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

Abstract

The invention discloses an urban low-altitude multi-unmanned aerial vehicle autonomous route planning system and method, and belongs to the technical field of low-altitude airspace intelligent route planning. Comprising a gridding airspace construction module for constructing a digital urban low-altitude airspace model, an air route planning module for generating respective initial air routes for a plurality of unmanned aerial vehicles on the basis of the gridding airspace model, and a conflict detection and resolution module for ensuring that all finally output air routes are globally conflict-free, the invention further discloses an urban low-altitude multi-unmanned-aerial-vehicle autonomous route planning method based on the system. Through airspace grid division, an improved three-dimensional jump point search algorithm and a conflict resolution mechanism based on conflict search, efficient generation and global conflict-free collaboration of an unmanned aerial vehicle route are realized, and the method is suitable for multiple scenes such as urban logistics distribution, routing inspection and emergency response.

Description

Technical Field

[0001] The present invention belongs to the technical field of low-altitude airspace intelligent route planning, and specifically relates to an urban low-altitude multi-UAV autonomous route planning system and method. Background Art

[0002] With the rapid development of the low-altitude economy, drone applications in logistics, inspection, agriculture, and other fields have seen explosive growth. According to the Association for Unmanned Vehicle Systems International (AUVSI), the global drone market is expected to exceed $50 billion by 2025, and the number of daily low-altitude drone flights in cities worldwide will exceed one million by 2025. This growth trend is evident not only in the consumer drone market but also in the industrial drone sector. The efficiency of drone routing directly impacts operational effectiveness. Efficient and autonomous multi-drone routing methods can support efficient drone operations in multiple scenarios and promote the large-scale application of drones.

[0003] However, traditional route planning technology currently faces the following bottlenecks. First, current route planning methods are highly dependent on manual labor. Route planning relies on manually drawn fixed routes and cannot dynamically adapt to the mission requirements of arbitrary starting and ending points. It has poor flexibility and is very time-consuming. Second, there is insufficient safety redundancy. In a high-density drone environment, manually planned routes are difficult to avoid dense urban obstacles (such as buildings and meteorological risk areas), resulting in a surge in collision risks. Finally, the conflict resolution efficiency of multi-UAV routes is low. Existing methods mostly use fixed priority rules (such as "first come, first served") or simple avoidance protocols, lack intelligent conflict resolution mechanisms, have a high secondary conflict rate, and are difficult to ensure global conflict-free.

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

[0005] To address the above shortcomings, the present invention provides an autonomous route planning system for multiple UAVs at low altitude in cities. Through airspace gridding, an improved three-dimensional jump point search algorithm, and a conflict resolution mechanism based on conflict search, this system achieves efficient UAV route generation and global conflict-free coordination. It is suitable for multiple scenarios such as urban logistics distribution, inspection, and emergency response. It includes the following modules: A gridded airspace construction module for constructing a digital urban low-altitude airspace model, which includes a gridded airspace division submodule and a trafficability assessment submodule; A route planning module for generating 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. And a conflict detection and resolution module for ensuring that all routes outputted in the end are globally conflict-free. The conflict detection and resolution module includes a route conflict detection and processing submodule and a route replanning submodule.

[0006] Furthermore, the airspace division submodule is used to perform low-altitude airspace grid division and coding modeling based on the input static map data to construct a multi-level and multi-scale three-dimensional airspace grid map; The accessibility assessment submodule is used to quantitatively calculate the grid accessibility weight based on the attribute data of the input static map.

[0007] Furthermore, 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 grid map based on the UAV mission information.

[0008] Furthermore, the route conflict detection and processing submodule is used to detect and determine conflicts on all UAV routes output by the route planning module, and impose constraints on UAVs that are determined to be in conflict; The route replanning submodule is used to replan the routes of the UAVs that are determined to be in conflict according to the output of the route conflict detection and processing submodule, so as to obtain a global conflict-free multi-UAV route.

[0009] Furthermore, the route replanning submodule is used to replan the routes of the conflicting UAVs with constraints output by the route conflict detection submodule, and return them to the route conflict detection processing submodule to re-determine the route conflict, and finally iterate to obtain a global conflict-free route for multiple UAVs.

[0010] A method for autonomous route planning of multiple UAVs at low altitude in an urban area, comprising the above-mentioned system, further comprising the following steps: S1. Airspace modeling: The gridded airspace construction module divides the input static low-altitude airspace environment map into multi-scale airspace grids, calculates the grid passability weights, and stores the grid map data in the storage medium of the computer; S2. Initial route planning: The route planning module selects a grid scale, plans the initial optimal route for each UAV at the corresponding grid scale based on the input mission requirements, and outputs the initial routes for all UAVs. S3, conflict detection and re-planning: All the initial routes of the UAVs output in step S2 are input into the conflict detection and resolution module. Based on the conflicts detected between the initial routes, constraint information is imposed on the UAVs and used for re-planning the UAV routes.

[0011] Furthermore, the calculation process of the trafficability weight in step S1 is as follows: ; Where, W is the accessibility weight of the airspace grid, C 障碍 is the obstacle density, R 风险 is the risk level, α、β is the weight coefficient.

[0012] Furthermore, in step S2, the initial route planning of a single UAV adopts a three-dimensional jumping point search algorithm: ; Where, f(n) is the cost estimate from the starting point through grid node n to the end point, g(n) is the actual cost from the starting point to the grid node n, h(n) is the heuristic function for estimating the cost from grid node n to the end point, w(n) is the grid risk weight, the accessibility weight calculated in step S1 W get.

[0013] Furthermore, the conflicts detected between the initial routes in step S3 include point conflicts and edge conflicts; The definition formula of point conflict is: ; The definition formula of edge conflict is: ; Where, t is the time step timestep time scale, assuming that the time step required for the drone to move each airspace grid node is 1, i, j They are a1, a2 The drone number, loc For the location where the conflict occurs, x, y, z are the coordinates of the conflict locations respectively.

[0014] Furthermore, the drone route is replanned in step S3 by improving the three-dimensional jump point search method and introducing an incremental update method. The calculation process is: ; Where, n’ For the current noden The parent node of rhs ( n ) is the grid node based n’ The minimum cost, c ( n',n ) is 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.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. Provides a conflict-free route planning system for multiple UAVs at low altitude in cities. This system reduces computational overhead through an incremental update and re-routing conflict resolution mechanism, and can support application scenarios involving hundreds of UAVs. 2. Provides a method for autonomous route planning for multiple UAVs at low altitude in cities. By simply inputting a static urban environment map and the starting and ending points of the UAV mission, a conflict-free route for multiple UAVs can be directly planned. 3. Compared with traditional manual route planning, it can realize route planning with arbitrary starting and ending points, with the characteristics of strong flexibility and high computational efficiency, and can provide technical support for the large-scale application of UAVs in the low-altitude economy. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is the overall structure diagram of the autonomous route planning of multiple UAVs at low altitude in cities in the present invention.

[0017] Figure 2 This is a workflow diagram of an urban low-altitude multi-UAV autonomous route planning system in the present invention.

[0018] Figure 3 This is a workflow diagram of the route planning module in the present invention.

[0019] Figure 4 This is a workflow diagram of the conflict detection and processing submodule and the route replanning submodule in the present invention.

[0020] 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.

[0021] Figure 6 This is the interface diagram after selecting the gridded airspace construction option on the initial homepage interface of the present invention.

[0022] Figure 7 This is the interface diagram after selecting the route planning and conflict detection and resolution option on the initial homepage interface of the present invention. DETAILED DESCRIPTION

[0023] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0024] Example

[0025] like Figure 1 As shown, this embodiment provides an urban low-altitude multi-UAV autonomous route planning system, including: Gridded airspace construction module, route planning module, and conflict detection and resolution module; The gridded airspace construction module is provided with a gridded airspace division submodule for multi-scale grid division based on static map data and a passability assessment submodule for quantitatively calculating grid passability weights based on map attribute data; The route planning module is equipped with a function to calculate the airspace grid scale based on the size and performance of the drone. L The grid scale selection submodule and the initial route planning submodule calculate the optimal initial route according to the starting and ending points in each UAV mission information; 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 impose constraints on UAVs that are determined to be in conflict; The route replanning submodule is used to replan the routes of the UAVs that are determined to be in conflict according to the output of the conflict detection and processing submodule, so as to obtain a global conflict-free multi-UAV route.

[0026] In detail, the route conflict detection processing submodule performs conflict detection between each pair of the initial routes of each UAV output by the route planning module, and outputs an empty set with no conflict or the conflict information of the UAV; The route replanning submodule is used to apply the UAV constraint information obtained by 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 the route conflict, and finally iterating to obtain a global conflict-free route for multiple UAVs.

[0027] like Figures 2 to 4 As shown, the planning method based on the above system is as follows: S1. Use the gridded airspace construction module to perform multi-scale grid division and grid accessibility weight calculation on the input static low-altitude airspace map data, and store the obtained data in the storage medium in the computer (through Figure 5-7 Perform interface synchronization display), based on the submitted static airspace map data (such as Figure 6 The gridded airspace construction module on the display page (shown) obtains and displays grid map data, where the grid accessibility weight is calculated as follows: Substitute the static low-altitude airspace attribute data into the following equation: (1); In equation (1), W is the accessibility weight of the airspace grid, C 障碍 is the obstacle density, R 风险 is the risk level, α、β is the weight coefficient; S2. The route planning module selects the grid scale according to the size and performance of the UAV, calculates and outputs the optimal initial route according to the starting and ending points in each UAV mission information, and displays it on Figure 7 The route planning module interface is used to submit task information and display routes. The calculation process of the route planning module is as follows: Substitute the drone's dimensions and performance data into the following equation: (2); In equation (2), L is the grid scale, k is the scaling factor of the drone size, usually 1, D 无人机 is the characteristic size of the UAV, that is, the maximum size parameter of the UAV, S It is the safety redundancy of the drone, generally 30% of the maximum speed of the drone.

[0028] Substitute the start and end point data in the drone mission information into the following equation: (3); In equation (3), f(n) is the cost estimate from the starting point through grid node n to the end point, g(n) is the actual cost from the starting point to the grid node n, h(n) is a heuristic function that represents the cost estimate from grid node n to the end point, w(n) is the grid risk weight, the accessibility weight calculated in step S1 WThe expansion calculation of specific route nodes first prunes and filters the spatial grid nodes according to the neighbor pruning rule of the three-dimensional jump point search algorithm. Among these grid nodes, the node with the smallest total cost estimate is selected to perform jump point search and expansion until the end point is reached, thus obtaining the complete route node.

[0029] S3: Input all the initial routes of the UAVs output in step S2 into the conflict detection and resolution module. According to the conflicts detected between the initial routes, constraint information is applied to the UAVs and used for re-planning the UAV routes. S3-1, multi-UAV route conflict detection and processing; detect whether there is a conflict between multiple UAV routes, and output route conflict information as follows Figure 7 As shown in the route conflict detection and resolution module; S3-1-1. Detect and determine the conflicts between all the initial routes of the UAVs obtained in step S2 or the new routes of the conflicting UAVs obtained in step S3-2 through the conflict detection processing submodule to obtain route conflict information. The calculation process is as follows: The detection and judgment of conflicts between routes are defined by the following formula: (4); (5); In formulas (4) to (5), t is the time step timestep time scale, assuming that the time step required for the drone to move each airspace grid node is 1, i, j They are a1, a2 The drone number, loc For the location where the conflict occurs, x、 y, z are the coordinates of the conflict positions, respectively. Formula (4) and Formula (5) represent point conflict and edge conflict, respectively. Based on the input UAV initial route or the replanned new route, the conflict between UAV routes is determined, the conflict information is output, and constraints are imposed on the conflicting UAVs.

[0030] S3-1-2, extract the route conflict information obtained in step S3-1-1. If the route conflict information is an empty set, obtain the conflict-free routes of all drones and Figure 7 Perform interface presentation and data saving; otherwise, output the constraint information of the UAV that has caused the route conflict; 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 UAV, obtain a 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: The route is replanned based on the UAV conflict information and an incremental update method is introduced. The calculation process is as follows: (6); In formula (6), n' is the parent node of the current node n, rhs(n) is the minimum cost based on the grid node n' introduced, 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 the successor nodes of the current node n, min represents the selection of the node that minimizes rhs(n) among all the 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.

[0031] Specifically, when re-planning the route in step S3-2, initialization is generally performed first so that rhs ( n ) =g(n) , according to the conflict information, the conflict node is set as a temporary obstacle, and its minimum cost based on the parent node becomes positive infinity, rhs ( n ) ≠g(n) By diffusing the impact of this local inconsistency, all the route nodes affected by obstacles can be found. On this basis, new temporary starting points can be set and the affected routes can be replanned to obtain a new UAV route.

[0032] It should be noted that the structure of the present invention can be implemented in a variety of different forms and is not limited to the embodiments. Any equivalent transformations made by ordinary technicians in this field using the contents of the present invention description and drawings, or directly or indirectly applied to other related technical fields, such as the loading and unloading of other items, are included in the scope of protection of the present invention.

Claims

1. An urban low-altitude multi-UAV autonomous route planning system, characterized by: Includes the following modules: A gridded airspace construction module for constructing a digital urban low-altitude airspace model, which includes a gridded airspace division submodule and a trafficability assessment submodule; A route planning module for generating 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. And a conflict detection and resolution module for ensuring that all routes outputted in the end are globally conflict-free. The conflict detection and resolution module includes a route conflict detection and processing submodule and a route replanning submodule.

2. The urban low-altitude multi-UAV autonomous route planning system according to claim 1, characterized in that: The airspace division submodule is used to perform low-altitude airspace grid division and coding modeling based on the input static map data, and construct a multi-level and multi-scale three-dimensional airspace grid map; The accessibility assessment submodule is used to quantitatively calculate the grid accessibility weight based on the attribute data of the input static map.

3. The urban low-altitude multi-UAV autonomous route planning system according to claim 1, characterized in that: 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 grid map based on the UAV mission information.

4. The urban low-altitude multi-UAV autonomous route planning system according to claim 1, characterized in that: The route conflict detection and processing submodule is used to detect and determine conflicts on all UAV routes output by the route planning module, and impose constraints on UAVs that are determined to be in conflict; The route replanning submodule is used to replan the routes of the UAVs that are determined to be in conflict according to the output of the route conflict detection and processing submodule, so as to obtain a global conflict-free multi-UAV route.

5. The urban low-altitude multi-UAV autonomous route planning system according to claim 4, characterized in that: The route replanning submodule is used to replan the routes of the conflicting UAVs with constraints output by the route conflict detection submodule, and return them to the route conflict detection processing submodule to re-determine the route conflict, and finally iterate to obtain a global conflict-free route for multiple UAVs.

6. A method for autonomous route planning of multiple UAVs at low altitude in cities, characterized in that: The system according to claim 1 to claim 5 further comprises the following steps: S1. Airspace modeling: The gridded airspace construction module divides the input static low-altitude airspace environment map into multi-scale airspace grids, calculates the grid passability weights, and stores the grid map data in the storage medium of the computer; S2. Initial route planning: The route planning module selects a grid scale, plans the initial optimal route for each UAV at the corresponding grid scale based on the input mission requirements, and outputs the initial routes for all UAVs. S3, conflict detection and re-planning: All the initial routes of the UAVs output in step S2 are input into the conflict detection and resolution module. Based on the conflicts detected between the initial routes, constraint information is imposed on the UAVs and used for re-planning the UAV routes.

7. The method for autonomous route planning of multiple UAVs at low altitude in an urban area according to claim 6, characterized in that: The calculation process of the trafficability weight in step S1 is as follows: ； Where, W is the accessibility weight of the airspace grid, C 障碍 is the obstacle density, R 风险 is the risk level, α、β is the weight coefficient.

8. The method for autonomous route planning of multiple UAVs at low altitude in an urban area according to claim 7, characterized in that: In step S2, the initial route planning of a single UAV adopts a three-dimensional jump point search algorithm: ； Where, f(n) is the cost estimate from the starting point through grid node n to the end point, g(n) is the actual cost from the starting point to the grid node n, h(n) is the heuristic function for estimating the cost from grid node n to the end point, w(n) is the grid risk weight, the accessibility weight calculated in step S1 W get.

9. The method for autonomous route planning of multiple UAVs at low altitude in an urban area according to claim 8, characterized in that: In step S3, the conflicts detected between the initial routes include point conflicts and edge conflicts; The definition formula of point conflict is: ； The definition formula of edge conflict is: ； Where, t is the time step timestep time scale, assuming that the time step required for the drone to move each airspace grid node is 1, i, j They are a1, a2 The drone number, loc For the location where the conflict occurs, x, y, z are the coordinates of the conflict locations respectively.

10. The method for autonomous route planning of multiple UAVs at low altitude in an urban area according to claim 8, characterized in that: The replanning of the UAV route in step S3 is to improve the three-dimensional jump point search method and introduce an incremental update method. The calculation process is: ； Where, n’ For the current node n The parent node of rhs ( n ) is the grid node based n’ The minimum cost, c ( n',n ) is 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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