Automatic guided vehicle (AGV) trajectory planning-based intelligent transportation method and system for lyocell fiber raw materials

Abstract

The invention relates to the technical field of AGV intelligent transportation, and discloses a lyocell fiber raw material intelligent transportation method and system based on AGV trajectory planning, and the method comprises the steps: obtaining a collision-free path of an AGV from an initial position to a target position through employing a self-adaptive target guidance RRT random search algorithm; deleting redundant nodes of the collision-free path, and fitting the path by adopting an NURBS curve to obtain a fitted path; segmenting the fitting path according to the curvature value of the fitting path and a preset segmentation number; calculating the speed constraint of each segment point in combination with the resultant gravity center and curvature value of the AGV and the carried raw material; and finally, performing acceleration and deceleration planning on each segmented path when the AGV moves along the fitting path by using an S-shaped speed curve to obtain motion control parameters of the AGV, and controlling the AGV to transport raw materials along the fitting path. According to the method, the optimal track planning of the AGV for transporting the Lyocell pulp raw material can be realized, the path accuracy and the transportation stability of the trolley for transporting the large-weight pulp raw material are ensured, and the continuity of Lyocell production is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of AGV intelligent transportation, and more specifically to an intelligent transportation method and system for lyocell fiber raw materials based on AGV trajectory planning. Background Art

[0002] In traditional lyocell production lines, the transportation of pulp raw materials from the raw material line side warehouse of the stock liquid workshop to the feeding line, and the transportation of finished products from the packaging line of the packaging workshop to the side warehouse of the finished product line mainly rely on the transportation of raw materials by manually driven clamping vehicles. This method has problems such as low transportation efficiency, high labor intensity of workers, and high labor costs, which is not conducive to the long-term development of the enterprise.

[0003] With the continuous development and intelligent upgrading of the manufacturing industry, AGV, as a transportation tool with good application adaptability and high reliability, has been widely used in various production scenarios and has become an important role in realizing the integration of production and transportation of production lines and building intelligent factories. AGV can drive autonomously according to preset paths or instructions to complete the task of handling raw materials, effectively improving production efficiency, reducing labor costs, and reducing errors and risks caused by human operations.

[0004] In the prior art, although there are a variety of AGV trolley conveying systems and path planning methods involving material transportation, most of these technical solutions are aimed at other fields and are not specifically aimed at the special needs of lyocell fiber production workshops. For lyocell fiber production lines, their production process has unique raw material handling requirements. Lyocell fiber is a high-performance green fiber material, and its production process involves the transportation and handling of a large number of raw material pulp bales. The weight of a large bale of raw material pulp is usually around 2 tons, and the weight is very large in tons, so high requirements are placed on the carrying capacity and stability of the transportation tool. In actual production, large bales of raw material pulp need to be transported from the line-side warehouse of the stock liquid workshop to the location of the feeding port of the feeding line. These transportation tasks not only require the AGV trolley to have sufficient carrying capacity, but also need to ensure the correctness of the transportation path and the stability of the transportation process and transportation efficiency.

[0005] However, there are many challenges in using AGVs to transport overweight pulp raw materials. First, the AGVs need to navigate autonomously in a complex production environment to avoid collisions with obstacles in the workshop and ensure the correctness of the transportation path and efficiency. Second, because the raw pulp bags are very heavy, and the overall center of gravity of the materials is high and the volume is large during stacking, the AGVs are prone to tipping over during transportation.

[0006] Therefore, when using AGV carts to transport overweight raw pulp in a lyocell fiber production line, how to achieve effective obstacle avoidance during the cart transportation process, and ensure the correctness of the cart transportation path as well as the stability and efficiency of the transportation process are technical problems that need to be solved urgently. Summary of the invention

[0007] The present invention proposes an intelligent transportation method and system for lyocell fiber raw materials based on AGV trajectory planning. The present invention can realize the effective obstacle avoidance of the AGV trolley in transporting pulp raw materials for lyocell fiber production in a complex production environment, accurately transport the pulp raw materials from an initial storage position to a target position, and ensure the stability and transportation efficiency of the trolley in the process of transporting overweight pulp raw materials, thereby ensuring the continuity of lyocell fiber production.

[0008] In order to achieve the above-mentioned object of the invention, the technical solution of the present invention is as follows: On the one hand, the present invention discloses an intelligent transportation method for lyocell fiber raw materials based on AGV trajectory planning, and the method comprises the following steps: According to the initial position, terminal position and feasible domain range of the target AGV, a collision-free path search is performed using an adaptive target-guided RRT random search algorithm to obtain a collision-free path for the target AGV; wherein the initial position is the initial storage position of the pulp raw material for Lyocell fiber production in the side warehouse of the raw material line, and the terminal position is the target position where the pulp raw material for Lyocell fiber production needs to be processed and fed; Deleting redundant nodes to optimize the collision-free path, and fitting the optimized collision-free path using a NURBS curve to obtain a fitting path; The fitting path is segmented according to the curvature value of the fitting path and the preset number of segments; According to the height of the combined center of gravity of the AGV and the pulp raw material it carries and the curvature value of the segmentation point of the fitting path, the speed constraint of the target AGV when it moves along the fitting path is calculated; The synthetic center of gravity height is obtained by combining the weighing sensor equipped on the trolley with the total mass of the trolley and the moment balance equation, and the calculation method is as follows: Formula (39); in, is the composite center of gravity height; is the horizontal distance from the weighing sensor to the center of gravity, is the vertical force measured by the load cell; is the number of weighing sensors; is the total mass of the target AGV; is the acceleration due to gravity; Use the S-shaped speed curve to plan the acceleration and deceleration of each segmented path when the target AGV moves along the fitting path, and obtain the motion control parameters of the target AGV; The target AGV transports the pulp raw material from the initial storage location to the target location along the planned fitting path according to the motion control parameters.

[0009] Preferably, the method uses an adaptive target-guided RRT random search algorithm to search for a collision-free path based on the initial position, target position and feasible domain range of the target AGV to obtain a collision-free path for the target AGV, including the following steps: Step B1. Initialize the geometric dimensions of the obstacle envelope model in the working environment of the target AGV, as well as the initial position, terminal position and feasible domain range of the target AGV movement; Step B2. Take the initial position of the target AGV as the starting point Stored in random tree nodes middle; Step B3. Sampling and generating random points within the global feasible domain , and search Middle distance The nearest node ; Step B4. Along vector and The angle direction is in steps of Generate new node , generate a new node The calculation expression is: Formula (1); in, is the target location; is the target guidance component coefficient, which is defined as: Formula (2); in, is the preset maximum value of the target guidance component coefficient; Estimate distance to target; for point to Minimum safe distance; α Estimate the distance factor for the target; β is the minimum safety distance factor; Step B5. Connect and Forming a path , determine the target AGV car along the path Whether the movement collides with obstacles in the environment. If no collision occurs, Stored in ; Step B6. Judgment and If the distance is less than the set error value, the collision-free path search ends, otherwise, steps B3 to B5 are repeated until the collision-free path search is completed.

[0010] Preferably, deleting redundant nodes to optimize the collision-free path comprises the following steps: Step C1. If the collision-free path is found Include n nodes, the starting point Save to the new path node and Assign to ; Step C2. With nodes Connect, create a connection , and then connect the lines Perform collision detection between obstacles and between the AGV and obstacles until a collision-free node is found ,Will Save to new path middle; Step C3. Assign to Repeat step C2 until the new path node contains the end point , the one-way deletion of redundant nodes is completed, and the collision-free path after one-way deletion optimization is obtained.

[0011] Preferably, the path points in the optimized collision-free path are used as the shape value points, and the optimized collision-free path is fitted using a cubic NURBS curve to obtain a fitting path. The calculation expression for the fitting is as follows: Formula (3); in, is the weight factor, and satisfies the adjacent k The weight factors are not all 0 at the same time; is the control vertex; node vector ; for k Subnormal B-spline basis functions.

[0012] Preferably, segmenting the fitting path according to the curvature value of the fitting path and a preset number of segments includes the following: Derivative of the NURBS curve, any point on the NURBS curve r Order derivative The calculation expression is as follows: Formula (4); Formula (5); in, For the NURNS curve r The first derivative curve j Control vertices; is the first j Control vertices; For NURBS curves l The first derivative curve j Control vertices; For NURBS curves l-1 The first derivative curve j Control vertices; For NURBS curves l-1 The first derivative curve j-1 Control vertices; According to equations (4) and (5), the first-order derivative and the second-order derivative of the NURBS curve are calculated, and then the curvature of the NURBS curve is obtained by solving the following calculation expression (6): Formula (6); in, is the curvature of the NURBS curve, is the first-order derivative of the NURBS curve; is the second-order derivative of the NURBS curve; After calculating the curvature of the NURBS curve, assuming that the number of segments of the curve is num, find the path points corresponding to the local extreme values ​​of the first num-1 curvatures, and use these points as segmentation points of the curve to segment the fitting path.

[0013] Preferably, according to the height of the combined center of gravity of the AGV and the carried pulp raw material and the curvature value of the segmentation point of the fitting path, the speed constraint of the target AGV moving along the fitting path is calculated, including the following contents: Step F1. During the turning process of the target AGV, when the vehicle body is in a critical state of rollover, the overturning moment generated by the centrifugal force is equal to the stabilizing moment generated by gravity, then: Formula (7); Formula (8); in, is the total mass of the target AGV, is the turning speed of the target AGV, is the turning radius of the target AGV, is the curvature of the NURBS curve, is the composite center of gravity height, is the acceleration due to gravity, is the wheelbase of the target AGV; is the critical speed when the vehicle body is in the critical state of rollover; Step F2. During the turning process of the target AGV, when the vehicle body is in a critical state of slippage, the centrifugal force is equal to the maximum static friction between the tires of the vehicle and the ground, then: Formula (9); Formula (10); in, is the friction coefficient between the target AGV tire and the ground; is the critical speed when the vehicle body is in the critical state of slip; Step F3. When the target AGV moves along the fitting path, the speed constraint for: Formula (11).

[0014] Preferably, an S-shaped speed curve is used to perform acceleration and deceleration planning on each segmented path when the target AGV moves along the fitting path, and the motion control parameters of the target AGV are obtained, including the following steps: Step G1. Based on the speed constraint of the target AGV , set the initial speed of the target AGV car in the current segment path , Final speed , initial acceleration , Maximum speed , maximum acceleration and maximum jerk , and solve for the path length , is the first-order derivative of the NURBS curve; Step G2. Use the S-shaped speed curve planning algorithm to control the speed of the target AGV in the current segmented path to obtain the acceleration time of the target AGV during its movement. , acceleration time , uniform speed period , deceleration time , Acceleration and deceleration time , the maximum acceleration that the car can reach in the acceleration section , the minimum acceleration that can be achieved in the deceleration stage , and the maximum speed that the car can reach in the current segment path ; Step G3. Determine the uniform speed time of the target AGV and output the S-shaped speed curve parameters; Step G4. According to the S-shaped speed curve parameters output in step G3, the trajectory points are interpolated to obtain the first n The motion parameters of the path interpolation points are the motion control parameters of the target AGV.

[0015] Preferably, step G2 specifically includes the following contents: Assuming that the maximum speed can be achieved when the current segmented path is planned using a standard seven-segment S-shaped speed curve, then: Step G21. Determine whether the target AGV has a uniform acceleration segment in the current segmented path; like , then the target AGV in the acceleration section cannot reach the set maximum acceleration , the car does not have a uniform acceleration phase, at this time: Formula (12); Formula (13); Formula (14); in, The target AGV accelerates for a period of time; The acceleration time of the target AGV; is the maximum acceleration that the target AGV can achieve in the acceleration section; like , then the target AGV car in the acceleration section can reach the set maximum acceleration , the car has a uniform acceleration stage, at this time: Formula (15); Formula (16); Formula (17); Step G22. Determine whether the target AGV has a uniform deceleration segment in the current segmented path; like , then the target AGV in the deceleration stage cannot reach the minimum acceleration, and there is no uniform deceleration stage. At this time, there are: Formula (18); Formula (19); Formula (20); in, is the deceleration time of the target AGV; is the acceleration and deceleration time of the target AGV; The minimum acceleration that the target AGV can achieve during the deceleration phase; like , then the target AGV car in the deceleration stage can reach the minimum acceleration, and there is a uniform deceleration stage, at this time: Formula (21); Formula (22); Formula (23); Step G23. Calculate the uniform speed time of the target AGV in the current segmented path. The calculation expression is: Formula (24); in, The target AGV car has a uniform speed period; is the path length; is the displacement of the target AGV during the acceleration phase; Furthermore, if , then: Formula (25); Furthermore, if , then: Formula (26).

[0016] Preferably, the step G3 specifically includes the following contents: Step G31. If , then the target AGV car has a uniform speed segment in the current segmented path and the car's speed extreme value Able to reach the set maximum speed , output the parameters calculated in steps G21-G23 , , , , , and , and the maximum speed that the target AGV can reach during the entire movement process , as the S-type speed curve parameter; Step G32. If , then there is no uniform speed segment for the target AGV in the current segmented path. Assuming that the maximum acceleration of the acceleration segment can reach And the minimum acceleration of the deceleration section can reach , then the maximum speed that the car can reach is for: Formula (27); in, Formula (28); Formula (29); Formula (30); Further, Formula (31); Formula (32); Formula (33); Formula (34); Formula (35); Step G33. If the calculated value in step G32 is or , then the acceleration of the target AGV in the acceleration or deceleration section cannot reach or , then reduce until the value of or Then repeat step G32 to calculate the parameters , , , , and , and finally output the parameters , , , , , , , and the maximum speed that the target AGV can reach during the entire movement process , as the S-type speed curve parameter; Preferably, the step G4 specifically includes: T s is the interpolation period, then n Path interpolation points ( ) are: when hour, , , ; when hour, , , ; when hour, , , ; when hour, ; when hour, , , ; when hour, , , ; when hour, , , .

[0017] Preferably, the step C also includes: step C4. performing equal-spacing interpolation on the collision-free path after the one-way deletion optimization to obtain a path , then from the end point Start to delete redundant nodes in reverse order, and finally get the path after secondary optimization , this path is the final optimized collision-free path.

[0018] Based on the same inventive concept, the present invention also discloses an intelligent transportation system for lyocell fiber raw materials based on AGV trajectory planning, the system is used to implement the above-mentioned intelligent transportation method, including an AGV trajectory planning module and a target AGV trolley; wherein, The AGV trajectory planning module uses an adaptive target-guided RRT random search algorithm to search for a collision-free path according to the initial position, target position and feasible domain range of the target AGV trolley, and obtains a collision-free path for the target AGV trolley; then, the redundant nodes are deleted to optimize the collision-free path, and the optimized collision-free path is fitted using a NURBS curve to obtain a fitted path; then, the fitted path is segmented according to the curvature value of the fitted path and the preset number of segments; then, according to the composite center of gravity height of the AGV trolley and the carried pulp raw material and the curvature value of the segmentation point of the fitting path, the speed constraint of the target AGV trolley when moving along the fitting path is calculated; finally, an S-shaped speed curve is used to perform acceleration and deceleration planning on each segmented path when the target AGV trolley moves along the fitting path, and the motion control parameters of the target AGV trolley are obtained; The target AGV trolley transports the pulp raw material from the initial storage position to the target position along the planned fitting path according to the motion control parameters, thereby completing the transportation of the pulp raw material.

[0019] On the other hand, the present invention further discloses a computer device, including a memory, a processor, and a computer program stored in the memory and executable in the processor, wherein when the processor executes the computer program, the above-mentioned intelligent transportation method is implemented.

[0020] On the other hand, the present invention further discloses a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed in a computer processor, the above-mentioned intelligent transportation method is implemented.

[0021] Beneficial effects of the present invention: 1. First, the weight of lyocell pulp raw materials is usually measured in tons, which is very heavy. Ordinary AGV carts cannot transport overweight pulp raw materials; secondly, due to the compact layout of various equipment on the lyocell production line, the problem of obstacle avoidance in trolley transportation needs to be fully considered; finally, based on the consideration of production continuity and efficiency, it is necessary to ensure the correctness of the trolley transportation path and the transportation efficiency of the trolley. Therefore, in the trajectory planning of the AGV trolley automatic transportation of raw materials, the present invention fully considers the characteristics of the trolley's transportation obstacle avoidance, transportation path accuracy, transportation efficiency and transportation weight of raw materials, integrates and improves multiple methods, and forms a set of adaptive RRT algorithm search collision-free path, redundant point deletion optimization collision-free path, NURBS curve fitting collision-free path, curvature extreme value segmented fitting path, synthetic center of gravity height constraint and S-type speed curve trajectory interpolation combination to form a complete AGV trolley trajectory planning method. The method of the present invention can realize the optimal trajectory planning of the AGV trolley for transporting pulp raw materials for lyocell. The trolley can accurately transport the raw materials from the initial storage position to the target position along the planned optimal path, and the transportation is time-saving and efficient. In addition, collisions can be avoided during transportation and the speed of the trolley can be controlled in real time to prevent the trolley from rolling over, thereby ensuring the continuity of lyocell fiber production.

[0022] 2. The adaptive target-guided RRT algorithm of the present invention takes into account both the guidance and randomness of the path search random tree growth. While ensuring the obstacle avoidance capability, it has strong guidance, can effectively reduce the path overhead, shorten the search time, and significantly improve the path planning efficiency of the vehicle, so that the searched collision-free path is the optimal path. The AGV vehicle travels along the planned optimal path, and can quickly and accurately transport the raw pulp to the designated location for feeding, ensuring the continuity of Lyocell fiber production.

[0023] 3. In the path planning of the present invention, the collision-free path obtained by searching is smoothly fitted, which can reduce the sharp corners in the path and avoid the drastic acceleration changes of the car during driving, which affects the driving stability.

[0024] 4. In the path planning of the present invention, the collision-free path after fitting is segmented by the curvature extreme value. The curvature extreme value is the point with the smallest turning radius. The segmentation is based on the turning point, and then the acceleration and deceleration planning is performed on the trolley. This can achieve smooth turning of the trolley, ensure the stability of the trolley in transporting heavy pulp raw materials, and avoid rollover.

[0025] 5. The present invention replaces the traditional manually driven clamping car with the automated transportation of the AGV trolley, reduces the time and error of manual operation, and can quickly and accurately complete the task of transporting the pulp raw materials for Lyocell production from the line-side warehouse to the feeding line, greatly improving the transportation efficiency and significantly improving the production efficiency of Lyocell fiber; and reduces the activities of personnel in a complex production environment, reduces the safety risks caused by human operational errors, and greatly reduces labor costs.

[0026] 6. The present invention integrates the scheduling, trajectory planning and motion control of the AGV trolley, realizes the intelligent transportation of the Lyocell fiber production line, and improves the automation and intelligence level of production.

[0027] 7. The present invention reduces human operation errors through automated transportation, ensures the stability and consistency of raw material transportation, and thus improves the product quality of lyocell fibers. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The foregoing and following detailed description of the present invention will become more apparent when read in conjunction with the following drawings, in which: Figure 1 is a flow chart of the method of the present invention; Figure 2 This is a system architecture diagram of the present invention; Figure 3 It is a schematic diagram of the S-shaped speed curve of the present invention; Figure 4 This is the effect diagram of the AGV vehicle trajectory planning from the first initial position to the target position in Example 2; Figure 5 This is the motion curve diagram of the AGV from the first initial position to the target position in Example 2; Figure 6 This is the effect diagram of the AGV vehicle trajectory planning from the second initial position to the target position in Example 2; Figure 7 This is the motion curve of the AGV from the second initial position to the target position in Example 2. DETAILED DESCRIPTION

[0029] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the technical solutions for achieving the purpose of the present invention will be further described below through specific embodiments. It should be noted that the technical solutions claimed for protection in the present invention include but are not limited to the following embodiments. Based on the embodiments in the present invention, all other embodiments obtained by those skilled in the art without creative work shall fall within the scope of protection of the present invention.

[0030] Example 1 The embodiment of the present invention proposes an intelligent transportation method for lyocell fiber raw materials based on AGV trajectory planning. Figure 1 For the flow chart of the method of the present invention, please refer to the attached specification Figure 1 , the method specifically includes the following contents: Step S1. First, the production plan is imported into the MES system. After receiving the production plan, the material preparation information management module of the MES system generates a batching instruction according to the process requirements and sends the batching instruction to the material calling module; then the material calling module generates a loading instruction according to the batching instruction and sends it to the AGV scheduling module; the AGV scheduling module first determines the initial storage position of the lyocell pulp raw material required for production in the loading instruction in the raw material line side warehouse and the target position where the material needs to be processed and fed, and then the AGV scheduling module sends a control signal (i.e., the scheduling instruction) to the target AGV car. After receiving the control signal, the target AGV car automatically drives to the initial storage position to receive the raw materials; and the AGV scheduling module sends the initial storage position and the target position where the material needs to be processed and fed to the AGV trajectory planning module.

[0031] In this embodiment, the batching instruction mainly includes information such as the batch number and target position of the required pulp raw material. Then, the material calling module matches the initial storage position of the pulp raw material according to the batch number of the pulp raw material and the storage situation of the raw material line side warehouse, and naturally obtains the initial position of the AGV cart transporting the raw material. That is to say, the initial storage position of the pulp raw material is the initial position of the cart transporting the raw material. Then, the material calling module packages the information such as the initial storage position and the target position to generate a loading instruction and sends it to the AGV scheduling module.

[0032] In the present embodiment, Lyocell is the abbreviation of Lyocell fiber.

[0033] It can be understood that the target AGV car is usually an idle AGV car in the area. How the AGV scheduling module selects an idle car as the target AGV car involves the AGV scheduling algorithm, but the scheduling algorithm does not belong to the innovation and improvement of the present invention. The existing scheduling method can be used and will not be explained in detail here.

[0034] Furthermore, in some embodiments, in order to facilitate the management of AGVs in the production area, a rest area is usually planned in the area, and idle AGVs are parked in the rest area. Therefore, after the target AGV completes the current transportation task, it will return to the rest area to rest and wait for the next transportation task.

[0035] Step S2. The AGV trajectory planning module takes the initial storage position and the target position where the feeding needs to be processed as the initial position and the terminal position of the trolley respectively, and uses the adaptive target to guide the RRT random search algorithm to search for a collision-free path according to the initial position, the terminal position and the feasible domain range to obtain the collision-free path of the trolley; then the collision-free path is optimized by deleting redundant nodes, and the optimized collision-free path is fitted with a NURBS curve to obtain a fitted path; then the fitted path is segmented according to the curvature value of the fitted path and the preset number of segments.

[0036] Step S3. After the target AGV receives the dispatch instruction and arrives at the initial storage position, the loading device at the initial storage position takes out multiple bags of pulp raw materials from the line-side warehouse, and then stacks them on the cart. After the AGV clamps the large bags of pulp raw materials, the weighing sensor equipped on the cart detects and obtains the vertical force, and then transmits the data obtained by the detection to the AGV trajectory planning module. The AGV trajectory planning module obtains the synthetic center of gravity height based on the vertical force measured by the weighing sensor, combined with the total mass of the cart and the moment balance equation.

[0037] In this embodiment, the weighing sensors are installed on the chassis of the trolley, generally evenly distributed at the supporting points of the AGV chassis (such as the four corners of a four-wheel AGV), and each sensor is installed at the same height. The specific method for synthesizing the center of gravity height is as follows: Get the vertical force measured by each load cell And the installation location coordinates of each sensor ; Establish the moment balance equation: Let the coordinates of the center of gravity be ; The total mass of the target AGV ; According to the moment balance: Formula (40); Formula (41); The moment balance of each sensor support force to the composite center of gravity height must satisfy: Formula (39); in, is the composite center of gravity height; is the horizontal distance from the weighing sensor to the center of gravity, is the vertical force measured by the load cell; is the number of weighing sensors; is the total mass of the target AGV; is the acceleration due to gravity; Further, ; Solve the equations (40) and (41) to calculate , and then further calculate to obtain Finally, substitute into formula (39) to calculate the synthetic center of gravity height of the target AGV car .

[0038] The AGV trajectory planning module calculates the speed constraint of the target AGV when it moves along the fitting path according to the synthetic center of gravity height and the curvature value of the fitting path segmentation point; then uses the S-shaped speed curve to plan the acceleration and deceleration of each segment path when the target AGV moves along the fitting path to obtain the motion control parameters of the target AGV.

[0039] Step S4. The AGV trajectory planning module controls the target AGV vehicle to transport the pulp raw material from the initial storage position to the target position along the planned fitting path according to the motion control parameters to complete the transportation task. After completing the transportation task, the vehicle returns to the rest area to wait; the motion parameters include speed, acceleration, jerk and other motion parameters.

[0040] In this embodiment, it can be understood that for the trajectory planning of the trolley when it moves from the rest area position to the initial storage position of the raw materials (or the trajectory planning from the target position back to the rest area), the trajectory planning method when the trolley transports the raw materials can be referred to (i.e., refer to the real-time trajectory planning strategy of step S3), except that the initial position and target position defined here are different.

[0041] It can be understood that the fitting path is the final motion path of the target AGV car after planning. The target AGV car moves along the fitting path, and based on the motion control parameters, the pulp raw material can be sent from the initial storage position to the target position where the material needs to be processed and fed.

[0042] The specific process of AGV vehicle trajectory planning will be described in detail below.

[0043] In the embodiment described in the present invention, the AGV trajectory module uses the adaptive target-guided RRT random search algorithm to search for a collision-free path according to the initial position, terminal position and feasible domain range of the target AGV trolley, and obtains the collision-free path of the target AGV trolley, including the following steps: First, in order to simplify the subsequent collision detection operation between the target AGV and the environmental obstacles and improve the response speed, this embodiment uses a standard geometric body (sphere and capsule) bounding box to envelop the irregular target AGV and environmental obstacles to achieve model simplification.

[0044] Specifically, the steps include: Step B1. Initialize the size and dimensions of the obstacle envelope model in the working environment of the target AGV, as well as the starting point (initial position), end point (end position) and global feasible domain range of the target AGV movement; Step B2. Take the initial position of the target AGV as the starting point Stored in random tree nodes middle; Step B3. Sampling and generating random points within the global feasible domain , and search Middle distance The nearest node ; Step B4. Along the vector and The angle direction is in steps of (Multiplied by the adaptive adjustment coefficient) to generate a new node , the new node The calculation formula is as follows: Formula (1); in, is the target location; Further, is the target guidance component coefficient, and its calculation formula is: Formula (2); in, is the preset maximum value of the target guidance component coefficient; Estimate distance to target; for point to The minimum safety distance is equal to the adjacent The closest and line segment Intersecting obstacles and The distance between them minus the radius of the obstacle envelope model; α Estimate the distance factor for the target; β is the minimum safety distance factor; During the path search The value can be adaptively adjusted dynamically according to the distribution of obstacles in the environment: when approaching an obstacle, The value decreases to bypass obstacles; when escaping obstacles or approaching the target point, As the value increases, the guidance is enhanced or the oscillation of the path at the end point is reduced; Step B5. Connect and Forming a path , determine the target AGV car along the path Whether the movement collides with obstacles in the environment. If no collision occurs, Stored in ; Specifically, the path The principle of collision detection with an obstacle is: first solve the path The difference between the shortest distance to the obstacle envelope model (the center of the sphere or the axis of the capsule) and the radius of the obstacle is subtracted, and then the difference is compared with the minimum safe distance. If the difference is less than the minimum safe distance, a collision is considered to have occurred. Step B6. Judgment and If the distance is less than the set error value, the collision-free path search ends, otherwise repeat steps B3 to B5 until the collision-free path search is completed (the collision-free path is a broken line).

[0045] Furthermore, since the adaptive target-guided RRT algorithm in step B has a certain degree of randomness, the path automatically searched and generated will have some redundant nodes, which increases unnecessary path overhead. Therefore, in order to shorten the path length and increase transportation efficiency, this embodiment optimizes the collision-free path searched and obtained in step B by deleting redundant nodes in the collision-free path. The main steps of the optimization method are as follows: Step C1. If the collision-free path is found Include n nodes, the starting point Save to the new path node and Assign to ; Step C2. With nodes Connect, create a connection , and then connect the lines Perform collision detection between obstacles and between the AGV and obstacles until a collision-free node is found ,Will Save to new path middle; Step C3. Assign to Repeat step C2 until the new path node contains the end point , the one-way deletion of redundant nodes is completed, and the collision-free path after one-way deletion optimization is obtained; Step C4. After the above one-way deletion, most of the redundant nodes in the path can be deleted, but this method always searches for the farthest connectable collision-free point in the direction of the end point, so the path optimized by one-way deletion is not necessarily the global optimal path. To solve this problem, reverse secondary deletion is further used to further optimize the path. The method is as follows: first, the collision-free path optimized by forward deletion is interpolated with equal spacing to obtain the path , then from the end point Start to delete redundant nodes in reverse. The deletion method is similar to the forward deletion disclosed in steps C1-C3, and finally obtain the collision-free path after secondary optimization. .

[0046] Furthermore, since the AGV path after the RRT algorithm is automatically searched and redundant nodes are deleted through the adaptive target guidance is still a broken line, there are non-smooth inflection points at the node connections. This causes the AGV to change direction quickly when performing tasks along this path, affecting its stability. A continuous cubic non-uniform rational B-spline curve is used to fit the collision-free path optimized in the above steps C1-C4, and the path is smoothed. Specifically, the following contents are included: The path points on the optimized collision-free path are used as the shape value points, and the NURBS curve is used to fit the optimized path. The calculation expression of the fitting is as follows: Formula (3); in, is the weight factor, and satisfies the adjacent k The weight factors are not all 0 at the same time; is the control vertex; node vector ; for k The sub-canonical B-spline basis function, in this embodiment, uses a cubic NURBS curve to fit the optimized collision-free path.

[0047] It can be understood that the NURBS curve is used to fit the optimized collision-free path, that is, a corresponding number of linear equations are constructed according to the given path points, and the weight factors in the NURBS curve formula are inversely solved. and control vertices , the fitted NURBS curve is determined and the corresponding fitting path can be obtained.

[0048] Since the path before fitting is completely collision-free, whether the AGV will collide with obstacles in the environment during the movement along the path after NURBS curve fitting is a problem that needs attention. Therefore, in order to avoid the collision between the fitted path after fitting and obstacles in the environment, this embodiment first interpolates the collision-free path before path fitting, and then fits the interpolated path to achieve a high degree of overlap between the fitting curve and the broken line path before fitting, so as to avoid the collision between the fitting path and obstacles in the environment as much as possible. In addition, by controlling the number of interpolation points, the overlap between the fitting curve and the broken line can be controlled to achieve more accurate path fitting.

[0049] Furthermore, in order to reduce the vibration and impact of the target AGV moving along the NURBS curve fitting path, the movement speed of the target AGV needs to be reasonably planned. As the curvature of the curve changes, the speed constraints at each point of the curve are different. At the extreme point of curvature (turning point, that is, the segmentation point of the fitting path), the speed constraint is the largest, that is, the speed allowed to pass is the smallest, so the corresponding deceleration treatment should be performed at this point. Therefore, before speed planning, the fitting path is first segmented with the extreme point of curvature as the segmentation point. The specific steps are as follows: First, the derivative of the NURBS curve is obtained. r Order derivative The calculation expression is as follows: Formula (4); Formula (5); in, For the NURNS curve r The first derivative curve j Control vertices; is the first j Control vertices; For NURBS curves l The first derivative curve j Control vertices; For NURBS curves l-1 The first derivative curve j Control vertices; For NURBS curves l-1 The first derivative curve j-1 Control vertices; According to the above calculation expressions (4) and (5), the first-order derivative and the second-order derivative of the NURBS curve can be solved, and then the curvature of the NURBS curve can be obtained according to the following calculation expression: Formula (6); in, is the curvature of the NURBS curve, is the first-order derivative of the NURBS curve; is the second-order derivative of the NURBS curve; After calculating the curvature of the curve, assuming that the number of segments of the curve is num, find the path points corresponding to the local extreme values ​​of the first num-1 curvatures, and use these points as segmentation points of the fitting path to segment the fitting path. The path between two adjacent segmentation points is the segmented path.

[0050] After the fitting path is segmented, the AGV is prone to rollover or slip when turning, accelerating or decelerating (sudden stop and start) during driving due to the large weight of the raw pulp bales transported by the trolley, the high overall center of gravity, and the large volume. Therefore, in order to ensure the stability of the AGV's movement along the fitting path, the movement speed of the AGV needs to be reasonably planned and constrained. This embodiment constrains the speed of the target AGV trolley when it moves along the fitting path in combination with the height of the synthetic center of gravity of the trolley and the curvature at the segmentation points of the fitting path, specifically including the following contents: Step F1. When the target AGV is turning, gravity and the support force of the ground keep the vehicle balanced in the vertical direction, and gravity will also affect the center of gravity and rollover of the vehicle. The static friction provided by the ground provides the necessary centripetal force when turning, so that the vehicle can smoothly complete the turning action; further, when the vehicle turns, a virtual centrifugal force will be generated due to inertia. The direction of this force is opposite to the centripetal force and deviates from the center of the circle. It will cause the vehicle to roll outward, which may cause the vehicle to roll or lose control. Therefore, considering that the vehicle body is in a critical state of rollover, the overturning moment generated by the centrifugal force is equal to the stabilizing moment generated by gravity, then: Formula (7); Formula (8); in, is the total mass of the target AGV, is the turning speed of the target AGV, is the turning radius of the target AGV, is the curvature of the NURBS curve, is the composite center of gravity height, is the acceleration due to gravity, is the wheelbase of the target AGV; is the critical speed when the vehicle body is in the critical state of rollover; Therefore, in order to prevent the target AGV from rolling over when turning, the overturning moment generated by the centrifugal force must be less than or equal to the stabilizing moment generated by gravity. Then the upper speed limit of the target AGV anti-rollover constraint is: Formula (37); Step F2. When the target AGV is turning, consider that when the vehicle body is in a critical state of slippage, the centrifugal force is equal to the maximum static friction between the tires of the vehicle and the ground, then: Formula (9); Formula (10); in, is the friction coefficient between the target AGV tire and the ground; is the critical speed when the vehicle body is in the critical state of slip; Therefore, in order to prevent the target AGV from slipping when turning, the centrifugal force must be less than or equal to the maximum static friction between the tire and the ground. Then the upper speed limit of the target AGV anti-slip constraint is: Formula (38); Step F3. In summary, the speed constraint of the target AGV moving along the NURBS fitting path for: Formula (11).

[0051] In the embodiment described in the present invention, the weighing sensor can be a mainstream weighing sensor currently on the market. The weighing sensor and the method for synthesizing the center of gravity height are not the innovation of the present invention and will not be described in detail here.

[0052] Based on the above path optimization, fitting, segmentation and speed constraints, this embodiment uses the S-shaped speed curve to plan the acceleration and deceleration of the vehicle in the segmented path, and finally outputs the motion instructions of the target AGV vehicle. Figure 3 As shown in the figure, the seven-segment S-shaped velocity curve with non-zero initial velocity and initial acceleration includes: Acceleration section (divided into acceleration section, uniform acceleration section, deceleration section), uniform speed section, deceleration section (divided into acceleration and deceleration section, uniform deceleration section, deceleration and deceleration section); details are as follows: Acceleration segment : At maximum jerk Make the acceleration from becomes ; ; Uniform acceleration section : Taking the maximum acceleration as accelerate; ; Deceleration stage : At maximum jerk Make the acceleration 0; ; Uniform speed section : At a constant speed uniform motion; ; Acceleration and deceleration section : With minimum jerk The acceleration changes from 0 to ; ; Uniform deceleration section : At minimum acceleration slow down; ; Deceleration stage : At maximum jerk J max Make the acceleration from becomes 0; ; in, T is the total movement time of the target AGV in the current segmented path, .

[0053] It should be noted that the jerk is the rate of change of acceleration with time, that is, the derivative of acceleration, usually expressed as Said, the formula is: .

[0054] The specific target AGV vehicle speed planning process is as follows: Step G1. Set the initial speed of the target AGV in the current segment path , Final speed , initial acceleration , Maximum speed , maximum acceleration , minimum acceleration and maximum jerk , and when setting parameters, ensure that all speeds are less than the speed constraint ; Then solve for the path length , is the first derivative of the NURBS curve.

[0055] Step G2. Use the S-shaped speed curve planning algorithm to control the speed of the target AGV in the current segmented path to obtain the acceleration time of the target AGV during its movement. , acceleration time , uniform speed period , deceleration time And acceleration and deceleration time , the maximum acceleration that the car can reach in the acceleration section , the minimum acceleration that can be achieved in the deceleration stage , and the maximum speed that the car can reach in the current path .

[0056] Assuming that the target AGV can reach the maximum speed when planning the current segmented path with the standard seven-segment S-shaped speed curve, step G2 specifically includes the following contents: Step G21. Determine whether the target AGV has a uniform acceleration segment in the current segmented path; like , then the target AGV car in the acceleration section cannot reach the set maximum acceleration, and the target AGV car does not have a uniform acceleration stage. At this time: Formula (12); Formula (13); Formula (14); in, is the acceleration time of the target AGV; is the acceleration period; is the maximum acceleration that the target AGV can achieve in the acceleration section; like , then the acceleration section can reach the set maximum acceleration , the target AGV car has a uniform acceleration stage, at this time: Formula (15); Formula (16); Formula (17); Step G22. Determine whether the target AGV has a uniform deceleration segment in the current segmented path; like , then the target AGV car in the deceleration stage cannot reach the set minimum acceleration, so the car does not have a uniform deceleration stage, at this time: Formula (18); Formula (19); Formula (20); in, is the deceleration time of the target AGV; is the acceleration and deceleration time of the target AGV; The minimum acceleration that the target AGV can achieve during the deceleration phase like , then the target AGV car in the deceleration stage can reach the set minimum acceleration, and the car has a uniform deceleration stage, at this time: Formula (21); Formula (22); Formula (23); Step G23. Calculate the uniform speed time of the target AGV in the current segmented path. The calculation expression is: Formula (24); in, When the target AGV is in the uniform speed section; is the path length; is the displacement of the target AGV during the acceleration phase; Furthermore, if , then the displacement of the car in the acceleration section is The following formula (25) is used to calculate: Formula (25); Furthermore, if , then the displacement of the car in the acceleration section is The following formula (26) is used to calculate: Formula (26); Step G3. Determine the size and output the motion parameters of the S-shaped speed curve. The details are as follows: Step G31. If , it means that the target AGV car has a uniform speed segment in the current segmented path and the car's speed extreme value Able to reach the set maximum speed , then directly output the parameters calculated in steps G21-G23 , , , , , and , and the maximum speed that the target AGV can reach during the entire movement process , as the S-type speed curve parameter; Step G32. If , it means that there is no uniform speed segment in the current segmented path. If the maximum acceleration of the acceleration segment can reach , and the minimum acceleration of the deceleration section can reach , then the maximum speed that the car can reach is for: Formula (27); Furthermore, the parameters in formula (27) The following calculation expression is used to obtain: Formula (28); Formula (29); Formula (30); And, at this time, the acceleration period , acceleration time , deceleration time , Acceleration and deceleration time And the uniform speed period The calculation formula is as follows: Formula (31); Formula (32); Formula (33); Formula (34); Formula (35); Step G33. If the calculated value in step G32 is or , then the acceleration of the target AGV in the acceleration or deceleration section cannot reach or , then reduce until the value of or Then return to step G32 to recalculate the parameters , , , , and , and finally output the parameters , , , , , , and As the S-type speed curve parameter.

[0057] Step G4. Perform trajectory point interpolation based on the S-type velocity curve parameters output in the above steps. T s is the interpolation period, then the first n interpolation path points ( ) are: ① When hour, , , ; ② When hour, , , ; ③When hour, , , ; ④ When hour, ; ⑤When hour, , , ; ⑥ When hour, , , ; ⑦ When hour, , , .

[0058] Step G5. Interpolate the first n The motion parameters of the interpolated path points ( v ( t )、 a ( t )、 J ( t )) is output to the target AGV car as the motion control parameter. After the car moves to the path point, it moves with the corresponding speed, acceleration and jerk.

[0059] Based on the same inventive concept, the present embodiment also discloses a lyocell fiber raw material intelligent transportation system based on AGV trajectory planning. Since the principle of solving the problem of the system is similar to that of the intelligent transportation method, the implementation of the system can refer to the implementation of the method, and the repeated parts will not be repeated. As used below, the term "unit" or "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived. Figure 2 The schematic diagram of the structure of the lyocell fiber raw material intelligent transportation system based on AGV trajectory planning provided by the embodiment of the present invention is as follows: Figure 2 As shown, the system may include: a material calling module 201, an AGV scheduling module 202 and an AGV trajectory planning module 203.

[0060] The structure of the system is described in detail below. The material calling module 201 produces a material loading instruction according to the batching instruction issued by the material preparation information management module in the MES system, and then issues the material loading instruction to the AGV scheduling module; The AGV scheduling module 202 receives the feeding instruction issued by the material calling module, first determines the initial storage position of the pulp raw material for lyocell fiber production required for production in the raw material line side warehouse and the target position where the material needs to be processed and fed in the feeding instruction, and then issues a scheduling instruction to the target AGV car; and sends the initial storage position and the target position to the AGV trajectory planning module; The AGV trajectory planning module 203 uses the initial storage position and the target position where the feeding needs to be processed as the initial position and the terminal position of the trolley respectively, and uses the adaptive target-guided RRT random search algorithm to search for a collision-free path based on the initial position, the terminal position and the feasible domain range to obtain the collision-free path of the trolley; then the collision-free path is optimized by deleting redundant nodes, and the optimized collision-free path is fitted using a NURBS curve to obtain a fitting path; then the fitting path is segmented according to the curvature value of the fitting path and the preset number of segments; then the speed constraint of the target AGV trolley when it moves along the fitting path is calculated according to the synthetic center of gravity height of the trolley and the curvature value of the segmentation point of the fitting path; finally, the S-shaped speed curve is used to perform acceleration and deceleration planning for each segmented path of the target AGV trolley when it moves along the fitting path to obtain the motion control parameters of the target AGV trolley; The target AGV trolley transports the pulp raw material from the initial storage position to the target position along the planned fitting path according to the motion control parameters, thereby completing the transportation of the pulp raw material. It should be noted that the system, device, model or unit described in the above embodiments can be specifically implemented by a computer chip or entity, or by a product with a certain function. For the convenience of description, in this specification, the above system is described in various modules according to their functions. Of course, when implementing the present invention, the functions of each module can be implemented in the same or more software and / or hardware.

[0061] Furthermore, in the present specification, adjectives such as first and second may be used merely to distinguish one element or action from another, without necessarily or implying any actual such relationship or order.

[0062] Furthermore, on the other hand, this embodiment also provides a computer device, which includes a processor, an input device, an output device and a memory, and the processor, the input device, the output device and the memory are interconnected; wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute the method in the above embodiment.

[0063] Furthermore, another aspect of this embodiment also provides a computer-readable storage medium, characterized in that: the computer-readable storage medium stores a computer program, the computer program includes program instructions, and when the program instructions are executed by a processor, the processor executes the method in the above embodiment.

[0064] In this embodiment, the processor may be a central processing unit (CPU). The processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or a combination of the above chips.

[0065] The memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer executable programs and units, such as the corresponding program units in the above method embodiments of the present invention. The processor executes various functional applications of the processor and works data processing by running the non-transitory software programs, instructions and modules stored in the memory, that is, implementing the method in the above method embodiments.

[0066] The memory may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created by the processor, etc. In addition, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0067] The one or more units are stored in the memory, and when executed by the processor, the method in the above embodiment is performed.

[0068] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware.

[0069] Example 2 In order to verify the effectiveness of the trajectory planning method for the AGV trolley of the present invention, a simulation map of the AGV trolley working area from the raw material line side warehouse of the pulp raw material storage position to the loading line was created, in which the black is the obstacle space. A computer equipped with an AMD Ryzen 7 5800H CPU was used as the hardware simulation platform, and the path search simulation experiment was carried out using the MATLAB language program. The simulation map range is 20×20 m, and the coordinates of the first initial storage position are set to (6, 5) and the coordinates of the second initial storage position are set to (15, 5), and the coordinates of the target position are set to (3, 17). The maximum number of iterations of the adaptive target guided RRT algorithm path search is set to 10000, and the number of path segmentation points is set to 1 during speed planning. Other parameter settings are shown in Table 1 below.

[0070]

[0071] like Figures 4 to 7 The figure shows the simulation effect diagram of the AGV vehicle trajectory planning in this embodiment. Figure 4 and Figure 6 The red curve in the middle is the implementation result of the adaptive target guided RRT random search algorithm, which successfully searches for collision-free transportation paths for AGVs under different initial storage position conditions. Compared with other similar path search algorithms, the path search time of the present invention is the shortest, the path search success rate is the highest, and the path obtained is the best. Figure 4 and Figure 6 The black curve in the middle is the result of the implementation of bidirectional deletion of redundant nodes in the AGV vehicle motion path. It can be seen that the optimized path length is greatly reduced. Figure 4 and Figure 6 The purple-red curve in the middle is the result of the implementation of the AGV trolley motion trajectory planning. It can be seen that the position interpolation points around the initial storage position (starting point), target position (target point) and path segmentation point (curvature extreme point) are denser, which realizes the acceleration and deceleration planning of the AGV trolley and ensures the stability of the AGV trolley motion. The S-shaped speed curves of the AGV trolley motion corresponding to different initial storage positions are as follows: Figure 5 and Figure 7 As shown, the speed at the turn is effectively constrained. In addition, Figure 4 and Figure 6The green curve in the middle is the forward deletion path of redundant points, the blue curve is the collision-free path node search process of the AGV car; the red curve is the search path of the adaptive target-guided RRT random search algorithm.

[0072] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Any simple modification or equivalent change made to the above embodiment based on the technical essence of the present invention shall fall within the protection scope of the present invention.

Claims

1. An intelligent transportation method for lyocell fiber raw materials based on AGV trajectory planning, characterized in that: The following steps are involved: According to the initial position, terminal position and feasible domain range of the target AGV, a collision-free path search is performed using an adaptive target-guided RRT random search algorithm to obtain a collision-free path for the target AGV; wherein the initial position is the initial storage position of the pulp raw material for Lyocell fiber production in the side warehouse of the raw material line, and the terminal position is the target position where the pulp raw material for Lyocell fiber production needs to be processed and fed; Deleting redundant nodes to optimize the collision-free path, and fitting the optimized collision-free path using a NURBS curve to obtain a fitting path; The fitting path is segmented according to the curvature value of the fitting path and the preset number of segments; According to the composite center of gravity height of the AGV and the pulp raw material carried and the curvature value of the segmentation point of the fitting path, the speed constraint of the target AGV when moving along the fitting path is calculated; wherein the composite center of gravity height is obtained by combining the weighing sensor equipped on the trolley with the total mass of the trolley and the moment balance equation; Use the S-shaped speed curve to plan the acceleration and deceleration of each segmented path when the target AGV moves along the fitting path, and obtain the motion control parameters of the target AGV; The target AGV transports the pulp raw material from the initial storage location to the target location along the planned fitting path according to the motion control parameters.

2. The method for intelligent transportation of lyocell fiber raw materials based on AGV trajectory planning according to claim 1, characterized in that: The method of searching for a collision-free path using an adaptive target-guided RRT random search algorithm based on the initial position, target position and feasible domain of the target AGV to obtain a collision-free path for the target AGV includes the following steps: Step B1. Initialize the geometric dimensions of the obstacle envelope model in the working environment of the target AGV, as well as the initial position, terminal position and feasible domain range of the target AGV movement; Step B2. Take the initial position of the target AGV as the starting point Stored in random tree nodes middle; Step B3. Sampling and generating random points within the global feasible domain , and search Middle distance The nearest node ; Step B4. Along vector and The angle direction is in steps of Generate new node , new node The calculation method is: Formula (1); in, is the target location; is the target guidance component coefficient, which is defined as: Formula (2); in, is the preset maximum value of the target guidance component coefficient; Estimate distance to target; for point to Minimum safe distance; α Estimate the distance factor for the target; β is the minimum safety distance factor; Step B5. Connect and Forming a path , determine the target AGV car along the path Whether the movement collides with obstacles in the environment. If no collision occurs, Stored in ; Step B6. Judgment and If the distance is less than the set error value, the collision-free path search ends, otherwise, steps B3 to B5 are repeated until the collision-free path search is completed.

3. The method for intelligent transportation of lyocell fiber raw materials based on AGV trajectory planning according to claim 1, characterized in that: Deleting redundant nodes to optimize the collision-free path includes the following steps: Step C1. If the collision-free path is searched Include n nodes, the starting point Save to the new path node and Assign to ; Step C2. With nodes Connect, create a connection , and then connect the lines Perform collision detection between obstacles and between the AGV and obstacles until a collision-free node is found ,Will Save to new path middle; Step C3. Assign to Repeat step C2 until the new path node contains the end point , the one-way deletion of redundant nodes is completed, and the collision-free path after one-way deletion optimization is obtained.

4. The method for intelligent transportation of lyocell fiber raw materials based on AGV trajectory planning according to claim 1, characterized in that: The path points in the optimized collision-free path are used as the shape value points, and the optimized collision-free path is fitted using a cubic NURBS curve to obtain a fitting path. The fitting calculation expression is as follows: Formula (3); in, is the weight factor, and satisfies the adjacent k The weight factors are not all 0 at the same time; is the control vertex; node vector ; for k Subnormal B-spline basis functions.

5. The method for intelligent transportation of lyocell fiber raw materials based on AGV trajectory planning according to claim 4, characterized in that: The fitting path is segmented according to the curvature value of the fitting path and the preset number of segments, including the following: Derivative of the NURBS curve, any point on the NURBS curve r Order derivative The calculation expression is as follows: Formula (4); Formula (5); in, For the NURNS curve r The first derivative curve j Control vertices; is the first j Control vertices; For NURBS curves l The first derivative curve j Control vertices; For NURBS curves l-1 The first derivative curve j Control vertices; For NURBS curves l-1 The first derivative curve j-1 Control vertices; According to equations (4) and (5), the first-order derivative and the second-order derivative of the NURBS curve are calculated, and then the curvature of the NURBS curve is obtained by solving the following calculation expression: Formula (6); in, is the curvature of the NURBS curve, is the first-order derivative of the NURBS curve; is the second-order derivative of the NURBS curve; After calculating the curvature of the NURBS curve, based on the set number of curve segments num, find the path points corresponding to the local extreme values ​​of the first num-1 curvatures, and use these points as segmentation points of the curve to segment the fitting path.

6. The method for intelligent transportation of lyocell fiber raw materials based on AGV trajectory planning according to claim 1, characterized in that: The synthetic center of gravity height is obtained by combining the weighing sensor equipped on the trolley with the moment balance equation, and the calculation method is as follows: Formula (39); in, is the composite center of gravity height; is the horizontal distance from the weighing sensor to the center of gravity; is the vertical force measured by the load cell; is the number of weighing sensors; is the total mass of the target AGV; is the acceleration due to gravity.

7. The method for intelligent transportation of lyocell fiber raw materials based on AGV trajectory planning according to claim 1, characterized in that: According to the combined center of gravity of the AGV and the pulp material it carries and the curvature value of the segmentation point of the fitting path, the speed constraint of the target AGV moving along the fitting path is calculated, including the following contents: Step F1. During the turning process of the target AGV, when the vehicle body is in a critical state of rollover, the overturning moment generated by the centrifugal force is equal to the stabilizing moment generated by gravity, then: Formula (7); Formula (8); in, is the total mass of the target AGV, is the turning speed of the target AGV, is the turning radius of the target AGV, is the curvature of the NURBS curve, is the composite center of gravity height, is the acceleration due to gravity, is the wheelbase of the target AGV; is the critical speed when the vehicle body is in the critical state of rollover; Step F2. During the turning process of the target AGV, when the vehicle body is in a critical state of slippage, the centrifugal force is equal to the maximum static friction between the tires of the vehicle and the ground, then: Formula (9); Formula (10); in, is the friction coefficient between the target AGV tire and the ground; is the critical speed when the vehicle body is in the critical state of slip; Step F3. When the target AGV moves along the fitting path, the speed constraint for: Formula (11).

8. The method for intelligent transportation of lyocell fiber raw materials based on AGV trajectory planning according to claim 1, characterized in that: Use the S-shaped speed curve to plan the acceleration and deceleration of each segment path when the target AGV moves along the fitting path, and obtain the motion control parameters of the target AGV, including the following steps: Step G1. Based on the speed constraint of the target AGV , set the initial speed of the target AGV car in the current segment path , Final speed , initial acceleration , Maximum speed , maximum acceleration and maximum jerk , and solve for the path length , is the first-order derivative of the NURBS fitting curve; Step G2. Use the S-shaped speed curve planning algorithm to control the speed of the target AGV in the current segmented path to obtain the acceleration time of the target AGV during its movement. , acceleration time , uniform speed period , deceleration time , Acceleration and deceleration time , the maximum acceleration that the car can reach in the acceleration section , the minimum acceleration that can be achieved in the deceleration stage , and the maximum speed that the car can reach in the current segment path ; Step G3. Determine the uniform speed time of the target AGV and output the S-shaped speed curve parameters; Step G4. According to the S-shaped speed curve parameters output in step G3, the trajectory points are interpolated to obtain the first n The motion parameters of the path interpolation points are the motion control parameters of the target AGV.

9. The method for intelligent transportation of lyocell fiber raw materials based on AGV trajectory planning according to claim 8, characterized in that: The step G2 specifically includes the following contents: Assuming that the maximum speed can be achieved when planning the current segmented path with the standard seven-segment S-shaped speed curve, then: Step G21. Determine whether the target AGV has a uniform acceleration segment in the current segmented path; like , then the target AGV in the acceleration section cannot reach the set maximum acceleration , the car does not have a uniform acceleration phase, at this time: Formula (12); Formula (13); Formula (14); in, The target AGV accelerates for a period of time; The acceleration time of the target AGV; is the maximum acceleration that the target AGV can achieve in the acceleration section; like , then the target AGV car in the acceleration section can reach the set maximum acceleration , the car has a uniform acceleration stage, at this time: Formula (15); Formula (16); Formula (17); Step G22. Determine whether the target AGV has a uniform deceleration segment in the current segmented path; like , then the target AGV in the deceleration stage cannot reach the minimum acceleration, and there is no uniform deceleration stage. At this time, there are: Formula (18); Formula (19); Formula (20); in, is the deceleration time of the target AGV; is the acceleration and deceleration time of the target AGV; The minimum acceleration that the target AGV can achieve during the deceleration phase; like , then the target AGV car in the deceleration stage can reach the minimum acceleration, and there is a uniform deceleration stage, at this time: Formula (21); Formula (22); Formula (23); Step G23. Calculate the uniform speed time of the target AGV in the current segmented path. The calculation expression is: Formula (24); in, The target AGV car has a uniform speed period; is the path length; is the displacement of the target AGV during the acceleration phase; Furthermore, if , then: Formula (25); Furthermore, if , then: Formula (26).

10. The method for intelligent transportation of lyocell fiber raw materials based on AGV trajectory planning according to claim 9, characterized in that: The step G3 specifically includes the following contents: Step G31. If , then the target AGV car has a uniform speed segment in the current segmented path and the car's speed extreme value Able to reach the set maximum speed , output the parameters calculated in steps G21-G23 , , , , , and , and the maximum speed that the target AGV can reach during the entire movement process are used as the parameters of the S-type speed curve; Step G32. If , then there is no uniform speed segment for the target AGV in the current segmented path. Assuming that the maximum acceleration of the acceleration segment can reach And the minimum acceleration of the deceleration section can reach , then the maximum speed that the car can reach is for: Formula (27); in, Formula (28); Formula (29); Formula (30); Further, Formula (31); Formula (32); Formula (33); Formula (34); Formula (35); Step G33. If the calculated value in step G32 is or , then the acceleration of the target AGV in the acceleration or deceleration section cannot reach or , then reduce until the value of or Then repeat step G32 to calculate the parameters , , , , , , and finally output the parameters , , , , , , , and the maximum speed that the target AGV can reach during the entire movement process , as the S-type speed curve parameter.

11. The method for intelligent transportation of lyocell fiber raw materials based on AGV trajectory planning according to claim 10, characterized in that: The step G4 specifically includes: T s is the interpolation period, then n Path interpolation points ( ) are: when hour, , , ; when hour, , , ; when hour, ， ， ； when hour, ; when hour, ， ， ； when hour, ， ， ； when hour, ， ， 。 12. The method for intelligent transportation of lyocell fiber raw materials based on AGV trajectory planning according to claim 3, characterized in that: The step C also includes: step C4. performing equal-spacing interpolation on the collision-free path after the one-way deletion optimization to obtain a path , then from the end point Start to delete redundant nodes in reverse order, and finally get the path after secondary optimization , this path is the final optimized collision-free path.

13. An intelligent transportation system for lyocell fiber raw materials based on AGV trajectory planning, characterized in that: The system is used to implement the intelligent transportation method described in any one of claims 1 to 12, including an AGV trajectory planning module and a target AGV vehicle; wherein, The AGV trajectory planning module uses an adaptive target-guided RRT random search algorithm to search for a collision-free path according to the initial position, target position and feasible domain range of the target AGV trolley, and obtains a collision-free path for the target AGV trolley; then, the redundant nodes are deleted to optimize the collision-free path, and the optimized collision-free path is fitted using a NURBS curve to obtain a fitted path; then, the fitted path is segmented according to the curvature value of the fitted path and the preset number of segments; then, according to the composite center of gravity height of the AGV trolley and the carried pulp raw material and the curvature value of the segmentation point of the fitting path, the speed constraint of the target AGV trolley when moving along the fitting path is calculated; finally, an S-shaped speed curve is used to perform acceleration and deceleration planning on each segmented path when the target AGV trolley moves along the fitting path, and the motion control parameters of the target AGV trolley are obtained; The target AGV trolley transports the pulp raw material from the initial storage position to the target position along the planned fitting path according to the motion control parameters, thereby completing the transportation of the pulp raw material.

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