Mechanical arm vibration suppression method and system based on trajectory optimization

Abstract

The invention discloses a mechanical arm vibration suppression method and system based on trajectory optimization, and relates to the technical field of computer platform load balancing, and the mechanical arm vibration suppression method comprises the steps that the pose of a first device is solved through a first modeling method, and the spatial position of the first device is calculated based on first modeling parameters; solving an optimal inverse solution based on the pose and angle of the first device; and establishing a first device structure model through a second model theory, and analyzing and optimizing the device structure. According to the method, the D-H modeling method and the SPONG linear torsion spring model are combined, so that the control precision and stability are improved; the optimal inverse solution algorithm is combined with structure and angle constraints, so that a non-ideal solution is avoided, energy consumption is reduced, and vibration is effectively suppressed; according to the feedforward control method based on trajectory optimization, additional sensors and actuators are avoided, hardware cost and system complexity are reduced, and control time lag introduced by hardware is avoided; the improved trajectory optimization algorithm can rapidly converge in a complex task, and a local optimal solution is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of robot motion planning, and in particular to a robot arm vibration suppression method and system based on trajectory optimization. Background Art

[0002] In recent years, robotic arms, as an automated equipment, have been widely used in manufacturing, medical, aerospace and other fields. With the development of intelligent technology, the accuracy and stability requirements of robotic arms are constantly improving when performing high-precision and high-load tasks. However, in practical applications, the vibration problem of robotic arms has become a key factor limiting their performance, especially when the end effector of the robotic arm (such as clamps, welding tools, etc.) moves at high speed. Due to the vibration of flexible joints and structures, elastic deformation of the robotic arm is often caused, which leads to end oscillation. The vibration not only affects the task accuracy, but also increases the energy consumption and loss of the system. Therefore, how to suppress the vibration of the robotic arm during operation and improve its control accuracy and system stability has become a hot topic in current research.

[0003] At present, the control technology for robot arm vibration mainly focuses on feedback control and feedforward control. Feedback control technology detects vibration information in real time and compensates by installing sensors and actuators. Common methods include PID control, fuzzy control and artificial intelligence optimization algorithms. Although these methods can suppress vibration to a certain extent, they require additional hardware facilities, which increases the complexity and cost of the system. At the same time, they may introduce large control lags, affecting the response speed and stability in practical applications. On the other hand, feedforward control technology reduces vibration by optimizing trajectory planning, avoiding the use of additional hardware. Traditional trajectory optimization methods mostly use simple mathematical models or empirical formulas, which cannot fully consider the flexible characteristics of the robot arm joints and the influence of external excitations, resulting in limited optimization effects.

[0004] Most of the current trajectory optimization methods ignore the dynamic coupling relationship between the end effector of the robot arm and the flexible characteristics of the joint, and lack effective modeling of joint vibration. Even in some advanced optimization methods, there are still unsatisfactory vibration suppression effects under high load or complex task scenarios. In addition, the inverse solution algorithms in the prior art often fail to take into account structural constraints and angle limitations, resulting in non-ideal solutions in the solution process, which in turn affects the accuracy of the trajectory and the stability of execution. In contrast, the trajectory optimization method based on the DH modeling method combined with the SPONG linear torsion spring model proposed in the present invention not only fully considers the kinematics of the robot arm and the dynamic characteristics of the flexible joints, but also ensures the minimization of the joint angles through the optimal inverse solution algorithm. The optimization process is more accurate, thereby effectively improving the vibration suppression effect and having higher computational efficiency. Summary of the invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the technical problem solved by the present invention is: the existing robot arm vibration suppression method has complex feedback control hardware, insufficient feedforward control effect, and inaccurate vibration model, and the problem of how to achieve efficient vibration suppression under high load and complex task conditions.

[0007] To solve the above technical problems, the present invention provides the following technical solutions: a method for suppressing vibration of a robotic arm based on trajectory optimization, comprising solving the posture of a first device through a first modeling method, and calculating the spatial position of the first device based on first modeling parameters; solving the optimal inverse solution based on the posture and angle of the first device; establishing a structural model of the first device through a second model theory, and analyzing and optimizing the device structure.

[0008] As a preferred solution of the robot arm vibration suppression method based on trajectory optimization described in the present invention, wherein: solving the posture of the first device through the first modeling method includes using the first modeling method to obtain the posture of the first device through matrix calculation.

[0009] As a preferred solution of the robot arm vibration suppression method based on trajectory optimization described in the present invention, wherein: the calculation of the spatial position of the first device based on the first modeling parameters includes establishing a device kinematic model according to the first modeling parameters, and calculating the spatial position of the first device.

[0010] As a preferred solution of the robot arm vibration suppression method based on trajectory optimization described in the present invention, wherein: the solving of the optimal inverse solution includes using a first algorithm to avoid inconsistencies based on the solved posture and angle of the first device, and selecting the optimal solution through a second algorithm.

[0011] As a preferred solution of the robot arm vibration suppression method based on trajectory optimization described in the present invention, wherein: solving the optimal inverse solution also includes selecting the optimal solution through a second algorithm.

[0012] As a preferred solution of the robot arm vibration suppression method based on trajectory optimization described in the present invention, wherein: establishing the structural model of the first device through the second model theory includes establishing the structural dynamics equation of the first device through the second model theory.

[0013] As a preferred solution of the robot arm vibration suppression method based on trajectory optimization described in the present invention, wherein: the analysis and optimization of the device structure includes calculating the vibration response of the first device structure according to the influencing factors, and performing vibration analysis; simulating and analyzing the structural deformation under different working postures, determining the influencing factors of the vibration of the first device, and analyzing the influence of the elastic deformation of the structure on the vibration of the first device based on the established first device structural model; by analyzing the structural vibration characteristics, determining the main factors affecting the vibration, and then optimizing the vibration suppression method.

[0014] Another object of the present invention is to provide a robot arm vibration suppression system based on trajectory optimization, which can accurately calculate the end position of the robot arm and optimize the joint inverse solution by combining the DH modeling method with the SPONG linear torsion spring model, thereby effectively reducing the vibration amplitude and solving the technical problem that the joint flexibility and dynamic coupling effects cannot be fully considered in the prior art.

[0015] As a preferred solution of the robot arm vibration suppression system based on trajectory optimization described in the present invention, it includes: a spatial position calculation module, an optimal inverse solution module, and an analysis and optimization module; the spatial position calculation module is used to solve the posture of the first device through a first modeling method, and calculate the spatial position of the first device based on the first modeling parameters; the optimal inverse solution module is used to solve the optimal inverse solution based on the posture and angle of the first device; the analysis and optimization module is used to establish a structural model of the first device through a second model theory, and analyze and optimize the device structure.

[0016] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement a method for suppressing vibration of a robot arm based on trajectory optimization.

[0017] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a robot arm vibration suppression method based on trajectory optimization.

[0018] Beneficial effects of the invention: The robot arm vibration suppression method based on trajectory optimization provided by the invention can accurately model the flexible joints and vibration characteristics of the robot arm by combining the robot arm kinematic model based on the DH modeling method and the SPONG linear torsion spring model, thereby optimizing the joint motion trajectory, which not only reduces the terminal vibration, but also improves the control accuracy and stability of the robot arm; through the optimal inverse solution algorithm, combined with structural constraints and angle constraints, the optimal joint solution is selected, avoiding the non-ideal solution that occurs in the traditional inverse solution method, so that the robot arm not only minimizes the energy consumption when performing tasks, but also effectively suppresses unnecessary vibrations, thereby improving the operating efficiency and accuracy; through trajectory optimization The feedforward control method not only avoids the use of additional sensors and actuators, reduces hardware costs and system complexity, but also avoids the control lag introduced by hardware and improves the system response speed; through the improved trajectory optimization algorithm, the particle swarm algorithm and the genetic algorithm are combined, which can not only converge quickly in a variety of complex tasks, but also avoid the local optimal solution problem that may occur in traditional algorithms, and provide more reliable and efficient optimization results; the present invention can not only adapt to different working environments and load changes, but also deal with the vibration problem of the robot arm in high-precision tasks, such as assembly, welding and other operations, to ensure that the robot arm can still maintain high stability and control accuracy in a dynamically changing environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0020] Figure 1 An overall flow chart of a robot arm vibration suppression method based on trajectory optimization provided in the first embodiment of the present invention.

[0021] Figure 2 An algorithm flow chart of a robot arm vibration suppression method based on trajectory optimization provided in the first embodiment of the present invention.

[0022] Figure 3 A simulation comparison diagram of an improved GAPSO, a standard PSO and a chaotic PSO for a robot arm vibration suppression method based on trajectory optimization provided in the second embodiment of the present invention.

[0023] Figure 4 A vibration displacement diagram of the end trajectory in the X direction of a robot arm vibration suppression method based on trajectory optimization provided in the second embodiment of the present invention.

[0024] Figure 5A vibration displacement diagram of the end trajectory in the Y direction of a robot arm vibration suppression method based on trajectory optimization provided in the second embodiment of the present invention.

[0025] Figure 6 A vibration displacement diagram of the end trajectory in the Z direction of a robot arm vibration suppression method based on trajectory optimization provided in the second embodiment of the present invention.

[0026] Figure 7 An overall flow chart of a robot arm vibration suppression system based on trajectory optimization provided in the third embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0028] Example 1, reference Figure 1-Figure 2 , as an embodiment of the present invention, provides a robot arm vibration suppression method based on trajectory optimization, comprising:

[0029] S1: Solve the posture of the first device through a first modeling method, and calculate the spatial position of the first device based on first modeling parameters.

[0030] Furthermore, solving the posture of the first device through the first modeling method includes using the first modeling method to obtain the posture of the first device through matrix calculation.

[0031] The method used in the first modeling method may be the DH modeling method, the neural network modeling method, or other modeling methods that can solve the position and posture of the first device.

[0032] The first device includes but is not limited to a robotic arm end.

[0033] It should be noted that calculating the spatial position of the first device based on the first modeling parameters includes establishing a device kinematic model according to the first modeling parameters, and calculating the spatial position of the first device.

[0034] It should also be noted that in the embodiment of the present application, the first modeling method is the DH modeling method, and the specific steps are:

[0035] Using the DH modeling method, the DH matrix is ​​solved according to the DH parameters of the robot arm, all matrices are multiplied sequentially, and then the end position of the robot arm is solved to obtain the kinematic model of the robot arm.

[0036] The kinematic model of the robot arm is expressed as:

[0037]

[0038] in, represents the transformation matrix from the base coordinate system to the first link coordinate system, represents the rotation matrix from the base coordinate system to the end effector, represents the translation vector from the base coordinate system to the end effector, and q represents the joint angle.

[0039] It should also be noted that, in an optional embodiment, the first modeling method is a neural network modeling method, and the specific steps are:

[0040] Collect the joint rotation angles of the robotic arm. According to the experimental measurement data, collect the spatial coordinates and orientation information of the end position of the robotic arm. Annotate the spatial coordinates and orientation information corresponding to each set of joint rotation angles as the training data set of the neural network. Build the robotic arm kinematic model by using a feedforward neural network. The input layer receives the rotation angles of each joint of the robotic arm. Assuming that the robotic arm has z' degrees of freedom, the size of the input layer is z', and the output layer gives the coordinates and posture information of the end position of the robotic arm.

[0041] S2: Based on the position and angle of the first device, find the optimal inverse solution.

[0042] Furthermore, solving the optimal inverse solution includes using a first algorithm to avoid inconsistencies based on the solved state and angle of the first device, and selecting the optimal solution through a second algorithm.

[0043] It should be noted that finding the optimal inverse solution also includes selecting the optimal solution through a second algorithm.

[0044] It should also be noted that in the embodiment of the present application, the first algorithm is an inverse solution algorithm; the second algorithm is an improved GAPSO algorithm, and the specific steps are:

[0045] Under the premise of knowing the end position of the robotic arm, the inverse solution algorithm is used to solve the rotation angle value of each joint. The structural constraints and angle constraints of the robotic arm are considered to avoid unreasonable solutions. According to the principle of minimum joint variables, the optimal solution is selected through the improved GAPSO algorithm to ensure that the robotic arm minimizes energy consumption and oscillation when performing tasks and improves operation accuracy.

[0046] The optimal inverse solution selection principle is expressed as:

[0047]

[0048] Among them, x represents the joint variable, represents the joint variable of the i-th joint at time t, λ i represents the motion cost coefficient of the i-th joint. The larger the load, the larger the λ value. i,min represents the minimum variable of the i-th joint, x i,max Represents the maximum variable of the i-th joint.

[0049] The improved GAPSO algorithm includes using a particle swarm algorithm and introducing its ideas into a genetic algorithm, and improving the genetic operator. First, the selection operator of the genetic algorithm arranges the individuals in descending order according to the size of the fitness value, removes a quarter of the individuals with lower fitness values, copies one-third of the individuals with middle fitness values ​​among the remaining individuals to the next generation and retains the other two parts of individuals to re-form a new population, and then the particle swarm algorithm updates the speed and position of the individuals to further mature the individuals. The crossover algorithm adopts an elite competition strategy, randomly selects two groups, each with two individuals, selects two individuals with high fitness values ​​in the two groups for arithmetic crossover, and the remaining two individuals are again updated with the PSO algorithm for speed and position. Finally, the particle swarm idea is introduced into the mutation operator to mutate the individuals that have completed the crossover operation.

[0050] The solution of each trajectory point is completed in the joint space, and the objective function and constraints are converted into logical terms and substituted into the matrix form of quintic polynomial. The improved GAPSO algorithm is used to find the optimization step to ensure that it is updated to the optimal value. When the number of iterations reaches the maximum number of cycles, the iteration is stopped and the result is output.

[0051] The improved GAPSO algorithm optimization steps include:

[0052] S201: Initialize relevant parameters, the number of individuals in the population pop_size, generate pop_size individuals,

[0053] S202: Calculate the corresponding fitness value.

[0054] S203: Let gen=1.

[0055] S204: If gen≤max_gen, execute S205, otherwise execute S206.

[0056] S205: Arrange all individuals in descending order according to their fitness values.

[0057] S206: Delete the 1 / 4 individuals with the worst fitness values.

[0058] S207: Copy the 1 / 3 individuals with the middle fitness among the remaining three equal parts of individuals and keep the other two individuals to the next generation to form a new population.

[0059] S208: Let k=1.

[0060] S209: If k≤max_k, go to S210, otherwise go to S211.

[0061] S210: According to and Update the speed and position of the individual, expressed as:

[0062]

[0063] Where i = 1, 2, ..., m, represents the velocity of the ith particle at the n+1th iteration, represents the position of the ith particle at the n+1th iteration, w represents the weight coefficient, n represents the number of iterations, c1 and c2 represent different acceleration constants, r1 and r2 represent random numbers (0,1), represents the best position found by the i-th particle in history, represents the global optimal position found in the entire group, and the speed of the particle cannot exceed the maximum speed V max , k=k+1, go to execute S209.

[0064] S211: Two individuals form a group, two groups are selected, and the two individuals with higher fitness values ​​in the two groups are subjected to crossover and mutation operations.

[0065] S212: Update the speed and position of the remaining two individuals again.

[0066] S213: Merge the mutated particles and the particles that undergo PSO evolution again to form new pop_size individuals.

[0067] S214: gen=gen+1, go to execute S204.

[0068] S215: Output the obtained optimal solution and function value.

[0069] S3: Establish the first device structure model through the second model theory to analyze and optimize the device structure.

[0070] Furthermore, establishing the structural model of the first device through the second model theory includes establishing the structural dynamics equation of the first device through the second model theory.

[0071] The second model theory may be a linear torsion spring model theory based on SPONG, or a SLSM standard linear solid model theory, or other theories suitable for constructing the structural model of the first device.

[0072] The first device structure includes but is not limited to a flexible joint of a robotic arm.

[0073] It should be noted that analyzing and optimizing the device structure includes calculating the vibration response of the first device structure according to the influencing factors and performing vibration analysis;

[0074] The structural deformation under different working conditions is simulated and analyzed to determine the influencing factors of the vibration of the first device. Based on the established structural model of the first device, the influence of the elastic deformation of the structure on the vibration of the first device is analyzed. By analyzing the structural vibration characteristics, the main factors affecting the vibration are determined, and then the vibration suppression method is optimized.

[0075] It should also be noted that in the embodiment of the present application, the second model theory is based on the linear torsion spring model theory of SPONG, and the specific steps are: based on the SPONG linear torsion spring theory, the flexible joint of the robot arm is equivalent to a torsion spring, and a flexible joint model of the robot arm is established. The dynamic equation of the flexible joint is expressed as:

[0076]

[0077] Where q represents the joint rotation angle, represents the angular velocity, represents angular acceleration, θ represents the rotor angle, represents the rotor angular acceleration, M(q) represents the inertia of the robot arm, G(q) represents the gravity vector, represents the Coriolis force matrix, K represents the joint torsional stiffness, J represents the moment of inertia matrix, and τ represents the torque generated by the motor on the arm.

[0078] Calculate the vibration response of the robot arm joints and perform vibration analysis.

[0079] In order to analyze the cause of the mechanical arm's body vibration, the system vibration differential equation is established. With q≈θ in G(q), The oscillating differential equation is expressed as:

[0080]

[0081] Let x = q-θ, and simplify the vibration differential equation to an ordinary differential equation, which can be expressed as:

[0082]

[0083] Considering the equation as a multi-degree-of-freedom forced vibration equation, we can solve it to get

[0084] Where χ is the scaling factor, represents a feature vector.

[0085] Let x = φy, φ is the eigenvector matrix corresponding to y, y is the modal coordinate, and we get

[0086] in, M p =φ T Mφ represents the mass matrix, K p =φ T Kφ represents the stiffness matrix.

[0087] It is concluded that

[0088] in, represents displacement acceleration, and P represents external excitation force.

[0089] The formula has two sets of solutions, one for homogeneous solutions and one for non-homogeneous solutions. The expressions of the solutions are:

[0090] y j (t) = y 1j (t)+y 2j (t)

[0091]

[0092] Among them, y j (t) represents the total solution, y 1j (t) represents the homogeneous solution, y 2j (t) represents a non-homogeneous solution, a j 、b j Represent different constants, represents angular frequency, and t represents time.

[0093] The deformation of the flexible joint is determined by the joint modal coordinates. The input of the joint is solved through the output data of the motor, and the change in the joint end position and acceleration can also be solved.

[0094] In the process of solving the steady-state solution of the system, when the system produces a small vibration, the non-homogeneous part is solved and expressed as:

[0095]

[0096] Using elastic volatility β i ,The terminal vibration caused by the elastic deformation of the joint is quantitatively analyzed to ensure that the suppression method can effectively reduce the vibration amplitude.

[0097] The vibration of the end of the robot arm is mainly manifested in the speed change area. In this area, the changes of gravity and Coriolis force can be ignored, and only the influence of the change of inertial force is considered, that is, The vibration is improved by changing the rate of change of external excitation and introducing the joint elastic fluctuation rate β i , expressed as:

[0098]

[0099] In the optimized vibration suppression trajectory, the vibration at the end of the robot arm is significantly reduced by guiding the joint acceleration change curve to be smoother.

[0100] It should also be noted that, in an optional embodiment, the second model theory is the SLSM standard linear solid model theory, and the specific steps are: determine the elasticity and damping characteristics of the robotic arm joints, use the standard linear solid model to describe the dynamic behavior of the joints, construct linear dynamic equations, establish the flexible model joints of the robotic arm, calculate the displacement of each joint according to the model, and combine the relative motion of each joint to obtain the position and posture of the end, obtain the dynamic response of the joint by solving the linear dynamic equations, and thus obtain the response of the entire robotic arm system, analyze the vibration modes of each joint and the end through modal analysis, identify the possible vibration frequency and resonance phenomenon of the system, and formulate optimized trajectory planning and control strategies based on the dynamic response of the robotic arm to reduce the vibration amplitude and improve accuracy and stability.

[0101] Example 2, reference Figure 3-Figure 6 , which is an embodiment of the present invention, provides a robot arm vibration suppression method based on trajectory optimization. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0102] First, in order to verify the effectiveness and feasibility of a robot vibration suppression method based on trajectory optimization, a comparison and simulation experiment was conducted on the robot vibration suppression method based on trajectory optimization and the original vibration suppression amplitude data on MATLAB. All experiments were conducted on a 13th Gen Intel(R) Core(TM) i5-13490F 2.50GHz computer with 32G memory.

[0103] Under the condition that the constraints and parameter settings are consistent, the standard particle swarm algorithm, the chaotic particle swarm algorithm and the improved GAPSO algorithm are used to conduct optimization tests on the same objective function to verify the performance of the improved GAPSO algorithm. The improved GAPSO is simulated and compared with the standard PSO and chaotic PSO. The performance is as follows Figure 3 shown.

[0104] The experimental results show that the improved GAPSO algorithm has higher optimization ability and faster computing power, and performs significantly better in dealing with high-dimensional function problems. When dealing with other functions, it can converge quickly in the first 20 generations or so, and has a greater advantage in convergence efficiency, which fully reflects the high efficiency performance of the algorithm of the present invention.

[0105] Specify the task point position in the joint space, complete the trajectory planning from the initial position point A to the position point B, sample the trajectory, analyze and process the optimized motion trajectory of the robot end, and optimize the displacement of the end trajectory in the X, Y, and Z directions before and after optimization. Figure 4-Figure 6 As shown in the figure, it can be seen that the amplitude of the optimized terminal trajectory in the X, Y, and Z directions is significantly reduced, the optimized trajectory is smoother as a whole, and the elastic vibration at the flexible joint is effectively suppressed, which proves that the vibration suppression trajectory planning method has an obvious vibration suppression effect.

[0106] Example 3, reference Figure 7 , which is an embodiment of the present invention, provides a robot arm vibration suppression system based on trajectory optimization, including a spatial position calculation module, an optimal inverse solution module, and an analysis and optimization module.

[0107] The spatial position calculation module is used to solve the posture of the first device through the first modeling method, and calculate the spatial position of the first device based on the first modeling parameters; the optimal inverse solution module is used to solve the optimal inverse solution based on the posture and angle of the first device; the analysis and optimization module is used to establish the structural model of the first device through the second model theory, and analyze and optimize the device structure.

[0108] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0109] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0110] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0111] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limited. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

[0112] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A robot arm vibration suppression method based on trajectory optimization, characterized in that: include: Solving the posture of the first device by a first modeling method, and calculating the spatial position of the first device based on the first modeling parameters; Based on the position and angle of the first device, an optimal inverse solution is obtained; The first device structure model is established through the second model theory to analyze and optimize the device structure.

2. The robot arm vibration suppression method based on trajectory optimization according to claim 1, characterized in that: Solving the position and posture of the first device by using the first modeling method includes using the first modeling method to obtain the position and posture of the first device through matrix calculation.

3. The robot arm vibration suppression method based on trajectory optimization according to claim 2, characterized in that: Calculating the spatial position of the first device based on the first modeling parameters includes establishing a device kinematic model according to the first modeling parameters, and calculating the spatial position of the first device.

4. The robot arm vibration suppression method based on trajectory optimization according to claim 3, characterized in that: The method of solving the optimal inverse solution includes using a first algorithm to avoid inconsistencies based on the solved position and angle of the first device, and selecting the optimal solution through a second algorithm.

5. The robot arm vibration suppression method based on trajectory optimization according to claim 4, characterized in that: The method of solving the optimal inverse solution also includes selecting the optimal solution through a second algorithm.

6. The robot arm vibration suppression method based on trajectory optimization according to claim 5, characterized in that: The establishing of the structural model of the first device through the second model theory includes establishing the structural dynamics equation of the first device through the second model theory.

7. The robot arm vibration suppression method based on trajectory optimization according to claim 6, characterized in that: The analyzing and optimizing the device structure includes calculating the vibration response of the first device structure according to the influencing factors and performing vibration analysis; The structural deformation under different working postures is simulated and analyzed to determine the influencing factors of the vibration of the first device. Based on the established structural model of the first device, the influence of the elastic deformation of the structure on the vibration of the first device is analyzed. By analyzing the structural vibration characteristics, the main factors affecting the vibration are determined, and then the vibration suppression method is optimized.

8. A system using the robot arm vibration suppression method based on trajectory optimization as claimed in any one of claims 1 to 7, characterized in that: Including spatial position calculation module, optimal inverse solution module, analysis and optimization module; The spatial position calculation module is used to solve the posture of the first device by a first modeling method, and calculate the spatial position of the first device based on the first modeling parameters; The optimal inverse solution module is used to solve the optimal inverse solution based on the posture and angle of the first device; The analysis and optimization module is used to establish a first device structure model through a second model theory, and analyze and optimize the device structure.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the robot arm vibration suppression method based on trajectory optimization according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the robot arm vibration suppression method based on trajectory optimization according to any one of claims 1 to 7 are implemented.

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