Full trajectory planning method and system for robotic arms

Abstract

This application relates to the field of robotic arm technology, and more specifically to a method and system for full trajectory planning of a robotic arm. The method includes: acquiring spatial dimension and position information of a palletizing area, information on the boxes to be palletized, and information on the conveyor belt; classifying the boxes and determining their stacking arrangement; acquiring parameter information of the robotic arm and establishing a mathematical model of the robotic arm; generating a first motion trajectory based on the mathematical model of the robotic arm and the conveyor belt information; optimizing the first motion trajectory based on obstacle information in the robotic arm's workspace; generating a second motion trajectory based on the mathematical model of the robotic arm, the conveyor belt information, the target box information, and distance information; generating a third motion trajectory based on the mathematical model of the robotic arm, the spatial dimension and position information of the palletizing area, and static obstacle information in the robotic arm's workspace; and optimizing the third motion trajectory based on dynamic obstacle information in the robotic arm's workspace.

Description

Technical Field

[0001] This application relates to the field of robotic arm technology, and more specifically to a method and system for full trajectory planning of a robotic arm. Background Technology

[0002] With the continuous acceleration of logistics speed, the process of packing and transporting goods inevitably involves palletizing boxes to facilitate forklift transfer to transport vehicles. Thanks to the continuous development of automation control technology, palletizing has become a very common and important task in warehousing and logistics industries.

[0003] Currently, most automated solutions for palletizing tasks in the industry are based on the feasibility of implementation in specific application scenarios. However, due to the uncertainty of the workspace, when a robotic arm grasps items and places them in the palletizing area, it needs to avoid collisions with all obstacles in the workspace to ensure operational safety. Collision detection by the robotic arm is often complex in its operation steps and algorithms, resulting in lengthy trajectory planning and consequently impacting the robotic arm's operational efficiency. Summary of the Invention

[0004] Therefore, this application is made in response to the problems and needs existing in the prior art.

[0005] The purpose of this application is to provide a method and system for full trajectory planning of a robotic arm, which, compared with the shortcomings of the prior art, can effectively perform obstacle avoidance of the robotic arm and improve the efficiency of collision detection, thereby improving the safety and efficiency of robotic arm operation.

[0006] The objective of this application is achieved through the following technical solution:

[0007] In a first aspect, embodiments of this application provide a method for full trajectory planning of a robotic arm, the method comprising:

[0008] Obtain spatial dimension and location information of the palletizing area, information of the boxes to be palletized, and information of the conveyor belt; classify the boxes to be palletized according to the classification criteria; and determine the stacking arrangement method according to the classified boxes and the spatial dimension information of the palletizing area.

[0009] Obtain the parameter information of the robotic arm, and establish a mathematical model of the robotic arm using the DH model based on the parameter information of the robotic arm to determine the initial pose information of the robotic arm.

[0010] A first motion trajectory is generated based on the mathematical model of the robotic arm and the conveyor belt information. Obstacle information of the robotic arm's workspace is obtained, and the first motion trajectory is optimized based on the obstacle information of the robotic arm's workspace. The obstacle information of the robotic arm's workspace includes static obstacle information and dynamic obstacle information. The obstacle information of the robotic arm's workspace is collected and obtained according to the obstacle information collection strategy.

[0011] The system acquires target item box information and distance information, where the distance information is the distance between the target item box and the next item box on the conveyor belt. A second motion trajectory is generated based on the robotic arm's mathematical model, conveyor belt information, target item box information, and distance information. A third motion trajectory is generated based on the robotic arm's mathematical model, spatial dimensions and position information of the palletizing area, and static obstacle information in the robotic arm's workspace. The third motion trajectory is then optimized based on dynamic obstacle information in the robotic arm's workspace.

[0012] Secondly, embodiments of this application provide a full trajectory planning system for a robotic arm, the system comprising:

[0013] The first determining module is used to classify and determine the stacking arrangement based on the obtained spatial size and location information of the palletizing area, the information of the boxes to be palletized, and the conveyor belt information, and includes the following units:

[0014] The first acquisition unit is used to acquire spatial dimension information and location information of the palletizing area, information of the boxes of items to be palletized, and information of the conveyor belt.

[0015] The first classification unit is used to classify boxes of items to be palletized based on size and pattern;

[0016] The first calculation unit is used to determine the stacking arrangement based on the spatial size information of the stacking area and the size information of the boxes of items to be stacked.

[0017] The first acquisition module is used to acquire parameter information of the robotic arm;

[0018] The first construction module is used to establish a mathematical model of the robotic arm using the DH model based on the parameter information of the robotic arm, and to determine the position information of the robotic arm.

[0019] The second acquisition module is used to acquire obstacle information in the robotic arm's workspace and construct collision areas, including:

[0020] The second acquisition unit is used to acquire static obstacle information in the workspace of the robotic arm according to the static obstacle acquisition strategy;

[0021] The third acquisition unit is used to acquire dynamic obstacle information in the workspace of the robotic arm according to the dynamic obstacle acquisition strategy;

[0022] The first building unit is used to construct the obstacle collision area based on obstacle information and a safe distance threshold;

[0023] The third acquisition module is used to acquire information about the target item box and distance information;

[0024] The first trajectory planning module is used to generate the first motion trajectory based on the mathematical model of the robotic arm and the information of the conveyor belt, and to optimize the first motion trajectory based on the obstacle information of the robotic arm's workspace.

[0025] The second trajectory planning module is used to generate a second motion trajectory based on the mathematical model of the robotic arm, conveyor belt information, target item box information, and distance information.

[0026] The third trajectory planning module is used to generate a third motion trajectory based on the mathematical model of the robotic arm, the spatial size and position information of the palletizing area, and the static obstacle information of the robotic arm's workspace, and to optimize the third motion trajectory based on the dynamic obstacle information of the robotic arm's workspace.

[0027] The storage module is used to store various basic parameter information, classification and calculation results information, trajectory planning information, and palletizing space change information.

[0028] Thirdly, embodiments of this application provide a computer-readable storage medium storing instructions executable by the aforementioned full trajectory planning system for a robotic arm, the instructions being executed by a processor included in the aforementioned full trajectory planning system for a robotic arm to implement the aforementioned full trajectory planning method for a robotic arm.

[0029] Compared with the prior art, the beneficial effects of the present invention are at least as follows:

[0030] The full trajectory planning method and system for the robotic arm described in this application simplifies the steps of obstacle information collection and processing through an obstacle information collection strategy. It improves computational efficiency during robotic arm collision detection, effectively enables obstacle avoidance operations of the robotic arm, and thus improves the safety and efficiency of robotic arm palletizing operations. Attached Figure Description

[0031] Figure 1 The figure illustrates a flowchart of a method for full trajectory planning of a robotic arm according to an embodiment of this application;

[0032] Figure 2 The figure illustrates a schematic diagram of an obstacle collision area according to an embodiment of this application;

[0033] Figure 3 The figure shows a schematic diagram of a full trajectory planning system for a robotic arm provided according to an embodiment of this application. Detailed Implementation

[0034] Hereinafter, exemplary embodiments according to this application will be described in detail with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only a part of the embodiments of this application, and this application is not limited to the exemplary embodiments described herein.

[0035] It should be noted that if the embodiments of this application involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features.

[0036] Figure 1 The figure illustrates a flowchart of a method for full trajectory planning of a robotic arm according to an embodiment of this application.

[0037] like Figure 1 As shown, the full trajectory planning method for a robotic arm according to an embodiment of this application may include steps S100, S200, S300 and S400.

[0038] In step S100, S101 obtains the spatial size and location information of the palletizing area, the information of the boxes to be palletized, and the information of the conveyor belt; S102 classifies the boxes to be palletized according to the classification criteria; S103 determines the stacking arrangement method according to the classified boxes and the spatial size information of the palletizing area.

[0039] Specifically, the palletizing area consists of multiple palletizing substrates, which are generally the same size. In the robotic arm's workspace, these substrates are arranged in linear, square, or other patterns, with gaps between them to facilitate palletizing operations by the robotic arm or forklift. To ensure the stability of the stack after palletizing, the maximum height of the stack needs to be specified. The length, width, and initial height of each palletizing substrate can be obtained manually, and the substrates are numbered. The distance between adjacent substrates can be measured manually. The conveyor belt of the transport line is fixed in position within the workspace, and its length, width, and height can be measured. Basic information, such as the speed information of the conveyor belt, can be obtained through the conveyor belt control system; a Cartesian coordinate system can be established based on the position of the conveyor belt, with the center point of the conveyor belt as the origin, the direction of the conveyor belt movement as the X-axis, and the vertical direction as the Z-axis, to determine the position of the conveyor belt in the coordinate system, thereby determining the position information of each palletizing substrate in the coordinate system; the basic information of the length, width, and height of the boxes to be palletized can be obtained in advance through measurement, and the image information of the boxes can be obtained and saved in advance by taking pictures with a camera. The acquired data can be stored in the storage module as the raw data for trajectory planning.

[0040] Next, the boxes to be stacked need to be initially classified according to different sizes. For boxes of the same size, they are further classified into different boxes according to different box patterns. The types of boxes after classification and the size and pattern information of each type of box are recorded and saved.

[0041] Finally, the optimal arrangement is calculated based on the size classification of the item boxes and the size of the palletizing substrate. The classified item boxes and their corresponding stacking arrangements are recorded and stored as initial data.

[0042] In step S200, the parameter information of the robotic arm is obtained, and a mathematical model of the robotic arm is established using the DH model based on the parameter information of the robotic arm to determine the initial pose information of the robotic arm.

[0043] It should be understood that the robotic arm described in this application is a multi-jointed robotic arm used to grasp boxes of goods on a transport line and place them into a palletizing base. The robotic arm base is a movable base, which expands the operating range. Therefore, the parameter information of the robotic arm includes, but is not limited to, the included angle between adjacent links, the distance between adjacent links, the link torsion angle, the link length, the position of the robotic arm end effector, and the rotational speed information of each joint. The DH model is a modeling method widely used in robotic arm modeling. The DH model has four parameters, where θ j and d j Used to describe the relationship between adjacent links, α represents the included angle between adjacent links and the distance between adjacent links. j and a j The link is described by the link torsion angle and link length. A multi-coordinate mathematical model of each joint is established based on the DH model and the parameter information of the robotic arm. Then, the position and orientation information of the end effector coordinate system relative to the robot reference coordinate system are obtained through coordinate system transformation and forward kinematics analysis. The robot reference coordinate system can be the Cartesian space coordinate system established based on the conveyor belt mentioned above.

[0044] In step S300, S301 generates a first motion trajectory based on the mathematical model of the robotic arm and the conveyor belt information; S302 acquires obstacle information in the robotic arm's workspace; S303 optimizes the first motion trajectory based on the obstacle information in the robotic arm's workspace; wherein, the obstacle information in the robotic arm's workspace includes static obstacle information and dynamic obstacle information, and the obstacle information in the robotic arm's workspace is acquired according to an obstacle information acquisition strategy.

[0045] Specifically, the robotic arm's movement includes the movement before grasping and the movement of grasping the item box and moving it to the corresponding palletizing base for stacking. It can be understood that the trajectory of the robotic arm before grasping the item box is the first movement trajectory mentioned above. The movement before grasping can include two parts: moving from its original position to the front of the conveyor belt and moving the gripping part of the robotic arm to a safe position above the conveyor belt. The movement capability of each joint of the robotic arm is greatest before grasping an item. Based on the working environment, a bounding sphere can be selected when choosing the bounding box for the robotic arm. A path planning search algorithm is used to plan the path for the robotic arm to move to the front of the conveyor belt based on the mathematical model of the robotic arm and the position information of the conveyor belt. Optionally, this embodiment uses a neural network algorithm to plan the path for the robotic arm to move forward to the front of the conveyor belt before grasping an item.

[0046] Understandably, the workspace of a robotic arm is not an idealized scenario; other objects may exist that could affect its operation. These obstacles may be stationary or changing positions within the workspace—the aforementioned static and dynamic obstacles. The shapes of these obstacles may also be regular or irregular. For irregular objects, obstacle avoidance requires constructing a suitable bounding box to ensure all parts of the irregular object are included, and collision detection calculations should be relatively simple, reducing the complexity of collision detection. Therefore, based on the aforementioned obstacle characteristics, static and dynamic obstacle information is collected using different acquisition strategies.

[0047] For static obstacles in the workspace of a robotic arm, three obstacle models can be categorized: regular cylinders or cubes, and irregular objects. For irregular objects, extreme points can be collected. For example, extreme points in four directions on the XY cross-section can be collected. If the number of extreme points is 3, the irregular object can be fitted as a cylinder; if the number of extreme points is 4, it can be fitted as a cube. The highest point of the irregular object on the Z-axis (vertical direction) is taken as the height point. When the regular object or the fitted regular object is a cylinder, any three points on the circumference of the cylinder's cross-section are taken as auxiliary points. The center, i.e., the target point position, is determined based on these three auxiliary points. Any point on the highest plane of the cylinder in the Z-axis direction is taken as the height auxiliary point. Based on these three auxiliary points, the center, and the height auxiliary point, the position and space occupied by the cylindrical obstacle are determined. The space occupied by the cylinder is the obstacle collision area. When the regular object or the fitted regular object is a cube, refer to... Figure 2 This is handled in a certain way.

[0048] Figure 2 The illustration shows a schematic diagram of an obstacle collision area provided according to an embodiment of this application.

[0049] like Figure 2 As shown, when the regular object or the fitted regular object is a cube, the four vertices of the cube are taken as auxiliary points. The center of the circle, i.e. the target point, is determined by the intersection of the diagonals. Half the length of the diagonal is taken as the radius r. A point on the highest plane of the cube's Z-axis is taken as the height auxiliary point to collect the height h. The obstacle collision area is determined based on the target point, radius r, height h, and safety distance threshold. The safety distance threshold can be adjusted according to the degree of enclosure of the fitted regular object on the irregular object.

[0050] For dynamic obstacles in the workspace of the robotic arm, a laser radar can be used to scan the workspace to obtain them. In this embodiment, a 3D laser radar is selected as the scanning device to scan objects on the robotic arm's running path. Optionally, the 3D laser radar can be mounted on the gripper of the robotic arm or on the bottom movable base. Based on the obstacle contour information and position information obtained by scanning, it is determined whether it is a dynamic obstacle. If it is a dynamic obstacle, multiple extreme points are obtained based on the contour as auxiliary points. Based on the multiple auxiliary points and the safety distance threshold, the minimum enclosing cylinder of the dynamic obstacle is determined as the collision area of ​​the dynamic obstacle. The position information of the dynamic obstacle is continuously collected, and the speed and direction of movement of the dynamic obstacle are calculated.

[0051] Return to reference Figure 1 For step S303, the path of the first motion trajectory to the conveyor belt can be optimized based on the static obstacle information in the robotic arm's workspace. The optimal path that avoids collisions with static obstacles is selected. During the movement of the robotic arm, the intersection points of the trajectories that may collide between the robotic arm and the dynamic obstacles are calculated by acquiring the speed and direction of the dynamic obstacles and the collision area of ​​the dynamic obstacles. The robotic arm is accelerated or decelerated in advance to avoid the dynamic obstacles. Finally, based on information such as the height of the conveyor belt, the size and orientation of the item box, the path for the robotic arm gripper to move to a safe position above the conveyor belt is planned. Optionally, the optimal path in terms of time can be calculated using the 3-5-3 polynomial interpolation function and the cuckoo search algorithm.

[0052] In step S400, S401 acquires target item box information and distance information, wherein the distance information is the distance between the target item box and the next item box on the conveyor belt. S402 generates a second motion trajectory based on the mathematical model of the robotic arm, the conveyor belt information, the target item box information, and the distance information. S403 generates a third motion trajectory based on the mathematical model of the robotic arm, the spatial size and position information of the palletizing area, and the static obstacle information of the robotic arm's workspace. The third motion trajectory is optimized based on the dynamic obstacle information of the robotic arm's workspace.

[0053] Understandably, the gripping part of the robotic arm is equipped with a camera for image analysis and recognition. The gripping part can also be equipped with the aforementioned dynamic obstacle detection lidar. By scanning the item boxes on the assembly line with the radar, i.e., the item box closest to the gripping part of the robotic arm, the size information of the item box is obtained. When the size information of the item box is not unique, the specific classification of the item box is determined by image recognition captured by the camera. The distance between the next item box and the current item box is obtained by the radar. Based on the speed of the conveyor belt and the distance between the two item boxes, the time threshold for the robotic arm to grasp the item box can be calculated. The time of the robotic arm's grasping action must be less than this time threshold, and the height lifted after grasping must be greater than the height of the next item box or the gripping part of the robotic arm must leave the conveyor belt area. At the same time, the movement capability of the robotic arm and the load-bearing capacity of each joint must be considered. Based on the above constraints, according to the mathematical model of the robotic arm, the path of the robotic arm's grasping is planned with the premise of optimal time, using a 3-5-3 polynomial interpolation function and a cuckoo search algorithm. That is, the second motion trajectory.

[0054] The palletizing operation then falls into the following categories:

[0055] (1) When the sorted item box being grabbed is the first palletizing operation, since there is no corresponding palletizing board in the record, the nearest palletizing board can be found based on the available palletizing board and the position of the robotic arm at this time. The selected palletizing board is the target position.

[0056] (2) When the sorted item box being grabbed is not the first time it is palletized, the corresponding palletizing board can be found in the record. If the height of the palletizing board is not at its maximum value or the height is at its maximum but there is an empty space on the top layer, that is, it is not marked as completed, the board is still selected as the target palletizing position. If the palletizing board is marked as completed, the empty palletizing board that is closest to the robotic arm is selected.

[0057] At this point, with the target palletizing substrate as the endpoint of the path planning, the minimum envelope of the robotic arm is determined based on its mathematical model. A neural network algorithm is used to plan the robotic arm's path to the target palletizing substrate, avoiding static obstacles. Based on the acquired dynamic obstacle information and the constructed collision area, the probability of collision and the intersection of collision trajectories are detected, and the trajectory of the robotic arm to the target palletizing substrate is optimized to avoid dynamic obstacles. When the robotic arm is stacking the boxes, its movement is planned according to the pre-calculated arrangement rules and the current placement state. At this point, the third motion trajectory planning is completed. The size change information of the substrate is recorded. When the height of the substrate reaches the set maximum value and there is no gap in the top layer, the substrate is marked as completed.

[0058] Below, for reference Figure 3 This application describes a full trajectory planning system for a robotic arm according to embodiments of the present application.

[0059] Figure 3 The figure shows a schematic diagram of a full trajectory planning system for a robotic arm provided according to an embodiment of this application.

[0060] like Figure 3 As shown, the full trajectory planning system for a robotic arm according to an embodiment of this application may include:

[0061] The first determining module is used to classify and determine the stacking arrangement based on the obtained spatial size and location information of the palletizing area, the information of the boxes to be palletized, and the conveyor belt information, and includes the following units:

[0062] The first acquisition unit is used to acquire spatial size information and location information of the palletizing area, information of the item box to be palletized and information of the conveyor belt. For the pattern information of the item box, the pattern on multiple faces can be acquired to ensure that the specific classification information of the item box on the conveyor belt can be accurately identified.

[0063] The first classification unit is used to classify the boxes to be palletized based on their size and pattern. It performs a preliminary classification based on the size of the boxes and a secondary classification based on the pattern information of the boxes when the length, width and height of the boxes are the same.

[0064] The first calculation unit is used to determine the stacking arrangement method by calculating based on the spatial size information of the stacking area and the size information of the boxes to be stacked. It can calculate the number of layers that can be placed based on the maximum allowable height of the stacking base plate and the height of the boxes. It records and saves the stacking arrangement method and the number of layers that can be placed for each type of box.

[0065] The first acquisition module is used to acquire parameter information of the robotic arm;

[0066] The first construction module is used to establish a mathematical model of the robotic arm using the DH model based on the parameter information of the robotic arm, and to determine the pose information of the robotic arm, which includes position and attitude information.

[0067] The second acquisition module is used to acquire obstacle information in the robotic arm's workspace and construct collision areas, including:

[0068] The second acquisition unit is used to acquire static obstacle information of the robotic arm's workspace according to the static obstacle acquisition strategy, including the processing of fitting irregular objects into regular objects.

[0069] The third acquisition unit is used to acquire dynamic obstacle information in the workspace of the robotic arm according to the dynamic obstacle acquisition strategy, including acquiring the speed and direction of motion of the dynamic obstacles;

[0070] The first construction unit is used to construct the obstacle collision area based on obstacle information and safety distance threshold. For regular cylinders or fitted regular cylinders, the safety distance threshold can be determined based on the encirclement degree of the cylinder over the obstacle. For cases where the extreme point is far from the outer edge of the regular object, the minimum safety distance threshold can be taken. For cases where the extreme point is close to the outer edge of the regular object, the maximum safety distance threshold can be taken to construct the obstacle collision area. The safety distance threshold for regular squares or fitted regular squares can be operated in the same way.

[0071] The third acquisition module is used to acquire target item box information and distance information, including a camera unit, a sensor acquisition unit, and a recognition and calculation unit. It acquires the size and pattern information of the target item box and the distance information to the next item box. Based on the size and pattern information of the target item box, it acquires the corresponding category and stacking arrangement from the stored item box categories, acquires the corresponding palletizing board or allocates the nearest free palletizing board, and calculates the maximum time threshold of the robotic arm's grasping action.

[0072] The first trajectory planning module is used to generate the first motion trajectory based on the mathematical model of the robotic arm and the information of the conveyor belt, and to optimize the first motion trajectory based on the obstacle information of the robotic arm's workspace.

[0073] The second trajectory planning module is used to generate a second motion trajectory based on the mathematical model of the robotic arm, conveyor belt information, target item box information, and distance information.

[0074] The third trajectory planning module is used to generate a third motion trajectory based on the mathematical model of the robotic arm, the spatial size and position information of the palletizing area, and the static obstacle information of the robotic arm's workspace, and to optimize the third motion trajectory based on the dynamic obstacle information of the robotic arm's workspace.

[0075] The storage module may include RAM (Random-Access Memory) and non-volatile memory, used to store various basic parameter information, classification and calculation result information, trajectory planning information, and palletizing space change information.

[0076] The specific functions and operations of each module and unit in the above-described full trajectory planning system for the robotic arm have been described in detail in the full trajectory planning method for the robotic arm described above. Therefore, their repeated description will be omitted here.

[0077] Furthermore, embodiments of this application may also be computer-readable storage media storing executable instructions of the full trajectory planning system for the robotic arm described above, which, when executed by a processor included in the full trajectory planning system for the robotic arm described above, are used to implement the full trajectory planning method for the robotic arm as described in the embodiments of this specification.

[0078] The basic principles of this application have been described above with reference to specific embodiments. It should be understood that the specific details disclosed above are for illustrative and illustrative purposes only, and are not intended to limit the scope of protection of this invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A full trajectory planning method of a robot arm applied to a palletizing placement of articles of a transport line, characterized in that, The method comprises: S100, acquiring space size information and position information of a stacking area, information of a to-be-stacked article box, and information of a conveying belt, classifying the to-be-stacked article box according to a classification standard, and respectively determining a stacking arrangement mode according to the classified article box and the space size information of the stacking area; S200, acquiring parameter information of a mechanical arm, establishing a mathematical model of the mechanical arm by using a D-H model according to the parameter information of the mechanical arm, and determining initial pose information of the mechanical arm; S300, generating a first motion trajectory according to the mathematical model of the mechanical arm and the information of the conveying belt, acquiring obstacle information of a working space of the mechanical arm, and optimizing the first motion trajectory according to the obstacle information of the working space of the mechanical arm; the obstacle information of the working space of the mechanical arm comprises static obstacle information and dynamic obstacle information, the obstacle information of the working space of the mechanical arm is acquired according to an obstacle information acquisition strategy, and an obstacle collision region is constructed according to the obstacle information and a safety distance threshold value, comprising: for a regular cylinder or a fitted regular cylinder, a safety distance threshold value is determined according to the degree of the cylinder surrounding the obstacle, when a pole point is far away from a periphery of the regular cylinder or the fitted regular cylinder, a minimum safety distance threshold value is taken, and when the pole point is close to the periphery of the regular cylinder or the fitted regular cylinder, a maximum safety distance threshold value is taken to construct the obstacle collision region; for a regular square or a fitted regular square, a safety distance threshold value is determined according to the degree of the square surrounding the obstacle, when a pole point is far away from a periphery of the regular square or the fitted regular square, a minimum safety distance threshold value is taken, and when the pole point is close to the periphery of the regular square or the fitted regular square, a maximum safety distance threshold value is taken; the optimization of the first motion trajectory comprises: selecting an optimal path without collision with the static obstacle, in the process of moving the mechanical arm, calculating a trajectory intersection point where a collision between the mechanical arm and a dynamic obstacle is possible by using the speed and the motion direction of the dynamic obstacle and the dynamic obstacle collision region, and performing acceleration or deceleration processing on the mechanical arm in advance to avoid the dynamic obstacle; S400, acquiring target article box information and distance information, the distance information being the distance between the target article box and a next article box on the conveying belt, generating a second motion trajectory according to the mathematical model of the mechanical arm, the information of the conveying belt, the target article box information, and the distance information, generating a third motion trajectory according to the mathematical model of the mechanical arm, the space size information and the position information of the stacking area, and the static obstacle information of the working space of the mechanical arm, and optimizing the third motion trajectory based on the dynamic obstacle information of the working space of the mechanical arm; The obstacle information acquisition strategy in step S300 comprises static obstacle information acquisition strategies and dynamic obstacle information acquisition strategies; The static obstacle information collection strategy is to determine a plurality of auxiliary points according to an obstacle model, to determine a target point according to the plurality of auxiliary points, and to collect obstacle position and space information according to the plurality of auxiliary points and the target point; the determination of the plurality of auxiliary points according to the obstacle model comprises: fitting a non-regular object as a cylinder or a square body when the obstacle model is a non-regular object; determining any three points on the circumference of the cross section of the cylinder or the four vertices of the square body as auxiliary points, and determining any point on the highest plane of the cylinder or the square body as a height auxiliary point; The dynamic obstacle information collection strategy is to collect a plurality of auxiliary points of a dynamic obstacle, to construct a dynamic obstacle collision region according to the plurality of auxiliary points and a safety distance threshold, and to collect dynamic obstacle speed and motion trajectory information; The mechanical arm is a multi-joint mechanical arm for grabbing an article box on a transportation line and placing the article box on a stacking substrate, and the mechanical arm base is a movable base. 2.The method of Claim 1, wherein, The space size information of the stacking area includes length and width of the stacking substrate, initial height of the stacking substrate, and maximum placement height information of the stacking substrate, and the position information of the stacking area includes position coordinates of the stacking substrate and distance between the stacking substrates; the article box information to be stacked includes size and pattern information of the article box; the conveyor belt information includes length, width, height, position, and speed information of the conveyor belt; and the classification standard is classification according to size information and patterns of the article box. 3.The method of Claim 1, wherein, The parameter information of the mechanical arm includes included angle of adjacent connecting rods, distance between adjacent connecting rods, connecting rod torsion angle, connecting rod length, end position of the mechanical arm, and rotation speed of each joint. 4.The method of Claim 1, wherein, When the stacking area changes, article box information and space size change information of each stacking substrate are recorded respectively, and when the height of a certain stacking substrate reaches the maximum placement height and the uppermost layer has no gap, the stacking substrate is marked as a completed state; when an article box to be placed is the same classification article box as the article box placed on the stacking substrate in the completed state, a new stacking substrate is selected.

5. A full trajectory planning system for a robotic arm, the system comprising: The system comprises: A first determination module is configured to classify and determine a stacking arrangement mode according to acquired space size information and position information of a stacking area, article box information to be stacked, and conveyor belt information, and comprises the following units: A first acquisition unit is configured to acquire space size information and position information of a stacking area, article box information to be stacked, and conveyor belt information; A first classification unit is configured to classify article boxes to be stacked based on size and pattern; A first calculation unit is configured to determine a stacking arrangement mode by calculation according to space size information of a stacking area and size information of article boxes to be stacked; A first acquisition module is configured to acquire parameter information of a mechanical arm; A first construction module is configured to establish a mathematical model of the mechanical arm by using a D-H model according to the parameter information of the mechanical arm, and to determine pose information of the mechanical arm; A second acquisition module is configured to acquire obstacle information of a working space of the mechanical arm and to construct a collision region, and comprises: A second acquisition unit is configured to acquire static obstacle information of a working space of the mechanical arm according to a static obstacle collection strategy; A third acquisition unit is configured to acquire dynamic obstacle information of a working space of the mechanical arm according to a dynamic obstacle collection strategy. The first construction unit is configured to construct the obstacle collision region according to the obstacle information and the safety distance threshold; The third acquisition module is configured to acquire target article box information and distance information; The first trajectory planning module is configured to generate a first motion trajectory according to a mathematical model of the robot arm, the conveyor belt information, and optimize the first motion trajectory based on the obstacle information of the robot arm workspace; The second trajectory planning module is configured to generate a second motion trajectory according to the mathematical model of the robot arm, the conveyor belt information, the target article box information and the distance information; The third trajectory planning module is configured to generate a third motion trajectory according to the mathematical model of the robot arm, the space size information and the position information of the stacking area, and the static obstacle information of the robot arm workspace, and optimize the third motion trajectory based on the dynamic obstacle information of the robot arm workspace; The storage module is configured to store various basic parameter information, classification and calculation result information, trajectory planning information and stacking space change information acquired.

6. A computer readable storage medium having stored therein instructions executable by the system of claim 5, wherein, The instructions, when executed by the processor included in the system of claim 5, are configured to implement the full trajectory planning method of the robot arm according to any one of claims 1-4.

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