such as the number of inner and outer claw toes, the length and width of the inner and outer claw toes can be obtained through mechanical design documents and models. During the design stage of the robotic arm, the number, length, and width of the claw toes can be directly read from the engineering design drawings.

[0079] The damping coefficient of the claw toes can be obtained through dynamic experiments. Apply a periodic load to the robotic claw toes, and by measuring its vibration attenuation characteristics, period, and amplitude, the damping coefficient can be calculated. Or use finite element analysis software to simulate the dynamic response of the claw toes under different loads, and infer the damping coefficient from the simulation results.

[0080] The equivalent stiffness of the claw toes of the oil-tea picking robotic arm is calculated by performing static or dynamic mechanical analysis on the claw toe material and structure and using Hooke's law formula.

[0081] Based on the above parameters, calculate the actual grasping force of the robotic arm during previous picking processes. The specific formula for calculating the actual grasping force is:

[0082] F ZA =F J -F HY +F DB

[0083] In the formula, F ZA is the actual grasping force, F J is the static frictional force, F HY is the dynamic slip force, F DB is the dynamic compensation force;

[0084] The static frictional force is determined by the clamping force setting value and the friction coefficient of the contact surface. The dynamic slip force represents the force loss caused by the vibration of the oil tea tree branch and the slip of the claw toes of the oil tea picking robotic arm. The dynamic compensation force represents the compensation force provided by the stiffness and damping characteristics of the claw toes of the oil tea picking robotic arm.

[0085] The static frictional force F J The calculation formula is as follows:

[0086] F J = μ z * F<( N

[0087] In the formula, μ z is the friction coefficient of the contact surface between the claw toe and the tree trunk, and F N is the effective pressure of the force applied by the claw toe distributed on the contact surface;

[0088] The friction coefficient μ z of the contact surface between the claw toe and the tree trunk is specifically based on the following formula:

[0089]

[0090] In the formula, μ c is the intrinsic friction coefficient of the contact surface between the claw toe and the tree trunk, is the surface texture correction factor; where the surface texture correction factor is used to represent the friction effect of the processed robotic arm surface texture on the contact surface of the tree trunk, such as the enhancement effect after increasing the roughness or applying a surface coating.

[0091] The method for obtaining the intrinsic friction coefficient μ c of the contact surface between the claw toe and the tree trunk is as follows: Under laboratory conditions, conduct friction tests on the claw toe material and the tree trunk surface material using a friction testing machine. Standard methods such as ASTM D1894 can be used for measurement to obtain the friction coefficient of the material in the dry or wet state. If the surface material of the tree trunk of the oil tea tree is known, relevant literature can be consulted to obtain the friction coefficient data of similar materials and conditions.

[0092] The surface texture correction factor is obtained by measuring the roughness of the claw toe surface using a surface profiler or roughness meter, conducting friction tests, recording the friction coefficients at different roughness levels, finding the relationship with the intrinsic friction coefficient, and recording the influence of each treatment on the friction coefficient for different surface treatments such as sandblasting, coating, and polishing, so as to determine the surface texture correction factor.

[0093] The effective pressure F NIt is transformed from the set value of the clamping force of the oil-tea camellia picking robotic arm. The specific calculation formula is as follows:

[0094]

[0095] In the formula, F S is the force applied by the oil-tea camellia picking robotic arm set, θ grip is the clamping angle between the robotic arm and the oil-tea camellia branch, and A is the contact area between the robotic arm and the oil-tea camellia branch. The total contact area is determined by the number of claw toes, their length and width. The formula for determining the contact area A between the robotic arm and the oil-tea camellia branch is as follows:

[0096] A = n in *(L in *W in ) + n out *(L out *W out )

[0097] In the formula, n in and n out are the numbers of inner and outer claw toes of the oil-tea camellia picking robotic arm respectively, L in and W in are the length and width of the inner claw toes respectively, and L out and W out are the length and width of the outer claw toes respectively.

[0098] Among them, a larger contact area between the robotic arm and the oil-tea camellia branch can reduce the contact pressure per unit area and increase the grasping stability. The clamping angle θ grip of the robotic arm and the oil-tea camellia branch affects the magnitude of the normal pressure.

[0099] Since the direction of the frictional force changes periodically in the opposite direction with the change of the slip direction, the actual work done will be evenly distributed in the positive and negative slip directions. Specifically: in the first half cycle of the positive slip direction, the frictional force does positive work; in the second half cycle of the negative slip direction, the frictional force does negative work. The average frictional force, or effective frictional force, is half of the static frictional force on average over the entire cycle, resulting in a frictional loss caused by slip of When the tree trunk is vibrated by an external force, the claw toes slip relative to the tree trunk, resulting in partial frictional force loss. The formula for calculating the dynamic slip force is as follows:

[0100]

[0101] In the formula, m is the equivalent mass of the oil-tea camellia branch, ω is the vibration angular frequency applied by the oil-tea camellia picking robotic arm, f z is the vibration amplitude, and δ zz is the correction factor for flexible energy absorption. Among them, the correction factor δ zzIt is characterized by the relative sliding distance between the oil-tea camellia picking robotic arm and the oil-tea camellia branches. The specific formula is as follows:

[0102]

[0103] In the formula, Δx is the relative sliding distance between the oil-tea camellia picking robotic arm and the oil-tea camellia branches, and k is the equivalent stiffness of the claw toes of the oil-tea camellia picking robotic arm.

[0104] The formula for calculating the dynamic compensation force is as follows:

[0105] F DB =(k*L toe )*f z +C z *v

[0106] In the formula, L toe is the equivalent length of the claw toes, C z is the damping coefficient of the claw toes, and v is the vibration speed of the oil-tea camellia picking robotic arm.

[0107] Among them, the vibration angular frequency ω, vibration amplitude f z applied by the oil-tea camellia picking robotic arm and the vibration speed v of the oil-tea camellia picking robotic arm are determined by the applied control signal.

[0108] Step 2: Construct a finite element analysis model for obtaining the relative sliding distance, calibrate the finite element analysis model based on the data in Step S1, and input the physical parameters of the robotic arm structure, the set value of the clamping force, the clamping characteristic parameters, and the average humidity of the surface of the clamping part under the current working conditions of the oil-tea camellia picking robotic arm into the finite element analysis model to obtain the predicted value of the relative sliding distance and calculate the predicted value of the actual gripping force under the current working conditions.

[0109] The specific steps for constructing a finite element analysis model for obtaining the relative sliding distance include: using CAD software such as SolidWorks, AutoCAD, CATIA, etc. to create the geometric models of the robotic arm and the oil-tea camellia branches. Export the models in a format supported by finite element analysis software such as STEP, IGES or STL.

[0110] Assign material properties to different components, the robotic arm and the oil-tea camellia branches, in the finite element analysis software. Select an appropriate mesh type according to the complexity of the model. Define fixed boundary conditions at certain positions of the model to simulate the actual working state. Simulate the corresponding relative sliding process by applying contact forces and record the relative sliding distance. Conduct finite element analysis on the constructed preliminary model, calculate the relative sliding distance corresponding to the relevant data in step S1, compare the calculated relative sliding distance with the relative sliding distance of the historical data in step S1, compare the relative sliding distance in the historical data with the calculation results of the preliminary model, and adjust the built-in parameters of the finite element analysis model through the comparison error to complete the calibration of the model.

[0111] Step 3: Construct a relationship expression based on the branch deflection, physiological parameters, and actual grasping force data during previous picking processes, and determine the fitting parameters in the relationship expression by least squares fitting. Input the physiological parameters and predicted values of the actual grasping force of the oil-tea camellia branches under the current working conditions into the relationship expression to obtain the branch deflection of the oil-tea camellia branches under the current working conditions.

[0112] Collect several samples of oil-tea camellia branches with different physiological parameters, and the physiological parameters include the diameter and corresponding elastic modulus of the oil-tea camellia branches.

[0113] Construct a relationship expression among branch deflection, physiological parameters, and actual grasping force. The relationship expression between branch deflection and actual grasping force is specifically:

[0114]

[0115] In the formula, d is the diameter of the oil-tea camellia branch, E is the corresponding elastic modulus of the oil-tea camellia branch, Y is the branch deflection, β and ε are fitting coefficients, and the fitting coefficients β and ε are determined by least squares fitting. Specifically, the value range of the coefficient β is 168 ± 20, and the value range of the coefficient ε is 3.102 ± 0.421.

[0116] Nonlinear Least Squares (NLS) is a statistical method for data fitting. By minimizing the sum of the squares of the errors between the observed data and the model predicted values, the best estimated values of the model parameters are found. Among them, fitting is performed through the branch deflection, physiological parameters, and the actual grasping force data of the robotic arm during previous picking processes, and the fitting coefficients β and ε are determined through the historical data during previous picking processes.

[0117] Since the least squares method is a common statistical method for data fitting, the specific steps of determining the fitting coefficients β and ε by least squares fitting are not elaborated here.

[0118] Research on the relationship between the actual grasping force and deflection of Camellia oleifera branches based on the hyperbolic tangent function, determining the expression of the relationship between branch deflection changes, physiological parameters, and actual grasping force. The bending deformation and flexural strength of Camellia oleifera branches directly determine whether the mechanical gripper can effectively grasp the branches during the picking process and achieve precise picking under high-frequency vibration excitation. Existing research has mostly focused on the design of mechanical picking equipment for fruit trees, but lacks a systematic study on the mechanical response characteristics of branches. To fill this gap, this study systematically analyzed the relationship between the actual grasping force and deflection of Camellia oleifera branches of different varieties through three-point bending tests, that is, the bending load-deflection relationship, and constructed a nonlinear mechanical model using the hyperbolic tangent function to provide precise mechanical parameters and boundary condition support for the design of mechanical grippers. The test materials for this study were sourced from the Large Fruit Camellia oleifera Technology Integration Demonstration Garden in Yangjiaping Village, Chating Town, Wangcheng District, Changsha City, Hunan Province, and the main planted varieties of Camellia oleifera trees in Hunan Province (Huashuo, Huajin, Huaxin, Xianglin) were selected. The diameter range of the test picking branches was 5 to 40 mm. To ensure the uniformity and comparability of the test samples, relatively straight branches without knots were selected as specimens. Immediately after the branches were detached from the tree, the tests were carried out. A universal material testing machine was used to conduct three-point bending tests on the Camellia oleifera branches at a loading speed of 20 mm / min. To systematically analyze the mechanical properties of the branches, the branch specimens were divided into 4 groups according to their diameters: 5 to 10 mm, 10 to 20 mm, 20 to 30 mm, and 30 to 40 mm. For Camellia oleifera branches of different varieties within each diameter range, three-point bending tests were carried out, and the corresponding actual grasping force-deflection change curves were recorded. The elastic modulus E corresponding to the Camellia oleifera branches was obtained through static bending tests. The specific elastic moduli of Camellia oleifera branches of different planting varieties are shown in Table 1.

[0119] Table 1: Elastic moduli of branches of 4 Camellia oleifera varieties

[0120]

[0121] As shown in Table 1, the elastic modulus of the branches of Huashuo generally shows a relatively low level, with a variation range between 459.01 Mpa and 682.26 MPa, and an average value of about 536 MPa. The relatively low elastic modulus indicates that the branches of Huashuo are structurally softer and have a weaker anti-bending ability. The elastic modulus of the branches of Huajin shows relatively large fluctuations, with a fluctuation range between 553.73 Mpa and 789.62 MPa, and an average value of about 678 MPa. The branches of Huajin show a higher elastic modulus than those of Huashuo, and their structure is generally tougher. Among the four varieties, the elastic modulus value of the branches of Huaxin is the highest, with a range between 609.95 Mpa and 983.33 MPa, and an average value of about 808 MPa. The high elastic modulus of the branches of Huaxin indicates that it has higher rigidity and anti-deformation ability. The elastic modulus of the branches of Xianglin ranges between 529.08 Mpa and 748.03 MPa, and an average value of about 658 MPa. The elastic modulus curve of Xianglin has obvious fluctuations. Generally, the elastic modulus of the branches of Xianglin is slightly higher than those of Huashuo and Huajin, but lower than that of Huaxin.

[0122] The hyperbolic tangent function (tanh) is an S-shaped saturation curve that gradually approaches ±1 as the independent variable changes. This non-linear relationship is common in many physical systems and is particularly suitable for describing the non-linear relationship between stress and strain. The actual holding force-deflection curve of the oil-tea camellia tree branches in the three-point bending test has a similar trend to the tanh curve. The actual holding force curve increases rapidly and linearly in the initial stage and then gradually levels off, showing a trend similar to the tanh function. This characteristic has been widely verified in the tests of fiber-reinforced concrete and shape memory alloys.

[0123] Step 4: Set the range of the branch deflection threshold, collect the ambient temperature at the location of the oil-tea camellia tree, dynamically adjust it based on the ambient temperature to obtain an adaptive branch deflection threshold range, compare the branch deflection under the current working condition with the adaptive branch deflection threshold range, and issue a corresponding effect evaluation according to the comparison result.

[0124] Dynamically adjust based on the ambient temperature to obtain an adaptive branch deflection threshold range. The specific logic of the dynamic adjustment is as follows: Set a reference temperature. If the reference temperature is greater than or equal to the ambient temperature, dynamically reduce the upper limit of the branch deflection threshold. If the reference temperature is less than the ambient temperature, dynamically increase the upper limit of the branch deflection threshold. The specific adjustment logic is based on the following formula:

[0125]

[0126] In the formula, yz′ max is the upper limit of the threshold of the adaptive branch deflection threshold range, yz max is the initial value of the upper limit of the threshold of the set branch deflection threshold range, T ST0 and T0 are the ambient temperature and the reference temperature respectively, and γ is the temperature correction constant;

[0127] It should be noted that the oil-tea camellia branches are mainly composed of lignin, cellulose and other organic substances. The physical properties of these materials, such as the elastic modulus and the flexural strength, will change with the temperature. The maximum deflection also increases accordingly. Generally speaking, when the temperature rises, the elastic modulus of the wood decreases, resulting in an increase in the deflection of the branches when subjected to external forces. Therefore, when the ambient temperature is greater than the reference temperature, the initial value of the upper limit of the set branch deflection threshold range should be appropriately increased.

[0128] In a low-temperature environment, the materials of the oil-tea camellia branches become more brittle. At low temperatures, the moisture in the cell walls of the wood may freeze, resulting in a decrease in the toughness of the wood. This makes the branches more likely to break when subjected to external forces, and the maximum deflection may decrease. Therefore, when the ambient temperature is less than or equal to the reference temperature, the initial value of the upper limit of the set branch deflection threshold range should be appropriately reduced to prevent the oil-tea camellia fruits from being damaged.

[0129] At the same time, the setting method of the temperature correction constant γ is to be set by combining the change of the branch deflection under the specific ambient temperature with the expert experience, and generally takes a value between 0 and 1. The reference temperature T0 is generally set at 25 °C, and can be specifically adjusted according to the growth and planting environment of the oil-tea camellia fruit trees.

[0130] Compare the branch deflection corresponding to the actual gripping force of the to-be-evaluated oil-tea camellia picking robotic arm with the adaptive branch deflection threshold range, and issue a corresponding effect evaluation according to the comparison result. The specific logic of issuing a corresponding effect evaluation according to the comparison result is as follows:

[0131] When the branch deflection Y′ corresponding to the actual gripping force of the to-be-evaluated oil-tea camellia picking robotic arm ∈ [yz min , yz′ max , it is judged that the gripping effect of the oil-tea camellia picking robotic arm is excellent;

[0132] When the branch deflection corresponding to the actual gripping force of the to-be-evaluated oil-tea camellia picking robotic arm , it is judged that the gripping effect of the oil-tea camellia picking robotic arm is poor.

[0133] Please refer to Figure 8 , the present invention also provides a gripping effect evaluation system for an oil-tea camellia picking robotic arm. The gripping effect evaluation system for an oil-tea camellia picking robotic arm is used to execute the above-mentioned gripping effect evaluation method for an oil-tea camellia picking robotic arm, and includes:

[0134] A historical data acquisition module, which is used to obtain the physical parameters of the robotic arm structure of the oil-tea camellia picking robotic arm, as well as the clamping force setting values, clamping characteristic parameters, average humidity on the surface of the clamping part, relative slip distance between the robotic arm and the oil-tea camellia branches, and the deflection and physiological parameters of the oil-tea camellia branches during previous picking processes, and calculate the actual grasping force of the robotic arm during previous picking processes based on these;

[0135] A prediction model construction module, which is used to construct a finite element analysis model for obtaining the relative slip distance, calibrate the finite element analysis model based on the data in step S1, input the physical parameters of the robotic arm structure, clamping force setting values, clamping characteristic parameters, and average humidity on the surface of the clamping part of the oil-tea camellia picking robotic arm under the current working conditions into the finite element analysis model to obtain the predicted value of the relative slip distance, and calculate the predicted value of the actual grasping force under the current working conditions;

[0136] A branch deflection correlation module, which is used to construct a relationship expression based on the branch deflection, physiological parameters, and actual grasping force data during previous picking processes, determine the fitting parameters in the relationship expression by least squares fitting, and input the physiological parameters and predicted value of the actual grasping force of the oil-tea camellia branches under the current working conditions into the relationship expression to obtain the deflection of the oil-tea camellia branches under the current working conditions;

[0137] A grasping effect evaluation module, which is used to set a threshold range for the branch deflection, collect the environmental temperature at the location of the oil-tea camellia tree, dynamically adjust to obtain an adaptive branch deflection threshold range based on the environmental temperature, compare the branch deflection under the current working conditions with the adaptive branch deflection threshold range, and issue a corresponding effect evaluation according to the comparison result.

[0138] The present invention also provides a grasping effect evaluation device for an oil-tea camellia picking robotic arm, including one or more processors; a storage device for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the above-mentioned grasping effect evaluation method for an oil-tea camellia picking robotic arm.

[0139] In this embodiment, to comprehensively improve the performance of the tree-grabbing manipulator, the claw toes are first changed to high-strength and lightweight carbon fiber materials by replacing the materials, and the support frame is made of 6061 aluminum alloy to enhance the overall structural stiffness. The length of the inner finger is adjusted from 150 mm to 200 mm, and a 120-degree bent finger is added. When the inner fingers are closed, a cross-encircling state can be formed, and the width of the outer finger is widened from 20 mm to 30 mm, thus significantly enhancing the grasping force and wrapping ability. A silicone finger sleeve is covered on the surface of the claw toes to increase the friction. To further improve the performance of the mechanical claw, an optimized design is carried out. Aiming at the problem of interference between the wrist structure and the rope during the pulling process, the wrist part is redesigned with a more compact geometric shape, and the wrist length is shortened from 200 mm to 50 mm. This improvement effectively reduces the contact opportunity between the rope and the wrist by reducing the wrist length and mass, and reduces the possibility of interference. In addition, the surface of the wrist is smoothed and made of lightweight nylon material by 3D printing technology, further reducing the friction and improving the operation flexibility. Subsequently, the support frame material is replaced with 7075 aluminum alloy with higher strength, significantly enhancing the stiffness and stability of the overall structure.

[0140] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0141] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those skilled in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or by the combination of computer software and electronic hardware. Whether these functions are executed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0142] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units. They can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0143] The above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in this application, and all should be covered within the protection scope of this application.

Claims

1. A method for evaluating the gripping effect of a camellia picking robot arm, characterized in that: The specific steps include: S1: Obtain the physical parameters of the mechanical arm structure of the oil-tea camellia picking robot arm, as well as its gripping force setting value, gripping characteristic parameters, average surface humidity of the gripping part, relative slip distance between the mechanical arm and the oil-tea camellia branch, and the deflection and physiological parameters of the oil-tea camellia branch during previous picking processes, and use these to calculate the actual gripping force of the mechanical arm during previous picking processes; The physiological parameters include the diameter of the tea tree branches and the corresponding elastic modulus; S2: Construct a finite element analysis model for obtaining the relative slip distance, and calibrate the finite element analysis model based on the data in step S1. Input the physical parameters of the robot arm structure, the clamping force setting value, the clamping characteristic parameters, and the average humidity of the clamping part surface under the current working condition of the oil-tea picking robot arm into the finite element analysis model to obtain the relative slip distance prediction value, and calculate the actual gripping force prediction value under the current working condition; S3: Based on the branch deflection, physiological parameters, and actual gripping force data from previous picking processes, a relational expression is constructed, and the fitting parameters in the relational expression are determined using the least squares method. The physiological parameters and actual gripping force prediction values of the oil-tea camellia branches under the current working conditions are input into the relational expression to obtain the branch deflection under the current working conditions. S4: Set the branch deflection threshold range, and collect the ambient temperature where the oil tea fruit tree is located. Dynamically adjust the adaptive branch deflection threshold range based on the ambient temperature, compare the branch deflection under the current working conditions with the adaptive branch deflection threshold range, and issue a corresponding effect evaluation based on the comparison results.

2. The method for evaluating the gripping effect of a tea-oil picking robot arm according to claim 1, wherein: The clamping characteristic parameters include the clamping angle between the manipulator and the tea tree branch, the contact area, and the friction coefficient between the claw and the trunk. The physical parameters of the manipulator structure include the number of inner and outer claws, the length and width of the inner and outer claws, the claw damping coefficient, and the equivalent stiffness of the manipulator claws. Based on the parameters, the actual gripping force of the robotic arm during the previous picking process is calculated. The actual gripping force is calculated based on the following formula: F ZA =F J -F HY +F DB Where, F ZA is the actual gripping force, F J is the static friction force, F HY is the dynamic slip force, F DB is the dynamic compensation force; The static friction force is determined by the clamping force setting value and the contact surface friction coefficient, the dynamic slip force represents the force loss caused by the vibration of the tea tree branches and the slip of the claws of the tea tree picking robot arm, and the dynamic compensation force represents the compensation force provided by the stiffness and damping characteristics of the claws of the tea tree picking robot arm.

3. The method for evaluating the gripping effect of a tea-oil picking robot arm according to claim 2, wherein: The static friction force F J The calculation is based on the formula: F J =μ z *F N Where μ z is the friction coefficient between the claw and the trunk, F N is the effective pressure of the force applied by the claw toe distributed to the contact surface; The friction coefficient μ between the contact surface of the claw and the trunk is z The specific formula is: Where μ c is the intrinsic friction coefficient of the contact surface between the claw and the trunk, is the surface texture correction factor; The effective pressure F N It is converted from the force applied by the oil-tea camellia picking robot arm. The specific calculation formula is: Where, F S The force applied by the oil-tea camellia picking robot arm is θ grip is the clamping angle between the robotic arm and the oil-tea camellia branch, and A is the contact area between the robotic arm and the oil-tea camellia branch. The number of claws, their length, and width determine the total contact area. The specific formula for determining the contact area A between the robotic arm and the oil-tea camellia branch is: A=n in *(L in *W in )+n out *(L out *W out ) Where n in and n out are the number of inner and outer claws of the oil-tea camellia picking robot arm, L in and W in are the length and width of the inner claw, L out and W out are the length and width of the outer claw toe, respectively.

4. The method for evaluating the gripping effect of a tea-oil picking robot arm according to claim 3, wherein: The dynamic slip force calculation is based on the formula: Where m is the equivalent mass of the oil-tea camellia branch, ω is the vibration angular frequency applied by the oil-tea camellia picking robot arm, and f is the vibration angular frequency applied by the oil-tea camellia picking robot arm. z is the vibration amplitude, δ zz is the correction factor of flexible energy absorption, where the correction factor of flexible energy absorption δ zz The relative sliding distance between the oil-tea camellia picking robot arm and the oil-tea camellia branches is used to characterize the sliding distance. The specific formula is: Where Δx is the relative sliding distance between the oil-tea camellia picking robot arm and the oil-tea camellia branch, and k is the equivalent stiffness of the claw toe of the oil-tea camellia picking robot arm.

5. The method for evaluating the gripping effect of a tea-oil picking robot arm according to claim 4, characterized in that: The dynamic compensation force is calculated based on the formula: F DB =(k*L toe )*f z +C z *v Where, L toe is the equivalent length of the claw, C z is the claw toe damping coefficient, and v is the vibration velocity of the oil-tea camellia picking robot arm.

6. The method for evaluating the gripping effect of a tea-oil picking robot arm according to claim 4, wherein: The relationship expression between branch deflection, physiological parameters and actual gripping force is constructed, where the relationship expression between branch deflection and actual gripping force is specifically as follows: Where d is the diameter of the tea tree branch, E is the elastic modulus of the tea tree branch, Y is the branch deflection, β and ε are fitting coefficients, and the fitting coefficients β and ε are determined by the least squares fitting method. Specifically, the value range of the coefficient β is 168±20, and the value range of the coefficient ε is 3.102±0.

421.

7. The method for evaluating the gripping effect of a camellia picking robot arm according to claim 1, wherein: The adaptive branch deflection threshold range is obtained based on the dynamic adjustment of the ambient temperature. The specific logic of the dynamic adjustment is: set a reference temperature. If the reference temperature is greater than or equal to the ambient temperature, the upper limit of the branch deflection threshold is dynamically reduced. If the reference temperature is less than the ambient temperature, the upper limit of the branch deflection threshold is dynamically increased. The specific adjustment logic is based on the formula: Where y′ zmax is the upper threshold of the adaptive branch deflection threshold range, y zmax To set the initial value of the upper threshold of the branch deflection threshold range, T S and T0 are the ambient temperature and reference temperature respectively, γ is the temperature correction constant; The branch deflection corresponding to the actual grasping force of the oil-tea picking robot arm to be evaluated is compared with the adaptive branch deflection threshold range. Based on the comparison result, a corresponding effect evaluation is issued. The specific logic for issuing the corresponding effect evaluation based on the comparison result is as follows: When the branch deflection Y′∈[y zmin , yz′ max ], it is judged that the gripping effect of the oil-tea camellia picking robot arm is excellent; When the actual grasping force of the oil-tea picking robot arm is evaluated, the branch deflection corresponding to the actual grasping force of the oil-tea picking robot arm is , it is judged that the gripping effect of the oil-tea picking robot arm is poor.

8. A gripping effect evaluation system for a tea-oil picking robot arm, characterized by: The system for evaluating the gripping effect of a camellia oil-picking robot arm is used to execute the method for evaluating the gripping effect of a camellia oil-picking robot arm according to any one of claims 1 to 7, comprising: A historical data acquisition module is used to obtain the physical parameters of the mechanical arm structure of the oil-tea camellia picking robot arm, as well as its gripping force setting value, gripping characteristic parameters, average surface humidity of the gripping area, relative slip distance between the mechanical arm and the oil-tea camellia branch, and the deflection and physiological parameters of the oil-tea camellia branch during previous picking processes. This is used to calculate the actual gripping force of the mechanical arm during previous picking processes. The physiological parameters include the diameter of the oil-tea camellia branch and the corresponding elastic modulus. a prediction model construction module, for constructing a finite element analysis model for obtaining the relative slip distance, and calibrating the finite element analysis model based on the data in step S1, inputting the mechanical arm structural physical parameters, clamping force setting value, clamping characteristic parameters, and average humidity of the clamping part surface under the current working condition of the oil-tea picking mechanical arm into the finite element analysis model to obtain a relative slip distance prediction value, and calculating the actual gripping force prediction value under the current working condition; The branch deflection association module is used to construct a relational expression based on the branch deflection, physiological parameters, and actual gripping force data from previous picking processes, and to determine the fitting parameters in the relational expression using the least squares method. The physiological parameters and actual gripping force prediction values of the oil-tea camellia branches under the current working conditions are then input into the relational expression to obtain the branch deflection under the current working conditions. The gripping effect evaluation module is used to set the branch deflection threshold range and collect the ambient temperature of the oil-tea tree. The adaptive branch deflection threshold range is obtained based on the dynamic adjustment of the ambient temperature. The branch deflection under the current working conditions is compared with the adaptive branch deflection threshold range. According to the comparison results, a corresponding effect evaluation is issued.

9. A device for evaluating the gripping effect of a camellia picking robot arm, characterized by: The gripping effect evaluation device of the oil-tea picking robot arm includes one or more processors; a storage device for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the gripping effect evaluation method of the oil-tea picking robot arm as described in any one of claims 1 to 7.

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