CN-122021017-A - Parallel instrument dexterity performance enhancement method based on improved meta heuristic optimization algorithm

CN122021017ACN 122021017 ACN122021017 ACN 122021017ACN-122021017-A

Abstract

The invention discloses a parallel instrument flexibility performance enhancement method based on an improved meta-heuristic optimization algorithm, which comprises the steps of establishing a basic optimization model of a parallel instrument, including a single-objective or multi-objective optimization model, splitting a complete task into a plurality of subtasks according to task characteristics executed by the parallel instrument, establishing a mapping relation between the working space subregion and an optimization target according to the association of the subtasks with the working space subregion and the optimization target respectively, establishing an improved optimization model of the basic optimization model based on the mapping relation, carrying out objective optimization solution on the improved optimization model by using a meta-heuristic intelligent optimization algorithm improved by chaotic mapping and mutation, screening candidate solutions based on a flexibility performance sensitivity index to obtain an optimal solution, and constructing a motion performance comprehensive evaluation index of the parallel instrument to evaluate the flexibility performance enhancement design effect of the parallel instrument applied to the optimal solution. The invention can effectively improve the design performance of the parallel connection apparatus and realize the aim of enhancing the dexterity performance of the parallel connection apparatus.

Inventors

Tao Mingzhe
XU HONGXIA
ZHANG YIYI
WU RUIJIA
SONG WENJUN
WU JIAN
LIU ZUOZHU

Assignees

杭州市滨江区浙医二院经血管植入器械研究院

Dates

Publication Date: 20260512
Application Date: 20260129

Claims (10)

1. The parallel instrument dexterity performance enhancement method based on the improved meta-heuristic optimization algorithm is characterized by comprising the following steps of: Establishing a basic optimization model of the parallel instrument, wherein the basic optimization model comprises a single-target optimization model or a multi-target optimization model; Dividing a complete task into a plurality of subtasks according to task characteristics executed by parallel instruments, establishing a mapping relation between the working space subareas and the optimization targets according to the association of the subtasks with the working space subareas and the optimization targets respectively, and establishing an improved optimization model of a basic optimization model based on the mapping relation; Performing target optimization solution on the improved optimization model by using a meta-heuristic intelligent optimization algorithm subjected to chaotic mapping and mutation improvement, and screening candidate solutions based on a smart performance sensitivity index to obtain an optimal solution; And constructing a comprehensive evaluation index of the motion performance of the parallel instrument, and evaluating the dexterous performance enhancement design effect of the parallel instrument applying the optimal solution.
2. The parallel instrument dexterity performance enhancement method based on improved meta-heuristic optimization algorithm of claim 1, wherein the step of establishing a single-objective optimization model according to the use requirement comprises the steps of: Deviation of motion of parallel-connected instruments As a design target Dimensional parameters of the instrument to be connected in parallel As a design variable The range of the values of the dimension parameters of the parallel instrument Maximum design cost of parallel instrument as constraint condition The established single-objective optimization model is expressed as: , Wherein, the The search is indicated to be performed in a search, It is indicated that the minimization of the amount of the liquid, Representation of The real space of the dimensions is such that, The dimensions of the parameters are represented and, And a judging function for indicating whether the optimization model meets the cost constraint.
3. The parallel instrument dexterity enhancement method based on improved meta-heuristic optimization algorithm of claim 2, wherein developing a multi-objective optimization model by a kinematic jacobian matrix comprises: After the kinematic analysis of the parallel instrument and the acquisition of the jacobian matrix, the condition number of the jacobian matrix of the parallel instrument is calculated according to the jacobian matrix Maximum carrying capacity And maximum structural rigidity Expanding the single-target optimization model into a multi-target optimization model, wherein the established multi-target optimization model is expressed as: , Wherein, the Representing the design goals for the goal optimization model.
4. The parallel instrument dexterity performance enhancement method based on an improved meta-heuristic optimization algorithm according to claim 1, wherein the establishing a mapping relationship between the workspace sub-area and the optimization target according to the association of the sub-tasks with the workspace sub-area and the optimization target respectively comprises: And defining a set of all reachable poses of an actuator of the parallel instrument in a working space when each subtask is executed as a working space subarea, analyzing performance preference of each subtask on different optimization targets, and accordingly establishing a corresponding subarea mapping relation between a specific working space subarea and the specific optimization targets based on each subtask, and converting tasks in an abstract form which cannot be calculated into an actuator pose set space capable of being calculated.
5. The parallel instrument dexterity performance enhancement method based on improved meta-heuristic optimization algorithm of claim 1 or 4, wherein the building of the improved optimization model of the basic optimization model based on the mapping relation comprises the following steps: Splitting an optimization domain of a complete task flow into target areas associated with different optimization targets according to the mapping relation between the working space subareas and the optimization targets, and carrying out partition constraint adjustment on a design objective function in a basic optimization model based on the target areas to form an improved optimization model of task guidance.
6. The method for enhancing the dexterity performance of parallel instruments based on an improved meta-heuristic optimization algorithm according to claim 1, wherein the objective optimization solution for the improved optimization model by using the meta-heuristic intelligent optimization algorithm improved by chaotic mapping and mutation comprises the following steps: Generating a diversified initial population representing the parallel instrument size parameter combination by adopting a chaotic mapping mode; driving the population to iterate by adopting a meta heuristic optimization strategy so as to optimize the optimization target of the improved optimization model; in the iterative process, the individuals in the optimized population are disturbed by adopting a variation improvement mode, so that the individuals converged to the local extremum jump out of the local optimum; and updating the population by adopting a non-dominant sorting technology until a termination condition is met, and outputting a candidate solution set related to the parallel instrument size parameter combination.
7. The parallel instrument dexterity performance enhancing method based on improved meta-heuristic optimization algorithm according to claim 1 or 6, wherein the chaotic mapping manner at least comprises Logistic mapping, cubic mapping or Tent mapping, and the variation improvement manner at least comprises Cauchy variation, gaussian variation, differential variation or t distribution disturbance variation.
8. The parallel instrument dexterity performance enhancement method based on improved meta-heuristic optimization algorithm of claim 1, wherein the dexterity performance sensitivity index is expressed as: , Wherein, the The sensitivity index of the dexterous performance is represented, and the content of the sensitivity index is the precision of loss required for optimizing one unit of dexterous performance; And Representing any two different candidate solutions, An index representing the solution candidate is presented, Representing the motion deviation function of the parallel instrument, A jacobian condition number function representing the parallel instrument.
9. The method for enhancing the dexterity performance of parallel instruments based on the improved meta-heuristic optimization algorithm according to claim 1, wherein the motion performance comprehensive evaluation index is expressed as: , Wherein, the The method comprises the steps of representing a comprehensive evaluation index of the motion performance, wherein the meaning of the comprehensive evaluation index is a comprehensive evaluation function based on the condition number of the jacobian matrix and a bearing capacity safety threshold; Representing the jacobian condition number of the parallel instrument, Indicating the maximum load carrying capacity of the parallel instrument, Indicating the limit of the load-bearing capacity, Representing a safety factor.
10. The parallel instrument dexterous performance enhancing device based on the improved meta-heuristic optimization algorithm is realized by the parallel instrument dexterous performance enhancing method based on the improved meta-heuristic optimization algorithm according to any one of claims 1-9, and is characterized by comprising a basic model building module, an improved model building module, a target optimization solving module and a comprehensive performance evaluating module; The basic model construction module is used for establishing a basic optimization model of the parallel instrument, and comprises a single-target optimization model or a multi-target optimization model; the improved model construction module is used for splitting a complete task into a plurality of subtasks according to task characteristics executed by the parallel-connection instruments, establishing a mapping relation between the working space subregion and the optimization target according to the association of the subtasks with the working space subregion and the optimization target respectively, and establishing an improved optimization model of the basic optimization model based on the mapping relation; The target optimization solving module is used for carrying out target optimization solving on the improved optimization model by using a meta-heuristic intelligent optimization algorithm which is subjected to chaotic mapping and mutation improvement, and screening candidate solutions based on the dexterous performance sensitivity index to obtain an optimal solution; the comprehensive performance evaluation module is used for constructing a comprehensive evaluation index of the motion performance of the parallel instrument and evaluating the dexterous performance enhancement design effect of the parallel instrument applying the optimal solution.

Description

Parallel instrument dexterity performance enhancement method based on improved meta heuristic optimization algorithm Technical Field The invention belongs to the technical field of parallel instrument design, and particularly relates to a parallel instrument dexterity performance enhancement method based on an improved meta-heuristic optimization algorithm. Background The parallel-connection instrument is used as a typical complex instrument, and the application scene is quite diverse. In particular, the parallel connection apparatus plays an important role in various fields such as industry, agriculture, medical treatment and the like due to the advantages of high precision, high speed, high load and the like. In particular in the medical field, the parallel instrument has stronger anti-interference capability due to the closed structure, and is very suitable for being applied to medical operation equipment with extremely high requirements on safety and stability, such as percutaneous puncture auxiliary positioning equipment, ultrasonic probe tracking positioning equipment and the like. In order to design high-performance parallel instruments, many scientific research groups have studied methods for parallel instrument design. The university of canada An Da institute of technology research team （Coppola G, Zhang D, Liu K. A 6-DOF reconfigurable hybrid parallel manipulator[J]. Robotics and Computer-Integrated Manufacturing, 2014, 30(2): 99-106.） performs multi-objective optimization design on a parallel instrument to find a design scheme for balancing all performance indexes from the performance indexes such as rigidity, dexterity, working space, occupied volume and the like of the parallel instrument. The university of Italian second research team （Cirillo A, Cirillo P, De Maria G, et al. Optimal custom design of both symmetric and unsymmetrical hexapod robots for aeronautics applications[J]. Robotics and Computer-Integrated Manufacturing, 2017, 44: 1-16.）, nature, inc. proposes a parallel instrument optimization custom design approach that, by combining dynamic optimization with kinematic optimization, aims to maximize the payload and minimize the driving force required for each kinematic chain while avoiding reducing the working space of the parallel instrument. The university of golombia perhead research team （Quintero-Riaza H F, Mejía-Calderón L A, Díaz-Rodríguez M. Synthesis of planar parallel manipulators including dexterity, force transmission and stiffness index[J]. Mechanics Based Design of Structures and Machines, 2019, 47(6): 680-702.） proposes an optimal dimension design method for parallel instruments, so that the dexterity index, force transmission efficiency and rigidity of the parallel instruments are optimized, and the dimension of the parallel instruments is reduced as much as possible on the premise of meeting the working space. The performance enhancement design of parallel devices is a process of searching an optimal solution to meet specific design targets and constraint conditions through systematic methods and technologies, aiming at improving efficiency, reducing cost and improving performance and quality, and the core is to find a design parameter set which enables the optimization targets to reach optimal values through establishing a mathematical model and designing an optimization algorithm. Because of the coupling cooperative motion characteristic of the multi-motion branched chains of the parallel connection apparatus, the design for enhancing the smart performance has higher complexity and is easy to have design conflict with other performance indexes. This feature also presents a significant challenge for its rapid, high quality development and design in a variety of industries. The research on the design method for enhancing the dexterity performance of the parallel connection apparatus can promote the perfection of the design theory of the high-performance parallel connection apparatus, thereby promoting the landing of high and new technology, accelerating the promotion of industrial transformation and upgrading, and having important significance. Disclosure of Invention In view of the above, the present invention aims to provide a method for enhancing the dexterity performance of parallel instruments based on an improved meta-heuristic optimization algorithm, which comprises the steps of firstly constructing a basic optimization model of the parallel instruments, expanding the basic optimization model into an optimization model covering multiple targets such as dexterity, bearing capacity, etc. based on a kinematic jacobian matrix, and realizing system balance between performances. And secondly, by analyzing task characteristics, a mapping relation between the subtasks and the working space subregions and between the subtasks and the optimization targets is established, and an improved optimization model of task guidance is formed, so that the optimization process has mo