Intelligent collaborative operation method of automatic tire type yard crane and intelligent guide vehicle

Abstract

The invention provides an intelligent collaborative operation method for an automatic rubber-tyred gantry crane and an intelligent guide vehicle. A wharf management system distributes collaborative operation tasks to the automatic rubber-tyred gantry crane and the IGV; the IGV uses a small path dynamic planning method to generate an initial driving path, and if there is an obstacle in the planned path, the driving path is adjusted by using a wharf leading edge lane changing strategy; the automatic rubber-tyred field bridge confirms the container by using a laser identification method, corrects the position of the automatic rubber-tyred field bridge by using a cart diagonal driving method, lifts the container to a safe height, and then loads the container to an IGV or a specified stacking area; after the task is completed, the automatic tire type gantry crane and the IGV update the task condition and upload the task condition to a wharf management system, collaborative operation of the automatic tire type gantry crane and the IGV can be effectively optimized, the stability of container hoisting of the automatic tire type gantry crane is improved, the IGV delay problem caused by path congestion or obstacles is avoided, and the service life of the automatic tire type gantry crane and the IGV is prolonged. Therefore, the safety and the collaboration of container terminal operation are effectively improved.

Description

Technical Field

[0001] The present application relates to the technical field of port automation control, and in particular to a method for intelligent collaborative operation of an automated tire-type yard crane and an intelligent guided vehicle. Background Art

[0002] As global logistics demand continues to grow, ports' requirements for automation and intelligence are also increasing. Tire-type cranes and intelligent guided vehicles (IGVs) are two key equipment widely used in port operations and play an important role in achieving automation and intelligence. However, existing tire-type cranes mainly use manual control to load, unload and stack containers, and their operation relies on manual instructions, with limited flexibility and automation. In addition, the operating modes of existing tire-type cranes and intelligent guided vehicles are usually relatively independent, and efficient collaborative operations have not yet been achieved, limiting the overall level of automation and intelligence.

[0003] Therefore, it is necessary to propose a method for automatic stacking of automated tire-type yard cranes and intelligent collaborative operation of intelligent guided vehicles. By realizing automatic stacking of automated tire-type yard cranes and intelligent collaborative operation of tire-type yard cranes and intelligent guided vehicles, the terminal operation efficiency can be improved and the needs of modern ports for automation and intelligence can be met. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a method for intelligent collaborative operation of an automated tire-type yard crane and an intelligent guided vehicle in response to the above-mentioned problems, so as to realize the collaborative operation of the automated tire-type yard crane and the intelligent guided vehicle and improve the overall production operation efficiency of the terminal.

[0005] The embodiment of the present application is implemented as follows:

[0006] The embodiment of the present application provides an intelligent collaborative operation method of an automated tire-type field bridge and an intelligent guided vehicle, which is characterized by comprising the following steps:

[0007] Step a: The terminal management system assigns the operation task, including specifying the specific automated rubber-tyred crane and intelligent guided vehicle, the container location and number, and the target loading and unloading area;

[0008] Step b, the automated tire-type yard crane and the intelligent guided vehicle synchronize the current operation task information, including the container location, number and target loading and unloading area;

[0009] Step c, the intelligent guided vehicle drives along the path planned by the small path dynamic planning method to the target loading and unloading area;

[0010] Step d, the automated tire-type yard crane uses a laser recognition method to confirm the target container, and uses a large vehicle diagonal driving method to correct its own position to accurately align with the container;

[0011] Step e: The automated rubber-tyred yard crane uses a hoist to lift the container to a safe height;

[0012] Step f, the automated tire-type yard crane loads the container onto the intelligent guided vehicle or the designated stacking area;

[0013] Step g: After the task is completed, the automated tire-type terminal crane and the intelligent guided vehicle update the task status and upload it to the terminal management system.

[0014] In some optional implementation schemes, the small path dynamic planning method in step c is to divide the work area into several small grids, combine the real-time data of terminal traffic flow and road conditions, use the D*Lite algorithm to dynamically evaluate the path cost of each grid node, select the current optimal node for expansion in each step, and finally plan the optimal path for the intelligent guided vehicle to the designated work area; when encountering obstacles or path congestion, the driving path of the intelligent guided vehicle is dynamically adjusted according to the lane changing strategy at the front of the terminal.

[0015] In some optional implementation schemes, the diagonal driving method of the large vehicle in step d is to configure four independently controllable driving tires for the automated tire-type crane, and coordinately control the driving wheels on the diagonal through a PID algorithm, so that the automated tire-type crane can move accurately and flexibly in container-dense areas; the laser recognition method is to identify the container position and number information through the laser sensor of the automated tire-type crane to confirm the operating container.

[0016] In some optional embodiments, the sling in step e lifts the container using a four-rope winding method, wherein four ropes configured by an automated tire-type yard crane are fixed at the four corners of the sling, and the sling is used to grab the container so that each rope is evenly stressed during lifting.

[0017] In some optional implementation schemes, the D*Lite algorithm sets the current position of the intelligent guided vehicle as the starting point, sets the designated loading and unloading area as the target point, and optimizes the path search process by reverse searching from the target point to the starting point; wherein, let g(u) be the path cost from the starting point to the current node, rhs(u) be the path cost from the adjacent node to the current node u, h(u) be the estimated cost from the current node to the target node, and in the initial state, g(u) of all nodes is set to infinity, the rhs value of the target point is set to 0, and the rhs(u) of the remaining nodes is set to infinity;

[0018] Starting from the target point, use the priority queue to store the nodes to be expanded and sort them according to min(g(u), rhs(u))+h(u). Take the node u with the lowest path cost from the queue in turn, expand its adjacent node v, and update the rhs of the adjacent node. The calculation formula is:

[0019] rhs(v)=min(g(u)+c(u,v)),u∈pred(v)

[0020] Where pred(v) is the set of predecessor nodes of a node, and c(u, v) is the path cost from node to node;

[0021] Reinsert the expanded adjacent nodes into the priority queue, and continue to expand until the starting point is expanded or the priority queue is empty, generating the initial optimal path from the starting point to the target point. The path consists of a series of grid nodes. During the path generation process, select the adjacent node with the smallest g(u) value as the next node until the target node is reached;

[0022] During the driving process of the intelligent guided vehicle, environmental changes are monitored in real time. When an obstacle is detected in the path, the affected node is marked as an inaccessible node. According to the lane change strategy at the terminal front, the path replanning is triggered and only the affected part of the path is updated. The specific update process includes recalculating the affected nodes and inserting them into the priority queue. They are reordered according to the new path cost. The intelligent guided vehicle drives along the updated optimal path to the designated work area.

[0023] In some optional implementation schemes, the lane change strategy at the terminal front specifically includes the following contents:

[0024] 1) The body sensors of the intelligent guided vehicle sense the speed and location information of surrounding vehicles and obstacles;

[0025] 2) The intelligent guided vehicle analyzes the position and movement trajectory of the obstacle to determine whether it conflicts with the planned driving path of the intelligent guided vehicle and whether there is an intention to change lanes;

[0026] 3) If there is no intention to change lanes, continue driving along the original path;

[0027] 4) If the intention to change lanes is generated, the intelligent guided vehicle that intends to change lanes will sense the position information of the surrounding vehicles. When there is no vehicle within 3m of the adjacent lane, it will turn on the turn signal and make a small lateral movement to change lanes. If there is a vehicle within 3m of the adjacent lane, the intelligent guided vehicle that intends to change lanes will stop and wait for the vehicle in the opposite lane to pass before changing lanes.

[0028] In some optional implementation schemes, the PID algorithm is that the automated tire-type field crane uses a laser sensor to detect the position error and angle error between itself and the target container in real time, wherein the position error is the deviation between the current position of the automated tire-type field crane and the target container, and the angle error is the deviation between the current orientation of the automated tire-type field crane and the orientation of the container; the detected error is calculated by proportional, integral and differential, wherein the proportional value is used to quickly respond to the current error, the integral value is used to eliminate the long-standing error, and the differential value is used to predict the error trend and pre-adjust; the proportional, integral and differential values ​​are combined to generate a comprehensive control signal, and the calculation formula is as follows:

[0029]

[0030] Among them, K p is the proportionality coefficient, K i is the integration coefficient, K d is the differential coefficient, e(t) is the error value;

[0031] The automated tire crane adjusts the motion parameters of each drive tire based on the generated control signal, ensuring that the crane can accurately adjust its position and orientation to align with the target container.

[0032] In some optional implementation schemes, the division principle of the plurality of small grids is as follows:

[0033] According to the size of the intelligent guided vehicle, the grid size of the working area is set to 2m×2m. Each grid node represents a small area in the working area. The initial state is set to be passable, and the obstacle area is marked as an inaccessible node.

[0034] The beneficial effects of the present application are as follows: the present application provides an intelligent collaborative operation method of an automated tire-type yard crane and an intelligent guided vehicle, which dynamically adjusts the driving path of the intelligent guided vehicle by adopting a small path dynamic planning method of the terminal front lane change strategy, reduces delays of the intelligent guided vehicle caused by path congestion or obstacles, thereby improving the efficiency of the intelligent guided vehicle, reducing the waiting time of the tire-type yard crane, and improving the overall coordination of the operation; by adopting a large vehicle diagonal driving method, the automated tire-type yard crane is accurately controlled to accurately position the container, and the four-rope winding method for lifting is used to ensure stable lifting or horizontal movement of the container, so as to realize accurate and automatic stacking of containers by the automated tire-type yard crane, thereby improving the overall safety of the operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0036] Figure 1 This is a flowchart of an embodiment of the present application;

[0037] Figure 2 A flowchart of the lane changing strategy at the front of the wharf according to an embodiment of the present application;

[0038] Figure 3 This is a schematic diagram of the dock site for the dock front lane changing strategy according to an embodiment of the present application. DETAILED DESCRIPTION

[0039] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations.

[0040] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application for which protection is sought, but merely represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0041] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0042] The features and performance of the present application are further described in detail below in conjunction with the embodiments.

[0043] The present invention proposes an intelligent collaborative operation method of an automated tire-type field bridge and an intelligent guided vehicle, comprising the following steps:

[0044] Step a: The terminal management system assigns the operation task, including specifying the specific automated rubber-tyred crane and intelligent guided vehicle, the container location and number, and the target loading and unloading area;

[0045] Step b, the automated tire-type yard crane and the intelligent guided vehicle synchronize the current operation task information, including the container location, number and target loading and unloading area;

[0046] Step c, the intelligent guided vehicle drives along the path planned by the small path dynamic planning method to the target loading and unloading area;

[0047] Step d, the automated tire-type yard crane uses a laser recognition method to confirm the target container, and uses a large vehicle diagonal driving method to correct its own position to accurately align with the container;

[0048] Step e: The automated rubber-tyred yard crane uses a hoist to lift the container to a safe height;

[0049] Step f, the automated tire-type yard crane loads the container onto the intelligent guided vehicle or the designated stacking area;

[0050] Step g: After the task is completed, the automated tire-type terminal crane and the intelligent guided vehicle update the task status and upload it to the terminal management system.

[0051] The specific small path dynamic planning method is to divide the operation area into several small grids, combine the real-time data of terminal traffic flow, road conditions, etc., use the D*Lite algorithm to dynamically evaluate the path cost of each grid node, select the current optimal node for expansion in each step, and finally plan the optimal path for the IGV to the designated operation area. When encountering obstacles or path congestion, the IGV driving path is dynamically adjusted according to the lane change and jump strategy at the front of the terminal.

[0052] Specifically, the above-mentioned division of the operation area into several small grids is that the grid size of the operation area is set to 2 meters × 2 meters according to the size of the IGV. Each grid node represents a small area in the operation area, and the initial state is set to be passable. At the same time, combined with the real-time traffic flow and road condition data of the terminal, fixed facilities, temporary stacked containers and other obstacle areas that may affect the travel of IGVs are marked as inaccessible nodes.

[0053] Specifically, the above D*Lite algorithm sets the current position of the IGV as the starting point, the designated loading and unloading area as the target point, and optimizes the path search process by reverse searching from the target point to the starting point. Let g(u) be the path cost from the starting point to the current node, rhs(u) be the path cost from the adjacent node to the current node u, h(u) be the estimated cost from the current node to the target node, and in the initial state, g(u) of all nodes is set to infinity, the rhs value of the target point is set to 0, and the rhs(u) of the remaining nodes is set to infinity;

[0054] Starting from the target point, use the priority queue to store the nodes to be expanded and sort them according to min(g(u), rhs(u))+h(u). Take the node u with the lowest path cost from the queue in turn, expand its adjacent node v, and update the rhs of the adjacent node. The calculation formula is:

[0055] rhs(v)=min(g(u)+c(u,v)),u∈pred(v)

[0056] Among them, pred(v) is the set of predecessor nodes of the node, and c(u, v) is the path cost from node to node.

[0057] The expanded adjacent nodes are reinserted into the priority queue, and the expansion continues until the starting point is expanded or the priority queue is empty, generating the initial optimal path from the starting point to the target point. The path consists of a series of grid nodes. During the path generation process, the adjacent node with the smallest g(u) value is selected as the next step node until the target node is reached.

[0058] During the IGV's driving process, the environment changes are monitored in real time. When an obstacle is detected in the path, the affected node is marked as an inaccessible node. According to the lane change strategy at the front of the terminal, the path replanning is triggered and only the affected part of the path is updated. The specific update process includes recalculating the rhs of the affected nodes, inserting them into the priority queue, and reordering them according to the new path cost. The IGV drives along the updated optimal path to the designated operation area.

[0059] Specifically, Figure 2 As shown, the lane change strategy at the front of the above dock is:

[0060] (1) IGV body sensors sense the speed and location of surrounding vehicles and obstacles;

[0061] (2) The IGV analyzes the obstacle’s position and movement trajectory to determine whether it conflicts with the IGV’s intended driving path and whether there is an intention to change lanes;

[0062] (3) If the intention to change lanes is generated, proceed to step (4); otherwise, continue driving along the original path;

[0063] (4) The lane-changing IGV senses the position information of surrounding vehicles. When there is no vehicle within 3 m of the adjacent lane, it turns on the turn signal and makes a small lateral movement to change lanes. When there is a vehicle within 3 m of the adjacent lane, the lane-changing IGV stops and waits for the vehicle in the opposite lane to pass before changing lanes.

[0064] like Figure 3 The example is described as follows:

[0065] IGV1 encounters an obstacle that hinders its driving and intends to change lanes. IGV1 begins to sense the position information of surrounding vehicles and finds that IGV2 is driving normally within 3m of the adjacent lane. Figure 3 As can be seen in the figure below, IGV1 stops and waits for IGV2 to pass, then turns on the turn signal and makes a small move to change lanes.

[0066] The automated tire crane uses laser recognition to identify the target container. The laser sensor of the automated tire crane identifies the location and number of the container and confirms the operating container. The above-mentioned diagonal driving method of the large vehicle is to configure four independently controllable drive tires for the automated tire crane, and coordinate the control of the drive wheels on the diagonal line through the PID algorithm, so that the automated tire crane can move accurately and flexibly in the container-dense area.

[0067] Specifically, the PID algorithm is that the automated tire-type field crane uses a laser sensor to detect the position error and angle error between itself and the target container in real time, where the position error is the deviation between the current position of the field crane and the target container, and the angle error is the deviation between the current orientation of the field crane and the orientation of the container. The detected error is calculated by proportional, integral and differential, where the proportional value is used to quickly respond to the current error, the integral value is used to eliminate long-standing errors, and the differential value is used to predict the error trend and perform pre-adjustment. The proportional, integral and differential values ​​are combined to generate a comprehensive control signal, and the calculation formula is as follows:

[0068]

[0069] Among them, K p is the proportionality coefficient, K i is the integration coefficient, K d is the differential coefficient, and e(t) is the error value.

[0070] The automated tire crane adjusts the motion parameters of each drive tire based on the generated control signal, ensuring that the crane can accurately adjust its position and orientation to align with the target container.

[0071] The spreader lifts the container using the four-rope winding method, which is to fix the four ropes configured by the automated tire-type yard crane at the four corners of the spreader. The spreader grabs the container and makes each rope evenly stressed during lifting, thereby increasing the stability of the container when the tire-type yard crane is lifting or moving horizontally.

Claims

1. The intelligent collaborative operation method of the automated tire-type field bridge and the intelligent guided vehicle is characterized by: The steps include: Step a: The terminal management system assigns the operation task, including specifying the specific automated rubber-tyred crane and intelligent guided vehicle, the container location and number, and the target loading and unloading area; Step b, the automated tire-type yard crane and the intelligent guided vehicle synchronize the current operation task information, including the container location, number and target loading and unloading area; Step c, the intelligent guided vehicle drives along the path planned by the small path dynamic planning method to the target loading and unloading area; Step d, the automated tire-type yard crane uses a laser recognition method to confirm the target container, and uses a large vehicle diagonal driving method to correct its own position to accurately align with the container; Step e: The automated rubber-tyred yard crane uses a hoist to lift the container to a safe height; Step f, the automated tire-type yard crane loads the container onto the intelligent guided vehicle or the designated stacking area; Step g: After the task is completed, the automated tire-type terminal crane and the intelligent guided vehicle update the task status and upload it to the terminal management system.

2. The intelligent collaborative operation method of the automated tire-type field bridge and the intelligent guided vehicle according to claim 1 is characterized in that: The dynamic planning method for small paths in step c is to divide the work area into several small grids, combine the real-time data of terminal traffic flow and road conditions, use the D*Lite algorithm to dynamically evaluate the path cost of each grid node, select the current optimal node for expansion in each step, and finally plan the optimal path for the intelligent guided vehicle to the designated work area; when encountering obstacles or path congestion, the driving path of the intelligent guided vehicle is dynamically adjusted according to the lane changing strategy at the front of the terminal.

3. The intelligent collaborative operation method of the automated tire-type field bridge and the intelligent guided vehicle according to claim 2 is characterized in that: The diagonal driving method of the large vehicle in step d is to configure four independently controllable driving tires for the automated tire-type yard crane, and coordinately control the driving wheels on the diagonal line through the PID algorithm, so that the automated tire-type yard crane can move accurately and flexibly in the container-dense area; the laser recognition method uses the laser sensor of the automated tire-type yard crane to identify the container position and number information to confirm the operating container.

4. The intelligent collaborative operation method of the automated tire-type field bridge and the intelligent guided vehicle according to claim 3 is characterized in that: The sling in step e lifts the container using a four-rope winding method, wherein four ropes configured by an automated tire-type yard crane are fixed at the four corners of the sling, the sling is used to grab the container, and each rope is evenly stressed during lifting.

5. The intelligent collaborative operation method of the automated tire-type field bridge and the intelligent guided vehicle according to claim 2 is characterized in that: The D*Lite algorithm sets the current position of the intelligent guided vehicle as the starting point, the designated loading and unloading area as the target point, and optimizes the path search process by reverse searching from the target point to the starting point; wherein, let g(u) be the path cost from the starting point to the current node, rhs(u) be the path cost from the adjacent node to the current node u, and h(u) be the estimated cost from the current node to the target node. In the initial state, g(u) of all nodes is set to infinity, the rhs value of the target point is set to 0, and the rhs(u) of the remaining nodes is set to infinity; Starting from the target point, use the priority queue to store the nodes to be expanded and sort them according to min(g(u), rhs(u))+h(u). Take the node u with the lowest path cost from the queue in turn, expand its adjacent node v, and update the rhs of the adjacent node. The calculation formula is: rhs(v)=min(g(u)+c(u,v)), u∈pred(v) Where pred(v) is the set of predecessor nodes of a node, and c(u, v) is the path cost from node to node; Reinsert the expanded adjacent nodes into the priority queue, and continue to expand until the starting point is expanded or the priority queue is empty, generating the initial optimal path from the starting point to the target point. The path consists of a series of grid nodes. During the path generation process, select the adjacent node with the smallest g(u) value as the next node until the target node is reached; During the driving process of the intelligent guided vehicle, environmental changes are monitored in real time. When an obstacle is detected in the path, the affected node is marked as an inaccessible node. According to the lane change strategy at the terminal front, the path replanning is triggered and only the affected part of the path is updated. The specific update process includes recalculating the affected nodes and inserting them into the priority queue. They are reordered according to the new path cost. The intelligent guided vehicle drives along the updated optimal path to the designated work area.

6. The intelligent collaborative operation method of the automated tire-type field bridge and the intelligent guided vehicle according to claim 5 is characterized in that: The lane change strategy at the terminal front specifically includes the following contents: 1) The body sensors of the intelligent guided vehicle sense the speed and location information of surrounding vehicles and obstacles; 2) The intelligent guided vehicle analyzes the position and movement trajectory of the obstacle to determine whether it conflicts with the planned driving path of the intelligent guided vehicle and whether there is an intention to change lanes; 3) If there is no intention to change lanes, continue driving along the original path; 4) If the intention to change lanes is generated, the intelligent guide vehicle that intends to change lanes will sense the position information of surrounding vehicles. If there is no vehicle within 3m of the adjacent lane, it will turn on the turn signal and make a small lateral movement to change lanes; When there is a vehicle within 3m of the adjacent lane, the intelligent guided vehicle that intends to change lanes will stop and wait for the vehicle in the opposite lane to pass before changing lanes.

7. The intelligent collaborative operation method of the automated tire-type field bridge and the intelligent guided vehicle according to claim 3 or 6, characterized in that: The PID algorithm is that the automated tire crane uses a laser sensor to detect the position error and angle error between itself and the target container in real time, where the position error is the deviation between the current position of the automated tire crane and the target container, and the angle error is the deviation between the current orientation of the automated tire crane and the orientation of the container; the detected error is calculated by proportional, integral and differential, where the proportional value is used to quickly respond to the current error, the integral value is used to eliminate the long-standing error, and the differential value is used to predict the error trend and pre-adjust; the proportional, integral and differential values ​​are combined to generate a comprehensive control signal, and the calculation formula is as follows: Among them, K p is the proportionality coefficient, K i is the integration coefficient, K d is the differential coefficient, e(t) is the error value; The automated tire crane adjusts the motion parameters of each drive tire based on the generated control signal, ensuring that the crane can accurately adjust its position and orientation to align with the target container.

8. The intelligent collaborative operation method of the automated tire-type field bridge and the intelligent guided vehicle according to claim 2, 5 or 6, characterized in that: The division principles of the several small grids are as follows: According to the size of the intelligent guided vehicle, the grid size of the working area is set to 2m×2m. Each grid node represents a small area in the working area. The initial state is set to be passable, and the obstacle area is marked as an inaccessible node.

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