An AGV motion control method based on a pure tracking model

Abstract

This invention relates to the field of AGV path planning and control technology, and discloses an AGV motion control method based on a pure tracking model. First, multiple path points are used to generate a smooth motion trajectory through linear interpolation and a circular arc transition connection model algorithm. The circular arc transition connection model algorithm inserts a circular arc curve between two adjacent trajectories to achieve smooth and continuous trajectory connection. For longitudinal control of path tracking, this pure tracking model-based AGV motion control method employs a speed look-ahead planning method. By pre-analyzing the subsequent path length and speed constraints, adaptive speed adjustment is achieved. For lateral control, a fuzzy controller is used to adaptively determine an appropriate look-ahead distance based on the AGV's speed and lateral deviation. A longer look-ahead distance is used when the speed is high, and a shorter look-ahead distance is used when the speed is low. Finally, the vehicle's heading angle is adjusted based on the look-ahead distance, thereby improving the accuracy of path tracking.

Description

Technical Field

[0001] This invention relates to the field of AGV path planning and control technology, specifically to an AGV motion control method based on a pure tracking model. Background Technology

[0002] In vehicle motion control systems, the expected trajectory is analyzed by combining data from sensors such as LiDAR, GPS, and MU, and the optimal control inputs for the vehicle are calculated. Then, the motion control system controls the vehicle's actuators, such as the accelerator, brakes, and steering wheel, to achieve trajectory tracking. Commonly used methods for trajectory tracking control of AGVs include PID control, LQR (Low-Quick Rate), MPC (Multi-Process Control), and control based on pure tracking models.

[0003] PID control is a linear feedback method that processes errors through proportional (P), integral (I), and derivative (D) components to make the system output approach the desired value. LQR control is an optimal control strategy that controls a linear system by solving a linear quadratic cost function. MPC control is a model predictive control method that predicts future system behavior based on the current state and system model in each control cycle, then optimizes the cost function to determine the optimal control input. Pure tracking model control is a trajectory tracking method based on look-ahead information. It calculates the control input based on the current position and look-ahead point information by setting a look-ahead distance to achieve desired trajectory tracking.

[0004] Traditional pure tracking models have several problems in path tracking, primarily because the fixed preview distance cannot adapt to different speeds and lateral deviations, resulting in low path tracking accuracy. Furthermore, since it only focuses on the current state and ignores future conditions, it may exhibit reaction delays when encountering sudden changes, causing the vehicle to be unable to adjust its behavior in a timely manner. Summary of the Invention

[0005] (a) Technical problems to be solved

[0006] To address the shortcomings of existing technologies, this invention provides an AGV motion control method based on a pure tracking model, which has advantages such as improved path tracking accuracy and stability, and solves the aforementioned technical problems.

[0007] (II) Technical Solution

[0008] To achieve the above objectives, the present invention provides the following technical solution: an AGV motion control method based on a pure tracking model, comprising the following steps:

[0009] Step 1: Calculate the desired motion trajectory by using linear interpolation and circular arc transition connection models for multiple path points through algorithmic formulas;

[0010] Step 2: Perform velocity look-ahead planning on the motion trajectory and set the number of look-ahead segments N. p And pre-analyze the impact of subsequent path length and speed constraints on the maximum speed v at path corners. max Perform calculations;

[0011] Step 3: By setting up a fuzzy controller to monitor the AGV vehicle's speed, the speed after forward planning, and the lateral deviation between the current trajectory and the desired trajectory, the aiming distance is adjusted according to the different speeds of the vehicle.

[0012] Step 4: Calculate the front wheel angle of the AGV vehicle based on the calculated pre-aiming distance.

[0013] As a preferred technical solution of the present invention, the circular arc transition connection model for the current point P i-1 Inflection point P i End point P i+1 and its line segment and Length S i and S i+1 To determine this, the radius and transition distance of the transition arc are calculated, and P is also... i-1 P i and P i+1 The three points are not collinear.

[0014] As a preferred technical solution of the present invention, the line segment and The formula for calculating the included angle α between them is as follows:

[0015]

[0016] in, Represents line segment The vector, express The vector, |P i-1 P i | is a vector The model, |P i P i +1| is a vector The formula for calculating the radius R of the transition arc, where arccos() is an inverse trigonometric function, is as follows:

[0017]

[0018] Where, δ max The formula for calculating the predicted transition distance L, where the maximum trajectory error value is given, is as follows:

[0019]

[0020] in, The value represents the tangent of half the included angle α. Since the length of the line segment itself limits the transition distance, the actual transition distance l' is calculated using the following formula:

[0021]

[0022]

[0023] Where, min represents the... and The minimum value is taken out, where r' represents the actual radius of the transition arc. This represents the cotangent value of half the included angle α;

[0024] In summary, the transition arc radius r and transition distance l are:

[0025]

[0026] Wherein, min(r', R) represents the minimum value for both calculation methods.

[0027] As a preferred embodiment of the present invention, the speed look-ahead planning is performed according to the number of look-ahead segments N each time. p Gradually add a new trajectory, perform speed planning and parameter adjustments, as follows:

[0028] S1. Obtain the top N p The length of the trajectory segment and the maximum speed v max ;

[0029] S2. Based on the constraints and maximum speed limit, adjust the starting and ending speeds of each trajectory segment using bidirectional scanning technology;

[0030] S3. Calculate the time and maximum speed of each segment of the S-shaped velocity curve required for the current trajectory segment;

[0031] S4. Adjust the step number parameter of the S-shaped velocity curve to meet the constraints.

[0032] S5. The velocity curve of the initial trajectory is obtained by combining the S-shaped acceleration and deceleration control algorithm.

[0033] As a preferred technical solution of the present invention, the speed look-ahead planning adopts a smooth splicing strategy of straight lines and arcs for the input path points to regenerate multiple smooth look-ahead trajectories. The constraints in the bidirectional scanning step S2 include maximum speed, maximum acceleration, maximum emergency stop, line segment length, and starting and ending speeds.

[0034] As a preferred technical solution of the present invention, the maximum speed v maxThe calculation formula under constraints is as follows:

[0035]

[0036] Where ε is the maximum allowable contour error during operation, θ is the complementary angle value of ε, and α AB β represents the angle between vector AB and the positive x-axis. BC Let AB be the angle between vector BC and the positive x-axis, AB be the vector of the vehicle's current path, BC be the vector of the vehicle's path after the turn, and point B be the inflection point. X-max Let a be the maximum allowable acceleration in the x-axis direction. Y-max Cosβ is the maximum permissible acceleration in the y-axis direction. BC This represents β BC The cosine value, cosα AB It represents α AB The cosine value, sinα AB It represents α AB The sine value, sinβ BC This represents β BC The sine value, |*| represents taking the absolute value of the internal data, and T represents the period.

[0037] As a preferred embodiment of the present invention, the bidirectional scanning in step S2 includes reverse scanning and forward scanning. The reverse scanning is performed from the last segment of the trajectory towards the first segment of the trajectory, and the endpoint velocity of each segment of the trajectory is set to... b v and set the speed at the end of the last trajectory segment. b v N+1 =0, where N represents the number of paths after splitting. b v i+1 Let A be the velocity of the endpoint of the i-th trajectory determined by the (i+1)-th trajectory through reverse scanning, and set the tangential acceleration A. t The specific process of reverse scanning is as follows:

[0038] 1) Let i = N and b v N+1 =0, then proceed to step 2);

[0039] 2) If i = 1, execute 3); otherwise, execute 2).

[0040] 2a. Applying the bounded constraint of the S-curve jerk curve to adjust the endpoint velocity of each trajectory segment from the end to the beginning of the path, the bounded cubic equation of the tangential S-curve jerk can be obtained as follows:

[0041]

[0042] Among them, Jt For tangential jerk, s i The running time is calculated starting from the zero point, with the start time of the i-th trajectory as the zero point. The velocity at the end of the (i-1)-th segment of the trajectory under the bounded constraint of the tangential S-shaped jerk curve is obtained by solving the above formula. if This indicates that the constraint of bounded tangential jerk is satisfied, if The acceleration conditions are not met. and b v i+1 The relationship between the two conditions is as follows:

[0043]

[0044] The formula is used to determine the endpoint velocity of the (i-1)th segment under constraint reverse scanning. Solve the problem, then execute 2b;

[0045] 2b. The final velocity at the end of the (i-1)th segment of the reverse scan. b v i Ultimately, it is determined by the following formula:

[0046]

[0047] Among them, v max Let v(i) be the maximum velocity across the entire trajectory. max The maximum speed for each segment of the trajectory after segmentation is determined, and then 2c is executed;

[0048] 2c. Decrement the value of i by one, and repeat step 2) until the judgment condition is met before executing step 3);

[0049] 3) All of them b v i (i = 1, 2, ..., N) are stored in the buffer.

[0050] As a preferred embodiment of the present invention, the forward scan is used to determine the initial velocity of the i-th trajectory under tangential acceleration and acceleration constraints. The specific process of the forward scanning is as follows:

[0051] ① Set i=1 and the initial velocity v1=0 for the first trajectory, then execute step ②;

[0052] ② If i = N, execute step ③; otherwise, execute step ②.

[0053] 2d The solution can be found using the following relation, which is as follows:

[0054]

[0055] The initial velocity of the i-th trajectory under tangential acceleration and acceleration constraints can be obtained by solving the above formula. like This indicates that the bounded constraint of tangential jerk is satisfied. Then, continue solving using the following relation. After solving, execute 2e. The specific relation is as follows:

[0056]

[0057] 2e. The velocity v at the starting point of the (i+1)th path segment i+1 Determined by the following formula:

[0058]

[0059] in This represents the initial velocity obtained by the (i+1)th trajectory under tangential acceleration and acceleration constraints;

[0060] 2f. Increment the value of i by one, and repeat step ② until the judgment condition is met before executing step ③.

[0061] ③, all v i+1 (i = 1, 2, ..., N) are stored in the buffer, and then the speed curve of the AGV vehicle is generated based on the data obtained from the two scans.

[0062] As a preferred embodiment of the present invention, in step three, the fuzzy controller sets velocity V and lateral distance P as inputs and aiming distance L as output. The universe of discourse for velocity V is [0,2] m / s, and its discrete universe of discourse is {-5,-4,-3,-2,-1,0,1,2,3,4,5}. The quantization factor is set to 2, dividing the velocity universe of discourse into 5 fuzzy subsets. The fuzzy language values ​​are {very slow, slow, moderate, fast, very fast} = {VS,S,M,F,VF}. The universe of discourse for lateral distance P is [0,2] m, and its discrete universe of discourse is {-5,-4,-3,-2,-1,0,1,2,3,4,5}. The quantization factor is set to 1, dividing the lateral distance P universe of discourse into 5 fuzzy subsets. The fuzzy language values ​​are {very small, small, medium, large, very large} = {VS,S,M,F,VF}. P ,S P M p The universe of discourse for the aiming distance L is divided into 5 fuzzy subsets, and the fuzzy linguistic values ​​are {very close, close, moderate, far, very far} = {VN, N, M}. L ,F L VF L}

[0063] As a preferred embodiment of the present invention, the formula for calculating the front wheel steering angle is as follows:

[0064]

[0065] Where L is the aiming distance, δ is the front wheel steering angle, arctan() is the arctangent trigonometric function, and α is the angle between the vehicle body and Ld.

[0066] Compared with existing technologies, this invention provides an AGV motion control method based on a pure tracking model, which has the following advantages:

[0067] 1. This invention solves the problem of poor path tracking performance caused by fixed aiming distance by using a fuzzy controller to dynamically adjust the aiming distance. Based on velocity look-ahead planning control, it adaptively adjusts velocity and acceleration to ensure smooth and continuous velocity and acceleration, and smooth and bounded jerk, thereby realizing trajectory tracking tasks under maximum velocity and maximum acceleration constraints.

[0068] 2. The present invention can ensure smooth and continuous velocity and acceleration, differentiable and smoothly bounded jerk through path tracking method, realize trajectory tracking task under given maximum velocity and maximum acceleration constraints, and reduce impact during motion. Attached Figure Description

[0069] Figure 1 This is a schematic diagram of the arc transition connection model of the present invention;

[0070] Figure 2 This is a schematic diagram of the motion control algorithm of the present invention;

[0071] Figure 3 This is a schematic diagram of the maximum corner speed of the present invention;

[0072] Figure 4 This is a schematic diagram of the control surface of the present invention;

[0073] Figure 5 This is a schematic diagram of the trajectory planned by the present invention;

[0074] Figure 6 This is a schematic diagram illustrating the tracking error of the present invention;

[0075] Figure 7 This is a schematic diagram of the position, velocity, and acceleration variation curves of the present invention;

[0076] Figure 8 This is a pure tracking path diagram for the present invention;

[0077] Figure 9 This is a schematic diagram of the input and output of the fuzzy controller of the present invention;

[0078] Figure 10 This is a schematic diagram of the process of the present invention. Detailed Implementation

[0079] The embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and should not be construed as limiting the scope of the invention.

[0080] In the description of this invention, unless otherwise stated, "a plurality of" means two or more; the terms "upper," "lower," "left," "right," "inner," "outer," "front end," "rear end," "head," "tail," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, the terms "first," "second," "third," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0081] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "connected" and "linked" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0082] An AGV motion control method based on a pure tracking model includes the following steps:

[0083] Step 1, according to Figure 1 Multiple path points are calculated using linear interpolation and circular arc transition models through algorithmic formulas to generate a smooth desired motion trajectory.

[0084] The circular arc transition model for the current point P i-1 Inflection point P i End point P i+1 and its line segment and Length S i and S i+1 To determine this, the radius and transition distance of the transition arc are calculated, and P is also... i-1 P i and P i+1 The three points are not collinear, and point P in the diagram is... a and P e These are the starting and ending points of the arc, and the line segment. and The formula for calculating the included angle α between them is as follows:

[0085]

[0086] in, Represents line segment The vector, express The vector, |P i-1 P i | is a vector The model, |P i P i +1| represents vector P i P i+1 The formula for calculating the radius R of the transition arc, where arccos() is an inverse trigonometric function, is as follows:

[0087]

[0088] Where, δ max The formula for calculating the predicted transition distance L, where the maximum trajectory error value is given, is as follows:

[0089]

[0090] in, The value represents the tangent of half the included angle α. Since the length of the line segment itself limits the transition distance, the actual transition distance l' is calculated using the following formula:

[0091]

[0092]

[0093] Where, min represents the... and The minimum value is taken out, where r' represents the actual radius of the transition arc. This represents the cotangent value of half the included angle α;

[0094] In summary, the transition arc radius r and transition distance l are:

[0095]

[0096] Where min(r', R) represents the minimum value for both calculation methods.

[0097] Step 2: Perform velocity look-ahead planning on the motion trajectory and set the number of look-ahead segments N. p And pre-analyze the impact of subsequent path length and speed constraints on the maximum speed v at path corners. max Perform calculations;

[0098] The speed look-ahead planning is based on the number of look-ahead segments N each time. pGradually add a new trajectory, perform speed planning and parameter adjustments, as follows:

[0099] S1. Obtain the top N p The length of the trajectory segment and the maximum speed v max ;

[0100] S2. Based on the constraints and maximum speed limit, adjust the starting and ending speeds of each trajectory segment using bidirectional scanning technology;

[0101] S3. Calculate the time and maximum speed of each segment of the S-shaped velocity curve required for the current trajectory segment;

[0102] S4. Adjust the step number parameter of the S-shaped velocity curve to meet the constraints.

[0103] S5. The velocity curve of the initial trajectory is obtained by combining the S-shaped acceleration and deceleration control algorithm.

[0104] Furthermore, the main process of the speed look-ahead algorithm uses a smooth splicing strategy of straight lines and arcs to regenerate multiple smooth look-ahead trajectories from the input path points. Then, it calculates the maximum speed under the constraint of path segment corners and uses a bidirectional scanning strategy to adjust the speed at connection points, satisfying constraints such as maximum speed, maximum acceleration, maximum emergency stop, segment length, and starting and ending speeds, ensuring the reachability of both ends of the path segment. Finally, bidirectional scanning combined with an S-shaped acceleration / deceleration control algorithm generates a speed curve, thereby achieving smooth speed trajectory generation. The specific flowchart of the algorithm is shown below. Figure 2 As shown.

[0105] For further details, please refer to Figure 3 Maximum speed v max The calculation formula under constraints is as follows:

[0106]

[0107] Where ε is the maximum allowable contour error during operation, θ is the complementary angle value of ε, and α AB β represents the angle between vector AB and the positive x-axis. BC Let AB be the angle between vector BC and the positive x-axis, AB be the vector of the vehicle's current path, BC be the vector of the vehicle's path after the turn, and point B be the inflection point. X-max Let a be the maximum allowable acceleration in the x-axis direction. Y-max Cosβ is the maximum permissible acceleration in the y-axis direction. BC This represents β BC The cosine value, cosα AB It represents α AB The cosine value, sinα AB It represents αAB The sine value, sinβ BC This represents β BC The sine value, |*| represents taking the absolute value of the internal data, and T represents the period.

[0108] Furthermore, bidirectional scanning includes reverse scanning and forward scanning. The specific process of reverse scanning is as follows:

[0109] 1) Let i = N and b v N+1 =0, then proceed to step 2);

[0110] 2) If i = 1, execute 3); otherwise, execute 2).

[0111] 2a. Applying the bounded constraint of the S-curve jerk curve to adjust the endpoint velocity of each trajectory segment from the end to the beginning of the path, the bounded cubic equation of the tangential S-curve jerk can be obtained as follows:

[0112]

[0113] Among them, J t For tangential jerk, s i The running time is calculated starting from the zero point, with the start time of the i-th trajectory as the zero point. The velocity at the end of the (i-1)-th segment of the trajectory under the bounded constraint of the tangential S-shaped jerk curve is obtained by solving the above formula. if This indicates that the constraint of bounded tangential jerk is satisfied, if The acceleration conditions are not met. and b v i+1 The relationship between the two conditions is as follows:

[0114]

[0115] The formula is used to determine the endpoint velocity of the (i-1)th segment under constraint reverse scanning. Solve the problem, then execute 2b;

[0116] 2b. The final velocity at the end of the (i-1)th segment of the reverse scan. b v i Ultimately, it is determined by the following formula:

[0117]

[0118] Among them, v max Let v(i) be the maximum velocity across the entire trajectory. max The maximum speed for each segment of the trajectory after segmentation is determined, and then 2c is executed;

[0119] 2c. Decrement the value of i by one, and repeat step 2) until the judgment condition is met before executing step 3);

[0120] 3) All of them b v i (i = 1, 2, ..., N) are stored in the buffer.

[0121] The reverse scan proceeds from the last trajectory segment towards the first trajectory segment, with the endpoint velocity of each trajectory segment set to... b v and set the speed at the end of the last trajectory segment. b v N+1 =0, where N represents the number of paths after splitting. b v i+1 Let A be the velocity of the endpoint of the i-th trajectory determined by the (i+1)-th trajectory through reverse scanning. t This is the tangential acceleration.

[0122] Furthermore, the forward scan is used to determine the initial velocity of the i-th trajectory under tangential acceleration and acceleration constraints. The specific process is as follows:

[0123] ① Set i=1 and the initial velocity v1=0 for the first trajectory, then execute step ②;

[0124] ② If i = N, execute step ③; otherwise, execute step ②.

[0125] 2d The solution can be found using the following relation, which is as follows:

[0126]

[0127] The initial velocity of the i-th trajectory under tangential acceleration and acceleration constraints can be obtained by solving the above formula. like This indicates that the bounded constraint of tangential jerk is satisfied. Then, continue solving using the following relation. After solving, execute 2e. The specific relation is as follows:

[0128]

[0129] 2e. The velocity v at the starting point of the (i+1)th path segment i+1 Determined by the following formula:

[0130]

[0131] in This represents the initial velocity obtained by the (i+1)th trajectory under tangential acceleration and acceleration constraints;

[0132] 2f. Increment the value of i by one, and repeat step ② until the judgment condition is met before executing step ③.

[0133] ③, all v i+1 (i = 1, 2, ..., N) are stored in the buffer, and then the speed curve of the AGV vehicle is generated based on the data obtained from the two scans.

[0134] Step 3: By setting up a fuzzy controller to monitor the AGV vehicle's speed, the speed after forward planning, and the lateral deviation between the current trajectory and the desired trajectory, the aiming distance is adjusted according to the different speeds of the vehicle.

[0135] The fuzzy controller sets velocity V and lateral distance P as inputs and aiming distance L as output. The universe of discourse for velocity V is [0,2] m / s, and its discrete universe of discourse is {-5,-4,-3,-2,-1,0,1,2,3,4,5}. The quantization factor is set to 2. The velocity universe of discourse is divided into 5 fuzzy subsets, with fuzzy linguistic values ​​of {very slow, slow, moderate, fast, very fast} = [VS,S,M,F,VF}. The universe of discourse for lateral distance P is [0,2] m, and its discrete universe of discourse is {-5,-4,-3,-2,-1,0,1,2,3,4,5}. The quantization factor is set to 1. The lateral distance universe of discourse is divided into 5 fuzzy subsets, with fuzzy linguistic values ​​of {very small, small, medium, large, very large} = {VS... P ,S P M p The universe of discourse for the aiming distance L is divided into 5 fuzzy subsets, and the fuzzy linguistic values ​​are {very close, close, moderate, far, very far} = {VN, N, M}. L ,F L ,CF L The specific AGV vehicle control surface is as follows: Figure 4 As shown, please refer to the following for the output of the fuzzy controller. Figure 9 :

[0136] Step 4, according to Figure 6 The aiming distance calculated from the above parameters is used to calculate the front wheel angle of the AGV vehicle. The formula for calculating the front wheel angle is as follows:

[0137]

[0138] Where L is the aiming distance, δ is the front wheel turning angle, arctan() is the arctangent trigonometric function, and α is the angle between the vehicle body and Ld. The turning angle of the car's movement can be calculated using the above formula.

[0139] like Figure 5 and Figure 6 As shown, attached Figure 6(a) is the fuzzy dynamic pre-aiming distance proposed in this invention, attached. Figure 6 (b) Using a fixed aiming distance, it can be observed that when using the dynamic aiming distance method, the maximum error during turning is only 8cm, and path tracking is smoother. This result indicates that the control method proposed in this paper is significantly better than the performance achieved by using the traditional aiming control method.

[0140] Please see Figure 7 It can be observed that velocity, acceleration, and jerk are bounded, and the velocity curve transitions smoothly. It can achieve adaptive adjustment of velocity and acceleration under path constraints. Bounded constraints on acceleration and jerk can reduce the impact during motion.

[0141] Figure 8 A visual marker is added every 10 seconds. It can be seen that the visual markers at the beginning and end of the arc segment are relatively dense, indicating that the vehicle has reduced its speed to ensure stability and safety when turning.

[0142] This method utilizes a fuzzy controller to dynamically adjust the preview distance, thus solving the problem of poor path tracking performance caused by a fixed preview distance. Based on velocity look-ahead planning control, it adaptively adjusts velocity and acceleration, ensuring smooth and continuous velocity and acceleration, and smooth and bounded jerk, thereby achieving trajectory tracking under maximum velocity and maximum acceleration constraints.

[0143] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An AGV motion control method based on a pure tracking model, characterized in that: Includes the following steps: Step 1: Calculate the desired motion trajectory by using linear interpolation and circular arc transition connection models for multiple path points through algorithmic formulas; The circular arc transition model applies to the current point. ,inflection point End point and its line segment and length and To determine this, the radius and transition distance of the transition arc are calculated, and simultaneously... , and The three points are not collinear; The line segment and The angle between The calculation formula is as follows: in, Represents line segment The vector, express The vector, For vectors The model, For vectors The model, ( ) represents an inverse trigonometric function; Predicted transition radius The calculation formula is as follows: in, The maximum trajectory error value is given by sin(), which is a sine function. The predicted transition distance at this point is... The calculation formula is as follows: in, The included angle is indicated Half the tangent value, because the length of the line segment itself limits the transition distance, at this point the actual transition distance... The calculation formula is as follows: in, Indicates to and Take the minimum value from the middle. This indicates the actual radius of the transition arc. Indicates the included angle Half of the cotangent value; In summary, the radius of the transition arc is obtained. and transition distance for: in, This represents the minimum value calculated using both methods. Step 2: Perform velocity look-ahead planning on the motion trajectory and set the number of look-ahead segments. And pre-analyze the impact of subsequent path length and speed constraints on the maximum speed at path corners. Perform calculations; The maximum speed The calculation formula under constraints is as follows: in, This represents the maximum allowable profile error during operation. for complementary angle values, This represents the angle between vector AB and the positive x-axis. Let AB be the angle between vector BC and the positive x-axis, AB be the vector representing the vehicle's current path, BC be the vector representing the vehicle's path after the turn, and point B be the inflection point. This represents the maximum permissible acceleration in the x-axis direction. This represents the maximum permissible acceleration in the y-axis direction. This indicates taking the minimum value of the internal data. It means cosine value, It means cosine value, It means The sine value, It means The sine value, This indicates taking the absolute value of the internal data. This indicates a period; Step 3: By setting up a fuzzy controller to monitor the AGV vehicle's speed, the speed after forward planning, and the lateral deviation between the current trajectory and the desired trajectory, the aiming distance is adjusted according to the different speeds of the vehicle. Step 4: Calculate the front wheel angle of the AGV vehicle based on the calculated pre-aiming distance.

2. The AGV motion control method based on a pure tracking model according to claim 1, characterized in that: The speed look-ahead planning is based on the number of look-ahead segments each time. Gradually add a new trajectory, perform speed planning and parameter adjustments, as follows: S1, Before obtaining Length and maximum speed of the segment trajectory ; S2. Based on the constraints and maximum speed limit, adjust the starting and ending speeds of each trajectory segment using bidirectional scanning technology; S3. Calculate the time and maximum speed of each segment of the S-shaped velocity curve required for the current trajectory segment; S4. Adjust the step number parameter of the S-shaped velocity curve to meet the constraints. S5. The velocity curve of the initial trajectory is obtained by combining the S-shaped acceleration and deceleration control algorithm.

3. The AGV motion control method based on a pure tracking model according to claim 2, characterized in that: The velocity look-ahead planning uses a smooth stitching strategy of straight lines and arcs to regenerate multiple smooth look-ahead trajectories from the input path points. The constraints in step S2, bidirectional scanning, include maximum velocity. Maximum acceleration Maximum emergency stop line segment length Starting and ending speeds.

4. The AGV motion control method based on a pure tracking model according to claim 2, characterized in that: The bidirectional scanning in step S2 includes reverse scanning and forward scanning. Reverse scanning is performed from the last segment of the trajectory towards the first segment of the trajectory, and the endpoint velocity of each segment of the trajectory is set to... And set the speed at the end of the last segment of the trajectory. ,in Indicates the number of trajectories after segmentation. For the first The trajectory was determined by reverse scanning. The trajectory endpoint velocity is set, and the tangential acceleration is set. The specific process of reverse scanning is as follows: 1). Let and Then proceed to step 2). 2). If Execute step 3 if the condition is met, otherwise execute step 2). 2a. Applying the bounded constraint of the S-shaped jerk curve to adjust the endpoint velocity of each trajectory segment from the end to the beginning of the path, the bounded cubic equation of the tangential S-shaped jerk is obtained as follows: in, For tangential jerk, For the first The running time, calculated from the zero point using the trajectory start time, is used to solve for the bounded time under the tangential S-shaped acceleration curve constraint. velocity at the end of the segment trajectory ,if This indicates that the constraint of bounded tangential jerk is satisfied, if The acceleration conditions are not met. and The relationship that satisfies the following is as follows: The first step determined by the constrained reverse scan using this formula is... Segment end speed Solve the problem, then execute 2b; 2b. The final result of the reverse scan Segment end speed Ultimately, it is determined by the following formula: in, The maximum speed of the entire trajectory. The maximum speed for each segment of the trajectory after segmentation is determined, and then 2c is executed; 2c. will The value is decremented by one, and step 2) is executed again until the judgment condition is met before step 3 is executed. 3). All of them ( Stored in the buffer.

5. The AGV motion control method based on a pure tracking model according to claim 4, characterized in that: The forward scan is used to determine the first... The initial velocity of the trajectory under tangential acceleration and acceleration constraints The specific process of the forward scanning is as follows: ① Settings and the initial velocity of the first trajectory Then proceed to step ②; ② If If the first step is to execute step ③, then execute step ②. 2d The solution can be found using the following relation, which is as follows: Solve the above formula to get the first... The initial velocity of the trajectory under tangential acceleration and acceleration constraints ,like This indicates that the bounded constraint of tangential jerk is satisfied. Then, continue solving using the following relation. After solving, execute 2e. The specific relation is as follows: 2e, No. Segment path starting point velocity Determined by the following formula: in Indicates the first The initial velocity of a trajectory obtained under tangential acceleration and acceleration constraints; 2f, will Increment the value by one, and repeat step ② until the judgment condition is met before executing step ③. ③, all of them Store in the buffer; The speed curve of the AGV vehicle is then generated based on the data obtained from the two scans.

6. The AGV motion control method based on a pure tracking model according to claim 1, characterized in that: Setting the speed of the fuzzy controller in step three and lateral distance Set the aiming distance for the input value. The output quantity is the speed. The domain of discourse is The discrete universe is The quantization factor is set to 2, the velocity domain is divided into 5 fuzzy subsets, and the fuzzy language value is... Very slow, slow, moderate, fast, very fast = lateral distance The domain of discourse is The discrete universe is The quantization factor is set to 1, which will increase the horizontal distance. The universe of discourse is divided into 5 fuzzy subsets, and the fuzzy linguistic values ​​are {very small, small, medium, large, very large} = { }, Pre-aiming distance The domain of discourse is divided into 5 fuzzy subsets, and the fuzzy linguistic values ​​are {very near, near, moderate, far, very far}. .

7. The AGV motion control method based on a pure tracking model according to claim 1, characterized in that: The formula for calculating the front wheel steering angle is as follows: in, For the pre-aiming distance, For the front wheel steering angle, For the arctangent trigonometric function, For the body and The included angle.

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