Time-space coupled AGV action coordinated control method and apparatus, and medium

Abstract

A time-space coupled AGV action coordinated control method and apparatus, and a medium. The method comprises: classifying servo actions on the basis of parameter features of an AGV, and planning a global path of the AGV on the basis of task demands (S100); presetting the total number of required actions as n, and planning the time required for a single action and the total time required for executing the servo actions (S200); dividing the global path into n+1 nodes, and coupling each action to each node (S300); acquiring a local trajectory equation of the AGV (S400); and performing a state check (S500). The space-time coupled AGV action coordinated control method solves the problem of low task execution efficiency due to stopping and waiting of AGVs caused by separate vehicle movements and transfer actions.

Description

A spatiotemporal coupling AGV action cooperative control method and device and medium TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent control, more particularly, to a spatiotemporal coupling AGV action cooperative control method and device and medium. BACKGROUND

[0002] Under the background of intelligence and automatic driving, AGV (Automated Guided Vehicle) has been increasingly applied to various fields. In traditional research and application, in order to simplify the complexity of AGV task action, the moving action and the moving of AGV are often controlled and researched separately. This makes the application of AGV simpler and more universal.

[0003] In the traditional technology, the walking and servo action of AGV are generally separated by presetting a front point. Before AGV completes the pickup and leaves the pickup front point to the unloading front point of AGV at the unloading warehouse, the AGV is treated as an automatic driving in this section of the road, only considering the walking of AGV without considering the servo action of AGV. When AGV reaches the unloading front point, AGV is taken over by the servo action module, and the walking only considers the simple low-speed forward and backward condition. In this scheme, AGV often needs to stop and wait for the completion of the servo action when executing the task, which increases the task execution time of AGV. If the waiting time is too long, it will directly lead to the decrease of the task completion efficiency of AGV. In addition, stopping and waiting will also lead to conflicts or delays in the scheduling of AGV, for example, an AGV may block the path of other AGVs when waiting, thereby affecting the working efficiency of the entire material transportation system.

[0004] In summary, it is of great significance to study the AGV action cooperative control. SUMMARY

[0005] An object of the present application is to provide a spatiotemporal coupling AGV action cooperative control method and device and medium to solve the technical problem of stopping and waiting and low task execution efficiency caused by separating the vehicle movement and the moving action of AGV.

[0006] According to a first aspect of the present application, a spatiotemporal coupling AGV action cooperative control method is provided, comprising the following steps:

[0007] Step S100: obtaining the parameter characteristics of AGV, classifying the servo action of AGV based on the parameter characteristics of AGV, establishing an AGV action set, and planning a global path of AGV according to task requirements;

[0008] Step S200: preset the actions in the single task in the task requirement, preset the required total number of actions as n, and plan the time required for a single action and the total time T required for performing the servo action in the task process a ;

[0009] Step S300: according to the preset total number of actions n, divide the global path into corresponding n+1 nodes, and couple each action in the total number of actions n into each node;

[0010] Step S400: according to the real-time position and end state [x, y, v, a] of the AGV, obtain the local trajectory equation of the AGV by introducing a quintic polynomial equation of time parameter;

[0011] Step S500: perform state inspection on each node, when the state machine of the AGV appears asynchronous in the time domain, recalibrate the time state and make fusion again.

[0012] Optionally, the step S100 specifically comprises:

[0013] Step S110: the AGV establishes an AGV action set according to its mechanical structure, motor performance and dynamic characteristics;

[0014] Step S120: according to the task requirement, a global path is planned or a global path is issued by a scheduling system.

[0015] Optionally, the step S200 specifically comprises:

[0016] Step S210: preset the servo action in the task requirement, and establish the action set of the task as n, n∈N, N is a positive integer, and the task requirement includes any one or more of the action instructions of moving, rotating, grabbing and releasing;

[0017] Step S220: according to the corresponding action in the action set n, establish the total time T required for the servo action in the task a .

[0018] Optionally, the step S300 specifically comprises:

[0019] Step S310: according to the number of actions in the action set n, divide the global path into n+1 nodes;

[0020] Step S320: establish different order priorities of the servo actions in the action set n at each node position;

[0021] Step S330: according to the characteristic parameters of the AGV and the global path, estimate the total task time T m, according to the order priority of the servo actions at different nodes and the estimated total time T m , each servo action is coupled into different nodes;

[0022] Step S340: according to the time requirement of the servo action, time constraints are made on the path between adjacent nodes;

[0023] wherein T m represents the total time of the AGV single task.

[0024] Optionally, the step S400 specifically comprises:

[0025] Step S410: according to the constraint condition of AGV kinematics, a quintic polynomial equation of time parameter is introduced;

[0026] Step S420: through the quintic polynomial equation of time parameter, a time matrix and a coefficient matrix of the polynomial between any two nodes are obtained.

[0027] Optionally, the step S500 specifically comprises:

[0028] Step S510: when the AGV reaches the i-th node, wherein 0

[0029] Step S520: when the situation in step S510 does not meet the requirements, the moving and servo action time of the AGV is recalculated, and the planning and fusion are redone.

[0030] Optionally, in the step S510, the verification of the current state machine is whether the action of the (0, i) node has been in the completed state; the time domain verification of the next state machine is whether the moving time t mi meets the requirements wherein t mi represents the moving time of the AGV from the i-th node to the i+1-th node, and t ai represents the time required to execute the i-th action.

[0031] According to the second aspect of the present application, a space-time coupled AGV action cooperative control device is provided, which is controlled based on the above-mentioned space-time coupled AGV action cooperative control method, and the device comprises:

[0032] A motion control module: used for obtaining the real-time position of the AGV and storing each walking node under a single task;

[0033] A servo action module: used for obtaining the servo action state of the AGV and storing the action list under a single task;

[0034] Time fusion module: for estimating the total time T of AGV single task m And the total time T required for executing the servo action in the task list a ;

[0035] Judgment module: for judging whether the servo action state of the previous i-1 nodes under the current node has been completed, if yes, judging whether the time required for the next servo action meets the walking demand of AGV, if yes, continuing to the next step, if not, re-performing the time-space fusion.

[0036] According to a third aspect of the present application, a computer readable storage medium is provided, which stores computer executable instructions for causing a computer to execute the time-space coupled AGV action cooperative control method of the first aspect of the present application.

[0037] According to the time-space coupled AGV action cooperative control method, device and medium of the present disclosure, the following technical effects are achieved:

[0038] According to the task demand, the global path of AGV is planned, the action in a single task is preset, and the time required for a single action and the total time required for executing the servo action in the task process are planned, according to the total number of preset actions, the global path is divided into nodes, and the servo action is coupled into the nodes, according to the position and end state of AGV, the local trajectory equation of AGV is obtained by introducing a quintic polynomial equation of time parameter, and finally the local trajectory produced is matched with the action of AGV to obtain the optimal strategy of action execution in a single task. The motion planning algorithm and servo action control are effectively integrated, on the basis of real-time detection of AGV and environmental changes, the motion path and servo action parameters are dynamically adjusted, and the close combination of movement and servo action is realized. Through theoretical analysis and simulation calculation of the model, the optimal control strategy is determined to realize the precise moving action. The real-time position of AGV is collected in real time through the sensor, and the real-time optimization of AGV motion and action is realized through fuzzy control, thereby improving the stability and robustness of the system.

[0039] Other features of the present application and its advantages will become apparent from the following detailed description of exemplary embodiments thereof, taken in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0040] The accompanying drawings incorporated in and forming a part of the specification, illustrate embodiments of the present application and, together with the description, serve to explain the principles of the application.

[0041] Fig. 1 is a flowchart of the time-space coupled AGV action cooperative control method provided by the embodiment of the present application;

[0042] Fig. 2 is a flowchart of a spatiotemporal coupling AGV action coordination control method according to an embodiment of the present application;

[0043] Fig. 3 is a structural block diagram of a spatiotemporal coupling AGV action coordination control device according to an embodiment of the present application;

[0044] Fig. 4 is a schematic diagram of the fusion of a servo action and a path node;

[0045] Fig. 5 is a schematic diagram of the fusion of a servo action and a path node. DETAILED DESCRIPTION

[0046] Various exemplary embodiments of the present application will now be described in detail with reference to the accompanying drawings. It should be noted that the relative arrangement of the components and steps set forth in the embodiments, numerical expressions, and numerical values are not limiting to the scope of the present application unless specifically stated otherwise.

[0047] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way limiting to the scope of the application and its applications or uses.

[0048] Techniques, methods, and devices known to those of ordinary skill in the relevant art can not be discussed in detail herein, but should be considered as part of the specification, where appropriate.

[0049] In all examples shown and discussed herein, any specific values should be interpreted as merely illustrative and not as a limitation. Thus, other examples of the exemplary embodiments can have different values.

[0050] An embodiment of a spatiotemporal coupling AGV action coordination control method is provided by the present application, as shown in Figs. 1 and 2, including the following steps:

[0051] Step S100: Obtain the parameter characteristics of the AGV, classify the servo actions of the AGV based on the parameter characteristics of the AGV, and establish an AGV action set, and plan a global path for the AGV according to the task requirements. More specifically, based on the parameter characteristics of the AGV, the characteristics related to each action, such as speed, acceleration, turning angle, etc., are extracted. Based on the AGV action set, a corresponding path planning algorithm (such as Dijkstra algorithm, A* algorithm, RRT algorithm, etc.) is selected to plan the global path.

[0052] Step S200: Preset the actions in a single task in the task requirements, preset the total number of actions required as n, and plan the time required for a single action and the total time T required for the servo action in the task process aMore specifically, the execution order of the preset actions can also be included to create an action sequence list. The execution time of the actions can not be fixed but can vary based on the speed or acceleration of the AGV. Moreover, the global path can also be planned in combination with the transition time between actions (e.g., the time required to transition from one action to another).

[0053] Step S300: According to the preset total number of actions n, the global path is divided into corresponding n+1 nodes, and each action in the total number of actions n is coupled into each node. It should be noted that each node in the global path represents the position before the AGV performs a certain action or the position after the action is performed, as well as a possible path decision point. Since there are n actions, the global path is divided into n+1 nodes, of which the starting point or the ending point each occupies one node, and the remaining n nodes correspond to the n actions one by one.

[0054] Step S400: According to the real-time position and end state [x, y, v, a] of the AGV, the local trajectory equation of the AGV is obtained by introducing a quintic polynomial equation with a time parameter. More specifically, the AGV is equipped with various sensors, such as photoelectric sensors, ultrasonic sensors, laser sensors, inertial measurement units (IMUs), etc., for real-time collection of environmental information, position information, speed information, etc. The data collected by these sensors is the basis for the AGV to perform navigation, positioning, and action execution. In the AGV system, a fuzzy controller optimizes the motion and actions of the AGV in real time based on the data collected by the sensors. By introducing a real-time control strategy, the data collected by the sensors is used to monitor and adjust the system, and the fuzzy controller realizes real-time optimization of the motion and actions of the AGV, improving the stability and robustness of the system.

[0055] Step S500: State verification is performed at each node, and when the state machine of the AGV is asynchronous in the time domain, the time state is recalibrated and fusion is redone.

[0056] In the embodiment of the present application, the step S100 specifically includes:

[0057] Step S110: The AGV establishes an action set according to its mechanical structure, motor performance, and dynamic characteristics.

[0058] Step S120: According to the task requirements, a global path is planned or a global path is issued by the scheduling system.

[0059] In the embodiment of the present application, the step S200 specifically includes:

[0060] Step S210: presetting the servo action in the task demand, and establishing the action set n of the task, n∈N, N is a positive integer, and the task demand includes any one or more of the action instructions of moving, rotating, grabbing and releasing.

[0061] Step S220: establishing the total time T required by the servo action in the task according to the corresponding action in the action set n a .

[0062] In the embodiment of the application, the step S300 specifically includes:

[0063] Step S310: dividing the global path into n+1 nodes according to the number of actions in the action set n. More specifically, n action nodes are inserted on the global path, which are usually located at the decision points or action execution points between two continuous path segments, and it is ensured that the start node or the end node is not included in the n action nodes.

[0064] Step S320: establishing different sequential priorities of the servo actions in the action set n at each node position. More specifically, the priority of each action at different node positions is determined according to the task demand, the current state of the AGV, the environmental conditions and other factors. The action to be executed at each node can be determined according to the priority list and the current conditions by an algorithm in the control system or the task planning software of the AGV, wherein the current conditions include the current state of the AGV, the surrounding environmental changes, the battery power, the emergency and the like.

[0065] Step S330: estimating the total task time T m according to the characteristic parameters of the AGV and the global path, and coupling each servo action to different nodes according to the sequential priorities of the servo actions at different nodes and the estimated total task time T m . More specifically, the n preset actions are coupled with the n action nodes, and each action node corresponds to an action instruction, and when the AGV reaches the node, the corresponding action will be executed.

[0066] Step S340: making time constraints on the path between adjacent nodes according to the time requirements of the servo actions; wherein T m represents the total time of the single task of the AGV. When the servo action has specific time requirements, it is necessary to ensure that the AGV has enough time to execute the servo action of the current node before moving to the next node. Based on the path between adjacent nodes, the time required for the AGV to move from the current node to the next node is estimated, which depends on the speed of the AGV, the path length and possible obstacles and the like.

[0067] It should be noted that when the AGV receives the task and the path from the current node to the node n sent by the scheduling system, the AGV loads various actions (as an AGV action set) according to the structure and motor performance of the AGV, and then divides each action into a specific priority order according to different nodes. Referring to FIG. 4, in a complete task of taking goods from the warehouse location A to the warehouse location B, the priority of the action type divided is shown in the following table:

[0068] In specific embodiments, according to the different functions of the AGV, the priority of each action class at different nodes has certain differences. In the above table, only the actions with higher relevance to taking or unloading are executed at the warehouse location node. At each node, the corresponding servo action is selected according to the different priorities of the servo action. If multiple actions are included in a certain action class, the priority is set according to the different task orders.

[0069] Next, according to the task sent by the scheduling system, the servo action that may be needed in the AGV task is preset, and its action set n is listed, n∈N (N is a positive integer), and the total number of elements (action number) in the set is n. Then, according to the task requirement, the time t a required by each action is estimated, and finally the total time T a required by the servo action is obtained by adding the time required by each action.

[0070] More specifically, for each AGV, the time of the servo action can be obtained according to the task requirement. When the scheduling system sends the target position of the fork, the servo action module calculates the time required by each sequential action according to the real-time state of the AGV.

[0071] The estimated time of the fork lifting is:

[0072] Where v t represents the target speed during lifting; a acc represents the acceleration of the fork; a dec represents the deceleration of the fork; and x represents the target lifting position.

[0073] The estimated time of the gantry moving is:

[0074] Where v max represents the maximum speed of the gantry moving forward and backward; a max represents the acceleration of the gantry moving; and d represents the total length of the gantry moving.

[0075] In the embodiments of the present application, the step S400 specifically comprises:

[0076] Step S410: Introduce a quintic polynomial equation with time parameter according to the constraint condition of AGV kinematics. In the path planning of AGV, in order to move from one position to another smoothly and considering the kinematics constraint of AGV (such as maximum speed and maximum acceleration), a polynomial trajectory planning method is used to ensure the continuity of position, speed and acceleration, and specific constraint conditions can be met by adjusting the coefficients of the polynomial equation.

[0077] Step S420: Obtain the time matrix and the coefficient matrix of the polynomial between any two nodes through the quintic polynomial equation with time parameter.

[0078] In the embodiment of the present application, the step S500 specifically comprises:

[0079] Step S510: When AGV reaches the i-th node, wherein 0 < i < n, check the time domain of the current node completion state machine and the next state machine. More specifically, check whether the time required for AGV to perform all tasks at the current node meets the predetermined time constraint, and whether there is enough time to transition to the next node.

[0080] Step S520: When the situation of not meeting occurs in step S510, recalculate the moving and servo action time of AGV, and make planning and fusion again. More specifically, when AGV reaches the i-th node and completes the current task, check whether the current time conflicts with the time of entering the next node. If a time conflict is detected (for example, the start time of the next node is earlier than the end time of the current node), re-planning is performed. According to new path, speed limit or obstacle information, etc., recalculate the moving time from the current node to the next node, and adjust the servo action time at the next node according to the new path or task requirement. Fuse the recalculated moving time and servo action time into the new time planning to ensure the time continuity of the whole path. Update the state machine of AGV with the new time planning and action sequence.

[0081] In the embodiment of the present application, in the step S510, the check of the current state machine is whether the action of the (0, i) node is in the completion state, and the time domain check of the next state machine is the moving time t mi whether to meet , wherein t mi represents the moving time of AGV from the i-th node to the i+1-th node, t ai represents the time required to perform the i-th action.

[0082] Further, when the AGV determines the servo action and the corresponding time, the AGV divides the motion nodes of the task according to the total number of actions, and limits the time between each adjacent node according to the time of the servo action. The time constraint between adjacent nodes is as follows:

[0083] Further, when the AGV needs to perform a pick-up preparation action or a delivery preparation action, it is desired that the AGV can accurately perform the action while reaching the storage location in a compact time interval, i.e., completing the action task when reaching the storage location, so a preparation action node needs to be inserted before the storage node, as shown in FIG. 5. Therefore, when considering the time limit of the motion nodes, the distance constraint between the path node 0 and the path node n-2 and the storage location also needs to be considered, as follows:

[0084] wherein, v m i n 、v max respectively represent the minimum speed and the maximum speed of the AGV; λ represents the distance between the inserted node and the storage location.

[0085] When the time of the AGV at each adjacent node is obtained, the five-order polynomial equation satisfied by the adjacent nodes is solved according to the state constraint [x, y, v, a] of the AGV at the adjacent nodes through the time parameter, as the local path between the two nodes. The five-order polynomial equation is represented as follows through the time state of the AGV at the node:

[0086] Further, each action in the action set n is marked with a state in the program variable, and the state is shown in the following table (not all actions are listed in the table). When the AGV reaches each node, the action set n is queried and verified, and if the state in the action set of the current node meets the target state, the movement and servo action time of the AGV is recalculated, and the planning and fusion is redone.

[0087] The embodiment of the application also provides an AGV action cooperative control device based on space-time coupling, which is controlled based on the AGV action cooperative control method based on space-time coupling, and the device comprises:

[0088] Motion control module: used for obtaining the real-time position of AGV and storing each walking node under single task. More specifically, the motion control module interacts with the photoelectric sensor, ultrasonic sensor, laser sensor, inertial measurement unit (IMU) and other sensors installed on the AGV, processes and analyzes the sensor data, and determines the accurate position of the AGV. The motion control module parses the task requirements issued by the scheduling system into a series of walking nodes, each node representing a specific position or state of the AGV on the path. These walking nodes are stored in the memory of the motion control module to guide the AGV to move according to the predetermined path. During task execution, the motion control module calculates the distance and direction between the current position of the AGV and the next walking node according to the real-time position information and stored walking nodes, and sends control instructions to the servo driver of the AGV.

[0089] Servo action module: used for obtaining the servo action state of AGV and storing the action list under single task. The servo action module communicates with the servo driver of the AGV to obtain the action state of the servo system in real time. When the AGV executes a single task, the servo action module is responsible for storing all servo action instructions under the task to form an action list (action set).

[0090] Time fusion module: used for estimating the total time T m of AGV single task a .

[0091] Judgment module: used for judging whether the servo action state of the previous i-1 nodes has been completed under the current node, if yes, judging whether the time required for the next servo action meets the walking requirements of the AGV, if yes, continuing to the next step, if not, re-performing the space-time fusion. More specifically, if the servo actions of the previous N nodes have been completed, the judgment module will further judge whether the time required for the next servo action meets the walking requirements of the AGV. If the time required for the next servo action does not meet the walking requirements of the AGV, the judgment module will trigger the corresponding processing mechanism, including sending warning information to the scheduling system and re-performing the space-time fusion (i.e. re-calculating the total time of the task and the time of each servo action), so as to ensure that the AGV can safely and efficiently complete the task.

[0092] The embodiment of the application also provides a computer readable storage medium, which stores computer executable instructions for making a computer execute the AGV action collaborative control method of space-time coupling.

[0093] The application provides a spatio-temporal coupling AGV action cooperation control method, device and readable storage medium.

[0094] It should be noted that the application dynamically adjusts the motion path and servo action parameters on the basis of real-time detection of AGV and environmental changes, realizes close combination of movement and servo action, and realizes seamless connection of servo action during movement of the AGV.

[0095] In the application, a servo action model of the AGV is established, time parameters are introduced to establish a quintic polynomial equation as a motion trajectory of the AGV between adjacent nodes, considering factors such as mechanical structure, motor performance and dynamic characteristics of the AGV, and through theoretical analysis and simulation calculation of the model, an optimal control strategy is determined to realize precise movement action. On the other hand, the application also introduces a real-time control strategy, uses data collected by sensors in real time to monitor and adjust the system, and realizes real-time optimization of AGV movement and action through a fuzzy controller, thereby improving stability and robustness of the system.

[0096] The above embodiments according to the drawings illustrate the structure, features and effects of the application, but the above are only preferred embodiments of the application, and it should be noted that the technical features involved in the above embodiments and preferred modes can be reasonably combined and matched into various equivalent schemes by those skilled in the art without departing from, changing the design idea and technical effects of the application. Therefore, the application is not limited in the scope of the drawings, and any change or modification within the scope of the application should be within the protection scope of the application.

Claims

1. A spatiotemporal coupling AGV action coordination control method, characterized in that, The method comprises the following steps: Step S100: acquiring parameter characteristics of the AGV, classifying servo actions of the AGV based on the parameter characteristics of the AGV, establishing an AGV action set, and planning a global path of the AGV according to a task requirement; Step S200: preset the actions in the single task in the task requirement, preset the total number of required actions as n, and plan the time required for single action and the total time T required for executing the servo action in the task process a ; Step S300: dividing the global path into corresponding n+1 nodes according to a preset total number n of actions, and coupling each action in the total number n of actions to each node; Step S400: acquiring a local trajectory equation of the AGV by introducing a quintic polynomial equation of a time parameter according to a real-time position and an end state [x, y, v, a] of the AGV; Step S500: performing state inspection on each node, and recalibrating a time state and making fusion again when an asynchronous state machine of the AGV appears in a time domain.

2. The spatio-temporal coupled AGV motion coordination control method according to claim 1, characterized in that, The step S100 specifically comprises: Step S110: establishing an AGV action set according to a mechanical structure, motor performance and dynamic characteristics of the AGV; Step S120: planning a global path according to a task requirement or issuing a global path by a scheduling system.

3. The spatio-temporal coupled AGV motion coordination control method according to claim 1, characterized in that, The step S200 specifically comprises: Step S210: presetting servo actions appearing in the task requirement, and establishing an action set n of the task, wherein n is an element of a natural number set N, and the task requirement comprises any one or more of action instructions of moving, rotating, grabbing and releasing; Step S220: Establish the total time T required for the servo actions in this task according to the corresponding actions in the action set n a .

4. The spatio-temporal coupled AGV motion coordination control method according to claim 1, characterized in that, The step S300 specifically comprises: Step S310: dividing the global path into n+1 nodes according to a number of actions in the action set n; Step S320: establishing different sequence priorities of the servo actions in each node position in the action set n; Step S330: estimating a total task time T according to the characteristic parameters of the AGV and the global path m , coupling each servo action into different nodes according to the sequence priority of the servo actions at different nodes and the estimated total task time T m . Step S340: making time constraints on paths between adjacent nodes according to time requirements of the servo actions; Where, T m represents the total time of AGV single task.

5. The spatio-temporal coupled AGV motion coordination control method according to claim 1, characterized in that, The step S400 specifically comprises: Step S410: introducing a quintic polynomial equation of a time parameter according to a constraint condition of kinematics of the AGV; Step S420: acquiring a time matrix and a coefficient matrix of a polynomial between any two nodes by the quintic polynomial equation of the time parameter.

6. The spatio-temporally coupled AGV motion coordination control method of claim 1, wherein, The step S500 specifically comprises: Step S510: when the AGV reaches an i-th node, wherein 0 Step S520: when a condition in the step S510 is not met, recalculating moving and servo action times of the AGV, and making planning and fusion again.

7. The spatio-temporal coupled AGV motion coordination control method according to claim 6, characterized in that, In the step S510, the check of the current state machine is whether the action of the (0, i) node has been in the completion state; the time domain check of the next state machine is the moving time t between (i, i+1) mi whether to meet where, t mi represents the moving time of the AGV from the i node to the i+1 node, t ai represents the time required to perform the i th action.

8. A space-time coupling AGV action coordination control device, characterized in that, The device is controlled by the spatiotemporal coupling AGV action cooperative control method according to any one of claims 1 to 7, and the device comprises: a motion control module for acquiring a real-time position of the AGV and storing each walking node under a single task; a servo action module for acquiring a servo action state of the AGV and storing an action list under a single task; Time fusion module: for estimating the total time T of AGV single task m and the total time T required for executing the servo action in the task list a ; a judgment module for judging whether servo action states of the first i-1 nodes under a current node have been completed, if yes, judging whether a time required by a next servo action meets a walking requirement of the AGV, if yes, continuing to the next step, and if not, performing spatiotemporal fusion again.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer executable instructions for causing a computer to execute the AGV action coordination control method of space-time coupling as claimed in any one of claims 1 to 7.

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