ADAMS-based method for establishing biped robot parameterized model

Abstract

The invention discloses a method for establishing a biped robot parameterized model based on an ADAMS, which comprises the following steps: S1, creating a global coordinate system MARKERG on a GROUND layer of ADAMS software, and setting an initial position as (0, 0, 0), and S2, constructing basic parts of a biped robot; s3, setting parameters for the design variable of each component in ADAMS software; s4, a LOCRELATIVETO function is used for defining the spatial position relation of all the components relative to the superior component; s5, the components are subjected to linkage adjustment; s6, creating rotation pair connection at key joint positions of the biped robot model; s7, motion planning of the biped robot is completed; s8, developing a user interaction interface; and S9, integrating the final model to a main menu of the ADAMS, and performing function closed loop of parameter input, simulation execution and real-time monitoring. According to the invention, stable operation of the biped robot in a complex motion environment is realized.

Description

Technical Field

[0001] The present invention relates to the field of robot technology, and in particular to a method for establishing a biped robot parameterized model based on ADAMS. Background Art

[0002] With the development of robotics and artificial intelligence, bipedal robots have gradually become an important research direction in the field of humanoid robots. Bipedal robots can simulate human walking patterns, and achieving a gait similar to that of humans is a research focus in the fields of many service robots and industrial robots. In terms of adaptability to complex terrain, energy-saving walking, and task execution efficiency, bipedal robots show broad application prospects. However, existing design and implementation methods face many technical challenges, especially in multi-degree-of-freedom structure design, motion planning and simulation processes.

[0003] At present, the design of bipedal robots is mainly based on physical models and experimental tests, and each joint and component needs to be independently designed, adjusted and optimized. In this process, designers need to manually adjust the parameters of the model so that the model can maintain balance in different gait modes. This operation is not only time-consuming and cumbersome, but also easy to cause model instability due to improper parameter adjustment. In addition, due to the complexity of the multi-degree-of-freedom structure, the debugging of each component usually needs to be carried out one by one, and there is a lack of systematic linkage adjustment methods, which makes the design process slow and error-prone. Traditional methods often use fixed parameter models, which makes it impossible for other components to adjust automatically when modifying a certain part, increasing the difficulty of model design and optimization.

[0004] Existing gait planning methods usually rely on complex kinematic and dynamic calculations, but due to the real-time nature of data and the limitations of system computing power, planning algorithms are difficult to respond quickly in practical applications. The simulation system needs to continuously adjust the angles and motion trajectories of each joint. Existing motion planning methods perform poorly when faced with dynamic changes and real-time feedback. In addition, in robot modeling and simulation, MATLAB and Simulink tools are mostly used to calculate motion data separately, but the lack of close integration with simulation software leads to delays and deviations in the gait data transmission process.

[0005] In summary, the existing technologies have significant deficiencies in parameter adjustment, gait planning, and simulation efficiency of bipedal robots. The lack of linked parametric design leads to long design cycles and difficult debugging, while the efficiency of motion planning algorithms limits real-time control capabilities. In addition, the deficiencies of existing simulation interfaces and tool chains increase the complexity and usage threshold of the development process. Therefore, there is an urgent need for a new method that can improve design efficiency, optimize motion planning, and achieve real-time debugging and control through the combination of parametric modeling and simulation tools to solve the above technical problems. Summary of the invention

[0006] One object of the present invention is to propose a method for establishing a parameterized model of a biped robot based on ADAMS, and the present invention realizes the stable operation of the biped robot in a complex motion environment.

[0007] A method for establishing a biped robot parameterized model based on ADAMS according to an embodiment of the present invention comprises the following steps:

[0008] S1. Create a global coordinate system MARKER_G on the GROUND layer of the ADAMS software and set the initial position to (0,0,0);

[0009] S2. Construct the basic components of the biped robot, including the left foot, calf, thigh, and joint cylinder, and define an independent local coordinate system for each component;

[0010] S3. Set the parameters of the design variables for each component in ADAMS software;

[0011] S4. Based on parametric design principles, use LOC R ELATIVE T The O function defines the spatial position relationship of each component relative to the parent component;

[0012] S5. Use ORI R ELATIVE T The O function sets the direction of each component so that the components can be adjusted in linkage;

[0013] S6. Create revolute joints at key joint positions of the biped robot model to simulate the gait motion of the biped robot;

[0014] S7, importing the cubic spline interpolation gait planning data generated by MATLAB, binding it to the driving function in the biped robot model, and completing the motion planning of the biped robot;

[0015] S8. Develop a user interaction interface, including a parameter adjustment dialog box for users to modify the structural parameters and initial angles of the biped robot; a real-time simulation window for displaying the trajectory of the biped robot's motion and changes in joint angles;

[0016] S9. Integrate the final model into the main menu of ADAMS to complete the functional closed loop of parameter input, simulation execution and real-time monitoring.

[0017] Optionally, S2 includes the following steps:

[0018] S21. Create the left foot model: through the global coordinate system MARKER G Define the local coordinate system of the left foot for the reference ref_foot 1, set the size parameters of the left foot, including length foot_l, width foot_w, and thickness foot_h, and calculate the mass properties of the left foot so that it reflects the change of center of gravity in gait simulation:

[0019]

[0020] Among them, P foot represents the mass property of the left foot, ρ material (x, y, z) is the density function of the material, which indicates the density distribution of the material at different locations;

[0021] S22. Create the left calf model: Based on the left foot model, use ref_foot 1 Construct the left shank model for the reference coordinate system and define the left shank local coordinate system ref_shank 1 , set the length of the calf length_xi and the radius of the cylinder rad_leg, the expression of kinetic energy during the movement is as follows:

[0022]

[0023] Among them, T shank (t) represents the kinetic energy of the calf model; m shank is the mass of the calf; I is the position vector of the calf mass center at time t; shank is the moment of inertia of the calf, which represents the inertia of the calf relative to the center of mass; θ(t) is the joint angle at time t;

[0024] S23. Create the left thigh model: Use the left calf local coordinate system ref_shank 1 As a reference, construct the left thigh model and define the left thigh local coordinate system ref_thigh 1 , set the length of the left thigh length_kuan 1 and the radius of the joint cylinder joint_rad, the dynamics of the left thigh are described by the Lagrange equation:

[0025]

[0026] Where L is the Lagrangian function, L = T thigh -V thigh , T thigh and V thigh Respectively represent the kinetic energy and potential energy of the left thigh model; q i is the generalized coordinate, describing the state of the left thigh during the movement; Q i It is a generalized force, used to reflect the effect of external driving force;

[0027] S24. Create a joint cylinder: Set a joint cylinder between the left thigh and left calf parts, and define the moment of inertia of the joint cylinder:

[0028]

[0029] Among them, I joint is the moment of inertia of the joint; ρ joint (r,l) is the density function of the material at the joint radius r and length l;

[0030] S25. Use nonlinear elasticity and damping equations in ADAMS software to set the linkage relationship between components. When adjusting the parameters of a certain component, other components can be automatically adjusted according to the linkage rules to achieve parameter optimization in gait planning:

[0031]

[0032] in, k is the linkage force between components; i represents the elastic coefficient, which controls the restoring force between components; q i and q 0i are the current parameter value and the initial parameter value respectively; c i is the damping coefficient, which describes the magnitude of the resistance during the linkage process.

[0033] Optionally, S4 includes the following steps:

[0034] S41. Use LOC R ELATIVE T O function is the local coordinate system ref_foot of the left foot 1 Define its spatial position relative to the global coordinate system MARKER_G, and give the center of mass of the left foot a periodic swing during gait motion:

[0035]

[0036] in, represents the spatial position vector of the left foot; foot_l and foot_w are the length and width of the left foot respectively; A and B are the amplitudes during the swing process; ω represents the angular frequency in the gait simulation, and t is the time variable;

[0037] S42, based on the local coordinate system of the left foot, use LOC R ELATIVE T The O function defines the local coordinate system ref_shank of the left shank 1 Position relationship:

[0038]

[0039] in, is the spatial position vector of the calf; length_huai 2 is the distance from the sole to the ankle joint; C is the elastic expansion coefficient, which indicates the change in the force state of the calf; θ ankle (t) is the angle of the ankle joint changing with time;

[0040] S43, based on the local coordinate system of the left calf, use LOC R ELATIVE T The O function defines the local coordinate system ref_thigh of the left thigh 1 Spatial position relationship:

[0041]

[0042] in, is the spatial position vector of the thigh; joint_length is the length of the joint; D is the adjustment coefficient of the joint force state; θ knee (t) is the time angle change of the knee joint;

[0043] S44, use LOC between left thigh and calf R ELATIVE T The O function defines the position relationship of the local coordinate system ref_joint of the joint cylinder:

[0044]

[0045] in, is the spatial position vector of the joint; E is the amplitude coefficient of the hip joint; θ hip (t) is the time angle change of the hip joint.

[0046] Optionally, S5 includes the following steps:

[0047] S51. Use ORI R ELATIVE T O function is the local coordinate system ref_foot of the left foot 1 Set its orientation so that the left foot is aligned with the global coordinate system MARKER_G:

[0048] ORI_RELATIVE_TO({0,0,0},MARKER_G);

[0049] S52. Use ORI R ELATIVE T The O function is the local coordinate system ef_shank of the left shank 1Set the direction relative to the left foot so that the left calf and left foot are synchronized during gait movement:

[0050] ORI_RELATIVE_TO({90,θ ankle (t),0},ref_foot 1 );

[0051] Among them, θ ankle (t) is the angle of the ankle joint at time t, which controls the swing direction of the calf relative to the left foot;

[0052] S53. Use ORI R ELATIVE T The O function is the local coordinate system ref_thigh of the left thigh 1 Set the orientation so that the left thigh's direction relative to the left calf automatically adjusts as the gait changes:

[0053] ORI_RELATIVE_TO({0,θ knee (t),-90},ref_shank 1 );

[0054] Among them, θ knee (t) is the angle of the knee joint at time t;

[0055] S54. Use ORI R ELATIVE T The O function sets the direction of the local coordinate system ref_joint of the joint cylinder so that the movement direction of the joint cylinder at the hip joint meets the gait planning requirements:

[0056] ORI_RELATIVE_TO({θ hip (t),0,0},ref_thigh 1 );

[0057] Among them, θ hip (t) is the angle of the hip joint at time t, which is used to control the direction change of the joint cylinder during gait movement;

[0058] S55, through ORI R ELATIVE T The O function realizes the linkage adjustment between the components. During the gait planning process, when the direction of the upper-level component changes, the lower-level component automatically adjusts synchronously:

[0059]

[0060] in, is the angular velocity vector of the i-th component at time t; θ i(t) is the rotation angle of the component; is the rotation axis unit vector.

[0061] Optionally, the S7 includes the following steps:

[0062] S71. Based on the gait parameters of the biped robot in the MATLAB environment, the cubic spline interpolation method is used to generate gait planning data to describe the relationship between the joint angles and spatial positions of the robot during walking. The interpolation function is as follows:

[0063] θ joint (t) = a i +b i (tt i )+c i (tt i ) 2 +d i (tt i ) 3 (t i ≤t <t i+1 );

[0064] Among them, θ joint (t) represents the angle of the joint at time t; a i ,b i ,c i ,d i is the interpolation coefficient; t i and t i+1 is the start and end time of the interpolation interval;

[0065] S72, import the generated gait planning data into the ADAMS environment, and bind it to the key joints in the robot model so that the gait data drives the movement of each joint. The driving function is as follows:

[0066]

[0067] Among them, q joint (t) is the joint position at time t; θ joint (t) is the joint angle generated by cubic spline interpolation; q offset (t) is the offset, which is used to adjust the initial state of the joint;

[0068] S73. Set the driving function in the ADAMS environment to dynamically bind the data generated by MATLAB to each joint of the biped robot model so that each joint changes synchronously during gait motion:

[0069]

[0070] in, is the joint state vector at time t, including the states of the ankle, knee and hip joints;

[0071] S74. Set a driving torque for each joint so that the joint meets the requirements of gait planning. The driving torque expression is as follows:

[0072]

[0073] Among them, τ joint (t) is the driving torque of the joint at time t; I joint is the moment of inertia of the joint; C joint and K joint are the damping coefficient and elastic coefficient respectively;

[0074] S75. During the gait planning process, the effect of the driving function is verified through real-time simulation and the interpolation coefficient and driving torque are adjusted to make the biped robot stable and coordinated during gait movement.

[0075] The beneficial effects of the present invention are:

[0076] (1) The present invention adopts a parametric modeling method, and uses the LOC_RELATIVE_TO and ORI_RELATIVE_TO functions to establish the spatial position and direction relationship between the various components of the robot, thereby realizing the linkage adjustment of the multi-degree-of-freedom structure. When the parameters of a certain component change, other related components will be automatically adjusted according to the linkage rules, thereby eliminating the disadvantage of each component needing to be debugged one by one in the traditional method. Through the combination of MATLAB and ADAMS tool chains, the real-time simulation support of the present invention enables designers to quickly adjust the robot model parameters and observe the adjustment effects in real time, which greatly improves the model debugging efficiency and avoids the parameter inconsistency problem caused by manual operation errors.

[0077] (2) The present invention adopts a cubic spline interpolation algorithm to generate motion trajectory data, and calculates the relationship between the change of joint angle and time through MATLAB. Compared with the traditional fixed gait planning method, the interpolation algorithm of the present invention can automatically adjust the interpolation coefficient according to the gait data to achieve a smooth and continuous motion trajectory. In addition, the driving function of the joint is deeply bound to the simulation system to ensure the synchronous coordination of each joint in motion. Through the efficient transmission of the interpolation algorithm and the MATLAB simulation results, the present invention effectively shortens the calculation time of gait planning and improves the real-time performance of the simulation response. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0079] Figure 1A flow chart of a method for establishing a parametric model of a biped robot based on ADAMS proposed by the present invention;

[0080] Figure 2 This is a schematic diagram of the local structure of a biped robot model in an ADAMS environment in a method for establishing a biped robot parameterized model based on ADAMS proposed by the present invention. DETAILED DESCRIPTION

[0081] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0082] refer to Figure 1-Figure 2 A method for establishing a parametric model of a biped robot based on ADAMS includes the following steps:

[0083] S1. Create a global coordinate system MARKER_G on the GROUND layer of the ADAMS software and set the initial position to (0,0,0);

[0084] S2. Construct the basic components of the biped robot, including the left foot, calf, thigh, and joint cylinder, and define an independent local coordinate system for each component;

[0085] S3. Set the parameters of the design variables for each component in ADAMS software;

[0086] S4. Based on parametric design principles, use LOC R ELATIVE T The O function defines the spatial position relationship of each component relative to the parent component;

[0087] S5. Use ORI R ELATIVE T The O function sets the direction of each component so that the components can be adjusted in linkage;

[0088] S6. Create revolute joints at key joint positions of the biped robot model to simulate the gait motion of the biped robot;

[0089] S7, importing the cubic spline interpolation gait planning data generated by MATLAB, binding it to the driving function in the biped robot model, and completing the motion planning of the biped robot;

[0090] S8. Develop a user interaction interface, including a parameter adjustment dialog box for users to modify the structural parameters and initial angles of the biped robot; a real-time simulation window for displaying the trajectory of the biped robot's motion and changes in joint angles;

[0091] S9. Integrate the final model into the main menu of ADAMS to complete the functional closed loop of parameter input, simulation execution and real-time monitoring.

[0092] In this implementation, S2 includes the following steps:

[0093] S21. Create the left foot model: through the global coordinate system MARKER G Define the local coordinate system of the left foot for the reference ref_foot 1 , set the size parameters of the left foot, including length foot_l, width foot_w, and thickness foot_h, and calculate the mass properties of the left foot so that it reflects the change of center of gravity in gait simulation:

[0094]

[0095] Among them, P foot represents the mass property of the left foot, ρ material (x, y, z) is the density function of the material, which indicates the density distribution of the material at different locations;

[0096] S22. Create the left calf model: Based on the left foot model, use ref_foot 1 Construct the left shank model for the reference coordinate system and define the left shank local coordinate system ref_shank 1 , set the length of the calf length_xi and the radius of the cylinder rad_leg, the expression of kinetic energy during the movement is as follows:

[0097]

[0098] Among them, T shank (t) represents the kinetic energy of the calf model; m shank is the mass of the calf; I is the position vector of the calf mass center at time t; shank is the moment of inertia of the calf, which represents the inertia of the calf relative to the center of mass; θ(t) is the joint angle at time t;

[0099] S23. Create the left thigh model: Use the left calf local coordinate system ref_shank 1 As a reference, construct the left thigh model and define the left thigh local coordinate system ref_thigh 1 , set the length of the left thigh length_kuan 1 and the radius of the joint cylinder joint_rad, the dynamics of the left thigh are described by the Lagrange equation:

[0100]

[0101] Where L is the Lagrangian function, L = T thigh -V thigh , T thigh and V thigh Respectively represent the kinetic energy and potential energy of the left thigh model; q i is the generalized coordinate, describing the state of the left thigh during the movement; Q i It is a generalized force, used to reflect the effect of external driving force;

[0102] S24. Create a joint cylinder: Set a joint cylinder between the left thigh and left calf parts, and define the moment of inertia of the joint cylinder:

[0103]

[0104] Among them, I joint is the moment of inertia of the joint; ρ joint (r,l) is the density function of the material at the joint radius r and length l;

[0105] S25. Use nonlinear elasticity and damping equations in ADAMS software to set the linkage relationship between components. When adjusting the parameters of a certain component, other components can be automatically adjusted according to the linkage rules to achieve parameter optimization in gait planning:

[0106]

[0107] in, k is the linkage force between components; i represents the elastic coefficient, which controls the restoring force between components; q i and q 0i are the current parameter value and the initial parameter value respectively; c i is the damping coefficient, which describes the magnitude of the resistance during the linkage process.

[0108] In this implementation, S4 includes the following steps:

[0109] S41. Use LOC R ELATIVE T O function is the local coordinate system ref_foot of the left foot 1 Define its spatial position relative to the global coordinate system MARKER_G, and give the center of mass of the left foot a periodic swing during gait motion:

[0110]

[0111] in, represents the spatial position vector of the left foot; foot_l and foot_w are the length and width of the left foot respectively; A and B are the amplitudes during the swing process; ω represents the angular frequency in the gait simulation, and t is the time variable;

[0112] S42, based on the local coordinate system of the left foot, use LOC R ELATIVE T The O function defines the local coordinate system ref_shank of the left shank 1 Position relationship:

[0113]

[0114] in, is the spatial position vector of the calf; length_huai 2 is the distance from the sole to the ankle joint; C is the elastic expansion coefficient, which indicates the change in the force state of the calf; θ ankle (t) is the angle of the ankle joint changing with time;

[0115] S43, based on the local coordinate system of the left calf, use LOC R ELATIVE T The O function defines the local coordinate system ref_thigh of the left thigh 1 Spatial position relationship:

[0116]

[0117] in, is the spatial position vector of the thigh; joint_length is the length of the joint; D is the adjustment coefficient of the joint force state; θ knee (t) is the time angle change of the knee joint;

[0118] S44, use LOC between left thigh and calf R ELATIVE T The O function defines the position relationship of the local coordinate system ref_joint of the joint cylinder:

[0119]

[0120] in, is the spatial position vector of the joint; E is the amplitude coefficient of the hip joint; θ hip (t) is the time angle change of the hip joint.

[0121] In this implementation, S5 includes the following steps:

[0122] S51. Use ORI R ELATIVE T O function is the local coordinate system ref_foot of the left foot 1 Set its orientation so that the left foot is aligned with the global coordinate system MARKER_G:

[0123] ORI_RELATIVE_TO({0,0,0},MARKER_G);

[0124] S52. Use ORI R ELATIVE T The O function is the local coordinate system ef_shank of the left shank 1 Set the direction relative to the left foot so that the left calf and left foot are synchronized during gait movement:

[0125] ORI_RELATIVE_TO({90,θ ankle (t),0},ref_foot 1 );

[0126] Among them, θ ankle (t) is the angle of the ankle joint at time t, which controls the swing direction of the calf relative to the left foot;

[0127] S53. Use ORI R ELATIVE T The O function is the local coordinate system ref_thigh of the left thigh 1 Set the orientation so that the left thigh's direction relative to the left calf automatically adjusts as the gait changes:

[0128] ORI_RELATIVE_TO({0,θ knee (t),-90},ref_shank 1 );

[0129] Among them, θ knee (t) is the angle of the knee joint at time t;

[0130] S54. Use ORI R ELATIVE T The O function sets the direction of the local coordinate system ref_joint of the joint cylinder so that the movement direction of the joint cylinder at the hip joint meets the gait planning requirements:

[0131] ORI_RELATIVE_TO({θ hip (t),0,0},ref_thigh 1 );

[0132] Among them, θ hip (t) is the angle of the hip joint at time t, which is used to control the direction change of the joint cylinder during gait movement;

[0133] S55, through ORI R ELATIVE TThe O function realizes the linkage adjustment between the components. During the gait planning process, when the direction of the upper-level component changes, the lower-level component automatically adjusts synchronously:

[0134]

[0135] in, is the angular velocity vector of the i-th component at time t; θ i (t) is the rotation angle of the component; is the rotation axis unit vector.

[0136] In this implementation, S7 includes the following steps:

[0137] S71. Based on the gait parameters of the biped robot in the MATLAB environment, the cubic spline interpolation method is used to generate gait planning data to describe the relationship between the joint angles and spatial positions of the robot during walking. The interpolation function is as follows:

[0138] θ joint (t) = a i +b i (tt i )+c i (tt i ) 2 +d i (tt i ) 3 (t i ≤t <t i+1 );

[0139] Among them, θ joint (t) represents the angle of the joint at time t; a i ,b i ,c i ,d i is the interpolation coefficient; t i and t i+1 is the start and end time of the interpolation interval;

[0140] S72, import the generated gait planning data into the ADAMS environment, and bind it to the key joints in the robot model so that the gait data drives the movement of each joint. The driving function is as follows:

[0141]

[0142] Among them, q joint (t) is the joint position at time t; θ joint (t) is the joint angle generated by cubic spline interpolation; q offset (t) is the offset, which is used to adjust the initial state of the joint;

[0143] S73. Set the driving function in the ADAMS environment to dynamically bind the data generated by MATLAB to each joint of the biped robot model so that each joint changes synchronously during gait motion:

[0144]

[0145] in, is the joint state vector at time t, including the states of the ankle, knee and hip joints;

[0146] S74. Set a driving torque for each joint so that the joint meets the requirements of gait planning. The driving torque expression is as follows:

[0147]

[0148] Among them, τ joint (t) is the driving torque of the joint at time t; I joint is the moment of inertia of the joint; C joint and K joint are the damping coefficient and elastic coefficient respectively;

[0149] S75. During the gait planning process, the effect of the driving function is verified through real-time simulation and the interpolation coefficient and driving torque are adjusted to make the biped robot stable and coordinated during gait movement.

[0150] Embodiment 1:

[0151] In this embodiment, the experimenters simulated an inspection task scenario in a smart factory. The scenario required a bipedal robot to walk stably in a complex workshop environment and automatically avoid obstacles through gait planning. The experimenters used the parametric model based on ADAMS of the present invention to compare with the traditional fixed parameter model, and verified the superiority of the solution proposed in the present invention in simulation efficiency, gait planning accuracy and debugging convenience under different task scenarios.

[0152] Inspection tasks in smart factories require robots to be able to bypass complex equipment and obstacles, check the status of machines, and upload real-time data. During the inspection process, the robot needs to adapt to various ground environments, such as slippery floors, steps, and narrow passages. Traditional bipedal robot simulation models require manual adjustment of the parameters of each joint and test their gait planning one by one, which makes model debugging complicated and time-consuming. In this embodiment, the experimenters used the method of the present invention to verify the robot's motion performance in the smart factory environment through parametric model design and cubic spline interpolation gait planning in ADAMS.

[0153] In order to simulate the entire process of the robot performing the inspection task, the experimenters first established a parametric model of the bipedal robot in ADAMS, used MARKER_G as the global coordinate system and created local coordinate systems for the left foot, calf and thigh. The spatial position of each component was defined by the LOC_RELATIVE_TO function, and the ORI_RELATIVE_TO function was used to set the linkage direction relationship between the components, so that when the parameters of a certain component change, other components can be automatically adjusted, thereby ensuring the stability of the robot's gait.

[0154] Next, the experimenters imported the cubic spline interpolation gait planning data generated by MATLAB into the ADAMS system. The planned gait data included the angle change curves of each joint of the robot during the step, and the motion trajectories of the ankle and knee joints, to ensure that the robot can smoothly complete each step. The experimenters defined two typical scenarios for simulation:

[0155] The robot needs to cross a slippery ground and maintain balance. The experimenters set the friction coefficient of the ground to 0.4 in the simulation system, and calculated the optimal gait through MATLAB so that the angle and frequency of each step of the robot can be accurately controlled. Using the parametric model of the present invention, the debugging time is 45 minutes. In contrast, the traditional method requires adjusting the joint parameters one by one and performing multiple tests, which takes up to 3 hours.

[0156] In the stair scenario, the robot needs to raise its legs and dynamically adjust the step length according to the step height. The experimenters adjust the parameters of the thigh and calf to ensure that the robot can pass smoothly in the gait simulation. The traditional model cannot achieve linkage adjustment. Each time the leg length is changed, the parameters of other components need to be recalculated, resulting in a long debugging time. After the parameters of the model of the present invention are modified, the position and direction of each joint will be automatically adjusted synchronously, which greatly shortens the debugging cycle.

[0157] In order to verify the feasibility and superiority of the present invention, the experimenters designed two experimental groups, respectively using the method of the present invention and the traditional method for comparison, and the data are shown in Table 1:

[0158] project Method of the present invention Traditional methods Slippery ground inspection and debugging time 45 minutes 3 hours Step through debugging time 50 minutes 4 hours Gait planning simulation time 10 seconds 30 seconds The number of automatic linkages after parameter adjustment 100 times None (manual adjustment required) Gait planning error (angle) ±0.5° +2° Gait stability in complex environments Stable, no falling Unstable, falling down many times Simulation interface operation complexity Simple and friendly Complicated, requires multiple calculations

[0159] The experimenters used five training samples in gait planning. Based on different inspection paths and obstacle combinations, they generated the optimal gait trajectory of the robot under different conditions. Table 2 shows the data of some training samples:

[0160]

[0161] The cubic spline interpolation algorithm of the present invention can generate a continuous and stable gait trajectory for each scenario through the data of the training samples. Compared with the traditional method, the present invention has significantly improved the gait simulation accuracy and response speed.

[0162] Through the simulation process of this embodiment 1, the experimenters verified the feasibility and effectiveness of the present invention in a complex environment. Compared with the traditional method, the present invention realizes rapid debugging and automatic linkage adjustment through parametric modeling and optimizes the accuracy and efficiency of gait planning with the help of cubic spline interpolation algorithm. In addition, the friendly simulation interface greatly reduces the user's usage threshold and improves the design efficiency. The method of the present invention can significantly reduce the development time in practical applications, improve the stability and simulation accuracy of gait planning, and provide strong technical support for the development of robots in smart factory inspections and other complex task scenarios.

[0163] The present invention adopts a parametric modeling method, and uses LOC_RELATIVE_TO and ORI_RELATIVE_TO functions to establish the spatial position and direction relationship between the various components of the robot, thereby realizing the linkage adjustment of the multi-degree-of-freedom structure. When the parameters of a certain component change, other related components will be automatically adjusted according to the linkage rules, thereby eliminating the disadvantage that each component needs to be debugged one by one in the traditional method. Through the combination of MATLAB and ADAMS tool chains, the real-time simulation support of the present invention enables designers to quickly adjust the robot model parameters and observe the adjustment effects in real time, which greatly improves the model debugging efficiency and avoids the parameter inconsistency problem caused by manual operation errors.

[0164] The present invention adopts a cubic spline interpolation algorithm to generate motion trajectory data, and calculates the change relationship of joint angles over time through MATLAB. Compared with the traditional fixed gait planning method, the interpolation algorithm of the present invention can automatically adjust the interpolation coefficient according to the gait data to achieve a smooth and continuous motion trajectory. In addition, the driving function of the joint is deeply bound to the simulation system to ensure the synchronous coordination of each joint in motion. Through the efficient transmission of the interpolation algorithm and MATLAB simulation results, the present invention effectively shortens the calculation time of gait planning and improves the real-time performance of the simulation response.

[0165] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for establishing a parametric model of a biped robot based on ADAMS, characterized in that: The steps include: S1. Create a global coordinate system MARKER_G on the GROUND layer of the ADAMS software and set the initial position to (0,0,0); S2. Construct the basic components of the biped robot, including the left foot, calf, thigh, and joint cylinder, and define an independent local coordinate system for each component; S3. Set the parameters of the design variables for each component in ADAMS software; S4. Based on parametric design principles, use LOC R ELATIVE T The O function defines the spatial position relationship of each component relative to the parent component; S5. Use ORI R ELATIVE T The O function sets the direction of each component so that the components can be adjusted in linkage; S6. Create revolute joints at key joint positions of the biped robot model to simulate the gait motion of the biped robot; S7, importing the cubic spline interpolation gait planning data generated by MATLAB, binding it to the driving function in the biped robot model, and completing the motion planning of the biped robot; S8. Develop a user interaction interface, including a parameter adjustment dialog box, for users to modify the structural parameters and initial angles of the biped robot; Real-time simulation window, used to display the trajectory and joint angle changes of the biped robot; S9. Integrate the final model into the main menu of ADAMS to complete the functional closed loop of parameter input, simulation execution and real-time monitoring.

2. The method for establishing a parametric model of a biped robot based on ADAMS according to claim 1, characterized in that: The S2 comprises the following steps: S21. Create the left foot model: through the global coordinate system MARKER G Define the local coordinate system ref_foot1 of the left foot as the reference, set the size parameters of the left foot, including length foot_l, width foot_w, thickness foot_h, and calculate the mass properties of the left foot so that it reflects the change of the center of gravity in the gait simulation: Among them, P foot represents the mass property of the left foot, ρ material (x, y, z) is the density function of the material, which indicates the density distribution of the material at different locations; S22. Create a left calf model: Based on the left foot model, build a left calf model with ref_foot1 as the reference coordinate system, define the left calf local coordinate system ref_shank1, set the calf length length_xi and the cylinder radius rad_leg, and the expression of kinetic energy during the movement is as follows: Among them, T shank (t) represents the kinetic energy of the calf model; m shank is the mass of the calf; I is the position vector of the calf mass center at time t; shank is the moment of inertia of the calf, which represents the inertia of the calf relative to the center of mass; θ(t) is the joint angle at time t; S23. Create a left thigh model: Use the left calf local coordinate system ref_shank1 as a reference to construct the left thigh model, define the left thigh local coordinate system ref_thigh1, set the length of the left thigh length_kuan1 and the radius of the joint cylinder joint_rad, and the dynamic relationship of the left thigh is described by the Lagrange equation: Where L is the Lagrangian function, L = T thigh -V thigh , T thigh and V thigh Respectively represent the kinetic energy and potential energy of the left thigh model; q i is the generalized coordinate, describing the state of the left thigh during the movement; Q i It is a generalized force, used to reflect the effect of external driving force; S24. Create a joint cylinder: Set a joint cylinder between the left thigh and left calf parts, and define the moment of inertia of the joint cylinder: Among them, I joint is the moment of inertia of the joint; ρ joint (r,l) is the density function of the material at the joint radius r and length l; S25. Use nonlinear elasticity and damping equations in ADAMS software to set the linkage relationship between components. When adjusting the parameters of a certain component, other components can be automatically adjusted according to the linkage rules to achieve parameter optimization in gait planning: in, k is the linkage force between components; i represents the elastic coefficient, which controls the restoring force between components; q i and q 0i are the current parameter value and the initial parameter value respectively; c i is the damping coefficient, which describes the magnitude of the resistance during the linkage process.

3. The method for establishing a parametric model of a biped robot based on ADAMS according to claim 1, characterized in that: The S4 comprises the following steps: S41. Use LOC R ELATIVE T The O function defines the spatial position relationship of the local coordinate system ref_foot1 of the left foot relative to the global coordinate system MARKER_G, and gives the center of mass of the left foot a periodic swing during gait motion: in, represents the spatial position vector of the left foot; foot_l and foot_w are the length and width of the left foot respectively; A and B are the amplitudes during the swing process; ω represents the angular frequency in the gait simulation, and t is the time variable; S42, based on the local coordinate system of the left foot, use LOC R ELATIVE T The O function defines the position relationship of the local coordinate system ref_shank1 of the left shank: in, is the spatial position vector of the calf; length_huai2 is the distance from the sole to the ankle joint; C is the elastic expansion coefficient, which indicates the change of the force state of the calf; θ ankle (t) is the angle of the ankle joint changing with time; S43, based on the local coordinate system of the left calf, use LOC R ELATIVE T The O function defines the spatial position relationship of the local coordinate system ref_thigh1 of the left thigh: in, is the spatial position vector of the thigh; joint_length is the length of the joint; D is the adjustment coefficient of the joint force state; θ knee (t) is the time angle change of the knee joint; S44, use LOC between left thigh and calf R ELATIVE T The O function defines the position relationship of the local coordinate system ref_joint of the joint cylinder: in, is the spatial position vector of the joint; E is the amplitude coefficient of the hip joint; θ hip (t) is the time angle change of the hip joint.

4. The method for establishing a parametric model of a biped robot based on ADAMS according to claim 1, characterized in that: The S5 The following steps are involved: S51. Use ORI R ELATIVE T The O function sets the orientation of the local coordinate system ref_foot1 of the left foot so that it is aligned with the global coordinate system MARKER_G: ORI_RELATIVE_TO({0,0,0},MARKER_G); S52. Use ORI R ELATIVE T The O function sets the direction of the local coordinate system ef_shank1 of the left shank relative to the left foot, so that the left shank and the left foot are synchronized and coordinated in the gait movement: ORI_RELATIVE_TO({90,θ ankle (t),0},ref_foot1); Among them, θ ankle (t) is the angle of the ankle joint at time t, which controls the swing direction of the calf relative to the left foot; S53. Use ORI R ELATIVE T The O function sets the orientation of the local coordinate system ref_thigh1 of the left thigh so that the orientation of the left thigh relative to the left calf automatically adjusts according to gait changes: ORI_RELATIVE_TO({0,θ knee (t),-90},ref_shank1); Among them, θ knee (t) is the angle of the knee joint at time t; S54. Use ORI R ELATIVE T The O function sets the direction of the local coordinate system ref_joint of the joint cylinder so that the movement direction of the joint cylinder at the hip joint meets the gait planning requirements: ORI_RELATIVE_TO({θ hip (t),0,0},ref_thigh1); Among them, θ hip (t) is the angle of the hip joint at time t, which is used to control the direction change of the joint cylinder during gait movement; S55, through ORI R ELATIVE T The O function realizes the linkage adjustment between the components. During the gait planning process, when the direction of the upper-level component changes, the lower-level component automatically adjusts synchronously: in, is the angular velocity vector of the i-th component at time t; θ i (t) is the rotation angle of the component; is the rotation axis unit vector.

5. The method for establishing a parametric model of a biped robot based on ADAMS according to claim 1, characterized in that: The S7 comprises the following steps: S71. Based on the gait parameters of the biped robot in the MATLAB environment, the cubic spline interpolation method is used to generate gait planning data to describe the relationship between the joint angles and spatial positions of the robot during walking. The interpolation function is as follows: θ joint (t)=a i +b i (t-t i )+c i (t-t i ) 2 +d i (t-t i ) 3 (t i ≤t<t i+1 ); Among them, θ joint (t) represents the angle of the joint at time t; a i ,b i ,c i ,d i is the interpolation coefficient; t i and t i+1 is the start and end time of the interpolation interval; S72, import the generated gait planning data into the ADAMS environment, and bind it to the key joints in the robot model so that the gait data drives the movement of each joint. The driving function is as follows: Among them, q joint (t) is the joint position at time t; θ joint (t) is the joint angle generated by cubic spline interpolation; q offset (t) is the offset, which is used to adjust the initial state of the joint; S73. Set the driving function in the ADAMS environment to dynamically bind the data generated by MATLAB to each joint of the biped robot model so that each joint changes synchronously during gait motion: in, is the joint state vector at time t, including the states of the ankle, knee and hip joints; S74. Set a driving torque for each joint so that the joint meets the requirements of gait planning. The driving torque expression is as follows: Among them, τ joint (t) is the driving torque of the joint at time t; I joint is the moment of inertia of the joint; C joint and K joint are the damping coefficient and elastic coefficient respectively; S75. During the gait planning process, the effect of the driving function is verified through real-time simulation and the interpolation coefficient and driving torque are adjusted to make the biped robot stable and coordinated during gait movement.

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