Unmanned aerial vehicle route planning method, device, equipment, medium and program product

Abstract

The application relates to the technical field of unmanned aerial vehicle management, in particular to an unmanned aerial vehicle route planning method, device, equipment, medium and program product, wherein the method comprises the following steps: collecting current position data and current flight route information of an unmanned aerial vehicle; determining airspace space-time constraint state data set based on the current position data and the current flight route information, so as to obtain corresponding flight constraint regions; if the flight constraint regions change, then based on the changed flight constraint regions, a re-planning intelligent agent is controlled to generate a route adjustment instruction, and the route adjustment instruction is delivered to the corresponding unmanned aerial vehicle, so as to re-plan the flight route. Therefore, the problems in the related art that invalid operation is caused by airspace micro-variation of a local algorithm, the response is slow due to limited computing power, the global re-planning unified re-computing of multiple machines is large in cost and poor in real-time performance, the step-by-step decoupling scheme lacks airspace segment mapping, it is difficult to quickly locate the affected segments, and the route visualization display function is lacked are solved.

Description

Technical Field

[0001] This application relates to the field of unmanned aerial vehicle (UAV) management technology, and in particular to a method, apparatus, equipment, medium, and program product for unmanned aerial vehicle (UAV) route planning. Background Technology

[0002] In related technologies, path planning algorithms can be integrated into UAV onboard flight control software. After the onboard equipment senses changes in no-fly zones or flight threats, the planning algorithm can be run independently on a single machine, performing local recalculation only for its own flight path. Alternatively, a central scheduling node can be built to uniformly manage all airspace data and all UAV flight paths. After the no-fly zone status is updated, the central node refreshes the global airspace model and batches and uniformly calculates obstacle avoidance routes for all affected UAVs. Furthermore, multi-UAV scheduling can be split into two independent solution stages: task allocation and route planning. First, the task locations are allocated to each aircraft, and then the flight path is planned for each aircraft. When airspace constraints change, the complete calculation process needs to be repeated twice.

[0003] However, in related technologies, embedded local algorithm replanning passively triggers calculations, and even minor airspace changes can result in invalid calculations, with single-machine computing power limiting response speed; centralized global replanning requires unified recalculation of multi-aircraft routes, resulting in high overhead and weak real-time performance, and lacks decision visualization methods; the step-by-step decoupling scheme for mission tracks requires two complete calculations when encountering no-fly zone changes, lacks airspace and segment tracking mapping, and has low efficiency in locating affected segments, which urgently needs improvement. Summary of the Invention

[0004] This application provides a method, apparatus, equipment, medium, and program product for unmanned aerial vehicle (UAV) route planning, to solve the problems in related technologies, such as: embedded local algorithm replanning passively triggers calculations, even minor airspace changes can lead to invalid calculations, and single-machine computing power limits response speed; centralized global replanning requires unified recalculation of routes for multiple aircraft, resulting in high overhead and weak real-time performance, and lack of decision visualization means; and the step-by-step decoupling scheme for mission tracks requires two complete calculations when encountering no-fly zones, lacks airspace and segment tracking mapping, and has low efficiency in locating affected segments.

[0005] The first aspect of this application provides a method for unmanned aerial vehicle (UAV) route planning, comprising the following steps: collecting current position data and current flight route information of at least one UAV; determining a spatiotemporal constraint state dataset matching the UAV based on the current position data and the current flight route information, and determining a flight constraint area corresponding to the UAV based on the spatiotemporal constraint state dataset; if the flight constraint area changes, controlling a replanning agent to generate a route adjustment instruction for the UAV based on the changed flight constraint area, and sending the route adjustment instruction to the corresponding UAV, and replanning the flight route of the UAV according to the route adjustment instruction.

[0006] Optionally, in one embodiment of this application, before the control replanning agent generates the flight path adjustment command for the unmanned aerial vehicle based on the modified flight constraint area, the method further includes: extracting spatial position parameters of the flight constraint area based on the airspace spatiotemporal constraint state dataset; constructing airspace discrete units based on the spatial position parameters and establishing a mapping relationship between the airspace discrete units and the current flight path information; and determining the first airspace discrete unit covered by the flight constraint area based on the mapping relationship.

[0007] Optionally, in one embodiment of this application, the step of controlling the replanning agent to generate the flight path adjustment command for the unmanned aerial vehicle based on the modified flight constraint area includes: determining the second airspace discrete unit covered by the modified flight constraint area based on the mapping relationship; determining the actual modified area of ​​the flight constraint area and the modified flight constraint area based on the first airspace discrete unit and the second airspace discrete unit; determining the planning strategy of the replanning agent based on the actual modified area, and generating the flight path adjustment command according to the planning strategy.

[0008] Optionally, in one embodiment of this application, before issuing the route adjustment command to the corresponding unmanned aerial vehicle (UAV), the method further includes: collecting the communication link status between the replanning agent and the UAV; if the communication link status meets preset communication conditions, then allowing the issuance of the route adjustment command to the UAV; if the communication link status does not meet the preset communication conditions, then generating an adjustment command for the communication link status, and adjusting the communication link status according to the adjustment command until an adjusted communication link status that meets the preset communication conditions is obtained, and allowing the issuance of the route adjustment command to the UAV.

[0009] Optionally, in one embodiment of this application, after replanning the flight path of the unmanned aerial vehicle according to the flight path adjustment instruction, the method further includes: obtaining the actual flight path information of the replanned flight path; based on the current location data and the actual flight path information, controlling the replanning agent to generate a visualization display instruction for the unmanned aerial vehicle, and executing the flight path visualization display operation according to the visualization display instruction.

[0010] A second aspect of this application provides an unmanned aerial vehicle (UAV) route planning device, comprising: a first acquisition module for acquiring current position data and current flight route information of at least one UAV; a first determination module for determining, based on the current position data and the current flight route information, a spatiotemporal constraint state dataset matching the UAV, and determining, based on the spatiotemporal constraint state dataset, a flight constraint area corresponding to the UAV; and a planning module for, when the flight constraint area changes, controlling a replanning agent to generate a route adjustment instruction for the UAV based on the changed flight constraint area, and sending the route adjustment instruction to the corresponding UAV, and replanning the flight route of the UAV according to the route adjustment instruction.

[0011] Optionally, in one embodiment of this application, it further includes: an extraction module, configured to extract spatial position parameters of the flight constraint region based on the airspace spatiotemporal constraint state dataset before the control replanning agent generates the flight route adjustment command for the unmanned aerial vehicle based on the changed flight constraint region; a construction module, configured to construct airspace discrete units based on the spatial position parameters and establish a mapping relationship between the airspace discrete units and the current flight route information; and a second determination module, configured to determine the first airspace discrete unit covered by the flight constraint region based on the mapping relationship.

[0012] Optionally, in one embodiment of this application, the planning module includes: a first determining unit, configured to determine a second airspace discrete unit covered by the modified flight constraint area based on the mapping relationship; a second determining unit, configured to determine the actual changed area of ​​the flight constraint area and the modified flight constraint area based on the first airspace discrete unit and the second airspace discrete unit; and a third determining unit, configured to determine the planning strategy of the replanning agent based on the actual changed area, and generate the route adjustment instruction according to the planning strategy.

[0013] Optionally, in one embodiment of this application, it further includes: a second acquisition module, configured to acquire the communication link status between the replanning agent and the unmanned aerial vehicle before issuing the route adjustment command to the corresponding unmanned aerial vehicle; a first issuing module, configured to allow the issuance of the route adjustment command to the unmanned aerial vehicle when the communication link status meets preset communication conditions; and a second issuing module, configured to generate an adjustment command for the communication link status when the communication link status does not meet the preset communication conditions, and adjust the communication link status according to the adjustment command until an adjusted communication link status that meets the preset communication conditions is obtained, and allow the issuance of the route adjustment command to the unmanned aerial vehicle.

[0014] Optionally, in one embodiment of this application, it further includes: an acquisition module, configured to acquire actual flight path information of the replanned flight path after the flight path of the unmanned aerial vehicle is replanned according to the flight path adjustment instruction; and a generation module, configured to control the replanning agent to generate a visualization display instruction for the unmanned aerial vehicle based on the current location data and the actual flight path information, and to execute a flight path visualization display operation according to the visualization display instruction.

[0015] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the unmanned aerial vehicle route planning method as described in the above embodiments.

[0016] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described unmanned aerial vehicle (UAV) route planning method.

[0017] A fifth aspect of this application provides a computer program product, including a computer program that, when executed, implements the above-described unmanned aerial vehicle (UAV) route planning method.

[0018] This application's embodiments can determine the corresponding airspace spatiotemporal constraint state dataset based on the collected current position data and current flight path information of the unmanned aerial vehicle (UAV), thereby determining the corresponding flight constraint area for the UAV. When the flight constraint area changes, the control replanning agent generates flight path adjustment instructions for the UAV and replans the flight path. By collecting real-time UAV position and flight path data to construct a dedicated airspace spatiotemporal constraint dataset for a single UAV, the corresponding flight constraint area is accurately matched and delineated. The independent replanning agent is only scheduled to output flight path adjustment instructions to update the flight path when the constraint area changes. This achieves precise constraint determination and targeted replanning, effectively reducing redundant calculations and improving the response efficiency of dynamic flight path adjustments. Therefore, it solves the problems in related technologies, such as: embedded local algorithm replanning passively triggering calculations, even minor airspace changes generating invalid calculations, and single-machine computing power limiting response speed; centralized global replanning requiring unified recalculation of multiple UAV routes, resulting in high overhead, weak real-time performance, and lack of decision visualization methods; and task trajectory step-by-step decoupling schemes requiring two complete calculations when encountering no-fly zone changes, lacking airspace and segment tracking mapping, and having low efficiency in locating affected segments.

[0019] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0020] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of an unmanned aerial vehicle (UAV) route planning method according to an embodiment of this application; Figure 2 This is a block diagram of an unmanned aerial vehicle (UAV) route planning device provided according to an embodiment of this application; Figure 3 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application. Detailed Implementation

[0021] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0022] The following description, with reference to the accompanying drawings, describes the unmanned aerial vehicle (UAV) route planning method, apparatus, equipment, medium, and program product according to embodiments of this application. To address the issues mentioned in the background technology, such as the passive triggering of embedded local algorithm replanning calculations leading to invalid calculations even with minor airspace changes, and the limitation of single-machine computing power on response speed; centralized global replanning requiring unified recalculation of multi-aircraft routes, resulting in high overhead, weak real-time performance, and lack of decision visualization methods; and the step-by-step decoupling scheme for mission tracks requiring two complete calculations when encountering no-fly zones, lacking airspace and segment tracking mapping, and having low efficiency in locating affected segments, this application provides a method for unmanned aerial vehicle (UAV) route planning. In this method, based on the collected current position data and current flight route information of the UAV, the corresponding airspace spatiotemporal constraint state dataset can be determined, thereby identifying the corresponding flight constraint area for the UAV. When the flight constraint area changes, the replanning agent is controlled to generate route adjustment instructions for the UAV and replan the flight route. By collecting real-time UAV position and route data to construct a single-aircraft dedicated airspace spatiotemporal constraint dataset, the corresponding flight constraint area is accurately matched and delineated. Only when the constraint area changes is the independent replanning agent scheduled to output route adjustment instructions to update the route, achieving precise constraint determination and targeted replanning, effectively reducing redundant calculations and improving the response efficiency of dynamic route adjustment. This solves the problems in related technologies, such as: embedded local algorithm replanning passively triggers calculations, even minor airspace changes can lead to invalid calculations, and single-machine computing power limits response speed; centralized global replanning requires unified recalculation of multi-aircraft routes, resulting in high overhead and weak real-time performance, and lack of decision visualization methods; and the step-by-step decoupling scheme for mission tracks requires two complete calculations when encountering no-fly zones, lacks airspace and segment tracking mapping, and has low efficiency in locating affected segments.

[0023] Specifically, Figure 1 This is a flowchart of an unmanned aerial vehicle (UAV) route planning method provided according to an embodiment of this application.

[0024] like Figure 1 As shown, the unmanned aerial vehicle (UAV) route planning method includes the following steps: In step S101, the current position data and current flight path information of at least one unmanned aerial vehicle are collected.

[0025] It is understood that, in the embodiments of this application, the unmanned aerial vehicle (UAV) can be understood as a drone with autonomous flight capability, such as various civilian / industrial UAVs for logistics, plant protection, inspection, reconnaissance, etc., and this application does not impose specific limitations; and the current location data can be used to describe the real-time spatial positioning information of the UAV, which may include, but is not limited to, latitude and longitude coordinates, flight altitude, and three-dimensional spatial coordinates, and can be collected in real time by the airborne GPS (Global Positioning System) and Beidou positioning modules, and this application does not impose specific limitations; the current flight route information can be used to describe the complete flight route data pre-loaded or being executed by the UAV, which may include, but is not limited to, the coordinates of all waypoints, the flight time of each segment, the flight speed, the flight heading, etc., and this application does not impose specific limitations.

[0026] In some embodiments, the present application can collect the current location data and current flight path information of one or more unmanned aerial vehicles in real time.

[0027] For example, in the embodiments of this application, the communication interaction between the ground control station and the unmanned aerial vehicle can be used to collect real-time latitude and longitude coordinates, three-dimensional spatial coordinates, flight altitude and other current position data transmitted back by one or more unmanned aerial vehicles, and simultaneously obtain the current flight route information of the unmanned aerial vehicle, including waypoint coordinates, flight time of the segment, flight speed and flight heading.

[0028] In step S102, based on the current location data and the current flight path information, a spatiotemporal constraint state dataset matching the unmanned aerial vehicle is determined, and based on the spatiotemporal constraint state dataset, the flight constraint area corresponding to the unmanned aerial vehicle is determined.

[0029] It is understood that, in this embodiment of the application, the airspace spatiotemporal constraint state dataset refers to a unique record generated by spatiotemporal filtering through an airspace no-fly zone time window state table, which matches each unmanned aerial vehicle (UAV). The airspace no-fly zone time window state table can store a structured data table containing the geographical boundaries of each no-fly airspace, the effective period of the constraint, and the control level. Each record is bound to the airspace number, spatial range, start and end time window, constraint type, and control level, as shown in Table 1. Table 1 is a schematic diagram of an airspace no-fly zone time window state table provided according to an embodiment of this application.

[0030] The airspace number can be understood as a unique identifier for each independently controlled airspace, used for quick indexing and retrieval of corresponding airspace constraint data, and to distinguish different constraint types such as permanent no-fly zones and temporary control zones.

[0031] The spatial range is defined by latitude and longitude, polygon coordinates, and altitude range to define the three-dimensional geographic boundary of the airspace, accurately delineating the actual spatial coverage area of ​​no-fly / restricted flights, which is used to determine the spatial intersection with the drone flight path and real-time location.

[0032] The start and end time windows are used to describe the continuous time interval in which control constraints are effective. They record the start and end times of constraints, distinguish between two scenarios: permanent effect throughout the day and temporary effect in time periods. They are used to match the flight time of drones and determine whether airspace constraints are effective for the current flight.

[0033] The type of constraint refers to the classification of airspace control, which may include, but is not limited to, permanent no-fly zones, temporary activity no-fly zones, time and altitude restrictions, and traffic control.

[0034] Control levels are used to describe the hierarchical classification of the strictness of airspace constraints. They may include, but are not limited to, special level, level one, level two, etc. The higher the level, the higher the priority of avoidance. In scenarios with multiple overlapping constraints, the flight constraint area is delineated according to the higher level of constraints.

[0035] Table 1

[0036] Furthermore, in the embodiments of this application, the flight constraint area can be a restricted airspace range that is only effective for the current unmanned aerial vehicle, obtained by combining the airspace spatiotemporal constraint status dataset with the current position data and current flight route information of the unmanned aerial vehicle. It may include, but is not limited to, temporary no-fly zones, permanent no-fly zones, and time-limited restricted airspaces. This application does not impose specific restrictions.

[0037] In some embodiments, this application can retrieve the airspace no-fly zone time window status table based on the current position data and current flight route information of the unmanned aerial vehicle, determine the airspace spatiotemporal constraint status dataset that matches the unmanned aerial vehicle, and then obtain the corresponding flight constraint area based on the airspace spatiotemporal constraint status dataset.

[0038] For example, in this application embodiment, the current location data and current flight route information of the unmanned aerial vehicle 1 can be used to retrieve the no-fly zone time window status table. From the no-fly zone time window status table, no-fly control records that overlap with the unmanned aerial vehicle 1 in both space and time can be found. Thus, an airspace spatiotemporal constraint status dataset is constructed. Then, based on the airspace spatiotemporal constraint status dataset, the spatial range, constraint type, control level, etc. of each record are extracted. Multiple overlapping no-fly airspaces are fused to obtain all continuous airspace ranges that will restrict the unmanned aerial vehicle's current flight. Finally, a flight constraint area that is only suitable for this unmanned aerial vehicle is generated.

[0039] Optionally, in one embodiment of this application, before the control replanning agent generates the flight path adjustment command for the unmanned aerial vehicle based on the changed flight constraint area, the method further includes: extracting the spatial position parameters of the flight constraint area based on the airspace spatiotemporal constraint state dataset; constructing airspace discrete units based on the spatial position parameters and establishing a mapping relationship between the airspace discrete units and the current flight path information; and determining the first airspace discrete unit covered by the flight constraint area based on the mapping relationship.

[0040] It is understood that, in the embodiments of this application, spatial location parameters may include, but are not limited to, three-dimensional boundary coordinates of the spatial range, longitude information, latitude information, vertex information, etc., and this application does not impose specific limitations; the airspace discretization unit is the independent gridded smallest spatial unit obtained by dividing the flight constraint area according to a uniform size, and the entire airspace is discretized in the form of three-dimensional grid blocks, which is the basic granularity unit for airspace and route association calculation.

[0041] Furthermore, in the embodiments of this application, the mapping relationship between airspace discrete units and current flight route information can be understood as a one-to-one correspondence index between each flight route segment, waypoint and the airspace discrete unit it passes through. The route can be quickly queried in reverse through the grid unit, and the affected flight route segment can be quickly located through the grid unit. The first airspace discrete unit can be understood as the airspace discrete unit corresponding to the flight constraint area before it changes.

[0042] In some embodiments, the present application embodiments can first extract the spatial position parameters corresponding to the flight constraint area from the airspace spatiotemporal constraint state dataset, and construct airspace discrete units based on the spatial position parameters, thereby establishing a mapping relationship between the airspace discrete units and the current flight route information of the unmanned aerial vehicle, and then quickly locate the first airspace discrete unit covered by the flight constraint area based on the mapping relationship.

[0043] For example, to support the rapid response capability of the replanning agent, this application employs a mapping method based on grid-based spatial discretization. Specifically, spatial location parameters of the flight constraint region are first extracted from the spatial spatiotemporal constraint state dataset. Then, based on the spatial location parameters, the flight constraint region is divided into regular three-dimensional grid cells according to longitude, latitude, and altitude information. After each flight path is determined, the current flight path information is sampled, and a mapping index is established between each grid cell and the list of flight segments passing through that grid, thereby determining the first spatial discretization cell covered by the flight constraint region.

[0044] Optionally, in one embodiment of this application, before issuing the route adjustment command to the corresponding unmanned aerial vehicle (UAV), the method further includes: collecting the communication link status between the replanning agent and the UAV; if the communication link status meets preset communication conditions, then allowing the issuance of the route adjustment command to the UAV; if the communication link status does not meet the preset communication conditions, then generating an adjustment command for the communication link status, and adjusting the communication link status according to the adjustment command until an adjusted communication link status that meets the preset communication conditions is obtained, and allowing the issuance of the route adjustment command to the UAV.

[0045] It is understood that, in the embodiments of this application, the replanning agent, as an independent software entity, is deployed on a ground control station or airborne computing platform and runs continuously throughout the entire mission cycle. Furthermore, the replanning agent is an autonomously operating agent that organically encapsulates functions such as perception, mapping, and planning. It can autonomously maintain current location data, current flight path information, and airspace spatiotemporal constraint state datasets, thereby improving the system's automated response capability to changes in no-fly zone status and enhancing the transparency and interpretability of the decision-making process through situational visualization.

[0046] Furthermore, the communication link status in this application embodiment refers to a set of quantitative indicators of communication channel quality, which may include, but is not limited to, signal-to-noise ratio, signal strength, data packet loss rate, transmission delay, communication bandwidth, bit error rate, link connectivity, degree of obstruction, remaining communication battery life, etc. This application does not impose specific limitations.

[0047] In some embodiments, before replanning the flight route according to the route adjustment instruction, this application first collects the communication link status between the replanning agent and the unmanned aerial vehicle, and checks whether the communication link status meets preset communication conditions. If it does, the route adjustment instruction is directly issued; if not, an adjustment instruction for the communication link status is generated, and the communication link status is optimized according to the adjustment instruction until the preset communication conditions are met, before the route adjustment instruction is issued. The preset communication conditions can be set by those skilled in the art according to actual conditions, and this application does not impose specific limitations.

[0048] For example, in this embodiment of the application, before preparing to issue a route adjustment command, the replanning agent first collects the communication link status between the replanning agent and the unmanned aerial vehicle 1 in real time. If the preset communication conditions are a packet loss rate of less than 0.5% and a signal strength of greater than -85dBm.

[0049] Furthermore, in this embodiment of the application, when the packet loss rate of the communication link is 0.2% and the signal strength is -72dBm, it is determined that the communication link status meets the preset communication conditions, and the complete route adjustment command is directly sent to the corresponding unmanned aerial vehicle.

[0050] In addition, in this embodiment of the application, when the UAV flies to an area obstructed by buildings and the link packet loss rate is 3.2%, it is determined that the communication link status does not meet the preset communication conditions, and an adjustment instruction for the communication link status is generated, such as increasing the radio transmission power, switching to the backup communication frequency, and reducing the image transmission code rate, etc., to adjust the corresponding communication link status until the link packet loss rate is less than 0.5% and the signal strength is greater than -85dBm. At this time, a flight path adjustment instruction is pushed to the UAV.

[0051] In step S103, if the flight constraint area changes, the control replanning agent generates a flight path adjustment command for the unmanned aerial vehicle based on the changed flight constraint area, and sends the flight path adjustment command to the corresponding unmanned aerial vehicle, and replans the flight path of the unmanned aerial vehicle according to the flight path adjustment command.

[0052] In some embodiments, when a change in the flight constraint area is detected, the present application can drive the replanning agent to generate a route adjustment instruction adapted to the unmanned aerial vehicle (UAV) based on the changed flight constraint area, and send the route adjustment instruction to the corresponding UAV, thereby controlling the UAV to complete the replanning of the flight route according to the received route adjustment instruction.

[0053] For example, in this embodiment of the application, when the flight constraint area changes, the replanning agent can automatically receive the changed flight constraint area and trigger the corresponding processing flow, without waiting for operator instructions. It generates a flight path adjustment instruction and sends it to the corresponding unmanned aerial vehicle (UAV) via a communication link for execution, thereby completing the replanning of the flight path. This closed-loop process is entirely autonomously driven by the replanning agent, significantly reducing the workload of operators in dynamic airspace management.

[0054] Optionally, in one embodiment of this application, the control replanning agent generates flight path adjustment instructions for the unmanned aerial vehicle based on the modified flight constraint area, including: determining the second airspace discrete unit covered by the modified flight constraint area based on the mapping relationship; determining the actual modified area of ​​the flight constraint area and the modified flight constraint area based on the first airspace discrete unit and the second airspace discrete unit; determining the planning strategy of the replanning agent based on the actual modified area, and generating flight path adjustment instructions according to the planning strategy.

[0055] It is understood that the planning strategies in the embodiments of this application include local adjustment strategies and global planning strategies.

[0056] The local adjustment strategy can be understood as follows: when the impact range of the actual changed area is relatively small, a local search algorithm can be run between the beginning and end points of the affected flight segment to generate an alternative flight segment. The global planning strategy, on the other hand, can be understood as follows: when the impact range of the actual changed area is relatively large, a global planning algorithm can be invoked, such as a genetic algorithm, to uniformly resolve the flight paths of multiple unmanned aerial vehicles. The impact range can be set by those skilled in the art according to the actual situation, and this application does not impose specific limitations.

[0057] In some embodiments, this application can locate the second airspace discrete unit covered by the changed flight constraint area based on the mapping relationship between airspace discrete units and flight routes. By comparing the first airspace discrete unit and the second airspace discrete unit, the actual changed area is obtained. Then, the planning strategy corresponding to the replanning agent is obtained by matching the actual changed area, thereby generating the corresponding flight route adjustment instruction.

[0058] For example, in the case of a change in the flight constraint area, the replanning agent can determine the second airspace discrete unit covered by the changed flight constraint area based on the mapping relationship, and determine the actual changed area by comparing the first airspace discrete unit with the second airspace discrete unit.

[0059] Furthermore, in this embodiment, when the influence range of the actual changed area is relatively small, a local adjustment strategy can be invoked to obtain the corresponding route adjustment instruction; when the influence range of the actual changed area is relatively large, a global planning strategy can be invoked to obtain the corresponding route adjustment instruction. This transforms repetitive geometric intersection calculations into one-time index construction and multiple fast table lookup operations, providing real-time assurance for the automated operation of the agent.

[0060] Optionally, in one embodiment of this application, after replanning the flight path of the unmanned aerial vehicle according to the flight path adjustment instruction, the method further includes: obtaining the actual flight path information of the replanned flight path; based on the current location data and the actual flight path information, controlling the replanning agent to generate a visualization display instruction for the unmanned aerial vehicle, and executing the flight path visualization display operation according to the visualization display instruction.

[0061] In some embodiments, after the flight path of the unmanned aerial vehicle is replanned, the actual flight path information of the replanned flight path can be obtained, and the current position data of the unmanned aerial vehicle and the actual flight path information can be combined to drive the replanning agent to output a visualization display command, and then execute the visualization display operation of the flight path according to the visualization display command.

[0062] For example, in the replanning agent of this application embodiment, not only autonomously completes the route adjustment during operation, but also simultaneously generates a decision description for this adjustment. After completing the route adjustment, the replanning agent generates a structured decision description, which records the no-fly zone event that triggered the adjustment, the affected flight segment identifier, and the specific changes to the old and new routes.

[0063] Furthermore, in this embodiment, based on the decision description, corresponding visualization instructions are generated, and the route visualization operation is executed according to the visualization instructions. This explicit recording of the decision basis and reasoning chain gives the agent's decision-making process interpretability, enabling operators to understand the causal relationship of the system's behavior, and facilitating auditing or manual review when necessary.

[0064] The unmanned aerial vehicle (UAV) route planning method proposed in this application can determine the corresponding airspace spatiotemporal constraint state dataset based on the collected current position data and current flight route information of the UAV, thereby determining the corresponding flight constraint area of ​​the UAV. When the flight constraint area changes, the replanning agent is controlled to generate route adjustment instructions for the UAV and replan the flight route. By collecting real-time position and route data of the UAV to construct a dedicated airspace spatiotemporal constraint dataset for a single UAV, the corresponding flight constraint area is accurately matched and delineated. The independent replanning agent is only scheduled to output route adjustment instructions to update the route when the constraint area changes. This can achieve precise constraint determination and targeted replanning, effectively reducing redundant calculations and improving the response efficiency of dynamic route adjustment. Thus, it solves the problems in related technologies, such as: embedded local algorithm replanning passively triggers calculations, even small airspace changes will produce invalid calculations, and single-machine computing power limits response speed; centralized global replanning requires unified recalculation of multiple aircraft routes, which has high overhead and weak real-time performance, and lacks decision visualization means; and the step-by-step decoupling scheme of mission trajectory requires two complete calculations when encountering no-fly zone changes, lacks airspace and segment tracking mapping, and has low efficiency in locating affected segments.

[0065] Next, the unmanned aerial vehicle (UAV) route planning device according to the embodiments of this application is described with reference to the accompanying drawings.

[0066] Figure 2 This is a block diagram of an unmanned aerial vehicle (UAV) route planning device provided according to an embodiment of this application.

[0067] like Figure 2 As shown, the unmanned aerial vehicle route planning device 10 includes: a first acquisition module 100, a first determination module 200, and a planning module 300.

[0068] The first acquisition module 100 is used to acquire the current location data and current flight path information of at least one unmanned aerial vehicle.

[0069] The first determining module 200 is used to determine the airspace spatiotemporal constraint state dataset that matches the unmanned aerial vehicle based on the current location data and the current flight route information, and to determine the flight constraint area corresponding to the unmanned aerial vehicle based on the airspace spatiotemporal constraint state dataset.

[0070] The planning module 300 is used to control the replanning agent to generate flight path adjustment instructions for the unmanned aerial vehicle (UAV) based on the changed flight constraint area when the flight constraint area changes, and to send the flight path adjustment instructions to the corresponding UAV, and replan the flight path of the UAV according to the flight path adjustment instructions.

[0071] Optionally, in one embodiment of this application, it further includes: an extraction module, a construction module, and a second determination module.

[0072] The extraction module is used to extract the spatial position parameters of the flight constraint area based on the airspace spatiotemporal constraint state dataset before the control replanning agent generates the flight route adjustment command for the unmanned aerial vehicle based on the changed flight constraint area.

[0073] The module is used to construct airspace discrete units based on spatial location parameters and establish a mapping relationship between airspace discrete units and current flight route information.

[0074] The second determining module is used to determine the first airspace discrete unit covered by the flight constraint area based on the mapping relationship.

[0075] Optionally, in one embodiment of this application, the planning module 300 includes: a first determining unit, a second determining unit, and a third determining unit.

[0076] The first determining unit is used to determine the second airspace discrete unit covered by the changed flight constraint area based on the mapping relationship.

[0077] The second determining unit is used to determine the actual changed area of ​​the flight constraint region and the changed flight constraint region based on the first spatial discretization unit and the second spatial discretization unit.

[0078] The third determining unit is used to determine the planning strategy of the replanning agent based on the actual changed area, and generate route adjustment instructions according to the planning strategy.

[0079] Optionally, in one embodiment of this application, it further includes: a second acquisition module, a first sending module, and a second sending module.

[0080] The second acquisition module is used to collect the communication link status between the replanning agent and the unmanned aerial vehicle before issuing the route adjustment command to the corresponding unmanned aerial vehicle.

[0081] The first sending module is used to allow the sending of route adjustment commands to the unmanned aerial vehicle when the communication link status meets the preset communication conditions.

[0082] The second sending module is used to generate an adjustment instruction for the communication link status when the communication link status does not meet the preset communication conditions, and adjust the communication link status according to the adjustment instruction until the adjusted communication link status meets the preset communication conditions, and allow the sending of route adjustment instructions to the unmanned aerial vehicle.

[0083] Optionally, in one embodiment of this application, it further includes an acquisition module and a generation module.

[0084] The acquisition module is used to obtain the actual flight path information of the replanned flight path after the flight path of the unmanned aerial vehicle is replanned according to the flight path adjustment instruction.

[0085] The generation module is used to control the replanning agent to generate visualization instructions for the unmanned aerial vehicle based on the current location data and actual flight path information, and to execute the flight path visualization operation according to the visualization instructions.

[0086] It should be noted that the foregoing explanation of the unmanned aerial vehicle (UAV) route planning method embodiment also applies to the UAV route planning device of this embodiment, and will not be repeated here.

[0087] According to the unmanned aerial vehicle (UAV) route planning device proposed in this application, it can determine the corresponding airspace spatiotemporal constraint state dataset based on the collected current position data and current flight route information of the UAV, thereby determining the flight constraint area corresponding to the UAV. When the flight constraint area changes, it controls the replanning agent to generate route adjustment instructions for the UAV and replans the flight route. By collecting real-time position and route data of the UAV to construct a single-aircraft dedicated airspace spatiotemporal constraint dataset, it accurately matches and delineates the corresponding flight constraint area. It only schedules the independent replanning agent to output route adjustment instructions to update the route when the constraint area changes, which can achieve accurate constraint judgment and targeted replanning, effectively reducing redundant calculations and improving the response efficiency of dynamic route adjustment. Thus, it solves the problems in related technologies, such as: embedded local algorithm replanning passively triggers calculations, even small airspace changes will produce invalid calculations, and single-machine computing power limits response speed; centralized global replanning requires unified recalculation of multiple aircraft routes, which has high overhead and weak real-time performance, and lacks decision visualization means; and the step-by-step decoupling scheme of mission trajectory requires two complete calculations when encountering no-fly zone changes, lacks airspace and segment tracking mapping, and has low efficiency in locating affected segments.

[0088] Figure 3 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application. The electronic device may include: The memory 301, the processor 302, and the computer program stored on the memory 301 and capable of running on the processor 302.

[0089] When the processor 302 executes the program, it implements the unmanned aerial vehicle route planning method provided in the above embodiments.

[0090] Furthermore, electronic devices also include: Communication interface 303 is used for communication between memory 301 and processor 302.

[0091] The memory 301 is used to store computer programs that can run on the processor 302.

[0092] The memory 301 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0093] If the memory 301, processor 302, and communication interface 303 are implemented independently, then the communication interface 303, memory 301, and processor 302 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 3 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0094] Optionally, in a specific implementation, if the memory 301, processor 302, and communication interface 303 are integrated on a single chip, then the memory 301, processor 302, and communication interface 303 can communicate with each other through an internal interface.

[0095] Processor 302 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0096] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described unmanned aerial vehicle (UAV) route planning method.

[0097] This application also provides a computer program product, including a computer program that, when executed, implements the above-described unmanned aerial vehicle (UAV) route planning method.

[0098] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0099] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0100] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0101] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). In addition, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically by optically scanning paper or other media, then editing, interpreting or otherwise processing them as necessary, and then storing them in computer memory.

[0102] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or more of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0103] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0104] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0105] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for unmanned aerial vehicle (UAV) route planning, characterized in that, Includes the following steps: Collect the current location data and current flight path information of at least one unmanned aerial vehicle; Based on the current location data and the current flight path information, a spatiotemporal constraint state dataset matching the unmanned aerial vehicle is determined, and based on the spatiotemporal constraint state dataset, the flight constraint area corresponding to the unmanned aerial vehicle is determined. If the flight constraint area changes, the control replanning agent generates a flight path adjustment instruction for the unmanned aerial vehicle (UAV) based on the changed flight constraint area, and sends the flight path adjustment instruction to the corresponding UAV, and replans the flight path of the UAV according to the flight path adjustment instruction.

2. The method according to claim 1, characterized in that, Before the control replanning agent generates the flight path adjustment command for the unmanned aerial vehicle based on the modified flight constraint region, the following steps are also included: Based on the aforementioned spatial-temporal constraint state dataset, the spatial location parameters of the flight constraint region are extracted. Based on the spatial location parameters, a spatial discrete unit is constructed, and a mapping relationship is established between the spatial discrete unit and the current flight route information; Based on the mapping relationship, the first discrete airspace unit covered by the flight constraint area is determined.

3. The method according to claim 2, characterized in that, Based on the modified flight constraint region, the control replanning agent generates flight path adjustment instructions for the unmanned aerial vehicle, including: Based on the mapping relationship, the second airspace discrete unit covered by the modified flight constraint area is determined; Based on the first spatial discretization unit and the second spatial discretization unit, the actual changed area of ​​the flight constraint region and the modified flight constraint region is determined. Based on the actual changed area, the planning strategy of the replanning agent is determined, and the route adjustment instruction is generated according to the planning strategy.

4. The method according to claim 1, characterized in that, Before issuing the route adjustment command to the corresponding unmanned aerial vehicle, the process also includes: Collect the communication link status between the replanning agent and the unmanned aerial vehicle; If the communication link status meets the preset communication conditions, then the route adjustment command can be sent to the unmanned aerial vehicle. If the communication link status does not meet the preset communication conditions, an adjustment instruction for the communication link status is generated, and the communication link status is adjusted according to the adjustment instruction until an adjusted communication link status that meets the preset communication conditions is obtained, and the route adjustment instruction is allowed to be issued to the unmanned aerial vehicle.

5. The method according to claim 1, characterized in that, After replanning the flight path of the unmanned aerial vehicle according to the flight path adjustment instruction, the process also includes: Obtain the actual flight route information after the replanned flight route; Based on the current location data and the actual flight path information, the replanning agent is controlled to generate visualization instructions for the unmanned aerial vehicle and execute the flight path visualization operation according to the visualization instructions.

6. A flight path planning device for unmanned aerial vehicles, characterized in that, include: The first data acquisition module is used to collect the current position data and current flight path information of at least one unmanned aerial vehicle; The first determining module is used to determine, based on the current location data and the current flight route information, an airspace spatiotemporal constraint state dataset matching the unmanned aerial vehicle, and to determine the flight constraint area corresponding to the unmanned aerial vehicle based on the airspace spatiotemporal constraint state dataset. The planning module is used to control the replanning agent to generate a flight path adjustment instruction for the unmanned aerial vehicle (UAV) based on the changed flight constraint area when the flight constraint area changes, and to send the flight path adjustment instruction to the corresponding UAV, and replan the flight path of the UAV according to the flight path adjustment instruction.

7. The apparatus according to claim 6, characterized in that, Also includes: The extraction module is used to extract the spatial position parameters of the flight constraint area based on the airspace spatiotemporal constraint state dataset before the control replanning agent generates the flight path adjustment command for the unmanned aerial vehicle based on the changed flight constraint area. The construction module is used to construct airspace discrete units based on the spatial location parameters, and to establish a mapping relationship between the airspace discrete units and the current flight route information; The second determining module is used to determine the first airspace discrete unit covered by the flight constraint area based on the mapping relationship.

8. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the unmanned aerial vehicle route planning method as described in any one of claims 1-5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the unmanned aerial vehicle route planning method as described in any one of claims 1-5.

10. A computer program product, characterized in that, Includes a computer program, which, when executed, is used to implement the unmanned aerial vehicle route planning method as described in any one of claims 1-5.

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