A dynamic obstacle avoidance trajectory planning method for a robotic arm based on dynamic motion primitives and steering force field

Abstract

The present invention proposes a dynamic obstacle avoidance trajectory planning method for a robotic arm based on dynamic motion primitives and a steering force field, belonging to the field of robotics. The method comprises the following steps: first, the robotic arm obtains a taught trajectory and performs preprocessing; then, an improved dynamic motion primitive (DMP) algorithm is used to learn the taught trajectory after preprocessing in step 1, so that the DMP can generate a motion pattern similar to the taught trajectory; through a depth camera installed around the robotic arm or at a fixed position, obstacle information, shape, and motion state are obtained in real time, the obstacle avoidance repulsion force is calculated, and the steering force is quantified to achieve obstacle avoidance of freely moving obstacles; a Lyapunov equation is constructed, and based on the Lyapunov stability theory, the stability of the improved DMP algorithm is proved to ensure that the motion trajectory of the robotic arm in a dynamic environment converges to a target point; finally, an obstacle avoidance experiment is carried out to verify the dynamic obstacle avoidance capability of the robotic arm; the present invention significantly improves the trajectory generalization capability of the robotic arm in a confined working space.

Description

Technical Field

[0001] The present invention belongs to the field of robotics technology, and in particular relates to a dynamic obstacle avoidance trajectory planning method for a robotic arm based on dynamic motion primitives and a steering force field. Background Art

[0002] When a robotic arm performs on-orbit tasks in space, the operating space is confined and the environment is constantly changing. This poses numerous challenges for trajectory planning. For example, the robotic arm must precisely operate within a limited space while avoiding dynamic obstacles to ensure the smooth execution of the task. However, current trajectory planning methods mostly rely on manual programming, which is not only inefficient but also cumbersome to modify, making it difficult to adapt to the demands of tasks in complex dynamic environments. To improve the adaptability and operational efficiency of robotic arms in complex environments, learning from demonstration (LfD) is an emerging technology that can transfer the skills of human operators to robotic arms and generalize these skills, thus providing a new approach to trajectory planning for robotic arms. Dynamic movement primitives (DMPs) are a commonly used learning from demonstration method and are widely used in trajectory planning and task execution for robotic arms. DMPs decompose complex motion trajectories into a series of simple motion patterns, enabling robotic arms to perform complex, human-like movements in a flexible and creative manner. However, traditional DMPs have significant limitations in complex dynamic environments. For example, it is difficult to effectively avoid dynamic obstacles, especially when the obstacles are rotating. In addition, although existing improvement methods have improved the adaptability of DMP to a certain extent, there are still many deficiencies in its obstacle avoidance capabilities, which are mainly manifested in the following aspects:

[0003] Excessive free space loss: Existing methods often sacrifice too much free space during obstacle avoidance, which limits the range of motion of the robot arm and reduces operational efficiency.

[0004] Falling into local minima: In complex environments, the combination of DMP and traditional potential fields is prone to falling into local minima, resulting in trajectory planning failure or trajectory mutation, affecting the motion stability of the robot arm.

[0005] Not suitable for rotational motion of obstacles: Most existing methods can only handle the translational motion of obstacles, but lack effective response strategies for the rotational motion of obstacles, which limits the application of robotic arms in complex dynamic environments.

[0006] In response to the above problems, it is particularly important to propose a new trajectory planning method to meet the needs of on-orbit operation tasks of the robotic arm, improve the adaptability and dynamic obstacle avoidance capability of the DMP, and reduce the free space loss. Summary of the Invention

[0007] To address the problem of insufficient obstacle avoidance capability of existing DMP methods in complex dynamic environments, the present invention proposes a dynamic obstacle avoidance trajectory planning method for a robotic arm based on dynamic motion primitives (DMP) and a steering force field. By introducing steering force field constraints, the obstacle avoidance performance of DMP is enhanced and free space loss is reduced, making it suitable for robotic arm operations in complex dynamic environments.

[0008] A dynamic obstacle avoidance trajectory planning method for a robotic arm based on dynamic motion primitives and steering force fields:

[0009] The method specifically comprises the following steps:

[0010] Step 1: Make the robot arm acquire the taught trajectory and perform preprocessing;

[0011] Step 2: Use the improved dynamic motion primitive (DMP) algorithm to learn the teaching trajectory preprocessed in step 1, so that the DMP can generate a motion pattern similar to the teaching trajectory;

[0012] Step 3: Use depth cameras installed around the robotic arm or in fixed positions to obtain obstacle information, shape, and motion status in real time, calculate the obstacle avoidance repulsion force, and quantify the steering force;

[0013] Step 4: Construct the Lyapunov equation and prove the stability of the improved DMP algorithm based on the Lyapunov stability theory to ensure that the motion trajectory of the robot in a dynamic environment converges to the target point;

[0014] Step 5: Conduct an obstacle avoidance experiment to verify the dynamic obstacle avoidance capability of the robotic arm.

[0015] Furthermore, in step 1,

[0016] The teaching trajectory is the motion path of the robot arm when completing a specific task, which is manually demonstrated by a human operator or given in advance;

[0017] The obtained teaching trajectory is preprocessed, and the preprocessing includes noise removal, smoothing and normalization; the preprocessed trajectory data will be used as the basic data for DMP learning.

[0018] Furthermore, in step 2,

[0019] The specific implementation process of the improved dynamic motion primitive algorithm is as follows: on the basis of the traditional DMP, a steering force field coupling term is introduced, including the obstacle avoidance repulsion force of different obstacles;

[0020] The traditional dynamic motion primitive (DMP) equation is:

[0021]

[0022]

[0023] Where τ is the time scale factor, the system duration can be adjusted by τ, x is the current position, x init is the initial position, g is the target position, v and are velocity and acceleration respectively, and the diagonal d×d matrix A=diag[α1,α2,…,α d ],B=diag[β1,β2,…,β d ] are elastic terms and damping terms respectively, λ is a predefined positive constant, and f(s) is a forcing term consisting of N weighted radial basis functions, which can be expressed as:

[0024]

[0025] in is the radial basis function, ω i Represents the weight of the radial basis function. When the initial state or target state of the trajectory changes, ω i It has the ability to generalize and generate similar trajectories;

[0026] By adding the obstacle avoidance coupling term to DMP, the improved DMP control equation is obtained as follows:

[0027]

[0028]

[0029] Among them, the additional items is the obstacle avoidance coupling term.

[0030] Furthermore, in step 3,

[0031] The specific implementation process of the quantified steering force is as follows: by analyzing the shape and motion state of the obstacle, the obstacle avoidance repulsive force is quantified, and the direction and magnitude of the repulsive force are dynamically adjusted according to the generalized distance and generalized angle of the obstacle;

[0032] The steering force is defined as follows:

[0033]

[0034] Where k, n, μ1, μ2 are constant coefficients, v is the velocity of the current position, d is the generalized distance, θ is the generalized angle, and R is the rotation matrix of π / 2 around the axis generated by XO×v;

[0035] For ellipses, arbitrary quadrilaterals, and triangles, they are mapped to circles, regular rectangles, and triangles by left-multiplying the affine transformation matrix R1. The quantized steering force expression is:

[0036] X a =R1X

[0037] v a =R1v

[0038]

[0039] Among them, R1 is a two-dimensional affine transformation matrix, and its expression is determined by the shape of the obstacle. is the angle between the long axis and the x-axis, X a is the virtual position after affine transformation, v a is the virtual velocity after affine transformation, d a ,θ a are the generalized distance and generalized angle determined by the virtual position and velocity of the affine transformation.

[0040] Furthermore, in step 3, the specific process of generalized distance and generalized angle is:

[0041] Define the generalized distance to obtain smooth equidistance lines. The generalized distances for circular, rectangular, and triangular obstacles are defined as follows:

[0042]

[0043]

[0044] Where d1 is the generalized distance of a circular obstacle, d2 is the generalized distance of a triangular obstacle, d3 is the generalized distance of a rectangular obstacle, r is the radius of the circular obstacle, a2 is 1 / 2 of the side length of the rectangular obstacle, a3 is 1 / 3 of the side length of the triangular obstacle, and l is the Euclidean distance between the current position and the nearest geometric structure of the contour;

[0045] Determine the angle between the current velocity direction and the obstacle. For a circular obstacle, where O is the center of the obstacle, X is the current position, and o is the point of intersection between the current position and the obstacle, the generalized angle is defined as follows:

[0046]

[0047] Among them, θ1 is the angle between v and XO, θ2 is the angle between Oo and OX;

[0048] For rectangular, triangular, and free-form obstacles, the relative angle is defined in a simplified manner. For rectangular and triangular obstacles, only the corresponding virtual circular obstacles are effective at different positions. When the current position changes, the virtual circular obstacle moves with it to ensure that the outer envelope of the virtual circular trajectory can cover the entire obstacle and that the trajectory does not intersect with the obstacle.

[0049] Furthermore, in step 4,

[0050] When t→∞, the phase variable x tends to 0; therefore, the simplified DMP equation It can be expressed as:

[0051]

[0052]

[0053] point is the stationary point of the equation; construct a Lyapunov function V and prove Therefore, it is shown that all points will converge to this stationary point; using the energy function of the damped spring mass system

[0054]

[0055]

[0056] Since R is a matrix rotated by π / 2, we can get v T Rv=0, It is proved that DMP converges to

[0057] A dynamic obstacle avoidance trajectory planning system for a robotic arm based on dynamic motion primitives and steering force fields:

[0058] The trajectory planning system includes: a teaching trajectory module, a DMP learning module, a steering force quantification module and a verification module;

[0059] The teaching trajectory module enables the robotic arm to obtain the teaching trajectory and perform preprocessing;

[0060] The DMP learning module uses an improved dynamic motion primitive (DMP) algorithm to learn the teaching trajectory preprocessed by the teaching trajectory module, so that the DMP can generate a motion pattern similar to the teaching trajectory;

[0061] The steering force quantification module uses depth cameras installed around the robotic arm or in fixed positions to obtain obstacle information, shape, and motion status in real time, calculate the obstacle avoidance repulsion force, and quantify the steering force;

[0062] The verification module constructs the Lyapunov equation and proves the stability of the improved DMP algorithm based on the Lyapunov stability theory, ensuring that the motion trajectory of the robot arm in a dynamic environment converges to the target point; finally, an obstacle avoidance experiment is performed to verify the dynamic obstacle avoidance capability of the robot arm.

[0063] An electronic device includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0064] A computer-readable storage medium is used to store computer instructions, which implement the steps of the above method when executed by a processor.

[0065] Beneficial effects of the present invention

[0066] Compared with the prior art, the present invention has the following effects:

[0067] Unlike traditional methods based on DMP and potential fields, and solutions based on steering angles, this invention introduces a steering force field, enabling the robot arm to effectively avoid three-dimensional dynamic obstacles of varying shapes and sizes, while allowing the obstacles to be moved freely with six degrees of freedom. This approach avoids trajectory abrupt changes and local minima, while minimizing free space loss.

[0068] Furthermore, analysis based on Lyapunov stability theory ensures the stability and reliability of the robot's trajectory in dynamic environments. The robot's trajectory planned using this method can provide reliable path planning support for operations in complex dynamic environments, significantly improving the robot's trajectory generalization capabilities in confined working spaces. This provides a new solution for the application of the robot in fields such as aerospace and industrial manufacturing, and has strong practical application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 Schematic diagram of the equidistant line distribution of basic obstacles.

[0070] Figure 2 is the angle between the current speed and the obstacle.

[0071] Figure 3 Schematic diagram of affine transformation.

[0072] Figure 4 This is an example diagram of setting up the experimental system of the present invention.

[0073] Figure 5 This is a snapshot of the experiment of the present invention DETAILED DESCRIPTION

[0074] 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. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0075] The experimental methods used in the following examples are conventional methods unless otherwise specified. The materials, reagents, methods, and instruments used are conventional in the art and can be obtained commercially by those skilled in the art unless otherwise specified.

[0076] Combine Figures 1 to 5 The present invention proposes a dynamic obstacle avoidance trajectory planning method for a robotic arm based on dynamic motion primitives (DMP) and steering force fields. The method includes improving the dynamic motion primitives (DMP) algorithm, quantizing the steering force, Lyapunov stability analysis and obstacle avoidance experimental verification.

[0077] In an embodiment, the system is set up as Figure 4 The image shows a 7-DOF Franka EmikaPanda robotic arm, a table with obstacles and a Realsense D435 RGB depth camera, with the obstacles driven by servo motors. The camera detects the location of the obstacles, and the robotic arm is controlled using the ROS framework on Ubuntu 20.04, moving across the table while avoiding obstacles.

[0078] Step 1, trajectory learning:

[0079] Acquiring and preprocessing the taught trajectory: The taught trajectory is the motion path of the robot arm when completing a specific task. It is usually manually demonstrated or pre-determined by a human operator. For example, a drag-and-drop teaching method can be used, allowing the operator to manually guide the robot arm to complete a complete task operation, recording its position, speed, and time information. The acquired taught trajectory is preprocessed, including noise removal, smoothing, and normalization. This preprocessed trajectory data serves as the basis for DMP learning.

[0080] The teaching trajectory of this example is given in advance, and the teaching trajectory equation is: x = 0.5sin(-t), y = 0.5cos(t), z = 0.3, where t∈(0,π), x, y, z are the Cartesian coordinates of the end of the robot arm in the base coordinate system.

[0081] Step 2: DMP parameter learning: The improved DMP algorithm is used to learn the preprocessed taught trajectory. Optimization algorithms (such as local weighted regression (LWR)) are used to adjust the radial basis function weights in the DMP, enabling it to generate motion patterns similar to the taught trajectory. Once learning is complete, the DMP will be able to generate similar trajectories when the target state changes.

[0082] The specific implementation process of the improved dynamic motion primitive (DMP) algorithm is as follows: Based on the traditional DMP, a steering force field coupling term is introduced, including the obstacle avoidance repulsion force of different obstacles, to achieve obstacle avoidance of dynamic obstacles. The traditional dynamic motion primitive (DMP) equation is:

[0083]

[0084]

[0085] Where τ is the time scale factor, the system duration can be adjusted by τ, x is the current position, x init is the initial position, g is the target position, v and are velocity and acceleration respectively, and the diagonal d×d matrix A=diag[α1,α2,…,α d ],B=diag[β1,β2,…,β d ] are elastic terms and damping terms respectively, λ is a predefined positive constant, and f(s) is a forcing term consisting of N weighted radial basis functions, which can be expressed as:

[0086]

[0087] where ω i represents the weights of the radial basis functions. An optimization algorithm such as Locally Weighted Regression (LWR) can be used to select appropriate model parameters for these basis functions so that the forcing function matches the desired trajectory. When the initial state or target state of the trajectory changes, ω i It has the ability to generalize and can generate similar trajectories. In addition, f depends on the phase variable s, which is a reparameterization of time t and is controlled by the so-called gauge system. The transient behavior of s is defined as:

[0088]

[0089] Where λ is a predefined positive constant. Radial basis function It can be defined as follows:

[0090]

[0091] Where exp(·) is the exponential function. The width of the Gaussian function h is i It can be calculated by the following formula:

[0092] h i =(c i+1 -c i ) -2 , h N =h N-1

[0093] The center c of the Gaussian function i It can be calculated as follows:

[0094]

[0095] By adding the obstacle avoidance coupling term to DMP, the improved DMP control equation is obtained as follows:

[0096]

[0097]

[0098] Among them, the additional items is the obstacle avoidance coupling term.

[0099] Step 3: Obtain obstacle information and calculate steering force:

[0100] Obstacle detection and identification: Using depth cameras mounted around the robot arm or in fixed locations, real-time information about obstacles in the work environment is acquired. Image processing or point cloud analysis algorithms are used to identify the shape, position, and motion of obstacles. For example, video analysis algorithms such as optical flow can be used to extract the speed and direction of an obstacle. In this example, the obstacle is elliptical, with a major axis a of 0.4 m and a minor axis b of 0.2 m. The linear velocity is 4 mm / s and the angular velocity is 0.04 rad / s.

[0101] Data import and processing: Detected obstacle information (including shape, location, speed, etc.) is imported into the computer system. Further processing of the obstacle information, such as coordinate conversion and data filtering, is performed to ensure data accuracy and real-time performance. The data is then used as input for calculating steering force.

[0102] Determine the generalized distance and generalized angle, and calculate the generalized distance and generalized angle between the current position of the robot arm and the obstacle based on the shape and position of the obstacle.

[0103] The specific implementation of the quantized steering force involves analyzing the shape and motion of the obstacle to quantify the repulsive force used to avoid freely moving obstacles. The direction and magnitude of the repulsive force are dynamically adjusted based on the generalized distance and angle to the obstacle, ensuring that the robot arm does not collide with the obstacle during the avoidance process.

[0104] Use Figure 1 The generalized distance is defined in this way to obtain a smooth equidistance line. Region 2 is the boundary straight line area, and region 1 is the arc transition area. The generalized distances of circular, rectangular, and triangular obstacles are defined as follows:

[0105]

[0106]

[0107]

[0108] Where d1 is the generalized distance of a circular obstacle, d2 is the generalized distance of a triangular obstacle, d3 is the generalized distance of a rectangular obstacle, r is the radius of the circular obstacle, a2 is 1 / 2 of the side length of the rectangular obstacle, and a3 is 1 / 3 of the side length of the triangular obstacle. In area 1, l is the Euclidean distance between the current position and the nearest endpoint of the contour. In area 2, l is the Euclidean distance between the current position and the nearest straight line of the contour.

[0109] The steering force is defined as follows:

[0110]

[0111] Where k, n, μ1, μ2 are constant coefficients, v is the velocity of the current position, d is the generalized distance, θ is the generalized angle, and R is the rotation matrix of π / 2 around the axis generated by XO×v. Figure 3 The process shown

[0112] For an ellipse, any quadrilateral, or triangle, it can be mapped to a circle, a regular rectangle, or a triangle simply by multiplying the affine transformation matrix R1 on the left, as shown in Figure 3 As shown, at this time:

[0113] X a =R1X

[0114] v a =R1v

[0115]

[0116] Among them, R1 is a two-dimensional affine transformation matrix, and its expression is determined by the shape of the obstacle, such as the affine transformation matrix of an ellipse mapped to a circle is the angle between the long axis and the x-axis, X a is the virtual position after affine transformation, v a is the virtual velocity after affine transformation, d a ,θ a are the generalized distance and generalized angle determined by the virtual position and velocity of the affine transformation.

[0117] Note that the generalized distance and angle definitions of the steering force field scheme are not limited to the above forms. The analytical equations can be designed independently as needed, and graphics of arbitrary shapes can be obtained by combining basic graphics.

[0118] Step 4: Update the steering force field coupling term in real time based on Lyapunov stability analysis: During the manipulator's motion, the steering force field coupling term is calculated in real time and added to the DMP's governing equations. The steering force field coupling term is adjusted in real time based on the dynamic changes of obstacles, ensuring that the manipulator can effectively avoid them.

[0119] The specific implementation process of the Lyapunov stability analysis is as follows: constructing the Lyapunov equation, and based on the Lyapunov stability theory, proving the stability of the improved DMP algorithm to ensure that the motion trajectory of the robot arm in a dynamic environment converges to the target point.

[0120] When t→∞, the phase variable x tends to 0. Therefore, the simplified DMP equation It can be expressed as:

[0121]

[0122]

[0123] point is the stationary point of the equation. Construct a Lyapunov function V and prove that Therefore, it is shown that all points will converge to this stationary point. Using the energy function of the damped spring mass system

[0124]

[0125]

[0126] Since R is a matrix rotated by π / 2, we can get v T Rv=0, It is proved that DMP converges to

[0127] Step 5, Experimental Verification: The effectiveness of the proposed method was verified through a series of numerical simulations and Franka manipulator experiments. The experimental results show that the method can provide reliable trajectory planning for the manipulator in complex dynamic environments, significantly improving the manipulator's generalized operational capabilities.

[0128] The center of the Franka robot base is the coordinate origin. Figure 5As shown in the figure, the snapshots of the experimental process show the trajectory of the Franka robot end effector. Each row of figures represents an experimental process consisting of 3 snapshots. The initial position and target position are: x init = [-0.5, 0.0, 0.3] and g = [0.0, 0.5, 0.3]. To verify the effectiveness of the method, the obstacle avoidance performance was tested in the presence of elliptical, rectangular, and triangular obstacles. The obstacles were placed at [-0.375, 0.3, 0.31]. Since the free space loss in this scheme is extremely small, a 5mm safety margin was left between the end of the robotic arm and the obstacle for experimental safety.

[0129] Performance Evaluation: The experimental results were evaluated based on metrics such as obstacle avoidance success rate, trajectory smoothness, free space loss, and computational efficiency. The experimental results demonstrate that the proposed method can effectively avoid obstacles, achieves good trajectory smoothness, minimizes free space loss, and achieves computational efficiency that meets real-time requirements.

[0130] A dynamic obstacle avoidance trajectory planning system for a robotic arm based on dynamic motion primitives and steering force fields is proposed.

[0131] The trajectory planning system includes: a teaching trajectory module, a DMP learning module, a steering force quantification module and a verification module;

[0132] The teaching trajectory module enables the robotic arm to obtain the teaching trajectory and perform preprocessing;

[0133] The DMP learning module uses an improved dynamic motion primitive (DMP) algorithm to learn the teaching trajectory preprocessed by the teaching trajectory module, so that the DMP can generate a motion pattern similar to the teaching trajectory;

[0134] The steering force quantification module uses depth cameras installed around the robotic arm or in fixed positions to obtain obstacle information, shape, and motion status in real time, calculate the obstacle avoidance repulsion force, and quantify the steering force to achieve obstacle avoidance for freely moving obstacles;

[0135] The verification module constructs the Lyapunov equation and proves the stability of the improved DMP algorithm based on the Lyapunov stability theory, ensuring that the motion trajectory of the robot arm in a dynamic environment converges to the target point; finally, an obstacle avoidance experiment is performed to verify the dynamic obstacle avoidance capability of the robot arm.

[0136] An electronic device includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0137] A computer-readable storage medium is used to store computer instructions, which implement the steps of the above method when executed by a processor.

[0138] The memory in the embodiments of the present application can be volatile memory or non-volatile memory, or can include both volatile and non-volatile memory. Among them, the non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct RAM bus random access memory (DR RAM). It should be noted that memory of the methods described herein is intended to comprise, but not be limited to, these and any other suitable types of memory.

[0139] In the above embodiments, all or part of the embodiments can be implemented using software, hardware, firmware, or any combination thereof. When implemented using software, all or part of the embodiments can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired connection such as a coaxial cable, optical fiber, digital subscriber line (DSL), or wireless connection such as infrared, wireless, or microwave. The computer-readable storage medium can be any available medium that can be accessed by a computer, or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium such as a floppy disk, hard disk, magnetic tape, an optical medium such as a high-density digital video disc (DVD), or a semiconductor medium such as a solid-state disc (SSD).

[0140] During implementation, each step of the above method can be completed by an integrated logic circuit of the hardware in the processor or by instructions in the form of software. The steps of the method disclosed in conjunction with the embodiments of the present application can be directly embodied as being executed by a hardware processor, or can be executed by a combination of hardware and software modules in the processor. The software module can be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in conjunction with its hardware. To avoid repetition, it will not be described in detail here.

[0141] It should be noted that the processor in the embodiments of the present application can be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above-described method embodiment can be completed by hardware integrated logic circuits in the processor or by software instructions. The above-described processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic device, discrete gate or transistor logic device, or discrete hardware components. The various methods, steps, and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of the present application can be directly implemented and executed by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium well-known in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. The storage medium is located in the memory, and the processor reads the information in the memory and, in conjunction with its hardware, completes the steps of the above-described method.

[0142] The above is a detailed introduction to the dynamic obstacle avoidance trajectory planning method for a robotic arm based on dynamic motion primitives and steering force fields proposed in the present invention, and the principles and implementation methods of the present invention are explained. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for general technical personnel in this field, according to the ideas of the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A dynamic obstacle avoidance trajectory planning method for a robotic arm based on dynamic motion primitives and a steering force field, characterized by: The method specifically comprises the following steps: Step 1: Make the robot arm acquire the taught trajectory and perform preprocessing; The teaching trajectory is the motion path of the robot arm when completing a specific task, which is manually demonstrated by a human operator or given in advance; Preprocessing the acquired teaching trajectory, including noise removal, smoothing, and normalization; the preprocessed trajectory data will be used as the basic data for DMP learning; Step 2: Use the improved dynamic motion primitive (DMP) algorithm to learn the teaching trajectory preprocessed in step 1, so that the DMP can generate a motion pattern similar to the teaching trajectory; The specific implementation process of the improved dynamic motion primitive algorithm is as follows: on the basis of the traditional DMP, a steering force field coupling term is introduced, including the obstacle avoidance repulsion force of different obstacles; The traditional dynamic motion primitive (DMP) equation is: Where, is the time scale factor, and the system duration can be expressed by adjust, is the current location, is the initial position, is the target position, and are velocity and acceleration respectively, diagonal matrix ， are the elastic term and the damping term, is a predefined positive constant, is a forcing term consisting of N weighted radial basis functions, which can be expressed as: in is the radial basis function, Represents the weight of the radial basis function. When the initial state or target state of the trajectory changes, It has the ability to generalize and generate similar trajectories; By adding the obstacle avoidance coupling term to DMP, the improved DMP control equation is obtained as follows: Among them, the additional items is the obstacle avoidance coupling term; Step 3: Use depth cameras installed around the robotic arm or in fixed positions to obtain obstacle information, shape, and motion status in real time, calculate the obstacle avoidance repulsion force, and quantify the steering force; The specific implementation process of the quantified steering force is as follows: by analyzing the shape and motion state of the obstacle, the obstacle avoidance repulsive force is quantified, and the direction and magnitude of the repulsive force are dynamically adjusted according to the generalized distance and generalized angle of the obstacle; The steering force is defined as follows: in, is a constant coefficient, is the speed of the current position, is the generalized distance, From a broad perspective, It's around Generated axis rotation The rotation matrix of For ellipses, arbitrary quadrilaterals, and triangles, by left-multiplying the affine transformation matrix , mapping it into a circle, a regular rectangle, and a triangle. The quantitative steering force expression is: in, is a two-dimensional affine transformation matrix, the expression is determined by the shape of the obstacle, The long axis and x The angle between the axes, is the virtual position after affine transformation, is the virtual velocity after affine transformation, 、 are the generalized distance and generalized angle determined by the virtual position and velocity of the affine transformation; In step 3, the specific process of generalized distance and generalized angle is: Define the generalized distance to obtain smooth equidistance lines. The generalized distances for circular, rectangular, and triangular obstacles are defined as follows: in, is the generalized distance of the circular obstacle, is the generalized distance of the triangle obstacle, is the generalized distance of rectangular obstacles, is the radius of the circular obstacle, is 1 / 2 of the side length of the rectangular obstacle, is 1 / 3 of the side length of the triangular obstacle, is the Euclidean distance between the current position and the nearest geometric structure of the contour; Determine the angle between the current velocity direction and the obstacle. For a circular obstacle, where O is the center of the obstacle, X is the current position, and o is the point of intersection between the current position and the obstacle, the generalized angle is defined as follows: in, for and The angle of for and The angle between For rectangular, triangular, and free-form obstacles, the relative angle is defined in a simplified way. For rectangular and triangular obstacles, only the corresponding virtual circular obstacle is effective at different positions. When the current position changes, the virtual circular obstacle moves with it, ensuring that the outer envelope of the virtual circular trajectory can cover the entire obstacle and that the trajectory does not intersect with the obstacle. Step 4: Construct the Lyapunov equation and prove the stability of the improved DMP algorithm based on the Lyapunov stability theory to ensure that the motion trajectory of the robot in a dynamic environment converges to the target point; Step 5: Conduct an obstacle avoidance experiment to verify the dynamic obstacle avoidance capability of the robotic arm.

2. The planning method according to claim 1, characterized in that: In step 4, when When the phase variable tends to 0; therefore, the simplified DMP equation It can be expressed as: point is the stationary point of the equation; construct a Lyapunov equation V and prove , thus indicating that all points will converge to this stationary point; using the energy function of the damped spring mass system ; Since R is a rotation The matrix can be obtained = 0, It is proved that DMP converges to .

3. A trajectory planning system for executing the dynamic obstacle avoidance trajectory planning method for a robotic arm based on dynamic motion primitives and a steering force field as described in claim 1 or 2, characterized in that: The trajectory planning system includes: a teaching trajectory module, a DMP learning module, a steering force quantification module and a verification module; The teaching trajectory module enables the robotic arm to obtain the teaching trajectory and perform preprocessing; The DMP learning module uses an improved dynamic motion primitive (DMP) algorithm to learn the teaching trajectory preprocessed by the teaching trajectory module, so that the DMP can generate a motion pattern similar to the teaching trajectory; The steering force quantification module uses depth cameras installed around the robotic arm or in fixed positions to obtain obstacle information, shape, and motion status in real time, calculate the obstacle avoidance repulsion force, and quantify the steering force; The verification module constructs the Lyapunov equation and proves the stability of the improved DMP algorithm based on the Lyapunov stability theory, ensuring that the motion trajectory of the robot arm in a dynamic environment converges to the target point; finally, an obstacle avoidance experiment is performed to verify the dynamic obstacle avoidance capability of the robot arm.

4. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to claim 1 or 2 are implemented.

5. A computer-readable storage medium for storing computer instructions, characterized in that: When the computer instructions are executed by a processor, the steps of the method according to claim 1 or 2 are implemented.

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