A multi-arm space robot grasping planning method

Abstract

The embodiment of the present application provides a kind of multi-arm space robot's grasp planning method, comprising: according to the stable contact quality index of the end of multi-arm space robot and object surface condition, determine stable contact;According to the stable contact quality index, generate including multi-arm space robot's stable grasp point selection strategy to target object;Utilize the kinematics and dynamics equation of three-dimensional model of multi-arm space robot;According to the stable grasp point of multi-arm space robot to target object and kinematics and dynamics equation, determine the performance evaluation index of the grasp configuration of multi-arm space robot;And, according to the performance evaluation index generation multi-arm space robot's grasp configuration multi-objective optimization model;According to the stable grasp point of multi-arm space robot to target object and multi-objective optimization model realizes grasp planning.According to the technical scheme provided in the embodiment of the present application, the design of multi-arm space robot's grasp planning method can be provided with reference.

Description

[Technical Field]

[0001] This invention relates to the field of space robot technology, and in particular to a grasping planning method for a multi-armed space robot. [Background Technology]

[0002] With the increasing demand for space exploration, space robots have demonstrated enormous development potential and have been widely applied to perform various tasks on large spacecraft, becoming one of the most important avenues for on-orbit servicing. Extravehicular target grasping, as a crucial link in on-orbit operations, is affected by environmental factors and faces a series of problems such as contact process disturbances. Compared to astronaut extravehicular activities, space robot operations offer higher safety and flexibility, enabling more effective task execution in complex space environments, reducing human risk and improving operational efficiency. Most existing space robots are single-armed structures, capable of performing on-orbit tasks such as cabin assembly and equipment maintenance. However, single-armed robots have limitations in handling diverse on-orbit tasks, including insufficient operational capabilities and limited task types. Therefore, multi-armed space robots have been proposed. Through the collaborative cooperation of multiple arms, they utilize contact-type rigid connections to achieve an envelope-like grasping of target objects, significantly improving operational flexibility and adaptability.

[0003] Compared to single-arm robots, multi-arm space robots possess higher operational precision in complex spatial environments, significantly improving the reliability and efficiency of spatial target grasping. In multi-arm grasping tasks, successful object grasping typically requires pre-selection of grasping points and generation of grasping configurations. Appropriate grasping points and configurations can effectively improve grasping stability and task success rate. Therefore, grasping planning is crucial for the execution of grasping tasks by multi-arm space robots, necessitating research in both grasping point selection and grasping configuration optimization. [Summary of the Invention]

[0004] In view of this, embodiments of the present invention provide a grasping planning method for a multi-armed space robot, comprising:

[0005] Using the three-dimensional model of the target object, the stable contact quality index is determined based on the stable contact conditions between the end effector of the multi-arm space robot and the object surface.

[0006] Based on the stable contact quality index, a stable gripping point selection strategy for the multi-arm space robot is generated, wherein the stable gripping point selection strategy includes the stable gripping points of the multi-arm space robot on the target object.

[0007] Using a 3D model of a multi-armed space robot, the kinematic and dynamic equations of the multi-armed space robot are generated;

[0008] Based on the stable grasping point of the multi-armed space robot on the target object and the kinematic and dynamic equations of the multi-armed space robot, the performance evaluation index of the grasping configuration of the multi-armed space robot is determined; and based on the performance evaluation index, a multi-objective optimization model of the grasping configuration of the multi-armed space robot is generated.

[0009] Based on the stable grasping points and multi-objective optimization model of the multi-armed space robot for the target object, and with stable contact as the goal, the grasping plan between the multi-armed space robot and the target object is carried out.

[0010] In the above method, the three-dimensional model of the target object is a point cloud model containing the shape information of the target object, and each point contains corresponding normal vector information;

[0011] The stable contact conditions between the end effector of the multi-armed space robot and the surface of the object include:

[0012] for There exists f such that:

[0013]

[0014] Among them, w ext The force spiral generated by the external force, F S The combined force spiral generated at the center of mass of the target object at each contact point, G = [G1G] k [This refers to the comprehensive grasping matrix for multi-armed space robots.] is the grasping force vector generated at the end of each robotic arm of the multi-armed space robot, where k is the number of contact points;

[0015] The forces acting at the contact point between the multi-armed space robot and the target object must satisfy the following:

[0016]

[0017] Where, μ f f is the Coulomb friction coefficient. in f is the force at the end of the robotic arm along the normal direction of the contact surface. io f it The force is along the tangential direction.

[0018] In the above method, the stable contact quality index includes the force spiral space ellipsoid volume index and the maximum resistance force spiral;

[0019] Among them, the volume index of the force-spiral space ellipsoid is:

[0020]

[0021] Where G is the comprehensive capture matrix, M f The volume index of the force-spiral space ellipsoid;

[0022] Maximum resistance spiral quality index:

[0023]

[0024] Among them, M r r is the quality index of the maximum resistance spiral. max (a,b) represents the function for calculating the maximum inscribed sphere radius within the convex hull b with center a. The convex hull of the linearized force spiral of the combination l is represented.

[0025] In the above method, generating a stable grasping point selection strategy for the multi-arm space robot based on the stable contact quality index includes:

[0026] Construct a random combination of n-1 points and construct the corresponding force spiral set G. asc ={ω1,ω2,...,ω n-1} and W asc ={ω 1,1 ,ω 1,2 ,...,ω 1,m ,ω 2,1 ,...,ω n-1,m}∪{O}。 For the force spiral set G asc Dimensionality reduction is performed to obtain the reduced force spiral set Y. Gmds :

[0027]

[0028] Where, Θ G3 With Λ G3 For a dual-centered matrix The first three eigenvectors and eigenvalues; D G 2 It is the squared distance matrix obtained by calculating the squared Euclidean distance between pairs of data points; H is the centering matrix;

[0029] Force spiral set W asc Dimensionality reduction is performed to obtain the reduced force spiral set Y. Wmds , for Y Gmds With Y Wmds Recombined to form Y Gasc With Y Wasc and reconstruct Coh(Y) Wasc ), calculate the set U of convex hull supporting hyperplanes passing through point O;

[0030] like Then point O is located at Coh(Y) Wasc Internally, for All conform to the grasping force closure, constructing G σ={ω1,ω2,...,ω n-1 ,ω c};

[0031] like Point O is located at Coh(Y) Wasc ) surface, make Constructing a force spiral set

[0032] like for Constructed G σ ={ω1,ω2,...,ω n-1 ,ω t The force closure condition is satisfied;

[0033] like Randomly select ω rmds ∈{N1∪N2}, replace Y Gmds In and ω rmds The closest ω in Euclidean geometry imds reformed And rebuild the corresponding and Restart the algorithm calculation;

[0034] Based on the obtained G σ A multi-criteria decision-making operation based on the entropy weight method and the distance comprehensive evaluation method is performed on the volume of the force spiral spatial ellipsoid and the maximum resistance spiral to select a stable set of grasping points:

[0035]

[0036] in, Let i be the stable gripping point at the end of the i-th robotic arm in the l-th combination, where i = 1, 2, ..., k.

[0037] In the above method, the three-dimensional model of the multi-armed space robot consists of multiple 7-DOF robotic arms. The step of generating the kinematic and dynamic equations of the multi-armed space robot using its three-dimensional model includes:

[0038] Constructing the kinematic equations of a multi-armed space robot:

[0039]

[0040] in, This represents the velocity vector at the end effector of a multi-arm robot. This represents the velocity vector of the base of the multi-arm robot system; J represents the velocity of each joint of a multi-armed robot. b J m These represent the Jacobian matrices related to the base and the multiple robotic arms, respectively.

[0041] Constructing the forward dynamic equations of a multi-armed space robot:

[0042]

[0043] Where H(Θ) is the comprehensive generalized inertia matrix of the multi-armed space robot, and τ is the comprehensive generalized driving torque of the multi-armed space robot. The system is divided into linear terms.

[0044] In the above method, determining the performance evaluation index of the grasping configuration of the multi-armed space robot based on the stable grasping point of the multi-armed space robot on the target object and the kinematic and dynamic equations of the multi-armed space robot includes:

[0045] Based on the kinematic equations of multi-arm space robots, an operability performance evaluation index M is constructed. o ;

[0046] Based on the dynamic equations of multi-armed space robots, a motion energy consumption performance evaluation index M is constructed. s ;

[0047] Based on the changes in joint torque during the joint space planning process of multi-arm space robots, a peak joint torque performance evaluation index M is constructed. τ ;

[0048] Based on the performance evaluation metrics, a multi-objective optimization model for the grasping configuration of the multi-arm space robot is generated, including: based on the operability performance evaluation metric M. o Sports energy consumption performance evaluation index M s Performance evaluation index M of joint peak torque τ Based on the motion constraints of the multi-armed space robot, a multi-objective optimization model for the grasping configuration of the multi-armed space robot is generated.

[0049] In the above method, the operability performance evaluation index M is constructed based on the kinematic equations of the multi-armed space robot. o ,include:

[0050] Based on the joint velocities during the motion of a multi-armed space robot Limit values, construct the joint angular velocity constraint weight matrix:

[0051]

[0052] in, This is the joint angular velocity constraint weight matrix. Let be the joint angular velocity of joint j of robotic arm i. Let the joint angular velocity constraint value of joint j of robotic arm i satisfy...

[0053] Based on the joint angular velocity constraint weight matrix, an operability performance evaluation index is constructed:

[0054]

[0055] Among them, M o G is the overall grasping matrix of the multi-arm space robot, used as an operational performance evaluation index. Let J be the pseudo-inverse of the Jacobian matrix J; under a fixed base, J = J m J m Let be the Jacobian matrix of the robotic arm.

[0056] In the above method, the motion energy consumption performance evaluation index M is constructed based on the dynamic equations of the multi-armed space robot. s ,include:

[0057] Based on the initial configuration of the multi-armed space robot configuration with target state Fifth-order polynomial interpolation is used for joint space planning to obtain the joint angular velocities at each time step.

[0058] Based on the forward dynamic equation of the multi-armed space robot, the time-varying function τ(t) of the torque of each joint of the multi-armed space robot is obtained, and the motion energy consumption performance evaluation index of the multi-armed space robot is constructed based on the changing parameters:

[0059]

[0060] Among them, M s λ is the energy consumption performance evaluation index for the multi-arm space robot from Θ. init To Θ end The number of segments, t, in joint space trajectory planning i-1 t represents the start time of the i-th segment. i This represents the end time of the i-th segment.

[0061] In the above method, the peak joint torque performance evaluation index M is constructed based on the changes in joint torque during the joint space planning process of the multi-arm space robot. τ ,include:

[0062] Based on the forward dynamic equation of a multi-armed space robot and the time-varying function τ(t) of the joint torque of the multi-armed space robot, a peak joint torque performance evaluation index is constructed:

[0063]

[0064] Among them, M τThis is a performance evaluation index for the peak joint torque, and for any given time, This represents the joint torque of joint j of robotic arm i.

[0065] In the above method, the motion constraints of the multi-armed space robot are the joint angle constraints of the multi-armed space robot; the method further includes:

[0066] Based on the joint angle constraints of the multi-arm space robot, the joint angle constraints of the multi-arm space robot are established as follows:

[0067] Θ min ≤Θ≤Θ max

[0068] in, Let the vector be composed of the joint angles of the multi-armed space robot. Let the vector be composed of the maximum limits of the joint angles of the multi-armed space robot. This is a vector composed of the minimum limits of the joint angles of a multi-armed space robot.

[0069] The operability performance evaluation index M is used as a basis. o Sports energy consumption performance evaluation index M s Performance evaluation index M of joint peak torque τ And the motion constraints of the multi-armed space robot, generating a multi-objective optimization model for the grasping configuration of the multi-armed space robot, including:

[0070] Based on the operability performance evaluation index M o Sports energy consumption performance evaluation index M s Performance evaluation index M of joint peak torque τ By combining the joint angle constraints of the multi-armed space robot, a multi-objective optimization model for the grasping configuration of the multi-armed space robot is generated:

[0071] findΘ opt

[0072] min M(Θ)=[M o (Θ)M s (Θ)M τ (Θ)] T

[0073] s.tΘ min ≤Θ≤Θ max

[0074] Where, Θ opt To determine the optimal configuration for the grasping multi-arm space robot, Θ represents the angle vector of each joint of the multi-arm space robot.

[0075] The grasping planning method for the multi-armed space robot designed in this embodiment of the invention effectively solves the problem of poor stability of the grasping points generated by the target object. At the same time, considering the coupling characteristics of the multi-armed space robot system itself, it realizes the optimal grasping configuration by combining multi-objective optimization methods, effectively meeting the grasping planning requirements of the multi-armed space robot before grasping the target object. [Attached Image Description]

[0076] 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.

[0077] Figure 1 This is an operation flowchart provided in the embodiments of the present invention;

[0078] Figure 2 This is a schematic diagram of a multi-armed space robot-target object grasping model provided in an embodiment of the present invention;

[0079] Figure 3 This is a flowchart of the algorithm for selecting stable gripping points for a multi-armed space robot provided in an embodiment of the present invention;

[0080] Figure 4 This is a schematic diagram of the DH coordinate system of a single robotic arm provided in an embodiment of the present invention;

[0081] Figure 5 This is a schematic diagram of the equivalent dynamic model of the multi-armed space robot provided in the embodiment of the present invention;

[0082] Figure 6 These are diagrams illustrating the selection effect of three gripping points for different target objects provided in embodiments of the present invention.

[0083] Figure 7 This is the Pareto diagram of the optimized grasping configuration of the multi-arm space robot provided in this embodiment of the invention;

[0084] Figure 8 This is the initial grasping diagram of the multi-arm space robot provided in the embodiment of the present invention;

[0085] Figure 9 This is a diagram illustrating the grasping effect of the multi-armed space robot provided in an embodiment of the present invention.

Detailed Implementation Methods

[0086] 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.

[0087] It should be understood that the described embodiments are merely 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.

[0088] This invention provides a grasping planning method for a multi-armed space robot. The method includes the following steps; please refer to the operation flowchart. Figure 1 :

[0089] Step 101: Using the three-dimensional model of the target object, determine the stable contact quality index based on the stable contact conditions between the end effector of the multi-armed space robot and the surface of the object.

[0090] For details, please refer to Figure 2 This is a target object grasping model for a multi-armed space robot. The 3D model of the target object is a point cloud model containing the shape information of the target object, and each point contains corresponding normal vector information.

[0091] Force generated at the end effector of the i-th robotic arm of a multi-arm space robot At contact point c i Force spiral generated at the location It can be represented as:

[0092]

[0093] Among them, B i Contact point c i The force helical basis at the contact point is determined by the type of frictional contact at the contact point. When the end effector of the robotic arm contacts the object, considering the transformation of the gripping force from the local coordinate system to the coordinate system of the object's center of mass, the contact point c... i The pose relative to the object's center of mass coordinate system is R Si Then it can be expressed as:

[0094]

[0095] The end effector of the i-th robotic arm of the multi-arm robot is at c i gripping force at the point The torque generated about the center of mass of the object is Then gripping force Force spiral generated relative to the object's center of mass It is expressed as follows:

[0096]

[0097] in,

[0098]

[0099] It is the force spiral transformation matrix relative to the position of the contact point and the center of mass of the target object. for The antisymmetric matrix G. i Let be the linear mapping matrix that transforms the contact force at the end of robotic arm i to the coordinate system of the object's center of mass. Then, the combined helical force F generated at each contact point at the target object's center of mass is... S for:

[0100]

[0101] Among them, F S The combined force spiral generated at the center of mass of the target object at each contact point, G = [G1 … G k [This refers to the comprehensive grasping matrix for multi-armed space robots.] This is the grasping force vector generated at the end of each robotic arm of the multi-armed space robot;

[0102] First, we analyze the stable contact conditions between the end effector of the multi-armed space robot and the surface of the object. There exists f such that:

[0103]

[0104] Among them, w ext The force spiral is generated by the external force applied. The multi-armed space robot and the surface of the target object are considered to have frictional contact points. Its force spiral basis... To prevent slippage at the contact point, the forces acting at the contact point with the target object must satisfy the following:

[0105]

[0106] Where, μ f f is the Coulomb friction coefficient. in f is the force at the end of the robotic arm along the normal direction of the contact surface. io f it This is the force along the tangential direction. Specifically, in this embodiment, the Coulomb friction coefficient μ is taken as... f =0.3.

[0107] Step 102: Based on the stable contact quality index, generate a stable gripping point selection strategy for the multi-arm space robot, wherein the stable gripping point selection strategy includes the stable gripping points of the multi-arm space robot on the target object.

[0108] Specifically, the first step is to construct stable contact quality indicators for multi-armed space robots, including two quality indicators: the volume of the force spiral space ellipsoid and the maximum resistance spiral.

[0109] (1) Volume of the force-spiral space ellipsoid

[0110] For the combined force spiral F S satisfy:

[0111] ||F S || 2 =F S T F S =f T (G+) T G + f = f T (GG T ) -1 f (9)

[0112] Among them, G + To comprehensively extract the generalized inverse matrix of matrix G, let ||F S || 2 If the value is ≤1, then the external force spiral can be equivalent to an ellipsoid. According to the singular value decomposition theory, the singular values ​​of the grasping matrix G correspond to the major and minor axes of the ellipsoid, with the minor axis representing the minimum singular value and the major axis representing the maximum singular value. A larger ellipsoid volume indicates better grasping stability. Based on this, the volume index of the force spiral space ellipsoid is established as follows:

[0113]

[0114] Where G is the comprehensive capture matrix, M f For the volume index of the force-spiral space ellipsoid

[0115] (2) Maximum resistance spiral

[0116] Based on the contact type between the multi-armed space robot and the target object, the friction cone at the contact point is linearized into an m-polygonal three-dimensional polyhedral convex cone, let f i U f in The limit value, then

[0117]

[0118] Among them, s i,j Indicates contact point c i The j-th edge can be constructed using the above decomposition to create a linearized force spiral ω. i,j =(s i,j τ i,j ) T , where τ i,j =p i ×s i,j For the set of contact points between the multi-armed robot and the object surface, C = {p1, p2, ..., p...} k There exists an unlinearized set of force spirals F. S ={ω1,ω2,...,ωi ,...,ω n Based on the friction cone at the linearized contact point, a corresponding set of linearized frictional contact force spirals can be constructed:

[0119] F SL ={ω 1,1 ,ω 1,2 ,...,ω 1,m ,ω 2,1 ,...,ω i,j ,...,ω k,m} (12)

[0120] in, Let F be the force spiral of the j-th edge after linearizing the contact point i, where i = 1, 2, ..., k, j = 1, 2, ..., m, k is the number of robotic arms in the multi-arm robot, and m is the number of linearized edges of the friction cone. The set F of linearized force spirals constructed from k contact points is defined as follows: SL There are k×m single-force spirals.

[0121] The necessary and sufficient condition for the closure of the grasping force is that the origin O of the force spiral space lies inside the convex hull of the force spiral, i.e. The grasping quality index is defined as the maximum force spindle max(w) that is independent of the direction of the disturbance. ext This can be equivalent to having O as the center in Coh(F) SL The maximum inscribed sphere radius in the sphere is used to construct the maximum resistance spiral quality index as shown below:

[0122]

[0123] Where, r max (a,b) represents the function for calculating the maximum inscribed sphere radius within the convex hull b with center a. The convex hull of the linearized force spiral of the combination l is represented.

[0124] Secondly, stable gripping points for the target object are selected according to the following process. Please refer to the algorithm flowchart. Figure 3 :

[0125] Step 1: Construct a random combination of n-1 points and construct the corresponding force spiral set G. asc ={ω1,ω2,...,ω n-1} and W asc ={ω 1,1 ,ω 1,2 ,...,ω 1,m ,ω 2,1 ,...,ω n-1,m}∪{O}.

[0126] Step 2: Apply Multidimensional Scaling (MDS) to the nonlinearized force helix G asc Dimensionality reduction is performed to obtain the reduced force spiral set Y. Gmds As shown below:

[0127]

[0128] Where, Θ G3 With Λ G3 For a dual-centered matrix The first three eigenvectors and eigenvalues; D G 2 It is the squared distance matrix obtained by calculating the squared Euclidean distance between pairs of data points; H is the centering matrix;

[0129] Step 3: Similarly, apply the force spiral set W asc Dimensionality reduction yields the linearized force spiral set Y. Wmds And recombine to form Y Gasc With Y Wasc Construct Coh(Y) Wasc ), calculate the set U of convex hull supporting hyperplanes passing through point O;

[0130] Step 4: If Then point O is located at Coh(Y) Wasc Internally, for All conform to the grasping force closure, constructing G σ ={ω1,ω2,...,ω n-1 ,ω c If the above is true, proceed to step 8; otherwise, skip this step.

[0131] Step 5: If Point O is located at Coh(Y) Wasc ) surface, make Constructing a force spiral set

[0132] Step 6: If for Constructed G σ ={ω1,ω2,...,ωω n-1 ,ω t The force closure condition is satisfied;

[0133] Step 7: If This indicates that a force-closed gripping point cannot be constructed, therefore ω is randomly selected. rmds ∈{N1∪N2}, replace Y Gmds In and ω rmds The closest ω in Euclidean geometry imdsreformed Return to step 3 and rebuild the corresponding structure. With G k Otherwise, skip this step;

[0134] Step 8: Based on the obtained G k (or G) σ ), calculate the force spiral space ellipsoid volume M corresponding to each combination of grasping points. f With the maximum resistance spiral M r Quality indicators;

[0135] Step 9: After calculating the quality indices of multiple sets of grasping point combinations, perform a multi-criteria decision-making operation based on the entropy weight method and the distance comprehensive evaluation method on the force spiral spatial ellipsoid volume and the maximum resistance spiral to select a stable set of grasping points; first, perform standardization processing on the generated data, and the standardized result of the quality index j of the i-th set of grasping points is r. ij Calculate the entropy value E corresponding to each quality indicator. j :

[0136]

[0137] Where ε is the number of generated crawl point combinations, and the weight w corresponding to each quality indicator is calculated based on the entropy value. j As shown below:

[0138]

[0139] This allows us to construct the relative proximity C for each set of grab points. i As shown below:

[0140]

[0141] Among them, v ij For r ij Multiplied by weight w j Post-transformation result, v ij =w j ×r ij , Let j be the positive and negative ideal solutions for quality index j. Based on relative proximity C i A stable set of crawl points can be obtained. in Let i be the contact position of the end effector of the i-th robotic arm in the l-th combination, i = 1, 2, ..., k. This completes the selection of the stable grasping point for the multi-arm space robot to grasp the target object.

[0142] Step 103: Using the three-dimensional model of the multi-armed space robot, generate the kinematic and dynamic equations of the multi-armed space robot.

[0143] Specifically, constructing a single robotic arm DH coordinate system is as follows: Figure 4 As shown, the kinematic equations of the multi-armed space robot are constructed:

[0144]

[0145] in, This represents the velocity vector at the end effector of a multi-arm robot. This represents the velocity vector of the base of the multi-arm robot system; J represents the velocity of each joint of a multi-armed robot. b J m These represent the Jacobian matrices related to the base and the multiple robotic arms, respectively.

[0146] The equivalent dynamic model of a multi-armed space robot is as follows: Figure 5 As shown, the forward dynamic equations of the multi-armed space robot are constructed as follows:

[0147]

[0148] Where H(Θ) is the comprehensive generalized inertia matrix of the multi-armed space robot, and τ is the comprehensive generalized driving torque of the multi-armed space robot. The system is divided into linear terms.

[0149] Step 104: Based on the stable grasping point of the multi-armed space robot on the target object and the kinematic and dynamic equations of the multi-armed space robot, determine the performance evaluation index of the grasping configuration of the multi-armed space robot; and, based on the performance evaluation index, generate a multi-objective optimization model of the grasping configuration of the multi-armed space robot.

[0150] Specifically, based on the kinematic equations of the multi-armed space robot, an operability performance evaluation index M is constructed. o ,include:

[0151] Based on the speed of each joint of the robotic arm during the movement of the multi-arm space robot Limit values, construct the joint angular velocity constraint weight matrix:

[0152]

[0153] in, This is the joint angular velocity constraint weight matrix. Let be the joint angular velocity of joint j of robotic arm i. Let the joint angular velocity constraint value of joint j of robotic arm i satisfy...

[0154] Based on the joint angular velocity constraint weight matrix, an operability performance evaluation index is constructed:

[0155]

[0156] Among them, M o G is the overall grasping matrix of the multi-arm space robot, used as an operational performance evaluation index. Let J be the pseudo-inverse of the Jacobian matrix J; under a fixed base, J = J m J m Let be the Jacobian matrix of the robotic arm.

[0157] Based on the dynamic equations of multi-armed space robots, a motion energy consumption performance evaluation index M is constructed. s ,include:

[0158] Based on the initial configuration of the multi-armed space robot configuration with target state Joint space trajectory planning is performed using fifth-order polynomial interpolation.

[0159]

[0160] Assume t0, t f Given the initial and final times, based on the configuration (Θ0, Θ) of the multi-armed space robot at the initial and final times. f ), joint angular velocity Joint angular acceleration The boundary can be solved to obtain the joint angular velocity at each moment.

[0161] Based on the forward dynamic equation of the multi-armed space robot, the time-varying function τ(t) of the torque of each joint of the multi-armed space robot is obtained, and the motion energy consumption performance evaluation index of the multi-armed space robot is constructed based on the changing parameters:

[0162]

[0163] Among them, M s λ is the energy consumption performance evaluation index for the multi-arm space robot from Θ. init To Θ end The number of segments during joint space trajectory planning. t i-1 t represents the start time of the i-th segment. i Represents the end time of the i-th segment;

[0164] Based on the changes in joint torque during the joint space planning process of multi-arm space robots, a peak joint torque performance evaluation index M is constructed. τ ,include:

[0165] Based on the forward dynamic equation of the multi-arm space robot and the time-varying function τ(t) of the joint torque of the multi-arm space robot, a peak joint torque performance evaluation index is constructed:

[0166]

[0167] Among them, M τ This is a performance evaluation index for the peak joint torque, and for any given time, This represents the joint torque of joint j of robotic arm i.

[0168] The motion constraints of the multi-armed space robot are the joint angle constraints of the multi-armed space robot. Based on the joint angle constraints of the multi-armed space robot, the joint angle constraints of the multi-armed space robot are established as follows:

[0169] Θ min ≤Θ≤Θ max (25)

[0170] in, Let the vector be composed of the joint angles of the multi-armed space robot. This represents the angle of the j-th joint of robotic arm i. Let the vector be composed of the maximum limits of the joint angles of the multi-armed space robot. This is a vector composed of the minimum limits of the joint angles of a multi-armed space robot.

[0171] The operability performance evaluation index M is used as a basis. o Sports energy consumption performance evaluation index M s Performance evaluation index M of joint peak torque τ And the motion constraints of the multi-armed space robot, generating a multi-objective optimization model for the grasping configuration of the multi-armed space robot, including:

[0172] Based on the operability performance evaluation index M o Sports energy consumption performance evaluation index M s Performance evaluation index M of joint peak torque τ Based on the motion constraints of the multi-armed space robot, a multi-objective optimization model for the grasping configuration of the multi-armed space robot is generated:

[0173]

[0174] Where, Θ opt To determine the optimal configuration for the grasping multi-arm space robot, Θ represents the angle vector of each joint of the multi-arm space robot.

[0175] Step 105: Based on the stable grasping point of the multi-armed space robot on the target object and the multi-objective optimization model, and with stable contact as the goal, perform grasping planning between the multi-armed space robot and the target object.

[0176] Specifically, the stable grasping plan for the multi-armed space robot to grasp the target object is implemented according to the following process:

[0177] Step 1: Based on the 3D model of the target object and the grasping quality indicators, generate stable grasping points for the target object according to the grasping point selection strategy;

[0178] Step 2: Based on the generated stable gripping points, solve for the optimal gripping configuration of the multi-arm space robot through multi-objective optimization;

[0179] Step 3: Combining the stable grasping points and optimal grasping configuration of the multi-armed space robot on the target object, solve the joint trajectory of the multi-armed space robot from the initial state to the target state, and realize the stable grasping planning of the multi-armed space robot.

[0180] Based on the method provided in the embodiments of the present invention, a simulation experiment study was conducted on the grasping planning method of a multi-armed space robot.

[0181] In this embodiment, it is assumed that the target object to be grasped is one of five types: sphere, cube, cylinder, and space station cargo ship model. The stable grasping point generation strategy is verified, and its size and contained data are shown in Table 1.

[0182] Table 1 Parameters corresponding to the target object

[0183]

[0184] Assume the coefficient of friction between the end effector of the multi-arm space robot and the surface of the target object is μ. f =0.2, the number of linearized sides of the friction cone is m=10, and the contact force is f. in =[00-1.0] T The negative sign indicates that the direction is along the normal to the inside of the target object. The above parameter values ​​and the target object are used to carry out simulation verification of the generation of three-contact, four-contact and five-contact grasping points.

[0185] Please refer to the simulation results. Figure 6 The figures show the resulting gripping points for four different target objects at three contact points. Each target object includes three different stable gripping qualities, where the C value represents the corresponding gripping quality. As can be seen from the figures, Figure 6(a), (d), (g) and (j) are all stable grasping points, and higher mass output corresponds to more stable grasping points, which proves the effectiveness of the proposed grasping point generation strategy for multi-armed space robots. The pose information of the grasping points (relative to the centroid of each target object) for the four target objects in the simulation results is shown in Table 2.

[0186] Table 2 Simulation results of the pose of the target object's stable grasping point.

[0187]

[0188] Assuming that each arm of the multi-armed space robot has the same structure and parameters, the dynamic parameters and initial configuration of the robotic arms are shown in Tables 3 and 4.

[0189] Table 3. DH and dynamic parameters of a single robotic arm

[0190]

[0191]

[0192] Table 4 Initial parameter configuration of the multi-arm space robot

[0193]

[0194] Based on the data shown in Tables 2-4 as input, and with operability, motion energy consumption and joint peak torque performance evaluation indicators as the optimization objectives of the grasping configuration of the multi-arm space robot, a simulation experiment of multi-objective grasping configuration optimization based on MOPSO was carried out using MATLAB language. The algorithm program setting parameters are shown in Table 5.

[0195] Table 5. MOPSO Multi-Objective Optimization Parameter Settings

[0196]

[0197]

[0198] After initializing the simulation program, it was run to optimize the multi-objective grasping configuration of the multi-arm space robot using the MOPSO algorithm. After 3000 iterations, the final multi-objective optimization simulation results for the simplified cargo ship model of the space station are as follows: Figure 7 As shown.

[0199] Figure 7 The three-dimensional surface formed by the scatter plot in the figure is the Pareto front of the multi-objective optimization result. The three coordinate axes in the figure represent the three objective functions of the grasping configuration optimization. The values ​​of the decision variables and optimization objectives corresponding to points A, B, and C are shown in Table 6.

[0200] Table 6. Multi-objective optimization results for the multi-armed space robot (points A, B, C, and D)

[0201]

[0202]

[0203] Depend on Figure 7 It can be seen that the closer to point A, the better the global operability of the multi-armed space robot; the closer to point C, the lower the energy consumption during the movement; and the closer to point D, the smaller the peak value of the joint torque. Based on the Pareto optimal solution set, vertex B of the three-dimensional leading edge is selected as the optimization result, so that the values ​​of the three optimization objectives are at a relatively reasonable level. Thus, the optimal grasping configuration of the multi-armed space robot can be obtained.

[0204] By combining the stable grasping point and optimal grasping configuration of the target object, the grasping planning of the multi-armed space robot for the target object can be realized. The final simulation results before and after are for reference. Figure 8 , Figure 9 . Figure 8 This refers to the grasping state of a multi-armed space robot before it grasps a target object. Figure 9 For the actual capture state, combined with Figure 8 , Figure 9 As can be concluded from Table 6, this gripping configuration optimization method can achieve low motion energy consumption and low peak joint torque while ensuring high operability, thus effectively realizing the gripping operation of the target object.

[0205] The technical solutions of the embodiments of the present invention have the following beneficial effects:

[0206] In the technical solution of this invention embodiment, a grasping planning method for a multi-armed space robot is designed. Using a known three-dimensional model of the target object, the stable contact conditions between the end effector of the multi-armed space robot and the object surface are analyzed, and a stable contact quality index is constructed based on these conditions. Based on the stable contact quality index, a stable grasping point selection strategy for the multi-armed space robot is designed, generating stable grasping points for the multi-armed space robot on the target object. A three-dimensional model of the multi-armed space robot is obtained, and its kinematic and dynamic equations are constructed based on this model. Based on the stable grasping points of the multi-armed space robot on the target object and its kinematic and dynamic equations, a performance evaluation index for the grasping configuration of the multi-armed space robot is constructed. Based on this performance evaluation index, a multi-objective optimization model for the grasping configuration of the multi-armed space robot is constructed, and the MOPSO algorithm is used to perform multi-objective optimization of the grasping configuration. Finally, based on the stable grasping points and optimal grasping configuration of the multi-armed space robot on the target object, a grasping plan is developed to achieve stable contact between the multi-armed space robot and the target object.

[0207] 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.

[0208] The contents not described in detail in this specification are common knowledge to those skilled in the art.

Claims

1. A method for grasp planning of a multi-arm space robot, characterized in that, The method comprises: determining a stable contact quality index according to a stable contact condition of an end of a multi-arm space robot and a surface of an object based on a three-dimensional model of the object, the stable contact quality index comprising a force screw space ellipsoid volume index and a maximum resistance force screw index; generating a stable grasping point selection strategy of the multi-arm space robot according to the stable contact quality index, wherein the stable grasping point selection strategy comprises a stable grasping point of the multi-arm space robot to the object; generating kinematics and dynamics equations of the multi-arm space robot based on the three-dimensional model of the multi-arm space robot; determining a performance evaluation index of a grasping configuration of the multi-arm space robot based on the stable grasping point of the multi-arm space robot to the object and the kinematics and dynamics equations of the multi-arm space robot, the performance evaluation index comprising an operability performance evaluation index, a motion energy consumption performance evaluation index, and a joint torque peak performance evaluation index; and generating a multi-objective optimization model of the grasping configuration of the multi-arm space robot according to the performance evaluation index; performing grasping planning between the multi-arm space robot and the object based on the stable grasping point of the multi-arm space robot to the object and the multi-objective optimization model, and taking stable contact as a target.

2. The method of claim 1, wherein, The three-dimensional model of the object is a point cloud model containing shape information of the object, and each point contains corresponding normal vector information; The stable contact condition of the end of the multi-arm space robot and the surface of the object comprises: For all of such that: wherein, is the force screw generated by the external force, is the resultant force screw generated by the contact points at the center of mass of the target object, is the resultant grasp matrix of the multi-arm space robot, is the grasp force vector generated by the end of each arm of the multi-arm space robot, is the number of contact points; The force at the contact point between the multi-arm space robot and the object needs to satisfy: wherein, is the Coulomb friction coefficient, is the force along the tangent direction. , is the force along the tangent direction.

3. The method of claim 1, wherein, The stable contact quality index comprises a force screw space ellipsoid volume index and a maximum resistance force screw index. The force screw space ellipsoid volume index comprises: wherein, is the integrated grasp matrix, is the force helix space ellipsoid volume index; The maximum resistance force screw quality index comprises: wherein, is the maximum resistance helix mass index, represents the calculation function of the maximum inscribed sphere radius in the convex hull centered at the convex hull maximum inscribed sphere radius, represents the convex hull constructed by linearization of the force helix of the combination 4. The method of claim 1, wherein, The stable grasping point selection strategy of the multi-arm space robot is generated according to the stable contact quality index, comprising: Constructing random combinations of points, and constructing a corresponding set of force spirals with , the set of force spirals dimensionality reduction, obtaining a set of reduced dimensionality force spirals : wherein, with is a double centered matrix the first three eigenvectors and eigenvalues of is a squared distance matrix obtained by computing the squared Euclidean distance between pairs of data points; is a centered matrix; force spirals dimensionality reduction to obtain a reduced dimensionality set of force spirals , the force spirals with recombined to form with and reconstructed , the force spirals a set of convex hull support hyperplanes ; If , then The point is located inside, for , both meet the grasp force closure, build ; If , point is located surface, so that , the construction force spiral set , ; like ,for , constructed The force closure condition is satisfied; If , randomly select , replace , the nearest , Euclidean distance , reform , and reconstruct the corresponding , and , restart the algorithm calculation; According to the results The multi-criteria decision operation based on entropy weight method and distance comprehensive evaluation method is performed on the force helical spatial ellipsoid volume and the maximum resistance force helix to select a stable set of gripping points: wherein, is the group of the mechanical arm end-stable grasping point, .

5. The method of claim 1, wherein, The three-dimensional model of the multi-arm space robot is composed of a plurality of 7-DOF (degree of freedom) arms, and the kinematics and dynamics equations of the multi-arm space robot are generated based on the three-dimensional model of the multi-arm space robot, comprising: constructing a kinematics equation of the multi-arm space robot; wherein, represents a velocity vector of the end of the robot arm; represents a velocity vector of the base of the multi-arm robot system; represents a velocity of each joint of the multi-arm robot; , respectively represent the Jacobian matrices associated with the base and the multi-arm robot; constructing a forward dynamics equation of the multi-arm space robot; wherein, is the combined generalized inertia matrix of the multi-arm space robot, is the combined generalized driving force moment of the multi-arm space robot, is the system decoupling term, is the joint angular acceleration of the multi-arm space robot.

6. The method of claim 1, wherein, The performance evaluation index of the grasping configuration of the multi-arm space robot is determined based on the stable grasping point of the multi-arm space robot to the object and the kinematics and dynamics equations of the multi-arm space robot, comprising: According to the kinematics equation of the multi-arm space robot, a manipulability performance evaluation index is constructed ; According to the dynamic equation of a multi-arm space robot, a motion energy consumption performance evaluation index is constructed ; According to the joint torque change in the joint space planning process of a multi-arm space robot, a joint torque peak performance evaluation index is constructed ; According to the performance evaluation index, a multi-objective optimization model of the grabbing configuration of the multi-arm space robot is generated, including: generating a multi-objective optimization model of the grabbing configuration of the multi-arm space robot according to the operability performance evaluation index , the motion energy consumption performance evaluation index , and the joint torque peak performance evaluation index , and the motion constraint condition of the multi-arm space robot.

7. The method of claim 6, wherein, The operability performance evaluation index is constructed according to a kinematics equation of the multi-arm space robot , comprising: Joint velocity in motion of a multi-arm space robot Limit values, construct joint angular velocity constraint weight matrix: wherein, is a joint angular velocity constraint weight matrix, is a joint angular velocity constraint weight matrix, is a joint angular velocity constraint weight matrix, is a joint angular velocity constraint weight matrix, is a joint angular velocity constraint weight matrix, is a joint angular velocity constraint weight matrix, is a joint angular velocity constraint weight matrix, , , ; an operability performance evaluation index is constructed according to a joint angular velocity constraint weight matrix; wherein, is an operability performance evaluation index, is a comprehensive grasping matrix of a multi-arm space robot, is a pseudo-inverse matrix of the Jacobian matrix under the fixed base, , is a Jacobian matrix of the robot arm.

8. The method of claim 6, wherein, The performance evaluation index of motion energy consumption is constructed according to a dynamics equation of the multi-arm space robot , comprising: According to the initial state configuration of the multi-arm space robot With the target state configuration , using five polynomial difference value to plan joint space, get the joint angular velocity at each time ; According to the forward dynamics equation of the multi-arm space robot, a function of the joint torque of the multi-arm space robot changing with time is obtained And according to the change parameter, a motion energy consumption performance evaluation index of the multi-arm space robot is constructed wherein, is a motion energy consumption performance evaluation index, is a multi-arm space robot from to the number of segments in joint space trajectory planning, represents the start time of the first segment, represents the end time of the first segment.

9. The method of claim 6, wherein, The joint torque peak performance evaluation index is constructed according to the change of the joint torque in the joint space planning process of the multi-arm space robot , comprising: According to the forward dynamics equation of the multi-arm space robot and the joint torque time-varying function of the multi-arm space robot , construct a joint torque peak performance evaluation index wherein, is the joint torque peak performance evaluation index, and for any time, , denotes robotic arm joint torque of the joint.

10. The method of claim 6, wherein, The motion constraint condition of the multi-arm space robot is a joint angle constraint condition of the multi-arm space robot; the method further comprises: a joint angle constraint condition of the multi-arm space robot is established according to a joint angle limit of the multi-arm space robot; wherein, is a vector composed of each joint angle of the multi-arm space robot, is a vector composed of each joint angle limit maximum value of the multi-arm space robot, is a vector composed of each joint angle limit minimum value of the multi-arm space robot.

11. The method of claim 6, wherein, The operability performance evaluation index , the motion energy consumption performance evaluation index , and the joint torque peak performance evaluation index , and the motion constraint condition of the multi-arm space robot, a multi-objective optimization model of the multi-arm space robot grabbing configuration is generated, comprising: According to the operability performance evaluation index , the motion energy consumption performance evaluation index , the joint torque peak performance evaluation index , and the joint angle constraint condition of the multi-arm space robot, a multi-objective optimization model of the multi-arm space robot grasping configuration is generated: wherein, is the optimal configuration for a multi-arm space robot to grasp, is the joint angle vector of the multi-arm space robot.

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