Intelligent logistics transportation palletizing AGV carts and their control methods and systems

Abstract

This disclosure provides an intelligent logistics transportation palletizing AGV (Automated Guided Vehicle) and its control method and system, wherein the AGV has an X-type Mecanum wheel structure. The method includes: obtaining whether the AGV is empty and the real-time weight of the cargo; real-time monitoring of the AGV's center of gravity position and heading angle in the world coordinate system, and the cargo's center of gravity position and offset angle in the vehicle coordinate system, thereby constructing a cargo center of gravity acceleration matrix; calculating the required three-dimensional driving force based on the acceleration information; constructing a slip velocity matrix by monitoring the wheel speed and roller speed of each Mecanum wheel; further calculating the real-time driving force required for each wheel, and combining a state variable feedback method to solve for the optimal real-time driving force for each wheel, ultimately obtaining the optimal real-time speed of each wheel. This method can effectively adapt to dynamic load changes and non-ideal driving states, improving the AGV's dynamic stability, path control accuracy, and intelligent response capability in complex logistics scenarios.

Description

Technical Field

[0001] This invention belongs to the field of intelligent AGV vehicles, specifically relating to an intelligent logistics transportation palletizing AGV vehicle and its control method and system. Background Technology

[0002] With the development of modern logistics, automated forklifts are widely used in logistics warehousing. However, due to the limitations of the wheel system structure, traditional forklifts can only move forward, backward, and make limited turns, making it difficult to flexibly cope with the confined space and complex paths of warehouse operations.

[0003] Mecanum wheel forklifts offer omnidirectional mobility, enabling forward, backward, lateral, diagonal, and in-situ rotation, making them ideal for precise and flexible transport tasks in logistics and warehousing environments. However, current solutions struggle to guarantee motion control precision and safety stability when Mecanum wheel forklifts are simultaneously transporting and lifting goods, as the load's center of gravity shifts. Therefore, a viable solution is urgently needed to achieve precise control of Mecanum wheel forklifts during simultaneous transport and lifting. Summary of the Invention

[0004] To address the aforementioned issues, this disclosure provides an intelligent logistics palletizing AGV (Automated Guided Vehicle) and its control method and system, based on exemplary embodiments. This disclosure employs an improved coefficient matrix state variable feedback method to optimize the driving force, ultimately obtaining the optimal real-time rotational speed of each wheel. This method improves the AGV's attitude control stability and driving accuracy under complex loads and dynamic scenarios, making it suitable for efficient anti-tipping palletizing operations.

[0005] In a first aspect of this disclosure, a control method for an intelligent logistics transportation palletizing AGV is provided, wherein the AGV is an X-type Mecanum wheel AGV, characterized by comprising the following steps:

[0006] S1: Obtain information on whether the AGV is empty. If it is carrying goods, obtain the real-time quality of the goods.

[0007] S2: Real-time monitoring of the center of gravity position and heading angle of the AGV carrying goods in the world coordinate system, as well as the center of gravity position and offset angle of the goods in the vehicle coordinate system, and constructing the real-time acceleration matrix of the center of gravity of the goods carried on the AGV in the vehicle coordinate system.

[0008] S3: Based on the real-time acceleration matrix of the cargo's center of gravity constructed in step S2, calculate the overall three-dimensional driving force required by the AGV carrying the cargo.

[0009] S4: Based on the rotational speed of each Mecanum wheel and the rotational speed of the roller obtained from real-time monitoring, construct the calculation matrix of the sliding speed of each wheel in the vehicle coordinate system when the AGV is carrying goods and moving and lifting goods in real time;

[0010] S5: Based on the calculation results of steps S3-S4, further calculate the real-time driving force required for each wheel of the AGV trolley carrying the goods;

[0011] S6: Further based on the state variable feedback method, the optimal real-time driving force of each wheel of the AGV carrying goods is solved, and finally the optimal real-time rotation speed of each wheel of the AGV carrying goods is obtained.

[0012] According to a second aspect of this disclosure, a control system for an intelligent logistics transport palletizing AGV that performs the control method described above is provided, the control system comprising:

[0013] The empty and loaded detection module is used to obtain information on whether the AGV is empty and, if it is carrying goods, the real-time weight of the goods.

[0014] An IMU (Inertial Measurement Unit) is used to acquire the attitude and angular velocity of the entire cargo-carrying vehicle in the world coordinate system in real time.

[0015] The real-time dynamic differential positioning module is used to provide the real-time center of gravity position and heading angle ψ of the AGV carrying goods in the world coordinate system.

[0016] The cargo offset angle detection unit is used to obtain the real-time center of gravity position and offset angle of the cargo in the vehicle coordinate system; the encoder is used to obtain the rotational angular velocity of each Mecanum wheel and the rotational angular velocity of each Mecanum wheel roller.

[0017] The cargo center acceleration matrix construction module is used to construct the real-time acceleration matrix of the cargo carried on the AGV vehicle;

[0018] The three-dimensional driving force calculation module is used to calculate the overall three-dimensional driving force required by the AGV trolley carrying cargo based on the data from the empty and loaded detection module, the IMU inertial measurement unit, the real-time dynamic differential positioning module, the cargo offset angle monitoring unit, and the cargo center acceleration matrix construction module.

[0019] The sliding speed calculation module is used to construct the sliding speed calculation matrix of each wheel of the AGV in the vehicle coordinate system when the AGV is carrying goods and moving and lifting goods in real time;

[0020] The real-time driving force calculation module is used to further calculate the required real-time driving force for each wheel of the AGV trolley carrying goods, based on the calculation results of the three-dimensional driving force calculation module and the slip velocity calculation module.

[0021] The state feedback optimization solution module is used to solve for the optimal real-time driving force of each wheel of the AGV carrying goods based on the state variable feedback method, and finally obtain the optimal real-time rotational speed of each wheel of the AGV carrying goods.

[0022] According to a third aspect of the present invention, an intelligent logistics transport palletizing AGV trolley is provided. The AGV trolley includes a base, four Mecanum wheels mounted on the base in an X-shape, and the AGV trolley also includes the control system described above, an elevator housing disposed above the rear end of the base, and a transport box carrying component.

[0023] The elevator housing is equipped with several lifting shafts for lifting and lowering the cargo inside the cargo box fixed by the transport box bearing component;

[0024] The transport container carrying component is equipped with a cargo box flipping mechanism for flipping the cargo box.

[0025] It should be understood that the description in the Summary of the Invention is not intended to limit the key or essential features of the embodiments of this disclosure, nor is it intended to restrict the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0026] The invention will now be described in more detail with reference to embodiments and the accompanying drawings.

[0027] Figure 1 The mounting structure of the four Mecanum wheels of the AGV trolley on the chassis is shown;

[0028] Figure 2 A schematic flowchart of the intelligent logistics transportation palletizing AGV trolley control method of this disclosure is shown;

[0029] Figure 3 The heading angle of the AGV is shown in the world coordinate system;

[0030] Figure 4 This shows the offset angle of the cargo's center of mass relative to the vehicle's overall center of mass within the vehicle's coordinate system;

[0031] Figure 5 This shows the offset angle α of the Mecanum wheel center point relative to the x-axis of the vehicle coordinate system. i ;

[0032] Figure 6A comparison graph is shown showing the effect of the cargo center acceleration constructed using the method of this disclosure on path deviation control, compared to not using it;

[0033] Figure 7 The wheel plane of the Mecanum wheel and the direction of roller rolling are shown;

[0034] Figure 8 This invention illustrates the overall center of gravity change trajectory of a cargo-carrying trolley over 25 minutes, along with the required driving force and optimal real-time rotational speed monitoring diagram, as shown in one embodiment of this disclosure.

[0035] Figure 9 This disclosure illustrates the load Q on the four wheels of the AGV trolley during the transportation and palletizing of goods. L,i The changes;

[0036] Figure 10 This paper presents a comparison chart showing the improvement effect of the state variable feedback method on the coefficient matrix in the optimization model.

[0037] Figure 11 This illustrates the optimal driving force variation required for the four Mecanum wheels in one embodiment of the present disclosure;

[0038] Figure 12 This illustrates the optimal real-time rotational speed variation of the four Mecanum wheels in one embodiment of this disclosure;

[0039] Figure 13 A block diagram of the intelligent logistics transportation palletizing AGV control system of this disclosure is shown;

[0040] Figure 14 This disclosure illustrates the flipping state structure of an intelligent logistics transport palletizing AGV trolley.

[0041] Figure 15 The diagram shows several lifting shaft structures installed inside the housing of the trolley elevator;

[0042] Figure 16 The initial state structure of the trolley with and without cargo boxes is shown;

[0043] Figure 17 A schematic diagram of the cargo box tilting mechanism of the trolley is shown;

[0044] Figure 18 The image shows the location of the trolley during the process of loading and stacking goods.

[0045] Figure 19 The initial state and lifting state of the trolley are shown. Detailed Implementation

[0046] The embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0047] In the description of embodiments of this disclosure, the term "comprising" and similar terms should be understood as open-ended inclusion, i.e., "including but not limited to". The term "based on" should be understood as "at least partially based on". The term "one embodiment / implementation" or "this embodiment / implementation" should be understood as "at least one embodiment / implementation". The terms "first", "second", etc., may refer to different or the same objects. Other explicit and implicit definitions may also be included below.

[0048] The AGV used in this public application is Figure 1 The X-shaped Mecanum wheel AGV shown has a unique mounting structure for its four Mecanum wheels. This structure enables complex movements such as translation, rotation, and oblique movement in any direction within a two-dimensional plane, exhibiting excellent flexibility and path control capabilities. It is widely used in automated tasks related to logistics handling, palletizing, and warehousing in confined spaces. The X-shaped Mecanum wheel has an ABAB structure: the left front wheel is wheel A, with its roller facing -45° to the right rear; the right front wheel is wheel B, with its roller facing +45° to the right front; the left rear wheel is wheel B, with its roller facing +45° to the right front; and the right rear wheel is wheel A, with its roller facing -45° to the left front. Figure 2 The flowchart illustrates the control steps of the intelligent logistics transportation palletizing AGV trolley provided in this disclosure. The method provided in this disclosure includes the following steps:

[0049] S1: Obtain information on whether the AGV is empty. If it is carrying goods, obtain the real-time quality of the goods.

[0050] S2: Real-time monitoring of the center of gravity position and heading angle of the AGV carrying goods in the world coordinate system, as well as the center of gravity position and offset angle of the goods in the vehicle coordinate system, to construct the real-time acceleration matrix of the center of gravity of the goods on the AGV in the vehicle coordinate system.

[0051] S3: Based on the real-time acceleration matrix of the cargo's center of gravity constructed in step S2, calculate the overall three-dimensional driving force required by the AGV carrying the cargo.

[0052] S4: Based on the rotational speed of each Mecanum wheel and the rotational speed of the roller obtained from real-time monitoring, construct the calculation matrix of the sliding speed of each wheel in the vehicle coordinate system when the AGV is carrying goods and moving and lifting goods in real time;

[0053] S5: Based on the calculation results of steps S3-S4, further calculate the real-time driving force required for each wheel of the AGV trolley carrying goods;

[0054] S6: Further based on the state variable feedback method, the optimal real-time driving force of each wheel of the AGV carrying goods is solved, and finally the optimal real-time rotation speed of each wheel of the AGV carrying goods is obtained.

[0055] This method enables multi-wheel coordinated control under complex loads and motion states, significantly improving the dynamic stability, steering accuracy, and path execution reliability of AGVs in logistics palletizing scenarios, and effectively enhancing the transportation safety and intelligent collaboration level of the entire vehicle under complex working conditions.

[0056] When an AGV carrying goods moves or lifts, its overall center of gravity changes, especially when the goods are unevenly distributed. Only by knowing the real-time position and acceleration of the goods' center of gravity within the vehicle's coordinate system can the control algorithm accurately determine the vehicle's dynamic stability and adjust the driving force and speed of each wheel in real time. This ensures the center of gravity projection falls within the supporting polygon, preventing tipping, and thus accurately realizing state variable feedback in predictive model control (such as MPC). In some embodiments, step S2 includes:

[0057] S21: Figure 3 It shows the x-coordinate system in the world coordinate system. o Oy o The relationship between the heading angle ψ, yaw angle φ, and sideslip angle δ of the AGV traveling downwards is based on real-time monitoring of the overall center of gravity (x) of the AGV carrying goods in the world coordinate system. v ,y v ,z v Given the heading angle ψ, construct the rotation matrix Rotate(ψ) and obtain the rigid body transformation matrix T based on the rotation matrix. WB Further inverse transformation yields the rigid body inverse transformation matrix T. BW Based on the inverse rigid body transformation matrix and the real-time position of the center of gravity (x) in the world coordinate system v ,y v ,z v The coordinates (x, y, z) of the real-time center of gravity of the cargo-carrying vehicle in the vehicle coordinate system are obtained.

[0058]

[0059] t is the coordinate matrix of the center of gravity of the cart carrying goods in the world coordinate system, t = [x v y v z v ] T ; Represents T WB T is a 4×4 matrix in the real number field. WB The transformation matrix is ​​used to convert the coordinates of the center of gravity of the cargo-carrying vehicle in the vehicle coordinate system to the world coordinate system;

[0060]

[0061] T BW This is a transformation matrix for converting the coordinates of the center of gravity of a cargo-carrying vehicle from the world coordinate system to the vehicle body coordinate system; For T WB The inverse transformation matrix, Rotate T (ψ) is the transpose of Rotate(ψ);

[0062] Take the real-time centroid coordinate matrix obtained after transformation x, y, and z in the coordinate system are the real-time coordinates (x, y, z) of the center of gravity of the cargo-laden vehicle within the vehicle coordinate system.

[0063] S22: Figure 4 This shows the offset angle θ of a cargo of mass m from the overall center of gravity of the cart carrying the cargo, obtained through real-time monitoring. x θ y , Figure 5 The projection of the line connecting the center point of the i-th Mecanum wheel and the center of gravity O of the cart carrying the cargo onto the xy-plane of the vehicle coordinate system shows the offset angle α relative to the x-axis of the vehicle coordinate system. i And the required driving force F for each Mecanum wheel i The parameters include orientation, and the offset angle θ between the center of gravity of the cargo carried by the AGV and the center of gravity of the AGV in the xz plane of the vehicle coordinate system, obtained from real-time monitoring. x And the offset angle θ in the yz plane of the vehicle coordinate system y The real-time center of gravity (x, y, z) of the AGV carrying the cargo, obtained in step S21, and the coordinates (x, y, z) of the cargo of mass m in the vehicle coordinate system are also obtained. C ,Y C Z C ), calculate the distance H between the center of gravity of the cargo and the center of gravity of the entire cart carrying the cargo in the vehicle coordinate system:

[0064]

[0065] S23: Construct the real-time acceleration matrix of the cargo's center of gravity a p :

[0066]

[0067] in, and These represent the real-time accelerations of a cargo of mass m along the x, y, and z axes in the vehicle coordinate system. and They are respectively equipped with, These are the angular velocities of the center of mass of the goods carried by the AGV (Automated Guided Vehicle) relative to the center of mass of the AGV in the xz plane of the vehicle coordinate system, and the angular velocities of the center of mass of the AGV relative to the center of mass of the AGV in the yz plane of the vehicle coordinate system, respectively. These are the angular accelerations relative to the center of gravity of the goods carried by the AGV (Automated Guided Vehicle) of mass m in the xz plane of the vehicle coordinate system, and the angular accelerations relative to the center of gravity of the AGV in the yz plane of the vehicle coordinate system.

[0068] The formula for calculating the total three-dimensional driving force required by the AGV carrying goods in step S3 is as follows:

[0069]

[0070] Among them, v x v y These are the linear velocities of the cargo-carrying trolley along the x and y axes in the vehicle coordinate system, respectively. and These represent the x-axis velocity and y-axis velocity of the cart carrying the cargo, relative to its center of gravity in the world coordinate system.

[0071] M represents the mass of the AGV without any cargo (i.e., the empty mass of the AGV); F x F y and F z These represent the driving forces required by the entire vehicle carrying a cargo of mass m along the x, y, and z axes of the vehicle coordinate system.

[0072] F z The first term (M+m)g represents the total weight of the cargo carrying mass m; the sum of the second and third terms is: The vertical load on the trolley caused by the vertical (z-axis direction of the vehicle coordinate system) acceleration component of the carried cargo; Item 4 The vertical load component is caused by the inertial acceleration resulting from the rotation of the trolley. When the trolley rotates about the z-axis of the vehicle coordinate system, the total mass (m+M) of the trolley carrying the cargo rotates in the horizontal direction, resulting in an additional upward inertial force. The negative sign indicates that the inertial force is upward, offsetting part of the downward load force of the first to third terms, thus reducing the load force provided by the ground to the trolley with mass m; F x F y and F z Used for subsequent tire load distribution calculations.

[0073] Figure 6 This disclosure shows the acceleration of the cargo carried on the AGV in the real-time acceleration matrix of the center of gravity in the vehicle coordinate system, constructed in steps S21-S23. and The required three-dimensional driving force for the AGV carrying the goods is calculated in step S3, thereby obtaining the driving force F required for each Mecanum wheel. i The comparison of the effects of introducing state variable feedback methods as feedback variables on path offset control is shown in the figure. The black dashed line represents the circular turning path of the target reference. Figure 6 (a) shows the trajectory comparison. Figure 6 (b) for constructing a according to methods S21-S23 of this disclosure p The matrix and the real-time acceleration of the cargo's center of gravity in steps S21-S23 of this disclosure are used to measure F. x F y F z The calculated F i The control error comparison chart shows that the red trajectory represents the area where matrix a was not used. p The method for constructing the driving force Fi as the four elements of the control input U is as follows: Figure 6(b) It can be seen that after the 6th minute, the path begins to deviate significantly from the target reference path due to the disturbance caused by the change in the center of mass of the cargo. The error oscillation details show that the error exceeds 10cm at its highest. The blue trajectory is the result of using the method of this disclosure. The trajectory tracking results of the method of this disclosure show that its control trajectory is closer to the target trajectory and responds to the disturbance caused by the change in the center of mass of the cargo more quickly and stably. The error amplitude of the blue trajectory relative to the target trajectory of the black dashed line is always small, with a maximum of no more than 5cm. The root mean square error of the path deviation of the red trajectory is 0.051m, and the root mean square error of the path deviation of the blue trajectory is 0.026m. This indicates that by introducing the real-time acceleration calculation of the center of mass of the cargo in steps S21-S23 into the control input considerations, the system can suppress the trajectory disturbance caused by the cargo deviation more quickly and maintain a more accurate path.

[0074] As another embodiment of the present invention, step S4 includes:

[0075] S41: Construct the conversion coefficient matrix T of the i-th wheel roller. i :

[0076] When i = 1, the i-th Mecanum wheel is the left front wheel; when i = 2, the i-th Mecanum wheel is the right front wheel; when i = 3, the i-th Mecanum wheel is the left rear wheel; and when i = 4, the i-th Mecanum wheel is the right rear wheel. α i Let the angle of offset of the line connecting the center point of the i-th Mecanum wheel and the center of gravity of the cart carrying the cargo in the xy plane of the vehicle coordinate system relative to the x-axis of the vehicle coordinate system be the angle of this line.

[0077] in, Represents matrix T i It is a real matrix with dimensions of 2 rows × 3 columns; T i The first line indicates the velocity v of the entire x-axis of the car carrying the cargo within the vehicle coordinate system. x y-axis velocity v x and rotational speed Switch to the rolling direction along the i-th wheel roller. The velocity component (i.e., the tilt angle γ) i In the direction in which it is located, since the roller rotates freely, there are no constraints; T i The second line indicates the vehicle's y-axis velocity v y and x-axis velocity v x Switch to the direction perpendicular to the i-th wheel roller. Direction; T i The first list shows the vehicle's three-dimensional velocity v x v y and Along the direction of roller rolling The velocity components; in the vehicle coordinate system, the x-axis is the forward direction and the y-axis is the lateral displacement direction; Figure 7 The diagram shows the wheel surface plane formed by axes a and b, where axis b is the roller tilting axis, and axis a represents the direction orthogonal to the roller tilting axis, i.e., perpendicular to the i-th roller direction. direction, Figure 7 The green arrow in the diagram indicates the rolling direction of the i-th wheel roller. The direction.

[0078] That is, T i It involves placing the car on the vehicle body and specifying the parameters within the system. The linear transformation matrix mapped to the local coordinate system of the wheel;

[0079] S42: Based on the i-th wheel roller conversion coefficient matrix T i Obtain the velocity component coefficient matrix block B in the slip direction of the i-th wheel. i and the i-th wheel drive mapping matrix block C i :

[0080] B i =[-sin(γ) i cos(γ) i ) d i cos(γ i -α i )];d i Let be the distance from the center point of the i-th Mecanum wheel to the center of gravity of the vehicle. L i W i Let L1+L3=L2+L4=2L, W1+W2=W3+W4=2W, and W1+W2=W3+W4=2W, respectively, be the real-time longitudinal distance along the y-axis and the lateral distance along the x-axis of the entire trolley carrying the cargo in the vehicle coordinate system.

[0081] C i =[R i cos(γ i ) r i ]; where R i Let r be the radius of the i-th Mecanum wheel. i Let be the radius of the roller of the i-th Mecanum wheel;

[0082] C i It is used to measure the rotational speed of the wheel. and roller rotation speed Converted to the velocity component of the wheel's contact point with the ground in the sliding direction (i.e., perpendicular to the roller's rolling direction). The transformation matrix. That is, C iThe first component in the matrix C represents the component of the wheel axle rotation in the slip direction; i The second component in the matrix It represents the direct projection of the roller's rotation about its own axis in the sliding direction;

[0083] Then obtain the i-th wheel roller conversion coefficient matrix T constructed in step S41. i The second row of elements constitutes matrix block B. i At the same time, according to T i Further calculate the drive mapping matrix block C for each wheel. i ;

[0084] S43: Construct the slip velocity calculation matrix v for each wheel in the vehicle coordinate system. slip :

[0085]

[0086] in, The status and weight of the cart carrying goods. Let be the rotational angular velocity of the i-th Mecanum roller. To monitor the rotational angular velocity of the i-th Mecanum wheel in real time;

[0087]

[0088] R i Let r be the radius of the i-th Mecanum wheel. i Let be the radius of the i-th Mecanum roller;

[0089] coefficient matrix It shows the mapping relationship between the slip velocity of each wheel and the system state (position velocity, angular velocity of each wheel), in 4 rows and 11 columns. Since the matrix is ​​1 row and 11 columns, the result of multiplying the two is the slip velocity v of each wheel in the vehicle coordinate system, which is 4 rows and 1 column. slip,i v slip That is, obtained through the above calculations

[0090] Figure 8 The trajectory of the overall center of gravity change of a vehicle carrying mass m is shown during a monitoring time of 25 minutes. Figure 8 (a) and the forces on the four wheels F i and sliding speed v slip,i Magnification value 100V slip,i ( Figure 8 (b)), Figure 8In (a), the black box line represents the overall chassis frame of the AGV carrying goods, the red track in the middle is the projection trajectory of the entire vehicle in the vehicle coordinate system over time, and the four blue "×" points represent the four Mecanum wheels. Figure 8 (a) shows that the dynamic trajectory of the center of gravity of the trolley carrying the cargo clearly converges and is stably distributed in the central region of the supporting polygon, that is, by constructing the cargo center of gravity acceleration a in real time. p It is fed back to the multi-wheel drive force optimization module (F i (Optimal solution) effectively realizes multi-round coordinated control and disturbance rejection dynamic balance, avoids the risk of the overall center of gravity exceeding the frame of the AGV chassis, and prevents the AGV from overturning or skidding due to unstable center of gravity.

[0091] Figure 8 (b) shows the slip velocity v slip,i Multiples of 100v slip,i With the corresponding required driving force F i Exhibiting a nonlinear but correlated trend, the driving force model constructed in this invention fully considers the functional relationship between slip velocity and ground friction and adopts... The degree of wheel slippage relative to the overall longitudinal travel speed v x The proportion correction, v slip,i Let be the local slip disturbance experienced by the i-th Mecanum wheel in its local coordinate system. The ratio of to is used to measure the slip of the wheel along the constraint direction. It is not necessary for both to be in the same coordinate system. The ratio is a normalization representation. Figure 8 (b) This demonstrates that the method of this disclosure provides the optimal driving force F. i,best and optimal real-time speed This ensures that the force and slip speed of the four Mecanum wheels change in a basically synchronized manner, presenting a good dynamic coordination relationship. The tire working point remains stable within a controllable range, avoiding slippage and / or stalling.

[0092] This disclosure significantly improves the multi-wheel coordinated control capability of AGV vehicles under load disturbances and path changes by integrating dynamic modeling of cargo center acceleration, wheel slip speed feedback and friction model correction. Figure 8 The overall center of gravity trajectory of the car shown in the figure did not exceed the limit, and the driving force and sliding speed were coordinated and synchronized, which fully verified its significant advantages in improving dynamic stability, path control accuracy and tire force utilization.

[0093] As another example of the present invention, step S5 includes the following steps:

[0094] S51: Calculate the real-time lateral load transfer of the i-th Mecanum wheel. and real-time vertical load transfer

[0095]

[0096] 2W represents the total length from the center point of the left wheel to the center point of the right wheel in the horizontal y-axis direction of the vehicle coordinate system; 2L represents the total length from the center point of the rear wheel to the center point of the front wheel in the vertical x-axis direction of the vehicle coordinate system.

[0097] S52: Calculate the real-time total vertical load Q of the i-th Mecanum wheel. L,i :

[0098] Right now:

[0099]

[0100] Where, α i Let s be the angle between the line connecting the center point of the i-th Mecanum wheel and the center of gravity of the cart carrying the cargo, and the y-axis relative to the cart's horizontal coordinate system; x,i s y,i Real-time lateral load transfer amount and real-time vertical load transfer The sign coefficient; s x,1 =-1,s y,1 =-1; s x,2 =-1,s y,2 =1; s x,3 =1,s y,3 =-1; s x,4 =1,s y,4 =1;

[0101] S53: Calculate the real-time driving force F required for the i-th Mecanum wheel. i :

[0102]

[0103] Where, μ i Let μ be the coefficient of friction between the i-th Mecanum wheel and the ground. i =0.3~0.5, μ i The value of μ depends on the material of the Mecanum wheel and the surface condition. For example, if the Mecanum wheel is covered with soft rubber and the surface is a flat, dry floor (such as epoxy flooring, wood flooring, or cement floor), then μ... i =0.4~0.5, if the wheels are hard and the ground is dusty, then μ i =0.3~0.4.

[0104] Figure 9 The diagram illustrates the application of the dynamic vertical load of the four Mecanum wheels, calculated using the above method, to the intelligent logistics transport and palletizing AGV cart in this disclosure when transporting and palletizing goods.L,i The changes in the vertical load Q of the four wheels L,i The cyclical fluctuations throughout the 250-second interval reflect the wheel load imbalance caused by factors such as center of gravity shift, acceleration and deceleration, and yaw rate disturbance during the dynamic operation of the vehicle.

[0105] During certain time periods, such as [60s, 120s] and [190s, 230s], Q L,3 With Q L,4 The large fluctuations may be due to the more intense load on the rear wheels during steering. Compared with the traditional static average distribution, dynamic load can more realistically reflect the force situation of each wheel, and thus be more reasonably used for tire friction modeling and slip control.

[0106] As another example of the present invention, the state variable feedback method in step S6 includes the following steps:

[0107] S61: Construct a state-space model that includes the vehicle's motion state and the cargo's posture:

[0108] Where X is the state vector matrix, The state derivative vector matrix, i.e. Each element in X is the first derivative of the corresponding element in X with respect to time. and These represent the heading angular velocity and heading angular acceleration of the AGV carrying goods in the world coordinate system; U is the control input vector matrix, U = [F1, F2, F3, F4]. T A is the state vector coefficient matrix. E is the control input vector coefficient matrix.

[0109]

[0110] In matrix A, the first, third, fifth, seventh, and ninth rows represent pairs of vectors. These are the constraints where the coefficient is 1; the coefficients in the second row are paired with the matrix. After the action, the lateral dynamic equation of the vehicle is obtained. It embodies three key couplings: This is the product of the yaw angular velocity and the longitudinal velocity of the vehicle body. It belongs to the Coriolis acceleration term in a non-inertial coordinate system and affects the lateral acceleration; -gθ x and -gθ yThe term represents the projection of gravity along the offset angle of the pendulum, reflecting the restoring force in the vehicle coordinate system caused by the disturbance of the cargo's center of gravity during the overall movement of the trolley carrying the cargo. The fourth row relates to the matrix. After the action, This is a simplified longitudinal dynamics model, showing that due to the cargo at θ y The directional shift causes a change in the vehicle's forward and backward motion, indicating that the vehicle's acceleration in the x-direction is mainly affected by θ. y The disturbance, such as the cargo deflecting forward, causes the vehicle to accelerate forward. All coefficients in the sixth row of matrix A are 0 because in matrix A... The sixth element in is Its main influence is on the input control vector of matrix E, that is, the driving force of the entire vehicle caused by the rotation of each Mecanum wheel. This is achieved by adjusting the matrix through the eighth row of matrix A. After the action, the result is This is a linearized representation in the xz plane of the one-dimensional cargo center of mass m offset relative to the overall center of gravity of the vehicle, indicating that the entire control system will shift by an angle θ from the center of gravity. x The restoring torque is generated, and the tenth row of the matrix is ​​similar, representing a linearized expression of the center of gravity offset model in the yz plane.

[0111]

[0112] in, Consider a single vehicle body of mass M (without cargo) as a rectangular structure (length 2L, width 2W), with a moment of inertia about its vertical axis around its center of mass as... The simplified result considers the rotational inertia of the individual trolley. To consider the moment of inertia of a cargo of mass m with its own center of gravity as the point of rotation, the sum of the two is the total moment of inertia of the cart carrying the cargo of mass m.

[0113] β x β y These are the centroid offset coupling coefficients of the cargo carried, β. x =cosβ,β y =sinβ; If the center of mass of the cargo being carried is biased towards pure forward and backward motion, then β = 0°, β x =1,β y =0; if the center of mass of the cargo being carried is biased towards a purely left-right shift, then β = 90°, β x =0,β y =1.

[0114] It is the key "input action channel mapping matrix" in the state-space variable framework constructed by the state variable feedback method in the coupled control system of the Mecanum wheel four-wheel drive AGV trolley carrying the cargo during the trolley's movement and / or lifting to the destination, which controls the center of gravity shift. Each column corresponds to the input force F of one wheel. i Each row represents the effect of this input force on the derivative of a certain state variable (i.e., acceleration). The four control inputs U = [F1, F2, F3, F4] are represented by matrix E. T This is transformed into an "acceleration" or dynamic driving effect on each dynamic quantity (velocity, angular velocity, sway angle, etc.) in the state vector; the second row of matrix E represents the effect of the lateral resultant force related to the lateral action of the four wheels on the lateral velocity. The influence, its coefficient Applying the control force at a 45° angle results in lateral (i.e., sideways) acceleration. The fourth row is similar, showing the longitudinal resultant force related to the longitudinal velocity of the four wheels. The influence of the wheel drive force on the vehicle's vertical axis is shown in the sixth line. The coefficients in the sixth line represent the yaw moment arm of each Mecanum wheel relative to the current center of gravity of the entire system. The coefficients in the sixth line represent the yaw angular velocity, indicating the influence of the wheel drive force on the yaw moment about the vertical axis of the vehicle. The further away from the overall center of gravity, the greater the influence. The coefficients in the eighth line indicate the yaw angular velocity of the torque generated by the four-wheel drive force in the xz plane relative to the vehicle's vertical axis. The effect (the negative sign indicates the opposite), the tenth line shows the angular velocity of the four-wheel drive force in the yz plane. The coupling effect influences the tilt angle.

[0115] S62: Constructing the optimization objective function:

[0116]

[0117] Among them, [1,N p [] represents the prediction time domain length, [0, N] c -1] is used to control the length of the time domain, X ref For the reference state trajectory, n is the nth step predicted backward from the current time k, Q is the state value weight matrix, and G is the input control value weight matrix; X k+n|k Based on the state value X at time k k|k The predicted state value at time k+n; U k+n|k To control the input value U based on time k k|k The control input adjustment value at time k+n; For U k+n|k The transpose of the matrix;

[0118] S63: Repeat the optimized iteration of step S62, if... Stop the iteration and output each element in the control state variable matrix U at this moment, which serves as the optimal real-time driving force F for each wheel of the AGV carrying the goods. i,best Furthermore, based on this, the optimal real-time rotational speed of each wheel of the AGV carrying goods can be determined.

[0119] Improved methods for the state value weight matrix Q and input control value weight matrix G in step S62 include:

[0120] S621: Construct the state vector data sample matrix X data And the sample matrix U of driving force observations of four Mecanum wheels data :

[0121] X (p) This is the data sample submatrix for the p-th sampling point, containing ten state variables.

[0122] U data =[U (1) U (2) ,...,U (N) ];in, For the four Mecanum wheel drive force observations at the p-th sampling point, and These are the driving force observations at the p-th sampling points for the left front wheel, right front wheel, left rear wheel, and right rear wheel, respectively.

[0123] S622: Construct the first spatial weight matrix W1 and the second spatial weight matrix W2:

[0124] For the element w in the p-th row and q-th column of the first spatial weight matrix W1 pq,1 : And p≠q, that is, X (q) This is the data sample submatrix for the q-th sampling point; The element in the a-th row and b-th column of the second space weight matrix W2 is defined as w ab For w ab If the interaction strength between adjacent wheels is 1, and that between non-adjacent wheels is 0, then the left front wheel is adjacent to the right front wheel and also to the left rear wheel, but not to the right rear wheel. Therefore, w 12 =1, w 13 =1, w 14 =0, which has no effect on its own function, therefore w 11 =0;

[0125] S623: Constructing a Consistency Coefficient for Spatial Variation Measurement Input distributed coupling coefficient h ab:

[0126]

[0127] Where, x e,p x e,q These are the values ​​of the e-th state variable in the p-th sample and the e-th state variable in the q-th sample, respectively. Let x be the e-th state variable e The average value in the p-th sample; where x is y, x、 ψ、 θ x , θ y or

[0128]

[0129] in, For the nth observation in the a-th Mecanum round, Let a = 1, 2, 3, 4; b = 1, 2, 3, 4; This is the nth observation in the b-th Mecanum cycle. These are the average of N observations in the a-th Mecanum cycle and the average of N observations in the b-th Mecanum cycle, respectively.

[0130] S624: Measuring Consistency Coefficient Based on Spatial Variation Construct a diagonal state weight matrix Q, and then determine the coupling coefficient h based on the input distribution. ab Construct the input control value weight matrix G:

[0131]

[0132] Spatial variation measurement consistency coefficient The smaller the value, the more drastic the state change → the more important the state → the larger the weight. The spatial change measurement consistency coefficient reflects the spatial autocorrelation or spatial continuity of the state variable.

[0133] Spatial variation measurement consistency coefficient The smaller (i.e.) The stronger the spatial continuity, the stronger the similarity or clustering of variables in space.

[0134] The larger (i.e.) The weaker the spatial continuity, the greater the spatial differences or discontinuities of the variables.

[0135] when The smaller, The larger the value, the higher the weight of matrix X;

[0136] when When it is larger, The smaller the value, the lower the weight of matrix X;

[0137] Therefore, the stronger its correlation with other variables, the greater its weight in matrix X during the optimization process of the state variable feedback method. Consequently, the error of this state variable is more important in the optimization control, has a greater impact on the optimization control effect, and the controller is more sensitive to the error of this variable in matrix X.

[0138] In the input control value weight matrix G, the diagonal elements h aa , represents the spatial autocorrelation (autocorrelation strength) of the a-th Mecanum wheel itself. That is, it clearly reflects the spatial or temporal continuity and stability of the input data controlled by the a-th Mecanum wheel across all sample observations:

[0139] h aa The larger the value, the more stable and autocorrelated the control input of the a-th wheel is, and therefore more important in the control strategy;

[0140] h aa A smaller value indicates that the control input of that round has poor stability or autocorrelation in the spatial or temporal series, is not very stable, and has low importance.

[0141] In matrix H, the off-diagonal elements h ab Meaning:

[0142] off-diagonal element h ab This indicates the spatial coordination between different wheels, that is, it reflects the strength or degree of interaction between two wheel inputs. ab The higher the value, the stronger the coordination between the two wheels. ab The smaller or negative the value, the weaker the synergy or the less correlated the two wheels are.

[0143] In the optimization objective function constructed in step S62, matrix Q determines the degree of importance the system places on state errors (such as deviations in yaw angle, position, and center of gravity offset) during optimization. By optimizing the weights of Q, the tracking ability of the target trajectory can be enhanced. The consistency coefficient is measured by spatial variation. After constructing matrix Q, the correlation coefficient of matrix Q is used to weight the corresponding vectors of the state vector matrix X, adjusting the sensitivity to the system state accuracy and dynamically reflecting the spatial cooperative correlation between different state dimensions. This improves path tracking accuracy, yaw angle stability, control targeting, and overall system responsiveness. Matrix G determines the penalty for the system's use of control input (i.e., wheel driving force); setting an appropriate G can prevent excessive wheel driving force input, which could lead to slippage, instability, or energy waste. An input distribution coupling coefficient h is introduced. ab Then, matrix G can consider the input coordination between the four wheels, balance the amplitude of the wheel driving force, prevent excessive input from causing slippage or a surge in energy consumption, optimize the distribution structure of the control force, and improve stability and energy efficiency.

[0144] Figure 10 The consistency coefficient is shown as a measure of spatial variation. Improved Q-matrix and input distribution coupling coefficient h ab The improved G matrix is ​​used as a variable, and the method of this disclosure is applied respectively. Figure 10 (a) and traditional methods ( Figure 10 (b) The Mecanum wheel driving force F obtained by solving i The three-dimensional distribution. Figure 10 (a) shows a relatively smooth surface with obvious regular changes, F i The changes are relatively gradual overall, indicating that the control effect is stable and can reduce the drastic changes in the driving force of the Mecanum wheel, which is conducive to the smooth implementation of the control strategy. In addition, the peak positions of the surface are evenly distributed, which is beneficial to the overall performance optimization of the system.

[0145] Figure 10 (b) shows a relatively drastic change in the entire surface with obvious fluctuations and numerous and dispersed local peaks. Such fluctuations may lead to frequent adjustments and system instability in the actual control process; it shows the phenomenon of local optimization and fails to achieve global smooth optimization.

[0146] By comparison, the spatial variation metric of this invention is used to measure the consistency coefficient. and the coupling coefficient h of the input distribution ab As an improvement to matrices Q and G in the state feedback optimization model, this method can significantly enhance the stability and consistency of overall control, avoiding excessively frequent control fluctuations. This helps extend the service life of the Mecanum wheel and reduce wear and impact on mechanical components. Simultaneously, the control strategy is clearer, facilitating real-time application and further design improvements to the state feedback optimization control method.

[0147] Figure 11 The optimal driving force F for the four wheels obtained by the method of this disclosure is shown. i,bestThe curve changes over time; in the first turning point from 10 minutes to 45 minutes, the orange solid line represents F. 1,best The force quickly rises to approximately 245N and then stabilizes, indicating that the front left wheel initially experiences increased force but adjusts rapidly; the brown dashed line represents F. 2,best Slightly higher than F overall 1,best The torque reaches approximately 250N, indicating that the front right wheel bears a greater lateral force; the reddish-brown dotted line represents F. 3,best The pressure increases slowly from approximately 260N to 275N, with the rear left wheel bearing the greatest stress during this phase; the magenta dotted line represents F. 4,best Almost with F 3,best The load changes synchronously, but is slightly higher at the crest (approximately 278N), and the right rear wheel also bears a significant load. This demonstrates that the rear wheel load is significantly higher than the front wheel, especially at F. 4,best >F 3,best >F 2,best >F 1,best This indicates that the car exhibits dynamic characteristics of rear-wheel main steering during the initial turn.

[0148] During the second turn between 65 and 90 minutes, the overall driving force of all four wheels begins to decline, but F 1,best F 2,best The temperature drops relatively slowly, decreasing to approximately 225 N; during this phase, F 3,best F 4,best The decrease is relative to F 1,best and F 2,best It is faster, and the lowest value is close to 215N. The changes are synchronized well, and the waveform trend is not abrupt, showing overall smooth cornering. At this stage, the wheels tend to be balanced, indicating that the path curvature is gentler, the load distribution is more even, and the system regulation has entered a stable response range.

[0149] During the third turning phase from 110 to 160 minutes, the driving force required by each wheel re-enters an increasing phase, with significant fluctuations. 1,best Rise to a peak of 245N; F 2,best Rise to slightly above F 1,best At this point, it reaches 250N; F 3,best Rising sharply to 275 N; F 4,best Rising again to F 3,best Slightly higher, reaching 285N; similar to the first turning phase, the rear-wheel dominance characteristic still exists. This section is the load adjustment enhancement zone, indicating that the control system relies on the dynamic adjustment capability of the rear wheels during large-radius or high-speed steering.

[0150] During the 200-250 minute phase, the driving force required by each wheel decreases rapidly again, forming a convergent state. 1,best F 2,bestReduced to a minimum of approximately 145N to 160N; F 3,best F 4,best The minimum descent is approximately 160N to 175N; during this stage, the driving force required by each wheel decreases in an "inversely symmetrical" manner, indicating that this part of the steering is the final stage of transportation or a U-turn. The system evenly distributes the load to all wheel sets to prevent bias and slippage, achieving a stable transition at the end of the steering phase.

[0151] Based on the optimal real-time driving force F of each wheel of the AGV carrying goods. i,best The optimal real-time rotational speed of the i-th Mecanum wheel is determined based on the law of conservation of energy. The formula is as follows:

[0152]

[0153] Among them, P i The rated power of the drive motor for each Mecanum wheel is Pi = 800W to 1200W.

[0154] Figure 12 The diagram shows the rotational speeds of the four wheels calculated using this formula. The curve showing the change. Figure 11 Each F in i,best The peak of the curve corresponds to Figure 12 The corresponding The trough, and vice versa. For example, around 100 minutes, Figure 11 F 1,best Reaching the trough, corresponding to Figure 12 middle Then the wave reaches its peak, with all four wheels responding in unison: four... Although the magnitude of the change curves differs in each turning phase, the response patterns are highly consistent, indicating that the state feedback control method achieves multi-round collaborative optimization of the response.

[0155] This disclosure constructs a 10-dimensional state-space model including cargo acceleration, offset angle, and heading angle, and combines it with dynamic optimization feedback of predictive control model accuracy to enable each wheel to obtain the optimal driving force that matches its position and slip state. Compared with traditional constant or simplified proportional control, it can adapt to the center of gravity offset and acceleration changes under complex working conditions in real time, effectively enhancing motion stability and response accuracy. Figure 11 and Figure 12 The forces and speeds of the four wheels exhibit "coordinated changes and non-abrupt changes" in multiple turning ranges, verifying that the method can effectively suppress tire slippage and instability risks under high-speed turning and load change conditions. This disclosure considers the overall center of gravity change and individual slip characteristics of the vehicle for the drive strategy of each wheel, avoiding problems such as overturning or cargo tipping caused by control mismatch in traditional methods.

[0156] Another aspect of this disclosure provides a control system for an intelligent logistics transport palletizing AGV trolley that performs the control method described above. Figure 13 The control system shown includes:

[0157] The empty and loaded detection module is used to obtain information on whether the AGV is empty. If it is carrying goods, the real-time mass m of the goods is obtained. This module can use a capacitive sensor array or a pressure sensor array, which is deployed on the vehicle lifting platform to detect whether goods are placed.

[0158] An IMU (Inertial Measurement Unit) is used to acquire the attitude and angular velocity of the entire cargo-carrying vehicle in the world coordinate system in real time.

[0159] The real-time dynamic differential positioning module can employ an RTK-GPS module or a visual SLAM positioning unit, combined with an IMU (Inertial Measurement Unit) to provide the real-time center-of-gravity position (x, y) of the AGV carrying goods in the world coordinate system. v ,y v ,z v and heading angle ψ;

[0160] The cargo offset angle detection unit is used to obtain the real-time center of gravity position and offset angle of the loaded cargo in the vehicle coordinate system; that is, this unit is used to obtain the offset angle θ of the cargo in the xz plane and yz plane of the vehicle coordinate system. x θ y This unit can be equipped with a dual-axis gyroscope or a laser rangefinder;

[0161] An encoder is used to obtain the rotational angular velocity of each Mecanum wheel. and the rotational angular velocity of each Mecanum roller Installed on each Mecanum axle and roller;

[0162] The cargo center acceleration matrix construction module is used to construct the real-time acceleration matrix of the cargo carried on the AGV vehicle.

[0163] The three-dimensional driving force calculation module is used to calculate the overall three-dimensional driving force required by the AGV carrying cargo based on data from the empty and loaded detection module, the IMU inertial measurement unit, the real-time dynamic differential positioning module, the cargo offset angle monitoring unit, and the cargo center acceleration matrix construction module.

[0164] The sliding speed calculation module is used to construct the sliding speed calculation matrix v of each wheel of the AGV in the vehicle coordinate system when the AGV is carrying goods and moving and lifting goods in real time. slip ;

[0165] The real-time driving force calculation module is used to further calculate the real-time driving force required for each wheel of the AGV trolley carrying goods, based on the calculation results of the three-dimensional driving force calculation module and the slip velocity calculation module.

[0166] The state feedback optimization solution module is used to solve for the optimal real-time driving force F of each wheel of an AGV carrying goods based on the state variable feedback method. i,best Ultimately, the optimal real-time rotational speed of each wheel of the AGV carrying goods is obtained.

[0167] The third aspect of this disclosure, Figure 14 This disclosure illustrates an intelligent logistics transport palletizing AGV (Automated Guided Vehicle) trolley. The AGV trolley includes a base 1 and four Mecanum wheels 2. The four Mecanum wheels are mounted in an X-shape on the base 1. Figure 1 The Mecanum wheels shown are configured in type A rotation structure, while the Mecanum wheels on the right front and left rear are configured in type B rotation structure. The AGV trolley disclosed herein also includes the aforementioned... Figure 13 The control system shown includes the lifting housing 3 located above the rear end of the vehicle base and the transport box bearing component 4.

[0168] The elevator housing 3 is equipped with several lifting shafts for lifting and lowering the cargo inside the cargo box 5, which is fixed to the transport box bearing component 4.

[0169] Specifically Figure 15 The first lifting shaft 31, the second lifting shaft 32, the third lifting shaft 33, the fourth lifting shaft 34, the fifth lifting shaft 35, and the sixth lifting shaft 36 are nested and snapped together from the outside to the inside within the lifting housing 3. The transport image carrying component 4 is fixedly connected to the top of the innermost lifting shaft, that is, fixedly connected to the top of the sixth lifting shaft 36.

[0170] The transport container carrying component 4 is equipped with a cargo box flipping mechanism, which is used to flip the cargo box 5, raise or lower the goods to the required height for unloading, or load the goods and then flip them to an upright position and raise them to the target stacking position.

[0171] As one embodiment of this disclosure, Figure 14 , Figure 16 The diagram shows that the transport box receiving component 4 includes two L-shaped sub-receiving components 41 on the left and right sides, with the upper horizontal portion of each sub-receiving component 41 being integrally formed. Figure 14Each sub-supporting component 41 is shown to include a lower supporting component 411 and an upper supporting component 412, both of which are L-shaped. The lower supporting component 411 and the upper supporting component 412 are fixedly connected by a connecting shaft 410 provided at the front end of the lower supporting component 411. An inner hook 44 is provided on the upper part of the vertical section of the lower supporting component 411. Figure 14 , Figure 17 The cargo box tipping mechanism is shown, comprising a first electrically operated telescopic rod 42 disposed on the vertical section of each lower receiving component 411, a second electrically operated telescopic rod 43 disposed on the horizontal section of each lower receiving component 411, and a third electrically operated telescopic rod 46 disposed on the horizontal section of each upper receiving component 412. The telescopic end of the first electrically operated telescopic rod 42 is fixedly connected to a hook 44, which, driven by the first electrically operated telescopic rod 42, can rise or fall to its initial position along a sliding groove within the vertical section of the lower receiving component 411.

[0172] As one embodiment of this disclosure, Figure 17 As shown, the front end of each second electric telescopic rod 43 is fixedly mounted on the lower part of the upper receiving member 412 on the corresponding side by means of a rod head ring 431 and a fixing rod 432.

[0173] The telescopic end of the third electric telescopic rod 46 faces forward (i.e., the direction of movement of the AGV trolley), and a herringbone-shaped fixing block 45 is provided at both the motor end and the telescopic end of the third electric telescopic rod. The converging end of the herringbone-shaped fixing block 45 is fixedly connected to the outer circumferential wall of the same fixed rod 451. The upper part of the telescopic end of the second electric telescopic rod 43 is provided with a snap-fit ​​hole. In the initial state, that is, when the upper supporting component 412 and the lower supporting component 411 are in a state where their vertical sections are attached and their horizontal sections are attached, the fixed rod 451 at the telescopic end of the third electric telescopic rod is snapped into the snap-fit ​​hole at the telescopic end of the second electric telescopic rod 43 from top to bottom.

[0174] The motor end of the second electric telescopic rod 43 is fixed at the starting end (near the rear of the vehicle body) of the corresponding lower receiving component 411's hollowed-out groove 49, and in the initial state (retracted state) of the second electric telescopic rod 43, both the telescopic end and the motor end of the entire second electric telescopic rod 43 are located within the corresponding hollowed-out groove 49.

[0175] When it is necessary to unload the goods inside cargo box 5 or to load goods to be transported and / or lifted into cargo box 5, such as Figure 18 As shown, when the cargo box 5 is transported to other locations by the AGV and then lifted and stacked, it is necessary to control the cargo box tilting mechanism to move the cargo box 5 from... Figure 16 The initial state shown is that cargo box 5 is upright. It is then flipped to... Figure 14In the tilted state shown, when the control reverses, the first electric telescopic rod 42 is activated, which drives the hook 44 to rise from the initial position along the sliding groove, and then the hook 44 moves upward and disengages from the upper rear edge of the cargo box 5. Then, the movement of the first electric telescopic rod 42 is stopped, and the third electric telescopic rod 46 and the second electric telescopic rod 43 are activated. The telescopic end of the third electric telescopic rod 46 drives the fixed rod 451 at its front to move towards the front of the trolley. Since the fixed rod 451 is engaged in the telescopic end of the second electric telescopic rod 43, the second electric telescopic rod 43 is also in the activated state. Therefore, the second electric telescopic rod 43 and the second electric telescopic rod 46 move forward at the same speed. Since the second electric telescopic rod 43 is fixedly installed at the front end of the upper receiving component 412 through the rod head ring 431 and the fixed rod 432, under the parallel forward push of the second electric telescopic rod 43 and the third electric telescopic rod 46 in the horizontal plane, the upper receiving component 412 drives the cargo box 5 it supports to move forward. The entire upper component has not yet rotated at a large angle, but it has already shown a slight tendency to tilt forward.

[0176] In the third stage of flipping the cargo box 5, the forward extension and retraction of the third electric telescopic rod 46 is stopped, while the extension and retraction of the second electric telescopic rod 43 continues. At this time, due to the restriction of the hollow groove 49, the rod head ring 431 at the front end of the second electric telescopic rod 43 and the fixing point of the fixing rod 432 with the upper receiving component 412 serve as the force point, and the upper receiving component 412 and the lower receiving component 411 serve as the rotation fixing point through the connecting shaft 410. The extension and retraction of the second electric telescopic rod 43 gives the force point a forward and upward lifting force, forming a force couple, which in turn drives the upper receiving component 412 and the cargo box 5 on it to rotate along the direction of the rotation fixing point towards the lower receiving component 411, so that the vertical and horizontal sections of the upper receiving component 412 separate from the vertical and horizontal sections of the lower receiving component 411, respectively, forming Figure 14 As shown in the flipped state, it completes a 90° tilt.

[0177] When it is necessary to lift the cargo box 5, several lifting shafts installed inside the elevator housing 3 drive the transport box receiving component 4, gradually lifting the cargo box 5 to its designated position. Figure 19 The state shown.

[0178] It should be clarified that the mass m of the cargo carried in the method provided in this disclosure includes the net weight of the cargo and the mass of the cargo box 5, while the mass M of the trolley itself is only [missing information]. Figure 16 The left side shows the mass of the trolley excluding cargo box 5.

[0179] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction, which contains one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0180] The various embodiments of this disclosure have been described above. These descriptions are exemplary and not exhaustive, and are not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical applications, or technical improvements to the technology in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A control method for an intelligent logistics transportation and palletizing AGV trolley, wherein the trolley is an X-type Mecanum wheel AGV trolley, characterized in that, Includes the following steps: S1: Obtain information on whether the AGV is empty. If it is carrying goods, obtain the real-time quality of the goods. S2: Real-time monitoring of the center of gravity position and heading angle of the AGV carrying goods in the world coordinate system, as well as the center of gravity position and offset angle of the goods in the vehicle coordinate system, and constructing the real-time acceleration matrix of the center of gravity of the goods carried on the AGV in the vehicle coordinate system. S3: Based on the real-time acceleration matrix of the cargo's center of gravity constructed in step S2, calculate the overall three-dimensional driving force required by the AGV carrying the cargo. S4: Based on the rotational speed of each Mecanum wheel and the rotational speed of the roller obtained from real-time monitoring, construct the calculation matrix of the sliding speed of each wheel in the vehicle coordinate system when the AGV is carrying goods and moving and lifting goods in real time; S5: Based on the calculation results of steps S3-S4, further calculate the real-time driving force required for each wheel of the AGV trolley carrying the goods; S6: Further based on the state variable feedback method, the optimal real-time driving force of each wheel of the AGV carrying goods is solved, and finally the optimal real-time rotation speed of each wheel of the AGV carrying goods is obtained. The S4 step includes: Based on the positional relationship of each Mecanum wheel relative to the vehicle body coordinate system, a wheel roller transformation coefficient matrix is ​​constructed to describe the mapping between the linear velocity of the AGV and the local velocity of the wheel. Obtain the velocity component coefficient matrix block in the slip direction and construct the driving mapping matrix block; Based on the wheel rotation speed, roller rotation speed, and the linear velocity of the overall center of gravity of the trolley carrying goods in the world coordinate system, a calculation matrix for the sliding velocity of each Mecanum wheel in the vehicle coordinate system is constructed to describe the linear mapping relationship between the sliding velocity of each Mecanum wheel and the system state.

2. The method as described in claim 1, characterized in that, Step S2 includes: Based on the real-time center of gravity position and attitude angle of the AGV carrying goods in the world coordinate system, a rotation transformation matrix is ​​constructed and the rigid body transformation relationship is obtained, thereby calculating the center of gravity position of the AGV as a whole in the vehicle coordinate system; further, combined with the offset angle and relative distance of the center of gravity position of the goods, a real-time acceleration matrix of the center of gravity of the goods carried on the AGV is constructed.

3. The method as described in claim 2, characterized in that, The S3 step, which calculates the overall three-dimensional driving force required for the AGV carrying the goods, includes: Considering the overall mass and motion state of the AGV and the cargo, and combining the vertical acceleration component of the cargo in the vehicle coordinate system, the angular velocity and angular acceleration of the cargo offset, and the additional upward inertia term generated by the overall rotational motion of the AGV carrying the cargo, the three-dimensional driving force required by the AGV carrying the cargo in the vehicle coordinate system is calculated, which is used for subsequent tire load distribution and dynamic control.

4. The method according to claim 1, characterized in that, The S5 step includes the following steps: Based on the acceleration of the center of mass of the cargo carried by the AGV in the vehicle coordinate system and the cargo offset angle, calculate the lateral and vertical load transfer of each Mecanum wheel; Based on the lateral and vertical load transfer amounts, and integrating the overall vertical driving force required by the AGV trolley carrying goods, the total vertical load of each wheel is calculated. The real-time driving force of the four wheels is calculated by combining the overall longitudinal and lateral driving force required by the AGV and the slip speed of the Mecanum wheel, along with the friction coefficient of each wheel.

5. The method according to claim 4, characterized in that, The state variable feedback method in step S6 includes the following steps: Construct a state-space model that includes the vehicle's motion state and the cargo's posture; Design and construct a state feedback optimization objective function, taking into account both state error and control input; The state value weight matrix in the state feedback optimization objective function is improved by introducing a spatial variation metric consistency coefficient, and the input control value weight matrix in the state feedback optimization objective function is improved by introducing an input distribution coupling coefficient. Set a termination threshold condition and iteratively solve for the optimal control input; Further calculations yielded the optimal real-time rotational speed of each wheel of the AGV carrying cargo.

6. A control system for an intelligent logistics transport palletizing AGV that executes the control method described in any one of claims 1-5, characterized in that, The control system includes: The empty and loaded detection module is used to obtain information on whether the AGV is empty and, if it is carrying goods, the real-time weight of the goods. An IMU (Inertial Measurement Unit) is used to acquire the attitude and angular velocity of the entire cargo-carrying vehicle in the world coordinate system in real time. The real-time dynamic differential positioning module is used to provide the real-time center of gravity position and heading angle of the AGV carrying goods in the world coordinate system. ; The cargo offset angle detection unit is used to obtain the real-time center of gravity position and offset angle of the cargo in the vehicle coordinate system; the encoder is used to obtain the rotational angular velocity of each Mecanum wheel and the rotational angular velocity of each Mecanum wheel roller. The cargo center acceleration matrix construction module is used to construct the real-time acceleration matrix of the cargo carried on the AGV vehicle; The three-dimensional driving force calculation module is used to calculate the overall three-dimensional driving force required by the AGV trolley carrying cargo based on the data from the empty and loaded detection module, the IMU inertial measurement unit, the real-time dynamic differential positioning module, the cargo offset angle monitoring unit, and the cargo center acceleration matrix construction module. The sliding speed calculation module is used to construct the sliding speed calculation matrix of each wheel of the AGV in the vehicle coordinate system when the AGV is carrying goods and moving and lifting goods in real time; The real-time driving force calculation module is used to further calculate the required real-time driving force for each wheel of the AGV trolley carrying goods, based on the calculation results of the three-dimensional driving force calculation module and the slip velocity calculation module. The state feedback optimization solution module is used to solve for the optimal real-time driving force of each wheel of the AGV carrying goods based on the state variable feedback method, and finally obtain the optimal real-time rotational speed of each wheel of the AGV carrying goods.

7. An intelligent logistics transport palletizing AGV trolley, the AGV trolley comprising a base (1) and four Mecanum wheels (2) mounted on the base (1) in an X-shape, characterized in that, The AGV also includes the control system as described in claim 6, an elevator housing (3) located above the rear end of the vehicle base, and a transport box bearing component (4). The elevator housing (3) is provided with several lifting shafts for lifting the cargo inside the cargo box (5) fixed by the transport box bearing component (4); The transport box carrying component (4) is provided with a cargo box flipping mechanism for flipping the cargo box (5).

8. The intelligent logistics transportation and palletizing AGV trolley according to claim 7, characterized in that, The transport box carrying component (4) includes two sub-supporting components (41) that are both L-shaped. Each sub-supporting component (41) includes a lower supporting component (411) and an upper supporting component (412) that are both L-shaped. The lower supporting component (411) and the upper supporting component (412) are fixedly connected by a connecting shaft (410) provided at the front end of the lower supporting component (411). An inner hook (44) is provided on the upper part of the lower supporting component (411). The cargo box flipping mechanism includes a first electric telescopic rod (42) provided on the vertical section of each upper supporting component (412), a second electric telescopic rod (43) provided on the horizontal section of each lower supporting component (411), and a third electric telescopic rod (46) provided on the horizontal section of each upper supporting component (412).

9. The intelligent logistics transportation palletizing AGV trolley according to claim 8, characterized in that, The front end of each of the second electric telescopic poles (43) is fixedly mounted on the lower part of the upper receiving component (412) on the corresponding side by means of a pole head ring (431) and a fixing rod (432).

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