Urban low-altitude multi-unmanned aerial vehicle autonomous route planning system and method

Abstract

This invention discloses an autonomous route planning system and method for multiple unmanned aerial vehicles (UAVs) in urban low-altitude airspace, belonging to the field of intelligent route planning technology for low-altitude airspace. It includes a gridded airspace construction module for building a digital urban low-altitude airspace model, a route planning module for generating initial routes for multiple UAVs based on the gridded airspace model, and a conflict detection and resolution module to ensure global conflict-free operation of all output routes. It also includes an autonomous route planning method for multiple UAVs in urban low-altitude airspace based on the above system. Through airspace grid division, an improved three-dimensional jump point search algorithm, and a conflict resolution mechanism based on conflict search, it achieves efficient generation of UAV routes and globally conflict-free coordination, applicable to various scenarios such as urban logistics distribution, inspection, and emergency response.

Description

Technical Field

[0001] This invention belongs to the field of intelligent route planning technology in low-altitude airspace, and more specifically, relates to an autonomous route planning system and method for multiple unmanned aerial vehicles in urban low-altitude airspace. Background Technology

[0002] With the rapid development of the low-altitude economy, the application of drones in logistics, inspection, agriculture, and other fields has experienced explosive growth. According to the Association for Unmanned Vehicle Systems International (AUVSI), the global drone market is projected to exceed $50 billion by 2025, with daily urban low-altitude drone flights exceeding one million. This growth trend is not only reflected in the consumer drone market but also significantly in the industrial drone sector. At this point, the efficiency of drone route planning will directly impact operational effectiveness. Efficient and autonomous multi-drone route planning methods can support efficient drone operations in multiple scenarios, promoting the large-scale application of drones.

[0003] However, current traditional route planning technologies face the following bottlenecks. First, current route planning methods are highly dependent on manual intervention, relying on the manual delineation of fixed routes, which cannot dynamically adapt to the needs of any origin and destination missions, resulting in poor flexibility and significant time consumption. Second, there is insufficient safety redundancy. In high-density UAV environments, manually planned routes are difficult to avoid dense urban obstacles (such as buildings and meteorological risk areas), leading to a surge in collision risks. Finally, the conflict resolution efficiency of multi-UAV routes is low. Existing methods mostly adopt fixed priority rules (such as "first come, first served") or simple avoidance protocols, lacking intelligent conflict resolution mechanisms, resulting in a high secondary conflict rate and difficulty in ensuring a conflict-free global environment.

[0004] Therefore, the problem that needs to be solved is how to design an autonomous flight path planning system and method for multiple UAVs in urban low-altitude areas to overcome the above-mentioned shortcomings and apply it to multiple UAV flight path planning in various scenarios such as logistics and inspection, so as to provide support for the large-scale application of UAVs in the low-altitude economy. Summary of the Invention

[0005] To address the above shortcomings, this invention provides an urban low-altitude multi-UAV autonomous route planning system. Through airspace grid division, an improved 3D jump point search algorithm, and a conflict resolution mechanism based on conflict search, it achieves efficient UAV route generation and globally conflict-free coordination. Applicable to various scenarios such as urban logistics delivery, inspection, and emergency response, it includes the following modules:

[0006] A gridded airspace construction module for building digital urban low-altitude airspace models, comprising a gridded airspace division submodule and a trafficability assessment submodule;

[0007] A route planning module is used to generate initial routes for multiple UAVs based on a gridded airspace model. The route planning module includes a grid scale selection submodule and an initial route planning submodule.

[0008] And a conflict detection and resolution module to ensure that all routes in the final output are globally conflict-free. The conflict detection and resolution module includes a route conflict detection and processing submodule and a route replanning submodule.

[0009] Furthermore, the airspace partitioning submodule is used to perform low-altitude airspace grid partitioning and coding modeling based on the input static map data, and to construct a multi-level, multi-scale three-dimensional airspace gridded map.

[0010] The accessibility assessment submodule is used to quantify and calculate grid access weights based on the attribute data of the input static map.

[0011] Furthermore, the grid scale selection submodule is used to select the airspace grid scale according to the UAV model and performance;

[0012] The initial route planning submodule is used to plan the optimal initial route for each UAV in the airspace gridded map based on the UAV mission information.

[0013] Furthermore, the route conflict detection and processing submodule is used to detect and determine the conflict of all UAV routes output by the route planning module, and to impose constraints on UAVs that are determined to be in conflict.

[0014] The route replanning submodule is used to replan the routes of UAVs that are determined to be in conflict from the route conflict detection and processing submodule, so as to obtain a globally conflict-free multi-UAV route.

[0015] Furthermore, the route replanning submodule is used to replan the routes of UAVs that are subject to constraints and will cause conflicts, as output by the route conflict detection submodule, and return to the route conflict detection and processing submodule for re-determination of route conflicts, and finally iterate to obtain a global conflict-free route for multiple UAVs.

[0016] A method for autonomous flight path planning for multiple unmanned aerial vehicles (UAVs) in urban low-altitude areas, comprising the aforementioned system, and further comprising the following steps:

[0017] S1. Airspace Modeling: The input static low-altitude airspace environment map is divided into multi-scale airspace grids through the gridded airspace construction module, the grid accessibility weights are calculated, and the grid map data is stored in the storage medium of the computer.

[0018] S2. Initial route planning: Select the grid scale through the route planning module, plan the initial optimal route for each UAV at the corresponding grid scale according to the input task requirements, and output the initial routes of all UAVs.

[0019] S3. Conflict Detection and Replanning: Input all the initial UAV routes output from step S2 into the conflict detection and resolution module. Based on the detected conflicts between the initial routes, impose constraint information on the UAVs and use it for UAV route replanning.

[0020] Furthermore, the calculation process for the traversability weight in step S1 is as follows:

[0021] ;

[0022] In the formula, W For the traversability weights of the spatial grid, C 障碍 For obstacle density, R 风险 Risk level, α、β These are the weighting coefficients.

[0023] Furthermore, in step S2, the initial route planning for a single UAV adopts a three-dimensional jump point search algorithm:

[0024] ;

[0025] In the formula, f(n) To estimate the cost from the starting point through grid node n to the ending point, g(n) The actual cost from the starting point to grid node n. h(n) Let n be the heuristic function for cost estimation from grid node n to the destination. w(n) The grid risk weight is the accessibility weight calculated in step S1. W get.

[0026] Furthermore, the detection of initial inter-route conflicts in step S3 includes point conflicts and edge conflicts;

[0027] The definition formula for point conflict is:

[0028] ;

[0029] The definition formula for edge conflict is:

[0030] ;

[0031] In the formula, t For time step timestep The time scale assumes that the time step required for the drone to move one airspace grid node is 1. i, jThey are respectively a1, a2 The drone's serial number, loc The location where the conflict occurred. x, y, z These are the coordinates of the conflict locations.

[0032] Furthermore, in step S3, the UAV route replanning involves improving the three-dimensional jump point search method by introducing an incremental update method. The calculation process is as follows:

[0033] ;

[0034] In the formula, n’ For the current node n The parent node, rhs ( n ) is the introduced grid node-based n’ The minimum cost, c ( n',n ) represents the actual cost from the parent node to the current node. g(n') From the starting point to the parent grid node n’ The actual cost.

[0035] Compared with the prior art, the present invention has the following advantages:

[0036] 1. A conflict-free route planning system for multiple UAVs in low-altitude urban areas is provided. The system reduces computational overhead by using an incremental route replanning mechanism to resolve route conflicts and can support application scenarios with hundreds of UAVs.

[0037] 2. A method for autonomous flight path planning of multiple UAVs in urban low-altitude areas is provided. By simply inputting a static urban environment map and the start and end points of the UAV mission, a conflict-free flight path for multiple UAVs can be directly planned.

[0038] 3. Compared with traditional manual route planning, it can realize route planning with arbitrary start and end points, and has the characteristics of high flexibility and high computational efficiency, which can provide technical support for the large-scale application of UAVs in the low-altitude economy. Attached Figure Description

[0039] Figure 1 This is a general structural diagram of an urban low-altitude multi-UAV autonomous flight path planning method according to the present invention.

[0040] Figure 2 This is a flowchart of the workflow of an urban low-altitude multi-UAV autonomous route planning system according to the present invention.

[0041] Figure 3 This is a flowchart of the route planning module in this invention.

[0042] Figure 4 This is a flowchart illustrating the workflow of the conflict detection and processing submodule and the route replanning submodule in this invention.

[0043] Figure 5 This is the initial homepage interface diagram of an urban low-altitude multi-UAV autonomous route planning system according to the present invention.

[0044] Figure 6 This is a screenshot of the interface after selecting the gridded spatial domain construction option on the initial homepage of this invention.

[0045] Figure 7 This is a screenshot of the interface after selecting the route planning and conflict detection and resolution option on the initial homepage of this invention. Detailed Implementation

[0046] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0047] Example

[0048] like Figure 1 As shown, this embodiment provides an urban low-altitude multi-UAV autonomous route planning system, including:

[0049] The module includes a gridded airspace construction module, a route planning module, and a conflict detection and resolution module.

[0050] The gridded airspace construction module includes a gridded airspace division submodule that performs multi-scale grid division based on static map data and a trafficability evaluation submodule that quantifies and calculates grid traffic weights based on map attribute data.

[0051] The route planning module is configured to calculate the airspace grid scale based on the size and performance of the UAV. L The grid scale selection submodule and the initial route planning submodule calculate the optimal initial route based on the origin and destination in the mission information of each UAV.

[0052] The conflict detection and resolution module includes a route conflict detection and processing submodule and a route replanning submodule;

[0053] The route conflict detection and processing submodule is used to detect and determine conflicts in all UAV routes output by the route planning module, and to impose constraints on UAVs that are determined to be in conflict.

[0054] The route replanning submodule is used to replan the routes of UAVs that are determined to be in conflict by the conflict detection and processing submodule, so as to obtain a global conflict-free multi-UAV route.

[0055] In detail, the route conflict detection and processing submodule performs pairwise conflict detection on the initial routes of each UAV output by the route planning module, and outputs conflict information of the empty set or UAVs that do not have conflicts.

[0056] The route replanning submodule is used to apply the UAV constraint information obtained from the route conflict detection and processing submodule to the UAV, thereby completing the route replanning of the conflicting UAV, obtaining a new UAV route, and returning to the route conflict detection and processing submodule for re-determination of route conflicts, and finally iterating to obtain a global conflict-free route for multiple UAVs.

[0057] like Figures 2 to 4 As shown, the planning method based on the above system is as follows:

[0058] S1. The input static low-altitude airspace map data is divided into multi-scale grids and grid accessibility weights are calculated using the gridded airspace construction module. The resulting data is then stored in the computer's storage medium (via...). Figure 5-7 (To synchronize the interface display), based on the submitted static airspace map data (such as...) Figure 6 The gridded spatial domain construction module on the display page (as shown) will obtain and display grid map data, where the calculation process for grid accessibility weights is as follows:

[0059] Substitute the static low-altitude airspace attribute data into the following equation:

[0060] (1);

[0061] In equation (1), W For the traversability weights of the spatial grid, C 障碍 For obstacle density, R 风险 Risk level, α、β These are the weighting coefficients;

[0062] S2. The route planning module selects the grid scale based on the UAV's size and performance, calculates and outputs the optimal initial route based on the origin and destination points in the mission information of each UAV, and displays it. Figure 7 The route planning module interface is used for submitting task information and displaying routes. The calculation process of the route planning module is as follows:

[0063] Substitute the drone's size and performance data into the following equation:

[0064] (2);

[0065] In equation (2), L For grid scale, k This is a scaling factor for the drone's size, typically 1.D 无人机 These are the characteristic dimensions of the drone, i.e., the largest size parameter of the drone. S For safety redundancy of drones, it is generally 30% of the drone's maximum speed.

[0066] Substitute the start and end point data from the UAV mission information into the following equation:

[0067] (3);

[0068] In equation (3), f(n) To estimate the cost from the starting point through grid node n to the ending point, g(n) The actual cost from the starting point to grid node n. h(n) It is a heuristic function that represents the cost estimate from grid node n to the destination. w(n) The grid risk weight is the accessibility weight calculated in step S1. W The specific route node expansion calculation first involves pruning and filtering the airspace grid nodes according to the neighbor pruning rule of the 3D jump point search algorithm. Among these grid nodes, the node with the smallest total cost estimate is selected for jump point search and expansion until the destination is reached, thus obtaining the complete route node.

[0069] S3. Input all the initial flight paths of the UAVs output in step S2 into the conflict detection and resolution module. Based on the detected conflicts between the initial flight paths, apply constraint information to the UAVs and use it for UAV flight path replanning.

[0070] S3-1, Conflict Detection and Handling of Multiple UAV Flight Routes; Detects whether conflicts occur between multiple UAV flight routes and outputs flight route conflict information, such as... Figure 7 The route conflict detection and resolution module is shown below;

[0071] S3-1-1. The conflict detection and processing submodule performs inter-route conflict detection and determination on all the initial UAV routes obtained in step S2 or the conflicting new UAV routes obtained in step S3-2 to obtain route conflict information. The calculation process is as follows:

[0072] The following formula defines the determination of inter-route conflicts:

[0073] (4);

[0074] (5);

[0075] In formulas (4) to (5), t For time step timestep The time scale assumes that the time step required for the drone to move one airspace grid node is 1. i, jThey are respectively a1, a2 The drone's serial number, loc The location where the conflict occurred. x、 y, z The coordinates of the conflict locations are respectively. Formulas (4) and (5) represent point conflict and edge conflict, respectively. Based on the input UAV initial route or the new route obtained by replanning, the conflict between UAV routes is determined, the conflict information is output, and constraints are imposed on the conflicting UAVs.

[0076] S3-1-2, Extract information from the route conflict information obtained in step S3-1-1. If the route conflict information is an empty set, obtain the conflict-free routes for all UAVs, and as follows... Figure 7 Perform interface presentation and data saving; otherwise, output the UAV constraint information for the flight path conflict.

[0077] S3-2. Input the UAV constraint information obtained in step S3-1-2 into the route replanning submodule to complete the route replanning of the conflicting UAVs, obtain the new UAV route, and return to step S3-1-1 to re-determine the route conflict. The calculation process of route replanning is as follows:

[0078] Based on UAV conflict information, flight routes are replanned using an incremental update method. The calculation process is as follows:

[0079] (6);

[0080] In formula (6), n' is the parent node of the current node n, rhs(n) is the minimum cost introduced based on the grid node n', c(n',n) is the actual cost from the parent node to the current node, g(n') is the actual cost from the starting point to the parent grid node n', n∈Succ(n) represents all successor nodes of the current node n, min represents selecting the node that minimizes rhs(n) among all successor nodes, nstart is the starting node, if represents the case where the current node is equal to the starting node, and otherwise represents other cases.

[0081] Specifically, during the route replanning step S3-2, initialization is generally performed first to make... rhs ( n ) =g(n) Based on the conflict information, conflicting nodes are set as temporary obstacles, and their minimum cost based on their parent nodes becomes positive infinity. rhs ( n ) ≠g(n) By disseminating the effects of this local inconsistency, all route nodes affected by obstacles can be identified. Based on this, new temporary starting points can be set up, and the affected routes can be replanned to obtain new UAV routes.

[0082] It should be noted that the structure of the present invention can be implemented in many different forms and is not limited to the embodiments. Any equivalent transformations made by those skilled in the art based on the content of the present invention specification and drawings, or direct or indirect applications in other related technical fields, such as the loading and unloading of other items, are included within the protection scope of the present invention.

Claims

1. An urban low-altitude multi-UAV autonomous route planning system, characterized in that, Includes the following modules: A gridded airspace construction module for building digital urban low-altitude airspace models, comprising a gridded airspace division submodule and a trafficability assessment submodule; The airspace partitioning submodule is used to perform low-altitude airspace grid partitioning and coding modeling based on the input static map data, and to construct a multi-level, multi-scale three-dimensional airspace gridded map. The accessibility assessment submodule is used to quantify and calculate grid accessibility weights based on the attribute data of the input static map; A route planning module is used to generate initial routes for multiple UAVs based on a gridded airspace model. The route planning module includes a grid scale selection submodule and an initial route planning submodule. The grid scale selection submodule is used to select the airspace grid scale according to the UAV model and performance. The initial route planning submodule is used to plan the optimal initial route for each UAV in the airspace gridded map based on the UAV mission information. And a conflict detection and resolution module to ensure that all routes in the final output are globally conflict-free. The conflict detection and resolution module includes a route conflict detection and processing submodule and a route replanning submodule. The route conflict detection and processing submodule is used to detect and determine conflicts in all UAV routes output by the route planning module, and to impose constraints on UAVs that are determined to be in conflict. The route replanning submodule is used to replan the routes of UAVs that are determined to be in conflict by the route conflict detection and processing submodule, so as to obtain a globally conflict-free multi-UAV route. It also replans the routes of UAVs that are in conflict and subject to constraints by the route conflict detection submodule, and returns to the route conflict detection and processing submodule for a second determination of route conflicts. Finally, iteratively, a globally conflict-free route for multiple UAVs is obtained.

2. A method for autonomous flight path planning for multiple unmanned aerial vehicles (UAVs) in urban low-altitude environments, characterized in that: The system comprising the system of claim 1 further comprises the following steps: S1. Airspace Modeling: The input static low-altitude airspace environment map is divided into multi-scale airspace grids through the gridded airspace construction module, the grid accessibility weights are calculated, and the grid map data is stored in the storage medium of the computer. S2. Initial route planning: Select the grid scale through the route planning module, plan the initial optimal route for each UAV at the corresponding grid scale according to the input task requirements, and output the initial routes of all UAVs. S3. Conflict Detection and Replanning: Input all the initial UAV routes output from step S2 into the conflict detection and resolution module. Based on the detected conflicts between the initial routes, impose constraint information on the UAVs and use it for UAV route replanning.

3. The urban low-altitude multi-UAV autonomous route planning method as described in claim 2, characterized in that: The calculation process for the accessibility weight in step S1 is as follows: ； In the formula, W For the traversability weights of the spatial grid, C 障碍 For obstacle density, R 风险 Risk level, α、β These are the weighting coefficients.

4. The urban low-altitude multi-UAV autonomous route planning method as described in claim 3, characterized in that: In step S2, the initial route planning for a single UAV adopts a three-dimensional jump point search algorithm: ； In the formula, f(n) To estimate the cost from the starting point through grid node n to the ending point, g(n) The actual cost from the starting point to grid node n. h(n) Let n be the heuristic function for cost estimation from grid node n to the destination. w(n) The grid risk weight is the accessibility weight calculated in step S1. W get.

5. The urban low-altitude multi-UAV autonomous route planning method as described in claim 4, characterized in that: The detection of initial inter-route conflicts in step S3 includes point conflicts and edge conflicts; The definition formula for point conflict is: ； The definition formula for edge conflict is: ； In the formula, t For time step timestep The time scale assumes that the time step required for the drone to move one airspace grid node is 1. i, j They are respectively a1, a2 The drone's serial number, loc The location where the conflict occurred. x, y, z These are the coordinates of the conflict locations.

6. The urban low-altitude multi-UAV autonomous route planning method as described in claim 4, characterized in that: In step S3, the UAV route replanning involves improving the three-dimensional jump point search method by introducing an incremental update method. The calculation process is as follows: ； In the formula, n’ For the current node n The parent node, rhs ( n ) is the introduced grid node-based n’ The minimum cost, c ( n',n ) represents the actual cost from the parent node to the current node. g(n') From the starting point to the parent grid node n’ The actual cost.

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