Mobile manipulator grasping pose planning method and device

Abstract

The application provides a mobile manipulator grasping pose planning method and device, the method comprising: determining a target object grasping feasibility map; performing grid discretization processing on the feasibility map to obtain a plurality of grids; determining manipulator joint restriction constraint coefficients, obstacle safety restriction constraint coefficients and operation stability restriction constraint coefficients of all poses of each grid; calculating the hybrid operability of each pose of each grid; selecting the maximum value of the hybrid operability of each pose in the grid as the hybrid operability index of the grid, obtaining an evaluation index set of all grids, selecting the grid corresponding to the maximum value in the evaluation index set as the optimal mobile chassis position of the mobile manipulator, and the pose corresponding to the hybrid operability in the grid is the optimal grasping pose of the manipulator load of the mobile manipulator. The application realizes more flexible grasping operation of the manipulator through the hybrid operability and completes the obstacle avoidance function.

Description

Technical Field

[0001] This application belongs to the field of image processing technology, and specifically relates to a method and apparatus for planning the grasping posture of a mobile robotic arm. Background Technology

[0002] Mobile robotic arms, by combining a robotic arm with a mobile chassis, greatly enhance the workspace and operational flexibility of the robotic arm. A mobile robotic arm consists of a robotic arm payload and a mobile chassis. Its applications are extremely wide-ranging, including home service robots and industrial warehouse management. During task execution, the key to reducing path planning distance and avoiding collisions lies in selecting an appropriate chassis position. In recent years, various indicators have been proposed to evaluate the operational performance of robots under different chassis positions, such as accessibility and operability. However, these traditional evaluation indicators neglect the robot's consideration of environmental obstacles and the change in its center of mass after grasping a target object, failing to meet the safety performance requirements for robot operation under complex working conditions. Summary of the Invention

[0003] To address at least one of the aforementioned technical problems, this application provides a method and apparatus for planning the grasping posture of a mobile robotic arm, which determines the grasping position and posture of the robotic arm based on hybrid operability.

[0004] The first aspect of this application provides a method for planning the grasping pose of a mobile robotic arm, mainly including:

[0005] Step S1: Determine the feasibility map for capturing the target object;

[0006] Step S2: Discretize the feasibility map into a grid to obtain multiple grids;

[0007] Step S3: Determine the joint constraint coefficients, obstacle safety constraint coefficients, and operability stability constraint coefficients for all poses of the robotic arm in each grid.

[0008] Step S4: Calculate the mixed operability of each pose of each grid according to the joint constraint coefficient of the robotic arm, the obstacle safety constraint coefficient, and the operability stability constraint coefficient.

[0009] Step S5: Select the maximum value of the mixed operability of each pose in the grid as the mixed operability index of the grid, obtain the evaluation index set of all grids, select the grid corresponding to the maximum value in the evaluation index set as the optimal mobile chassis position of the mobile robot arm, and the pose corresponding to the mixed operability in the grid is the optimal grasping posture of the mobile robot arm load.

[0010] Preferably, in step S1, the feasibility map E for capturing the target object is determined using the following formula. FIRM :

[0011]

[0012] Where E represents the working environment area of ​​the mobile robotic arm, E out This refers to the area beyond the maximum working range of the mobile robotic arm's load capacity. This indicates the area occupied by the b-th obstacle; This represents the safe expansion zone of the b-th obstacle; This represents the area obscured by the b-th obstacle, where B is the number of obstacles within the workspace of the moving robotic arm around the target object.

[0013] Preferably, step S3 further includes:

[0014] (1) Determine the joint constraint coefficients of the robotic arm for each pose using the following formula:

[0015]

[0016] Specifically, for the i-th joint and j-th motion direction of the robotic arm at the q-th pose, J i,j (q) represents the Jacobian matrix, i represents the joint number of the robotic arm, j represents the direction of motion of the robotic arm joint in the three-dimensional coordinate system, the first three dimensions represent the motion on the x, y, and z axes respectively, and the last three dimensions represent the rotation about the x, y, and z axes, Γ i The positive and negative directions of movement for the i-th joint;

[0017] in, and These represent the negative joint constraint coefficient and the positive joint constraint coefficient, respectively, and their calculation methods under different conditions are as follows:

[0018]

[0019]

[0020] In the formula Let θ be the gradient of the joint constraint function h(q). i,max and θ i,min θ represents the maximum and minimum joint movement limits of the i-th joint, respectively. i Let h(q) be the angle by which the i-th joint deflects from the minimum joint motion constraint to the maximum joint motion constraint. The joint constraint function h(q) is:

[0021]

[0022] Where γ is the scaler gain and n is the number of joints;

[0023] (2) Determine the obstacle safety constraint coefficients for each pose using the following formula:

[0024]

[0025] Among them, and These represent the negative safety constraint coefficient and the positive safety constraint coefficient of the obstacle, respectively. Their calculation methods under different conditions are as follows:

[0026]

[0027]

[0028] In the formula Let v represent the gradient of the obstacle safety constraint function f(q). j It is the shortest distance vector between the obstacle and the robotic arm load, where the safety constraint function f(q) is:

[0029]

[0030] In the formula, d obs (q) represents the shortest distance from the obstacle to the moving robotic arm load when the robotic arm load is in attitude q, D * This represents the maximum safe working distance for the robotic arm's load, where η is the safety factor.

[0031] (3) Determine the operational stability constraint coefficients for each pose using the following formula:

[0032]

[0033] in, and These represent the negative and positive coefficients of the stability constraint, respectively. When the center of gravity projection of the robotic arm moves towards the support boundary of the mobile chassis during load operation, the negative coefficient of the stability constraint is selected. Its calculation method is as follows:

[0034]

[0035] When the center of gravity projection of the robotic arm moves away from the support boundary of the mobile chassis during operation, a positive coefficient for stability constraint is selected, with a value of [value missing].

[0036] In the above formula Let g(q) represent the gradient of the stability constraint function g(q), which is:

[0037]

[0038] In the formula, d stab The point v represents the projection of the center of gravity of the entire mobile robotic arm during its operation. COG The shortest distance between the support boundary of the mobile chassis and the support boundary, if d stab ≥0 indicates that the centroid projection point v COG Within the range of the mobile chassis support boundary, if d stab <0 indicates that v COG Outside the support boundary of the mobile chassis, α and β are adjustment coefficients.

[0039] Preferably, in step S4, the hybrid operability of each pose of each mesh is calculated using the following formula:

[0040]

[0041] in, Represents the mixed Jacobian matrix. Represents the mixed Jacobian matrix The corresponding eigenvalues, the mixed Jacobian matrix Each item in the formula is calculated using the following formula:

[0042]

[0043] Specifically, for the i-th joint and j-th motion direction of the q-th pose of the mobile robotic arm, L i,j (Γ,q) represents the joint constraint coefficients of the robotic arm, O i,j (Γ,q) represents the obstacle safety constraint coefficient, K i,j (Γ,q) represents the operational stability constraint coefficients, J i,j (q) represents the Jacobian matrix of the robotic arm.

[0044] The second aspect of this application provides a mobile robotic arm grasping pose planning device, mainly comprising:

[0045] The feasibility map determination module is used to determine the feasibility map of capturing the target object;

[0046] The gridding module is used to discretize the feasibility map into multiple grids.

[0047] The constraint coefficient calculation module is used to determine the constraint coefficients of the robot arm joints, obstacle safety constraint coefficients, and operability stability constraint coefficients for all poses of each grid.

[0048] The hybrid operability calculation module is used to calculate the hybrid operability of each pose of each grid based on the joint constraint coefficient of the robotic arm, the obstacle safety constraint coefficient, and the operability stability constraint coefficient.

[0049] The pose determination module is used to select the maximum value of the mixed operability of each pose in the grid as the mixed operability index of the grid, obtain the evaluation index set of all grids, and select the grid corresponding to the maximum value in the evaluation index set as the optimal mobile chassis position of the mobile robot arm. The pose corresponding to the mixed operability in the grid is the optimal grasping posture of the mobile robot arm for the robot arm load.

[0050] Preferably, in the feasibility map determination module, the feasibility map E for capturing the target object is determined using the following formula. FIRM :

[0051]

[0052] Where E represents the working environment area of ​​the mobile robotic arm, E out This refers to the area beyond the maximum working range of the mobile robotic arm's load capacity. This indicates the area occupied by the b-th obstacle; This represents the safe expansion zone of the b-th obstacle; This represents the area obscured by the b-th obstacle, where B is the number of obstacles within the workspace of the moving robotic arm around the target object.

[0053] Preferably, the constraint coefficient calculation module includes:

[0054] The robot arm joint constraint coefficient calculation unit is used to determine the robot arm joint constraint coefficients for each pose using the following formula:

[0055]

[0056] Specifically, for the i-th joint and j-th motion direction of the robotic arm at the q-th pose, J i,j (q) represents the Jacobian matrix, i represents the joint number of the robotic arm, j represents the direction of motion of the robotic arm joint in the three-dimensional coordinate system, the first three dimensions represent the motion on the x, y, and z axes respectively, and the last three dimensions represent the rotation about the x, y, and z axes, Γ i The positive and negative directions of movement for the i-th joint;

[0057] in, and These represent the negative joint constraint coefficient and the positive joint constraint coefficient, respectively, and their calculation methods under different conditions are as follows:

[0058]

[0059]

[0060] In the formula Let θ be the gradient of the joint constraint function h(q). i,max and θ i,min θ represents the maximum and minimum joint movement limits of the i-th joint, respectively. i Let h(q) be the angle by which the i-th joint deflects from the minimum joint motion constraint to the maximum joint motion constraint. The joint constraint function h(q) is:

[0061]

[0062] Where γ is the scaler gain and n is the number of joints;

[0063] The obstacle safety constraint coefficient calculation unit is used to determine the obstacle safety constraint coefficient for each pose using the following formula:

[0064]

[0065] Among them, and These represent the negative safety constraint coefficient and the positive safety constraint coefficient of the obstacle, respectively. Their calculation methods under different conditions are as follows:

[0066]

[0067]

[0068] In the formula Let v represent the gradient of the obstacle safety constraint function f(q). j It is the shortest distance vector between the obstacle and the robotic arm load, where the safety constraint function f(q) is:

[0069]

[0070] In the formula, d obs (q) represents the shortest distance from the obstacle to the moving robotic arm load when the robotic arm load is in attitude q, D * This represents the maximum safe working distance for the robotic arm's load, where η is the safety factor.

[0071] The operability stability constraint coefficient calculation unit is used to determine the operability stability constraint coefficients for each pose using the following formula:

[0072]

[0073] in, and These represent the negative and positive coefficients of the stability constraint, respectively. When the center of gravity projection of the robotic arm moves towards the support boundary of the mobile chassis during load operation, the negative coefficient of the stability constraint is selected. Its calculation method is as follows:

[0074]

[0075] When the center of gravity projection of the robotic arm moves away from the support boundary of the mobile chassis during operation, a positive coefficient for stability constraint is selected, with a value of [value missing].

[0076] In the above formula Let g(q) represent the gradient of the stability constraint function g(q), which is:

[0077]

[0078] In the formula, d stab The point v represents the projection of the center of gravity of the entire mobile robotic arm during its operation. COG The shortest distance between the support boundary of the mobile chassis and the support boundary, if d stab ≥0 indicates that the centroid projection point v COG Within the range of the mobile chassis support boundary, if d stab <0 indicates that v COG Outside the support boundary of the mobile chassis, α and β are adjustment coefficients.

[0079] Preferably, in the hybrid operability calculation module, the hybrid operability of each pose of each mesh is calculated using the following formula:

[0080]

[0081] in, Represents the mixed Jacobian matrix. Represents the mixed Jacobian matrix The corresponding eigenvalues, the mixed Jacobian matrix Each item in the formula is calculated using the following formula:

[0082]

[0083] Specifically, for the i-th joint and j-th motion direction of the q-th pose of the mobile robotic arm, L i,j (Γ,q) represents the joint constraint coefficients of the robotic arm, O i,j (Γ,q) represents the obstacle safety constraint coefficient, K i,j (Γ,q) represents the operational stability constraint coefficients, J i,j (q) represents the Jacobian matrix of the robotic arm.

[0084] A third aspect of this application is a computer device comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, the processor executing the computer program to implement the mobile robotic arm grasping pose planning method as described above.

[0085] A fourth aspect of this application is a readable storage medium storing a computer program that, when executed by a processor, is used to implement the mobile robotic arm grasping pose planning method as described above.

[0086] This application determines the grasping posture of the mobile robotic arm by using hybrid operability, which improves the adaptability of the robotic arm to complex working environments, enhances safety and stability, enables more dexterous grasping operations, and completes obstacle avoidance functions. Attached Figure Description

[0087] Figure 1 This is a flowchart of a preferred embodiment of the mobile robotic arm grasping pose planning method of this application.

[0088] Figure 2 A schematic diagram defining the various areas of the working environment for the mobile robotic arm.

[0089] Figure 3 This is a schematic diagram illustrating an example of a robotic arm load moving to the right of the centerline.

[0090] Figure 4 This is a schematic diagram illustrating an example of a robotic arm load moving to the left of the centerline.

[0091] Figure 5 This is a schematic diagram showing the robotic arm positioned to the left of the obstacle.

[0092] Figure 6 This is a schematic diagram showing the robotic arm positioned to the right of an obstacle.

[0093] Figure 7 This is a schematic diagram showing the distance between the robotic arm's load and the obstacle.

[0094] Figure 8 This is a schematic diagram of the structure of a computer device suitable for implementing the embodiments of this application, specifically a terminal or server. Detailed Implementation

[0095] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions in the embodiments of this application will be described in more detail below with reference to the accompanying drawings. In the drawings, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The described embodiments are only some, not all, of the embodiments of this application. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application. The embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0096] To address the issues of poor practicality, instability, and safety hazards in the grasping actions of existing mobile robotic arms guided by a single grasping evaluation index, this application proposes a novel mobile robotic arm grasping pose planning technology. It proposes a mobile chassis position and robotic arm grasping pose planning method based on "hybrid operability," incorporating obstacle safety constraint coefficients and operability stability constraint coefficients to solve problems such as insufficient consideration of multiple obstacles and displacement of the grasped object and the mobile robotic arm's center of mass.

[0097] The first aspect of this application provides a method for planning the grasping pose of a mobile robotic arm, such as... Figure 1 As shown, the specific steps include the following:

[0098] Step S1: Determine the feasibility map for capturing the target object;

[0099] Step S2: Discretize the feasibility map into a grid to obtain multiple grids;

[0100] Step S3: Determine the joint constraint coefficients, obstacle safety constraint coefficients, and operability stability constraint coefficients for all poses of the robotic arm in each grid.

[0101] Step S4: Calculate the mixed operability of each pose of each grid according to the joint constraint coefficient of the robotic arm, the obstacle safety constraint coefficient, and the operability stability constraint coefficient.

[0102] Step S5: Select the maximum value of the mixed operability of each pose in the grid as the mixed operability index of the grid, obtain the evaluation index set of all grids, select the grid corresponding to the maximum value in the evaluation index set as the optimal mobile chassis position of the mobile robot arm, and the pose corresponding to the mixed operability in the grid is the optimal grasping posture of the mobile robot arm load.

[0103] The following is a detailed explanation.

[0104] First, in step S1, the feasibility map E of capturing the target object is calculated. FIRM In some optional implementations, in step S1, the feasibility map E for capturing the target object is determined using the following formula. FIRM :

[0105]

[0106] Among them, such as Figure 2 As shown, E represents the working environment area of ​​the mobile robotic arm. out This refers to the area beyond the maximum working range of the mobile robotic arm's load capacity. This indicates the area occupied by the b-th obstacle; This represents the safe expansion zone of the b-th obstacle; This represents the area obscured by the b-th obstacle, where B is the number of obstacles within the workspace of the moving robotic arm around the target object.

[0107] Then, in step S2, the feasibility map E of the captured object is... FIRM The mesh is processed to obtain a discretized mesh G. FIRM ={C1,…,C m ,…,C M}, where M represents the number of meshes. The position in the pose planning of this application is derived from this mesh set G. FIRM Selected from the options. That is, in step S4, C for each grid cell will be calculated. m The combined operability of all poses is used to find the optimal pose G for each grid. m Then, the optimal mesh is found. The optimal mesh and the optimal pose are collectively referred to as the optimal pose, which can achieve the pose planning purpose of this application.

[0108] As mentioned above, it is necessary to exhaust all possible poses for each grid and evaluate the quality of these poses through hybrid operability. Hybrid operability covers the three constraint coefficients in step S3. Step S3 is explained in detail below to solve for these three constraint coefficients.

[0109] In some alternative implementations, step S3 further includes:

[0110] (1) Determine the joint constraint coefficients of the robotic arm for each pose using the following formula:

[0111]

[0112] Specifically, for the i-th joint and j-th motion direction of the robotic arm at the q-th pose, J i,j(q) represents the Jacobian matrix, i represents the joint number of the robotic arm, j represents the direction of motion of the robotic arm joint in the three-dimensional coordinate system, the first three dimensions (j≤3) represent the motion on the x, y, and z axes respectively, and the last three dimensions (3≤j<6) represent the rotation about the x, y, and z axes respectively. i Let Γ represent the positive and negative directions of movement for the i-th joint, where Γ ∈ {-1, 1}. 6 .

[0113] in, and These represent the negative joint constraint coefficient and the positive joint constraint coefficient, respectively, and their calculation methods under different conditions are as follows:

[0114]

[0115]

[0116] Figure 3 An example is given of the robotic arm load moving to the right of the centerline. Figure 4 An example is given where the load of the robotic arm moves to the left of the centerline. In the above formula... Let θ be the gradient of the joint constraint function h(q). i,max and θ i,min θ represents the maximum and minimum joint movement limits of the i-th joint, respectively. i Let h(q) be the angle by which the i-th joint deflects from the minimum joint motion constraint to the maximum joint motion constraint. The joint constraint function h(q) is:

[0117]

[0118] Where γ is the scaler gain and n is the number of joints.

[0119] Gradient of joint constraint function The calculation method is as follows:

[0120]

[0121] The load on the robotic arm is at its minimum when it is at the center of its joint range of motion; and it approaches infinity when the load moves to its limit position.

[0122] (2) Determine the obstacle safety constraint coefficients for each pose using the following formula:

[0123]

[0124] Among them, and These represent the negative safety constraint coefficient and the positive safety constraint coefficient of the obstacle, respectively. Their calculation methods under different conditions are as follows:

[0125]

[0126]

[0127] Figure 5 v is given j A diagram showing the robotic arm positioned to the left of the obstacle when the value is greater than 0. Figure 6 v is given j A schematic diagram showing the robotic arm positioned to the right of the obstacle when the value is less than 0. (The formula is incomplete in the original text.) Let v represent the gradient of the obstacle safety constraint function f(q). j It is the shortest distance vector between the obstacle and the robotic arm load, v j =P1-P2. When v j >0 indicates that the direction is positive on the j-axis.

[0128] The safety constraint function f(q) is:

[0129]

[0130] In the formula, such as Figure 7 As shown, d obs (q) represents the shortest distance from the obstacle to the moving robotic arm load when the robotic arm load is in attitude q, D * This represents the maximum safe working distance for the robotic arm's load, where η is the safety factor.

[0131] Gradient of obstacle safety constraint function f(q) The calculation method is as follows:

[0132]

[0133]

[0134]

[0135] In the formula, v b =P o -P m , representing the shortest distance vector from the robotic arm load to the obstacle. o and P m These represent the position coordinates corresponding to the shortest distance on the obstacle and the robotic arm load, respectively.

[0136] (3) Determine the operational stability constraint coefficients for each pose using the following formula:

[0137]

[0138] in, and These represent the negative and positive coefficients of the stability constraint, respectively. When the center of gravity projection of the robotic arm moves towards the support boundary of the mobile chassis during load operation, the negative coefficient of the stability constraint is selected. Its calculation method is as follows:

[0139]

[0140] When the center of gravity projection of the robotic arm moves away from the support boundary of the mobile chassis during operation, a positive coefficient for stability constraint is selected, with a value of [value missing].

[0141] In the above formula Let g(q) represent the gradient of the stability constraint function g(q), which is:

[0142]

[0143] In the formula, d stab The point v represents the projection of the center of gravity of the entire mobile robotic arm during its operation. COG The shortest distance between the support boundary of the mobile chassis and the support boundary, if d stab ≥0 indicates that the centroid projection point v COG Within the range of the mobile chassis support boundary, if d stab <0 indicates that v COG Outside the support boundary of the mobile chassis, α and β are adjustment coefficients.

[0144] gradient The calculation method is as follows:

[0145]

[0146]

[0147]

[0148] In the formula, v COG This represents the coordinates of the center of gravity projection of the entire mobile robotic arm onto the robotic arm's base coordinates.

[0149] As described above, the joint constraint coefficient L of the robotic arm was calculated in step S3. i,j (Γ,q), obstacle safety constraint coefficient O i,j (Γ,q) and operational stability constraint coefficient K i,j (Γ,q) can be used to calculate the hybrid operability in step S4.

[0150] In some alternative implementations, in step S4, the hybrid operability of each pose of each mesh is calculated using the following formula:

[0151]

[0152] in, Represents the mixed Jacobian matrix. Represents the mixed Jacobian matrix The corresponding eigenvalues, the mixed Jacobian matrix Each item in the formula is calculated using the following formula:

[0153]

[0154] Specifically, for the i-th joint and j-th motion direction of the q-th pose of the mobile robotic arm, L i,j (Γ,q) represents the joint constraint coefficients of the robotic arm, O i,j (Γ,q) represents the obstacle safety constraint coefficient, K i,j (Γ,q) represents the operational stability constraint coefficients, J i,j (q) represents the Jacobian matrix of the robotic arm.

[0155] Finally, in step S5, when the mobile chassis is in a single grid C m At that time, the corresponding set of effective grasping postures of the robotic arm load. And calculate For each pose, the corresponding "hybrid operability" index yields a set of evaluation indexes for a single grid. Take its maximum value As a mobile chassis, it is located in C. m "Hybrid operability" at different locations Among them, K m This indicates that the mobile robotic arm chassis is at position C. m In this situation, the number of gestures by which the robotic arm effectively grasps the target object. Through the above method, the "hybrid operability" index G for each grid is... m You can then obtain the set of evaluation metrics:

[0156] A FIRM ={G1,…,G m ,…,G M}

[0157] This set corresponds to the mesh set G described in step S2. FIRM ={C1,…,C m ,…,C M}, where the grid position corresponding to the maximum value of the evaluation index is the optimal mobile chassis position, and its corresponding optimal grasping posture is the optimal grasping posture of the robotic arm load. For example, in set A FIRM If the maximum value is G3, then in the grid set G FIRMThe corresponding position C3 is the optimal chassis position, and the posture corresponding to G3 is the optimal posture. Subsequently, the robot can be controlled to move to the position and adopt the grasping posture corresponding to G3 to grasp the target.

[0158] This application incorporates obstacle safety constraint coefficients into the hybrid operability evaluation index, enabling the mobile robotic arm to consider multiple obstacles and improving its adaptability to complex working environments. It also adds operability stability constraint coefficients to the hybrid operability evaluation index, considering the overall change in the mobile robotic arm's center of mass after grasping an object, resulting in a more stable grasping posture and improved safety and stability. Furthermore, this application proposes a mobile platform posture determination algorithm based on hybrid operability, selecting the optimal platform posture by positioning the platform at the location with the highest hybrid operability. Typical experiments demonstrate that the mobile robotic arm based on "hybrid operability" can achieve more dexterous grasping operations and complete obstacle avoidance functions.

[0159] The second aspect of this application provides a mobile robotic arm grasping pose planning device corresponding to the above method, mainly comprising:

[0160] The feasibility map determination module is used to determine the feasibility map of capturing the target object;

[0161] The gridding module is used to discretize the feasibility map into multiple grids.

[0162] The constraint coefficient calculation module is used to determine the constraint coefficients of the robot arm joints, obstacle safety constraint coefficients, and operability stability constraint coefficients for all poses of each grid.

[0163] The hybrid operability calculation module is used to calculate the hybrid operability of each pose of each grid based on the joint constraint coefficient of the robotic arm, the obstacle safety constraint coefficient, and the operability stability constraint coefficient.

[0164] The pose determination module is used to select the maximum value of the mixed operability of each pose in the grid as the mixed operability index of the grid, obtain the evaluation index set of all grids, and select the grid corresponding to the maximum value in the evaluation index set as the optimal mobile chassis position of the mobile robot arm. The pose corresponding to the mixed operability in the grid is the optimal grasping posture of the mobile robot arm for the robot arm load.

[0165] In some alternative implementations, in the feasibility map determination module, the feasibility map E for capturing the target object is determined using the following formula. FIRM :

[0166]

[0167] Where E represents the working environment area of ​​the mobile robotic arm, E out This refers to the area beyond the maximum working range of the mobile robotic arm's load capacity. This indicates the area occupied by the b-th obstacle; This represents the safe expansion zone of the b-th obstacle; This represents the area obscured by the b-th obstacle, where B is the number of obstacles within the workspace of the moving robotic arm around the target object.

[0168] In some optional implementations, the constraint coefficient calculation module includes:

[0169] The robot arm joint constraint coefficient calculation unit is used to determine the robot arm joint constraint coefficients for each pose using the following formula:

[0170]

[0171] Specifically, for the i-th joint and j-th motion direction of the robotic arm at the q-th pose, J i,j (q) represents the Jacobian matrix, i represents the joint number of the robotic arm, j represents the direction of motion of the robotic arm joint in the three-dimensional coordinate system, the first three dimensions represent the motion on the x, y, and z axes respectively, and the last three dimensions represent the rotation about the x, y, and z axes, Γ i The positive and negative directions of movement for the i-th joint;

[0172] in, and These represent the negative joint constraint coefficient and the positive joint constraint coefficient, respectively, and their calculation methods under different conditions are as follows:

[0173]

[0174]

[0175] In the formula Let θ be the gradient of the joint constraint function h(q). i,max and θ i,min θ represents the maximum and minimum joint movement limits of the i-th joint, respectively. i Let h(q) be the angle by which the i-th joint deflects from the minimum joint motion constraint to the maximum joint motion constraint. The joint constraint function h(q) is:

[0176]

[0177] Where γ is the scaler gain and n is the number of joints;

[0178] The obstacle safety constraint coefficient calculation unit is used to determine the obstacle safety constraint coefficient for each pose using the following formula:

[0179]

[0180] Among them, and These represent the negative safety constraint coefficient and the positive safety constraint coefficient of the obstacle, respectively. Their calculation methods under different conditions are as follows:

[0181]

[0182] In the formula Let v represent the gradient of the obstacle safety constraint function f(q). j It is the shortest distance vector between the obstacle and the robotic arm load, where the safety constraint function f(q) is:

[0183]

[0184] In the formula, d obs (q) represents the shortest distance from the obstacle to the moving robotic arm load when the robotic arm load is in attitude q, D * This represents the maximum safe working distance for the robotic arm's load, where η is the safety factor.

[0185] The operability stability constraint coefficient calculation unit is used to determine the operability stability constraint coefficients for each pose using the following formula:

[0186]

[0187] in, and These represent the negative and positive coefficients of the stability constraint, respectively. When the center of gravity projection of the robotic arm moves towards the support boundary of the mobile chassis during load operation, the negative coefficient of the stability constraint is selected. Its calculation method is as follows:

[0188]

[0189] When the center of gravity projection of the robotic arm moves away from the support boundary of the mobile chassis during operation, a positive coefficient for stability constraint is selected, with a value of [value missing].

[0190] In the above formula Let g(q) represent the gradient of the stability constraint function g(q), which is:

[0191]

[0192] In the formula, d stab The point v represents the projection of the center of gravity of the entire mobile robotic arm during its operation.COG The shortest distance between the support boundary of the mobile chassis and the support boundary, if d stab ≥0 indicates that the centroid projection point v COG Within the range of the mobile chassis support boundary, if d stab <0 indicates that v COG Outside the support boundary of the mobile chassis, α and β are adjustment coefficients.

[0193] In some alternative implementations, in the hybrid operability calculation module, the hybrid operability of each pose of each mesh is calculated using the following formula:

[0194]

[0195] in, Represents the mixed Jacobian matrix. Represents the mixed Jacobian matrix The corresponding eigenvalues, the mixed Jacobian matrix Each item in the formula is calculated using the following formula:

[0196]

[0197] Specifically, for the i-th joint and j-th motion direction of the q-th pose of the mobile robotic arm, L i,j (Γ,q) represents the joint constraint coefficients of the robotic arm, O i,j (Γ,q) represents the obstacle safety constraint coefficient, K i,j (Γ,q) represents the operational stability constraint coefficients, J i,j (q) represents the Jacobian matrix of the robotic arm.

[0198] A third aspect of this application is a computer device comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, the processor executing the computer program to implement the mobile robotic arm grasping pose planning method as described above.

[0199] A fourth aspect of this application provides a readable storage medium storing a computer program that, when executed by a processor, implements the mobile robotic arm grasping pose planning method described above. This computer-readable storage medium may be included in the apparatus described in the above embodiments; or it may exist independently and not incorporated into the apparatus. The aforementioned computer-readable storage medium carries one or more programs that, when executed by the apparatus, process data according to the described method.

[0200] The computer program for the mobile robotic arm's grasping pose planning method described in this application can be set on the mobile robot chip or on a computer device remotely connected to the mobile robot. When installed on a remote computer device, refer to... Figure 8 It shows a schematic diagram of the structure of a computer device 400 suitable for implementing the embodiments of this application. Figure 8 The computer device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments described in this application.

[0201] like Figure 8 As shown, the computer device 400 includes a central processing unit (CPU) 401, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 402 or a program loaded from a storage section 408 into a random access memory (RAM) 403. The RAM 403 also stores various programs and data required for the operation of the device 400. The CPU 401, ROM 402, and RAM 403 are interconnected via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0202] The following components are connected to I / O interface 405: an input section 406 including a keyboard, mouse, etc.; an output section 407 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN card, modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to I / O interface 405 as needed. A removable medium 411, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 410 as needed so that computer programs read from it can be installed into storage section 408 as needed.

[0203] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 409, and / or installed from removable medium 411. When the computer program is executed by central processing unit (CPU) 401, it performs the functions defined in the methods of this application. It should be noted that the computer storage medium of this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on a computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0204] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0205] The modules or units described in the embodiments of this application can be implemented in software or hardware. The described modules or units can also be located in a processor, and the names of these modules or units do not necessarily constitute a limitation on the module or unit itself.

[0206] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for planning the grasping pose of a mobile robotic arm, characterized in that, include: Step S1: Determine the feasibility map for capturing the target object; Step S2: Discretize the feasibility map into a grid to obtain multiple grids; Step S3: Determine the joint constraint coefficients, obstacle safety constraint coefficients, and operability stability constraint coefficients for all poses of the robotic arm in each grid. Step S4: Calculate the mixed operability of each pose of each grid according to the joint constraint coefficient of the robotic arm, the obstacle safety constraint coefficient, and the operability stability constraint coefficient. Step S5: Select the maximum value of the mixed operability of each pose in the grid as the mixed operability index of the grid, obtain the evaluation index set of all grids, select the grid corresponding to the maximum value in the evaluation index set as the optimal mobile chassis position of the mobile robot arm, and the pose corresponding to the mixed operability in the grid is the optimal grasping posture of the mobile robot arm load. Step S3 further includes: (1) Determine the joint constraint coefficients of the robotic arm for each pose using the following formula: ； Among them, for the q-th pose of the robotic arm, the first... The first joint, the first One direction of movement, Represents the Jacobian matrix. This indicates the joint number of the robotic arm. This indicates the direction of motion of the robotic arm joints in a three-dimensional coordinate system. The first three dimensions represent the motion along the x, y, and z axes, respectively, while the last three dimensions represent the rotation about the x, y, and z axes. For the first The positive and negative directions of movement of each joint; in, and These represent the negative joint constraint coefficient and the positive joint constraint coefficient, respectively, and their calculation methods under different conditions are as follows: ； ； In the formula Joint constraint function gradient, and They represent the first Maximum and minimum joint movement limits for each joint For the first The angle by which a joint deflects from its minimum joint motion constraint towards its maximum joint motion constraint, and the joint constraint function. for: ； Where γ is the scaler gain and n is the number of joints; (2) Determine the obstacle safety constraint coefficients for each pose using the following formula: ； Among them, and These represent the negative safety constraint coefficient and the positive safety constraint coefficient of the obstacle, respectively, and their calculation methods under different conditions are as follows: ； ； In the formula Represents obstacle safety limit constraint function gradient, It is the shortest distance vector between the obstacle and the robotic arm load, where the safety constraint function is... for: ； In the formula, This indicates that the load on the robotic arm is in the following posture: At that time, the shortest distance from the obstacle to the load of the moving robotic arm, This indicates the maximum safe working distance for the robotic arm's load capacity. For safety factor; (3) Determine the operational stability constraint coefficients for each pose using the following formula: ； in, and These represent the negative and positive coefficients of the stability constraint, respectively. When the center of gravity projection of the robotic arm moves towards the support boundary of the mobile chassis during load operation, the negative coefficient of the stability constraint is selected. Its calculation method is as follows: ； When the center of gravity projection of the robotic arm moves away from the support boundary of the mobile chassis during operation, a positive coefficient for stability constraint is selected, with a value of [value missing]. ; In the above formula Represents the stability constraint function gradient, stability constraint function for: ； In the formula, This represents the projection point of the center of gravity of the entire mobile robotic arm during its operation. The shortest distance between the support boundary of the mobile chassis, if , indicating the centroid projection point Within the support boundary of the mobile chassis, if ,express Outside the support boundary of the mobile chassis and It is an adjustment factor; In step S4, the hybrid operability of each pose of each mesh is calculated using the following formula: ； in, Represents the mixed Jacobian matrix. Represents the mixed Jacobian matrix The corresponding eigenvalues, the mixed Jacobian matrix Each item in the formula is calculated using the following formula: ； Among them, for the q-th pose of the mobile robotic arm, the first... The first joint, the first One direction of movement, The constraint coefficient for the robotic arm joints. This represents the obstacle safety limit constraint coefficient. This represents the operational stability constraint coefficient. This represents the Jacobian matrix of the robotic arm.

2. The mobile robotic arm grasping pose planning method as described in claim 1, characterized in that, In step S1, the feasibility map (EFIRM) for capturing the target object is determined using the following formula: ； Where E represents the working environment area of ​​the mobile robotic arm, and Eout represents the area outside the maximum working range of the mobile robotic arm's load capacity. This indicates the area occupied by the b-th obstacle; This represents the safe expansion zone of the b-th obstacle; This represents the area obscured by the b-th obstacle, where B is the number of obstacles within the workspace of the moving robotic arm around the target object.

3. A mobile robotic arm grasping posture planning device, characterized in that, For implementing the mobile robotic arm grasping pose planning method as described in claim 1, the apparatus includes: The feasibility map determination module is used to determine the feasibility map of capturing the target object; The gridding module is used to discretize the feasibility map into multiple grids. The constraint coefficient calculation module is used to determine the constraint coefficients of the robot arm joints, obstacle safety constraint coefficients, and operability stability constraint coefficients for all poses of each grid. The hybrid operability calculation module is used to calculate the hybrid operability of each pose of each grid based on the joint constraint coefficient of the robotic arm, the obstacle safety constraint coefficient, and the operability stability constraint coefficient. The pose determination module is used to select the maximum value of the mixed operability of each pose in the grid as the mixed operability index of the grid, obtain the evaluation index set of all grids, and select the grid corresponding to the maximum value in the evaluation index set as the optimal mobile chassis position of the mobile robot arm. The pose corresponding to the mixed operability in the grid is the optimal grasping posture of the mobile robot arm for the robot arm load.

4. The mobile robotic arm grasping pose planning device as described in claim 3, characterized in that, In the feasibility map determination module, the feasibility map (EFIRM) for capturing the target object is determined using the following formula: ； Where E represents the working environment area of ​​the mobile robotic arm, and Eout represents the area outside the maximum working range of the mobile robotic arm's load capacity. This indicates the area occupied by the b-th obstacle; This represents the safe expansion zone of the b-th obstacle; This represents the area obscured by the b-th obstacle, where B is the number of obstacles within the workspace of the moving robotic arm around the target object.

5. The mobile robotic arm grasping pose planning device as described in claim 3, characterized in that, The constraint coefficient calculation module includes: The robot arm joint constraint coefficient calculation unit is used to determine the robot arm joint constraint coefficients for each pose using the following formula: ； Among them, for the q-th pose of the robotic arm, the first... The first joint, the first One direction of movement, Represents the Jacobian matrix. This indicates the joint number of the robotic arm. This indicates the direction of motion of the robotic arm joints in a three-dimensional coordinate system. The first three dimensions represent the motion along the x, y, and z axes, respectively, while the last three dimensions represent the rotation about the x, y, and z axes. For the first The positive and negative directions of movement of each joint; in, and These represent the negative joint constraint coefficient and the positive joint constraint coefficient, respectively, and their calculation methods under different conditions are as follows: ； ； In the formula Joint constraint function gradient, and They represent the first Maximum and minimum joint movement limits for each joint For the first The angle by which a joint deflects from its minimum joint motion constraint towards its maximum joint motion constraint, and the joint constraint function. for: ； Where γ is the scaler gain and n is the number of joints; The obstacle safety constraint coefficient calculation unit is used to determine the obstacle safety constraint coefficient for each pose using the following formula: ； Among them, and These represent the negative safety constraint coefficient and the positive safety constraint coefficient of the obstacle, respectively, and their calculation methods under different conditions are as follows: ； ； In the formula Represents obstacle safety limit constraint function gradient, It is the shortest distance vector between the obstacle and the robotic arm load, where the safety constraint function is... for: ； In the formula, This indicates that the load on the robotic arm is in the following posture: At that time, the shortest distance from the obstacle to the load of the moving robotic arm, This indicates the maximum safe working distance for the robotic arm's load capacity. For safety factor; The operability stability constraint coefficient calculation unit is used to determine the operability stability constraint coefficients for each pose using the following formula: ； in, and These represent the negative and positive coefficients of the stability constraint, respectively. When the center of gravity projection of the robotic arm moves towards the support boundary of the mobile chassis during load operation, the negative coefficient of the stability constraint is selected. Its calculation method is as follows: ； When the center of gravity projection of the robotic arm moves away from the support boundary of the mobile chassis during operation, a positive coefficient for stability constraint is selected, with a value of [value missing]. ; In the above formula Represents the stability constraint function gradient, stability constraint function for: ； In the formula, This represents the projection point of the center of gravity of the entire mobile robotic arm during its operation. The shortest distance between the support boundary of the mobile chassis, if , indicating the centroid projection point Within the support boundary of the mobile chassis, if ,express Outside the support boundary of the mobile chassis and It is an adjustment factor.

6. The mobile robotic arm grasping pose planning device as described in claim 5, characterized in that, In the hybrid operability calculation module, the hybrid operability of each pose of each mesh is calculated using the following formula: ； in, Represents the mixed Jacobian matrix. Represents the mixed Jacobian matrix The corresponding eigenvalues, the mixed Jacobian matrix Each item in the formula is calculated using the following formula: ； Among them, for the q-th pose of the mobile robotic arm, the first... The first joint, the first One direction of movement, The constraint coefficient for the robotic arm joints. This represents the obstacle safety limit constraint coefficient. This represents the operational stability constraint coefficient. This represents the Jacobian matrix of the robotic arm.

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