A method for establishing a parametric model of a biped robot based on ADAMS

Abstract

The present invention discloses a method for establishing a parametric model of a biped robot based on ADAMS. 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. S3. Set parameters for the design variables of each component in the ADAMS software. S4. Use the LOC R ELATIVE T O function to define the spatial position relationship of each component relative to the superior component. S5. Make the components perform linkage adjustment. S6. Create revolute joints at the key joint positions of the biped robot model. S7. Complete the motion planning of the biped robot. S8. Develop a user interaction interface. S9. Integrate the final model into the main menu of ADAMS to form a functional closed-loop for parameter input, simulation execution, and real-time monitoring. The present invention realizes the stable operation of the biped robot in a complex motion environment.

Description

Technical Field

[0001] The present invention relates to the technical field of robots, and particularly to a method for establishing a parametric model of a biped robot based on ADAMS. Background Art

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

[0003] Currently, the design of biped robots is mainly based on physical models and experimental tests. Each joint and component needs to be designed, adjusted, and optimized independently. During this process, designers need to manually adjust various parameters of the model to make the model maintain balance in different gait patterns. This operation is not only time-consuming and cumbersome but also prone to 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, lacking a systematic means of linkage adjustment, resulting in a slow and error-prone design process. In traditional methods, fixed-parameter models are often used, so when modifying a certain part, other components cannot be automatically adjusted, increasing the difficulty of model design and optimization.

[0004] Existing gait planning methods usually rely on complex kinematic and dynamic calculations. However, due to the real-time nature of data and the limitations of system computing power, the planning algorithm is difficult to respond quickly in practical applications. The simulation system needs to continuously adjust the joint angles and motion trajectories. Existing motion planning methods perform poorly in the face of dynamic changes and real-time feedback. In addition, in robot modeling and simulation, MATLAB and Simulink tools are mostly used to calculate motion data independently, but the combination with simulation software is not tight enough, resulting in delays and deviations in the gait data transmission process.

[0005] In summary, the existing technology has significant deficiencies in the parameter adjustment, gait planning, and simulation efficiency of biped robots. The lack of linked parametric design leads to a long design cycle and difficult debugging, while the efficiency problem of the motion planning algorithm limits the real-time control ability. In addition, the deficiencies of existing simulation interfaces and toolchains increase the complexity and usage threshold of the development process. Therefore, there is an urgent need for a new method that can improve the design efficiency, optimize motion planning, and achieve real-time debugging and control by combining parametric modeling and simulation tools to solve the above technical problems. Summary of the Invention

[0006] An object of the present invention is to propose a method for establishing a parametric 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 parametric model of a biped robot based on ADAMS according to an embodiment of the present invention includes 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 cylinders, and define independent local coordinate systems for each component;

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

[0011] S4. Based on the parametric design principle, use LOC R ELATIVE T O function to define the spatial position relationship of each component relative to the superior component;

[0012] S5. Use ORI R ELATIVE T O function to set the directions of each component so that the components are linked and adjusted;

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

[0014] S7. Import the cubic spline interpolation gait planning data generated by MATLAB, and bind it to the drive function in the biped robot model to complete the motion planning of the biped robot;

[0015] S8. Develop a user interface, including a parameter adjustment dialog box for the user 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 movement and the change of joint angles;

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

[0017] Optionally, the S2 includes the following steps:

[0018] S21. Create a left foot model: Define the local coordinate system ref_foot of the left foot with the global coordinate system MARKER G as a reference 1, Set the size parameters of the left foot, including the length foot_l, width foot_w, and thickness foot_h, and calculate the mass properties of the left foot to reflect the center of gravity change in gait simulation:

[0019]

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

[0021] S22. Create the left calf model: Based on the left foot model, construct the left calf model with ref_foot 1 as the reference coordinate system, and define the local coordinate system of the left calf ref_shank 1 . Set the length length_xi and the radius of the cylinder rad_leg of the calf. 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; is the position vector of the center of mass of the calf at time t; I shank is the moment of inertia of the calf, indicating 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: Using the local coordinate system of the left calf ref_shank 1 as a reference, construct the left thigh model, and define the local coordinate system of the left thigh ref_thigh 1 . Set the length length_kuan 1 of the left thigh and the radius joint_rad of the joint cylinder. The dynamic relationship of the left thigh is described by the Lagrangian equation:

[0025]

[0026] Among them, 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 is the generalized force, used to reflect the action of the external driving force;

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

[0028]

[0029] where 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 non - linear elastic and damping equations in ADAMS software to set the linkage relationship between components. When adjusting the parameters of a certain component, other components can automatically adjust according to the linkage rules to achieve parameter optimization in gait planning:

[0031]

[0032] where is the linkage force between components; k i represents the elastic coefficient, controlling 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, describing the magnitude of the resistance during the linkage process.

[0033] Optionally, the S4 includes the following steps:

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

[0035]

[0036] where 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; ω represents the angular frequency in gait simulation, and t is the time variable;

[0037] S42. Based on the local coordinate system of the left foot, use the LOC R ELATIVE T O function to define the positional relationship of the local coordinate system ref_shank of the left calf 1 :

[0038]

[0039] Among them, is the spatial position vector of the calf; length_huai 2 is the distance from the sole of the foot to the ankle joint; C is the elastic expansion coefficient, indicating the change in the force-bearing 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 O function to define the spatial position relationship of the local coordinate system ref_thigh of the left thigh 1 :

[0041]

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

[0043] S44. Use LOC R ELATIVE T O function to define the position relationship of the local coordinate system ref_joint of the joint cylinder between the left thigh and the calf:

[0044]

[0045] Among them, 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, the S5 includes the following steps:

[0047] S51. Use ORI R ELATIVE T O function to set the direction of the local coordinate system ref_foot of the left foot so that the left foot is aligned with the global coordinate system MARKER_G: 1 ORI_RELATIVE_TO({0,0,0},MARKER_G);

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

[0049] S52. Use ORI R ELATIVE T O function to be the local coordinate system ef_shank of the left calf 1Set the direction relative to the left foot to synchronize and coordinate the left calf and left foot 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, controlling the swinging direction of the calf relative to the left foot;

[0052] S53. Use the ORI R ELATIVE T O function to set the direction for the local coordinate system ref_thigh of the left thigh, so that the direction of the left thigh relative to the left calf is automatically adjusted according to gait changes: 1 Set the direction, so that the direction of the left thigh relative to the left calf is automatically adjusted according to 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 the ORI R ELATIVE T O function to set the direction for 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 requirements of gait planning:

[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, used to control the direction change of the joint cylinder during gait movement;

[0058] S55. Through the ORI R ELATIVE T O function to achieve the linkage adjustment between components. During gait planning, when the direction of the upper-level component changes, the lower-level component automatically makes synchronous adjustments:

[0059]

[0060] Among them, 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 unit vector of the rotation axis.

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

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

[0063] 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 );

[0064] Among them, θ joint (t) represents the joint angle at time t; a i , b i , c i , d i are interpolation coefficients; t i and t i+1 are the start and end times 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 to drive the movement of each joint with the gait data. 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 used to adjust the initial state of the joint;

[0068] S73. Set the driving function in the ADAMS environment, dynamically bind the data generated by MATLAB to each joint of the biped robot model, and make each joint change synchronously during the gait movement:

[0069]

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

[0071] S74. Set the driving torque for each joint to make the joint meet the requirements of gait planning. The expression of the driving torque is as follows:

[0072]

[0073] where 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, verify the effect of the driving function through real-time simulation and adjust the interpolation coefficient and driving torque to make the biped robot stable and coordinated during gait movement.

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

[0076] (1) The present invention adopts a parametric modeling method, uses the LOC_RELATIVE_TO and ORI_RELATIVE_TO functions to establish the spatial position and direction relationship between the components of the robot, realizes the linkage adjustment of the multi-degree-of-freedom structure. When the parameters of a certain component change, other related components will automatically adjust according to the linkage rules, thus eliminating the drawback that each component needs to be debugged one by one in the traditional method. Through the combination of the MATLAB and ADAMS toolchains, the real-time simulation support of the present invention enables designers to quickly adjust the robot model parameters and observe the adjustment effect in real time, greatly improving the model debugging efficiency and avoiding the problem of inconsistent parameters 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 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 during movement. 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. Description of the Drawings

[0078] The 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 to the present invention. In the 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 form a functional closed-loop for parameter input, simulation execution, and real-time monitoring.

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

[0093] S21. Create a left-foot model: Define the local coordinate system ref_foot of the left foot with the global coordinate system MARKER G as the reference, set the dimension parameters of the left foot, including the length foot_l, width foot_w, and thickness foot_h, and calculate the mass properties of the left foot to reflect the change of the center of gravity in the gait simulation: 1 where P

[0094]

[0095] represents the mass property of the left foot, and ρ foot is the density function of the material, indicating the density distribution of the material at different positions; material (x, y, z)

[0096] S22. Create a left-shank model: Based on the left-foot model, construct the left-shank model with ref_foot 1 as the reference coordinate system, and define the local coordinate system ref_shank of the left shank 1 . Set the length length_xi of the shank and the radius rad_leg of the cylinder. The expression of the kinetic energy during the movement is as follows:

[0097]

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

[0099] S23. Create a left-thigh model: Using the local coordinate system ref_shank of the left shank 1 as the reference, construct the left-thigh model and define the local coordinate system ref_thigh of the left thigh 1 . Set the length length_kuan of the left thigh 1 and the radius joint_rad of the joint cylinder. The dynamic relationship of the left thigh is described by the Lagrange equation:

[0100]

[0101] Among them, \(L\) is the Lagrangian function, and \(L = T\) 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, which describes the state of the left thigh during movement; \(Q\) i is the generalized force, which is used to reflect the action of the external driving force;

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

[0103]

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

[0105] S25. Use the non-linear elasticity and damping equations in the 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] Among them, is the linkage force between components; \(k\) 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 embodiment, S4 includes the following steps:

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

[0110]

[0111] Among them, 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; \(\omega\) represents the angular frequency in gait simulation, and \(t\) is the time variable;

[0112] S42. Based on the local coordinate system of the left foot, use the LOC R ELATIVE T O function to define the positional relationship of the local coordinate system ref_shank of the left lower leg 1 as follows:

[0113]

[0114] wherein, is the spatial position vector of the lower leg; length_huai 2 is the distance from the sole of the foot to the ankle joint; C is the elastic expansion coefficient, indicating the change in the force-bearing state of the lower leg; θ ankle (t) is the angle of the ankle joint changing with time;

[0115] S43. Based on the local coordinate system of the left lower leg, use the LOC R ELATIVE T O function to define the spatial positional relationship of the local coordinate system ref_thigh of the left thigh 1 as follows:

[0116]

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

[0118] S44. Use the LOC R ELATIVE T O function to define the positional relationship of the local coordinate system ref_joint of the joint cylinder between the left thigh and the lower leg:

[0119]

[0120] wherein, 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 embodiment, S5 includes the following steps:

[0122] S51. Use the ORI R ELATIVE T O function to set the direction of the local coordinate system ref_foot of the left foot 1 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 calf 1 Set the direction relative to the left foot to synchronize and coordinate the left calf and the left foot during gait movement:

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

[0126] Where θ ankle (t) is the angle of the ankle joint at time t, controlling 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 direction so that the direction of the left thigh relative to the left calf is automatically adjusted according to gait changes:

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

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

[0130] S54. Use ORI R ELATIVE T The O function is used to set 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 requirements of gait planning:

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

[0132] Where θ hip (t) is the angle of the hip joint at time t, 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 components. During gait planning, when the direction of the upper-level component changes, the lower-level components automatically perform synchronous adjustment:

[0134]

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

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

[0137] S71. Based on the gait parameters of the biped robot in the MATLAB environment, use the cubic spline interpolation method to generate gait planning data for describing the joint angles and spatial position relationships of the robot during walking. The interpolation function is as follows:

[0138] 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 );

[0139] Among them, θ joint (t) represents the joint angle at time t; a i , b i , c i , d i are interpolation coefficients; t i and t i+1 are the start and end times 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 to make the gait data drive 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 used to adjust the initial state of the joint;

[0143] S73. Set the driving function in the ADAMS environment, dynamically bind the data generated by MATLAB to each joint of the biped robot model, and make each joint change synchronously during the gait movement:

[0144]

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

[0146] S74. Set the driving torque for each joint to make the joint meet the requirements of gait planning. The expression of the driving torque 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, verify the effect of the driving function through real-time simulation and adjust the interpolation coefficient and driving torque to make the biped robot stable and coordinated during the gait movement.

[0150] Example 1:

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

[0152] The inspection task in the intelligent factory requires the robot to bypass complex equipment and obstacles, check the status of the 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. For the traditional biped robot simulation model, it is necessary to manually adjust the parameters of each joint and test its gait planning one by one, resulting in complex and time-consuming model debugging. In this embodiment, the experimenter used the method of the present invention, through parametric model design and cubic spline interpolation gait planning in ADAMS, and verified the motion performance of the robot in the intelligent factory environment.

[0153] To simulate the whole process of a robot performing an inspection task, the experimenters first established a parametric model of a biped robot in ADAMS. They used MARKER_G as the global coordinate system and created local coordinate systems for the left foot, calf, and thigh. The spatial positions of each component were defined using the LOC_RELATIVE_TO function, and the linkage direction relationship between components was set using the ORI_RELATIVE_TO function, so that when the parameters of a certain component changed, other components could be automatically adjusted to ensure 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 during the robot's stepping process and the motion trajectories of the ankle and knee joints to ensure that the robot could smoothly complete each step. The experimenters defined two typical scenarios for simulation:

[0155] The robot needed 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 could be precisely controlled. Using the parametric model of the present invention, the debugging time was 45 minutes. In contrast, the traditional method required adjusting the joint parameters one by one and conducting multiple tests, which took up to 3 hours.

[0156] In the step scenario, the robot needed to lift its leg and dynamically adjust the step length according to the step height. The experimenters adjusted the parameters of the thigh and calf to ensure that the robot could pass smoothly in the gait simulation. The traditional model could not achieve linkage adjustment, and after changing the leg length each time, it was necessary to recalculate the parameters of other components, resulting in too long a debugging time. However, after modifying the parameters of the model of the present invention, the positions and directions of each joint would be automatically synchronized and adjusted, greatly shortening the debugging cycle.

[0157] To verify the feasibility and superiority of the present invention, the experimenters designed two experimental groups and compared them using the method of the present invention and the traditional method respectively. The data are shown in Table 1:

[0158] Project The method of the present invention Traditional method Inspection and commissioning time on slippery ground 45 minutes 3 hours Commissioning time for passing steps 50 minutes 4 hours Time-consuming for gait planning simulation 10 seconds 30 seconds 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 falls Unstable, multiple falls Operation complexity of the simulation interface Simple, user-friendly Complicated, multiple calculations required

[0159] The experimenters used 5 training samples in gait planning. Based on different inspection paths and obstacle combinations, they generated the optimal gait trajectories of the robot under different conditions. Table 2 shows the data of some training samples:

[0160]

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

[0162] Through the simulation process of Embodiment 1, the experimenters verified the feasibility and effectiveness of the present invention in complex environments. Compared with traditional methods, 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 the 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 of gait planning and simulation accuracy, and provide strong technical support for the development of robots in intelligent factory patrol and other complex task scenarios.

[0163] The present invention adopts a parametric modeling method, uses the LOC_RELATIVE_TO and ORI_RELATIVE_TO functions to establish the spatial position and orientation relationship between the components of the robot, and realizes the linkage adjustment of the multi-degree-of-freedom structure. When the parameters of a certain component change, other related components will automatically adjust according to the linkage rules, thus eliminating the drawback that each component needs to be debugged one by one in the traditional method. Through the combination of the MATLAB and ADAMS toolchains, the real-time simulation support of the present invention enables designers to quickly adjust the parameters of the robot model and observe the adjustment effect in real time, greatly improving the model debugging efficiency and avoiding the problem of inconsistent parameters caused by manual operation errors.

[0164] The present invention uses a cubic spline interpolation algorithm to generate motion trajectory data, and calculates the relationship between joint angles and time through MATLAB. Compared with the traditional fixed gait planning method, the interpolation algorithm of the present invention can automatically adjust the interpolation coefficients according to the gait data to achieve a smooth and continuous motion trajectory. In addition, the driving function of the joints is deeply bound to the simulation system to ensure the synchronous coordination of each joint during 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.

[0165] The above is only a preferred specific embodiment 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, according to the technical solution and inventive concept of the present invention, makes equivalent substitutions or changes, and 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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