Air-ground coordinated material transportation method, device, electronic equipment and storage medium

Abstract

The present invention provides an air-ground coordinated material transportation method, device, electronic equipment and storage medium, which belongs to the technical field of path planning. In this method, the difference between the robot time cost of the robot to the target handover point and the drone time cost of the target drone corresponding to the target handover point to reach the target handover point is minimized, that is, the waiting time between the robot and the target drone at the target handover point is shortest, which greatly enhances the air-ground coordinated transportation of air-ground cooperation and improves the efficiency of air-ground coordinated transportation. In addition, the optimal material transportation path of the target drone obtained by planning with the time cost-impeded A-star algorithm can ensure that when the target drone flies according to the optimal material transportation path, it will not collide with other drones in flight, that is, it avoids collisions between drones in multi-drone transportation, and has good safety.

Description

Technical Field

[0001] The present invention relates to the technical field of path planning, and in particular to an air-ground coordinated material transportation method, device, electronic equipment and storage medium. Background Art

[0002] With the rapid development of technology, the logistics and transportation industry is undergoing profound changes. Traditional logistics and transportation methods are particularly inadequate in certain scenarios, especially when facing public health emergencies. Therefore, developing new logistics and transportation methods, particularly for transporting supplies within the complex environments of specialized hospitals, is crucial.

[0003] Robots have been widely used in the logistics sector. For example, in warehouse management, robots can automatically navigate to storage locations and accurately pick goods, significantly improving efficiency. In order processing, automated order processing systems and intelligent sorting systems enable rapid and efficient order processing. Furthermore, robots can be used for inventory management, providing real-time monitoring of inventory status and automatic updates of inventory data.

[0004] Drone technology has also made significant progress in the logistics sector in recent years. Drones can deliver goods quickly and flexibly, making them particularly suitable for remote areas or those with inaccessible transportation. Drone delivery not only significantly reduces delivery times but also improves customer satisfaction. Furthermore, drones offer advantages such as low cost and flexible dispatch, potentially filling gaps in traditional air transport capacity.

[0005] Currently, transporting materials within the air-land collaborative system faces numerous challenges. For one thing, the collaboration between land robots and aerial drones involves waiting time, significantly reducing transport efficiency if tasks are not properly allocated. Furthermore, multiple drones simultaneously carrying materials pose a risk of collision.

[0006] In summary, how to strengthen air-ground collaborative transportation and avoid collisions between drones in multi-UAV transportation have become technical problems that need to be solved urgently. Summary of the Invention

[0007] In view of this, the purpose of the present invention is to provide an air-ground coordinated material transportation method, device, electronic equipment and storage medium to alleviate the technical problems of low efficiency of traditional air-ground coordinated air-ground transportation and collisions between drones in multi-drone transportation.

[0008] In a first aspect, an embodiment of the present invention provides an air-ground coordinated material transportation method, comprising:

[0009] When a robot located inside the warehouse receives supplies, it calculates the robot time cost of reaching each handover point, and obtains the drone time cost of the drone associated with each handover point to reach the corresponding handover point. The handover point is the location where the robot hands over the supplies to the drone.

[0010] According to the robot time cost of the robot to each handover point and the drone time cost of each drone to reach the corresponding handover point, a target handover point is determined among the handover points, and then the robot transports the materials to the target handover point, wherein the target handover point is the point where the difference between the robot time cost of the robot to the target handover point and the drone time cost of the target drone corresponding to the target handover point to reach the target handover point is minimized;

[0011] When the target UAV receives the materials at the target handover point, the A-star algorithm with time cost barrier is used to plan the path of the target UAV to obtain the optimal material transportation path of the target UAV, so that the target UAV can transport the materials to the destination of the materials according to the optimal material transportation path. When the target UAV flies according to the optimal material transportation path, it will not collide with other UAVs in flight.

[0012] Furthermore, the path of the target UAV is planned using the time-cost-impeded A-star algorithm, including:

[0013] Calculate the total time cost for the UAV in flight to reach each flight node, where each flight node is a flyable node obtained by planning the UAV's path using the A-star algorithm with time cost barrier / A-star algorithm;

[0014] Put the target intersection point as the starting node into the first list;

[0015] Determine whether the first list is empty;

[0016] If it is empty, the search fails;

[0017] If the target node exists in the first list, the search is successful, wherein the target node is the destination of the target UAV's flight, and the destination is the destination of the supplies;

[0018] The node with the smallest f value is taken from the first list as the current node, and is added to the second list. The node with the smallest f value taken first is the starting node, and the f value represents the total cost from the starting node to the target node through the node.

[0019] Calculate all reachable nodes adjacent to the current node to obtain a set of child nodes;

[0020] For each child node:

[0021] If the child node is a flight node of another UAV, then calculate the absolute value of the difference between the total time cost required for the target UAV to fly from the starting node to the child node and the total time cost of other UAVs to fly to the child node;

[0022] If the absolute value of the difference is less than a preset time threshold, the child node is discarded;

[0023] If the child node is in the second list, discard the child node;

[0024] If the child node is in the first list, check whether its f value calculated by the current node is smaller; if so, update its f value and set its parent node to the current node;

[0025] If the child node is not in the first list, add it as a node to the first list, calculate its f value through the current node, and set its parent node to the current node;

[0026] Return to the step of determining whether the first list is empty.

[0027] Furthermore, the node includes: a truncated octahedron with an edge length of a preset value.

[0028] Furthermore, according to the time cost calculation formula Calculate the time cost from the current node to its adjacent reachable child nodes, where: Indicates the current node To its adjacent reachable child nodes The time cost, Indicates the current node The average velocity function, represents the edge length of the truncated octahedron.

[0029] Furthermore, the total time cost required for the target UAV to travel from the starting node to the child node is: ,in, Indicates that the target UAV moves from the starting node to the child node The total time cost required, Indicates the parent node of the target UAV from the starting node to the child node The total time cost required, Indicates the parent node To this child node time cost.

[0030] Furthermore, the f value of the current node is calculated, including:

[0031] Calculation formula based on f value Calculate the f value of the child node, where Indicates the child node The f-value, Represents the path from the starting node to the child node Estimated consumption, Represents a child node Estimated cost to the target node, Represents a child node Other factors affect consumption.

[0032] Furthermore, the handover point is set outside the material warehouse.

[0033] In a second aspect, an embodiment of the present invention further provides an air-ground coordinated material transport device, comprising:

[0034] a calculation and acquisition unit, configured to calculate, when a robot located inside the material warehouse receives materials, a robot time cost for the robot to reach each handover point, and to acquire a drone time cost for a drone associated with each handover point to reach the corresponding handover point, wherein the handover point is a location where the robot transfers the materials to the drone;

[0035] a determination and delivery unit, configured to determine a target handover point among the handover points based on a robot time cost of the robot to each handover point and a drone time cost of each drone to the corresponding handover point, so that the robot delivers the materials to the target handover point, wherein the target handover point is the point at which the difference between the robot time cost of the robot to the target handover point and the drone time cost of the target drone corresponding to the target handover point to reach the target handover point is minimized;

[0036] A planning unit is used to plan the path of the target UAV using the time-cost-impeded A-star algorithm when the target UAV receives the materials at the target handover point, so as to obtain the optimal material transportation path of the target UAV, so that the target UAV can transport the materials to the destination of the materials according to the optimal material transportation path, wherein the target UAV will not collide with other UAVs in flight when flying according to the optimal material transportation path.

[0037] In a third aspect, an embodiment of the present invention further provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of any one of the methods described in the first aspect when executing the computer program.

[0038] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores machine-executable instructions. When the machine-executable instructions are called and executed by a processor, the machine-executable instructions prompt the processor to execute any method described in the first aspect above.

[0039] In an embodiment of the present invention, an air-ground coordinated material transportation method is provided, comprising: when a robot located inside a material warehouse receives materials, calculating a robot time cost for the robot to reach each handover point, and obtaining a drone time cost for a drone associated with each handover point to reach the corresponding handover point, wherein the handover point is the location where the robot transfers the materials to the drone; determining a target handover point among the handover points based on the robot time cost for the robot to reach each handover point and the drone time cost for each drone to reach the corresponding handover point, and then the robot transports the materials to the target handover point, wherein the difference between the robot time cost for the robot to reach the target handover point and the drone time cost for the target drone corresponding to the target handover point to reach the target handover point is minimized; when a target drone receives the materials at the target handover point, planning a path for the target drone using a time-cost-impeded A-star algorithm to obtain an optimal material transportation path for the target drone, so that the target drone transports the materials to the destination along the optimal material transportation path, wherein the target drone will not collide with other drones in flight when flying along the optimal material transportation path. From the above description, it can be seen that in the air-ground collaborative material transportation method of the present invention, the difference between the robot time cost of the robot to the target handover point and the drone time cost of the target drone corresponding to the target handover point to reach the target handover point is the smallest, that is, the waiting time between the robot and the target drone at the target handover point is the shortest, which greatly enhances the air-ground collaborative transportation of air-ground cooperation and improves the efficiency of air-ground collaborative transportation. In addition, the optimal material transportation path of the target drone obtained by planning with the time cost-hindered A-star algorithm can prevent the target drone from colliding with other drones in flight when flying according to the optimal material transportation path, that is, avoiding collisions between drones in multi-drone transportation, and having good safety, alleviating the technical problems of low efficiency of traditional air-ground collaborative air-ground transportation and collisions between drones in multi-drone transportation. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0041] Figure 1 A flow chart of an air-ground coordinated material transportation method provided by an embodiment of the present invention;

[0042] Figure 2 A schematic diagram of a truncated octahedron provided in an embodiment of the present invention;

[0043] Figure 3 A schematic diagram of a tessellated truncated octahedron provided in an embodiment of the present invention;

[0044] Figure 4 A schematic diagram of an air-ground coordinated material transport device provided in an embodiment of the present invention;

[0045] Figure 5 A schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0046] The following will clearly and completely describe the technical solutions of the present invention in conjunction with the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0047] Traditional air-ground collaborative transportation has low efficiency, and there is a risk of collision between drones during multi-drone transportation.

[0048] Based on this, in the air-ground collaborative material transportation method of the present invention, the difference between the robot time cost of the robot to the target handover point and the drone time cost of the target drone corresponding to the target handover point to reach the target handover point is minimized, that is, the waiting time between the robot and the target drone at the target handover point is the shortest, which greatly enhances the air-ground collaborative transportation of air-ground cooperation and improves the efficiency of air-ground collaborative transportation. In addition, the optimal material transportation path of the target drone obtained by planning the A-star algorithm with time cost obstruction can ensure that the target drone will not collide with other drones in flight when flying according to the optimal material transportation path, which avoids collisions between drones in multi-drone transportation and has good safety.

[0049] To facilitate understanding of this embodiment, an air-ground coordinated material transportation method disclosed in an embodiment of the present invention is first introduced in detail.

[0050] Example 1:

[0051] According to an embodiment of the present invention, an embodiment of an air-ground coordinated material transportation method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0052] Figure 1 FIG. 1 is a flow chart of an air-ground coordinated material transportation method according to an embodiment of the present invention. Figure 1 As shown, the method includes the following steps:

[0053] In step S102, when a robot located inside the warehouse receives supplies, it calculates the robot time cost of reaching each handover point and obtains the drone time cost of the drone associated with each handover point to reach the corresponding handover point. The handover point is the location where the robot transfers supplies to the drone.

[0054] In the embodiment of the present invention, an unmanned aircraft is referred to as a "drone", abbreviated as "UAV" in English, which is an unmanned aircraft controlled by a radio remote control device and a self-contained program control device, or is operated completely or intermittently autonomously by an onboard computer.

[0055] The method of the present invention is realized by the collaborative transportation of robots and drones. Professional transportation intelligent robots are deployed inside the material warehouse, and professional transportation drones are deployed outside the material warehouse. The robots pick up materials in the material warehouse and deliver them to the target handover point. At the target handover point, the materials are handed over between the robot and the drone, and then the drone transports the materials to the destination of the materials.

[0056] This invention allocates collaboration between aerial drones and ground robots using the time dimension. When a robot receives supplies from a warehouse, it calculates the robot's time cost to reach each handover point and also calculates the drone time cost for the drone associated with each handover point. The robot time cost is specifically the time required for the robot to reach each handover point, while the drone time cost is specifically the time required for the drone to reach the corresponding handover point.

[0057] Professional intelligent robots are deployed within the warehouse. Following instructions, they automatically navigate to designated areas, use their gripping modules to retrieve items, and then deliver them. Equipped with multiple sensors, the robots provide reliable environmental perception and dynamic obstacle avoidance, enabling high-precision positioning and navigation in complex environments. At the transfer point, the robots transfer the supplies to drones. The drones then receive the supplies and deliver them quickly and accurately to their destinations. Using drones, companies can cover a wider geographic area and reach more potential customers.

[0058] Step S104: Based on the robot time cost of the robot reaching each handover point and the drone time cost of each drone reaching the corresponding handover point, a target handover point is determined among the handover points, and the robot then delivers the materials to the target handover point. The target handover point is the one where the difference between the robot time cost of the robot reaching the target handover point and the drone time cost of the target drone corresponding to the target handover point reaching the target handover point is minimized.

[0059] In step S106, when the target UAV receives the materials from the target handover point, the A-star algorithm with time cost barrier is used to plan the path of the target UAV to obtain the optimal material transportation path of the target UAV, so that the target UAV can transport the materials to the destination of the materials according to the optimal material transportation path. When the target UAV flies according to the optimal material transportation path, it will not collide with other UAVs in flight.

[0060] Specifically, the traditional A-star algorithm is a classic heuristic search algorithm, evolved from combining the advantages of Dijkstra's algorithm and Breadth-First Search (BFS) algorithms. The present invention's time-cost-impeded A-star algorithm adds a time-cost impediment (i.e., using the time dimension to calculate path-finding obstacles to form a path plan) to the traditional A-star algorithm (i.e., the drone's pathfinding algorithm) to prevent collisions between drones operating in different time dimensions. This means that when a target drone follows the optimal material transport path (planned using the time-cost-impeded A-star algorithm), it will not collide with other drones in flight.

[0061] In an embodiment of the present invention, an air-ground coordinated material transportation method is provided, comprising: when a robot located inside a material warehouse receives materials, calculating a robot time cost for the robot to reach each handover point, and obtaining a drone time cost for a drone associated with each handover point to reach the corresponding handover point, wherein the handover point is the location where the robot transfers the materials to the drone; determining a target handover point among the handover points based on the robot time cost for the robot to reach each handover point and the drone time cost for each drone to reach the corresponding handover point, and then the robot transports the materials to the target handover point, wherein the difference between the robot time cost for the robot to reach the target handover point and the drone time cost for the target drone corresponding to the target handover point to reach the target handover point is minimized; when a target drone receives the materials at the target handover point, planning a path for the target drone using a time-cost-impeded A-star algorithm to obtain an optimal material transportation path for the target drone, so that the target drone transports the materials to the destination along the optimal material transportation path, wherein the target drone will not collide with other drones in flight when flying along the optimal material transportation path. From the above description, it can be seen that in the air-ground collaborative material transportation method of the present invention, the difference between the robot time cost of the robot to the target handover point and the drone time cost of the target drone corresponding to the target handover point to reach the target handover point is the smallest, that is, the waiting time between the robot and the target drone at the target handover point is the shortest, which greatly enhances the air-ground collaborative transportation of air-ground cooperation and improves the efficiency of air-ground collaborative transportation. In addition, the optimal material transportation path of the target drone obtained by planning with the time cost-hindered A-star algorithm can prevent the target drone from colliding with other drones in flight when flying according to the optimal material transportation path, that is, avoiding collisions between drones in multi-drone transportation, and having good safety, alleviating the technical problems of low efficiency of traditional air-ground collaborative air-ground transportation and collisions between drones in multi-drone transportation.

[0062] The above content briefly introduces the air-ground coordinated material transportation method of the present invention. The specific contents involved are described in detail below.

[0063] In an optional embodiment of the present invention, the path of the target UAV is planned using the time-cost-impeded A-star algorithm, which specifically includes the following steps:

[0064] (1) Calculate the total time cost for a UAV in flight to fly to each flight node, where each flight node is a flyable node obtained by planning the UAV's path using the A-star algorithm with time cost barrier / A-star algorithm;

[0065] Specifically, before this step, the starting node and target node of all running drones are determined, and the latitude, longitude and altitude information of the drone at the current moment are converted into scene coordinates (x, y, z). The same coordinate system facilitates calculation. This process is the process of establishing a scene coordinate system and then converting the latitude, longitude and altitude information of the drone into the scene coordinate system.

[0066] When the first drone is deployed, the flight node is the flyable node obtained by planning its path using the A-star algorithm. It should be noted that drones take off one by one in sequence, not simultaneously. This means that after one drone's path planning is complete, another drone that has received the cargo can take off.

[0067] The calculation process of the above total time cost is: first calculate the time cost of the drone from one flight node to another adjacent flight node (the calculation process of the time cost is described below, please refer to the introduction below), and then add the calculated time cost from the current flight node to any flight node to obtain the total time cost of any flight node.

[0068] (2) Put the target intersection point as the starting node into the first list;

[0069] (3) Determine whether the first list is empty;

[0070] (4) If it is empty, the search fails;

[0071] (5) If the target node exists in the first list, the search is successful, where the target node is the destination of the target UAV’s flight, and the destination is the destination of the supplies;

[0072] Specifically, after a successful search, the nodes in the second list are used as flight nodes on the planned optimal material transportation route. In implementation, starting from the target node, the stored parent nodes are continuously searched until the starting node is found. The route formed by all nodes is the optimal material transportation route for the target UAV. The first list mentioned above can specifically be an open list.

[0073] (6) Take the node with the smallest f value from the first list as the current node and add it to the second list. The node with the smallest f value taken for the first time is the starting node. The f value represents the total cost from the starting node to the target node through this node.

[0074] Specifically, the node is a node corresponding to the value f. The second list may be a close list.

[0075] (7) Calculate all reachable nodes adjacent to the current node and obtain a set of child nodes;

[0076] (8) For each child node:

[0077] If the child node is a flight node of another UAV, the absolute value of the difference between the total time cost required for the target UAV to fly from the starting node to the child node and the total time cost of other UAVs to fly to the child node is calculated;

[0078] Specifically, the other drones mentioned above are other drones in flight.

[0079] If the absolute value of the difference is less than the preset time threshold, the child node is discarded;

[0080] Specifically, if the absolute value of the difference is less than a preset time threshold, it indicates that the target drone will collide with other drones in flight, and the child node cannot be used as the target drone's flight node. This prevents the target drone's planned path from colliding with a drone in flight at the same time. This means that when the traditional A-star algorithm calculates f(n) for the topological grid, a new total time cost is simultaneously calculated. The result of this total time cost comparison is used to plot the real-time state of the topological grid that the A-star algorithm currently reaches. After discarding the child node, the f-values ​​of the remaining child nodes are calculated using the current node.

[0081] If the child node is in the second list, discard the child node;

[0082] Specifically, after discarding the child node, the f-values ​​of the remaining child nodes are calculated through the current node.

[0083] If the child node is in the first list, check whether its f value calculated by the current node is smaller. If it is smaller, update its f value and set its parent node to the current node;

[0084] Specifically, if the child node is already in the first list and the f value of the child node in the first list is greater than the f value of the child node calculated by the current node, then the smaller f value is used to update the f value of the child node in the first list, and its parent node is set as the current node.

[0085] If the child node is not in the first list, add it as a node to the first list, calculate its f value through the current node, and set its parent node to the current node;

[0086] Specifically, after the f value calculation is completed, the f value is stored in the current node, that is, the f value of the current node is obtained, and its parent node is set as the current node.

[0087] (9) Return to the step of determining whether the first list is empty.

[0088] In an optional embodiment of the present invention, the node comprises: a truncated octahedron with an edge length of a preset value.

[0089] Specifically, the nodes here include the parent node, child node, flight node, start node, target node and node mentioned above, which are all truncated octahedrons with edge lengths of preset values, such as Figure 2 The corresponding schematic diagram of the tacked truncated octahedron is shown in Figure 3 shown.

[0090] The truncated octahedron is a semi-regular polyhedron with fourteen faces. It belongs to the Archimedean solid and is also a parallelepiped. It is composed of 6 squares and 8 regular hexagons, with a total of 14 faces, 36 edges and 24 vertices. Because each face has point symmetry, the truncated octahedron is also a ring-shaped polyhedron. It can be seen that the path-finding algorithm for three-dimensional space in the present invention uses truncated octahedra with preset edge lengths to densely pave the flight area of ​​the drone and serve as the basic table of star A. On the one hand, the truncated octahedron can densely pave the space, and on the other hand, the truncated octahedron has a sufficient number of faces compared to the cube, and the flight direction of the drone is more comprehensive and rich.

[0091] In an optional embodiment of the present invention, according to the time cost calculation formula Calculate the time cost from the current node to its adjacent reachable child nodes, where Indicates the current node To its adjacent reachable child nodes The time cost, Indicates the current node The average velocity function, represents the edge length of a truncated octahedron.

[0092] In an optional embodiment of the present invention, the total time cost required for the target UAV to travel from the starting node to the child node is: ,in, Indicates that the target drone moves from the starting node to the child node The total time cost required, Indicates the target drone's path from the starting node to the parent node of this child node The total time cost required, Indicates the parent node To this child node time cost.

[0093] In an optional embodiment of the present invention, calculating the f value of the current node includes:

[0094] Calculation formula based on f value Calculate the f value of the child node, where Indicates the child node The f-value, Represents the path from the starting node to the child node Estimated consumption, Represents a child node Estimated cost to the target node, Represents a child node Other factors affect consumption.

[0095] Specifically, the above process of calculating the f-value is the same as the process of calculating the f-value in the traditional technology, which will not be repeated here.

[0096] In an optional embodiment of the present invention, the handover point is set outside the material warehouse.

[0097] It should be noted that every time a new drone takes off from the starting node or takes off from the target node back to the starting node, the above-mentioned A-star algorithm with time cost barrier is repeated to plan the path of the target drone.

[0098] The method of the present invention can solve transportation problems in scenarios that are not suitable for human transportation due to infection risks, etc.; it can solve transportation problems in scenarios that are not suitable for land transportation due to terrain problems, etc.; it can solve the problem of drone collisions encountered in multi-drone transportation and strengthen air-ground cooperation; it is a path planning method that avoids multi-drone collisions in the time dimension.

[0099] The proposed method utilizes truncated octahedral tessellation and the A-star algorithm, along with a time-cost function and f-value calculation method, to achieve intelligent planning and dynamic adjustment of drone flight paths. This path planning method can be flexibly adjusted based on actual conditions, ensuring that drones can complete their missions efficiently and accurately.

[0100] Example 2:

[0101] An embodiment of the present invention also provides an air-ground coordinated material transportation device, which is mainly used to execute the air-ground coordinated material transportation method provided in Example 1 of the present invention. The air-ground coordinated material transportation device provided in the embodiment of the present invention is specifically introduced below.

[0102] Figure 4 is a schematic diagram of an air-ground coordinated material transport device according to an embodiment of the present invention, such as Figure 4 As shown, the device mainly includes: a calculation and acquisition unit 10, a determination and delivery unit 20, and a planning unit 30, wherein:

[0103] A calculation and acquisition unit is configured to calculate the robot time cost for the robot to reach each handover point when the robot receives the materials, and to obtain the drone time cost for the drone associated with each handover point to reach the corresponding handover point, wherein the handover point is the location where the robot transfers the materials to the drone;

[0104] The determination and delivery unit is used to determine the target handover point among the handover points based on the robot time cost of the robot to each handover point and the drone time cost of each drone to reach the corresponding handover point, and then the robot delivers the materials to the target handover point, wherein the target handover point is the one where the difference between the robot time cost of the robot to the target handover point and the drone time cost of the target drone corresponding to the target handover point to reach the target handover point is the minimum;

[0105] The planning unit is used to plan the path of the target UAV using the time-cost-impeded A-star algorithm when the target UAV receives the materials at the target handover point, and obtain the optimal material transportation path of the target UAV, so that the target UAV can transport the materials to the destination of the materials according to the optimal material transportation path. Among them, when the target UAV flies according to the optimal material transportation path, it will not collide with other UAVs in flight.

[0106] In an embodiment of the present invention, an air-ground coordinated material transportation device is provided, comprising: when a robot located inside a material warehouse receives materials, calculating the robot time cost of the robot to each handover point, and obtaining the drone time cost of the drone associated with each handover point to reach the corresponding handover point, wherein the handover point is the location where the robot transfers the materials to the drone; based on the robot time cost of the robot to each handover point and the drone time cost of each drone to reach the corresponding handover point, determining a target handover point among the handover points, and then the robot transports the materials to the target handover point, wherein the difference between the robot time cost of the robot to the target handover point and the drone time cost of the target drone corresponding to the target handover point to reach the target handover point is minimized; when the target drone receives the materials at the target handover point, planning the path of the target drone using the time cost-impeded A-star algorithm to obtain an optimal material transportation path for the target drone, so that the target drone can transport the materials to the destination of the materials along the optimal material transportation path, wherein the target drone will not collide with other drones in flight when flying along the optimal material transportation path. From the above description, it can be seen that in the air-ground collaborative material transportation device of the present invention, the difference between the robot time cost of the robot to the target handover point and the drone time cost of the target drone corresponding to the target handover point to reach the target handover point is the smallest, that is, the waiting time between the robot and the target drone at the target handover point is the shortest, which greatly enhances the air-ground collaborative transportation of air-ground cooperation and improves the efficiency of air-ground collaborative transportation. In addition, the optimal material transportation path of the target drone obtained by planning with the time cost barrier A-star algorithm can prevent the target drone from colliding with other drones in flight when flying according to the optimal material transportation path, that is, avoiding collisions between drones in multi-drone transportation, and having good safety, alleviating the technical problems of low efficiency of air-ground collaborative transportation and collisions between drones in multi-drone transportation in traditional air-ground cooperation.

[0107] Optionally, the planning unit is also used to: calculate the total time cost for the UAV in flight to fly to each flight node, wherein each flight node is a flyable node obtained by planning the path of the UAV using the A-star algorithm / A-star algorithm with time cost obstruction; put the target intersection point as the starting node into the first list; determine whether the first list is empty; if it is empty, the search fails; if there is a target node in the first list, the search is successful, wherein the target node is the end point of the target UAV's flight, and the end point is the destination of the materials; take the node with the smallest f value from the first list as the current node, and add it to the second list, wherein the node with the smallest f value taken for the first time is the starting node, and the f value represents the total consumption from the starting node through the node to the target node; calculate all the adjacent reachable nodes of the current node , and then obtain a group of child nodes; for each child node: if the child node is a flight node of other UAVs, calculate the absolute value of the difference between the total time cost required for the target UAV to fly from the starting node to the child node and the total time cost of other UAVs to fly to the child node; if the absolute value of the difference is less than the preset time threshold, discard the child node; if the child node is in the second list, discard the child node; if the child node is in the first list, check whether the f value calculated by the current node is smaller, if smaller, update its f value and set its parent node to the current node; if the child node is not in the first list, add it as a node to the first list, calculate its f value through the current node, and set its parent node to the current node; return to the step of determining whether the first list is empty.

[0108] Optionally, the node includes: a truncated octahedron whose edge length is a preset value.

[0109] Optionally, calculate the time cost according to the formula Calculate the time cost from the current node to its adjacent reachable child nodes, where Indicates the current node To its adjacent reachable child nodes The time cost, Indicates the current node The average velocity function, represents the edge length of a truncated octahedron.

[0110] Optionally, the total time cost required for the target UAV to travel from the starting node to the child node is: ,in, Indicates that the target drone moves from the starting node to the child node The total time cost required, Indicates the target drone's path from the starting node to the parent node of this child node The total time cost required, Indicates the parent node To this child node time cost.

[0111] Optionally, the planning unit is further configured to: calculate the formula according to the f value Calculate the f value of the child node, where Indicates the child node The f-value, Represents the path from the starting node to the child node Estimated consumption, Represents a child node Estimated cost to the target node, Represents a child node Other factors affect consumption.

[0112] Optionally, the handover point is set outside the material warehouse.

[0113] The device provided in the embodiment of the present invention has the same implementation principle and technical effects as those in the aforementioned method embodiment. For the sake of brief description, for matters not mentioned in the device embodiment, reference can be made to the corresponding content in the aforementioned method embodiment.

[0114] like Figure 5 As shown, an electronic device 600 provided in an embodiment of the present application includes: a processor 601, a memory 602 and a bus, wherein the memory 602 stores machine-readable instructions executable by the processor 601. When the electronic device is running, the processor 601 communicates with the memory 602 through the bus, and the processor 601 executes the machine-readable instructions to perform the steps of the above-mentioned air-ground coordinated material transportation method.

[0115] Specifically, the above-mentioned memory 602 and processor 601 can be general-purpose memories and processors, which are not specifically limited here. When the processor 601 runs the computer program stored in the memory 602, it can execute the above-mentioned air-ground coordinated material transportation method.

[0116] The processor 601 may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by an integrated logic circuit of hardware in the processor 601 or by instructions in the form of software. The above-mentioned processor 601 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The various methods, steps, and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in conjunction with the embodiments of the present application can be directly embodied as being executed by a hardware decoding processor, or can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium well-known in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. The storage medium is located in memory 602, and processor 601 reads the information in memory 602 and performs the steps of the above method in conjunction with its hardware.

[0117] Corresponding to the above-mentioned air-ground coordinated material transportation method, an embodiment of the present application also provides a computer-readable storage medium, which stores machine-executable instructions. When the computer-executable instructions are called and executed by the processor, the computer-executable instructions prompt the processor to execute the steps of the above-mentioned air-ground coordinated material transportation method.

[0118] The air-ground coordinated material transportation device provided in the embodiment of the present application can be specific hardware on the equipment or software or firmware installed on the equipment. The implementation principle and technical effects of the device provided in the embodiment of the present application are the same as those of the aforementioned method embodiment. For the sake of brief description, for parts not mentioned in the device embodiment, reference can be made to the corresponding content in the aforementioned method embodiment. Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can all refer to the corresponding processes in the aforementioned method embodiment, and will not be repeated here.

[0119] In the embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interface, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0120] For another example, the flowcharts and block diagrams in the accompanying drawings show the possible architectures, functions and operations of the devices, methods and computer program products according to multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of code, and the module, program segment or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0121] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0122] In addition, each functional unit in the embodiments provided in the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0123] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling an electronic device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the air-ground collaborative material transportation method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0124] It should be noted that similar numbers and letters represent similar items in the following figures. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. In addition, the terms "first", "second", "third", etc. are only used to distinguish the description and are not to be understood as indicating or implying relative importance.

[0125] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present application, which are used to illustrate the technical solutions of the present application, rather than to limit them. The scope of protection of the present application is not limited thereto. Although the present application has been described in detail with reference to the above-mentioned embodiments, those skilled in the art should understand that any person skilled in the art can modify or easily conceive of changes to the technical solutions described in the above-mentioned embodiments within the technical scope disclosed in the present application, or perform equivalent replacements for some of the technical features thereof. However, these modifications, changes, or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application. They should all be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. An air-ground coordinated material transportation method, characterized in that: include: When a robot located inside the warehouse receives supplies, it calculates the robot time cost of reaching each handover point, and obtains the drone time cost of the drone associated with each handover point to reach the corresponding handover point. The handover point is the location where the robot hands over the supplies to the drone. According to the robot time cost of the robot to each handover point and the drone time cost of each drone to reach the corresponding handover point, a target handover point is determined among the handover points, and then the robot transports the materials to the target handover point, wherein the target handover point is the point where the difference between the robot time cost of the robot to the target handover point and the drone time cost of the target drone corresponding to the target handover point to reach the target handover point is minimized; When the target UAV receives the materials at the target handover point, the A-star algorithm with time cost barrier is used to plan the path of the target UAV to obtain the optimal material transportation path of the target UAV, so that the target UAV can transport the materials to the destination of the materials according to the optimal material transportation path. When the target UAV flies according to the optimal material transportation path, it will not collide with other UAVs in flight.

2. The method according to claim 1, characterized in that The path of the target UAV is planned using the time-cost-impeded A-star algorithm, including: Calculate the total time cost for the UAV in flight to reach each flight node, where each flight node is a flyable node obtained by planning the UAV's path using the A-star algorithm with time cost barrier / A-star algorithm; Put the target intersection point as the starting node into the first list; Determine whether the first list is empty; If it is empty, the search fails; If the target node exists in the first list, the search is successful, wherein the target node is the destination of the target UAV's flight, and the destination is the destination of the supplies; The node with the smallest f value is taken from the first list as the current node, and is added to the second list. The node with the smallest f value taken first is the starting node, and the f value represents the total cost from the starting node to the target node through the node. Calculate all reachable nodes adjacent to the current node to obtain a set of child nodes; For each child node: If the child node is a flight node of another UAV, then calculate the absolute value of the difference between the total time cost required for the target UAV to fly from the starting node to the child node and the total time cost of other UAVs to fly to the child node; If the absolute value of the difference is less than a preset time threshold, the child node is discarded; If the child node is in the second list, discard the child node; If the child node is in the first list, check whether its f value calculated by the current node is smaller; if so, update its f value and set its parent node to the current node; If the child node is not in the first list, add it as a node to the first list, calculate its f value through the current node, and set its parent node to the current node; Return to the step of determining whether the first list is empty.

3. The method according to claim 2, characterized in that The node includes: a truncated octahedron with an edge length of a preset value.

4. The method according to claim 3, characterized in that Calculation formula based on time cost Calculate the time cost from the current node to its adjacent reachable child nodes, where: Indicates the current node To its adjacent reachable child nodes The time cost, Indicates the current node The average velocity function, represents the edge length of the truncated octahedron.

5. The method according to claim 4, characterized in that The total time cost required for the target UAV to travel from the starting node to the child node is: ,in, Indicates that the target UAV moves from the starting node to the child node The total time cost required, Indicates the parent node of the target UAV from the starting node to the child node The total time cost required, Indicates the parent node To this child node time cost.

6. The method according to claim 2, characterized in that Calculating the f value of the current node includes: Calculation formula based on f value Calculate the f value of the child node, where Indicates the child node The f-value, Represents the path from the starting node to the child node Estimated consumption, Represents a child node Estimated cost to the target node, Represents a child node Other factors affect consumption.

7. The method according to claim 1, characterized in that The handover point is arranged outside the material warehouse.

8. An air-ground coordinated material transport device, characterized in that: include: a calculation and acquisition unit, configured to calculate, when a robot located inside the material warehouse receives materials, a robot time cost for the robot to reach each handover point, and to acquire a drone time cost for a drone associated with each handover point to reach the corresponding handover point, wherein the handover point is a location where the robot transfers the materials to the drone; a determination and delivery unit, configured to determine a target handover point among the handover points based on a robot time cost of the robot to each handover point and a drone time cost of each drone to the corresponding handover point, so that the robot delivers the materials to the target handover point, wherein the target handover point is the point at which the difference between the robot time cost of the robot to the target handover point and the drone time cost of the target drone corresponding to the target handover point to reach the target handover point is minimized; A planning unit is used to plan the path of the target UAV using the time-cost-impeded A-star algorithm when the target UAV receives the materials at the target handover point, so as to obtain the optimal material transportation path of the target UAV, so that the target UAV can transport the materials to the destination of the materials according to the optimal material transportation path, wherein the target UAV will not collide with other UAVs in flight when flying according to the optimal material transportation path.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores machine-executable instructions. When the machine-executable instructions are called and executed by a processor, the machine-executable instructions prompt the processor to execute the method according to any one of claims 1 to 7.

Images