An Adams / Matlab co-simulation method for an underwater robot-manipulator system

Abstract

The present invention relates to a method for Adams / Matlab co-simulation of an underwater robot-manipulator system. By using the Lagrange equation method in the widely popular multi-rigid body dynamics theory in Adams software, the dynamic model of the system is automatically established, and the control system is established by using the visualization simulation tool Simulink in Matlab software, so as to simulate the underwater robot-manipulator system and obtain the coupling influence of the operation of the manipulator on the body under the corresponding input.

Description

Technical Field

[0001] The present invention belongs to the forefront field of robot and ocean engineering research, and relates to a simulation method for an autonomous underwater vehicle-manipulator system (UVMS). More specifically, it is a co-simulation of the multi-rigid body system dynamics model in Adams and Matlab software. Background Art

[0002] The ocean covers two-thirds of the entire earth's surface and has a huge impact on the future life and survival of mankind. UVMS can help humans better understand the true face of the ocean and understand the existing ocean environmental problems, so as to take specific measures to protect the ocean resources on the earth for different ocean environmental problems. On the basis of harmonious coexistence, the ocean resources can be used green and effectively for the benefit of mankind. In the development of robotics, underwater manipulators play an increasingly important role in ocean tasks such as the installation and maintenance of underwater equipment, the laying and detection of underwater networks, the exploration and repair of underwater pipelines, etc. by underwater robots.

[0003] However, at present, these tasks are mainly performed by manned submersibles and remotely operated vehicles (ROVs), both of which are equipped with one or more manipulators. Although ROVs have been recognized as a great asset in the scientific and engineering communities, their performance still has limitations. Its disadvantages are mainly manifested in:

[0004] (1) ROVs and manned submersibles are usually large and heavy robots, requiring customized large mother ships for support (transportation, launching, and recovery);

[0005] (2) Complex user interface information and high information latency significantly increase the diving cost and manpower input of ROVs;

[0006] (3) Task requirements often require operators to work continuously for several hours, and have high requirements for the operation skills of operators;

[0007] (4) ROVs need umbilical cables to provide them with energy. The presence of umbilical cables brings additional control problems and greatly limits the flexibility during operation.

[0008] Although many valuable research results have been obtained for space robot-manipulator systems and mobile manipulator systems, there are still significant technical differences and difficulties between underwater robot-manipulator systems and them. First, different from land or space robot-manipulator systems with fixed bases, underwater robot-manipulator systems belong to floating multi-rigid-body systems. Second, during the operation of the manipulator, the coupled motion between the manipulator and the robot body not only follows the conservation of angular momentum but is also affected by additional hydrodynamic forces. In addition, the restoring forces and restoring torques generated by the changes in the center of gravity and buoyancy during the operation of the manipulator will cause large roll and pitch of the robot. Summarizing these problems, the main difficulties are as follows:

[0009] (1) It is difficult to obtain accurate models of the robot body, manipulator, and operating environment, especially the hydrodynamic model;

[0010] (2) The underwater robot-manipulator system is a non-linear, strongly coupled multi-input multi-output kinematically redundant system, and the overall control is difficult;

[0011] (3) It is difficult to describe the coupled influence between the manipulator and the robot body with a mathematical model.

[0012] In summary, the establishment of the model of the underwater robot-manipulator system and its simulation have great guiding significance for practical applications. Summary of the Invention

[0013] Technical Problems to be Solved

[0014] In order to avoid the deficiencies of the prior art, the present invention provides an Adams / Matlab co-simulation method for an underwater robot-manipulator system.

[0015] Technical Solution

[0016] An Adams / Matlab co-simulation method for an underwater robot-manipulator system, characterized by the following steps:

[0017] Step 1: Perform solid modeling on each component of the underwater robot-manipulator system in the Creo 3D modeling software, and perform assembly in the Creo software to obtain the assembly of the underwater robot-manipulator system, and save it as a Parasolid (*.x_t) format file;

[0018] Step 2: Create a new model in Adams, and import the Parasolid (*.x_t) format file generated in the previous step into the model;

[0019] Step 3: Define the material properties of each component of the newly imported model, and automatically solve by Adams to obtain the mass characteristics, inertia properties, and centroid positions of each component;

[0020] Step 4: Apply constraints to each joint according to the actual motion of the robotic arm and add kinematic pairs; the kinematic pairs include fixed pairs and revolute pairs;

[0021] Step 5: Apply driving torques to each kinematic pair, which are subsequently defined as input variables and used as the inputs of Adams in the Matlab co-simulation;

[0022] Step 6: Use the Adams / View module to experimentally run the simulation of the underwater robot - robotic arm system under the action of gravity, and confirm that the mechanical system modeling is correct according to the simulation results;

[0023] Step 7: Add hydrodynamic forces to the robotic arm, and use the three-component generalized forces in Adams to simulate the hydrodynamic forces received by the robotic arm;

[0024] Step 8: Determine the input and output variables of Adams. Before defining the input variables, change the driving input used for experimental analysis in Step 6 to 0. The input variables are the driving torques of each rotating joint of the robotic arm, and the output is the change in the angles of each joint of the robotic arm;

[0025] Step 9: Select the established input and output unit variables in the Adams / Controls module before determining, select the co-simulation control software as Matlab, the analysis type as nonlinear, and the solver as the interactive FORTRAN, so as to generate four files, namely.m,.cmd,.adm, and.txt, for Matlab to load and call;

[0026] Step 10: Run the.m file in Matlab to load the relevant parameters and input and output of the model, and enter adams_sys, the Adams - Matlab interface command, in the command line to display the module window of adams_sys;

[0027] Step 11: Drag the adams_sub module and the output module in the module window of adams_sys to the newly created Simulink window and set the software interactive simulation parameters;

[0028] Step 12: Perform control system modeling in the newly created Simulink window in Step 11. The control system includes a trajectory generator, a PD controller, and an attitude feedback system of the robotic arm. The trajectory generator includes inverse kinematics solution and trajectory planning;

[0029] Step 13: Mathematically model the inverse kinematics solution part in the trajectory generator of the robotic arm to obtain the posture of the robotic arm when the end effector reaches the desired position, that is, the desired joint angles of the robotic arm;

[0030] Step 14: Based on the desired joint angles obtained in Step 13, perform trajectory planning to obtain the function change process of each joint angle of the robotic arm;

[0031] Step 15: Take the function change process of each joint angle calculated in Step 14 as the input, that is, the desired trajectory, subtract it from the posture feedback information of the output actual trajectory to obtain the joint angle error, and input it into the PD controller. Under the action of the PD controller, the output torque directly acts on the robot-manipulator system, so that the system moves to form a complete simulation system; set the Matlab simulation parameters, perform the co-simulation of the underwater robot-manipulator system, so as to obtain the actual change process of the joint angles of the robotic arm, the change of the robot posture under the coupling effect, and its generated speed and acceleration.

[0032] Further technical solution of the present invention: The hydrodynamic force is calculated using the approximate empirical formula Morrison formula in Step 7, and its expression is as follows:

[0033]

[0034] where ρ is the fluid density, D is the equivalent diameter of the robotic arm link, v is the fluid flow velocity, C D is the drag coefficient, and C M is the added mass force coefficient;

[0035] Specifically, when adding, the three-component generalized force in Adams is used to simulate:

[0036]

[0037] Further technical solution of the present invention: The process of obtaining the desired joint angles of the robotic arm in Step 13 is as follows:

[0038] First, it is known that the position of the target of the end effector of the robotic arm in the coordinate system of the robotic arm base. According to the inverse kinematics solution of the robotic arm, the joint angles q = [π / 6, π / 2, -π / 2, -π / 6] are obtained. Then, each joint of the robotic arm can reach the target position for operation at the above-mentioned obtained joint angles q i The following is the specific calculation process

[0039]

[0040] where is the position and posture matrix of the end effector of the four-degree-of-freedom robotic arm, and T ii-1 (i = 1, 2, 3, 4) is the homogeneous transformation matrix from the adjacent joint coordinate system i to the coordinate system i - 1, t ij (i = 1, 2, 3; j = 1, 2, 3) are the terms of the end - effector attitude matrix, 0 x4, 0 y4, 0 z4 are the terms of the position matrix respectively, t ij and 0 x4, 0 y4, 0 The specific expressions of z4 are as follows:

[0041]

[0042]

[0043] Among them, d i is the offset of the robotic arm link, a i is the length of the robotic arm link, q i is the joint angle of the robotic arm, α i is the rotation angle of the robotic arm, d i 、a i 、q i and α i are all the D - H parameters of the robotic arm; when the position and attitude of the end - effector are known, the joint angles q of each robotic arm can be solved according to equations (4) and (5) i .

[0044] The further technical solution of the present invention: The process of obtaining the function change of each joint angle of the robotic arm in step 14 is as follows:

[0045] Based on the joint angles of the desired robotic arm obtained, the trajectory planning of the robotic arm is carried out, and the fifth - order polynomial is used for trajectory planning. The specific process is as follows:

[0046] The expression of the fifth - order polynomial is as follows

[0047] q(t)=a0 + a1t + a2t 2 +a3t 3 +a4t 4 +a5t 5 (4)

[0048] Its initial constraint conditions are

[0049]

[0050] Its termination constraint conditions are

[0051]

[0052] Substituting the constraint conditions (7) and (8) into (6), a system of six equations with six unknowns can be obtained. Solving this system gives six coefficients a i (i = 1 to 6); thus, the functions of the desired joint angles, joint angular velocities, and joint angular accelerations of the robotic arm are as follows

[0053]

[0054] A computer system, comprising: one or more processors, and a computer-readable storage medium for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the above method.

[0055] A computer-readable storage medium, storing computer-executable instructions that are used to implement the above method when executed.

[0056] Beneficial effects

[0057] An Adams / Matlab co-simulation method for an underwater robot-manipulator system provided by the present invention automatically establishes a dynamic model of the system by using the Lagrangian equation method in the widely popular multi-rigid-body dynamics theory in Adams software, and establishes a control system with the visualization simulation tool Simulink in Matlab software, thereby simulating the underwater robot-manipulator system and obtaining the coupling influence of the operation of the manipulator on the body under corresponding inputs. Compared with the prior art, it has the following technical advantages:

[0058] (1) It does not rely on an accurate mathematical model, and the automatic system modeling function of Adams can meet the actual simulation requirements;

[0059] (2) The hydrodynamic force calculated using the Morrison formula approximately simulates the hydrodynamic force of the actual fluid acting on the operating manipulator, greatly saving the calculation time of the hydrodynamic force. Description of the drawings

[0060] The drawings are only for the purpose of showing specific embodiments and are not considered to be a limitation of the present invention. Throughout the drawings, the same reference signs denote the same components.

[0061] Figure 1 Adams and Matab co-simulation flowchart.

[0062] Figure 2 Schematic diagram of adding hydrodynamic force to the manipulator link.

[0063] Figure 3 Manipulator motion control block diagram.

[0064] Figure 4 Overall combined simulation structure diagram.

[0065] Figure 5 Desired joint angles, angular velocities, and angular accelerations of the robotic arm: (a) Variation of the desired joint angle; (b) Variation of the desired joint angular velocity; (c) Variation of the desired joint angular acceleration.

[0066] Figure 6 Variation of the desired and actual joint angles of the robotic arm: (a) Joint 1; (b) Joint 2; (c) Joint 3; (d) Joint 4.

[0067] Figure 7 Coupling effects on the robot body during the operation of the robotic arm: (a) Displacement of the robot body along each coordinate axis; (b) Acceleration of the robot body along each coordinate axis; (c) Angular velocity of the robot body around each coordinate axis; (d) Angular acceleration of the robot body around each coordinate axis. Specific implementation manners

[0068] In order to make the objectives, technical solutions, and advantages of the present invention more clear and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0069] As shown in the Figure 1 accompanying drawings is a block diagram of an embodiment of a simulation method for an underwater operation robot based on Adams and Matlab software, including the entire operation robot and robotic arm dynamics simulation module of Adams / View, the control virtual prototype system module of Adams / Control, and the Matlab / Simulink control system module.

[0070] The specific implementation manner of the combined simulation method is as follows:

[0071] (1) First, perform 3D solid modeling and assembly of each component of the operation robot in Creo software to form an assembly, and save it as a Parasolid (*.x_t) format file; import it into the newly created Adams model; and define the material properties for each component in turn. The mass properties, inertia properties, and centroid positions of each component are automatically calculated and solved by Adams software; apply constraints to each system of the underwater robot-manipulator according to the actual motion of the manipulator, and add kinematic pairs, including translational pairs, fixed pairs, and rotational pairs; add driving torques at the positions where kinematic pairs have been added, and define them as system input units, which serve as input variables in subsequent co-simulations; use the simulation results of the system under the action of gravity run by the Adams / View module to judge whether the system modeling is correct; and add hydrodynamic forces to each link of the manipulator. The hydrodynamic forces are calculated using the approximate empirical formula, the Morison formula, and its expression is as follows:

[0072]

[0073] where ρ is the fluid density, D is the equivalent diameter of the manipulator link, v is the fluid velocity, C D is the drag coefficient, C M is the added mass force coefficient, which needs to be determined by experience or experiment. When adding specifically, the three-component generalized force in Adams is used to simulate, and the calculation is shown in Equation (2). The addition schematic diagram is shown in Appendix Figure 2 as shown

[0074]

[0075] Define the input and output variables of Adams. The input variables are the driving torques of each rotating joint of the manipulator, and the output is the change in the angle of each joint of the manipulator; select the input and output unit variables established before in the Adams / Controls module, select the co-simulation control software as Matlab, the analysis type is nonlinear, and the solver selects the interactive FORTRAN, so as to generate four files, namely.m,.cmd,.adm, and.txt, for Matlab to load and call; finally, export the virtual prototype system of the control software Matlab for co-simulation analysis with Adams.

[0076] (2) Run the.m file generated in the previous step in Matlab, load various parameters of the virtual prototype system and check whether the input and output are the same as those set in the previous step. Then, type adams_sys (the interface command between Adams and Matlab) in the command line. In the Simulink, display the module window of adams_sys, and establish a control system based on the adams_sub module and the output module. The control system block diagram is shown in Appendix Figure 3As shown, it includes a trajectory generator for making the robotic arm move along a desired trajectory, a PD controller for controlling the robotic arm to move in a desired motion mode, and an attitude feedback system for the entire robot-robotic arm system;

[0077] (3) Assume to simulate the operation process of the underwater robot-robotic arm system in a static water environment (i.e., the robotic arm is deployed). First, the position of the end effector of the robotic arm in the base coordinate of the robotic arm is known. According to the inverse kinematics of the robotic arm, the joint angles q = [π / 6, π / 2, π / 2, π / 6] are obtained. Then, each joint of the robotic arm can reach the target position for operation at the above-mentioned joint angles q i The specific calculation process is as follows

[0078]

[0079] where is the position and attitude matrix of the end effector of the four-degree-of-freedom robotic arm, T i i-1 *i = 1, 2, 3, 4) is the homogeneous transformation matrix from the adjacent joint coordinate system i to the coordinate system i-1, t ij (i = 1, 2, 3; j = 1, 2, 3) are the items of the end effector attitude matrix, 0 x4, 0 y4, 0 z4 are the items of the position matrix respectively, t ij and 0 x4, 0 y4, 0 z4 are expressed specifically as follows

[0080]

[0081]

[0082] where d i is the offset of the robotic arm link, a i is the length of the robotic arm link, q i is the joint angle of the robotic arm, α i is the rotation angle of the robotic arm, d i , a i , q i and α i are all known quantities as shown in Table 1 in the appendix, collectively referred to as the D-H parameters of the robotic arm. When the position and attitude of the end effector are known, the joint angles q of each robotic arm can be solved according to equations (4) and (5) i .

[0083] Table 1 Standard D-H Parameter Table of the Robotic Arm

[0084]

[0085] (4) Based on the joint angles of the desired robotic arm obtained in the previous step, conduct trajectory planning for the robotic arm (here, the trajectory refers to the functional variation process of the joint angles of the robotic arm, so that the end effector can reach the target position along with the functional variation of the joint angles). Use a fifth-order polynomial for trajectory planning, and the specific process is as follows

[0086] The expression of the fifth-order polynomial is as follows

[0087] q(t) = a0 + a1t + a2t 2 + a3t 3 + a4t 4 + a5t 5 (13)

[0088] Its initial constraint conditions are

[0089]

[0090] Its termination constraint conditions are

[0091]

[0092] Substitute the constraint conditions in equations (7) and (8) into equation (6) to obtain a system of equations with six equations and six unknowns, and solve it to obtain the six coefficients a i (i = 1 to 6). Thus, the functions of the desired joint angles, joint angular velocities, and joint angular accelerations of the robotic arm are as follows

[0093]

[0094] (5) On the above basis, call the modules in the Robotics Toolbox in Matlab to build an inverse kinematics solution module and a trajectory planning module, which together with the previous control module and the virtual prototype adams_sub system form a co-simulation system. Its overall simulation structure is as shown in the appendix Figure 4 , and then co-simulation can be carried out to analyze the actual motion conditions of each joint of the robotic arm, the attitude results of the robot body, and the velocities and accelerations generated by it

[0095] Simulation analysis

[0096] As Figure 5 shown, according to the inverse kinematics solution, the variation laws of the desired joint angles, angular velocities, and angular accelerations are obtained. Under the action of the single-joint PD controller, the variation of each joint angle of the robotic arm is as shown in Figure 6As shown, during the operation and deployment of the robotic arm, the robot body of the floating base is affected by the coupling effect of the robotic arm deployment. Under the action of the coupling effect, the centroid displacement, centroid acceleration, angular velocity about the axis, and angular acceleration about the axis of the robot body change as Figure 7 shown.

[0097] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of various equivalent modifications or substitutions, and these modifications or substitutions should be covered within the protection scope of the present invention.

Claims

1. An Adams / Matlab co-simulation method for an underwater robot-manipulator system, characterized in that The steps are as follows: Step 1: Conduct solid modeling on each component of the underwater robot-manipulator system in the Creo 3D modeling software, assemble them in the Creo software to obtain the assembly of the underwater robot-manipulator system, and save it as a Parasolid (*.x_t) format file; Step 2: Create a new model in Adams and import the Parasolid (*.x_t) format file generated in the previous step into the model; Step 3: Define the material properties of each component of the newly imported model, and automatically solve by Adams to obtain the mass characteristics, inertia properties, and centroid positions of each component; Step 4: Apply constraints to each joint according to the actual motion of the manipulator and add kinematic pairs; the kinematic pairs include fixed pairs and revolute pairs; Step 5: Apply driving torques to each kinematic pair, which are subsequently defined as input variables and used as the input of Adams in the Matlab co-simulation; Step 6: Use the Adams / View module to experimentally run the simulation of the underwater robot-manipulator system under the action of gravity, and confirm that the mechanical system modeling is correct according to the simulation results; Step 7: Add hydrodynamic forces to the manipulator and use the three-component generalized force in Adams to simulate the hydrodynamic forces received by the manipulator; Step 8: Determine the input and output variables of Adams. Before defining the input variables, change the driving input used for experimental analysis in Step 6 to 0. The input variables are the driving torques of each rotating joint of the manipulator, and the output is the change in the joint angles of each joint of the manipulator; Step 9: In the Adams / Controls module, select the input and output unit variables established before, select the co-simulation control software as Matlab, the analysis type is nonlinear, and the solver selects the interactive FORTRAN, so as to generate four files, namely.m,.cmd,.adm, and.txt, for Matlab to load and call; Step 10: Run the.m file in Matlab to load the relevant parameters and input and output of the model, and input adams_sys, that is, the Adams-Matlab interface command, in the command line to display the module window of adams_sys; Step 11: Drag the adams_sub module and the output module in the module window of adams_sys to the newly created Simulink window and set the software interactive simulation parameters; Step 12: Conduct control system modeling in the newly created Simulink window in Step 11. The control system includes the trajectory generator of the manipulator, the PD controller, and the attitude feedback system of the system. The trajectory generator includes inverse kinematics solution and trajectory planning; Step 13: Conduct mathematical modeling on the inverse kinematics solution part in the trajectory generator of the manipulator to obtain the attitude of the manipulator when the end effector of the manipulator reaches the desired position, that is, the desired joint angles of each joint of the manipulator; Step 14: Conduct trajectory planning based on the desired joint angles obtained in Step 13 to obtain the function change process of each joint angle of the manipulator; Step 15: Take the function change process of each joint angle calculated in Step 14 as the input, i.e., the desired trajectory, subtract it from the attitude feedback information of the output actual trajectory to obtain the error of the joint angle, and input it into the PD controller. The torque output under the action of the PD controller directly acts on the robot-manipulator system, so that the system moves to form a complete simulation system; Set the Matlab simulation parameters and perform the co-simulation of the underwater robot-manipulator system to obtain the actual change process of the joint angles of the manipulator, the change of the robot attitude under the coupling effect, and the generated speed and acceleration.

2. The Adams / Matlab co-simulation method for the underwater robot-manipulator system according to claim 1, wherein In Step 7, the hydrodynamic force is calculated using the approximate empirical formula, the Morison formula, and its expression is as follows: where ρ is the fluid density, D is the equivalent diameter of the robotic arm link, v is the fluid velocity, C D is the drag coefficient, and C M is the added mass force coefficient; When specifically adding, the three-component generalized force in Adams is used to simulate:

3. The Adams / Matlab co-simulation method for an underwater robot-manipulator system according to claim 1, wherein The process of obtaining the desired joint angles of the manipulator in Step 13 is as follows: First, the position of the end effector of the robotic arm in the base coordinates of the robotic arm is known. By solving the inverse kinematics of the robotic arm, the joint angles q = [π / 6, -π / 2, π / 2, -π / 6] are obtained. Then, each joint of the robotic arm is at the above-mentioned joint angles q i The end effector can reach the target position for operation. The specific calculation process is as follows Among them is the position and attitude matrix of the end effector of the four-degree-of-freedom robotic arm, T i i-1 (i = 1, 2, 3, 4) is the homogeneous transformation matrix from the adjacent joint coordinate system i to the coordinate system i - 1, t ij (i = 1, 2, 3; j = 1, 2, 3) are the terms of the end effector attitude matrix, 0 x4, 0 y4, 0 z4 are the terms of the position matrix respectively, t ij and 0 x4, 0 y4, 0 The specific expressions of z4 are as follows: Among them, d i is the offset of the robotic arm link, a i is the length of the robotic arm link, q i is the joint angle of the robotic arm, α i is the rotation angle of the robotic arm, d i , a i , q i and α i are all D-H parameters of the robotic arm; when the position and attitude of the end effector are known, each robotic arm joint angle q i can be solved according to equations (4) and (5).

4. The Adams / Matlab co-simulation method for an underwater robot-manipulator system according to claim 3, characterized in that The process of obtaining the function change of each joint angle of the manipulator in Step 14 is as follows: Based on the obtained desired joint angles of the manipulator, perform the trajectory planning of the manipulator. The fifth-order polynomial is used for trajectory planning, and the specific process is as follows: The expression of the fifth-order polynomial is as follows q(t) = a0 + a1t + a2t 2 + a3t 3 + a4t 4 + a5t 5 (4) Its initial constraint conditions are Its termination constraint conditions are Substituting the constraint conditions in equations (7) and (8) into equation (6) can obtain a system of equations with six equations and six unknowns. Solving this system gives six coefficients a i (i = 1 to 6); thus, the functions of the desired joint angles, joint angular velocities, and joint angular accelerations of the robotic arm are as follows 5. A computer system, characterized in that Including: One or more processors, a computer-readable storage medium for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in claim 1.

6. A computer-readable storage medium, characterized in that Stored with computer-executable instructions, the instructions are used to implement the method described in claim 1 when executed.

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