Robot time optimal trajectory planning method based on dynamics constraint

Abstract

The invention belongs to the technical field of robot control, and particularly discloses a robot time optimal trajectory planning method based on dynamic constraints, which comprises the following steps: establishing a dynamic model, initializing a genetic algorithm population, and generating a joint space trajectory parameter set; performing genetic manipulation on the population; evaluating the trajectory execution time of each trajectory parameter group, and judging whether a constraint condition is met or not; if the conditions are not met, continuing iteration, otherwise, screening a plurality of trajectory parameter groups as local optimization initial solutions; taking the initial solution as input, constructing a trajectory model based on a B-spline curve, and adopting a sequential quadratic programming method to carry out iterative optimization on a trajectory parameter group under constraint conditions; and comparing the plurality of optimized trajectory schemes, and outputting a group which meets the constraint and has the shortest trajectory time as a final trajectory. According to the method, the global search capability of a genetic algorithm and the local fine optimization capability of sequential quadratic programming are fused, and global optimization of trajectory execution time can be realized under complex physical constraints.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of robot control, and particularly relates to a robot time-optimal trajectory planning method based on dynamics constraints. BACKGROUND

[0002] With the development of intelligent manufacturing and flexible automation, industrial robots are widely used in the fields of automobile manufacturing, 3C assembly, metal processing, etc., and the motion control and trajectory planning technology of the robots is gradually evolving from the traditional teaching and reproduction mode to the high-performance autonomous planning direction. In the working scene with high beat, high precision and high dynamic load, the robot not only needs to complete the fitting of the spatial path, but also needs to realize the optimization of the running time under the premise of meeting the dynamics and kinematics constraints, so as to maximize the production efficiency and response ability of the system.

[0003] The existing robot trajectory planning method is based on preset path and time parameters in industrial applications, and the trajectory is generated by interpolation or spline function, and then part of the parameters is adjusted by offline simulation or limited optimization means to meet the kinematics and dynamics constraints.

[0004] Although this method is convenient to implement, it often has problems such as long trajectory execution time, slow reaction to physical constraints, lack of global optimality, etc. in high dynamic tasks, and it is difficult to meet the trajectory quality requirements of high-speed and high-precision robot systems. On the other hand, traditional optimization methods such as SQP, QP, etc. are sensitive to initial values and are easy to fall into local minimum; although the pure genetic algorithm has certain global search ability, the convergence speed is slow, and the feasibility of the solution cannot be fully guaranteed; in the robot path optimization problem with complex constraints and multi-variable coupling, the traditional method lacks the ability to effectively integrate the dynamics model and the trajectory structure characteristics, and cannot achieve efficient and high-precision time-optimal trajectory output from a global perspective. SUMMARY

[0005] The application provides a robot time-optimal trajectory planning method based on dynamics constraints to solve the problem of being unable to achieve efficient and high-precision time-optimal trajectory output from a global perspective in the background art.

[0006] The technical scheme adopted by the application is as follows: The application provides a robot time-optimal trajectory planning method based on dynamics constraints, which comprises the following steps: Step S1, establishing a dynamics model, initializing a population, and generating a plurality of groups of trajectory parameters according to preset parameters; Step S2, performing genetic operations on the population to generate a new generation of trajectory parameter groups; Step S3, evaluating the fitness of each trajectory parameter group; Step S4, judging whether a preset termination condition is met or not; If the preset termination condition is not met, returning to execute step S2; Otherwise, executing step S5; Step S5, screening multiple trajectory parameter groups from the current population as initial solutions for subsequent optimization; Step S6, performing local optimization respectively with the screened trajectory parameter groups as initial inputs; Step S7, comparing the multiple trajectory parameter groups obtained through local optimization, and determining a group meeting the constraint condition and having the shortest trajectory time as the final output.

[0007] Further, in step S1, the following steps are included: Step S1-1, constructing a dynamic model based on the structural parameters of the target robot; Step S1-2, determining the dimension and boundary condition of the trajectory parameters according to the number of joints and the number of trajectory segments; The boundary condition includes the position range of the B-spline control points, the node time distribution range, and the individual number and coding length of the genetic algorithm population set; Step S1-3, describing the trajectory in the joint space of the robot using a quintic B-spline curve, expressing each trajectory as a control point sequence and a node time vector, and constructing the trajectory parameter group of a single individual in the population; Step S1-4, generating multiple trajectory parameter groups in a random disturbance or uniform distribution manner according to the trajectory structure and value range, as the initial population of the genetic algorithm.

[0008] Further, in step S1-1, the dynamic model is:

[0009] wherein, is the position of the joint; is the velocity of the joint; is the acceleration of the joint; is the torque vector of the joint; is the inertia matrix; is the centripetal force matrix; is the gravity vector; is the external disturbance, is the friction vector; is the external force acting on the end effector; is the Jacobian matrix.

[0010] Furthermore, in step S2, samples of the trajectory parameter groups in the population are selected, the trajectory control points and time nodes are combined and reorganized using the set crossover strategy, and disturbances are added to the control points or time nodes in the trajectory parameter group with a preset probability to generate a new generation of trajectory parameter groups.

[0011] Furthermore, in step S3, the fitness evaluation is based on the trajectory execution time, and determines whether each set of trajectory parameters satisfies the speed constraint, torque constraint, and trajectory accuracy constraint.

[0012] Furthermore, in step S4, the termination condition is set to and / or ;

[0013] in, is the Lagrangian function; is the gradient of the Lagrangian function with respect to the independent variable x, , To optimize the condition threshold, it can be set by the user. is the i-th Lagrange multiplier vector, is the i-th constraint function, is the change in the constraint target, is the optimal solution to the constraint objective.

[0014] Furthermore, in step S5, the genetic algorithm elite solution set is defined as:

[0015] in Sort by fitness in ascending order, = is the minimum Euclidean distance, is the diversity threshold, which is 5% to 10% of the diameter of the solution space; in, For candidate solutions, For individuals who have been selected into the elite set, Sort the fitness of all solutions in the population in ascending order, is the fitness function, K is the elite size threshold, and E is the set of elite solutions that have met the conditions.

[0016] Furthermore, in step S6, the local optimization includes: Set multiple constraints, including dynamic constraints, kinematic constraints, and trajectory accuracy constraints, to build a local sequence planning time optimization model; The joint motion trajectory model obtained by the quintic B-spline curve is used to obtain the velocity value of the i-th planning point , the speed constraint is that the absolute value of the speed at all times does not exceed the rated limit:

[0017] in is the rated upper limit of the joint speed, is the total exercise time; Kinematic constraints at each point during a given motion Defined as:

[0018] in, Represents the joint number, Number the constraints; Sampling points from the actual curve, the distance from the sampling point to the reference curve is solved in sequence, which is the trajectory accuracy error , according to the trajectory accuracy constraint requirements:

[0019] in, Indicates the error setting value in different areas of the trajectory. When the value is 1, it is a strong constraint for the working condition segment; When the value is 2, it is a weak constraint in the transition section; Set trajectory accuracy constraints at each point during motion : .

[0020] Furthermore, it is allowed to exceed the rated speed for a short period of time, but it cannot exceed the overload speed limit, and set the standard speed limit and overload speed limit , and set the overload speed gain coefficient ,in:

[0021] Absolute speed The overload speed limit cannot be exceeded at any time , if the speed is between the standard limit and the overload limit, that is , set the duration Cannot exceed the preset overload time ; Set the allowable torque constraint overload setting, the overload gain coefficient of the torque constraint A more conservative safety margin than the speed constraint is used in the setting, i.e. ;

[0022] in, is the duration of overload, is the real-time joint torque, is the permissible overload torque of the joint, is the dynamic torque constraint.

[0023] Furthermore, the construction of a local sequence planning time optimization model includes: Set up sequential quadratic programming, add the three constraints above, and aim for time optimization:

[0024] in, is a non-convex objective function, is the first nonlinear constraint, is the second nonlinear constraint, is the n-dimensional real vector of x, m is the number of first constraints, and p is the number of second constraints; Given the time optimization algorithm model:

[0025] in is the time-optimal objective function, is the inequality constraint function in the optimization, which includes three inequalities: speed, torque and trajectory accuracy.

[0026] It can be seen from the above technical solutions that the advantages of the present invention are: By adopting a staged, hybrid optimization strategy, this approach introduces a genetic algorithm at the global search level to generate diverse trajectory candidates. Constrained sequential quadratic programming (SQP) is then used in the local refinement phase to iteratively optimize trajectory parameters, thus balancing global exploration with local precision. Trajectories are parameterized using quintic B-splines in joint space. Combined with a comprehensive dynamics model, the trajectory optimization process uniformly considers multiple constraints, including velocity, torque, and accuracy, ensuring that the resulting trajectory not only minimizes time but also meets the physical feasibility and trajectory continuity required for practical execution. Furthermore, this method incorporates an overload limit and overload duration mechanism, allowing for flexible scheduling of highly dynamic motions within a set safety margin, thereby improving the overall timeliness and robustness of the trajectory. Compared to existing methods, this method effectively reduces trajectory execution time, improves motion smoothness, and enhances the interpretability and deployment reliability of optimization results, making it suitable for the general trajectory planning needs of various industrial robotic arm systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solution of the present invention, the following is a brief introduction to the drawings required for the description. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0028] Figure 1 This is a step diagram of the time-optimal trajectory planning method for robots based on dynamic constraints; Figure 2 Schematic diagram of key points of the end effector trajectory in the embodiment; Figure 3 This is a schematic diagram of the torque continuous overload time constraint in the embodiment; Figure 4 Schematic diagram of trajectory accuracy constraint in the embodiment. DETAILED DESCRIPTION

[0029] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0030] See also Figure 1 As shown, the present invention provides a robot time optimal trajectory planning method based on dynamic constraints, comprising the following steps: Step S1: Establish a dynamic model, initialize the population, and generate several sets of trajectory parameters according to preset parameters; Step S1-1, constructing a dynamic model based on the structural parameters of the target robot; The kinetic model is:

[0031] in, is the position of the joint; is the velocity of the joint; is the acceleration of the joint; is the torque vector of the joint; is the inertia matrix; is the centripetal force matrix; is the gravity vector; For external interference, is the friction vector; is the external force acting on the end effector; is the Jacobian matrix; Step S1-2: Determine the dimension and boundary conditions of the trajectory parameters according to the number of joints and the number of trajectory segments; The boundary conditions include the position range of B-spline control points, the time distribution range of nodes, and the number of individuals and coding length of the genetic algorithm population; Step S1-3: Use quintic B-spline curves to describe the trajectory in the joint space of each robot, and represent each trajectory as a sequence of control points and a node time vector to form a trajectory parameter set of a single individual in the population; Step S1-4: Generate multiple trajectory parameter groups as the initial population of the genetic algorithm in a random perturbation or uniform distribution manner according to the trajectory structure and value range; Step S2: performing genetic operations on the population to generate a new generation trajectory parameter group; Select samples of trajectory parameter groups in the population, combine and reorganize trajectory control points and time nodes using a set crossover strategy, and add disturbances to the control points or time nodes in the trajectory parameter group with a preset probability to generate a new generation of trajectory parameter groups; Step S3, evaluating the fitness of each trajectory parameter group; The fitness evaluation is based on the trajectory execution time and determines whether each set of trajectory parameters meets the speed constraint, torque constraint and trajectory accuracy constraint; Step S4: determine whether the preset termination condition is met; If the preset termination condition is not met, return to step S2; Otherwise, execute step S5; The termination condition is set to and / or ;

[0032] in, is the Lagrangian function; is the gradient of the Lagrangian function with respect to the independent variable x, , To optimize the condition threshold, it can be set by the user. is the i-th Lagrange multiplier vector, is the i-th constraint function, is the change in the constraint target, is the optimal solution to the constraint objective; Step S5: selecting multiple trajectory parameter groups from the current population as initial solutions for subsequent optimization; Define the genetic algorithm elite solution set as:

[0033] in Sort by fitness in ascending order, = is the minimum Euclidean distance, is the diversity threshold, which is 5% to 10% of the diameter of the solution space; in, For candidate solutions, For individuals who have been selected into the elite set, Sort the fitness of all solutions in the population in ascending order, is the fitness function, K is the elite size threshold, and E is the set of elite solutions that have met the conditions.

[0034] Step S6: using the selected trajectory parameter groups as initial inputs, respectively, to perform local optimization; Use forward kinematics to obtain the transformation relationship between joints and ends;

[0035] The position, velocity, and acceleration parameters of the end of the robotic arm are derived using a quintic B-spline curve:

[0036] For active joints, the discrete joint path points are converted into continuous and smooth trajectory functions through quintic B-splines. Then, the first-order derivative of the function in each joint space is calculated to obtain the velocity curve, and the second-order derivative is calculated to obtain the acceleration curve.

[0037]

[0038] At the starting and ending points of the loading and unloading robot, the speed, acceleration, and jerk must all be zero, and the corresponding joint space starting and ending points must also meet the above requirements. By using a quintic B-spline curve to construct the initial trajectory for optimization, the continuity of the robot's position, velocity, and acceleration during movement is achieved; In step S6, the local optimization includes: Set multiple constraints, including dynamic constraints, kinematic constraints, and trajectory accuracy constraints, to build a local sequence planning time optimization model; The joint motion trajectory model obtained by the quintic B-spline curve is used to obtain the velocity value of the i-th planning point , the speed constraint is that the absolute value of the speed at all times does not exceed the rated limit:

[0039] in is the rated upper limit of the joint speed, is the total exercise time; Kinematic constraints at each point during a given motion Defined as:

[0040] wherein, represents the joint number, is the constraint number; The sampling points are taken from the actual curve, and the distance from the sampling points to the reference curve is sequentially solved, which is the trajectory accuracy error , according to the trajectory accuracy constraint requirement:

[0041] wherein, represents the error setting value in different regions of the trajectory, and when the value is 1, it is a strong constraint for the working condition section; the value is 2, it is a weak constraint for the transition section; The trajectory accuracy constraint at each point in the motion process is set : ; The rated speed is allowed to be exceeded for a short time, but cannot exceed the overload speed upper limit, and the standard speed limit value and the overload speed limit value are set, and the overload speed gain coefficient is set, wherein:

[0042] The absolute speed cannot exceed the overload speed limit value at any time , if the speed is between the standard limit value and the overload limit value, i.e. , the duration length is set, which cannot exceed the preset overload time .

[0043] The overload setting of the allowable torque constraint is set, and the overload gain coefficient of the torque constraint is more conservative than the safety margin of the speed constraint, i.e. ;

[0044] wherein, is the overload duration, is the real-time joint torque, is the allowable overload torque of the joint, is the dynamics torque constraint; The local sequence planning time optimization model includes: Set the sequence quadratic programming, add the above three constraints, and take the time optimization as the target:

[0045] in, is a non-convex objective function, is the first nonlinear constraint, is the second nonlinear constraint, is the n-dimensional real vector of x, m is the number of first constraints, and p is the number of second constraints; Given the time optimization algorithm model:

[0046] in is the time-optimal objective function, is the inequality constraint function in the optimization, which includes three inequalities: speed, torque and trajectory accuracy; Step S7: compare the multiple trajectory parameter groups obtained through local optimization, and determine the group that meets the constraint conditions and has the shortest trajectory time as the final output.

[0047] In some embodiments, the present application provides a robot time-optimal trajectory planning system based on dynamic constraints, the system comprising: The dynamic modeling module, trajectory initialization module, genetic optimization module, fitness evaluation module, termination judgment module, initial solution screening module, local optimization module and result output module are used to achieve the time optimal planning of the robot joint trajectory while meeting multiple constraints.

[0048] The dynamics modeling module is used to build a complete dynamic model of the robot to be planned. This model describes the relationships between inertia, gravity, friction, external loads, and driving torques in different joint states. In this example, a modeling tool is used to establish the dynamic structure of the rigid body chain based on the robot's structural parameters, generating a symbolic representation that can be used for constraint calculations.

[0049] The trajectory initialization module is used to generate multiple trajectory parameter sets based on preset parameters, forming the initial population for the genetic algorithm. In this embodiment, a quintic B-spline function is used to describe the trajectory parameters in joint space. Each parameter set includes multiple control points and corresponding time nodes, and the population is constructed through a uniform distribution and perturbation generation mechanism.

[0050] The genetic optimization module is used to perform genetic operations on the current population to generate a new generation of trajectory parameter sets. In this embodiment, these genetic operations include individual selection based on set rules, cross-recombination of control points and time nodes, and probabilistically triggered small perturbation mutation operations to maintain population diversity.

[0051] An adaptability evaluation module is configured to evaluate the adaptability value of each trajectory parameter set. In the embodiment, the adaptability is optimized with the total trajectory execution time as the target, while detecting whether each trajectory set meets the speed constraint, torque constraint and trajectory accuracy constraint. Trajectories that do not meet the conditions are eliminated or assigned a low adaptability value.

[0052] A termination determination module is configured to determine whether the current population meets the termination condition. In the embodiment, the termination condition includes the convergence of the adaptability function, the evolution generation number of the population exceeding the upper limit, or the lack of diversity in the solution space. If any of the conditions is met, the genetic stage is terminated.

[0053] An initial solution screening module is configured to screen a plurality of trajectory parameter sets from the current population as initial solutions for local optimization. In the embodiment, an elite solution set is constructed, and the selected solutions are screened according to the adaptability ranking and the Euclidean distance distribution, so as to ensure the representativeness and distribution breadth of the selected solutions.

[0054] A local optimization module is configured to perform a local optimization operation on the screened trajectory parameter sets. In the embodiment, a sequential quadratic programming algorithm is adopted, a trajectory model is established based on a quintic B-spline curve, and speed, torque and trajectory accuracy constraints are set to minimize the trajectory time in the feasible solution space.

[0055] A result output module is configured to compare the plurality of trajectory parameter sets after the local optimization, determine the optimal trajectory and output. In the embodiment, the trajectory parameter set that meets all the constraints and has the shortest trajectory time is selected as the final output result, which is called by the robot execution control system.

[0056] The system can be deployed on an industrial robot simulation platform or an embedded trajectory control unit, supports trajectory planning tasks in complex path, high precision requirement and high speed beat operation scenarios, and has good universality and engineering adaptability.

[0057] In some embodiments, the present application provides a terminal, comprising: a memory configured to store a robot time-optimal trajectory planning program based on dynamic constraints; a processor configured to perform the robot time-optimal trajectory planning system based on dynamic constraints to realize the steps of the robot time-optimal trajectory planning method based on dynamic constraints.

[0058] In some embodiments, the present application provides a computer readable storage medium, which stores computer instructions. When the computer reads the computer instructions in the storage medium, the computer executes the robot time-optimal trajectory planning method based on dynamic constraints.

[0059] It is understood that the systems, devices, modules, or units described in the above embodiments may be implemented by computer chips or physical devices, or by products having certain functions. A typical implementation device is a computer, which may be a personal computer, a laptop computer, a personal digital assistant, a tablet computer, a wearable device, or a combination of any of these devices.

[0060] In a typical configuration, a computer includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0061] Memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0062] Computer-readable media include permanent and non-permanent, removable and non-removable media that can be used to store information using any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, disk storage, quantum memory, graphene-based storage media or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory media such as modulated data signals and carrier waves.

[0063] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0064] It should be understood that although the terms first, second, third, etc. may be used to describe various information in one or more embodiments of this specification, such information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of one or more embodiments of this specification, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when..." or "when..." or "in response to determining."

[0065] The above description is merely a preferred embodiment of one or more embodiments of this specification and is not intended to limit one or more embodiments of this specification. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of one or more embodiments of this specification shall be included in the scope of protection of one or more embodiments of this specification.

Claims

1. A robot time optimal trajectory planning method based on dynamic constraints, characterized by: The following steps are involved: Step S1: Establish a dynamic model, initialize the population, and generate several sets of trajectory parameters according to preset parameters; Step S2: performing genetic operations on the population to generate a new generation trajectory parameter group; Step S3, evaluating the fitness of each trajectory parameter group; Step S4: determine whether the preset termination condition is met; If the preset termination condition is not met, return to step S2; Otherwise, execute step S5; Step S5: selecting multiple trajectory parameter groups from the current population as initial solutions for subsequent optimization; Step S6: using the selected trajectory parameter groups as initial inputs, respectively, to perform local optimization; Step S7: compare the multiple trajectory parameter groups obtained through local optimization, and determine the group that meets the constraint conditions and has the shortest trajectory time as the final output.

2. The robot time optimal trajectory planning method based on dynamic constraints according to claim 1 is characterized in that: In step S1, the following steps are included: Step S1-1, constructing a dynamic model based on the structural parameters of the target robot; Step S1-2: Determine the dimension and boundary conditions of the trajectory parameters according to the number of joints and the number of trajectory segments; The boundary conditions include the position range of B-spline control points, the time distribution range of nodes, and the number of individuals and coding length of the genetic algorithm population; Step S1-3: Use quintic B-spline curves to describe the trajectory in the joint space of each robot, and represent each trajectory as a sequence of control points and a node time vector to form a trajectory parameter set of a single individual in the population; Step S1-4: Generate multiple trajectory parameter groups as the initial population of the genetic algorithm in a random perturbation or uniform distribution manner according to the trajectory structure and value range.

3. The robot time optimal trajectory planning method based on dynamic constraints according to claim 2 is characterized in that: In step S1-1, the kinetic model is: in, is the position of the joint; is the velocity of the joint; is the acceleration of the joint; is the torque vector of the joint; is the inertia matrix; is the centripetal force matrix; is the gravity vector; For external interference, is the friction vector; is the external force acting on the end effector; is the Jacobian matrix.

4. The robot time optimal trajectory planning method based on dynamic constraints according to claim 1 is characterized in that: In step S2, a sample of the trajectory parameter group in the population is selected, and the trajectory control points and time nodes are combined and reorganized using the set crossover strategy. Perturbations are added to the control points or time nodes in the trajectory parameter group with a preset probability to generate a new generation of trajectory parameter groups.

5. The robot time optimal trajectory planning method based on dynamic constraints according to claim 1 is characterized in that: In step S3, the fitness evaluation is based on the trajectory execution time and determines whether each set of trajectory parameters satisfies the speed constraint, torque constraint and trajectory accuracy constraint.

6. The robot time optimal trajectory planning method based on dynamic constraints according to claim 1 is characterized in that: In step S4, the termination condition is set to and / or ; in, is the Lagrangian function; is the gradient of the Lagrangian function with respect to the independent variable x, , To optimize the condition threshold, it can be set by the user. is the i-th Lagrange multiplier vector, is the i-th constraint function, is the change in the constraint target, is the optimal solution to the constraint objective.

7. The method for time-optimal trajectory planning of a robot based on dynamic constraints according to any one of claims 1 to 6, characterized in that: In step S5, the genetic algorithm elite solution set is defined as: in, Sort by fitness in ascending order, = is the minimum Euclidean distance, is the diversity threshold, which is 5% to 10% of the diameter of the solution space; in, For candidate solutions, For individuals who have been selected into the elite set, Sort the fitness of all solutions in the population in ascending order, is the fitness function, K is the elite size threshold, and E is the set of elite solutions that have met the conditions.

8. The method for time-optimal trajectory planning of a robot based on dynamic constraints according to claim 7, characterized in that: In step S6, the local optimization includes: Set multiple constraints, including dynamic constraints, kinematic constraints, and trajectory accuracy constraints, to build a local sequence planning time optimization model; The joint motion trajectory model obtained by the quintic B-spline curve is used to obtain the velocity value of the i-th planning point , the speed constraint is that the absolute value of the speed at all times does not exceed the rated limit: in is the rated upper limit of the joint speed, is the total exercise time; Kinematic constraints at each point during a given motion Defined as: in, Represents the joint number, Number the constraints; Sampling points from the actual curve, the distance from the sampling point to the reference curve is solved in sequence, which is the trajectory accuracy error , according to the trajectory accuracy constraint requirements: in, Indicates the error setting value in different areas of the trajectory. When the value is 1, it is a strong constraint for the working condition segment; When the value is 2, it is a weak constraint in the transition section; Set trajectory accuracy constraints at each point during motion : 。 9. The robot time optimal trajectory planning method based on dynamic constraints according to claim 8 is characterized in that: It is allowed to exceed the rated speed for a short time, but it cannot exceed the overload speed limit. Set the standard speed limit. and overload speed limit , and set the overload speed gain coefficient ,in: Absolute speed The overload speed limit cannot be exceeded at any time , if the speed is between the standard limit and the overload limit, that is , set the duration Cannot exceed the preset overload time ; Set the allowable torque constraint overload setting, the overload gain coefficient of the torque constraint A more conservative safety margin than the speed constraint is used in the setting, i.e. ; in, is the duration of overload, is the real-time joint torque, is the permissible overload torque of the joint, is the dynamic torque constraint.

10. The robot time optimal trajectory planning method based on dynamic constraints according to claim 9 is characterized in that: Building a local sequence planning time optimization model includes: Set up sequential quadratic programming, add the three constraints above, and aim for time optimization: in, is a non-convex objective function, is the first nonlinear constraint, is the second nonlinear constraint, is the n-dimensional real vector of x, m is the number of first constraints, and p is the number of second constraints; Given the time optimization algorithm model: in is the time-optimal objective function, is the inequality constraint function in the optimization, which includes three inequalities: speed, torque and trajectory accuracy.

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