A grasping posture planning method based on improved horned lizard optimization algorithm and simulation

Abstract

The present invention discloses a grasping posture planning method based on an improved horned lizard optimization algorithm combined with simulation, which relates to the field of grasping control of manipulators. The method comprises the following steps: setting the motion ranges of 11 joints obtained by a virtual five-finger dexterous hand prototype as the upper and lower limits of population initialization in an IHLOA algorithm, thereby randomly generating a joint angle matrix for controlling the motion of the virtual five-finger dexterous hand prototype; then, transmitting the joint angle matrix to a virtual five-finger dexterous hand in a CoppeliaSim simulation environment; the virtual five-finger dexterous hand performs an object grasping operation according to the received joint angle matrix, and feeds back the multi-point pressure tactile information obtained during the grasping process to the IHLOA algorithm; the IHLOA algorithm sums the multi-point pressure tactile information and the object's gravity, analyzes the obtained resultant force, and selects the multi-joint angle matrix corresponding to the minimum force-minimum angle criterion according to the minimum force-minimum angle criterion, and uses it as the multi-joint angle matrix of the optimal grasping posture, thereby realizing the optimal grasping planning of the five-finger dexterous hand for the object.

Description

Technical Field

[0001] The present invention relates to the technical field of manipulator grasping control, and in particular to a grasping posture planning method based on an improved horned lizard optimization algorithm combined with simulation. Background Art

[0002] In the process of developing intelligent robots, the multi-point grasping problem of dexterous hands has always been a technical challenge that needs to be overcome. Achieving optimal grasp planning for objects with five-fingered dexterous hands can not only significantly improve the robot's operational capabilities in various complex scenarios and expand its application areas, but also has important significance for promoting the development of robotics technology to a higher level. To address this problem, this patent proposes a grasping posture planning method (i.e., ICGPP method) based on the improved horned lizard optimization algorithm (i.e., IHLOA algorithm) combined with CoppeliaSim simulation. Summary of the Invention

[0003] In view of the above-mentioned prior art, the present invention provides a grasping posture planning method based on an improved horned lizard optimization algorithm combined with simulation, which mainly solves the technical problems existing in the above-mentioned background technology.

[0004] To achieve the above-mentioned purpose, the technical solution of the embodiment of the present invention is implemented as follows:

[0005] A grasping posture planning method based on an improved horned lizard optimization algorithm combined with simulation, the method comprising the following steps: step S1: generating a joint motion range through a virtual five-finger dexterous hand prototype, and setting the joint motion range as the upper and lower limits of population initialization in the improved horned lizard optimization algorithm, thereby randomly generating a joint angle matrix for controlling the motion of the virtual five-finger dexterous hand prototype; step S2: inputting the joint angle matrix into the virtual five-finger dexterous hand in the CoppeliaSim simulation environment, the virtual five-finger dexterous hand performing an object grasping operation based on the received joint angle matrix, and feeding back multi-point pressure tactile information obtained during the grasping process to the improved horned lizard optimization algorithm, the improved horned lizard optimization algorithm summing the received multi-point pressure tactile information with the object gravity to obtain a resultant force; step S3: the improved horned lizard optimization algorithm analyzing the obtained resultant force, and based on the minimum force-minimum angle criterion as an objective function, selecting the multi-joint angle matrix corresponding to the objective function, using it as the optimal multi-joint angle matrix of the grasping posture, and outputting it as feedback to the virtual five-finger dexterous hand to achieve optimal grasping planning.

[0006] As a preferred embodiment of the present invention, the improved horned lizard optimization algorithm in step S1 initializes the particles by introducing the Logistic-tent chaotic map in the population initialization, so that the generated particles are more evenly distributed. The specific formula is as follows:

[0007]

[0008] in, Indicates the The value of the random process exhibited by the population at time , represents the value that makes the Logistic-tent map exhibit chaotic behavior in the interval [0,1], Indicates the The value of the random process exhibited by the population at time t.

[0009] As a preferred solution of the present invention, in order to enhance the particle exploration capability, the improved horned lizard optimization algorithm in step S2 introduces new particles generated by a probabilistic perturbation strategy during the iterative optimization process.

[0010] As a preferred solution of the present invention, the improved horned lizard optimization algorithm in step S2 introduces a greedy mechanism into the probability perturbation strategy in order to accurately judge the superiority between the fitness of the new particles generated by the probability perturbation strategy and the fitness of the original particles. The probability perturbation strategy is based on the dimension of the objective function. , current iteration number and the maximum number of iterations Calculating perturbation probability and update the position, and then determine the position according to the greedy mechanism;

[0011] Its disturbance probability The calculation formula is as follows:

[0012]

[0013] The updated position calculation formula is as follows:

[0014]

[0015] in, Indicates the updated location. Indicates the The search position of the generation, Indicates the joint motion with degrees of freedom.

[0016] As a preferred embodiment of the present invention, the improved horned lizard optimization algorithm in step S2 introduces a fitness adjustment strategy to balance the exploration ability of particles in the algorithm. The fitness adjustment strategy calculates the fitness value of the current particle and selects a position update strategy that is adapted to the fitness of the particle according to the fitness status of the particle. When the number of iterations is large and the particle fitness is still not ideal, a reverse blood spray attack strategy is introduced into the position update strategy to increase the influence weight of the optimal position individual in the later iteration. The specific formula is as follows:

[0017]

[0018] in, represents the initial velocity of the particle, represents the error coefficient, represents the gravitational acceleration in projectile motion, Indicates the The best search position for the generation, Represents a constant.

[0019] As a preferred embodiment of the present invention, the specific formula of the minimum force-minimum angle criterion as the objective function in step S3 is as follows:

[0020]

[0021] in, is a function representing the net force, A function representing an angle, represents the joint angle matrix, represents the friction coefficient, Indicates the pressure value fed back by different pressure sensors. Indicates the mass of the object being grasped, represents the acceleration due to gravity, represents the initial joint angle, Indicates the joint angle corresponding to the grasping gesture, Indicates the minimum value operation.

[0022] As a preferred embodiment of the present invention, the specific process of improving the horned lizard optimization algorithm in step S3 to obtain the optimal multi-joint angle matrix of the grasping posture according to the objective function is as follows:

[0023] Step S31: setting algorithm parameters and initializing the population using Logistic-tent chaotic mapping;

[0024] Step S32: Determine whether the initialized population is in the first half of the iteration. If so, adopt a hiding strategy for individuals with good fitness, and determine whether to adopt a blood-spraying attack strategy or a moving escape strategy for individuals with poor fitness by generating random numbers. If not, adopt a reverse blood-spraying attack strategy for individuals with good fitness, and determine whether to adopt a hiding strategy or a moving escape strategy for individuals with poor fitness by generating random numbers.

[0025] Step S33: Select the individual with the worst fitness and update its position using the skin darkening and brightening strategy, calculate the MSH rate and make a judgment. If the MSH rate is less than 0.3, use the MSH rate position update strategy and add probability disturbance. Then select the optimal individual through the greedy mechanism to judge whether the objective function is met. If not, loop again, otherwise end.

[0026] The beneficial effects of the present invention are: this method is based on the improved horned lizard optimization algorithm and takes the minimum force-minimum angle criterion as the objective function, and selects the optimal grasping posture according to multi-point pressure information; in terms of grasping posture judgment, it not only ensures that the grasped object is not damaged due to excessive grasping force, thereby protecting the integrity of the object, but also minimizes the change in joint angle, reduces the motion loss of the robot joint, and improves the grasping efficiency. While ensuring the grasping quality, this method achieves the efficiency and stability of the grasping process. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 The present invention provides a schematic flow chart of the ICGPP method;

[0028] Figure 2 A schematic diagram of the improved horned lizard optimization algorithm provided by the present invention;

[0029] Figure 3 Schematic diagram of the virtual five-finger dexterous hand structure provided by the present invention Figure 1 ;

[0030] Figure 4 Schematic diagram of the virtual five-finger dexterous hand structure provided by the present invention Figure 2 ;

[0031] Figure 5 A schematic diagram of the cylinder envelope grasping posture of the virtual five-finger dexterous hand provided by the present invention;

[0032] Figure 6 A schematic diagram of the precise grasping posture of a cuboid by the virtual five-finger dexterous hand provided by the present invention;

[0033] Figure 7 A schematic diagram of a mixed grasping posture of a cube by the virtual five-finger dexterous hand provided by the present invention;

[0034] Figure 8 This is a schematic diagram of the objective function fitness curve provided by the present invention. DETAILED DESCRIPTION

[0035] The technical solution of the present invention is further elaborated in detail below in conjunction with the drawings and specific embodiments of the specification. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention. In the following description, reference is made to "some embodiments", which describes a subset of all possible embodiments, but it should be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict.

[0036] In the following description, numerous specific details are provided to provide a more thorough understanding of the present invention. However, it will be apparent to those skilled in the art that the present invention may be practiced without one or more of these details. In other instances, certain technical features well known in the art are not described to avoid confusion with the present invention.

[0037] It should be understood that the present invention can be implemented in different forms and should not be interpreted as being limited to the embodiments proposed herein. On the contrary, providing these embodiments will make the disclosure thorough and complete, and will fully convey the scope of the present invention to those skilled in the art. And the purpose of the terms used herein is only to describe specific embodiments and is not intended to limit the present invention. When used herein, the singular forms "one", "an" and "said / the" are also intended to include plural forms, unless the context clearly indicates another way. It should also be understood that the terms "comprising" and / or "comprising" when used in this specification determine the presence of the features, integers, steps, operations, elements and / or parts, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, parts and / or groups. When used herein, the term "and / or" includes any and all combinations of the relevant listed items.

[0038] It should also be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be an intermediate element. When an element is referred to as being "connected to" another element, it may be directly connected to the other element or there may be an intermediate element. The terms "vertical," "horizontal," "inner," "outer," "left," "right," and similar expressions used herein are for illustrative purposes only and do not represent the only implementation methods.

[0039] In order to fully understand the present invention, a detailed structure will be provided in the following description to illustrate the technical solution proposed by the present invention. Optional embodiments of the present invention are described in detail below. However, in addition to these detailed descriptions, the present invention may also have other implementations.

[0040] Please refer to the attached Figure 1 A grasping posture planning method based on an improved horned lizard optimization algorithm combined with simulation, the method comprising the following steps:

[0041] Step S1: Generate a joint motion range through a virtual five-finger dexterous hand prototype, and set the joint motion range as the upper and lower limits of population initialization in the improved horned lizard optimization algorithm, thereby randomly generating a joint angle matrix for controlling the motion of the virtual five-finger dexterous hand prototype;

[0042] Step S2: The joint angle matrix is ​​input into a virtual five-finger dexterous hand in the CoppeliaSim simulation environment. The virtual five-finger dexterous hand performs an object grasping operation based on the received joint angle matrix, and feeds back the multi-point pressure and tactile information obtained during the grasping process to the improved horned lizard optimization algorithm. The improved horned lizard optimization algorithm sums the received multi-point pressure and tactile information with the object's gravity to obtain a resultant force.

[0043] Step S3: The improved horned lizard optimization algorithm analyzes the obtained resultant force, and based on the minimum force-minimum angle criterion as the objective function, selects the multi-joint angle matrix corresponding to the objective function, uses it as the optimal multi-joint angle matrix for the grasping posture, and outputs it as feedback to the virtual five-finger dexterous hand to achieve the optimal grasping plan.

[0044] Specifically, in this method, the main function of the improved horned lizard optimization algorithm (IHLOA algorithm) is to optimize the multi-joint angle matrix that controls the grasping motion of the dexterous hand. This multi-joint angle matrix describes in detail the joint angles of 11 degrees of freedom during the grasping motion of the dexterous hand. By applying this multi-angle matrix to the virtual five-fingered dexterous hand in the CoppeliaSim simulation environment, it can provide precise instructions for its motion control, ensure the accuracy and stability of the dexterous hand during the grasping process, and lay a solid foundation for achieving optimal grasping planning.

[0045] As a preferred embodiment of the present invention, the improved horned lizard optimization algorithm in step S1 initializes the particles by introducing the Logistic-tent chaotic map in the population initialization, so that the generated particles are more evenly distributed. The specific formula is as follows:

[0046]

[0047] in, Indicates the The value of the random process exhibited by the population at time , represents the value that makes the Logistic-tent map exhibit chaotic behavior in the interval [0,1], Indicates the The value of the random process exhibited by the population at time t.

[0048] As a preferred solution of the present invention, in order to enhance the particle exploration capability, the improved horned lizard optimization algorithm in step S2 introduces new particles generated by a probabilistic perturbation strategy during the iterative optimization process.

[0049] Specifically, to improve the algorithm's global performance, this method first introduces a logistic-tent chaotic map during particle initialization. This reduces the probability of falling into a local optimum by making the particles more evenly distributed during the initialization phase. Secondly, a probabilistic perturbation strategy is incorporated into the iterative process to increase particle diversity and enhance their exploration capabilities, further preventing the optimization process from falling into a local optimum.

[0050] As a preferred solution of the present invention, the improved horned lizard optimization algorithm in step S2 introduces a greedy mechanism into the probability perturbation strategy in order to accurately judge the superiority between the fitness of the new particles generated by the probability perturbation strategy and the fitness of the original particles. The probability perturbation strategy is based on the dimension of the objective function. , current iteration number and the maximum number of iterations Calculating perturbation probability and update the position, and then determine the position according to the greedy mechanism; the disturbance probability The calculation formula is as follows:

[0051]

[0052] The updated position calculation formula is as follows:

[0053]

[0054] in, Indicates the updated location. Indicates the The search position of the generation, Indicates the joint motion with degrees of freedom.

[0055] Specifically, a greedy mechanism is introduced into the probabilistic perturbation strategy to quickly and efficiently make the optimal choice under the current state, thereby improving the algorithm's performance and optimization results. A greedy mechanism always makes the currently optimal choice when solving a problem. After each probabilistic perturbation, the greedy mechanism determines each action based on the current local information, hoping to ultimately reach a better result through a series of locally optimal choices. This approach has relatively low computational complexity and high execution efficiency.

[0056] As a preferred embodiment of the present invention, the improved horned lizard optimization algorithm in step S2 introduces a fitness adjustment strategy to balance the exploration ability of particles in the algorithm. The fitness adjustment strategy calculates the fitness value of the current particle and selects a position update strategy that is adapted to the fitness of the particle according to the fitness status of the particle. When the number of iterations is large and the particle fitness is still not ideal, a reverse blood spray attack strategy is introduced into the position update strategy to increase the influence weight of the optimal position individual in the later iteration. The specific formula is as follows:

[0057]

[0058] in, Indicates the initial velocity of the particle, with a value of 1. represents the error coefficient, defined as , represents the gravitational acceleration in projectile motion, Indicates the The best search position for the generation, Represents a constant, whose value is .

[0059] Specifically, such as Figure 2 As shown, the fitness adjustment strategy calculates the fitness value of the current particle and selects a position update strategy that is suitable for it according to the fitness status of the particle. The specific process is as follows: When the number of iterations is Less than the maximum number of iterations When the number of particles with good fitness reaches half, a hiding strategy is implemented for particles with good fitness. This strategy adjusts the positions of these particles to the positions of particles with the best fitness, thereby expanding the search range and fully exploring possible better solutions in the solution space. For particles with poor fitness, either a bleeding attack strategy or a moving escape strategy is adopted. The bleeding attack strategy incorporates the influence of the best fitness position into its own position, guiding particles to search towards better solutions, thereby achieving more efficient optimization.

[0060] When the number of iterations Greater than or equal to the maximum number of iterations When the number of iterations reaches half, the strategy is adjusted. For particles with poor fitness, a hiding strategy or a mobile escape strategy is adopted, allowing the particles to continue exploring within a relatively small range, further refining the search for possible local optimal solutions. For particles with good fitness, a reverse bleeding attack strategy is adopted. This strategy helps expand the influence range of the optimal particle position and propels the entire search process towards a more optimal solution. Especially when the number of iterations is high and the particle fitness is still not ideal, the reverse bleeding attack strategy can encourage the optimal particle to flexibly adjust its position and continue to search deeper, thereby increasing the probability of the algorithm ultimately finding the global optimal solution.

[0061] As a preferred embodiment of the present invention, the specific formula of the minimum force-minimum angle criterion as the objective function in step S3 is as follows:

[0062]

[0063] in, is a function representing the net force, A function representing an angle, represents the joint angle matrix, represents the friction coefficient, Indicates the pressure value fed back by different pressure sensors. Indicates the mass of the object being grasped, represents the acceleration due to gravity, represents the initial joint angle, Indicates the joint angle corresponding to the grasping gesture, Indicates the minimum value operation. .

[0064] Specifically, the minimum force-minimum angle criterion constructed by this method is as follows:

[0065] 1. Minimum force principle: This principle aims to minimize the net force exerted on the grasped object and maintain this minimum net force throughout the grasping process. This principle effectively avoids damage to the object caused by excessive force, ensuring the safety and stability of the grasping process.

[0066] 2. Minimum Angle Criterion: This requires that the sum of the angle changes of all joints in the dexterous hand be minimized. By meeting this criterion, the dexterous hand's range of motion can be minimized, thereby improving grasping speed and efficiency while reducing energy consumption.

[0067] In practical applications, when the dexterous hand grasps an object according to the above criteria, it must first strictly meet the minimum force criterion to ensure the safety of the object during the grasping process. On this basis, the minimum angle criterion is further met to improve the grasping efficiency. This patent uses the minimum force-minimum angle criterion as the objective function of the grasping posture planning method, and uses this objective function to accurately control the grasping posture of the dexterous hand. In order to construct this objective function, this method transforms the above problem into a minimization problem, that is, by finding a set of suitable joint angle matrices, the combined force of the friction force exerted by the dexterous hand on the object and the gravity acting on the object is minimized, while ensuring that the change in the joint angle matrix is ​​also minimized.

[0068] As a preferred embodiment of the present invention, Figure 2As shown, the specific process of the improved horned lizard optimization algorithm in step S3 obtaining the optimal multi-joint angle matrix of the grasping posture according to the objective function is as follows: step S31: setting algorithm parameters and initializing the population using Logistic-tent chaotic mapping; step S32: judging whether the initialized population is in the first half of the iteration, if it is in the first half, adopting a hiding strategy for individuals with good fitness, and judging whether to adopt a blood-spraying attack strategy or a moving escape strategy by generating random numbers (random numbers less than 0.3) for individuals with poor fitness; if it is not in the first half, adopting a reverse blood-spraying attack strategy for individuals with good fitness, and judging whether to adopt a hiding strategy or a moving escape strategy by generating random numbers (random numbers less than 0.3); step S33: selecting the individual with the worst fitness to update the position using the skin darkening and brightening strategy, calculating the MSH rate and judging, if the MSH rate is less than 0.3, using the MSH rate position update strategy and adding probability disturbance, and then selecting the optimal individual through the greedy mechanism to judge whether the objective function is met, if not, looping, and ending if met.

[0069] Specifically, the virtual five-finger dexterous hand constructed by this method is as follows:

[0070] CoppeliaSim, a powerful robotics simulation software, provides an ideal environment for the development and testing of virtual robots thanks to its high flexibility and scalability. The virtual five-fingered dexterous hand model constructed within this platform not only closely resembles a real dexterous hand in terms of mechanical structure and motion capabilities, but is also equipped with tactile sensors. These sensors, located at key locations within the dexterous hand, accurately sense the multi-point pressure applied to objects in real time and accurately during grasping.

[0071] The constructed virtual five-fingered dexterous hand is Figure 3 As shown in the figure, first, SOLIDWORKS software is used to generate the STL file of the 11-DOF five-finger dexterous hand prototype. All STL models are then imported into CoppeliaSim software. Second, to effectively control the motion of the virtual five-finger dexterous hand model, joints need to be added. In the virtual five-finger dexterous hand model, the revolute joints are closely connected to the finger structure, forming a unified part, and their structure exhibits an irregular shape. Finally, dynamic features are constructed. To impart dynamic features, the model needs to be converted into a convex body.

[0072] In CoppeliaSim software, convex bodies are approximated by stitching triangles. To optimize visual observation and ensure the normal operation of the model, this method turns off the visual properties of the convex body and sets it as the parent object of the original geometric body. In this way, the motion trajectory of the convex body and the original geometric body are kept consistent, while ensuring the accuracy of dynamic calculations. Regarding the property settings of the convex body, during the simulation process, the object is marked as controllable and dynamic to ensure that it has complete dynamic properties, which further assists in grasping operations, such as Figure 4 As shown in the figure, this method places a total of 12 tactile sensors on the fingers and palm of the virtual five-finger dexterous hand, thereby meeting the mechanical calculation requirements of the virtual five-finger dexterous hand when grasping objects and achieving a more realistic and accurate simulation effect.

[0073] For example, in order to verify the effectiveness of this method, a simulation experiment based on this method was first carried out.

[0074] This experiment uses CoppeliaSim and MATLAB for joint simulation to optimize the grasping postures of three different objects.

[0075] First, the friction coefficient was set to 0.574, and the ICGPP method was used to optimize the grasping postures of the three objects. After optimization, the optimal grasping postures for different objects were successfully obtained: for the cylinder, the envelope grasping posture was obtained; for the cuboid, the precise grasping posture was obtained; and for the cube, the mixed grasping posture was obtained. These optimized grasping postures are as follows: Figure 5 、 Figure 6 and Figure 7 As shown in .

[0076] The improved horned lizard optimization algorithm of this method is used to find the minimum force of three grasping postures. Figure 8 As shown in the figure, the yellow line represents the fitness curve of mixed grasping, the blue line represents the fitness curve of precise grasping, and the red line represents the fitness curve of envelope grasping. It can be seen that the minimum fitness values ​​of the objective function are 、 and The joint angles of the three grasping postures obtained are shown in Table 1, and the minimum force and minimum angle related data finally obtained by the simulation experiment are shown in Table 2. Through the analysis of the data, it can be found that the joint angles corresponding to the grasping posture optimized by the ICGPP method successfully achieved the minimum value of the angle change. This shows that the simulation experiment based on the ICGPP method is feasible, and fully proves that the grasping posture planning method of jointly simulating IHLOA and Coppeliasim can accurately generate envelope grasping, precise grasping and mixed grasping postures that meet the minimum force-minimum angle criteria according to the different shapes of the object.

[0077] Table 1 Grasping posture angle

[0078]

[0079] Table 2. Changes in joint angles and minimum forces of grasping gestures

[0080]

[0081] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. The scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A grasping posture planning method based on an improved horned lizard optimization algorithm combined with simulation, characterized in that: The method comprises the following steps: Step S1: Generate a joint motion range through a virtual five-finger dexterous hand prototype, and set the joint motion range as the upper and lower limits of population initialization in the improved horned lizard optimization algorithm, thereby randomly generating a joint angle matrix for controlling the motion of the virtual five-finger dexterous hand prototype; The improved horned lizard optimization algorithm introduces Logistic-tent chaotic mapping to initialize particles during population initialization, making the generated particles more evenly distributed. The specific formula is as follows: in, Indicates the The value of the random process exhibited by the population at time , represents the value that makes the Logistic-tent map exhibit chaotic behavior in the interval [0,1], Indicates the The value of the random process exhibited by the population at that moment; Step S2: The joint angle matrix is ​​input into a virtual five-finger dexterous hand in the CoppeliaSim simulation environment. The virtual five-finger dexterous hand performs an object grasping operation based on the received joint angle matrix, and feeds back the multi-point pressure and tactile information obtained during the grasping process to the improved horned lizard optimization algorithm. The improved horned lizard optimization algorithm sums the received multi-point pressure and tactile information with the object's gravity to obtain a resultant force. In order to improve the particle exploration ability, the improved horned lizard optimization algorithm introduces new particles generated by the probabilistic perturbation strategy during the iterative optimization process; In order to accurately judge the superiority between the fitness of the new particles generated by the probability perturbation strategy and the fitness of the original particles, the improved horned lizard optimization algorithm introduces a greedy mechanism in the probability perturbation strategy. The probability perturbation strategy is based on the dimension of the objective function. , current iteration number and the maximum number of iterations Calculating perturbation probability and update the position, and then determine the position according to the greedy mechanism; the disturbance probability The calculation formula is as follows: The updated position calculation formula is as follows: in, Indicates the updated location. Indicates the The search position of the generation, Indicates the joint motion with 3 degrees of freedom; Step S3: The improved horned lizard optimization algorithm analyzes the obtained resultant force, and based on the minimum force-minimum angle criterion as the objective function, selects the multi-joint angle matrix corresponding to the objective function, uses it as the optimal multi-joint angle matrix for the grasping posture, and outputs it as feedback to the virtual five-finger dexterous hand to achieve the optimal grasping plan.

2. The grasping posture planning method based on the improved horned lizard optimization algorithm combined with simulation according to claim 1 is characterized in that: In step S2, the improved horned lizard optimization algorithm introduces a fitness adjustment strategy to balance the exploration ability of particles in the algorithm. The fitness adjustment strategy calculates the fitness value of the current particle and selects a position update strategy that is adapted to the fitness of the particle according to the fitness of the particle. When the number of iterations is large and the particle fitness is still not ideal, a reverse bleeding attack strategy is introduced to the position update strategy to increase the influence weight of the optimal position individual in the later iteration. The specific formula is as follows: in, represents the initial velocity of the particle, represents the error coefficient, represents the gravitational acceleration in projectile motion, Indicates the The best search position for the generation, Represents a constant.

3. The grasping posture planning method based on the improved horned lizard optimization algorithm combined with simulation according to claim 2 is characterized in that: The specific formula for taking the minimum force-minimum angle criterion as the objective function in step S3 is as follows: in, is a function representing the net force, A function representing an angle, represents the joint angle matrix, represents the friction coefficient, Indicates the pressure value fed back by different pressure sensors. Indicates the mass of the object being grasped, represents the acceleration due to gravity, represents the initial joint angle, Indicates the joint angle corresponding to the grasping gesture, Indicates the minimum value operation.

4. The grasping posture planning method based on the improved horned lizard optimization algorithm combined with simulation according to claim 3 is characterized in that: The specific process of improving the horned lizard optimization algorithm in step S3 to obtain the optimal multi-joint angle matrix of the grasping posture according to the objective function is as follows: Step S31: setting algorithm parameters and initializing the population using Logistic-tent chaotic mapping; Step S32: Determine whether the initialized population is in the first half of the iteration. If so, adopt a hiding strategy for individuals with good fitness, and determine whether to adopt a blood-spraying attack strategy or a moving escape strategy for individuals with poor fitness by generating random numbers. If not, adopt a reverse blood-spraying attack strategy for individuals with good fitness, and determine whether to adopt a hiding strategy or a moving escape strategy for individuals with poor fitness by generating random numbers. Step S33: Select the individual with the worst fitness and update its position using the skin darkening and brightening strategy, calculate the MSH rate and make a judgment. If the MSH rate is less than 0.3, use the MSH rate position update strategy and add probability disturbance. Then select the optimal individual through the greedy mechanism to judge whether the objective function is met. If not, loop again, otherwise end.

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