A method for optimizing a pre-capture configuration of a dual branch modular robot

Abstract

The application provides a pre-capture configuration optimization method for a double-branch modular robot, comprising the following steps: constructing a kinematics and dynamics model of the double-branch modular robot by using a three-dimensional model of the double-branch modular robot; determining a contact model between the robot and a capture target according to the dynamics model of the double-branch modular robot, and constructing a contact and collision dynamics equation of the double-branch modular robot; constructing a performance evaluation index and an optimization model by considering key factors before the double-branch modular robot captures the target according to a contact and collision analysis before the double-branch modular robot captures the target; obtaining a pre-capture configuration solution set of the double-branch modular robot by using a multi-objective optimization algorithm; and obtaining an optimal pre-capture configuration according to a superior and inferior solution distance method. According to the technical scheme provided by the embodiment of the application, the end pose and base attitude disturbance of the double-branch modular robot before capturing can be effectively reduced, and the contact and collision force can be reduced.

Description

Technical Field

[0001] This invention belongs to the field of robot configuration optimization, and relates to a method for pre-capture configuration optimization of a bi-branch modular robot. Background Technology

[0002] Modular robots are complex systems comprising multiple modular units. Through adjustments to joint arrangement and connections, they can possess varying motion properties (such as workspace and dexterity) and operational capabilities (such as dynamic load capacity and end-effector force). In the space environment, modular robots need to operate precisely in microgravity, where inertia dominates motion. This results in significant dynamic coupling effects, impacting the controllability and stability of their movement. Configuration optimization helps modular robots avoid singularities to the greatest extent possible, ensuring smooth and continuous movement of each joint and preventing attitude loss or limited operational range. Furthermore, given the specific requirements and high-difficulty operations of space missions, pre-capture configuration optimization methods for modular robots enable them to perform tasks with high precision, stability, and efficiency, ensuring sufficient safety and adaptability in complex space missions.

[0003] Currently, configuration optimization methods for single-branch modular robots are quite comprehensive. Compared to single-branch modular robots, the coupling relationship between the manipulator and the base of bi-branch modular robots is more complex, with more degrees of freedom and more intricate singularity problems. However, most current configuration optimization methods for bi-branch modular robots focus on operational factors after the robot captures the target. For free-floating bi-branch modular robots, the contact and collision between the manipulator and the target during target capture can cause end-effector perturbation and base perturbation, potentially leading to capture failure. Therefore, this paper considers the contact and collision effects of bi-branch modular robots, combined with key factors in the task execution process, to select the optimal configuration of the bi-branch modular robot before target capture. Therefore, in-depth research on configuration optimization methods for bi-branch modular robots has significant theoretical and practical value for promoting the widespread application of modular robots in aerospace and other fields. Summary of the Invention

[0004] In view of this, this invention provides a pre-capture configuration optimization method for a bi-branch modular robot, targeting the context of on-orbit space operations. The proposed method can effectively reduce end-effector pose and base attitude disturbances before capture, thereby minimizing contact collision forces.

[0005] This invention provides a method for pre-capture configuration optimization of a dual-branch modular robot, comprising:

[0006] Step S1: Using the 3D model of the bi-branch modular robot, construct the kinematics and dynamics model of the bi-branch modular robot;

[0007] Step S2, based on the dynamic model of the dual-branch modular robot, determines the contact model between the robot and the captured target, and constructs the contact and collision dynamic equations of the dual-branch modular robot;

[0008] Step S3, based on the contact and collision analysis before the bi-branch modular robot captures the target, considers the key factors before the bi-branch modular robot captures the target and constructs performance evaluation indicators and optimization models.

[0009] Step S4 uses a multi-objective optimization algorithm to obtain the solution set of the pre-capture configuration of the bi-branch modular robot, and obtains the optimal configuration before capture based on the superior-inferior solution distance method.

[0010] In the above method, step S1 includes:

[0011] Step S1.1 Construct the kinematic equations of the dual-branch modular robot.

[0012]

[0013] in, and These represent the velocity vectors at the ends of the left and right branches, respectively. and These represent the Jacobian matrices representing the velocity transmission between the base and the left and right ends of the modular robot's left and right branches, respectively; This represents the velocity vector of the robot's base. and Represent the Jacobian matrices for the joint angles and end-effector velocity transfer of the left and right branches of the modular robot, respectively. and These represent the degrees of freedom of the left and right branches of the modular robot, respectively. This represents the joint angular velocity vector; it can be simplified as follows:

[0014]

[0015] in, This represents the end effector velocity vector of a dual-branch modular robot. The Jacobian matrix represents the velocity transfer between the base and end effector of a dual-branch modular robot. The Jacobian matrix representing the transmission of joint angles and end-effector velocity;

[0016] Therefore, based on the conservation of momentum and angular momentum of the bi-branch modular robot in a free-floating state, the kinematic equations can be further constructed as follows:

[0017]

[0018] in, The generalized Jacobian matrix representing a bi-branch modular robot;

[0019] Step S1.2 Construct the joint space forward dynamic equations of the bi-branch modular robot.

[0020] Neglecting the gravitational potential energy of the bi-branch modular robot in a space environment, the forward dynamic equations of the joint space can be obtained using the Lagrangian method:

[0021]

[0022] in, This represents the base and joint angle vectors of a dual-branch modular robot. The inertia matrix represents the inertia matrix of a dual-branch modular robot; This refers to the nonlinear term of the system. Indicates a generalized driving force; This indicates the output force at the end.

[0023] In the above method, step S2 includes:

[0024] Based on the continuous contact collision dynamics modeling method, a nonlinear damping model is used to describe the contact collision process, where the contact collision force in the nonlinear damping model is:

[0025]

[0026] in, This represents the left and right branches of a modular robot. Indicates Hertzian stiffness. Indicates the collision coefficient. Indicates the damping coefficient. This represents the relative compression along the normal direction of the contact surface. Representing relative compression velocity; combining the bi-branch modular robot dynamics model, constructing a contact collision dynamics model:

[0027]

[0028] in, , , , , express The normal direction vector of the contact surface of the branch;

[0029] In the above method, step S3 includes:

[0030] Step S3.1 Based on the contact collision dynamics model of the dual-branch modular robot, define three performance evaluation indicators: comprehensive contact collision force before capture, end-effector pose disturbance, and base attitude disturbance of the dual-branch modular robot.

[0031] Step S3.1.1 Based on the contact collision dynamics model of the dual-branch modular robot, construct a comprehensive contact collision force performance evaluation index;

[0032] The comprehensive contact collision force function is defined as the average of the contact collision forces of the left and right branches before the dual-branch modular robot captures the target:

[0033]

[0034] in, The function calculates the mean of the input data.

[0035] Step S3.1.2 Construct performance evaluation index for end-effector pose perturbation of the dual-branch modular robot;

[0036] After the spatial target comes into contact with the end effector of the dual-branch modular robot, a large perturbation in the robot's end effector pose may cause the end effector to deviate from the target, leading to mission failure. According to the impulse and impulse-moment theorem, we can obtain:

[0037]

[0038] in, For the equivalent mass of the dual-branch modular robot, The equivalent inertia of the dual-branch modular robot. and These represent the changes in angle and angular velocity of the bi-branch modular robot after it comes into contact with the target. and This represents the magnitude of the impulse of the contact force and the impulse of the torque generated on the end effector after a spatial target collides with the dual-branch modular robot.

[0039] Based on the end effector linear velocity direction vector of the dual-branch modular robot The direction vector of the rotation axis of the terminal angular velocity , can be obtained

[0040]

[0041] in, , , and Let represent the Jacobian matrices of the angular velocity and linear velocity of the bi-branch modular robot, respectively. From these, an evaluation index for the end-effector pose perturbation performance can be constructed:

[0042]

[0043] in, and The weighting coefficients for the linear and angular velocities of the end effector perturbation of the dual-branch modular robot;

[0044] Step S3.1.3 Construct the base attitude perturbation performance evaluation index for the dual-branch modular robot;

[0045] Based on the dynamic equations of the bi-branch modular robot, a functional relationship can be established between the base posture perturbation and the joint angles and contact collision pulses:

[0046]

[0047] in, This is the change in the angular velocity of the base. The inertial matrix is ​​related to the angular velocity of the base. This is the coupling matrix relating the base angular velocity to the joint angular velocity. This is the joint angle-dependent inertia matrix. The Jacobian matrix is ​​related to the joint angular velocity. The Jacobian matrix is ​​related to the joint angle. This indicates the contact impact pulse received by the end.

[0048] When the amplitude of the contact collision pulse remains constant, the base attitude disturbance is only related to the joint angles of the bi-branch modular robot. To optimize the base attitude disturbance caused by the contact collision, a corresponding objective function can be constructed:

[0049]

[0050] in, Let L be the L2 norm of any vector. The smaller the value, the smaller the base attitude disturbance.

[0051] Step S3.2 Considering the joint limit constraints of the modular robot, the constraints for the pre-capture configuration of the bi-branch modular robot are established as follows:

[0052]

[0053] in, This represents the minimum joint angle sequence of a bi-branch modular robot. This represents the maximum joint angle sequence of a bi-branch modular robot. The joint angles corresponding to the robot's capture configuration should be within this range.

[0054] Step S3.3, combining steps S3.1 and S3.2, defines the pre-capture configuration optimization model of the dual-branch modular robot as follows:

[0055]

[0056] in, The optimal configuration for capture of the dual-branch modular robot.

[0057] In the above method, step S4 includes:

[0058] Step S4.1 Based on the multi-objective optimization model constructed in S3.3, the non-dominated sorting genetic algorithm (NSGA-II) is used to solve the above optimization model to obtain the Pareto non-dominated solution set;

[0059] Step S4.2 uses the superior-inferior solution distance method to evaluate the Pareto solution set, thereby achieving a comprehensive evaluation of the results of the three performance evaluation indicators in S4.1, and sorting the solution set of the dual-branch modular robot before capture to obtain the optimal configuration before capture;

[0060] Step S4.2.1 Arrange the three performance evaluation index results in the Pareto solution set obtained in S4.1 to construct the performance evaluation index solution set matrix.

[0061]

[0062] in, Indicates the first The solution of the first... Results of each performance evaluation metric.

[0063] Step S4.2.2 Standardize the performance evaluation index solution set matrix to obtain a standardized performance evaluation index solution set matrix, where each matrix element... It can be represented as:

[0064]

[0065] Step S4.2.3 performs forward normalization on the results of each performance evaluation index to obtain the forward normalization performance evaluation index solution set matrix, where each matrix element... for:

[0066]

[0067] in, and Represent the first in the normalized matrix The maximum and minimum values ​​of each performance evaluation indicator.

[0068] Step S4.2.4: Set the weight coefficients for each performance evaluation index and calculate the first... The score of each optimization solution .

[0069]

[0070] in, Indicates the first The weight values ​​corresponding to each performance evaluation index function.

[0071] Step S4.2.5 Sort the Pareto solution set according to the score to obtain the optimal configuration of the dual-branch modular robot before capture corresponding to the highest score. Attached Figure Description

[0072] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort or labor.

[0073] Figure 1 This is a flowchart illustrating a pre-capture configuration optimization method for a dual-branch modular robot provided in an embodiment of the present invention.

[0074] Figure 2 This is a schematic diagram of the dual-branch modular robot structure in the simulation experiment of this invention embodiment;

[0075] Figure 3 This is a schematic diagram of the multi-objective optimization results of the simulation experiment in the embodiment of the present invention. Specific Implementation

[0076] To better understand the technical solution of the present invention, the embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0077] It should be understood that the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0078] This invention provides a method for optimizing the pre-capture configuration of a dual-branch modular robot. Please refer to [link / reference]. Figure 1 This is a flowchart illustrating a pre-capture configuration optimization method for a dual-branch modular robot provided in this invention, as shown in the example. Figure 1 As shown, the method includes the following steps:

[0079] Step 101: Using the 3D model of the bi-branch modular robot, construct the kinematics and dynamics model of the bi-branch modular robot.

[0080] Specifically, the first step is to construct the kinematic equations of the bi-branch modular robot:

[0081]

[0082] in, and These represent the velocity vectors at the ends of the left and right branches, respectively. and These represent the Jacobian matrices representing the velocity transmission between the base and the left and right ends of the modular robot's left and right branches, respectively; This represents the velocity vector of the robot's base. and Represent the Jacobian matrices for the joint angles and end-effector velocity transfer of the left and right branches of the modular robot, respectively. and These represent the degrees of freedom of the left and right branches of the modular robot, respectively. This represents the joint angular velocity vector.

[0083] Secondly, the above kinematic equations can be simplified as follows:

[0084]

[0085] in, This represents the end effector velocity vector of a dual-branch modular robot. The Jacobian matrix represents the velocity transfer between the base and end effector of a dual-branch modular robot. The Jacobian matrix represents the transmission of joint angle and end-effector velocity.

[0086] Therefore, based on the conservation of momentum and angular momentum of the bi-branch modular robot in a free-floating state, the kinematic equations can be further constructed as follows:

[0087]

[0088] in, The generalized Jacobian matrix represents the bi-branch modular robot.

[0089] Step 2: Construct the joint space forward dynamic equations for the bi-branch modular robot.

[0090] Neglecting the gravitational potential energy of the bi-branch modular robot in a space environment, the forward dynamic equations of the joint space can be obtained using the Lagrangian method:

[0091]

[0092] in, This represents the base and joint angle vectors of a dual-branch modular robot. The inertia matrix represents the inertia matrix of a dual-branch modular robot; This refers to the nonlinear term of the system. Indicates a generalized driving force; This indicates the output force at the end.

[0093] Step 102: Based on the dynamic model of the dual-branch modular robot, determine the contact model between the robot and the captured target, and construct the contact collision dynamic equation of the dual-branch modular robot.

[0094] Specifically, based on the continuous contact collision dynamics modeling method, a nonlinear damping model is used to describe the contact collision process, where the contact collision force in the nonlinear damping model is:

[0095]

[0096] in, This represents the left and right branches of a modular robot. Indicates Hertzian stiffness. Indicates the collision coefficient. Indicates the damping coefficient. This represents the relative compression along the normal direction of the contact surface. Representing relative compression velocity; combining the bi-branch modular robot dynamics model, constructing a contact collision dynamics model:

[0097]

[0098] in, , , , , express The normal direction vector of the contact surface of the branch.

[0099] Step 103: Based on the contact and collision analysis of the dual-branch modular robot before capturing the target, consider the key factors before the dual-branch modular robot captures the target, and construct performance evaluation indicators and optimization models.

[0100] Specifically, based on the contact collision dynamics model of the bi-branch modular robot, three performance evaluation indicators are defined: the comprehensive contact collision force before capture, the end-effector pose disturbance, and the base attitude disturbance behavior.

[0101] Step 1: Based on the contact and collision dynamics model of the dual-branch modular robot, construct a comprehensive contact and collision force performance evaluation index.

[0102] The comprehensive contact collision force function is defined as the average of the contact collision forces of the left and right branches before the dual-branch modular robot captures the target:

[0103]

[0104] in, The function calculates the mean of the input data.

[0105] Step 2: Construct performance evaluation indicators for the pose disturbance of the end effector of the dual-branch modular robot. After the spatial target comes into contact with the end effector of the dual-branch modular robot, a large pose disturbance of the robot's end effector may cause the end effector to deviate from the target, thus leading to mission failure. According to the impulse and impulse-moment theorem, we can obtain:

[0106]

[0107] in, For the equivalent mass of the dual-branch modular robot, The equivalent inertia of the dual-branch modular robot. and These represent the changes in angle and angular velocity of the bi-branch modular robot after it comes into contact with the target. and This represents the magnitude of the contact force impulse and torque impulse generated on the end effector after a spatial target collides with the dual-branch modular robot.

[0108] Based on the end effector linear velocity direction vector of the dual-branch modular robot The direction vector of the rotation axis of the terminal angular velocity We can obtain:

[0109]

[0110] in, , , and Let represent the Jacobian matrices of the angular velocity and linear velocity of the bi-branch modular robot, respectively. From these, an evaluation index for the end-effector pose perturbation performance can be constructed:

[0111]

[0112] in, and The linear and angular velocity weighting coefficients represent the end effector perturbation weights of a dual-branch modular robot.

[0113] Step 3: Construct an evaluation index for the base attitude perturbation performance of the dual-branch modular robot.

[0114] Based on the dynamic equations of the bi-branch modular robot, a functional relationship can be established between the base posture perturbation and the joint angles and contact collision pulses:

[0115]

[0116] in, This is the change in the angular velocity of the base. The inertial matrix is ​​related to the angular velocity of the base. This is the coupling matrix relating the base angular velocity to the joint angular velocity. This is the joint angle-dependent inertia matrix. The Jacobian matrix is ​​related to the joint angular velocity. The Jacobian matrix is ​​related to the joint angle. This indicates the contact impact pulse received by the end.

[0117] When the amplitude of the contact collision pulse remains constant, the base attitude disturbance is only related to the joint angles of the bi-branch modular robot. To optimize the base attitude disturbance caused by the contact collision, a corresponding objective function can be constructed:

[0118]

[0119] in, Let L be the L2 norm of any vector. The smaller the value, the smaller the base attitude disturbance.

[0120] Secondly, considering the joint limit constraints of the modular robot, the following constraints are established for the pre-capture configuration of the bi-branch modular robot:

[0121]

[0122] in, This represents the minimum joint angle sequence of a bi-branch modular robot. This represents the maximum joint angle sequence of a bi-branch modular robot. The joint angles corresponding to the robot's capture configuration should be within this range.

[0123] Finally, combining the above steps, the pre-capture configuration optimization model of the dual-branch modular robot is defined as follows:

[0124]

[0125] in, The optimal configuration for capture of the dual-branch modular robot.

[0126] Step 104: The solution set of the pre-capture configuration of the bi-branch modular robot is obtained by solving the multi-objective optimization algorithm. The optimal configuration before capture is obtained according to the superior-inferior solution distance method.

[0127] Specifically, based on the multi-objective optimization model constructed in step 103, the non-dominated sorting genetic algorithm (NSGA-II) is used to solve the above optimization model to obtain the Pareto non-dominated solution set.

[0128] Secondly, the Pareto solution set is evaluated using the superior-inferior solution distance method to achieve a comprehensive evaluation of the results of the three performance evaluation indicators in S4.1, thereby sorting the solution set of the bi-branch modular robot before capture and obtaining the optimal configuration before capture.

[0129] Step 1: Arrange the three performance evaluation index results in the Pareto solution set obtained in S4.1 to construct the performance evaluation index solution set matrix. :

[0130]

[0131] in, Indicates the first The solution of the first... Results of each performance evaluation metric.

[0132] Step 2 involves standardizing the performance evaluation index solution set matrix to obtain a standardized performance evaluation index solution set matrix, where each matrix element... It can be represented as:

[0133]

[0134] Step 3 involves performing forward processing on the results of each performance evaluation index to obtain a forward performance evaluation index solution set matrix, where each matrix element... for:

[0135]

[0136] in, and Represent the first in the normalized matrix The maximum and minimum values ​​of each performance evaluation indicator.

[0137] Step 4: Set the weight coefficients for each performance evaluation index and calculate the... The score of each optimization solution .

[0138]

[0139] in, Indicates the first The weight values ​​corresponding to each performance evaluation index function.

[0140] Step 5: Sort the Pareto solution set according to the score to obtain the optimal configuration of the dual-branch modular robot before capture, corresponding to the highest score.

[0141] Based on the method provided in the embodiments of the present invention, a simulation experiment was conducted using a dual-branch modular robot as the object, wherein the two branches have the same configuration, and each branch is composed of three modular motion units, such as... Figure 2 As shown.

[0142] The optimization result can be obtained based on the NSGA-II multi-objective optimization algorithm, as follows: Figure 2 As shown in Table 1, the Pareto solutions can be sorted according to their scores, yielding the optimal pre-capture configuration for the ideal dual-branch modular robot, i.e., the second set of data. The optimal configuration exhibits a comprehensive contact collision force of -1.602, representing a 14.15% improvement over the average Pareto solution level; an end-effector pose perturbation of 2.076, a 29.56% improvement over the average Pareto solution level; and a base pose perturbation of 0.445, a 49.28% improvement over the average Pareto solution level. These results demonstrate the effectiveness of the proposed method.

[0143] Table 1. Optimization Results and Pareto Solution Sets and Processing Results

[0144]

[0145] The technical solutions of the embodiments of the present invention have the following beneficial effects:

[0146] The contact collision dynamics of a bi-branch modular robot in free-floating state are characterized to achieve contact collision analysis before capture. The NSGA-II multi-objective optimization algorithm is used to optimize multiple indicators of the bi-branch modular robot, including contact collision force, end-effector pose perturbation, and base attitude perturbation. The optimal configuration before capture is achieved by using the superior-inferior solution distance method, which effectively reduces the mission failure rate of the bi-branch modular robot caused by contact collision when capturing the target.

[0147] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0148] The contents not described in detail in this specification are common knowledge to those skilled in the art.

Claims

1. A method for pre-capture configuration optimization of a dual-branch modular robot, characterized in that, The method includes: Step S1: Using the 3D model of the bi-branch modular robot, construct the kinematics and dynamics model of the bi-branch modular robot; Step S2, based on the dynamic model of the dual-branch modular robot, determines the contact model between the robot and the captured target, and constructs the contact and collision dynamic equations of the dual-branch modular robot; Step S3 is based on the contact and collision analysis before the dual-branch modular robot captures the target. It considers the comprehensive contact and collision force, end-effector pose disturbance, and base pose disturbance before the dual-branch modular robot captures the target, and constructs performance evaluation indicators and optimization models. Step S4 uses a multi-objective optimization algorithm to obtain the solution set of the pre-capture configuration of the bi-branch modular robot, and obtains the optimal configuration before capture based on the superior-inferior solution distance method. Step S1 includes: Step S1.1 Construct the kinematic equations of the bi-branch modular robot: in, and These represent the velocity vectors at the ends of the left and right branches, respectively. and These represent the Jacobian matrices representing the velocity transmission between the base and the left and right ends of the modular robot's left and right branches, respectively; This represents the velocity vector of the robot's base. and Represent the Jacobian matrices for the joint angles and end-effector velocity transfer of the left and right branches of the modular robot, respectively. and These represent the degrees of freedom of the left and right branches of the modular robot, respectively. This represents the joint angular velocity vector; it can be simplified as follows: in, This represents the end effector velocity vector of a dual-branch modular robot. The Jacobian matrix represents the velocity transfer between the base and end effector of a dual-branch modular robot. The Jacobian matrix representing the transmission of joint angles and end-effector velocity; Therefore, based on the conservation of momentum and angular momentum of the bi-branch modular robot in a free-floating state, the kinematic equations can be further constructed as follows: in, The generalized Jacobian matrix representing a bi-branch modular robot; Step S1.2 Construct the joint space forward dynamic equations of the bi-branch modular robot; Neglecting the gravitational potential energy of the bi-branch modular robot in a space environment, the forward dynamic equations of the joint space can be obtained using the Lagrangian method: in, This represents the base and joint angle vectors of a dual-branch modular robot. The inertia matrix represents the inertia matrix of a dual-branch modular robot; This refers to the nonlinear term of the system. Indicates a generalized driving force; Indicates the output force at the end; Step S2 includes: Based on the continuous contact collision dynamics modeling method, a nonlinear damping model is used to describe the contact collision process, where the contact collision force in the nonlinear damping model is: in, This represents the left and right branches of a modular robot. Indicates Hertzian stiffness. Represents the collision coefficient. Indicates the damping coefficient. This represents the relative compression along the normal direction of the contact surface. Representing relative compression velocity; combining the bi-branch modular robot dynamics model, constructing a contact collision dynamics model: in, , , , , express The normal direction vector of the contact surface of the branch.

2. The method according to claim 1, characterized in that, Step S3 includes: Step S3.1 Based on the contact collision dynamics model of the dual-branch modular robot, define three performance evaluation indicators: comprehensive contact collision force before capture, end-effector pose disturbance, and base attitude disturbance of the dual-branch modular robot. Step S3.1.1 Based on the contact collision dynamics model of the dual-branch modular robot, construct a comprehensive contact collision force performance evaluation index; The comprehensive contact collision force performance evaluation index is defined as the average contact collision force of the left and right branches of the dual-branch modular robot before it captures the target: in, The function calculates the mean of the input data. Step S3.1.2 Construct performance evaluation index for end-effector pose perturbation of the dual-branch modular robot; According to the impulse and impulse-moment theorem, we can obtain: in, For the equivalent mass of the dual-branch modular robot, The equivalent inertia of the dual-branch modular robot. and These represent the changes in angle and angular velocity of the bi-branch modular robot after it comes into contact with the target. and These represent the magnitudes of the contact force impulse and torque impulse generated on the end effector after a spatial target collides with the dual-branch modular robot. Based on the end effector linear velocity direction vector of the dual-branch modular robot The direction vector of the rotation axis of the terminal angular velocity , can be obtained in, , , and Let represent the Jacobian matrices of the angular velocity and linear velocity of the bi-branch modular robot, respectively. From these, an evaluation index for the end-effector pose perturbation performance can be constructed: in, and These represent the linear velocity and angular velocity weighting coefficients of the end effector of the dual-branch modular robot, respectively. Step S3.1.3 Construct the base attitude perturbation performance evaluation index for the dual-branch modular robot; Based on the dynamic equations of the bi-branch modular robot, a functional relationship can be established between the base posture perturbation and the joint angles and contact collision pulses: in, This is the change in the angular velocity of the base. The inertial matrix is ​​related to the angular velocity of the base. This is the coupling matrix relating the base angular velocity to the joint angular velocity. This is the joint angle-dependent inertia matrix. The Jacobian matrix is ​​related to the joint angular velocity. The Jacobian matrix is ​​related to the joint angle. This indicates the contact impact pulse received by the end. To optimize the base attitude perturbation caused by contact collisions, the corresponding objective function is constructed: in, Let L be the L2 norm of any vector. The smaller the value, the smaller the base attitude disturbance; Step S3.2 Considering the joint limit constraints of the modular robot, the constraints for the pre-capture configuration of the bi-branch modular robot are established as follows: in, This represents the minimum joint angle sequence of a bi-branch modular robot. This represents the maximum joint angle sequence of a bi-branch modular robot. The joint angles corresponding to the robot's capture configuration should be within this range. Step S3.3, combining steps S3.1 and S3.2, defines the pre-capture configuration optimization model of the dual-branch modular robot as follows: in, The optimal configuration for capture of the dual-branch modular robot.

3. The method according to claim 1, characterized in that, Step S4 includes: Step S4.1 Based on the pre-capture configuration optimization model constructed in step S3.3, the non-dominated sorting genetic algorithm NSGA-II is used to solve the optimization model to obtain the Pareto non-dominated solution set; Step S4.2 uses the superior-inferior solution distance method to evaluate the Pareto solution set in step S4.1, thereby achieving a comprehensive evaluation of the optimization model results, and sorting the solution set of the dual-branch modular robot before capture to obtain the optimal configuration before capture; Step S4.2.1 Arrange the three performance evaluation index results in the Pareto solution set obtained in S4.1 to construct the performance evaluation index solution set matrix. in, Indicates the first The solution of the first... Results of each performance evaluation index; Step S4.2.2 Standardize the performance evaluation index solution set matrix to obtain a standardized performance evaluation index solution set matrix, where each matrix element... It can be represented as Step S4.2.3 performs data forwarding processing on the results of each performance evaluation index to obtain the forwarded performance evaluation index solution set matrix, where each matrix element... for in, and Represent the first in the normalized matrix The maximum and minimum values ​​of each performance evaluation indicator; Step S4.2.4: Set the weight coefficients for each performance evaluation index and calculate the first... The score of each optimization solution in, Indicates the first The weight values ​​corresponding to each performance evaluation index function; Step S4.2.5 Sort the Pareto solution set according to the score to obtain the optimal configuration of the dual-branch modular robot before capture corresponding to the highest score.

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