Anode transport unmanned heavy load agv

Abstract

The application discloses an anode transfer unmanned heavy-load AGV, which comprises a left vehicle body and a right vehicle body, the left vehicle body and the right vehicle body are connected through a vehicle frame, the right side of the left vehicle body and the left side of the right vehicle body are symmetrically provided with a left material bracket and a right material bracket which are used for bearing an anode tray and are used in cooperation, a left single-line laser radar and a right single-line laser radar for collecting anode tray position data are arranged on the left vehicle body and the right vehicle body respectively, and the left single-line laser radar and the right single-line laser radar are connected with an AGV controller. Through the double single-line laser radars, environment and target recognition are realized, and the AGV controller is used to realize the whole unmanned operation from path planning, tray positioning to automatic docking, carrying and unloading, so that the overall efficiency of anode transfer is greatly improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of AGV application, in particular to an anode transfer unmanned heavy-load AGV. BACKGROUND

[0002] An anode refers to an anode material for aluminum smelting, which is generally a carbon paste or carbon block made of petroleum coke, pitch coke, etc. as aggregate and coal tar pitch as binder, and is mainly used as an anode for aluminum electrolysis cells. Currently, the anode transfer work in the electrolytic aluminum industry is mainly completed by manual work, and the working environment is high temperature and strong magnetic environment. The working environment is poor, and the entire operation requires 24 hours without stop, with great working intensity, which seriously affects the health of the operating personnel.

[0003] AGV is an industrial vehicle that loads goods by automatic or manual method, automatically travels according to the set route, drags the loading platform truck to the designated place, and then loads and unloads goods by automatic or manual method. With the gradual popularization of AGV application, how to apply AGV to the electrolytic aluminum industry to realize the automatic transfer of anodes has become an urgent technical problem to be solved. SUMMARY

[0004] In view of the technical problems pointed out in the above background art, the purpose of the present application is to provide an anode transfer unmanned heavy-load AGV.

[0005] To achieve the above purpose, the technical solution provided by the present application is as follows:

[0006] An anode transfer unmanned heavy-load AGV, comprising a left vehicle body and a right vehicle body, the left vehicle body and the right vehicle body are connected through a vehicle frame, the right side of the left vehicle body and the left side of the right vehicle body are symmetrically provided with left and right material carriers for carrying anode trays, the left and right vehicle bodies are respectively provided with left and right single-line laser radars for collecting anode tray position data, and the left and right single-line laser radars are connected with an AGV controller.

[0007] Compared with the prior art, the present application has the following advantages:

[0008] Through the environment and target recognition of the double single-line laser radars, the AGV controller is used to realize the whole process of unmanned operation from path planning, tray positioning to automatic docking, carrying and unloading. The workers can be liberated from the harsh working environment of high temperature, strong magnetism and high dust, and the operation safety and personnel health level can be significantly improved. The automatic operation mode eliminates the efficiency bottleneck caused by human factors, and can realize 24-hour uninterrupted operation, greatly improving the overall efficiency of anode transfer. BRIEF DESCRIPTION OF DRAWINGS

[0009] Figure 1This is a first schematic diagram of the overall structure of the unmanned heavy-duty AGV for anode transfer provided in an embodiment of this application;

[0010] In the diagram, the left vehicle body is 1, the right vehicle body is 2, the frame is 3, the left single-line lidar is 4, the left material support is 5, the right material support is 6, and the right single-line lidar is 7.

[0011] Figure 2 This is a second schematic diagram of the overall structure of the unmanned heavy-duty AGV for anode transfer provided in an embodiment of this application;

[0012] Figure 3 This is a third schematic diagram of the overall structure of the unmanned heavy-duty AGV for anode transfer provided in an embodiment of this application;

[0013] In the diagram, the left front drive wheel assembly is 8, the right front drive wheel assembly is 9, the left rear drive wheel assembly is 10, and the right rear drive wheel assembly is 11.

[0014] Figure 4 This is a fourth schematic diagram of the overall structure of the unmanned heavy-duty AGV for anode transfer provided in the embodiments of this application;

[0015] Figure 5 The fifth schematic diagram of the overall structure of the unmanned heavy-duty AGV for anode transfer provided in the embodiments of this application;

[0016] In the diagram, the first limit switch is 12.

[0017] Figure 6 This is a schematic diagram of the patrol navigation in this application;

[0018] In the diagram, the center line of the car is 13. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this 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. Therefore, they should not be construed as limitations on this invention.

[0021] In the description of this patent, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "setting" should be interpreted broadly. For example, they can refer to a fixed connection or setting, a detachable connection or setting, or an integral connection or setting; they can refer to a direct connection or an indirect connection. Those skilled in the art can understand the specific meaning of the above terms in this patent according to the specific circumstances.

[0022] like Figures 1-6 The diagram shows a structural embodiment of the present invention.

[0023] This embodiment provides an unmanned heavy-duty AGV for anode transfer, including a left vehicle body 1 and a right vehicle body 2. The left vehicle body 1 and the right vehicle body 2 are connected by a frame 3. The right side of the left vehicle body 1 and the left side of the right vehicle body 2 are symmetrically arranged with a left material bracket 5 and a right material bracket 6 for carrying anode trays and cooperating with each other. The left vehicle body 1 and the right vehicle body 2 are respectively equipped with a left single-line lidar 4 and a right single-line lidar 7 for collecting anode tray position data. The left single-line lidar 4 and the right single-line lidar 7 are connected to the AGV controller.

[0024] Preferably, the lower end of the left vehicle body 1 is provided with a left front drive wheel set 8 and a left rear drive wheel set 10 capable of moving forward, backward, lateral, and lifting, and the lower end of the right vehicle body 2 is provided with a right front drive wheel set 9 and a right rear drive wheel set 11 capable of moving forward, backward, lateral, and lifting.

[0025] The AGV controller is connected to the left front drive wheel group 8 and the left rear drive wheel group 10, the right front drive wheel group 9 and the right rear drive wheel group 11.

[0026] Preferably, a first limit switch 12 for detecting the position of the anode tray is provided on the side of the frame 3 near the left material tray 5, and a second limit switch for detecting the position of the anode tray is provided on the side of the frame 3 near the right material tray 6.

[0027] Preferably, when the AGV receives a transport task, it automatically parses the transport task route and autonomously travels to the designated area; specifically, it includes the following steps:

[0028] Step 11: The AGV enters a waiting state, listens to the task queue, and waits for a task to be triggered;

[0029] If there is no task, continue listening;

[0030] If there are new tasks, proceed to the next step;

[0031] Step 12: A new task has been detected, and the system begins to execute the task automatically.

[0032] The controller queries the location of the anode tray;

[0033] Based on the current map data and station information, the MPC algorithm is used to realize the dynamic planning of the automatic driving route and the automatic control of the AGV.

[0034] Control the left front drive wheel group 8 and the left rear drive wheel group 10, the right front drive wheel group 9 and the right rear drive wheel group 11 to drive the AGV to move towards the initial identification position of the pallet.

[0035] Step 13: The AGV moves to the initial pallet recognition position, which is the initial coarse positioning area to facilitate the subsequent recognition operation;

[0036] Step 14: Call the radar system consisting of the left single-line lidar 4 and the right single-line lidar 7 to identify and align the anode tray;

[0037] Use the left single-line lidar 4 and the right single-line lidar 7 to acquire the fused point cloud data of the anode tray contour;

[0038] Step 15: Filter, reduce noise, and extract features from the collected point cloud data to accurately locate the edge of the anode tray;

[0039] Obtain the center position and attitude angle data of the anode tray;

[0040] Calculate the deviation between the actual position and the expected position;

[0041] Determine if the docking conditions are met; if the docking conditions are met, execute the docking action.

[0042] Step 16: Fine-tune the position and angle of the AGV to ensure that the left material carrier 5 and the right material carrier 6 can be accurately inserted into the pallet; the AGV moves laterally when moving towards the pallet.

[0043] According to the instructions of the AGV controller, the docking operation is performed; after the left material tray 5 and the right material tray 6 can be accurately inserted into the anode tray, the left front drive wheel group 8 and the left rear drive wheel group 10, the right front drive wheel group 9 and the right rear drive wheel group 11 are lifted to lift the anode tray off the ground.

[0044] Step 17: The system checks whether the anode tray has been successfully picked up.

[0045] Confirm that the anode tray has been lifted stably;

[0046] If it fails, undo or realign and try again;

[0047] If successful, proceed to the next stage;

[0048] Step 18: The anode tray is stably supported.

[0049] Record the time and location of anode tray pickup;

[0050] Step 19: The AGV uses the MPC algorithm to realize dynamic planning of the automatic driving route and automatic control of the AGV;

[0051] Control the left front drive wheel group 8 and the left rear drive wheel group 10, the right front drive wheel group 9 and the right rear drive wheel group 11 to drive the AGV to the target position;

[0052] Step 110: After reaching the target position, the left front drive wheel group 8 and the left rear drive wheel group 10, the right front drive wheel group 9 and the right rear drive wheel group 11 descend, thereby causing the left material support 5 and the right material support 6 to descend and put down the anode tray.

[0053] Record the time and position of the anode tray being lowered;

[0054] Step 111: Send a task completion signal to the scheduling system;

[0055] Proceed to step 11.

[0056] Preferably, the Model Predictive Control (MPC) algorithm is used to realize the dynamic planning of the automatic driving route and the automatic control of the AGV. Specifically, a series of key points indicating important locations that the vehicle should pass through, issued by the scheduling system, are used as control points. A continuous and smooth curved reference path is generated using a cubic spline interpolation algorithm, which is used for the AGV to accurately track the vehicle during the local planning and control stages.

[0057] Preferably, the cubic spline interpolation algorithm specifically includes the following:

[0058] The key point is Cubic splines in each adjacent interval Construct a cubic polynomial above:

[0059]

[0060] in, Indicates the interval A cubic spline function defined within the interval, where x is the independent variable within that interval. Let x be the x-coordinate of the i-th interpolation node. The coefficient for each curve segment;

[0061] Construct a constraint system that includes the following constraints:

[0062] 1. Interpolation constraint: The curve is continuous at all key points;

[0063] 2. Smoothness constraint: The first derivative-velocity and the second derivative-acceleration are continuous in adjacent segments;

[0064] 3. Natural boundary or fixed derivative: boundary conditions;

[0065] By constructing the above constraint system, the coefficients of each curve segment can be uniquely determined. This process generates a path that is continuous in position, velocity, and acceleration, which is used as a reference path for the AGV.

[0066] The reference path is used as the target trajectory input in MPC. Based on this, MPC designs a cost function that not only considers the lateral and longitudinal errors between the actual position of the AGV and the reference path, but also introduces a heading angle deviation constraint to ensure that the vehicle attitude is consistent with the reference path. At the same time, the cost function further includes a penalty for the rate of change of control input, specifically including the rate of change of steering angle and the rate of change of acceleration.

[0067] MPC transforms the environmental state and system prediction model into a constrained optimization problem in each control cycle, solves for the optimal future control sequence, executes only the first control variable, and then re-optimizes using the updated state in the next cycle.

[0068] Preferably, when the AGV approaches the anode tray, the AGV's movement path is adjusted in real time according to the edge-following navigation strategy to ensure alignment between the AGV and the anode tray; specifically, this includes the following steps:

[0069] Step 21: Subscribe to the raw data of the single-line LiDAR, convert the polar coordinate scan points to Cartesian coordinates, filter out invalid values, and perform point cloud cropping based on ROI;

[0070] Step 22: Use the k-means algorithm to perform incremental clustering of point clouds based on distance thresholds to filter small-scale point clusters; use the sliding window curvature algorithm to detect inflection points: calculate the angle between adjacent vectors, mark it as a corner point when it exceeds the threshold, and divide the point cluster into independent line segments at the inflection point to lay the foundation for geometric feature extraction;

[0071] Step 23: Perform PCA principal component analysis on the segmented line segments to calculate the direction angle and length. Filter the effective line segments by angle range and minimum length, verify the right angle relationship between adjacent line segments, and finally output the center coordinates and angle deviation of the line segments.

[0072] Step 24: The AGV adjusts its movement direction in real time according to its relative position to the edge, and maintains a safe distance in combination with the edge-following navigation strategy to achieve precise obstacle avoidance along the edge of the anode tray.

[0073] Preferably, in step 4, the edge-following navigation strategy is as follows:

[0074] like Figure 6As shown, two guide lines are fitted using an edge detection algorithm, one on the left and one on the right. The edge-following navigation strategy uses the position of the guide lines and their offset from the vehicle center. With offset And the angle of deviation of the guide line relative to the center line 13 of the vehicle. As input, calculate the overall offset:

[0075] ;

[0076] In the formula: L is the width of the side of the anode tray;

[0077] This refers to the deviation of the far end of the anode tray relative to the centerline of the vehicle.

[0078] This refers to the deviation of the anode tray near the vehicle end relative to the vehicle centerline;

[0079] Dynamically adjust wheel speed difference using PID control The difference is calculated using the following formula:

[0080] ;

[0081] In the formula, The deviation of the near or far end of the vehicle relative to the vehicle centerline; For control parameters;

[0082] when When the value is greater than 0, the anode tray is relative to the direction closer to the front of the vehicle. The two driving angles that are far away from the tray when the AGV moves laterally are controlled. The deviation angle is input into the angle control algorithm to obtain the control target angle, so as to control the adjustment of the AGV's far end towards the rear of the vehicle.

[0083] when When <0, the anode tray is relative to the direction closer to the rear of the vehicle. The two driving angles that are far away from the tray when the AGV moves laterally are controlled. The deviation angle is input into the angle control algorithm to obtain the control target angle, so as to control the AGV to adjust the far end towards the front of the vehicle.

[0084] when When the value is greater than 0, the anode tray is relative to the direction closer to the front of the vehicle. The two driving angles that are close to the tray when the AGV moves laterally are controlled. The deviation angle is input into the angle control algorithm to obtain the control target angle, so as to control the adjustment of the AGV's near end towards the rear of the vehicle.

[0085] when When the value is less than 0, the anode tray is positioned relative to the direction near the rear of the vehicle. This corresponds to the two drive angles near the tray when the AGV moves laterally. The deviation angle is input into the angle control algorithm to obtain the target control angle, thereby controlling the AGV's near-vehicle end to adjust towards the front of the vehicle.

[0086] Preferably, in step 24, after the edge inspection and identification, the AGV's driving trajectory is adjusted by multi-drive differential cooperative control technology;

[0087] Each drive wheel of the AGV is equipped with an independent motor control system. All four drive wheel sets are drive steering wheels with independent driving and steering capabilities. By acquiring environmental information in real time, the AGV coordinates and controls each drive motor. It adopts a dynamic model-based control algorithm - multi-wheel steering instantaneous center control algorithm - to dynamically adjust the speed and steering angle of each drive motor.

[0088] Preferably, the multi-wheel steering instantaneous center control algorithm is specifically as follows:

[0089] The position of the j-th steering wheel in the vehicle coordinate system is P_j = (x_j, y_j);

[0090] The steering angle of the j-th steering wheel is θ_j;

[0091] The direction the wheel is facing is ;

[0092] Normal direction - perpendicular to the wheel direction is ;

[0093] Step 31: Calculate the current instantaneous center of gravity based on the current wheel direction; use the least squares method to find the intersection point in the sense of least square error;

[0094] The normal originates from point (x_j, y_j) and its direction is... Its linear equation is: ;

[0095] in, ;

[0096] The final instantaneous center position is ;

[0097] in, Let x and y be the coordinates of the instantaneous center;

[0098] Step 32: Calculate the equivalent front axle rotation angle at the corresponding positions of the centers of the left front drive wheel assembly 8 and the right front drive wheel assembly 9, and the equivalent rear axle rotation angle at the corresponding positions of the centers of the left rear drive wheel assembly 10 and the right rear drive wheel assembly 11, using the instantaneous center of rotation. and ;

[0099] ;

[0100] in, The coordinates of the front axle center position are: The coordinates are the center position of the rear axle;

[0101] Step 33: Calculate and input the corresponding front axle center angle and rear axle center angle control values;

[0102] Step 34: Calculate the instantaneous center of the target using the control variables;

[0103] Step 35: Calculate the target angles of all wheels using the instantaneous center of gravity.

[0104] It should be noted that this invention uses a combination of left and right dual single-line lidar and limit switches, supplemented by point cloud filtering, feature extraction and other algorithms, to achieve centimeter-level precise positioning of the anode tray; the original edge-following navigation strategy and PID control can fine-tune the AGV posture in real time during docking, and can accurately align even if there is a slight offset of the tray, ensuring that the material tray is successfully inserted in one go.

[0105] Each wheel set can be driven and steered independently. Combined with the "multi-wheel steering instantaneous center control algorithm", it can realize a variety of complex motion modes such as lateral movement, diagonal movement, and turning on the spot. In narrow or complex workshops, it can also plan the optimal path and flexibly avoid obstacles, which greatly improves the utilization rate of the space.

[0106] The system employs MPC and cubic spline algorithms: It uses an advanced model predictive control (MPC) algorithm combined with cubic spline interpolation to generate a smooth path; this not only ensures the optimal driving path and smooth motion, but also allows for dynamic adjustment based on real-time status in each control cycle, exhibiting excellent anti-interference capabilities and robustness.

[0107] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A driverless heavy-duty AGV for anode transfer, characterized in that, The vehicle includes a left vehicle body (1) and a right vehicle body (2), which are connected by a frame (3). The left vehicle body (1) and the right vehicle body (2) are symmetrically provided with a left material bracket (5) and a right material bracket (6) for carrying anode trays and for cooperating with each other. The left vehicle body (1) and the right vehicle body (2) are respectively provided with a left single-line laser radar (4) and a right single-line laser radar (7) for collecting anode tray position data. The left single-line laser radar (4) and the right single-line laser radar (7) are connected to the AGV controller. The frame (3) is connected to one side of the left vehicle body (1) and the right vehicle body (2). The lower end of the left vehicle body (1) is provided with a left front drive wheel set (8) and a left rear drive wheel set (10) capable of moving forward, backward, lateral, and lifting. The lower end of the right vehicle body (2) is provided with a right front drive wheel set (9) and a right rear drive wheel set (11) capable of moving forward, backward, lateral, and lifting. The AGV controller is connected to the left front drive wheel group (8) and the left rear drive wheel group (10), the right front drive wheel group (9) and the right rear drive wheel group (11) for control. When the AGV receives a transport task, it automatically parses the transport task route and autonomously travels to the designated area; specifically, it includes the following steps: Step 11: The AGV enters a waiting state, listens to the task queue, and waits for a task to be triggered; If there is no task, continue listening; If there are new tasks, proceed to the next step; Step 12: A new task has been detected, and the system begins to execute the task automatically. The controller queries the location of the anode tray; Based on the current map data and station information, the MPC algorithm is used to realize the dynamic planning of the automatic driving route and the automatic control of the AGV. Control the left front drive wheel group (8) and the left rear drive wheel group (10), the right front drive wheel group (9) and the right rear drive wheel group (11) to drive the AGV to move towards the initial identification position of the pallet; Step 13: The AGV moves to the initial pallet recognition position, which is the initial coarse positioning area to facilitate the subsequent recognition operation; Step 14: Call the radar system composed of the left single-line lidar (4) and the right single-line lidar (7) to identify and align the anode tray; The left single-line lidar (4) and the right single-line lidar (7) are used to obtain the fused point cloud data of the anode tray contour; Step 15: Filter, reduce noise, and extract features from the collected point cloud data to accurately locate the edge of the anode tray; Obtain the center position and attitude angle data of the anode tray; Calculate the deviation between the actual position and the expected position; Determine if the docking conditions are met; if the docking conditions are met, execute the docking action. Step 16: Fine-tune the position and angle of the AGV to ensure that the left material tray (5) and the right material tray (6) can be accurately inserted into the pallet; the AGV moves laterally when moving towards the pallet; The docking operation is performed according to the instructions of the AGV controller; after the left material tray (5) and the right material tray (6) can be accurately inserted into the anode tray, the left front drive wheel group (8) and the left rear drive wheel group (10), the right front drive wheel group (9) and the right rear drive wheel group (11) are lifted to lift the anode tray off the ground; Step 17: The system checks whether the anode tray has been successfully picked up. Confirm that the anode tray has been lifted stably; If it fails, undo or realign and try again; If successful, proceed to the next stage; Step 18: The anode tray is stably supported. Record the time and location of anode tray pickup; Step 19: The AGV uses the MPC algorithm to realize dynamic planning of the automatic driving route and automatic control of the AGV; Control the left front drive wheel group (8) and the left rear drive wheel group (10), the right front drive wheel group (9) and the right rear drive wheel group (11) to drive the AGV to the target position; Step 110: After reaching the target position, the left front drive wheel assembly (8) and the left rear drive wheel assembly (10), the right front drive wheel assembly (9) and the right rear drive wheel assembly (11) descend, thereby causing the left material support (5) and the right material support (6) to descend and put down the anode tray; Record the time and position of the anode tray being lowered; Step 111: Send a task completion signal to the scheduling system; Perform step 11; The Model Predictive Control (MPC) algorithm is used to realize the dynamic planning of the automated driving route and the automatic control of the AGV. Specifically, a series of key points indicating important locations that the vehicle should pass through, issued by the scheduling system, are used as control points. A continuous and smooth curved reference path is generated using a cubic spline interpolation algorithm, which is used for the AGV to accurately track the vehicle during the local planning and control stages. The cubic spline interpolation algorithm specifically includes the following: The key point is ; cubic splines in each adjacent interval Construct a cubic polynomial above: ； in, Indicates the interval A cubic spline function defined within the interval, where x is the independent variable within that interval. Let x be the x-coordinate of the i-th interpolation node. The coefficient for each curve segment; Construct a constraint system that includes the following constraints: (1) Interpolation constraint: The curve is continuous at all key points; (2) Smoothness constraint: The first derivative-velocity and the second derivative-acceleration are continuous in adjacent segments; (3) Natural boundary or fixed derivative: boundary conditions; By constructing the above constraint system, the coefficients of each curve segment can be uniquely determined. Generate a path that is continuous in position, velocity, and acceleration, which will be used as a reference path for the AGV. The reference path is used as the target trajectory input in MPC. Based on this, MPC designs a cost function that not only considers the lateral and longitudinal errors between the actual position of the AGV and the reference path, but also introduces a heading angle deviation constraint to ensure that the vehicle attitude is consistent with the reference path. At the same time, the cost function further includes a penalty for the rate of change of control input, specifically including the rate of change of steering angle and the rate of change of acceleration. MPC transforms the environmental state and system prediction model into a constrained optimization problem in each control cycle, solves for the optimal future control sequence, executes only the first control variable, and then re-optimizes using the updated state in the next cycle.

2. The unmanned heavy-duty AGV for anode transfer according to claim 1, characterized in that, A first limit switch (12) for detecting the position of the anode tray is provided on the side of the frame (3) near the left material tray (5), and a second limit switch for detecting the position of the anode tray is provided on the side of the frame (3) near the right material tray (6).

3. The unmanned heavy-duty AGV for anode transfer according to claim 1, characterized in that, When the AGV approaches the anode tray, its movement path is adjusted in real time according to the edge-following navigation strategy to ensure alignment between the AGV and the anode tray; specifically, the following steps are included: Step 21: Subscribe to the raw data of the single-line LiDAR, convert the polar coordinate scan points to Cartesian coordinates, filter out invalid values, and perform point cloud cropping based on ROI; Step 22: Use the k-means algorithm to perform incremental clustering of point clouds based on a distance threshold, filtering out small clusters. Modular point clusters; Inflection points are detected using a sliding window curvature algorithm: the angle between adjacent vectors is calculated, and points exceeding a threshold are marked as corner points. At the inflection points, the point clusters are segmented into independent line segments, laying the foundation for geometric feature extraction. Step 23: Perform PCA principal component analysis on the segmented line segments to calculate the direction angle and length. Filter the effective line segments by angle range and minimum length, verify the right angle relationship between adjacent line segments, and finally output the center coordinates and angle deviation of the line segments. Step 24: The AGV adjusts its movement direction in real time according to its relative position to the edge, and maintains a safe distance in combination with the edge-following navigation strategy to achieve precise obstacle avoidance along the edge of the anode tray.

4. The unmanned heavy-duty AGV for anode transfer according to claim 3, characterized in that, In step 4, the edge-following navigation strategy is as follows: Two guide lines are fitted using an edge detection algorithm. The edge-following navigation strategy uses the position of the guide lines and their offset from the vehicle center. With offset And the angle of deviation of the guide line relative to the center line of the car. As input, calculate the overall offset: ； ； In the formula: L is the width of the side of the anode tray; This refers to the deviation of the far end of the anode tray relative to the centerline of the vehicle. This refers to the deviation of the anode tray near the vehicle end relative to the vehicle centerline; Dynamically adjust wheel speed difference using PID control The formula is as follows: ； In the formula, The deviation of the near or far end of the vehicle relative to the vehicle centerline; For control parameters; when When the value is > 0, the anode tray is relative to the direction closer to the front of the vehicle. The two driving angles that are far away from the tray when the AGV moves laterally are controlled. The deviation angle is input into the angle control algorithm to obtain the control target angle, so as to control the adjustment of the far end of the AGV towards the rear of the vehicle. when When < 0, the anode tray is relative to the direction closer to the rear of the vehicle. The two driving angles that are far away from the tray when the AGV moves laterally are controlled. The deviation angle is input into the angle control algorithm to obtain the control target angle, so as to control the AGV to adjust the far end towards the front of the vehicle. when When the value is > 0, the anode tray is relative to the direction closer to the front of the vehicle. This corresponds to the two drive angles that are close to the tray when the AGV moves laterally. The deviation angle is input into the angle control algorithm to obtain the target control angle, thereby controlling the AGV to adjust from the near end to the rear of the vehicle. when When the value is less than 0, the anode tray is positioned relative to the direction closer to the rear of the vehicle. This corresponds to the two drive angles that control the AGV to move laterally towards the tray. The deviation angle is input into the angle control algorithm to obtain the target control angle, thereby controlling the AGV's near-vehicle end to adjust towards the front of the vehicle.

5. The unmanned heavy-duty AGV for anode transfer according to claim 4, characterized in that, In step 24, after the edge inspection and identification, the AGV's driving trajectory is adjusted through multi-drive differential cooperative control technology; Each drive wheel of the AGV is equipped with an independent motor control system. All four drive wheel sets are drive steering wheels with independent driving and steering capabilities. By acquiring environmental information in real time, the AGV coordinates and controls each drive motor. It adopts a dynamic model-based control algorithm - multi-wheel steering instantaneous center control algorithm - to dynamically adjust the speed and steering angle of each drive motor.

6. The unmanned heavy-duty AGV for anode transfer according to claim 5, characterized in that, The multi-wheel steering instantaneous center of gravity control algorithm is as follows: The position of the j-th steering wheel in the vehicle coordinate system is P_j = (x_j, y_j); The steering angle of the j-th steering wheel is θ_j; The direction the wheel is facing is ; Normal direction - perpendicular to the wheel direction is ; Step 31: Calculate the current instantaneous center of gravity based on the current wheel direction; use the least squares method to find the intersection point in the sense of least square error; The normal originates from point (x_j, y_j) and its direction is... Its linear equation is: ; in, ; The final instantaneous center position is ; in, Let x and y be the coordinates of the instantaneous center; Step 32: Calculate the equivalent front axle rotation angle at the corresponding positions of the centers of the left front drive wheel set (8) and the right front drive wheel set (9) and the equivalent rear axle rotation angle at the corresponding positions of the centers of the left rear drive wheel set (10) and the right rear drive wheel set (11) using the instantaneous center. and ; ； ； in, The coordinates of the front axle center position are: The coordinates are the center position of the rear axle; Step 33: Calculate and input the corresponding front axle center angle and rear axle center angle control values; Step 34: Calculate the instantaneous center of the target using the control variables; Step 35: Calculate the target angles of all wheels using the instantaneous center of gravity.

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