CN-121821415-B - Sparrow optimization algorithm-based optimal track planning method for material grabbing arm time

CN121821415BCN 121821415 BCN121821415 BCN 121821415BCN-121821415-B

Abstract

The invention discloses a sparrow optimization algorithm-based optimal track planning method for a material grabbing arm time, and belongs to the technical field of mechanical arm control of material grabbing machines. The method comprises the steps of constructing a grabbing arm kinematics model through a standard D-H parameter method, deducing a clamping jaw terminal attitude parameter, adopting a 3-5-3 segmentation polynomial design track interpolation function to determine angle, angular speed, angular acceleration continuity and limit constraint and solve coefficients, searching an optimal track parameter based on an improved sparrow optimization algorithm, improving optimizing performance through chaotic mapping, levy flight and a multiple information guiding strategy, and finally analyzing movable arm stress and bucket rod stress through transient dynamics simulation to verify track safety. The invention forms a complete technical link of modeling, design, optimization and verification, realizes time optimal movement on the premise of meeting various constraints, gives consideration to movement stability and structural reliability, effectively improves the working efficiency of the material grabbing machine, and provides powerful support for the intelligent upgrading of the material grabbing machine.

Inventors

HU XIAOBING
WEI CHENHAO
LI CHANGWU

Assignees

宜宾四川大学产业技术研究院

Dates

Publication Date: 20260512
Application Date: 20260313

Claims (6)

1. The utility model provides a material grabbing arm time optimal track planning method based on sparrow optimization algorithm, which is characterized by comprising the following steps: S1, performing kinematic modeling on a grabbing arm, constructing a coordinate system and coordinate transformation relation of a connecting rod, and deducing posture parameters of the tail end of a clamping jaw; S2, designing a track interpolation function by adopting 3-5-3 segmentation polynomials in combination with the tail end gesture parameters of the clamping jaw, selecting interpolation points to divide track segments, defining track constraint conditions, and simultaneously solving interpolation function coefficients; s3, calling a track interpolation function, constraint conditions and coefficients based on a sparrow optimization algorithm, and searching optimal track parameters which meet constraints and have minimum total running time; S4, extracting a change rule of the angle of the material grabbing arm joint according to the optimal track parameters, carrying out transient dynamics simulation, analyzing the change of the stress of the movable arm and the stress of the bucket rod, and judging the track safety; wherein, step S1 includes: S1.1, modeling a grabbing arm by adopting a standard D-H parameter method, carding connecting rod connection and motion characteristics, and determining the type and expression mode of the connecting rod parameters; s1.2, constructing a homogeneous transformation matrix of an adjacent coordinate system based on the type and the expression mode of the connecting rod parameter; S1.3, multiplying each homogeneous transformation matrix in sequence to obtain an integral transformation matrix from the clamping jaw coordinates to the base coordinates; S1.4, deducing a turning angle of the clamping jaw relative to a reference direction under a base coordinate based on the integral transformation matrix, wherein the turning angle is the terminal attitude parameter of the clamping jaw; wherein, step S2 includes: S2.1, combining with the tail end gesture parameters of the clamping jaw, selecting four interpolation points including a track starting point and a track middle point, dividing the track into three sections, wherein the first section and the third section adopt a cubic polynomial, and the second section adopts a penta polynomial to form a 3-5-3 segmented polynomial structure; S2.2, defining track constraint conditions, including angle, angular velocity, angular acceleration continuity constraint and limit constraint, wherein the limit constraint defines joint angle, angular velocity and angular acceleration allowable range; s2.3, constructing an equation set to solve interpolation function coefficients by combining polynomial expressions of all sections and constraint conditions, and ensuring that the tracks meet constraint and are smoothly connected; Wherein, step S3 includes: S3.1, optimizing initial population distribution by adopting a chaotic mapping method, generating initial individuals conforming to a solution space range and forming an initial population; s3.2, based on initial population distribution, introducing a Levy flight strategy to design a finder position updating mode; s3.3, designing a follower moving mode by adopting a multi-element information guiding strategy based on the individual fitness of the initial population; S3.4, setting an algorithm iteration termination condition, calling a track interpolation function, constraint conditions and coefficients, performing iterative search in a solution space through an initial population, a designed finder position updating mode and a designed follower moving mode, updating individual positions of the population, calculating total running time of the track, and screening optimal track parameters which meet all constraints and have minimum running time; Wherein, step S4 includes: S4.1, extracting a joint angle change rule from the optimal track parameter, and converting the joint angle change rule into a displacement change quantity of a hydraulic rod of the grabbing arm as transient dynamics simulation driving input; S4.2, applying a preset load to the tail end according to the actual operation scene of the material grabbing arm; s4.3, establishing a material grabbing arm transient dynamics simulation model, wherein the model comprises a movable arm and a bucket rod, setting simulation time step and termination time based on driving input and preset load, and starting a simulation track movement process; s4.4, acquiring stress variation data of the movable arm and the bucket rod, generating a stress variation curve, and analyzing a stress peak value; S4.5, determining allowable stress according to the material characteristics of the movable arm of the grabbing arm and the bucket rod, comparing a stress peak value with the allowable stress, judging that the track is safe and reliable if the stress peak value is smaller than the allowable stress, and returning to the step S3 to redesign the track parameters if the stress peak value is larger than or equal to the allowable stress.
2. The method of claim 1, wherein in step S3.1, the chaotic mapping adopts sinusoidal power mapping, and the specific operations include setting initial parameters of the sinusoidal power mapping, inputting initial random values, generating a series of chaotic sequences through formula iterative computation, normalizing the chaotic sequences, converting the chaotic sequences into initial individuals conforming to a solution space range, and forming the initial individuals into an initial population to ensure uniform distribution of the population in the solution space.
3. The method according to claim 1, wherein in step S3.2, the Levy flight search step length is determined by a gamma function and a normal distribution vector, and the specific operation includes setting Levy flight distribution parameters, generating two vectors obeying normal distribution as a flight direction random factor, combining a gamma function calculation result and the normal distribution vector to obtain a search step length, judging the relation between an environment early warning value and a safety value when the position of a finder is updated, and updating the position by adopting the search step length when the early warning value reaches the standard.
4. The method of claim 1, wherein in the step S3.3, the implementation process of the multi-element information guiding strategy comprises the steps of screening elite individuals and disadvantaged individuals according to fitness values in each generation of population, respectively processing the two types of individuals by adopting a reverse learning strategy to generate a reverse elite population and a reverse disadvantaged population, merging the elite population and the reverse elite population and sequencing, keeping 50% of non-repeated individuals before the combination of the elite population and the reverse disadvantaged population to form a new disadvantaged population, merging the disadvantaged population and the reverse disadvantaged population and sequencing, keeping 50% of individuals after the combination of the first 50% of individuals to form a new disadvantaged population, selecting reference individuals from the two types of new populations by adopting a roulette method, positively correlating the selection probability of the new elite population individuals with the self fitness, positively correlating the selection probability of the new elite population individuals with the reciprocal of the self fitness, taking the selected new elite population individuals as optimal reference objects, the new disadvantaged population individuals as worst reference objects, guiding the position updating of followers.
5. The method according to claim 1, wherein in step S2.2, the limit constraints include joint angle limit, joint angular velocity limit and joint angular acceleration limit, each constraint explicitly corresponds to a motion parameter allowable range, and is determined according to mechanical structure strength, hydraulic driving capability and operation safety requirements of the material grabbing arm, the angle continuity constraint requires that an end point of an adjacent track segment be consistent with a start point angle, the angular velocity continuity constraint requires that the end point of the adjacent track segment be consistent with the start point angular velocity, and the track start-end point angular velocity is zero, and the angular acceleration continuity constraint requires that the end point of the adjacent track segment be consistent with the start point angular acceleration, and the track start-end point angular acceleration is zero.
6. The method according to claim 1, wherein in step S3, key parameters based on a sparrow optimization algorithm are determined through an orthogonal experiment, the key parameters include a levy flight distribution parameter and a reverse population proportion parameter, the specific experimental process includes selecting a test function with nonlinear and multi-local extremum characteristics as a verification function, verifying that the characteristic of the function is similar to the space characteristics of the grasping arm trajectory planning solution, setting orthogonal experimental factors and levels, setting a plurality of different levels for each factor, repeating the experiment on each parameter combination according to a design scheme, calculating an average value of experimental results of each parameter combination, analyzing and determining a parameter combination with optimal optimizing effect of the algorithm, and taking the parameter combination as a fixed parameter based on the sparrow optimization algorithm.

Description

Sparrow optimization algorithm-based optimal track planning method for material grabbing arm time Technical Field The invention relates to the technical field of control of mechanical arms of a grabbing machine, in particular to a method for planning an optimal track of grabbing arm time based on a sparrow optimization algorithm. Background The material grabbing machine is used as core equipment in the field of engineering machinery, is widely applied to a plurality of industrial scenes such as solid waste treatment, wharf loading and unloading, material handling and the like, and the operation efficiency directly influences the circulation speed of the whole production link. Along with the promotion of industrialization progress, the requirements of various industries on the high efficiency and the accuracy of material handling are continuously improved, and the intelligent and automatic upgrading of the material grabbing machine becomes an important trend of industry development. In the core technology of the material grabbing machine, the mechanical arm track planning is a key link for determining the working efficiency and the running stability, wherein the time optimal track planning can furthest shorten the working period and improve the equipment utilization rate, and becomes the hot spot direction of the current research. At present, a track planning technology is mainly developed in joint space, a track function is constructed by a common polynomial interpolation method, and an optimal parameter is solved by an intelligent optimization algorithm. In the aspect of optimization algorithm, a particle swarm optimization algorithm, a butterfly algorithm, a longhorn swarm search algorithm and other meta-heuristic algorithms have been used for solving a time optimal problem, and diversified technical paths are provided for track parameter optimization. Meanwhile, kinematic modeling is used as a basis of track planning, and a standard D-H parameter method can accurately describe the motion relation of the connecting rod, so that the method becomes a mainstream method of mechanical arm modeling, and theoretical support is provided for subsequent track design. Although the track planning technology has been developed to some extent, many challenges to be solved are still faced in practical application, and it is difficult to fully satisfy the efficient and safe operation requirements of the material grabbing machine. Firstly, the connection between the existing kinematic modeling and the track design is not tight enough, part of schemes only focus on single link optimization, and a complete link of modeling-design-optimizing-checking cannot be formed, so that the overall suitability of the track planning is insufficient, and the actual motion characteristics of the mechanical arm are difficult to accurately match. In the process of solving, the optimization algorithm is difficult to quickly find a time optimal solution on the premise of meeting multiple constraints such as angle, angular velocity and angular acceleration, and the quality of the optimizing efficiency and the solution is required to be improved. In addition, the prior art generally lacks a systematic safety verification link for planning tracks, and track parameters cannot be effectively related to the structural strength of a mechanical arm movable arm and a bucket rod, so that part of planned tracks can meet kinematic constraint, but stress exceeding risks can exist under actual operation loads, and equipment operation safety is affected. These problems are mutually interwoven, so that the existing track planning scheme is difficult to realize the comprehensive targets of 'time optimal, stable motion and safe structure', further improvement of the operating efficiency and the intelligent level of the material grabbing machine is restricted, and a track planning method with full-link collaborative optimization is needed to solve the problems. Disclosure of Invention The invention aims to overcome the defects of the prior art and provides a sparrow optimization algorithm-based optimal track planning method for the material grabbing arm time. The aim of the invention is realized by the following technical scheme: The invention provides a sparrow optimization algorithm-based material grabbing arm time optimal track planning method, which comprises the following steps of: S1, performing kinematic modeling on a grabbing arm, constructing a coordinate system and coordinate transformation relation of a connecting rod, and deducing posture parameters of the tail end of a clamping jaw; S2, designing a track interpolation function by adopting 3-5-3 segmentation polynomials in combination with the tail end gesture parameters of the clamping jaw, selecting interpolation points to divide track segments, defining track constraint conditions, and simultaneously solving interpolation function coefficients; s3, calling a track interpolation function, constrai