Multi-aircraft path planning method, device, equipment, medium and program product

Abstract

The invention discloses a multi-aircraft path planning method, device and equipment, a medium and a program product, and the method comprises the following steps: carrying out the space-time grid modeling of a low-altitude environment, and generating a space-time grid situation map containing a time dimension and a space dimension; based on the space-time grid situation map, an initial path is planned for each aircraft, the initial path is composed of a plurality of space-time grids, and each space-time grid comprises a corresponding time code and a corresponding space code; detecting conflicts among the aircrafts in the initial path, wherein the conflicts comprise meeting conflicts, chasing conflicts and exchange conflicts; conflict resolution is carried out on the aircrafts with conflicts based on a preset priority strategy, wherein the priority strategy comprises speed priority, task attribute priority and path length priority; and re-planning the path after conflict resolution through an incremental A algorithm to obtain a conflict-free target path set. The method effectively reduces the grid redundancy of the paths of the aircrafts with different speeds, the planning time is obviously shortened compared with the traditional algorithm, and the adaptability is high.

Description

Technical Field

[0001] This invention relates to the field of aircraft technology, specifically to multi-aircraft path planning methods, devices, equipment, media, and program products. Background Technology

[0002] With the continuous opening up of low-altitude airspace and the significant increase in the frequency of airspace use by air-using entities, conflicts and contradictions between airspaces are constantly escalating. At the same time, airspaces are showing a trend of rapid renewal, and the pressure on low-altitude airspace management is increasing.

[0003] Existing technologies suffer from numerous problems. With the increasing number of aircraft, the solution space for path planning grows exponentially, leading to high computational costs for algorithms in large-scale aircraft scenarios. In real-world missions, aircraft typically face dynamic environments, but most current path planning methods assume a static environment. In dynamic environments, path planning lacks flexibility and real-time performance, and cannot adjust paths in real-time during flight. Furthermore, most mainstream path planning algorithms focus on optimizing and improving existing methods, prioritizing efficiency over the actual operational characteristics and mission requirements of aircraft in low-altitude environments. In solving collaborative path planning for multiple low-altitude aircraft, practical issues such as speed differences between aircraft, mission priorities, dynamic constraints, and airspace limitations are often simplified or ignored, making it difficult to effectively implement the planning results in complex low-altitude environments.

[0004] Therefore, how to accurately and quickly determine the conflict relationships between structured airspace and address the deficiencies and shortcomings of existing technologies has become an important problem that researchers in this field urgently need to solve. Summary of the Invention

[0005] In response, the present invention provides a multi-aircraft path planning method, apparatus, device, medium, and program product to at least partially solve the above-mentioned technical problems.

[0006] This invention provides a multi-aircraft path planning method, comprising the following steps: performing space-time gridded modeling of the low-altitude environment to generate a space-time grid situation map containing time and spatial dimensions; based on the space-time grid situation map, planning an initial path for each aircraft, wherein the initial path consists of multiple space-time grids, each containing corresponding time and spatial codes; detecting conflicts between aircraft in the initial path, the conflicts including encounter conflicts, overtaking conflicts, and exchange conflicts; resolving conflicts between aircraft based on a preset priority strategy, the priority strategy including speed priority, mission attribute priority, and path length priority; and using incremental A... The algorithm replans the paths after conflict resolution to obtain a set of conflict-free target paths.

[0007] In one aspect of this application, the "spatiotemporal gridding modeling of the low-altitude environment" includes: multi-scale subdivision of the spatial and temporal dimensions of the low-altitude environment; assigning a four-dimensional code to each subdivided spatiotemporal grid, the four-dimensional code including longitude code, latitude code, altitude code and time code; generating a spatiotemporal grid situation map based on the four-dimensional code, the situation map being used to characterize the grid occupancy status under different times and spaces.

[0008] In one aspect of this application, the "planning an initial path for each aircraft" includes: obtaining the starting position, ending position, speed, and mission attributes of the aircraft; and, based on the space-time grid situation map, employing incremental A... The algorithm plans the initial path from the starting position to the ending position; the incremental A The algorithm's evaluation function includes the actual distance from the starting point to the current grid, the estimated distance from the current grid to the ending point, and the distance value based on the previous grid layer.

[0009] In one aspect of this application, the "detection of conflicts between aircraft in the initial path" includes: detecting whether there is a conflict between two aircraft based on four-dimensional encoding of a space-time grid; the encounter conflict is when any two aircraft occupy the same space grid at the same time; the catch-up conflict is when, when there is a difference in the speed of the aircraft, the later aircraft catches up with the earlier aircraft and occupies the same space grid; the exchange conflict is when any two aircraft exchange space grids in adjacent time grids.

[0010] In one aspect of this application, the "conflict resolution of conflicting aircraft based on a preset priority strategy" includes: calculating the priority of each aircraft, the priority including speed priority, mission attribute priority and path length priority; for conflicting aircraft, determining the path of the higher priority aircraft to be retained according to the priority; and pruning conflicting nodes using a positive constraint method, the positive constraint including specifying that the aircraft must occupy or must not occupy a certain spacetime grid.

[0011] In one aspect of this application, the "through incremental A" The algorithm for replanning paths after conflict resolution includes: determining the affected spatiotemporal grid when obstacles are updated in the environment; and based on the incremental A... The algorithm uses the search results of the initial path to perform local replanning on the affected grid; the incremental A... The algorithm obtains the preferred search direction through the encoding operation of the space-time grid.

[0012] Another aspect of this application provides a multi-aircraft path planning device, comprising the following modules: a modeling module for performing spacetime gridded modeling of the low-altitude environment to generate a spacetime grid situation map containing time and space dimensions; an initial path planning module for planning an initial path for each aircraft based on the spacetime grid situation map, the initial path consisting of multiple spacetime grids, each spacetime grid containing corresponding time and space codes; a conflict detection module for detecting conflicts between aircraft in the initial path, the conflicts including encounter conflicts, overtaking conflicts, and exchange conflicts; a conflict resolution module for resolving conflicts between aircraft based on a preset priority strategy, the priority strategy including speed priority, mission attribute priority, and path length priority; and a path replanning module for incremental A The algorithm replans the paths after conflict resolution to obtain a set of conflict-free target paths.

[0013] This application also provides an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; the memory storing instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the multi-aircraft path planning method as described above.

[0014] In another aspect, this application provides a computer-readable storage medium having stored thereon computer program instructions that can be executed by a processor to implement the multi-aircraft path planning method as described above.

[0015] Another aspect of this application provides a computer program product, including a computer program that, when executed by a processor, implements the multi-aircraft path planning method as described above.

[0016] The solution provided in this application effectively reduces grid redundancy on the paths of aircraft at different speeds through multi-scale modeling of low-altitude airspace spatiotemporal grids; it significantly reduces the size of the conflict tree by adopting a priority strategy, and the planning time is significantly shortened compared to traditional algorithms; incremental A The algorithm can reuse the initial planning results and only performs local replanning when obstacles are dynamically updated, making it highly adaptable and meeting the needs of large-scale, dynamic path planning in low-altitude environments. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0018] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a schematic diagram of a multi-vehicle path planning method provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the flight path conflict between two aircraft in an embodiment of the present invention; Figure 3 This is a top view of the conflict resolution results between the two aircraft in an embodiment of the present invention; Figure 4 This is a schematic diagram of the simulation environment of the present invention; Figure 5 This is a schematic diagram of the path planning results of 300 aircraft using the path planning method described in this invention; Figure 6 This is a comparison chart of the replanning time of the path planning method described in this invention and other algorithms; Figure 7 This is a schematic diagram of the structure of a multi-aircraft path planning device provided in an embodiment of the present invention; Figure 8 This is a schematic diagram of the structure of a device provided in an embodiment of the present invention. Detailed Implementation

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

[0020] In a typical configuration of this application, the terminal and the service network devices each include one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0021] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0022] Computer-readable media include permanent and non-permanent, removable and non-removable media, which can store information by any method or technology. Information can be computer program instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, read-only optical disc (CD-ROM), digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0023] This application proposes a multi-vehicle path planning method, and the technical solution of this application will be described in detail below with reference to various embodiments.

[0024] like Figure 1 As shown in the diagram, this invention discloses a multi-vehicle path planning method, which includes the following steps: Step 101: Perform spatiotemporal grid modeling of the low-altitude environment to generate a spatiotemporal grid situation map containing time and spatial dimensions; Here, the spatiotemporal gridding modeling of the low-altitude environment involves multi-scale subdivision of the spatial and temporal dimensions of the low-altitude environment; each subdivided spatiotemporal grid is assigned a four-dimensional code, which includes longitude, latitude, altitude, and time codes; a spatiotemporal grid situation map is generated based on the four-dimensional codes, which is used to characterize the grid occupancy status under different times and spaces. It can be understood that by multi-scale processing of the time dimension, the redundancy of the path expression grid caused by the use of the same time step by aircraft at different speeds is effectively avoided, and a one-to-one correspondence between the aircraft and the spatiotemporal occupancy status is achieved.

[0025] Step 102: Based on the space-time grid situation map, plan an initial path for each aircraft. The initial path consists of multiple space-time grids, and each space-time grid contains corresponding time codes and spatial codes. In practice, the starting position, ending position, velocity, and mission attributes of the aircraft are obtained; based on the space-time grid situation map, incremental A / D is employed. The algorithm plans the initial path from the starting position to the ending position; the incremental A The algorithm's evaluation function includes the actual distance from the starting point to the current grid, the estimated distance from the current grid to the ending point, and the distance value based on the previous grid layer. Here, the incremental A... Algorithm Adoption A Algorithm evaluation function The metrics in the list are used to select "candidate grids" from the open list, where Indicates the grid starting from the origin. To the current grid The actual distance value of the optimal path. Indicates from the current grid To the target grid The estimated distance value is calculated using Euclidean distance, since the operational space of a low-altitude aircraft is its 26-neighborhood. Based on this, a third metric for each grid is considered: the actual distance value based on the previous grid. The calculation method is as follows:

[0026] The formula represents the starting grid. The estimated value is 0, and the values ​​of other grids are... The value is the set of the grid above it. The minimum value of the city-level distance from each grid to the starting point and the sum of the distances to the target grid. The value and its estimated shortest distance to the starting grid can be used to determine the state of the current grid path: when At this point, the grid is in a locally consistent state, meaning the environment has not changed. when At this point, the grid is in a partially occupied state, which means that some grids in the path grid set before this grid are occupied by obstacles, causing the path from the grid in the previous layer to the starting point to become longer; when At this point, the grid is in a partially empty state, which means that the obstacles near the grid path have disappeared, and there is a more ideal upper-layer grid that makes the cost of the grid to the starting point smaller.

[0027] The incremental A The evaluation function of the algorithm is:

[0028] Using incremental A When planning the initial path from the starting point to the ending point, the algorithm first uses the spatial three-dimensional encoding bit operations of the starting and ending point grids to determine the preferred search direction. Then, it performs iterative planning. In each iteration, it first obtains the minimum value in the candidate grid set. The corresponding grid is selected and removed from the candidate grid set. The evaluation value of the selected grid is compared to its local state, and subsequent operations are performed until the end of the current loop. The next selection process then continues from the candidate grid set. The grid with the smallest value is used as the parent grid for the next iteration, until the smallest value is found in the candidate grid set. Value greater than the endpoint grid Value, or target point Value and Values ​​are equal and not equal When the loop terminates, the static path planning is complete. Starting from the endpoint grid, the uppermost grid of each grid is traced back to the starting grid to obtain the initial path from the starting point to the endpoint. This ensures spatial connectivity of the path while using time encoding to represent the aircraft's dwell time sequence across grids.

[0029] Step 103: Detect conflicts between aircraft in the initial path, including encounter conflicts, overtaking conflicts, and exchange conflicts; Here, based on the four-dimensional encoding of the space-time grid, the existence of a conflict between two aircraft is detected. An encounter conflict occurs when any two aircraft occupy the same spatial grid at the same time. A catch-up conflict occurs when, due to a difference in speed, the later aircraft catches up with the earlier aircraft and occupies the same spatial grid. An exchange conflict occurs when any two aircraft exchange spatial grids in adjacent time grids. This invention uses an improved conflict search algorithm, Geo-CBS (Geo-Conflict Based Search), to detect conflicts between aircraft in the initial path. In high-level searches, Geo-CBS sets appropriate priorities for aircraft to quickly prune the conflict tree and prevent it from growing exponentially. In low-level searches, it uses the incremental A... Algorithms to improve adaptability to dynamic environments. Step 104: Resolve conflicts between conflicting aircraft based on a preset priority strategy, which includes speed priority, mission attribute priority, and path length priority; for conflicting aircraft, determine the priority based on the priority level to retain the path of the higher priority aircraft. Here, the priority of each aircraft is first calculated as follows:

[0030] Among them, priority is given to comparing the aircraft value; This indicates the principle of speed priority: The current speed of the aircraft, The speed of the fastest aircraft in the environment; If two aircraft If the values ​​are the same, compare. , The principle of priority based on mission attributes is as follows: priority is defined according to the specific mission attributes performed by the aircraft, and the larger the value, the higher the priority. if and If all values ​​are the same, then compare This refers to the path length priority principle.

[0031] If two aircraft have the same priority in the current environment, a positive constraint method is used to prune the conflicting nodes. This positive constraint includes specifying whether an aircraft must or must not occupy a certain spacetime grid. For example... Figure 2 The diagram illustrates a conflict that occurs when aircraft S1 and S2 perform flight missions at the same altitude. The action space for aircraft S1 and S2 in the current environment consists of a grid in four directions (forward, backward, left, and right) and the current grid. The weights of the cost function are... = , This means only the total path length is considered. The flight path of aircraft S1 is... This indicates that the path of spacecraft S1 consists of five spacetime grids; the path of spacecraft 2 is... Based on the space-time grid discretized from the paths of the two aircraft, it can be seen that both aircraft simultaneously occupy... The mesh is therefore determined to be in conflict. The conflict resolution method is as follows: first, split the nodes of spacecraft S1. At the first conflict, the following CT nodes are generated: Node 00: Spacecraft 1 must completely occupy the spacetime mesh. Node 01: Spacecraft 1 cannot occupy the spacetime grid. The spatial portion; in node 00, due to the space-time grid Because it is occupied by aircraft 1, aircraft S2 needs to have its path replanned. The replanned path is as follows: Figure 3 The path E2 is shown. Simultaneously, a new collision occurs in the spacetime grid. However, because node 00 specifies that aircraft S1 must completely occupy the spacetime grid. This is equivalent to spacecraft 1 also completely occupying the spacetime grid. Therefore, node 001 is pruned; simultaneously, a hovering operation needs to be performed on node 01, thus increasing the total cost to 9. Similarly, node 0001 can be pruned in the same way, ultimately generating only node 0000, with the same total cost of 9. It's understandable that using positive constraints significantly reduces the number of conflicting nodes. Compared to setting constraints on all conflicting nodes to prevent a particular aircraft from passing through the grid, this allows for faster pruning and improves algorithm efficiency. Furthermore, since positive constraints also ensure that only one of the two sub-constraints is true (i.e., the aircraft occupies or does not occupy the spacetime grid), this method also guarantees the completeness of the search process.

[0032] Step 105: Through incremental A The algorithm replans the paths after conflict resolution to obtain a set of conflict-free target paths.

[0033] Here, when obstacles are updated in the environment, the affected spatiotemporal grid is determined; based on the incremental A... The algorithm uses the search results of the initial path to perform local replanning on the affected grid; the incremental A... The algorithm obtains the preferred search direction through the encoding operation of the space-time grid.

[0034] Simulation test

[0035] This experiment used real and simulated data from a university in Shanghai as the underlying surface environment for the task. The underlying surface data for the university was provided by Beidou Fuxi Company. A schematic diagram of the simulation environment used in this experiment is shown below. Figure 4 As shown. The specific experimental content includes two sets of sub-experiments: a comparison experiment on the computational efficiency of path planning for different numbers of aircraft, and a path replanning experiment based on dynamic environmental updates.

[0036] A comparative experiment on the computational efficiency of path planning for different numbers of aircraft was conducted. Path planning experiments were randomly generated, including 30, 60, 120, and 300 aircraft flight missions, and the average value of 10 experiments was used as the result. Methods compared with the Geo-CBS proposed in this invention include those based on A... The algorithm includes sequential planning, a conflict search-based algorithm, and a current optimized variant of the conflict search algorithm, CBS-DB. In this experiment, path modeling complexity, task planning time, number of conflict nodes, and total path length are used as comparative validation metrics. Specific parameters are as follows: Environmental parameters: A university in Shanghai and its surrounding area. Dissected A total of 12,240 spatially partitioned voxels were used, with a planar mesh size of 32m and a height dimension of 8m. Aircraft parameters: Aircraft were divided into three categories with speeds of 2m / s, 4m / s, and 8m / s respectively. The flight process included takeoff, level flight, and landing. To compare algorithm efficiency, collisions should be maximized; therefore, this experiment set the level flight phases of all aircraft to be at the same altitude layer, i.e., 50m-55m. Cost function weight settings: Since in low-altitude scenarios, more attention is paid to the total path length and total flight time of the aircraft, with less focus on the flight time of a single aircraft. Therefore, the weights of the three indicators in the cost function were set as follows: Maximum planning time: Set the maximum planning time to 2 minutes. If the time is exceeded, the planning will fail.

[0037] The path planning experiments were conducted on different numbers of aircraft under this mission environment, and the results are shown in Table 1.

[0038] Table 1. Results of a single flight planning operation for 30 aircraft sorties

[0039] Table 2. Comparative test results of different sorties of aircraft (average value of 10 test groups)

[0040] Analysis of the experimental results shows that, in terms of planning time, the Geo-CBS algorithm significantly reduces the planning time compared to other methods when handling path planning problems for 30, 60, 120, and 300 aircraft in this environment. This advantage is particularly pronounced when aircraft density increases. When handling path planning for 300 aircraft in this environment, other algorithms have lower success rates, but Geo-CBS's planning time is far below the specified time. Regarding path length, the Geo-CBS algorithm outperforms sequential planning, but is slightly inferior to the other two conflict-resolving algorithms. Because the sequential planning method is simpler in principle, planning aircraft one by one according to the input order, and using the previously successfully planned aircraft path grid as the obstacle grid for the next aircraft, the path length is significantly longer compared to the latter three conflict resolution methods. Although the two methods based on conflict search and its variants have different constraint forms, the algorithms strictly follow the cost function to determine the node expansion when handling conflicts, and generate the path with the optimal cost function. Geo-CBS introduces a priority strategy, which prioritizes the expansion of high-priority path nodes when handling path conflicts between aircraft of different priorities, significantly shortening the path planning time.

[0041] In discrete A path replanning experiment was conducted in a simulated airspace under a grid environment, involving 600 sorties of aircraft performing individual missions. The obstacle update ratios were 10%, 20%, 50%, and 100%. The comparison algorithm was the same as in sub-experiment 1. Since sub-experiment 2 aimed to verify the dynamic adaptability of the proposed Geo-CBS algorithm, all parameters except the longest planning time were the same as in sub-experiment 1. Because the path replanning time due to environmental changes should be as short as possible in a real low-altitude environment, the longest planning time was set to 15 seconds. The path replanning experiment was conducted in a simulated airspace with 600 sorties of aircraft performing individual missions, with obstacle update ratios of 10%, 20%, 50%, and 100%. The longest planning time was set to 15 seconds. After updating the obstacles in the environment at different ratios, four algorithms were used to replan the paths. The initial planning time and replanning time results are as follows: Figure 6 .according to Figure 6 The results show that the replanning time of the other three algorithms remains relatively stable under different obstacle update ratios in the environment, while the algorithm of this invention exhibits a gradual increase in computation time. The algorithm of this invention uses incremental A / B algorithm in the low-level planning stage. The algorithm can reuse some results from the initial planning whenever obstacles are updated, making it more effective in handling obstacle updates. When the obstacle update ratio is 10%, the time consumption is reduced by 67.3% compared to the initial planning; when the obstacle update ratio is 20%, the time consumption is reduced by 50.4%; and when the obstacle update ratio is 50%, the time consumption is reduced by 43.8%. Therefore, the higher the obstacle update ratio, the longer the Geo-CBS replanning time will be, but its performance remains efficient. Experimental results show that the Geo-CBS algorithm proposed in this invention exhibits good adaptability in scenarios with dynamic obstacle updates.

[0042] Figure 7 A multi-aircraft path planning device 200 is shown. This device embodiment is similar to... Figure 1 Corresponding to the method embodiments shown, this device can be specifically applied to various electronic devices.

[0043] like Figure 7 As shown, the multi-aircraft path planning device 200 provided in this application embodiment includes: Modeling module 201 is used to perform spatiotemporal gridded modeling of the low-altitude environment and generate a spatiotemporal gridded situation map containing time and spatial dimensions. The initial path planning module 202 is used to plan an initial path for each aircraft based on the space-time grid situation map. The initial path is composed of multiple space-time grids, and each space-time grid contains a corresponding time code and spatial code. Conflict detection module 203 is used to detect conflicts between aircraft in the initial path, including encounter conflicts, pursuit conflicts and exchange conflicts; The conflict resolution module 204 is used to resolve conflicts between aircraft based on a preset priority strategy, which includes speed priority, mission attribute priority, and path length priority. Path replanning module 205 is used for incremental A The algorithm replans the paths after conflict resolution to obtain a set of conflict-free target paths.

[0044] Based on the same inventive concept, this application also provides an electronic device. The method corresponding to the electronic device can be the method in the foregoing embodiments, and its problem-solving principle is similar to that method. The electronic device provided in this application includes: at least one processor; and a memory communicatively connected to the at least one processor; characterized in that the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the methods and / or technical solutions of the foregoing embodiments of this application.

[0045] The electronic device can be a user device, or a device formed by integrating user devices and network devices through a network, or it can be an application running on the aforementioned devices. The user device includes, but is not limited to, various terminal devices such as computers, mobile phones, tablets, smartwatches, and wristbands. The network device includes, but is not limited to, network hosts, single network servers, multiple network server sets, or cloud computing-based computer sets, and can be used to implement some processing functions when setting an alarm clock. Here, the cloud consists of a large number of hosts or network servers based on cloud computing. Its characteristic is that cloud computing is a type of distributed computing, consisting of a virtual computer composed of a group of loosely coupled computer sets.

[0046] Figure 3The diagram illustrates the structure of an apparatus suitable for implementing the methods and / or technical solutions in the embodiments of this application. The apparatus 300 includes a Central Processing Unit (CPU) 301, which can perform various appropriate actions and processes based on a program stored in a Read Only Memory (ROM) 302 or a program loaded from a storage portion 308 into a Random Access Memory (RAM) 303. The RAM 303 also stores various programs and data required for system operation. The CPU 301, ROM 302, and RAM 303 are interconnected via a bus 304. An Input / Output (I / O) interface 305 is also connected to the bus 304.

[0047] The following components are connected to I / O interface 305: an input section 306 including a keyboard, mouse, touchscreen, microphone, infrared sensor, etc.; an output section 307 including a cathode ray tube (CRT), liquid crystal display (LCD), LED display, OLED display, etc., and speakers, etc.; a storage section 308 including one or more computer-readable media such as hard disk, optical disk, magnetic disk, semiconductor memory, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 309 performs communication processing via a network such as the Internet.

[0048] In particular, the methods and / or embodiments in this application can be implemented as computer software programs. For example, the embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowchart. When the computer program is executed by the central processing unit (CPU) 301, it performs the functions defined in the methods of this application.

[0049] Another embodiment of this application provides a computer-readable storage medium having computer program instructions stored thereon, which can be executed by a processor to implement the methods and / or technical solutions of any one or more embodiments of this application described above.

[0050] Specifically, this embodiment can employ any combination of one or more computer-readable media. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0051] A computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, characterized by carrying computer-readable program code. Such propagated data signals may take various forms, including—but not limited to—electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0052] The program code contained on a computer-readable medium may be transmitted using any suitable medium, including—but not limited to—wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0053] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0054] The flowcharts or block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of devices, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-specific system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0055] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0056] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or page components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between devices or units through some interfaces, and may be electrical, mechanical, or other forms.

[0057] The units described as separate components may or may not be physically separate. 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 the units characterized in this embodiment can be selected to achieve the purpose of this embodiment according to actual needs.

[0058] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in a combination of hardware and software functional units.

[0059] The integrated units implemented as software functional units described above can be stored in a computer-readable storage medium. These software functional units, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0060] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

[0061] Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices recited in a device claim may also be implemented by a single unit or device through software or hardware. The terms "first," "second," etc., are used to indicate names and do not indicate any specific order.

Claims

1. A multi-vehicle path planning method, characterized in that, Includes the following steps: Spatiotemporal gridded modeling of the low-altitude environment is performed to generate a spatiotemporal gridded situation map containing both time and spatial dimensions; Based on the space-time grid situation map, an initial path is planned for each aircraft. The initial path consists of multiple space-time grids, and each space-time grid contains a corresponding time code and spatial code. Detecting conflicts between aircraft in the initial path, including encounter conflicts, overtaking conflicts, and exchange conflicts; Conflict resolution is performed on aircraft with conflict based on a preset priority strategy, which includes speed priority, mission attribute priority, and path length priority. The conflict-free target path set is obtained by replanning the conflict-resolved paths using the incremental A* algorithm.

2. The method according to claim 1, characterized in that, The "spatiotemporal gridded modeling of the low-altitude environment" includes: multi-scale subdivision of the spatial and temporal dimensions of the low-altitude environment; and assignment of a four-dimensional code to each subdivided spatiotemporal grid, wherein the four-dimensional code includes longitude code, latitude code, altitude code and time code. Based on the four-dimensional encoding, a spatiotemporal grid situation map is generated, which is used to characterize the grid occupancy status under different times and spaces.

3. The method according to claim 1, characterized in that, The phrase "planning an initial path for each aircraft" includes: Obtain the aircraft's starting position, ending position, speed, and mission attributes; Based on the aforementioned spatiotemporal grid situation map, an incremental A* algorithm is used to plan the initial path from the starting point to the ending point. The evaluation function of the incremental A* algorithm includes the actual distance from the starting point to the current grid, the estimated distance from the current grid to the ending point, and the distance value based on the previous grid layer.

4. The method according to claim 1, characterized in that, The "detection of conflicts between aircraft in the initial path" includes: detecting whether there is a conflict between two aircraft based on four-dimensional encoding of the space-time grid; The encounter conflict occurs when any two aircraft occupy the same spatial grid at the same time; the overtaking conflict occurs when, when there is a difference in the speed of the aircraft, the latter aircraft overtakes the former aircraft and occupies the same spatial grid; the exchange conflict occurs when any two aircraft exchange spatial grids in adjacent time grids.

5. The method according to claim 1, characterized in that, The "resolving conflicts between conflicting aircraft based on a preset priority strategy" includes: calculating the priority of each aircraft, which includes speed priority, mission attribute priority, and path length priority; for conflicting aircraft, determining the path of the higher priority aircraft to be retained based on the priority; and pruning conflicting nodes using a positive constraint method, which includes specifying whether an aircraft must occupy or must not occupy a certain spacetime grid.

6. The method according to claim 1, characterized in that, The "replanning of the path after conflict resolution using the incremental A* algorithm" includes: when an obstacle is updated in the environment, determining the affected spatiotemporal grid; based on the incremental A* algorithm, using the search results of the initial path to perform local replanning of the affected grid; the incremental A* algorithm obtains the preferred search direction through the encoding operation of the spatiotemporal grid.

7. A multi-vehicle path planning device, characterized in that, Includes the following modules: The modeling module is used to perform spatiotemporal gridded modeling of the low-altitude environment, generating a spatiotemporal gridded situation map that includes time and spatial dimensions; The initial path planning module is used to plan an initial path for each aircraft based on the space-time grid situation map. The initial path consists of multiple space-time grids, and each space-time grid contains a corresponding time code and spatial code. The conflict detection module is used to detect conflicts between aircraft in the initial path, including encounter conflicts, pursuit conflicts, and exchange conflicts. The conflict resolution module is used to resolve conflicts between aircraft based on a preset priority strategy, which includes speed priority, mission attribute priority, and path length priority. The path replanning module is used to replan the conflict-resolved paths using the incremental A* algorithm to obtain a set of conflict-free target paths.

8. An electronic device, characterized in that, The electronic device includes: at least one processor; a memory communicatively connected to the at least one processor; characterized in that the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-6.

9. A computer-readable medium having computer program instructions stored thereon, characterized in that, The computer program instructions can be executed by a processor to implement the method as described in any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1-6.

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