Mechanical arm motion planning method for automatic stem cell preparation

Abstract

The embodiment of the invention provides a motion planning method for a mechanical arm for automatic preparation of stem cells, and belongs to the technical field of computation.The motion planning method specifically comprises the steps that 1, an RRT-Connect algorithm is adopted to plan a collision-free motion path node sequence of the mechanical arm moving to a stem cell culture bottle; step 2, constructing an interpolation model corresponding to the collision-free motion path node sequence by adopting a five-order NURBS curve, and constructing a multi-objective optimization problem taking the optimal time, the lowest energy consumption and the minimum impact as objectives according to the interpolation model; and 3, the multi-objective optimization problem is solved, and a uniformly-distributed Pareto leading-edge solution set is generated to serve as a final motion path scheme of the mechanical arm. Through the scheme of the invention, the efficiency, precision and adaptability of path planning are improved.

Description

Technical Field

[0001] The disclosed embodiments relate to the field of computing technology, and more particularly to a robotic arm motion planning method for automated stem cell preparation. Background Art

[0002] Currently, in the automated preparation of stem cells, the complex operating environment created by precision medical devices places stringent demands on the mechanical system, and the movement of the robotic arm must strictly avoid obstacles. In addition, during the transportation of cell culture flasks, the optimization of motion control parameters will directly affect cell activity: when the acceleration of the robotic arm suddenly changes, the fluid disturbance caused by inertial impact may destroy the integrity of the cell membrane; and although overly conservative motion strategies can reduce mechanical stress, they may cause cell metabolic disorders due to prolonged operation cycles. This requires that motion trajectory planning must simultaneously meet multiple constraints of optimal time, minimum energy consumption, and minimum impact. However, traditional multi-objective optimization algorithms generally face the difficult problem of balancing convergence rate and solution set distribution in engineering applications.

[0003] It can be seen that there is an urgent need for a robotic arm motion planning method for automated stem cell preparation with high path planning efficiency, accuracy and adaptability. Summary of the Invention

[0004] In view of this, the embodiments of the present disclosure provide a robotic arm motion planning method for automated stem cell preparation, which at least partially solves the problems of poor path planning efficiency, accuracy and adaptability in the prior art.

[0005] The present disclosure provides a method for robotic arm motion planning for automated stem cell preparation, comprising:

[0006] Step 1: Use the RRT-Connect algorithm to plan a collision-free motion path node sequence for the robot arm to move to the stem cell culture bottle;

[0007] Step 2: Use fifth-order NURBS curves to construct an interpolation model corresponding to the node sequence of the collision-free motion path, and based on this, construct a multi-objective optimization problem with the goals of optimal time, minimum energy consumption, and minimum impact;

[0008] Step 3: Solve the multi-objective optimization problem and generate a uniformly distributed Pareto frontier solution set as the final motion path solution for the robotic arm.

[0009] According to a specific implementation of the embodiment of the present disclosure, step 1 specifically includes:

[0010] Step 1.1: Construct a spherical envelope model based on the robot arm's configuration parameters. Set the center of the base coordinate system as the sphere center and the maximum reach as the radius to generate a spherical protection zone. Furthermore, based on the cylindrical geometry of the robot arm's connecting rod, a cylindrical encapsulation structure is used to wrap each motion unit layer by layer, establishing a hierarchically optimized interference detection architecture.

[0011] Step 1.2: Digitally model obstacles in the stem cell culture environment. By analyzing the external features of the biomedical device, a standardized geometric model is used to describe it. The spatial relationship of the outermost container is first verified. If no intersection is found in this layer of geometry, all subsequent sub-level detection processes are immediately terminated.

[0012] In step 1.3, the robotic arm and stem cell culture flask are used as root nodes respectively, and the RRT-Connect algorithm is used to iteratively perform random sampling, step size limitation, and collision detection until a collision-free motion path node sequence is obtained.

[0013] According to a specific implementation of the embodiment of the present disclosure, the step of describing using a standardized geometric model includes:

[0014] The circular tubular container is adapted to the axially symmetrical cylindrical shape for description, and the square instrument is adapted to the hexahedral structure for description.

[0015] According to a specific implementation of the embodiment of the present disclosure, the expression of the multi-objective optimization problem is:

[0016]

[0017] Among them, F1, F2 and F3 represent the time, energy and impact objective functions respectively. j P(t), j V(t), j A(t) and j J(t) represents the angular displacement, angular velocity, angular acceleration and angular jerk of the joint j of the manipulator at time t, j Q min 、 j Q max 、 j V max 、 j A max and j J max They represent the minimum angular displacement, maximum angular displacement, maximum angular velocity, maximum angular acceleration and maximum angular jerk of the robot joint j respectively, and T represents the total running time.

[0018] According to a specific implementation of the embodiment of the present disclosure, step 3 specifically includes:

[0019] Step 3.1, set the basic parameters of the fast non-dominated sorting algorithm and initialize the population using the good point set theory, where the basic parameters include the population size, the number and value range of decision variables, and the maximum number of iterations;

[0020] Step 3.2: Use the fast non-dominated sorting algorithm to divide the initialized population into levels and generate a Pareto optimal solution set hierarchy {L1, L2, ..., L i ,…,L N}, individuals in the same level are sorted by crowding, where L i represents the frontier of the i-th layer, i=1,...N;

[0021] Step 3.3, use the dynamic grid algorithm to select the optimal frontier layer L1 and the lowest adaptation layer L N Select the global leader With reverse guide

[0022] Step 3.4: Based on the sorting results and the evolutionary stage of the educational competition algorithm, the population is divided into student roles and school roles, and the update operator of the educational competition algorithm is generated.

[0023] Step 3.5: Generate the golden sine update operator according to the preset formula And perform weighted update based on this to generate the offspring population;

[0024] Step 3.6, apply the reverse learning strategy to construct the reverse candidate solution set;

[0025] Step 3.7, perform fast non-dominated sorting on the joint population consisting of the original population, the offspring population and its reverse candidate solution set;

[0026] Step 3.8, select the individuals with the top NP ranking to form a new population;

[0027] In step 3.9, when the preset maximum number of iterations is reached, the currently obtained Pareto optimal solution set will be output. If the preset maximum number of iterations is not reached, step 3.3 must be repeated for continuous optimization.

[0028] In step 3.10, the comprehensive fuzzy membership method is used to screen the Pareto optimal solution in the final result to obtain the final motion path plan of the robotic arm.

[0029] According to a specific implementation of the embodiment of the present disclosure, the evolutionary stages include a basic education stage, an advanced screening stage, and an elite training stage;

[0030] The expression of the update operator in the basic education stage is:

[0031]

[0032] The expression of the update operator in the advanced screening stage is

[0033]

[0034] The expression of the elite training stage is

[0035]

[0036] Among them, NP represents the population size, It max represents the maximum number of iterations, Indicates the current location, represents the update position, t represents the current number of iterations, w represents the adaptive step size, randn represents a random variable that obeys the normal distribution, Levy(dim) represents the Levy distribution, close(x) represents the school individual closest to x; H represents the judgment threshold of learning talent, R1 and R2 simulate the talent values ​​of different students and take random values ​​in the range of [0,1]. represents the current global optimal position, represents the average position of the population, Indicates the current global worst position.

[0037] According to a specific implementation of the embodiment of the present disclosure, the golden sine update operator The expression is

[0038]

[0039] The expression of the weighted update is

[0040]

[0041] Among them, r1 and r2 are random numbers in [0,2π] and [0,π] respectively, and g is the golden section number. α represents the weight of the golden sine update strategy.

[0042] According to a specific implementation of the embodiment of the present disclosure, the expression of the reverse learning strategy is:

[0043]

[0044] Among them, the scaling factor γ is adaptively adjusted according to the number of iterations.

[0045] The robotic arm motion planning scheme for automated stem cell preparation in the disclosed embodiment includes: step 1, using the RRT-Connect algorithm to plan a collision-free motion path node sequence for the robotic arm to move to the stem cell culture bottle; step 2, using a fifth-order NURBS curve to construct an interpolation model corresponding to the collision-free motion path node sequence, and based on this, constructing a multi-objective optimization problem with the goals of optimizing time, minimizing energy consumption, and minimizing impact; step 3, solving the multi-objective optimization problem to generate a uniformly distributed Pareto front solution set as the final motion path scheme for the robotic arm.

[0046] The beneficial effects of the embodiments of the present disclosure are as follows: through the scheme of the present disclosure, in the multi-objective trajectory planning method of the manipulator, the RRT-Connect algorithm is used to plan the collision-free motion path of the manipulator, and the segmented trajectory interpolation is performed through the fifth-order NURBS curve to ensure the geometric continuity of the path. For the multi-objective optimization problem with the goals of optimal time, minimum energy consumption and minimum impact in the trajectory planning of the manipulator, an improved non-dominated sorting educational competitive optimization algorithm is adopted, which integrates the good point set initialization strategy, the pinhole imaging reverse learning mechanism and the golden sine update method to effectively enhance the global optimization ability of the algorithm. The algorithm dynamically optimizes the control vertices of the NURBS trajectory, and generates a uniformly distributed Pareto front solution set in the three-dimensional target space composed of time-energy consumption-impact under the premise of strictly satisfying the constraints of the manipulator joint angular velocity, angular acceleration and angular acceleration. Compared with traditional methods, the scheme proposed by the present invention can achieve the coordinated optimization of execution efficiency and system dynamic characteristics in the trajectory planning process, thereby improving planning efficiency, accuracy and adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0048] Figure 1 A schematic flow chart of a robotic arm motion planning method for automated stem cell preparation according to an embodiment of the present disclosure;

[0049] Figure 2 A Pareto frontier diagram for solving a multi-objective trajectory planning model for a robotic arm provided in an embodiment of the present disclosure;

[0050] Figure 3 A schematic diagram of the comprehensive fuzzy membership of non-dominated solutions in a Pareto front provided by an embodiment of the present disclosure;

[0051] Figure 4A schematic diagram of the continuity characteristics of the angular displacement curves of each joint and its high-order kinematic parameters provided by an embodiment of the present disclosure, wherein (a) represents angular displacement, (b) represents angular velocity, (c) represents angular acceleration, and (d) represents angular jerk;

[0052] Figure 5 A schematic diagram showing energy consumption comparison results before and after optimization of each joint provided in an embodiment of the present disclosure, wherein (a) represents joint 1, (b) represents joint 2, (c) represents joint 3, (d) represents joint 4, (e) represents joint 5, and (f) represents joint 6;

[0053] Figure 6 A schematic diagram showing the impact comparison results before and after optimization of each joint provided in an embodiment of the present disclosure, wherein (a) represents joint 1, (b) represents joint 2, (c) represents joint 3, (d) represents joint 4, (e) represents joint 5, and (f) represents joint 6;

[0054] Figure 7 A schematic diagram of a simulated obstacle avoidance process of a robotic arm provided in an embodiment of the present disclosure, wherein (a) represents an initial state, (b) represents transition state one, (c) represents transition state two, and (d) represents a target state. DETAILED DESCRIPTION

[0055] The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

[0056] The following describes the embodiments of the present disclosure through specific examples, and those skilled in the art can easily understand other advantages and effects of the present disclosure from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all of the embodiments. The present disclosure can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that, in the absence of conflict, the following embodiments and features in the embodiments can be combined with each other. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present disclosure.

[0057] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein can be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on this disclosure, it should be understood by those skilled in the art that an aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects described herein can be used to implement an apparatus and / or practice a method. In addition, other structures and / or functionalities other than one or more of the aspects described herein can be used to implement this apparatus and / or practice this method.

[0058] It should also be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present disclosure. The illustrations only show components related to the present disclosure and are not drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component can be changed at will, and the component layout type may also be more complicated.

[0059] Additionally, in the following description, specific details are provided to provide a thorough understanding of the examples. However, one skilled in the art will appreciate that the aspects described can be practiced without these specific details.

[0060] The present disclosure provides a method for robotic arm motion planning for automated stem cell preparation, which can be applied to the automated stem cell preparation process.

[0061] See also Figure 1 , is a flow chart of a robotic arm motion planning method for automated stem cell preparation according to an embodiment of the present disclosure. Figure 1 As shown, the method mainly includes the following steps:

[0062] Step 1: Use the RRT-Connect algorithm to plan a collision-free motion path node sequence for the robot arm to move to the stem cell culture bottle;

[0063] In specific implementation, a rapid-exploring random tree path search algorithm (RRT-Connect) can be used to generate a collision-free motion path node sequence based on the manipulator collision detection strategy based on the hierarchical bounding box theory. The specific steps are as follows:

[0064] 1) A spherical envelope model is constructed based on the configuration parameters of the robotic arm. The center of the base coordinate system is set as the center of the sphere, and the maximum extension distance is used as the radius to generate a spherical protection area. In view of the cylindrical geometric characteristics of the robotic arm connecting rod, a cylindrical packaging structure is used to wrap each motion unit layer by layer to establish a hierarchically optimized interference detection architecture.

[0065] 2) Digital modeling of obstacles in stem cell culture environments requires analyzing the external features of biomedical devices and describing them using standardized geometric models. For example, circular tubular containers can be adapted to axially symmetrical cylinders, while large square instruments can be approximated using hexahedrons.

[0066] 3) Using the robotic arm and stem cell culture flask as root nodes, the RRT-Connect algorithm iteratively performs random sampling, step-size limiting, and collision detection until a collision-free motion path node sequence is obtained. Specifically, the system prioritizes verifying the spatial relationships of the outermost containers. If no intersection occurs at this level of geometry, all subsequent sub-level detection processes are immediately terminated. This hierarchical verification strategy, leveraging the principle of spatial exclusion, eliminates a large number of non-interacting objects at an early stage, significantly reducing the frequency of triggering accurate collision calculations.

[0067] Step 2: Use fifth-order NURBS curves to construct an interpolation model corresponding to the node sequence of the collision-free motion path, and based on this, construct a multi-objective optimization problem with the goals of optimal time, minimum energy consumption, and minimum impact;

[0068] In specific implementation, after obtaining the collision-free motion path node sequence of the robotic arm, the present invention also discloses a novel multi-target trajectory planning method for the robotic arm, and the specific steps are as follows:

[0069] In the joint space, the fifth-order NURBS curve is used to construct the interpolation model of the robot arm motion trajectory. On this basis, the interval time is taken To control variables, a robot trajectory optimization problem is constructed with time, energy consumption and impact as optimization objectives:

[0070]

[0071] Where F1, F2 and F3 represent the time, energy and impact objective functions respectively; j P(t), j V(t), j A(t) and j J(t) represents the angular displacement, angular velocity, angular acceleration and angular jerk of the joint j of the manipulator at time t respectively; j Q min 、 j Q max 、 j V max 、 j A max and j J max They represent the minimum angular displacement, maximum angular displacement, maximum angular velocity, maximum angular acceleration and maximum angular jerk of the robot arm joint j respectively; T represents the total running time.

[0072] Step 3: Solve the multi-objective optimization problem and generate a uniformly distributed Pareto frontier solution set as the final motion path solution for the robotic arm.

[0073] In specific implementation, the specific process of solving the multi-objective optimization problem and generating the final motion path plan for the robotic arm includes:

[0074] 1) Set the basic parameters of the algorithm, including population size, number of decision variables and their value range, and maximum number of iterations, and initialize the population using the good point set theory.

[0075] 2) Execute the fast non-dominated sorting algorithm to establish the population hierarchical structure {L1, L2, ...L N}, forming a Pareto ranking sequence. In the fast non-dominated sorting algorithm, each individual is associated with two key parameters: Q m represents the set of individuals dominated by individual m; C m Record the total number of individuals that dominate the current individual m. The algorithm execution process is as follows: First, C m = 0 are assigned to the first non-dominated level L1; secondly, for the current level L k For each individual m in the m Individual n in the execution C n Subtract one operation, if C n = 0, then individual n is classified into L k+1 Repeat step 2 until all individual layers are completed, forming {L1, L2, ...L N} Pareto hierarchy structure. Individuals in the same level are sorted by the degree of crowding, where the degree of crowding CD v The definition is as follows:

[0076]

[0077] Where, Represents the value of the objective function of individual v at the uth position; and They represent the minimum and maximum population values ​​of the u-th objective function respectively; m represents the number of objective functions.

[0078] 3) Using the dynamic grid algorithm, we can select the optimal frontier layer L1 and the lowest adaptation layer L N Select the global leader With reverse guide

[0079] 4) Based on the sorting results and the evolutionary stage of the algorithm, the population is divided into student and school roles, and the ECO update operator is generated. Among them, the basic education stage, advanced screening stage and elite training stage are updated according to formula (3), formula (4) and formula (5) respectively.

[0080]

[0081] Where NP represents the population size; It max Indicates the maximum number of iterations; represents the current position; t represents the current iteration number; w represents the adaptive step size; randn represents a random variable that follows a normal distribution; Levy(dim) represents the Levy distribution; close(x) represents the school individual closest to x; H represents the judgment threshold of learning talent, which is set to 0.5; R1 and R2 simulate the talent values ​​of different students and take random values ​​in the range of [0,1]. Indicates the current global optimal position; represents the average position of the population; Indicates the current global worst position.

[0082] 5) Generate the golden sine update operator according to formula (6) And perform weighted mixed position update according to formula (7) to generate the offspring population.

[0083]

[0084] Where r1 and r2 are random numbers in [0, 2π] and [0, π] respectively; g is the golden section number, which takes the value α represents the weight of the golden sine update strategy.

[0085] 6) Execute the reverse learning mechanism according to formula (8) to generate a set of reverse candidate solutions.

[0086]

[0087] In the formula, the scaling factor γ is adaptively adjusted according to the number of iterations.

[0088] 7) Perform fast non-dominated sorting on the original population, the offspring population, and the joint population constructed by their reverse decomposition.

[0089] 8) Select the individuals with the top NP ranking to form a new population.

[0090] 9) When the algorithm reaches the preset maximum number of iterations, the currently obtained Pareto frontier optimal solution set is output; if the termination criteria are not met, step 4 must be repeated for continued optimization. When all termination criteria are met, the system generates a non-dominated solution set as the final result and stops the operation.

[0091] 10) The comprehensive fuzzy membership method is used to screen the optimal solution, and its mathematical expression is shown in formula (9).

[0092]

[0093] Where, O i (n) represents the i-th target value in the n-th optimal solution; i～max and O i～min They represent the extreme value of the i-th objective in the solution set; M represents the number of optimization objectives; r i (n) represents the standard fuzzy membership of the i-th target value in the n-th optimal solution; r s (n) represents the comprehensive fuzzy membership of the nth optimal solution.

[0094] The present embodiment provides a robotic arm motion planning method for automated stem cell preparation. In the robotic arm multi-objective trajectory planning method, the RRT-Connect algorithm is used to plan the collision-free motion path of the robotic arm, and the segmented trajectory interpolation is performed through a fifth-order NURBS curve to ensure the geometric continuity of the path. For the multi-objective optimization problem in robotic arm trajectory planning with the goals of optimal time, minimum energy consumption, and minimum impact, an improved non-dominated sorting educational competitive optimization algorithm is adopted, which integrates the good point set initialization strategy, the pinhole imaging reverse learning mechanism, and the golden sine update method to effectively enhance the algorithm's global optimization capability. The algorithm dynamically optimizes the control vertices of the NURBS trajectory and generates a uniformly distributed Pareto front solution set in the three-dimensional target space composed of time, energy consumption, and impact, under the premise of strictly satisfying the constraints of the robotic arm joint angular velocity, angular acceleration, and angular acceleration. Compared with traditional methods, the solution proposed in the present invention can achieve coordinated optimization of execution efficiency and system dynamic characteristics during the trajectory planning process, thereby improving planning efficiency, accuracy, and adaptability.

[0095] The method disclosed herein will be further described below in conjunction with a specific embodiment. First, a collision detection algorithm based on a hierarchical bounding box and an RRT-Connect algorithm are used to plan a collision-free motion path for the robotic arm, as shown in Table 1.

[0096] Table 1

[0097]

[0098]

[0099] Then, based on the collision-free path node sequence, a fifth-order NURBS curve is used to construct an interpolation model of the robot's motion trajectory. This paper takes the JAKA ZU5 six-DOF collaborative robot as the research object, and the constraints of its joints are shown in Table 2.

[0100] Table 2

[0101]

[0102] The subsequent detailed steps are as follows:

[0103] The population size is set to 100, the maximum number of iterations is set to 50, and the range of decision variables is determined by the joint constraints of the manipulator in Table 2.

[0104] The Pareto frontier of the multi-objective trajectory optimization problem of the robotic arm is obtained by using the improved non-dominated sorting educational competitive optimization algorithm. Figure 2 shown.

[0105] In practical engineering applications, it is usually necessary to select the optimal solution from the Pareto solution set. To this end, this patent uses a comprehensive fuzzy membership method for quantitative analysis. The overall analysis results are as follows: Figure 3 shown.

[0106] Analyzing the results, the non-dominated solution numbered 21 performs best, and the time interval division scheme corresponding to this solution is

[0107] t i+1 -t i =[1.1460,0.7541,0.9881,0.8459,0.6044,0.8026,0.6667,0.9707].

[0108] Based on this scheme, combined with the control vertices of each joint trajectory, the change rules of each joint angular displacement, angular velocity, angular acceleration and angular jerk can be derived. Its dynamic characteristic curve is as follows: Figure 4 shown.

[0109] Through the visual comparative analysis of experimental data ( Figure 5 and Figure 6 ), the robot arm trajectory planning optimization method proposed in the present invention breaks through the technical bottleneck of the difficult balance between efficiency and energy consumption in traditional trajectory planning, and simultaneously reduces energy loss and system impact on the basis of improving operation efficiency.

[0110] A simulation platform was built in MATLAB 2023a to verify the obstacle avoidance process of the robotic arm. Figure 7 As shown in FIG, the simulation results show that the solution proposed in the present invention can achieve accurate obstacle avoidance of the robotic arm.

[0111] It should be understood that various parts of the present disclosure can be implemented in hardware, software, firmware, or a combination thereof.

[0112] The above description is merely a specific embodiment of the present disclosure, but the scope of protection of the present disclosure is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this disclosure should be included in the scope of protection of the present disclosure. Therefore, the scope of protection of the present disclosure should be based on the scope of protection of the claims.

Claims

1. A robotic arm motion planning method for automated stem cell preparation, characterized in that: include: Step 1: Use the RRT-Connect algorithm to plan a collision-free motion path node sequence for the robot arm to move to the stem cell culture bottle; Step 2: Use fifth-order NURBS curves to construct an interpolation model corresponding to the node sequence of the collision-free motion path, and based on this, construct a multi-objective optimization problem with the goals of optimal time, minimum energy consumption, and minimum impact; Step 3: Solve the multi-objective optimization problem and generate a uniformly distributed Pareto frontier solution set as the final motion path solution for the robotic arm.

2. The method according to claim 1, characterized in that The step 1 specifically includes: Step 1.1: Construct a spherical envelope model based on the robot arm's configuration parameters. Set the center of the base coordinate system as the sphere center and the maximum reach as the radius to generate a spherical protection zone. Furthermore, based on the cylindrical geometry of the robot arm's connecting rod, a cylindrical encapsulation structure is used to wrap each motion unit layer by layer, establishing a hierarchically optimized interference detection architecture. Step 1.2: Digitally model obstacles in the stem cell culture environment by analyzing the external features of biomedical devices and describing them using standardized geometric models. In step 1.3, the robotic arm and stem cell culture flask are used as root nodes respectively, and the RRT-Connect algorithm is used to iteratively perform random sampling, step size limitation, and collision detection until a collision-free motion path node sequence is obtained.

3. The method according to claim 2, characterized in that The steps of describing the method using a standardized geometric model include: The circular tubular container is adapted to the axially symmetrical cylindrical shape for description, and the square instrument is adapted to the hexahedral structure for description.

4. The method according to claim 3, characterized in that The expression of the multi-objective optimization problem is: Among them, F1, F2 and F3 represent the time, energy and impact objective functions respectively. j P(t), j V(t), j A(t) and j J(t) represents the angular displacement, angular velocity, angular acceleration and angular jerk of the joint j of the manipulator at time t, j Q min 、 j Q max 、 j V max 、 j A max and j J max They represent the minimum angular displacement, maximum angular displacement, maximum angular velocity, maximum angular acceleration and maximum angular jerk of the robot joint j respectively, and T represents the total running time.

5. The method according to claim 4, characterized in that The step 3 specifically includes: Step 3.1, set the basic parameters of the fast non-dominated sorting algorithm and initialize the population using the good point set theory, where the basic parameters include the population size, the number and value range of decision variables, and the maximum number of iterations; Step 3.2: Use the fast non-dominated sorting algorithm to divide the initialized population into levels and generate a Pareto optimal solution set hierarchy {L1, L2, ..., L i ,…,L N }, individuals in the same level are sorted by crowding, where L i represents the frontier of the i-th layer, i=1,...N; Step 3.3, use the dynamic grid algorithm to select the optimal frontier layer L1 and the lowest adaptation layer L N Select the global leader With reverse guide Step 3.4: Based on the sorting results and the evolutionary stage of the educational competition algorithm, the population is divided into student roles and school roles, and the update operator of the educational competition algorithm is generated. Step 3.5: Generate the golden sine update operator according to the preset formula And perform weighted update based on this to generate the offspring population; Step 3.6, apply the reverse learning strategy to construct the reverse candidate solution set; Step 3.7, perform fast non-dominated sorting on the joint population consisting of the original population, the offspring population and its reverse candidate solution set; Step 3.8, select the individuals with the top NP ranking to form a new population; In step 3.9, when the preset maximum number of iterations is reached, the currently obtained Pareto optimal solution set will be output. If the preset maximum number of iterations is not reached, step 3.3 needs to be executed again for continuous optimization. In step 3.10, the comprehensive fuzzy membership method is used to screen the Pareto optimal solution in the final result to obtain the final motion path plan of the robotic arm.

6. The method according to claim 5, characterized in that The said evolutionary stages include the basic education stage, the advanced screening stage and the elite training stage; The expression of the update operator in the basic education stage is: The expression of the update operator in the advanced screening stage is The expression of the elite training stage is Among them, NP represents the population size, It max represents the maximum number of iterations, Indicates the current location, represents the update position, t represents the current number of iterations, w represents the adaptive step size, randn represents a random variable that obeys the normal distribution, Levy(dim) represents the Levy distribution, close(x) represents the school individual closest to x; H represents the judgment threshold of learning talent, R1 and R2 simulate the talent values ​​of different students and take random values ​​in the range of [0,1]. represents the current global optimal position, represents the average position of the population, Indicates the current global worst position.

7. The method according to claim 6, characterized in that The golden sine update operator The expression is The expression of the weighted update is Among them, r1 and r2 are random numbers in [0,2π] and [0,π] respectively, and g is the golden section number. α represents the weight of the golden sine update strategy.

8. The method according to claim 7, characterized in that The expression of the reverse learning strategy is: Among them, the scaling factor γ is adaptively adjusted according to the number of iterations.

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