Vehicle-unmanned aerial vehicle cooperative distribution method and system supporting mobile take-off and landing

Abstract

The invention provides a vehicle-unmanned aerial vehicle cooperative distribution method and system supporting mobile take-off and landing. The method comprises the following steps: obtaining distribution data in a distribution area; operating an upper solver, and deciding a vehicle distribution task, a vehicle planning path and an unmanned aerial vehicle distribution task based on the distribution data; a lower-layer solver is operated, and on the basis of considering vehicle movement, a planned path of unmanned aerial vehicle distribution and return flight is solved based on a vehicle planned path and an unmanned aerial vehicle distribution task; the vehicle carrying the unmanned aerial vehicle team goes to the client point according to the vehicle planning path to execute a vehicle distribution task; after the unmanned aerial vehicle loads goods, the unmanned aerial vehicle distribution task is executed according to the unmanned aerial vehicle distribution and return flight planning path; all the unmanned aerial vehicles complete return voyage after executing the unmanned aerial vehicle distribution task, all the client points complete distribution, and the vehicle carries the unmanned aerial vehicle team to return to the warehouse; and after all the vehicles and the unmanned aerial vehicles return to the warehouse, state check and maintenance are carried out to complete the distribution process. According to the method, the overall distribution efficiency is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle (UAV) air-ground collaborative logistics, and in particular to a vehicle-unmanned aerial vehicle (UAV) collaborative delivery method and system supporting mobile take-off and landing. Background Art

[0002] With the continuous development of the e-commerce industry in recent years and users' demand for stronger delivery timeliness, the traditional logistics distribution model is becoming increasingly difficult to meet the growing terminal delivery needs.

[0003] The advantages of using drones to deliver eligible packages from the terminal distribution center to customers include fast response, efficient delivery, strong flexibility, high cost-effectiveness, and no impact from traffic congestion and ground obstacles. However, drone delivery is currently still limited by its relatively limited load capacity and battery capacity, and cannot achieve long-distance, multi-customer delivery tasks without one-way replenishment. Therefore, the application scenarios of independent drone delivery are relatively narrow, and the objects of delivery are often special goods with light weight and strong timeliness requirements, such as fresh food and emergency rescue supplies, and they often rely on ground facilities for replenishment, thereby extending the delivery range of drones and improving the endurance of delivery services.

[0004] In order to further improve the delivery efficiency and increase the utilization rate of drones, the delivery model based on the collaboration of vehicles and drones has attracted more and more attention. Although the research on the collaborative delivery of vehicles and drones has increased year by year, most of the delivery solutions proposed in existing studies restrict vehicles to parking points, launch and recover drones, increase the extra waiting time of trucks or drones, limit the flexibility of dynamic collaboration between drones and vehicles and further improve the delivery efficiency, and there is still a lot of room for optimization. Summary of the invention

[0005] In view of this, the present invention provides a vehicle-UAV collaborative delivery method and system supporting mobile take-off and landing to solve the above-mentioned problems.

[0006] The present invention provides a vehicle-UAV collaborative distribution method supporting mobile take-off and landing, comprising: obtaining distribution data in a distribution area, wherein the distribution data includes location information of customer points, cargo demand information corresponding to each customer point, the number of UAVs carried by a vehicle, performance parameters of the vehicle and each UAV, warehouse location information and other environmental data; running an upper-layer solver to decide vehicle distribution tasks, vehicle planning paths and UAV distribution tasks based on the distribution data; running a lower-layer solver to solve the planned paths for UAV distribution and return based on the vehicle planning paths and the UAV distribution tasks, taking into account vehicle movement; the vehicle carrying a UAV team goes to the assigned customer point according to the vehicle planning paths to perform the vehicle distribution tasks; after the UAV is loaded with cargo, it goes to the assigned customer point according to the planned paths for UAV distribution and return to perform the UAV distribution tasks; after all UAVs complete the return after performing the UAV distribution tasks and all customer points have completed distribution, the vehicle carrying the UAV team returns to the warehouse; after all vehicles and UAVs have returned to the warehouse, status inspection and maintenance are performed to complete the distribution process.

[0007] In another implementation of the present invention, it also includes: establishing a two-dimensional coordinate system of the distribution scene with the warehouse location as the coordinate origin; performing calculations based on the two-dimensional coordinate system to obtain the location information of the customer point, the warehouse location information, the vehicle's current location information and other environmental data.

[0008] In another implementation of the present invention, the upper-level solver is run to decide vehicle delivery tasks, vehicle path planning and drone delivery tasks based on the delivery data, including: running the upper-level solver to initialize a list of currently available drones and vehicles as objects to which tasks can be assigned; based on the delivery data, customer points are screened to retain customer points that have not completed delivery tasks and to which no vehicle drones are assigned for delivery; based on the respective load capacities and maximum ranges of different drones, a list of customer points that can complete delivery corresponding to each model of drone is obtained; the remaining customer points are sorted according to the distance from the vehicle, and starting from the customer point closest to the vehicle, tasks are assigned to available vehicles and drones according to specific priorities to obtain vehicle delivery tasks, vehicle path planning and drone delivery tasks.

[0009] In another implementation of the present invention, the priority includes: when tasks are assigned, they are first assigned to drones with faster speeds, followed by drones with slower speeds, and finally to vehicles.

[0010] In another implementation of the present invention, it also includes: the vehicle provides charging and replenishment services for the drone, and after the drone completes the sub-delivery task, it returns to the vehicle to perform replenishment operations.

[0011] In another aspect of the present invention, a vehicle-UAV collaborative distribution system supporting mobile take-off and landing is provided, comprising: a data acquisition module: acquiring distribution data within a distribution area, the distribution data including location information of customer points, cargo demand information corresponding to each customer point, the number of UAVs carried by the vehicle, performance parameters of the vehicle and each UAV, warehouse location information and other environmental data; a task allocation and path planning module: running an upper-level solver, deciding vehicle distribution tasks, vehicle planning paths and UAV distribution tasks based on the distribution data; running a lower-level solver, solving the planned paths for UAV distribution and return based on the vehicle planning paths and the UAV distribution tasks, taking into account vehicle movement; a distribution module: a vehicle carrying a UAV team goes to the assigned customer point according to the vehicle planning path to perform the vehicle distribution task; after the UAV is loaded with cargo, it goes to the assigned customer point according to the planned path for UAV distribution and return to perform the UAV distribution task; after all UAVs complete the return after performing the UAV distribution task, and all customer points have completed distribution, the vehicle carrying the UAV team returns to the warehouse; after all vehicles and UAVs return to the warehouse, status inspection and maintenance are performed to complete the distribution process.

[0012] In another aspect of the present invention, an electronic device is provided, 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 vehicle-UAV collaborative delivery method supporting mobile take-off and landing as described in any one of the above items are implemented.

[0013] Another aspect of the present invention provides a computer storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps in the vehicle-UAV collaborative delivery method supporting mobile take-off and landing as described in any one of the above items are implemented.

[0014] The vehicle-UAV collaborative delivery method supporting mobile take-off and landing of the present invention takes into account the launch and recovery of UAVs on mobile vehicles, so that UAVs can take off and land while the vehicle is driving, without the need for the vehicle to stop, eliminating the extra time overhead caused by the waiting of UAVs and vehicles at parking points, and achieving more flexible and efficient air-ground collaboration; on the one hand, it saves the time that vehicles and UAVs stay at each parking point, improves the parallelism of delivery, reduces the waste of delivery resources, and thus improves the overall delivery efficiency; on the other hand, since there is no need to plan the vehicles and UAVs to go to the parking points, the flexibility and optimization space of path planning are improved; in addition, the complete collaborative delivery scheduling solution is divided into two levels, the upper-level solver decides the task allocation of UAVs and vehicles and the launch scheduling of UAVs, and the lower-level solver solves the specific trajectory path planning for the UAV to complete delivery and return based on the movement of the vehicle, which simplifies the complexity of the collaborative scheduling problem and achieves efficient and high-quality solutions. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. By reading the detailed description of the following implementations, the advantages and benefits of the solutions become clear to those skilled in the art. The drawings are only used to illustrate the preferred implementations and are not to be considered as limiting the present invention.

[0016] In the attached picture:

[0017] Figure 1 The present invention is a flowchart of a vehicle-UAV collaborative delivery method supporting mobile take-off and landing according to an embodiment of the present invention.

[0018] Figure 2 This is a schematic diagram of vehicle-UAV collaborative delivery supporting mobile take-off and landing according to an embodiment of the present invention.

[0019] Figure 3 This is a schematic diagram comparing the delivery time of a UAV mobile take-off and landing and a UAV static take-off and landing at a parking point according to an embodiment of the present invention.

[0020] Figure 4 A schematic diagram of a scheduling solution algorithm framework for a hierarchical strategy according to an embodiment of the present invention. DETAILED DESCRIPTION

[0021] For ease of understanding, before describing in detail the specific embodiments of the present invention, an exemplary description of the prior art of a vehicle-UAV collaborative delivery method supporting mobile take-off and landing of the present invention is first given.

[0022] In the vehicle-drone collaborative delivery model, a vehicle carries one or more drones, and the two deliver goods to their respective customer points. Drones and vehicles have a collaborative relationship, and vehicles act as mobile warehouses and supply / charging stations for drones, thereby making up for the shortcomings of drones in terms of load capacity and battery capacity. In traffic congestion and narrow restricted areas in urban road networks, drones can replace vehicles to complete delivery more quickly and flexibly. Vehicle-drone collaborative delivery integrates drones with traditional truck transportation systems, giving full play to the respective advantages of traditional truck delivery and drone low-altitude delivery, and making up for their respective disadvantages in non-collaborative modes. Compared with the independent drone delivery model and the drone-vehicle parallel delivery model, it has a better overall performance and broader research prospects in terms of improving delivery timeliness, reducing delivery costs, and taking into account the stability and flexibility of delivery.

[0023] When it comes to solving the scheduling and planning problems of vehicle-drone collaborative delivery, existing research still mostly uses traditional operations research methods. However, traditional algorithms can only handle delivery problems with a smaller case size. As the scale of the problem increases, the time consumption will also increase greatly, and it cannot meet the real-time requirements. With the rapid development of deep reinforcement learning theory in recent years, many studies have shown that deep reinforcement learning methods have excellent performance in solving high-dimensional complex combinatorial optimization problems, allowing researchers to see new possibilities for solving drone-vehicle collaborative delivery scheduling and planning problems. However, existing related research has not fully utilized the potential of reinforcement learning in dealing with complex and dynamic scenarios, and has not proposed corresponding innovations in the distribution model based on the characteristics of reinforcement learning.

[0024] In order to enable those skilled in the art to better understand the technical solutions in the embodiments of the present invention, the technical solutions in the embodiments of the present invention will be described clearly and in detail below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in the field based on the embodiments in the embodiments of the present invention should fall within the scope of protection of the embodiments of the present invention.

[0025] Figure 1 A schematic flow chart of a vehicle-UAV collaborative delivery method supporting mobile take-off and landing provided by an embodiment of the present invention is shown in FIG. Figure 1 As shown, this embodiment mainly includes:

[0026] S101. Obtaining distribution data within the distribution area, wherein the distribution data includes location information of customer points, cargo demand information corresponding to each customer point, the number of drones carried by the vehicle, performance parameters of the vehicle and each drone, warehouse location information, and other environmental data.

[0027] Exemplarily, the distribution data required to solve the task allocation and collaborative distribution scheduling problems in the area are collected, including the location information of all customer points, the cargo demand information corresponding to each customer point (including the weight of the cargo, whether drone delivery is supported, etc.), the number of drones carried by the vehicle, the performance parameters of the vehicle and each drone (load capacity, endurance, flight speed, etc.), and other environmental data such as warehouse location information, to ensure the accuracy of the collected data and provide a basis for subsequent task allocation and scheduling planning.

[0028] S102: Run the upper-level solver to decide vehicle delivery tasks, vehicle planning paths, and drone delivery tasks based on the delivery data.

[0029] Exemplarily, a heuristic upper-level task allocation and drone launch scheduling solver based on a greedy strategy is designed. For currently available drones and trucks, the tasks that are allowed to be delivered and are closest to the customer points that have not been delivered within the allocation range are selected to obtain vehicle delivery tasks, vehicle planning paths and drone delivery tasks.

[0030] like Figure 4 As shown in the figure, the complete collaborative delivery task scheduling solution is divided into two levels: the upper-level solver decides the task allocation of drones and vehicles and the launch scheduling of drones.

[0031] In each time step, the information of currently available delivery resources (vehicles or drones that have not been assigned tasks) is collected. If there are available delivery resources in the scene, the upper-level solver centrally allocates tasks, decides whether drones that have been assigned tasks but have not yet been launched should be launched, and passes the results to the lower-level solvers distributed in various drones and vehicles.

[0032] S103, running the lower layer solver, taking into account the movement of the vehicle, based on the vehicle planning path and the drone delivery task, solving the planned path for drone delivery and return.

[0033] Exemplarily, the lower-level path planning solver uses a deep reinforcement learning algorithm to dynamically solve the path planning of the UAV agent in real time in a distributed manner. Since the dynamic takeoff and landing of the UAV introduces the consideration of vehicle movement, the solution space for solving the path planning of multiple UAVs is huge, the dynamics and constraints are complex, and it is difficult to solve it centrally. Therefore, the deep reinforcement learning algorithm is applied to solve the trajectory path of the UAV in a distributed manner, thereby ensuring higher long-term benefits with lower solution time complexity.

[0034] The lower-level solver considers the movement of the vehicle and solves the specific trajectory planning of the drone to the assigned task point to complete the delivery and the specific trajectory planning of the drone returning to the vehicle for recovery after completing the delivery in each time step.

[0035] It should be understood that the reinforcement learning algorithm considering the offline strategy has higher sample efficiency and has been widely used to solve the intelligent agent path planning problem. Among them, the SAC (Soft Actor-Critic) algorithm enhances the exploration ability of the model through entropy regularization. Therefore, the SAC algorithm is applied to learn the profit maximization strategy of the UAV agent to go to a stationary or moving target point; and in practical applications, the trained policy network will be deployed to the edge computing unit of each UAV to realize distributed real-time computing of path planning. Each UAV solves the path planning in real time based on its own observations and the status of neighboring UAVs.

[0036] S104: The vehicle carrying the drone fleet goes to the assigned customer point according to the vehicle planned path to perform the vehicle delivery task.

[0037] Exemplarily, the vehicle travels along the road to the assigned customer point for delivery, places the goods at the designated location according to the task requirements, and records the delivery status (such as the delivery time of the goods, success or abnormal status, etc.).

[0038] S105, after the drone is loaded with goods, it follows the planned route of delivery and return of the drone to go to the assigned customer point to perform the drone delivery task.

[0039] Exemplarily, after loading the cargo, the drone takes off from the vehicle and heads to the assigned customer point for delivery. According to the mission requirements, after the drone arrives at the customer point, it delivers the cargo to the designated location and records the delivery status.

[0040] S106. After all drones have completed the drone delivery mission and returned, and all customer points have completed delivery, the vehicle carrying the drone team returns to the warehouse.

[0041] Exemplarily, after confirming that all drones have completed their return flight and all delivery tasks assigned to the vehicle have been completed, the vehicle carries the drones back to the warehouse.

[0042] S107. After all vehicles and drones return to the warehouse, status inspection and maintenance are performed to complete the delivery process.

[0043] For example, after all vehicles and drones have returned to the warehouse, the system performs detailed status checks and maintenance to ensure that there is no equipment damage or abnormal status, and that this round of delivery process is completed.

[0044] The vehicle-UAV collaborative delivery method supporting mobile take-off and landing of the present invention takes into account the launch and recovery of UAVs on mobile vehicles, so that UAVs can take off and land while the vehicle is driving, without the need for the vehicle to stop, eliminating the extra time overhead caused by the waiting of UAVs and vehicles at parking points, and achieving more flexible and efficient air-ground collaboration; on the one hand, it saves the time that vehicles and UAVs stay at each parking point, improves the parallelism of delivery, reduces the waste of delivery resources, and thus improves the overall delivery efficiency; on the other hand, since there is no need to plan the vehicles and UAVs to go to the parking points, the flexibility and optimization space of path planning are improved; in addition, the complete collaborative delivery scheduling solution is divided into two levels, the upper-level solver decides the task allocation of UAVs and vehicles and the launch scheduling of UAVs, and the lower-level solver solves the specific trajectory path planning for the UAV to complete delivery and return based on the movement of the vehicle, which simplifies the complexity of the collaborative scheduling problem and achieves efficient and high-quality solutions.

[0045] In another implementation of the present invention, it also includes: establishing a two-dimensional coordinate system of the distribution scene with the warehouse location as the coordinate origin; performing calculations based on the two-dimensional coordinate system to obtain the location information of the customer point, the warehouse location information, the vehicle's current location information and other environmental data.

[0046] In another implementation of the present invention, the upper-level solver is run to decide vehicle delivery tasks, vehicle path planning and drone delivery tasks based on the delivery data, including: running the upper-level solver to initialize a list of currently available drones and vehicles as objects to which tasks can be assigned; based on the delivery data, customer points are screened to retain customer points that have not completed delivery tasks and to which no vehicle drones are assigned for delivery; based on the respective load capacities and maximum ranges of different drones, a list of customer points that can complete delivery corresponding to each model of drone is obtained; the remaining customer points are sorted according to the distance from the vehicle, and starting from the customer point closest to the vehicle, tasks are assigned to available vehicles and drones according to specific priorities to obtain vehicle delivery tasks, vehicle path planning and drone delivery tasks.

[0047] For example, the upper solver obtains the current location of the vehicle, the list of drones currently on board the vehicle and their respective load capacities, as well as the location information, cargo weight, and delivery completion status of the customer point from the environment. The solver initializes the list of currently available drones and vehicles as objects that can be assigned tasks.

[0048] According to the currently returned environmental status information, the customer points are first screened, and the customer points that have not completed the delivery task and have no vehicle drone assigned to deliver are retained. Then, according to the respective load capacity and maximum range of different drones, a list of customer points corresponding to each model of drone that can complete the delivery is obtained, that is, the type of customer point itself supports drone delivery, the weight of the cargo does not exceed the maximum load of the drone, and the distance between the customer point and the truck does not exceed 0.3 times the maximum range of the drone (0.3 times the maximum range takes into account the safe distance that the drone needs to return to the truck after completing the delivery under dynamic take-off and landing).

[0049] The remaining customer points are sorted according to their distance from the vehicle, and starting from the closest customer point, the tasks are assigned to the available vehicles and drones according to specific priorities. When assigning tasks, they are given priority to drones with faster speeds, followed by drones with slower speeds, and finally to vehicles.

[0050] In another implementation of the present invention, the priority includes: when tasks are assigned, they are first assigned to drones with faster speeds, followed by drones with slower speeds, and finally to vehicles.

[0051] In another implementation of the present invention, it also includes: the vehicle provides charging and replenishment services for the drone, and after the drone completes the sub-delivery task, it returns to the vehicle to perform replenishment operations.

[0052] like Figure 2 As shown in the figure, a vehicle is loaded with a group of drones of different models (different models of drones have different load capacities, endurances, and flight speeds, such as low-speed high-load drones and high-speed small drones), carrying goods from the warehouse to the customer point to complete the delivery. The drones load the goods from the vehicle and depart, go to the customer point to complete the delivery, and then return to the vehicle to charge and replenish the goods. The launch and recovery operations of the drones are not limited to when the vehicle is stationary at the parking point. In addition to serving as a temporary warehouse and a mobile base station for drone delivery, the vehicle itself will also go to the customer point to complete the delivery task. After the delivery tasks of all customer points have been completed and all vehicles and drones have returned to the warehouse, a round of delivery is completed.

[0053] like Figure 3 As shown, the drone can take off and land while the vehicle is moving, without the need for the vehicle to stop. This approach can significantly improve delivery efficiency and planning flexibility:

[0054] On the one hand, it saves the time that trucks and drones stay at each parking point, improves the parallelism of distribution, reduces the waste of distribution resources, and thus improves the overall distribution efficiency; on the other hand, since there is no need to plan trucks and drones to go to parking points, the flexibility of route planning and optimization space are improved.

[0055] In order to ensure the practical significance of the research scenario and the solvability of the scheduling problem, the following necessary assumptions are made for the scenario:

[0056] There is only one warehouse in the area. Initially, the vehicle and its fleet of heterogeneous drones all depart from the warehouse and need to return to the warehouse after completing the delivery to all customer points.

[0057] Movement constraints: Trucks need to travel along the road at a constant speed between different nodes in the scene, while drones travel at a constant speed regardless of road conditions. Therefore, the distance between the vehicle and a point in the scene is calculated using Manhattan distance, while the distance between the drone and a point in the scene is calculated using straight-line distance.

[0058] Load constraints: UAVs have load limits, and the weight of the cargo they carry cannot exceed their maximum load; there is no load limit for vehicles.

[0059] Cargo constraints: There is a one-to-one correspondence between the allocation of customer points to drones or trucks, that is, the cargo of a single customer point cannot be split and can only be delivered independently by a single drone or truck. A drone can only deliver to one customer point at a time, and the distance a drone can move for a one-way delivery cannot exceed its maximum range.

[0060] Customer point classification: Some customer points in the scene can be delivered by vehicles, while other customer points can be delivered by drones and trucks. For example, some customer points may require signatures for delivery, or there may be no conditions for drones to land or fly in the surrounding area, so drones cannot be used for delivery.

[0061] Mid-course take-off and landing operations: The drone mounted on the vehicle can be launched at any time during the delivery process and delivered to the customer point; after the drone reaches the customer point and completes the delivery and returns to the vehicle, it can be recovered and resupplied at any time during the delivery process. The above drone launch and recovery process does not require the vehicle to stop.

[0062] The time it takes for a drone to load cargo, change batteries, and complete delivery services at the customer point accounts for a very small proportion of the overall delivery time and is therefore negligible.

[0063] Another aspect of the present invention provides a vehicle-unmanned aerial vehicle collaborative delivery system supporting mobile take-off and landing, comprising:

[0064] Data acquisition module: acquires the delivery data within the delivery area, including the location information of customer points, the cargo demand information corresponding to each customer point, the number of drones carried by the vehicle, the performance parameters of the vehicle and each drone, the warehouse location information and other environmental data.

[0065] Task allocation and path planning module: Run the upper-level solver to decide vehicle delivery tasks, vehicle planning paths and drone delivery tasks based on the delivery data; run the lower-level solver to solve the planned paths for drone delivery and return based on the vehicle planning paths and the drone delivery tasks, taking into account vehicle movement.

[0066] Distribution module: the vehicle carrying the drone team goes to the assigned customer point according to the planned path of the vehicle to perform the vehicle distribution task; after the drone is loaded with goods, it goes to the assigned customer point according to the planned path of the drone distribution and return to perform the drone distribution task; after all drones complete the return after performing the drone distribution task and all customer points are delivered, the vehicle carrying the drone team returns to the warehouse; after all vehicles and drones return to the warehouse, status inspection and maintenance are carried out to complete the distribution process.

[0067] The vehicle-UAV collaborative delivery system supporting mobile take-off and landing of the present invention takes into account the launch and recovery of UAVs on mobile vehicles, so that UAVs can take off and land while the vehicle is driving, without the need for the vehicle to stop, eliminating the extra time overhead caused by the waiting of UAVs and vehicles at parking points, and achieving more flexible and efficient air-ground collaboration; on the one hand, it saves the time that vehicles and UAVs stay at each parking point, improves the parallelism of delivery, reduces the waste of delivery resources, and thus improves the overall delivery efficiency; on the other hand, since there is no need to plan the vehicles and UAVs to go to the parking points, the flexibility and optimization space of path planning are improved; in addition, the complete collaborative delivery scheduling solution is divided into two levels, the upper-level solver decides the task allocation of UAVs and vehicles and the launch scheduling of UAVs, and the lower-level solver solves the specific trajectory path planning for the UAV to complete delivery and return based on the movement of the vehicle, which simplifies the complexity of the collaborative scheduling problem and achieves efficient and high-quality solutions.

[0068] In another aspect of the present invention, an electronic device includes: a processor, a memory, a communication bus, and a communication interface.

[0069] in:

[0070] The processor, memory and communication interface communicate with each other through a communication bus.

[0071] Communication interface, used to communicate with other electronic devices or servers.

[0072] The processor is used to execute the program, and specifically can execute the steps of any one of the vehicle-UAV collaborative delivery methods supporting mobile take-off and landing in the above-mentioned embodiments.

[0073] Specifically, the program may include program codes including computer operation instructions.

[0074] The processor may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application. The one or more processors included in the smart device may be processors of the same type, such as one or more CPUs; or processors of different types, such as one or more CPUs and one or more ASICs.

[0075] The memory is used to store programs. The memory may include a high-speed RAM memory, and may also include a non-volatile memory, such as at least one disk memory.

[0076] The program can be specifically used to enable the processor to execute to implement the steps of any of the vehicle-UAV collaborative delivery methods supporting mobile take-off and landing described in the embodiments. The specific implementation of each step in the program can refer to the corresponding descriptions in the steps and units executed by any of the vehicle-UAV collaborative delivery methods supporting mobile take-off and landing in the above steps, which will not be repeated here. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the above-described devices and modules can refer to the corresponding process description in the aforementioned method embodiment.

[0077] The exemplary embodiments of the present application also provide a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to execute the methods of the various embodiments of the present application.

[0078] The above-described method according to an embodiment of the present invention may be implemented in hardware, firmware, or as software or computer code that may be stored in a recording medium (such as a CD ROM, RAM, floppy disk, hard disk, or magneto-optical disk), or as computer code that is originally stored in a remote recording medium or a non-temporary machine-readable medium downloaded over a network and will be stored in a local recording medium, so that the method described herein may be stored in such software processing on a recording medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware (such as an ASIC or FPGA). It is understood that a computer, processor, microprocessor controller, or programmable hardware includes a storage component (e.g., RAM, ROM, flash memory, etc.) that can store or receive software or computer code, and when the software or computer code is accessed and executed by a computer, processor, or hardware, the method described herein is implemented. In addition, when a general-purpose computer accesses the code for implementing the method shown herein, the execution of the code converts the general-purpose computer into a dedicated computer for executing the method shown herein.

[0079] Thus far, specific embodiments of the present invention have been described. Other embodiments are within the scope of the appended claims. In some cases, the actions recited in the claims may be performed in a different order and still achieve the desired results. Additionally, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing may be advantageous.

[0080] It should be noted that all directional indications in the embodiments of the present invention (such as up, down, left, right, back, etc.) are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0081] In the description of the present invention, the terms "first" and "second" are only used to facilitate the description of different components or names, and cannot be understood as indicating or implying a sequential relationship, relative importance, or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features.

[0082] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0083] It should be noted that although the specific embodiments of the present invention are described in detail in conjunction with the accompanying drawings, it should not be understood as limiting the scope of protection of the present invention. Within the scope described in the claims, various modifications and variations that can be made by those skilled in the art without creative work still belong to the scope of protection of the present invention.

[0084] The examples of the embodiments of the present invention are intended to concisely illustrate the technical features of the embodiments of the present invention so that those skilled in the art can intuitively understand the technical features of the embodiments of the present invention, and are not intended to be improper limitations of the embodiments of the present invention.

[0085] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A vehicle-UAV collaborative delivery method supporting mobile take-off and landing, characterized in that: include: Acquire delivery data within the delivery area, the delivery data including location information of customer points, cargo demand information corresponding to each customer point, the number of drones carried by the vehicle, performance parameters of the vehicle and each drone, warehouse location information and other environmental data; Running the upper solver to decide vehicle delivery tasks, vehicle path planning and drone delivery tasks based on the delivery data; Running the lower layer solver to solve the planned path of the drone delivery and return flight based on the vehicle planned path and the drone delivery task, taking into account the movement of the vehicle; The vehicle carrying the drone fleet follows the vehicle planned path to the assigned customer point to perform the vehicle delivery task; After the drone is loaded with goods, it follows the planned route for delivery and return of the drone and goes to the assigned customer point to perform the drone delivery mission; After all drones have completed the drone delivery mission and returned, and all customer points have completed delivery, the vehicle carrying the drone fleet returns to the warehouse; After all vehicles and drones return to the said warehouse, they undergo status inspection and maintenance to complete the delivery process.

2. The method according to claim 1, characterized in that Also includes: Taking the warehouse location as the coordinate origin, establish a two-dimensional coordinate system for the distribution scene; Calculation is performed based on the two-dimensional coordinate system to obtain the location information of the customer point, the warehouse location information, the vehicle's current location information and other environmental data.

3. The method according to claim 1, characterized in that The running of the upper solver, based on the delivery data, determines the vehicle delivery task, the vehicle planning path and the drone delivery task, including: Run the upper solver and initialize the list of currently available drones and vehicles as objects that can be assigned tasks; According to the delivery data, the customer points are screened, and the customer points for which the delivery tasks have not been completed and to which no vehicle drone is assigned for delivery are retained; According to the load capacity and maximum range of different drones, a list of customer points that can complete delivery corresponding to each type of drone is obtained; The remaining customer points are sorted according to their distance from the vehicle. Starting from the closest customer point, the tasks are assigned to the available vehicles and drones according to specific priorities, resulting in vehicle delivery tasks, vehicle planning paths and drone delivery tasks.

4. The method according to claim 3, characterized in that The priorities include: When assigning tasks, priority is given to faster drones, followed by slower drones, and finally to vehicles.

5. The method according to claim 1, characterized in that Also includes: The vehicle provides charging, battery replacement and replenishment services for the drone. After the drone completes the sub-delivery mission, it returns to the vehicle for replenishment operations.

6. A vehicle-UAV collaborative delivery system supporting mobile take-off and landing, characterized in that: include: Data acquisition module: acquires the delivery data in the delivery area, including the location information of the customer points, the cargo demand information corresponding to each customer point, the number of drones carried by the vehicle, the performance parameters of the vehicle and each drone, the warehouse location information and other environmental data; Task allocation and path planning module: Run the upper solver to decide vehicle delivery tasks, vehicle planning paths and drone delivery tasks based on the delivery data; run the lower solver to solve the planned paths for drone delivery and return based on the vehicle planning paths and drone delivery tasks, taking into account vehicle movement; Delivery module: The vehicle carrying the drone team goes to the assigned customer point according to the vehicle planned path to perform the vehicle delivery task; after the drone is loaded with goods, it goes to the assigned customer point according to the drone delivery and return planned path to perform the drone delivery task; After all drones have completed the drone delivery mission and returned, and all customer points have completed delivery, the vehicle carrying the drone fleet returns to the warehouse; After all vehicles and drones return to the said warehouse, they undergo status inspection and maintenance to complete the delivery process.

7. An electronic device, characterized in that: include: 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 vehicle-UAV collaborative delivery method supporting mobile take-off and landing as described in any one of claims 1 to 5 are implemented.

8. A computer storage medium, characterized in that: The computer storage medium stores a computer program, which, when executed by a processor, implements the steps in the vehicle-UAV collaborative delivery method supporting mobile take-off and landing as described in any one of claims 1 to 5.

Images