A mechanical arm trajectory planning method, system, device and storage medium based on an improved particle swarm algorithm

Abstract

The application discloses a mechanical arm trajectory planning method, system, device and storage medium based on an improved particle swarm algorithm, S1, a robot kinematics model is established; S2, tent chaos mapping, simulated annealing algorithm, adaptive inertia weight and learning rate are brought into a standard particle swarm algorithm, based on the robot kinematics model, an improved particle swarm algorithm is used to solve the running time in the movement trajectory planning of the mechanical arm; S3, a 3-5-3 interpolation function is used for trajectory planning on a robot joint space, optimal values solved by the improved particle swarm algorithm are brought into the 3-5-3 interpolation function, and the optimal trajectory of the mechanical arm is obtained. The utilization efficiency of the overall joint of the industrial robot is remarkably improved, and the mechanical arm trajectory planning optimization scheme oriented by the time optimization is realized.

Description

Technical Field

[0001] This invention belongs to the field of industrial robots and relates to a method, system, device and storage medium for planning the trajectory of a robotic arm based on an improved particle swarm optimization algorithm. Background Technology

[0002] Industrial robots play a vital role in modern industrial production. With slowing population growth, a shrinking working-age population, and rising labor costs, the importance of industrial robots is even more pronounced. However, traditional robot trajectory planning and optimization algorithms are prone to getting stuck in local optima and suffering from low search efficiency during the search for the optimal solution. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of the prior art and provide a robotic arm trajectory planning method, system, device and storage medium based on an improved particle swarm optimization algorithm, which significantly improves the overall joint utilization efficiency of industrial robots and realizes a time-optimal robotic arm trajectory planning optimization scheme.

[0004] To achieve the above objectives, the present invention employs the following technical solution:

[0005] A robotic arm trajectory planning method based on an improved particle swarm optimization algorithm includes the following steps:

[0006] S1, Establish the robot's kinematic model;

[0007] S2 incorporates the tent chaotic mapping, simulated annealing algorithm, adaptive inertia weights, and learning rate into the standard particle swarm optimization algorithm. Based on the robot's kinematics model, the improved particle swarm optimization algorithm is used to solve the running time in the motion trajectory planning of the robotic arm.

[0008] S3. The 3-5-3 interpolation function is used to plan the trajectory in the robot joint space. The optimal value obtained by the improved particle swarm algorithm is substituted into the 3-5-3 interpolation function to obtain the optimal trajectory of the robotic arm.

[0009] Preferably, the process of S1 is as follows:

[0010] S11. Model the robot body;

[0011] S12. Model the pose of the robotic arm using the improved DH method;

[0012] S13. Establish the robot kinematics model based on the robot arm pose model.

[0013] Furthermore, the S12 process is as follows: establish a coordinate system for the links between each joint, and use a homogeneous transformation matrix to transform the coordinates of multiple series links.

[0014] Preferably, the process of solving for the time parameters in the motion trajectory planning of the robotic arm using the improved particle swarm optimization algorithm is as follows:

[0015] S21. Determine the three curves between the starting point and target point of the robotic arm and the two interpolation points in the weld inspection task;

[0016] S22. Initialize the improved particle algorithm parameters, including standard particle swarm parameters, upper bound parameter Ub and lower bound parameter Lb of the search space of the tent chaos map, and the start temperature Tstar, end temperature Tend, and current temperature Tcur of the simulated annealing algorithm.

[0017] S23. Use the tent chaotic map to initialize the population position and randomly initialize the vector of each particle.

[0018] S24. Traverse each particle, and based on the current particle position, solve the interpolation function of the corner joint in the three-segment curve. Determine whether the corner joint velocity and angular acceleration meet the constraints based on the robot's physical limitations.

[0019] S25. If the constraints are not met, then abandon the point; if the constraints are met, then calculate the current particle fitness; if the fitness is less than the particle extreme value, then update the particle extreme value position and fitness.

[0020] S26. If the fitness is greater than the particle extreme value, then perform the simulated annealing optimization step to determine whether to accept the particle position.

[0021] S27. Update the global optimum;

[0022] S28. Update particle velocity and next position using adaptive inertia weights and learning rate;

[0023] S29. Repeat S25 to S28 and determine whether the termination condition is met. If it is met, exit. Obtain the running time of the robotic arm joints in the three motion trajectories.

[0024] Furthermore, the specific process of S23 is as follows: Initialize the population using the tent chaotic mapping to determine the chaotic parameter α; based on the particle population boundary, determine the initial particle generation formula as X = Lb + rand(N,D)*(Ub-Lb), where N is the initial population size, D is the particle dimension, Ub is the upper bound of the particle search, and Lb is the lower bound of the particle search. Based on the particle generation formula, obtain the general solution for the tent initialization population: Where x(n) = x(t) / Lb, and x(t) is the initial position of the previous particle.

[0025] Furthermore, the formula for calculating S28 is as follows:

[0026]

[0027] Where ω is the inertia weight, and k is the current iteration number. max The maximum number of iterations is given by c1 and c2, which represent the individual learning rate and the group learning rate, respectively.

[0028] Preferably, in S3, the joint time with the longest running time among the three trajectory segments is selected as the input value for polynomial interpolation, and the complete running trajectory of the robotic arm is obtained by using 3-5-3 interpolation trajectory planning in the robot joint space.

[0029] A robotic arm trajectory planning system based on an improved particle swarm optimization algorithm, comprising:

[0030] The kinematic model building module is used to build the robot's kinematic model;

[0031] The runtime solution module incorporates the tent chaotic mapping, simulated annealing algorithm, adaptive inertia weights, and learning rate into the standard particle swarm optimization algorithm. Based on the robot's kinematics model, it uses an improved particle swarm optimization algorithm to solve the runtime in the motion trajectory planning of the robotic arm.

[0032] The optimal trajectory acquisition module is used to perform trajectory planning in the robot joint space using the 3-5-3 interpolation function. The optimal value obtained by the improved particle swarm optimization algorithm is substituted into the 3-5-3 interpolation function to obtain the optimal trajectory of the robotic arm.

[0033] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the robotic arm trajectory planning method based on the improved particle swarm optimization algorithm.

[0034] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the robotic arm trajectory planning method based on the improved particle swarm optimization algorithm.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] This invention improves upon the standard particle swarm optimization (PSO) algorithm by introducing the probabilistic jump property from simulated annealing to avoid getting trapped in local optima during the search process. It uses a tent chaotic mapping to initialize the particle population, resulting in a more uniform initial particle distribution, and employs adaptive inertia weights and learning rates to enhance search efficiency and accelerate convergence. This significantly improves the overall joint utilization efficiency of industrial robots, enabling a time-optimal trajectory planning optimization scheme for robotic arms. This not only enhances the motion performance of the robotic arm but also greatly improves the efficiency and accuracy of weld seam inspection for aerospace workpieces. Through the application of this invention, industrial robots can utilize joint resources more efficiently when performing complex tasks, ensuring rapid and accurate weld seam inspection and providing strong support for precision manufacturing and quality control in the aerospace field. Attached Figure Description

[0037] Figure 1 Modeling diagram of the six joints of the KUKAKR50R2500 robotic arm of this invention;

[0038] Figure 2 This is a flowchart of the improved particle swarm optimization algorithm of the present invention;

[0039] Figure 3 The experimental comparison diagram of the improved particle swarm optimization algorithm of the present invention and the standard particle swarm optimization algorithm at the first joint of the robotic arm is shown.

[0040] Figure 4 This is an experimental comparison diagram of the improved particle swarm optimization algorithm of this invention and the standard particle swarm optimization algorithm at the second joint of the robotic arm;

[0041] Figure 5 This is an experimental comparison diagram of the improved particle swarm optimization algorithm and the standard particle swarm optimization algorithm of the present invention on the third robotic arm joint;

[0042] Figure 6 This is an experimental comparison diagram of the improved particle swarm optimization algorithm and the standard particle swarm optimization algorithm of the present invention at the fourth joint of the robotic arm;

[0043] Figure 7 This is an experimental comparison diagram of the improved particle swarm optimization algorithm and the standard particle swarm optimization algorithm of the present invention at the fifth joint of the robotic arm;

[0044] Figure 8 This is a comparative experimental diagram of the improved particle swarm optimization algorithm and the standard particle swarm optimization algorithm of the present invention applied to the sixth joint of the robotic arm.

[0045] Figure 9 This diagram illustrates the application of the improved particle swarm optimization algorithm of this invention in the joint space of a robotic arm trajectory. Detailed Implementation

[0046] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0047] It should be noted that the terms “front,” “back,” “left,” “right,” “up,” and “down” used in the following description refer to the directions shown in the attached diagram, while the terms “inside” and “outside” refer to the directions toward or away from the geometric center of a specific component, respectively.

[0048] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the specification of this invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0049] Definitions of abbreviations involved in this implementation method:

[0050] PSO: Particle Swarm Optimization, an algorithm proposed by James Kennedy and Russell Eberhart to find the optimal solution, inspired by the regularity of bird flock foraging behavior.

[0051] The robotic arm trajectory planning method based on the improved particle swarm optimization algorithm described in this invention includes the following process:

[0052] S1, such as Figure 1 As shown, kinematic modeling is performed on the industrial robot;

[0053] This step considers that traditional six-axis robotic arm pose modeling involves establishing a coordinate system on each joint and using homogeneous coordinate transformation to achieve the transformation of multiple joint coordinates. This method requires six parameters to uniquely represent the robotic arm pose. The improved DH method, however, establishes a coordinate system for each link and uses a homogeneous transformation matrix to achieve coordinate transformation for multiple connected links.

[0054] S11. Use Matlab’s robotics toolbox to model the body of an industrial robot.

[0055] S12. Model the pose of the six-axis robot arm using the improved DH method and establish the improved DH table. In the pose modeling of this sub-step, the homogeneous transformation matrix is ​​used to realize the coordinate transformation of multiple series links.

[0056]

[0057] S13. Establish the robot's kinematic model based on the pose model of the six-axis robotic arm.

[0058] S2, such as Figure 2 As shown, the improved particle swarm optimization algorithm is used to optimize the motion trajectory planning of the robotic arm.

[0059] This step specifically involves: S21, determining the angles of the six joints and the three curve segments of the robot arm at the starting point, target point, and two interpolation points in the weld inspection task.

[0060] Path points Joint 1 Joint 2 Joint 3 Joint 4 Joint 5 Joint 6 Path point 1 0 0 0 0 0 0 Path point 2 -0.277 0.8168 -1.0053 0.1309 0.2513 0.231 Path point 3 0.143 0.5628 0.6283 0.0157 0.0566 0.1257 Path point 4 -1.1347 1.0053 -1.4451 0.0785 0.4398 1.1385

[0061] S22. Determine the particle population size m, particle dimension D, and iteration count K_max; in this example, the particle population size m = 30, particle dimension D = 3, iteration count N = 100; and the start temperature Tstar, end temperature Tend, and current temperature Tcur of the simulated annealing algorithm.

[0062] S23. Initialize the population position using the tent chaotic map and perform random initialization, determining the chaotic parameter α, which is chosen as 0.5 in this example. Based on the particle population boundary, the initial particle generation formula is determined as X = Lb + rand(N,D)*(Ub-Lb). Where N is the initial population size, D is the particle dimension, Ub is the upper bound of the particle search (4 in this example), and Lb is the lower bound of the particle search (0 in this paper). Based on the particle generation formula, the general solution for tent initializing the population is obtained: Where x(n) = x(t) / Lb, and x(t) is the initial position of the previous particle.

[0063] S24. Traverse the current particle and determine whether the current particle satisfies the angle constraint and angular velocity constraint; if it does not satisfy the constraint, discard the solution; if it satisfies the constraint, update the learning rate.

[0064] S25. Update the particle velocity and next position using adaptive inertia weights and learning rate, whereby...

[0065]

[0066] Where ω is the inertia weight, and k is the current iteration number. max The maximum number of iterations is given by c1 and c2, which represent the individual learning rate and the group learning rate, respectively.

[0067] S26. Calculate the current particle fitness x. If x is less than the current particle extreme value, accept the solution and modify the particle extreme value. If x is greater than the current particle extreme value, do not immediately discard the solution, but calculate the probability p based on the probabilistic jump characteristic of the simulated annealing algorithm. Where ΔP is the difference between the current particle's fitness and its current extreme value. If the probability p is greater than the random number r, the solution is accepted; if the probability p is less than the random number r, the solution is discarded.

[0068] S27. Update the global optimum.

[0069] S28. Repeat steps S23 to S27 until the exit condition is met (the number of loops meets the set requirement).

[0070] S29. Execute the algorithm to obtain the running time of the six joints of the robotic arm in the three trajectory segments, and the maximum running time in the three trajectory segments. Figures 3 to 8 The experiment shows the proposed algorithm UPSO and the standard particle swarm optimization algorithm on six joints of a robotic arm. The experiment shows that the UPSO algorithm improves the trajectory running time of the six joints and also optimizes the number of convergences.

[0071] S3. Perform trajectory planning using 3-5-3 interpolation in the robot's joint space. Substitute the optimal value obtained from the improved particle swarm optimization algorithm into the 3-5-3 interpolation function to obtain the joint angle changes of the robotic arm during its movement, i.e., the optimal trajectory of the robotic arm. Figure 9 As shown.

[0072] S2 will obtain the motion time of the six joints of the robotic arm in three motion trajectories. The joint time with the longest running time in each of the three trajectories will be selected as the input value for polynomial interpolation. The complete running trajectory of the robotic arm will be obtained by using 3-5-3 interpolation trajectory planning in the robot's joint space.

[0073] The following are embodiments of the apparatus of the present invention, which can be used to execute embodiments of the method of the present invention. For details not omitted in the apparatus embodiments, please refer to the embodiments of the method of the present invention.

[0074] In another embodiment of the present invention, a robotic arm trajectory planning system based on an improved particle swarm optimization algorithm is provided. This robotic arm trajectory planning system based on the improved particle swarm optimization algorithm can be used to implement the above-mentioned robotic arm trajectory planning method based on the improved particle swarm optimization algorithm. Specifically, the robotic arm trajectory planning system based on the improved particle swarm optimization algorithm includes a kinematic model establishment module, a runtime solution module, and an optimal trajectory acquisition module.

[0075] The kinematic model building module is used to build the robot's kinematic model.

[0076] The runtime solution module incorporates the tent chaotic mapping, simulated annealing algorithm, adaptive inertia weights, and learning rate into the standard particle swarm optimization algorithm. Based on the robot's kinematics model, it uses an improved particle swarm optimization algorithm to solve the runtime in the motion trajectory planning of the robotic arm.

[0077] The optimal trajectory acquisition module is used to perform trajectory planning in the robot joint space using the 3-5-3 interpolation function. The optimal value obtained by the improved particle swarm optimization algorithm is substituted into the 3-5-3 interpolation function to obtain the optimal trajectory of the robotic arm.

[0078] In another embodiment of the present invention, a terminal device is provided, comprising a processor and a memory. The memory stores a computer program, the computer program including program instructions, and the processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), or field-programmable gate arrays (FPGAs). Gate Array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., are the computing and control core of the terminal. They are suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to realize the corresponding method flow or corresponding function. The processor described in this embodiment of the invention can be used for the operation of a robotic arm trajectory planning method based on an improved particle swarm optimization algorithm, including: S1, establishing a robot kinematic model; S2, incorporating the tent chaotic mapping, simulated annealing algorithm, adaptive inertia weight, and learning rate into the standard particle swarm optimization algorithm, and using the improved particle swarm optimization algorithm to solve the running time in the robotic arm's motion trajectory planning based on the robot kinematic model; S3, using a 3-5-3 interpolation function to perform trajectory planning in the robot joint space, and incorporating the optimal value obtained by the improved particle swarm optimization algorithm into the 3-5-3 interpolation function to obtain the optimal trajectory of the robotic arm.

[0079] In another embodiment, the present invention also provides a computer-readable storage medium (Memory), which is a memory device in a terminal device for storing programs and data. It is understood that the computer-readable storage medium here may include both the built-in storage medium in the terminal device and extended storage media supported by the terminal device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor, which may be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here may be high-speed RAM or non-volatile memory, such as at least one disk storage device.

[0080] One or more instructions stored in a computer-readable storage medium can be loaded and executed by the processor to implement the corresponding steps of the robotic arm trajectory planning method based on the improved particle swarm optimization algorithm in the above embodiments; one or more instructions in the computer-readable storage medium are loaded and executed by the processor in the following steps: S1, establish a robot kinematic model; S2, incorporate the tent chaotic mapping, simulated annealing algorithm, adaptive inertia weight, and learning rate into the standard particle swarm optimization algorithm, and solve the running time in the trajectory planning of the robotic arm using the improved particle swarm optimization algorithm based on the robot kinematic model; S3, perform trajectory planning in the robot joint space using the 3-5-3 interpolation function, and incorporate the optimal value solved by the improved particle swarm optimization algorithm into the 3-5-3 interpolation function to obtain the optimal trajectory of the robotic arm.

[0081] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0082] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0083] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0084] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0085] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0086] It should be understood that the above description is for illustrative purposes and not for limitation. Many embodiments and applications beyond the provided examples will be apparent to those skilled in the art upon reading the above description. Therefore, the scope of this teaching should not be determined by reference to the above description, but rather by reference to the foregoing claims and the full scope of their equivalents. For purposes of completeness, all articles and references, including patent applications and publications, are incorporated herein by reference. The omission of any aspect of the subject matter disclosed herein in the foregoing claims is not intended as a waiver of that subject matter, nor should it be construed as an indication that the applicant has not considered that subject matter as part of the disclosed inventive subject matter.

Claims

1. A robotic arm trajectory planning method based on an improved particle swarm optimization algorithm, characterized in that, Includes the following processes: S1, Establish the robot's kinematic model; S2 incorporates the tent chaotic mapping, simulated annealing algorithm, adaptive inertia weights, and learning rate into the standard particle swarm optimization algorithm. Based on the robot's kinematics model, the improved particle swarm optimization algorithm is used to solve the running time in the motion trajectory planning of the robotic arm. The process of solving the time parameters in the motion trajectory planning of the robotic arm using an improved particle swarm optimization algorithm is as follows: S21. Determine the three curves between the starting point and target point of the robotic arm and the two interpolation points in the weld inspection task; S22. Initialize the improved particle algorithm parameters, including standard particle swarm parameters, upper bound parameter Ub and lower bound parameter Lb of the search space for the tent chaos map, and the start temperature Tstar, end temperature Tend, and current temperature Tcur of the simulated annealing algorithm. S23. Initialize the population position using the tent chaotic map and randomly initialize the vector of each particle; S24. Traverse each particle, and based on the current particle position, solve the interpolation function of the corner joint in the three-segment curve. Determine whether the corner joint velocity and angular acceleration meet the constraints based on the robot's physical limitations. S25. If the constraints are not met, then abandon the point; if the constraints are met, then calculate the current particle fitness; if the fitness is less than the particle extreme value, then update the particle extreme value position and fitness. S26. If the fitness is greater than the particle extreme value, then perform the simulated annealing optimization step to determine whether to accept the particle position. S27. Update the global optimum; S28. Update particle velocity and next position using adaptive inertia weights and learning rate; S29. Repeat S25 to S28 and determine whether the termination condition is met. If it is met, exit. Obtain the running time of the robotic arm joints in the three motion trajectories. The specific process of S23 is as follows: Initialize the population using the tent chaotic mapping, and determine the chaotic parameters. Based on the particle population boundary, the initial particle generation formula is determined as follows: Where N is the initial population size, D is the particle dimension, Ub is the upper bound of the particle search, and Lb is the lower bound of the particle search. Based on the particle generation formula, the general solution for the tent initial population is obtained: ;in , The position initialized for the previous particle; The formula for calculating S28 is: ； in For inertial weights, This represents the current iteration number. The maximum number of iterations, , These are individual learning rate and group learning rate, respectively. S3. The 3-5-3 interpolation function is used to plan the trajectory in the robot joint space. The optimal value obtained by the improved particle swarm algorithm is substituted into the 3-5-3 interpolation function to obtain the optimal trajectory of the robotic arm.

2. The robotic arm trajectory planning method based on the improved particle swarm optimization algorithm according to claim 1, characterized in that, The process of S1 is as follows: S11. Model the robot body; S12. Model the pose of the robotic arm using the improved DH method; S13. Establish a robot kinematics model based on the robotic arm pose model.

3. The robotic arm trajectory planning method based on the improved particle swarm optimization algorithm according to claim 2, characterized in that, The S12 process is as follows: establish a coordinate system for the links between each joint, and use a homogeneous transformation matrix to transform the coordinates of multiple series links.

4. The robotic arm trajectory planning method based on the improved particle swarm optimization algorithm according to claim 1, characterized in that, In S3, the joint time with the longest running time among the three trajectory segments is selected as the input value for polynomial interpolation. The complete running trajectory of the robotic arm is obtained by using 3-5-3 interpolation trajectory planning in the robot's joint space.

5. A robotic arm trajectory planning system based on an improved particle swarm optimization algorithm according to claim 1, characterized in that, Include: The kinematic model building module is used to build the robot's kinematic model; The runtime solution module incorporates the tent chaotic mapping, simulated annealing algorithm, adaptive inertia weights, and learning rate into the standard particle swarm optimization algorithm. Based on the robot's kinematics model, it uses an improved particle swarm optimization algorithm to solve the runtime in the motion trajectory planning of the robotic arm. The optimal trajectory acquisition module is used to perform trajectory planning in the robot joint space using the 3-5-3 interpolation function. The optimal value obtained by the improved particle swarm optimization algorithm is substituted into the 3-5-3 interpolation function to obtain the optimal trajectory of the robotic arm.

6. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the robotic arm trajectory planning method based on the improved particle swarm algorithm as described in any one of claims 1 to 4.

7. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the robotic arm trajectory planning method based on the improved particle swarm algorithm as described in any one of claims 1 to 4.

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