An urban low-altitude unmanned aerial vehicle route planning method, device and storage medium

Abstract

The application discloses a kind of urban low-altitude unmanned aerial vehicle route planning method, device and storage medium, wherein method includes: obtaining the starting point and end point of flight, and obstacle information;According to starting point and end point, determine route search area, carry out preliminary route planning, obtain preliminary route;Optimize preliminary route, obtain final route;Consider that there can be more unmanned aerial vehicles simultaneously flying in urban low-altitude, by setting safety interval around aerial obstacle area, to separate the flight route of unmanned aerial vehicle in different directions, ensure navigation safety;Consider that there can be multiple unmanned aerial vehicles simultaneously passing through narrow flyable region, set different airway according to the width of narrow airspace, allocate airway for unmanned aerial vehicle entering narrow flyable region.The route of single unmanned aerial vehicle is planned in the application, and the planning optimization of multiple unmanned aerial vehicles is considered simultaneously, the rapidity, path optimality and safety of unmanned aerial vehicle route planning in low-altitude airspace can be considered.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) path planning technology, and in particular to a method, apparatus, and storage medium for planning urban low-altitude UAV routes. Background Technology

[0002] Drones integrate advanced manufacturing, artificial intelligence, and mobile internet technologies, driving a transformation in human society's production and lifestyles. Their main technologies include five modules: perception, localization, planning, control, and decision-making. Path planning, which plays a crucial role in bridging these modules, autonomously plans a safe and efficient path from the starting point to the target point based on perceived map information containing obstacles, and outputs this path to the control and decision-making module, thereby enabling the drone to operate efficiently.

[0003] With the widespread application of low-altitude technologies such as UAV monitoring, patrol, and mapping, long-distance low-altitude aerial path planning has become a challenge for low-altitude aircraft applications. UAVs need to generate routes based on maps, and these routes need to be optimized to enable the UAVs to quickly reach their destinations, achieving rapid and efficient mission execution. Currently, the mainstream global path planning algorithms include graph search, biomimetic intelligent algorithms, and fast random search tree algorithms. Graph search algorithms, such as A* and Dijkstra's algorithm, suffer from low efficiency due to the large number of nodes traversed during the planning process. Biomimetic intelligent algorithms, such as particle swarm optimization, genetic algorithms, and ant colony optimization, are characterized by high computational cost, long iteration times, and poor real-time performance. In contrast, the Rapidly-Exploring Random Tree (RRT) algorithm, proposed by LaValle in 1998, is widely used due to its probabilistic completeness, simple algorithm structure, and strong search capabilities in complex environments compared to the previous two types of planning algorithms. Traditional Rapidly Exploring Random Trees (RRT) can quickly generate routes, but the generated routes are not optimal, leading to significant energy consumption for UAVs during operation. In recent years, researchers have proposed various optimization methods to address the issues of high randomness in node expansion and poor path optimization in RRT algorithms. These include the RRT Goal Bias algorithm and the B-RRT algorithm (bidirectional search algorithm) to accelerate path generation; the RRT* algorithm and the inform RRT* algorithm to improve path quality; and fusion algorithms for secondary path pruning, all contributing to the development of RRT algorithms. However, there are still no good solutions to the problems of low sampling point utilization and poor path optimization in complex environments when RRT algorithms expand new nodes and collide with obstacles. Furthermore, RRT algorithms may fail to generate feasible paths quickly or even fail to generate paths in narrow spaces. Therefore, improving the low sampling point utilization and path optimization in the traditional RRT algorithm's path planning process is a pressing technical problem that needs to be solved. Summary of the Invention

[0004] In order to at least partially solve one of the technical problems existing in the prior art, the present invention aims to provide a method, device and storage medium for urban low-altitude unmanned aerial vehicle (UAV) route planning.

[0005] The technical solution adopted in this invention is:

[0006] A method for planning urban low-altitude unmanned aerial vehicle (UAV) routes, including single UAV route planning and route modification and adjustment considering simultaneous flight of multiple UAVs;

[0007] Single-drone route planning includes the following steps:

[0008] Obtain the start and end points of the flight, as well as information on obstacles;

[0009] The route search area is determined based on the start and end points. Based on the route search area and obstacle information, the RRT* algorithm is used to perform preliminary route planning to obtain a preliminary route.

[0010] The initial route is optimized to obtain the final route;

[0011] The steps to modify and adjust flight paths to accommodate multiple drones flying simultaneously include:

[0012] When the drone's flight path bypasses the obstacle area, a safety interval is designed according to the flight path's heading, and the flight path is modified according to the safety interval to avoid heading conflicts between drones with different headings.

[0013] The system detects whether a narrow flyable area exists. If so, it sets up multiple flight paths based on the width of the narrow airspace and the maneuverability of the UAV. Based on the UAV's heading, it assigns flight paths to UAVs entering the narrow flyable area to ensure that multiple UAVs with different headings fly on different flight paths.

[0014] Furthermore, determining the route search area based on the origin and destination includes:

[0015] Set the length and width of the minimum search area;

[0016] Connect the start and end points, and use the midpoint of the line as the center of the search area;

[0017] The length of the search area is determined by the horizontal distance between the start and end points, and the width of the search area is determined by the vertical distance between the start and end points, thus defining the first search area;

[0018] Determine if the area of ​​the first search region is greater than the area of ​​the minimum search region. If so, use the first search region as the route search region; or...

[0019] Overlap the center of the first search area with the center of the smallest search area to obtain the smallest area that contains both the first search area and the smallest search area, and use it as the route search area.

[0020] Furthermore, preliminary flight routes are obtained through the following methods:

[0021] A1. Starting from the origin, randomly scatter points within the flight path search area, select an X-rand as a sampling point, and find the node X-near that is closest to the sampling point X-rand from the constructed tree;

[0022] A2. Connect X-near and X-rand, and use the direction of the connecting line as the direction of tree growth; set the step size of tree growth, grow the tree by one step size in this direction, and a new node X-new will be generated at the end of the growth. Find the point closest to X-new among the existing nodes and connect the two points.

[0023] A3. Perform collision detection from X-rand to X-new. If the collision detection result is no collision, add the X-new node as a child node of X-near to the tree; continue to spread points and search within the flight path search area; if the collision detection result is a collision, node generation fails, delete X-new, and re-spread points and search.

[0024] A4. Repeat steps A1-A3 until the distance from the generated X-new to the endpoint is less than one step, then terminate the tree growth and connect the new node to the endpoint.

[0025] Furthermore, the optimization of the preliminary route to obtain the final route includes:

[0026] Determine the parent and child nodes of the key nodes from the generated tree, and connect the parent and child nodes; the key node refers to the point that connects two winding routes to avoid obstacles;

[0027] Collision detection is performed. If the collision detection result is no collision, the straight line formed by connecting the parent node and child node of the critical node is used as the new route. If the collision detection result is a collision, the critical node, parent node and child node are connected to generate two line segments. On these two line segments, the route collision detection and route optimization are iterated according to the preset step size.

[0028] Furthermore, the step of performing route collision detection and route optimization iteratively according to a preset step size on these two line segments includes:

[0029] Starting from the key node, and ending with the parent and child nodes;

[0030] The selected detection point is gradually advanced on the two line segments according to the preset step size. The two detection points are connected and collision detection is performed until a collision is detected. Then, the process returns to the previous collision detection point and records the result.

[0031] The key nodes are updated based on the recorded detection points, and iterative detection and updates are performed using the new key nodes as the new starting point until the optimized route is finally obtained.

[0032] Furthermore, when the UAV's flight path bypasses an obstacle area, a safety interval is designed based on the flight path's heading, and the flight path is modified according to the safety interval to avoid heading conflicts between UAVs with different headings, including:

[0033] Design a safety separation, and detect whether the obstacle area is on the left or right side of the flight path based on the flight path's heading;

[0034] If the detected obstacle area is located to the left of the flight path, modify the obstacle envelope information according to the safety interval, and modify the flight path according to the new obstacle information;

[0035] If the detected obstacle area is located on the right side of the flight path, no flight path modification is required.

[0036] Furthermore, the obstacle detection area is located on the left or right side of the flight path, including:

[0037] Find two points on the straight path before passing through the obstacle area, namely point A = (px, py) and point B = (qx, qy) on the subsequent path;

[0038] Find a point C (lx, ly) in the obstacle region that we are about to pass through;

[0039] Obtain vectors AB = (qx-px, qy-py) and AC = (lx-px, ly-py). Perform a cross product of the two vectors to obtain the result M. If M < 0, the obstacle area is on the right side of the flight path, and the flight path operation required for a right turn is executed. If M > 0, the obstacle area is on the left side of the flight path, and the flight path operation required for a left turn is executed.

[0040] Furthermore, the detection process includes identifying whether a narrow flyable area exists. If such an area exists, multiple fixed flight paths are established based on the width of the narrow airspace and the maneuverability of the unmanned aerial vehicle (UAV). Based on the UAV's heading, fixed flight paths are assigned to UAVs entering the narrow flyable area, including:

[0041] If the space between multiple obstacle areas is detected to be smaller than a preset value, the area is marked as a narrow flyable area.

[0042] The narrow flyable area is divided into multiple one-way routes based on the maneuverability of the UAV;

[0043] Based on the drone's heading, flight paths are assigned to drones entering narrow flyable areas, ensuring that drones entering these areas fly on fixed paths and avoiding heading conflicts between drone flight paths.

[0044] Another technical solution adopted in this invention is:

[0045] A low-altitude unmanned aerial vehicle (UAV) route planning device for urban areas, comprising:

[0046] At least one processor;

[0047] At least one memory for storing at least one program;

[0048] When the at least one program is executed by the at least one processor, the at least one processor implements the method described above.

[0049] Another technical solution adopted in this invention is:

[0050] A computer-readable storage medium storing a processor-executable program, which, when executed by a processor, performs the method described above.

[0051] The beneficial effects of this invention are: this invention plans the flight path of a single UAV, while also considering the planning optimization of multiple UAVs operating simultaneously, and can take into account the speed, path optimization and safety of UAV flight path planning in low-altitude airspace. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following description is provided with accompanying drawings of the relevant technical solutions in the embodiments of the present invention or the prior art. It should be understood that the accompanying drawings described below are only for the purpose of clearly illustrating some embodiments of the technical solutions of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1 This is a schematic diagram illustrating the dynamic determination of the route planning search space in an embodiment of the present invention;

[0054] Figure 2 This is a schematic diagram illustrating the determination of the route search area based on the starting point and the ending point in an embodiment of the present invention;

[0055] Figure 3 This is a schematic diagram of the minimum search area in an embodiment of the present invention;

[0056] Figure 4 This is a schematic diagram of the local sampling planning area of ​​UAV A1 in this embodiment of the invention;

[0057] Figure 5 This is a schematic diagram of the initial path generation process in an embodiment of the present invention;

[0058] Figure 6This is a schematic diagram of a direct connection to an optimized flight route in an embodiment of the present invention;

[0059] Figure 7 This is a schematic diagram of iterative look-back optimization of the flight path in an embodiment of the present invention;

[0060] Figure 8 This is a schematic diagram comparing the iterative planning path with the initial planning path in an embodiment of the present invention;

[0061] Figure 9 This is a schematic diagram illustrating the flight path conflict caused by two UAVs with different turns in an embodiment of the present invention;

[0062] Figure 10 This is a schematic diagram illustrating the situation where the right-turn route in an embodiment of the present invention is entirely within the safety interval zone;

[0063] Figure 11 This is a schematic diagram illustrating the situation where a portion of the left-turn route is within the safety interval zone in an embodiment of the present invention;

[0064] Figure 12 This is a schematic diagram of the UAV route planning after designing a safety interval in an embodiment of the present invention;

[0065] Figure 13 This is a schematic diagram illustrating the delineation of a narrow region in an embodiment of the present invention;

[0066] Figure 14 This is a schematic diagram showing the overlapping safety interval zones of multiple obstacles within a narrow area in an embodiment of the present invention;

[0067] Figure 15 This is a rendering of a planned flight path within a narrow area, as described in an embodiment of the present invention.

[0068] Figure 16 This is an optimization comparison diagram considering multiple drones in a city in an embodiment of the present invention;

[0069] Figure 17 This is a flowchart of a method for planning urban low-altitude unmanned aerial vehicle (UAV) routes in an embodiment of the present invention. Detailed Implementation

[0070] The embodiments of the present invention are described in detail below. Examples of these 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 are only used to explain the present invention, and should not be construed as limiting the present invention. The step numbers in the following embodiments are set only for ease of explanation, and there is no limitation on the order between the steps. The execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0071] In the description of this invention, it should be understood that the orientation descriptions, such as up, down, front, back, left, right, etc., are based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting this invention.

[0072] In the description of this invention, "several" means one or more, "more than" means two or more, "greater than," "less than," and "exceeding" are understood to exclude the stated number, while "above," "below," and "within" are understood to include the stated number. The use of "first" and "second" in the description is merely for distinguishing technical features and should not be construed as indicating or implying relative importance, or implicitly indicating the number of indicated technical features, or implicitly indicating the order of the indicated technical features.

[0073] In the description of this invention, unless otherwise explicitly defined, terms such as "set up," "install," and "connect" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this invention in conjunction with the specific content of the technical solution.

[0074] like Figure 17 As shown, this embodiment provides a method for planning urban low-altitude unmanned aerial vehicle (UAV) routes, including single UAV route planning and route modification and adjustment considering the simultaneous flight of multiple UAVs;

[0075] The single-drone route planning includes the following steps:

[0076] Obtain the start and end points of the flight, as well as information on obstacles;

[0077] The route search area is determined based on the start and end points. Based on the route search area and obstacle information, the RRT* algorithm is used to perform preliminary route planning to obtain a preliminary route.

[0078] The initial route is optimized to obtain the final route;

[0079] The steps to modify and adjust flight paths to accommodate multiple drones flying simultaneously include:

[0080] Considering the large number of drones operating simultaneously in urban airspace, when planning obstacle avoidance routes, safety intervals are designed to ensure sufficient spatial distance between routes of different flight paths in order to avoid potential conflicts between drones with different flight paths.

[0081] Considering the narrow flyable areas created by no-fly zones over cities, multiple flight paths are established based on the width of the narrow airspace and the performance of the drones, taking into account the possibility of multiple drones passing through the narrow flyable area simultaneously. Different flight paths are assigned to drones with different headings.

[0082] For single-UAV flight path planning, 1) for large cities, the low-altitude airspace of the entire city is quite large. Considering that the urban low-altitude airspace environment is relatively fixed and there are available static environment maps, searching the entire city's low-altitude airspace based on the starting point and mission point is inefficient. Therefore, this embodiment proposes a strategy of dynamically changing the search space according to the UAV's mission; 2) due to the limitations of step size and search time during UAV flight path planning, non-optimal flight paths may be generated when planning routes to avoid obstacles. A local adjustment optimization method based on iterative optimization is designed to generate flight paths with lower resource consumption.

[0083] To address the modification and adjustment of flight routes considering simultaneous flight of multiple UAVs, a flight route modification strategy based on the urban environment is considered under the condition of multiple UAV operation. 1) To address potential heading conflicts when multiple UAVs fly in obstacle areas, a flight route adjustment strategy is designed to establish safe intervals for multiple UAVs operating at low altitudes in urban areas, ensuring sufficient safe distance in space for flight routes with different headings. 2) For narrow flyable areas constrained by obstacles and no-fly zones, considering the high risk of airspace conflicts when many UAVs may pass through such narrow flyable areas simultaneously, flight routes are established. Heading conflict refers to the conflict when UAVs flying in opposite directions use the same flight route when bypassing the same obstacle area.

[0084] The above method will be explained in detail below with reference to the accompanying drawings.

[0085] S1: Single UAV route planning.

[0086] S101: First, we introduce the strategy for dynamically changing the search space based on the UAV mission. For example... Figure 1 As shown, we have already determined the drone's starting and ending points on the global map during flight path planning. When planning flight paths in unknown environments, we need to conduct a global search. For flight path planning in urban airspace, since we already have information on ground buildings and no-fly zones within the city, and this information is relatively fixed, we can dynamically determine the local search area based on the mission information when planning drone flight paths. This effectively reduces the time consumed in flight path planning.

[0087] The specific steps are as follows:

[0088] Decision-making methods for different local areas should be adopted according to different situations: in cases such as Figure 1 In the case shown, the distance between the starting point and the ending point is large. In this case, the center symmetrical to the center of the starting point and the ending point is taken as the center of the local planning rectangular area. The difference between the absolute values ​​of x and y between the starting point and the ending point is multiplied by a suitable value (e.g., greater than 1) to obtain the length and width of the local planning rectangular area, as shown. Figure 2 As shown, the planned rectangular region is obtained. When the starting and ending points are close, the first method may fail to explore feasible routes or fail to find the optimal route due to obstacles. Therefore, a local search planning region based on the minimum search area is adopted, with the center point of the starting and ending points as the center, and a fixed area as the route search region. This ensures the stability and reliability of the algorithm. The second type of planned rectangular region is shown below. Figure 3 As shown. When choosing between the two types of rectangular planning regions, the algorithm compares the sizes of the two regions and selects the one with the larger size as the rectangular planning region.

[0089] As an alternative implementation method, in the specific route planning process, both methods can be combined to determine the appropriate route planning area. For example, UAV A1 can delineate the planning area based on the origin and destination, resulting in a planning area such as... Figure 4 As shown, the planning method for the region refers to the first method on the y-axis and the second method on the x-axis.

[0090] After determining the route search area, preliminary route planning is carried out, the process of which is as follows: Figure 5 As shown, the specific steps include:

[0091] A1. Drone A1 starts from the starting point and then randomly scatters points within a local sampling area, selecting an X-rand as the sampling point. It then searches the constructed tree for the node X-near that is closest to the sampling point X-rand. If the tree contains only the starting point, then the closest node is the starting point.

[0092] A2. Begin the tree growth process. First, connect X-near and X-rand; the direction of this connection is the direction of tree growth. Set a step size as the step size for each growth cycle. Grow the tree by one step size in this direction, and a new node X-new will be generated at the end of the growth (choose one X-new between X-near and X-rand). Then, find the closest node to this new node among the existing X nodes and connect the two nodes.

[0093] A3. Next, collision detection is performed from X-rand to X-new. If the collision detection result is no collision, the X-new node is added to the tree as a child node of X-near, and the point search continues in the local sampling area starting from step 1. If the collision detection result is a collision, the node generation fails, X-new is deleted, and the point search is performed again.

[0094] A4. Repeat steps A1-A3 until the distance from the generated X-new to the target point is less than one step size, then terminate the tree growth. Directly connect the new node to the target point.

[0095] Figure 5 (1) A schematic diagram for adding nodes, Figure 5 (2) is a schematic diagram of node removal based on collision detection results. Figure 5 (3) is a schematic diagram of the growth tree of the connecting nodes. Figure 5 (4) A schematic diagram of generating the initial route by deleting useless nodes.

[0096] S102: As Figure 5 As shown, the path generated using the RRT* algorithm has some optimization potential. This mainly involves optimizing the obstacle-avoiding routes generated by the algorithm. Specific optimization methods are as follows:

[0097] Find the critical node in the random tree that connects two bends in the path due to obstacle avoidance; determine the parent and child nodes of this critical node from the random tree; connect the two nodes directly and perform a collision detection, such as... Figure 6 As shown ( Figure 6 To ensure sufficient clarity, some intermediate nodes have been omitted. If the collision detection result is no collision, the straight line formed by directly connecting the parent and child nodes of that node is used as the new flight path, and the consumption value and other related data are modified in the program. If the collision detection result is a collision, such as... Figure 7 As shown, these three nodes are connected to generate two line segments. On these two line segments, a search step size is determined by a specific length, and collision detection and route optimization are iteratively performed. Figure 6 (1) is a diagram showing the connection between the parent node and the child node. Figure 6 (2) is a schematic diagram after connecting the parent node and the child node. Figure 7 (1) A diagram showing a collision that occurs when connecting a parent node and a child node. Figure 7 (2) This is a schematic diagram of sampling detection points on two line segments and iteratively performing collision detection. Figure 7 (3) This is a schematic diagram of the new key nodes obtained after a single iteration. Figure 7 (4) is the optimized route map.

[0098] This method starts with a key node and terminates with its parent and child nodes respectively. Detection points are selected on two line segments based on step size. Two detection points are connected, and iterative collision detection is performed on their connection. This process is repeated alternately until a collision is detected, at which point the process returns to the previous detection point and records it. The process of iteratively searching from a key point to its parent node is called backtracking, and the process of iteratively searching from a key point to its child node is called look-ahead. If a collision occurs due to backtracking, backtracking stops, the previous backtracked node is recorded, and look-ahead searching continues until a collision is found between a connection and an obstacle. The optimized route is then determined based on the nearest collision-free search point. If a collision occurs due to look-ahead searching, look-ahead searching stops, and backtracking continues until a collision occurs during backtracking, returning to the previous backtracked node. After a single look-ahead backtracking iteration, two new key points are identified. Further iterative optimization is then performed around these new key points using the same method until the route optimization conditions are met, at which point the iterative optimization ends, ultimately yielding the optimized route, such as... Figure 7 (4) As shown in the figure; after obtaining the flight path, modify the relevant data such as the drone consumption value.

[0099] Based on the above method, each key node in the initial flight path that bypasses obstacles is iteratively optimized to achieve overall flight path optimization. After iterative optimization of the initial flight path of UAV A1, the resulting flight path is as follows: Figure 8 As shown.

[0100] S2: Consider modifying and adjusting flight paths for multiple drones flying simultaneously.

[0101] Through the above optimized search, an approximate optimized flight path can be obtained in an urban environment considering no-fly zones and obstacles. However, after planning the flight path for each drone, it is also necessary to consider scenarios where a large number of drones will fly simultaneously in the urban environment in the future. Taking two drones as an example, there may be situations where the flight paths of the drones overlap, such as... Figure 9 As shown, drones A1 and A2 have different starting and ending points, and may get too close when passing obstacles. When the number of drones operating in urban environments increases, the probability of multiple drones flying towards each other on the same path is higher due to the influence of obstacles.

[0102] S201: A flight path adjustment strategy is designed to address the need to navigate through obstacle zones, reducing the probability of flight path conflicts between UAVs flying towards each other while bypassing the obstacle zone. We consider setting a safe interval zone around the obstacle zone. When a UAV passes through the obstacle zone, if it turns right to bypass the designated obstacle zone, its initial flight path remains unchanged. However, for left turns, the safe interval zone needs to be treated as a no-fly zone, requiring local replanning of the flight path through the obstacle zone to generate a new left-turn flight path. Under this method, the following situations may arise during the optimization process: Figure 10 and Figure 11 As shown, the optimization process for a drone turning right and left when bypassing obstacles is illustrated. Among them, Figure 11 (1) is a schematic diagram of flight routes planned based on the original no-fly zone. Figure 11 (2) is a schematic diagram of flight routes based on the extended no-fly zone plan.

[0103] After setting up a safety interval zone, the computer needs to process the drone's flight path according to the different actions taken when turning left and right through the obstacle area. The computer uses a vector method to determine the directional relationship between the drone's flight path and the current obstacle.

[0104] Take two points on the straight path before passing the obstacle area, namely point A = (px, py) and point B = (qx, qy) on the subsequent path. Then take a point C = (lx, ly) in the obstacle area to be passed. Take vectors AB = (qx - px, qy - py) and AC = (lx - px, ly - py), and perform a cross product of the two vectors to obtain the result M. If M < 0, the obstacle area is on the right side of the path, and the path operation required for a right turn is executed; if M > 0, the obstacle area is on the left side of the path, and the path operation required for a left turn is executed. The new path obtained after judging, turning, and optimizing the two paths A1 and A2 of the UAV is as follows: Figure 12 As shown.

[0105] By establishing safe intervals around obstacles in space, the probability of multiple drones approaching each other too closely during flight can be reduced. However, in urban areas, several no-fly zones are located close together, resulting in relatively narrow local flyable airspace, such as... Figure 13 As shown. Two problems may exist in this situation: one is... Figure 14 When safety separation zones are established, the overlap between safety separation zones (obstacles) makes it impossible to adjust left-turn routes. Secondly, because this area is a critical airspace where many drones will fly simultaneously, without appropriate measures, the risk of multiple drones flying too close together in this narrow airspace is high. Figure 13 As shown.

[0106] S202: We propose designing evenly distributed flight paths in narrow areas. Within each path, UAVs can only fly in one direction. Furthermore, different paths are assigned to UAVs based on their entry points into the narrow area, ensuring efficient use of each path. After using flight path separation, UAVs flying in different directions are spatially separated in the narrow airspace. When dividing flight paths, the maneuverability of the UAVs needs to be considered. On the other hand, UAVs with different speeds should be assigned to different paths as much as possible to ensure efficient and smooth operation within the flight paths. The UAV flight paths set up in narrow airspace are as follows: Figure 15 As shown, based on the performance of the UAV operating in urban airspace, different numbers of airways can be set according to the width of the narrow airspace. When generating the UAV's flight path, the method of delineating fixed airways can be used to determine the UAV's local flight path.

[0107] After considering the modification of drone flight paths based on the urban environment under conditions of multiple drone operations, the before-and-after comparison diagram is as follows. Figure 16 As shown, where, Figure 16 (1) is a schematic diagram of the route generated directly based on RRT* planning. Figure 16 (2) A schematic diagram of the flight path modified based on the urban environment to take into account the operation of multiple UAVs.

[0108] This embodiment also provides an urban low-altitude unmanned aerial vehicle (UAV) route planning device, including:

[0109] At least one processor;

[0110] At least one memory for storing at least one program;

[0111] When the at least one program is executed by the at least one processor, the at least one processor implements Figure 17 The method shown.

[0112] This embodiment of the urban low-altitude UAV route planning device can execute the urban low-altitude UAV route planning method provided in the method embodiment of the present invention, and can execute any combination of implementation steps of the method embodiment, and has the corresponding functions and beneficial effects of the method.

[0113] This application also discloses a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium and execute the computer instructions, causing the computer device to perform... Figure 17 The method shown.

[0114] This embodiment also provides a storage medium storing instructions or programs that can execute the urban low-altitude UAV route planning method provided in the method embodiment of the present invention. When the instructions or programs are run, any combination of implementation steps of the method embodiment can be executed, and the method has the corresponding functions and beneficial effects.

[0115] In some alternative embodiments, the functions / operations mentioned in the block diagrams may not occur in the order shown in the operation diagrams. For example, depending on the functions / operations involved, two consecutively shown blocks may actually be executed substantially simultaneously, or the blocks may sometimes be executed in reverse order. Furthermore, the embodiments presented and described in the flowcharts of this invention are provided by way of example to provide a more comprehensive understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is altered and sub-operations described as part of a larger operation are executed independently.

[0116] Furthermore, although the invention has been described in the context of functional modules, it should be understood that, unless otherwise stated, one or more of the described functions and / or features may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the invention. Rather, given the properties, functions, and internal relationships of the various functional modules in the apparatus disclosed herein, the actual implementation of the module will be understood within the scope of conventional skill of an engineer. Therefore, those skilled in the art can implement the invention as set forth in the claims using ordinary techniques without excessive experimentation. It is also understood that the specific concepts disclosed are merely illustrative and not intended to limit the scope of the invention, which is determined by the full scope of the appended claims and their equivalents.

[0117] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0118] 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.

[0119] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), 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). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0120] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination 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.

[0121] In the foregoing description of this specification, references to terms such as "one embodiment," "another embodiment," or "some embodiments" indicate that a specific feature, structure, material, or characteristic described in connection with an embodiment or example is included in at least one embodiment or example of the present invention. In this specification, 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.

[0122] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

[0123] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of this application.

Claims

1. A method for planning flight paths of urban low-altitude unmanned aerial vehicles (UAVs), characterized in that, This includes single-drone flight path planning and flight path modification and adjustment considering simultaneous flight of multiple drones; Single-drone route planning includes the following steps: Obtain the start and end points of the flight, as well as information on obstacles; The route search area is determined based on the start and end points. Based on the route search area and obstacle information, the RRT* algorithm is used to perform preliminary route planning to obtain a preliminary route. The initial route is optimized to obtain the final route; The steps to modify and adjust flight paths to accommodate multiple drones flying simultaneously include: When the drone's flight path bypasses the obstacle area, a safety interval is designed according to the flight path's heading, and the flight path is modified according to the safety interval to avoid heading conflicts between drones with different headings. Detect the existence of narrow flyable areas. If they exist, set up multiple flight paths based on the width of the narrow airspace and the maneuverability of the UAVs. Assign flight paths to UAVs entering the narrow flyable areas according to the UAVs' headings to ensure that multiple UAVs with different headings fly on different flight paths. Obtain the initial route through the following methods: A1. Starting from the origin, randomly scatter points within the flight path search area, select an X-rand as a sampling point, and find the node X-near that is closest to the sampling point X-rand from the constructed tree; A2. Connect X-near and X-rand, and use the direction of the connecting line as the direction of tree growth; set the step size of tree growth, grow the tree by one step size in this direction, and a new node X-new will be generated at the end of the growth. Find the point closest to X-new among the existing nodes and connect the two points. A3. Perform collision detection from X-rand to X-new. If the collision detection result is no collision, add the X-new node as a child node of X-near to the tree and continue to spread the search within the flight path search area. If the collision detection result is a collision, the node generation fails, X-new is deleted, and the search is performed again. A4. Repeat steps A1-A3 until the distance from the generated X-new to the endpoint is less than one step, then terminate the tree growth and connect the new node to the endpoint. The optimization of the preliminary route to obtain the final route includes: Determine the parent and child nodes of the key nodes from the generated tree, and connect the parent and child nodes; the key node refers to the point that connects two winding routes to avoid obstacles; Collision detection is performed. If the collision detection result is no collision, the straight line formed by connecting the parent node and child node of the critical node is used as the new route. If the collision detection result is a collision, the critical node, parent node and child node are connected to generate two line segments. On these two line segments, the route collision detection and route optimization are iterated according to the preset step size.

2. The method for planning urban low-altitude unmanned aerial vehicle (UAV) routes according to claim 1, characterized in that, The process of determining the route search area based on the origin and destination includes: Set the length and width of the minimum search area; Connect the start and end points, and use the midpoint of the line as the center of the search area; The length of the search area is determined by the horizontal distance between the start and end points, and the width of the search area is determined by the vertical distance between the start and end points, thus defining the first search area; Determine if the area of ​​the first search region is greater than the area of ​​the minimum search region. If so, use the first search region as the route search region; or... Overlap the center of the first search area with the center of the smallest search area to obtain the smallest area that contains both the first search area and the smallest search area, and use it as the route search area.

3. The method for planning urban low-altitude unmanned aerial vehicle (UAV) routes according to claim 1, characterized in that, The process of performing route collision detection and route optimization iteratively on these two line segments according to a preset step size includes: Starting from the key node, and ending with the parent and child nodes; The selected detection point is gradually advanced on the two line segments according to the preset step size. The two detection points are connected and collision detection is performed until a collision is detected. Then, the process returns to the previous collision detection point and records the result. The key nodes are updated based on the recorded detection points, and iterative detection and updates are performed using the new key nodes as the new starting point until the optimized route is finally obtained.

4. The method for planning urban low-altitude unmanned aerial vehicle (UAV) routes according to claim 1, characterized in that, When the drone's flight path bypasses an obstacle area, a safety interval is designed based on the flight path's heading, and the flight path is modified according to the safety interval to avoid heading conflicts between drones with different headings, including: Design a safety separation, and detect whether the obstacle area is on the left or right side of the flight path based on the flight path's heading; If the detected obstacle area is located on the left side of the flight path, modify the obstacle information according to the safety interval, and modify the flight path according to the new obstacle information; If the detected obstacle area is located on the right side of the flight path, no flight path modification is required.

5. The method for planning urban low-altitude unmanned aerial vehicle (UAV) routes according to claim 4, characterized in that, The condition that the obstacle detection area is located on the left or right side of the flight path, based on the flight path's heading, includes: Find two points on the straight path before passing through the obstacle area, namely point A = (px, py) and point B = (qx, qy) on the subsequent path; Find a point C (lx, ly) in the obstacle region that we are about to pass through; Obtain vectors AB=(qx-px,qy-py) and AC=(lx-px,ly-py), perform a cross product of the two vectors to obtain the result M. If M<0, the obstacle area is on the right side of the flight path, and the flight path operation required for a right turn is executed; if M>0, the obstacle area is on the left side of the flight path, and the flight path operation required for a left turn is executed.

6. The method for planning urban low-altitude unmanned aerial vehicle (UAV) routes according to claim 1, characterized in that, The detection process involves identifying whether a narrow flyable area exists. If such an area exists, multiple flight paths are established based on the width of the narrow airspace and the maneuverability of the UAV. Fixed flight paths are assigned to UAVs entering the narrow flyable area based on their flight path, including: If the space between multiple obstacle areas is detected to be smaller than a preset value, the area is marked as a narrow flyable area. The narrow flyable area is divided into multiple one-way routes based on the maneuverability of the UAV; Based on the drone's heading, flight paths are assigned to drones entering narrow flyable areas, ensuring that drones entering these areas fly on fixed paths and avoiding heading conflicts between drone flight paths.

7. A flight path planning device for urban low-altitude unmanned aerial vehicles (UAVs), characterized in that, include: At least one processor; At least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the method according to any one of claims 1-6.

8. A computer-readable storage medium storing a processor-executable program, characterized in that, The processor-executable program, when executed by the processor, is used to perform the method as described in any one of claims 1-6.

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