CN116929356A

CN116929356A - Urban low-altitude unmanned aerial vehicle route planning method, device and storage medium

Info

Publication number: CN116929356A
Application number: CN202310597841.4A
Authority: CN
Inventors: 杨健; 陈育壕; 梁可柔; 李卓樾; 杨怀新; 李其銮; 叶梓晴; 穆浩荃; 陈伟
Original assignee: South China University of Technology SCUT
Current assignee: South China University of Technology SCUT
Priority date: 2023-05-24
Filing date: 2023-05-24
Publication date: 2023-10-24

Abstract

The application discloses a method, a device and a storage medium for planning a route of an urban low-altitude unmanned aerial vehicle, wherein the method comprises the following steps: acquiring a start point and an end point of flight and barrier information; determining a route searching area according to the starting point and the ending point, and performing route preliminary planning to obtain a preliminary route; optimizing the preliminary course to obtain a final course; considering that more unmanned aerial vehicles possibly exist in urban low altitude and fly simultaneously, the unmanned aerial vehicles with different heading are separated by setting a safety interval around an air obstacle area, so that the navigation safety is ensured; considering that a plurality of unmanned aerial vehicles can pass through a narrow flyable area simultaneously, different channels are set according to the width of a narrow airspace, and channels are allocated to the unmanned aerial vehicles entering the narrow flyable area. According to the method, the route of the single unmanned aerial vehicle is planned, simultaneously planning optimization of simultaneous operation of multiple unmanned aerial vehicles is considered, and rapidity, route optimality and safety of the route planning of the unmanned aerial vehicle flying in a low-altitude airspace can be considered.

Description

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

Technical Field

The application relates to the technical field of unmanned aerial vehicle path planning, in particular to a method, a device and a storage medium for planning an urban low-altitude unmanned aerial vehicle route.

Background

Unmanned aerial vehicles integrate advanced manufacturing, artificial intelligence, mobile Internet and other technologies, and promote the revolution of human society production modes and life modes. The main technology of the unmanned aerial vehicle comprises five modules of perception, positioning, planning, control and decision making, wherein the path planning with the function of going up and down is to autonomously plan a safe and effective path from a starting point to a target point from perceived map information with an obstacle, and output the path to the control decision making module, so that the unmanned aerial vehicle can work efficiently.

With wide application of low-altitude technologies such as unmanned aerial vehicle monitoring, inspection and mapping, low-altitude long-distance air path planning becomes a challenge for low-altitude aircraft application. The unmanned aerial vehicle needs to generate a route according to the map, and the route needs to be optimized so that the unmanned aerial vehicle can quickly reach a destination, and the effect that the unmanned aerial vehicle quickly and efficiently executes tasks is achieved. The main types of global path planning algorithms at the present stage include a graph searching method, a bionic intelligent algorithm and a fast random search tree algorithm, wherein the graph searching method is represented by an A-type algorithm, dijkstra and the like, but the number of nodes traversed is more due to the searching characteristics in the planning process, and the path searching efficiency is lower; the representative of the bionic intelligent algorithm is a particle swarm algorithm, a genetic algorithm, an ant swarm algorithm and the like, and the bionic intelligent algorithm has the defects of large operand, long iteration time and poor instantaneity; compared with the former two planning algorithms, the rapid-sampling random tree (RRT) based on the sampling proposed by LaValle in 1998 is widely used due to probability completeness, simple algorithm structure and strong searching capability in complex environments. The traditional fast-expansion random tree (Rapidly-Exploring Random Trees/RRT) can generate routes quickly, but the generated routes are not optimal, which results in a great amount of energy consumption during operation of the unmanned aerial vehicle. In recent years, various optimization methods are proposed by researchers aiming at the problems of high randomness of RRT algorithm expansion nodes, poor generated path optimality and the like, wherein the problems comprise an RRT Goal Bias algorithm (target Bias strategy) for accelerating path generation and a B-RRT algorithm (bidirectional search algorithm); RRT and form RRT algorithms aimed at improving the path quality; fusion algorithms and the like aimed at secondary pruning paths together promote the development of RRT algorithms. However, at present, there is still no better solution to the problems of low utilization rate of sampling points, poor route optimality obtained by planning, and the like caused by the collision between a larger probability and an obstacle when a new node is expanded in a complex environment by an RRT algorithm. In addition, the RRT algorithm has a problem that a feasible path may not be generated quickly in a road in a narrow space, or even a path may not be generated. In view of this, how to improve the low utilization rate of sampling points and the improved optimality of the path in the path planning process of the conventional RRT algorithm is a technical problem to be solved at present.

Disclosure of Invention

In order to solve at least one of the technical problems existing in the prior art to a certain extent, the application aims to provide a method, a device and a storage medium for planning a route of an urban low-altitude unmanned aerial vehicle.

The technical scheme adopted by the application is as follows:

a city low-altitude unmanned aerial vehicle route planning method comprises single unmanned aerial vehicle route planning and route modification adjustment considering simultaneous flight of multiple unmanned aerial vehicles;

the single unmanned aerial vehicle route planning comprises the following steps:

acquiring a start point and an end point of flight and barrier information;

determining a route searching region according to the starting point and the end point, and performing route preliminary planning by adopting an RRT algorithm according to the route searching region and the obstacle information to obtain a preliminary route;

optimizing the preliminary course to obtain a final course;

the course modification adjustment considering simultaneous flight of multiple unmanned aerial vehicles comprises the following steps:

when the route of the unmanned aerial vehicle bypasses the obstacle area, designing a safety interval according to the course of the route, and modifying the route according to the safety interval so as to avoid course conflict among the courses of the unmanned aerial vehicles with different courses;

detecting whether a narrow flyable area exists, if so, setting a plurality of channels according to the width of a narrow airspace and the maneuvering performance of the unmanned aerial vehicle, and distributing channels for the unmanned aerial vehicle entering the narrow flyable area according to the heading of the unmanned aerial vehicle so as to ensure that a plurality of unmanned aerial vehicles with different headings fly on different channels.

Further, the determining the route searching area according to the starting point and the ending point includes:

setting the length and width of the minimum search area;

connecting a starting point and an ending point, and acquiring the middle point of a connecting line as the center of a search area;

determining the length of a search area according to the transverse distance between the starting point and the end point, determining the width of the search area according to the longitudinal distance between the starting point and the end point, and determining a first search area;

judging whether the area of the first search area is larger than the area of the minimum search area, if so, taking the first search area as a route search area; or,

and overlapping the center of the first search area and the center of the minimum search area to obtain the minimum area containing the first search area and the minimum search area as the route search area.

Further, a preliminary course is obtained by:

a1, taking a starting point as a starting point, randomly scattering points in an airliner search area, selecting an X-rand as a sampling point, and searching a node X-near nearest to the sampling point X-rand from a constructed tree;

a2, connecting X-near and X-rand, and taking the direction of the connecting line as the direction of tree growth; setting a step length of tree growth, growing a step length in the direction of tree growth, generating a new node X-new at the tail end of the tree growth, finding a point closest to the X-new in the original existing nodes, and connecting the two points;

a3, performing collision detection from the X-rand to the X-new, and if the collision detection result is no collision, taking the X-new node as a child node of the X-near to be added into the tree; continuing to search the scattering points in the navigation line searching area; if the collision detection result is that a collision exists, the node generation fails, X-new is deleted, and scattering point searching is performed again;

a4, repeating the steps A1-A3 until the distance from the generated X-new to the end point is smaller than one step length, stopping the growth of the tree, and connecting the new node with the end point.

Further, the optimizing the preliminary course to obtain the final course includes:

determining father nodes and child nodes of the key nodes from the generated tree, and connecting the father nodes and the child nodes; the key nodes refer to points which are generated for avoiding obstacles and are used for connecting two bending routes;

performing collision detection, and if the collision detection result is no collision, taking a straight line formed by connecting a father node and a son node of the key node as a new route; if the collision detection result is that a collision exists, connecting three nodes, namely a key node, a father node and a child node, to generate two line segments; and on the two line segments, performing route collision detection and route optimization according to preset step length iteration.

Further, the performing route collision detection and route optimization on the two line segments according to preset step length iteration includes:

the key node is used as a starting point, and the father node and the child node are used as an ending point;

gradually advancing selected detection points on the two line segments according to a preset step length, connecting the two detection points, performing collision detection until collision is detected, returning to the last collision detection point, and recording;

and updating the key nodes according to the recorded detection points, and carrying out iterative detection and updating by taking the new key nodes as new departure points until the optimized route is finally obtained.

Further, when the course of the unmanned aerial vehicle bypasses the obstacle area, designing a safety interval according to the course of the course, and modifying the course according to the safety interval to avoid course collision between the courses of unmanned aerial vehicles with different courses, including:

designing a safety interval, and detecting that the obstacle area is positioned on the left side or the right side of the route according to the course of the route;

if the detected obstacle area is positioned at the left side of the route, modifying obstacle envelope information according to the safety interval, and modifying the route according to the new obstacle information;

if the detection obstacle region is located on the right side of the route, then no modification of the route is required.

Further, the detection obstacle region is located on the left side or the right side of the route, including:

acquiring two points in a straight line route before passing through an obstacle area, namely, a point= (px, py) and a point B= (qx, qy) in a subsequent route;

acquiring a point in the obstacle region to be passed as a point c= (lx, ly);

obtaining a vector AB= (qx-px, qy-py) and a vector AC= (lx-px, ly-py), and cross multiplying the two vectors to obtain a result M, and if M <0, the barrier area is on the right side of the route, executing the route operation required by right turn; if M >0, the obstacle region is on the left side of the course, and the course operation required at the time of left turn is performed.

Further, the detecting whether a narrow flyable area exists, if so, setting a plurality of fixed channels according to the width of the narrow airspace and the maneuvering performance of the unmanned aerial vehicle, and distributing the fixed channels for the unmanned aerial vehicle entering the narrow flyable area according to the heading of the unmanned aerial vehicle, including:

if the space size among the plurality of obstacle areas is detected to be smaller than a preset value, marking the area as a narrow flyable area;

dividing the narrow flyable area into a plurality of unidirectional channels according to the maneuvering performance of the unmanned aerial vehicle;

according to the course of the unmanned aerial vehicle, a course is distributed for the unmanned aerial vehicle entering the narrow flyable area, so that the unmanned aerial vehicle entering the narrow flyable area flies on the fixed course, and course conflicts among unmanned aerial vehicle courses are avoided.

The application adopts another technical scheme that:

an urban low-altitude unmanned aerial vehicle route planning device, comprising:

at least one processor;

at least one memory for storing at least one program;

the at least one program, when executed by the at least one processor, causes the at least one processor to implement the method described above.

The application adopts another technical scheme that:

a computer readable storage medium, in which a processor executable program is stored, which when executed by a processor is adapted to carry out the method as described above.

The beneficial effects of the application are as follows: according to the method, the route of the single unmanned aerial vehicle is planned, simultaneously planning optimization of simultaneous operation of multiple unmanned aerial vehicles is considered, and rapidity, route optimality and safety of the route planning of the unmanned aerial vehicle flying in a low-altitude airspace can be considered.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following description is made with reference to the accompanying drawings of the embodiments of the present application or the related technical solutions in the prior art, and it should be understood that the drawings in the following description are only for convenience and clarity of describing some embodiments in the technical solutions of the present application, and other drawings may be obtained according to these drawings without the need of inventive labor for those skilled in the art.

FIG. 1 is a schematic diagram of dynamically determining a routing search space in an embodiment of the present application;

FIG. 2 is a schematic diagram of determining an airline search region based on a start point and an end point in an embodiment of the present application;

FIG. 3 is a schematic diagram of a minimum search area in an embodiment of the application;

FIG. 4 is a schematic diagram of a partial sampling plan area of the drone A1 in an embodiment of the present application;

FIG. 5 is a schematic diagram of an initial path generation process in an embodiment of the application;

FIG. 6 is a schematic diagram of a direct connect optimization route in an embodiment of the application;

FIG. 7 is a schematic diagram of an iterative look-ahead traceback optimization route in an embodiment of the present application;

FIG. 8 is a schematic diagram of a comparison of an iteratively planned path with an initially planned path in an embodiment of the present application;

FIG. 9 is a schematic diagram of a lane conflict generated by two unmanned aerial vehicles with different turns in an embodiment of the present application;

FIG. 10 is a schematic diagram of a right turn route in an embodiment of the present application with the route completely within the safe interval;

FIG. 11 is a schematic illustration of a left turn lane with a lane portion within a safe interval in an embodiment of the present application;

FIG. 12 is a schematic illustration of a planned route of a drone after a security interval is designed in an embodiment of the present application;

FIG. 13 is a schematic illustration of a narrow region demarcation in an embodiment of the present application;

FIG. 14 is a schematic view of the overlap of the safety spacers of multiple obstacles in a confined area in an embodiment of the present application;

FIG. 15 is a graph of the effect of planned flight paths in a narrow area in an embodiment of the application;

FIG. 16 is a diagram of an optimization comparison of multiple unmanned aerial vehicles in a city;

fig. 17 is a flowchart of a method for planning a route of an urban low-altitude unmanned aerial vehicle according to an embodiment of the present application.

Detailed Description

Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative only and are not to be construed as limiting the application. The step numbers in the following embodiments are set for convenience of illustration only, and the order between the steps is not limited in any way, and the execution order of the steps in the embodiments may be adaptively adjusted according to the understanding of those skilled in the art.

In the description of the present application, it should be understood that references to orientation descriptions such as upper, lower, front, rear, left, right, etc. are based on the orientation or positional relationship shown in the drawings, are merely for convenience of description of the present application and to simplify the description, and do not indicate or imply that the apparatus or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus should not be construed as limiting the present application.

In the description of the present application, a number means one or more, a number means two or more, and greater than, less than, exceeding, etc. are understood to not include the present number, and above, below, within, etc. are understood to include the present number. The description of the first and second is for the purpose of distinguishing between technical features only and should not be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated or implicitly indicating the precedence of the technical features indicated.

In the description of the present application, unless explicitly defined otherwise, terms such as arrangement, installation, connection, etc. should be construed broadly and the specific meaning of the terms in the present application can be reasonably determined by a person skilled in the art in combination with the specific contents of the technical scheme.

As shown in fig. 17, the embodiment provides a method for planning a route of an urban low-altitude unmanned aerial vehicle, which comprises single unmanned aerial vehicle route planning and route modification adjustment considering simultaneous flight of multiple unmanned aerial vehicles;

wherein, single unmanned aerial vehicle route planning includes following steps:

acquiring a start point and an end point of flight and barrier information;

optimizing the preliminary course to obtain a final course;

in order to avoid possible collision of unmanned aerial vehicles with different heading directions when planning to avoid the route of obstacle, the safety interval is designed to ensure that the route with different heading directions has enough safety distance in space.

Consider a narrow flyable area formed by a no-fly area above a city. When the unmanned aerial vehicle passes through the narrow flyable area, a plurality of unmanned aerial vehicles can simultaneously pass through the narrow area, so that a plurality of channels are set according to the width of a narrow airspace and the performance of the unmanned aerial vehicle, and different channel routes are designated for the unmanned aerial vehicles with different heading.

Aiming at single unmanned aerial vehicle route planning, 1) the low-altitude airspace range of the whole city is larger for a large city, and the defects of low efficiency in searching the low-altitude airspace of the whole city when route planning is carried out based on a departure point and a mission point are overcome by considering that the low-altitude airspace environment of the city is relatively fixed and an available static environment map exists. Therefore, the embodiment provides a strategy for dynamically changing the search space according to the task of the unmanned aerial vehicle; 2) Aiming at the limitation of step length and search time in the course of unmanned aerial vehicle course planning, when planning a course avoiding obstacle, a non-optimal course may be generated, and a course local adjustment optimization method based on an iterative optimization method is designed to generate a course with less consumption.

Aiming at the route modification adjustment considering the simultaneous flight of multiple unmanned aerial vehicles, a route modification strategy based on urban environment under the operation condition of the multiple unmanned aerial vehicles is considered; 1) Aiming at the possible course conflict when the multiple unmanned aerial vehicle flies in the obstacle area, a course adjustment strategy is designed, a safety interval is designed for the multiple unmanned aerial vehicle to run in the low-altitude city, and the course with different courses is ensured to have enough safety distance in space. 2) For a narrow flyable region formed by an obstacle and a restricted no-fly zone, consider that there is a high risk of occurrence of airspace collision in the case where more unmanned aerial vehicles pass through the narrow flyable region at the same time, and thus, a flight path is set. The course collision refers to the collision that unmanned aerial vehicles flying oppositely use the same course when bypassing the same obstacle area.

The above method is explained in detail below with reference to the accompanying drawings.

S1: and planning a single unmanned aerial vehicle route.

S101: first, a strategy for dynamically changing the search space according to unmanned aerial vehicle tasks is introduced. As shown in fig. 1, we have determined the start and end points of the drone in the global map when planning the route. When planning an air route in an unknown environment, a global search needs to be developed. For the route planning problem of the urban airspace unmanned aerial vehicle, because the ground building information and the no-fly zone information in the city are relatively fixed, the local search area can be dynamically determined according to the task information when the route planning of the unmanned aerial vehicle is solved. Thus, the time consumption of route planning can be effectively reduced.

The method comprises the following specific steps:

the method for deciding the adaptive local area range is adopted for different situations: in the case shown in fig. 1, the distance between the starting point and the end point is large, in which case the center symmetry point of the starting point and the end point is taken as the center of the local planning rectangular area, and the difference between the absolute values of the x value and the y value between the starting point and the end point is multiplied by a proper value (for example, greater than 1) to be used as the length and the width of the local planning rectangular area, as shown in fig. 2, to obtain the planning rectangular area; for the situation that the distance between the starting point and the ending point is relatively close, the former method can cause the situation that the feasible route is explored to fail or the optimal route cannot be explored due to the obstruction of the obstruction, so that a local search planning area based on the minimum search area is adopted, the central points of the starting point and the ending point are taken as the center, and the fixed area range is taken as the route search area. This ensures the stability and reliability of the algorithm. The second type of planned rectangular area is shown in fig. 3. In the alternative of two planning rectangular areas, the algorithm compares the sizes of the two areas, and takes one method with a larger planning area as the rectangular planning area.

As an alternative, two methods may be used in combination to determine the appropriate route planning region during a particular route planning process. For example, the unmanned aerial vehicle A1 performs planning region demarcation based on a start point and an end point, and a planned region is obtained as shown in fig. 4, and the method of planning the region refers to a first method on the y-axis and refers to a second method on the x-axis.

After determining the route searching area, carrying out route preliminary planning, wherein the implementation process is shown in fig. 5, and specifically comprises the following steps:

a1, starting from a starting point, the unmanned aerial vehicle A1 randomly spreads points in a local sampling area, selects an X-rand as a sampling point, and searches a node X-near closest to the sampling point X-rand from the constructed tree. If there is only one node in the tree with a start point, the nearest node is the start point.

A2, starting the tree growth process. First, X-near and X-rand are connected, and the direction of the connecting line is the direction of tree growth. Setting a step size Stepsize as the step size of the tree for one growth, growing a step size in the direction of the tree growth, then generating a new node X-new at the end of the growth (selecting an X-new between X-near and X-rand), and finding the point nearest to the point in the original existing X to connect the two points.

A3, immediately performing collision detection from the X-rand to the X-new, if the collision detection result is no collision, adding the X-new node serving as a child node of the X-new into the tree, and continuing to perform scattering point search in the local sampling area from the step 1; if the collision detection result is that a collision exists, the node generation fails, X-new is deleted, and the scattering point search is performed again.

A4, repeating the steps A1-A3 until the distance from the generated X-new to the target point is smaller than one step length, and stopping the growth of the tree. The new node is directly connected to the target point.

Fig. 5 (1) is a schematic diagram of adding nodes, fig. 5 (2) is a schematic diagram of deleting nodes based on collision detection results, fig. 5 (3) is a schematic diagram of growing trees by connecting nodes, and fig. 5 (4) is a schematic diagram of generating initial routes by deleting useless nodes.

S102: as shown in fig. 5, the path generated using the RRT algorithm has a certain optimization space. It is mainly possible to optimize the route generated by the algorithm to bypass the obstacle. The specific optimization method is as follows:

searching a key node which is generated by avoiding obstacles and connects two bending routes in the random tree; determining parent nodes and child nodes of the key node from the random tree; and directly connecting the two nodes for one collision detection, as shown in fig. 6 (in order to ensure the process to be clear enough, some intermediate nodes are omitted in fig. 6), if the collision detection result is no collision, taking a straight line formed by directly connecting the father node and the child node of the node as a new route, and modifying related data such as consumption values in a program. If the collision detection result is that there is a collision, as shown in fig. 7, connecting the three nodes to generate two line segments, and iteratively performing route collision detection and route optimization on the two line segments by taking the determined length as a search step length. Fig. 6 (1) is a schematic diagram before the parent node and the child node are connected, and fig. 6 (2) is a schematic diagram after the parent node and the child node are connected. Fig. 7 (1) is a schematic diagram of collision generated by connecting a parent node and a child node, fig. 7 (2) is a schematic diagram of sampling detection points on two line segments and performing collision detection iteratively, fig. 7 (3) is a schematic diagram of obtaining new key nodes after a single iteration, and fig. 7 (4) is an optimized route diagram.

The method takes a key node as a starting point, and takes a father node and a child node as an ending point respectively. The selected detection point is advanced on the two line segments according to the step size. And connecting the two detection points, and carrying out iterative collision detection on the connecting line of the two detection points. And the process is alternated until collision is detected, and the last detection point is returned and recorded. We refer to the process of advancing an iterative search from a keypoint to its parent node as backtracking, and the process of advancing an iterative search from a keypoint to its child node as look-ahead. If the route collision is caused by backtracking, stopping backtracking, recording the last backtracked node, and continuing to search for forward looking until the forward looking search finds that the connection line collides with the obstacle, and determining the route optimization result according to the nearest collision-free search point. If the look-ahead search results in a link collision, stopping looking ahead, continuing backtracking until backtracking collides, and returning to the last backtracking node. After the single look-ahead backtracking is finished, two new key points can be determined, iterative optimization is further performed around the new key points by adopting the same method until the route optimization condition is met and the iterative optimization is finished, and finally an optimized route is obtained, as shown in fig. 7 (4); and after the route is obtained, modifying relevant data such as the consumption value of the unmanned aerial vehicle.

Based on the method, each key node bypassing the obstacle in the initial route is subjected to iterative optimization, so that the optimization of the whole route is realized. After iterative optimization of the initial route of the unmanned aerial vehicle A1, the obtained route is shown in fig. 8.

S2: consider course modification adjustments for simultaneous flights of multiple unmanned aerial vehicles.

By the above optimized search, an approximate optimized flight route considering the no-fly zone and the obstacle in the urban environment can be obtained. But after planning the route for each unmanned aerial vehicle, consider the future scenario that there will be a greater number of unmanned aerial vehicles flying simultaneously in the urban environment. Taking two unmanned aerial vehicles as an example, there may be a case where flight routes of the unmanned aerial vehicles overlap, as shown in fig. 9, starting points and ending points of the unmanned aerial vehicles A1 and A2 are different, and an excessively approaching situation may occur when passing through an obstacle. When the number of unmanned aerial vehicles running in the urban environment increases, the probability of the occurrence of the opposite flight of a plurality of unmanned aerial vehicles on the same path is high due to the influence of the obstacle.

S201: and designing an air route adjustment strategy aiming at the requirement of passing through the obstacle area so as to reduce the probability of air route collision of the opposite flying unmanned aerial vehicle and bypassing the obstacle area. We consider the provision of a safety spacer surrounding the obstacle region, the initial course being maintained as the drone passes over the obstacle region if it turns right around the prescribed obstacle region. While a left turn route requires the safe interval to be considered as a no-fly zone and requires local re-planning of the route through the obstacle region to create a new left turn route. Under this method, the conditions in the optimization process are shown in fig. 10 and 11, which respectively show the optimization process of turning right and left of the unmanned aerial vehicle when bypassing the obstacle. Fig. 11 (1) is a schematic route diagram based on the original no-fly zone range planning, and fig. 11 (2) is a schematic route diagram based on the extended no-fly zone planning.

After the safety interval area is arranged, the computer needs to process the route of the unmanned aerial vehicle according to specific conditions due to different measures taken when the left turn and the right turn navigate through the obstacle area. The computer adopts a vector method to judge the azimuth relation between the unmanned aerial vehicle route and the current obstacle:

two points in the straight line route before passing through the obstacle region are respectively point a= (px, py) and point b= (qx, qy) of the subsequent route, and one point in the obstacle region to be passed through is further taken as point c= (lx, ly). Taking the vector AB= (qx-px, qy-py) and the vector AC= (lx-px, ly-py), and cross multiplying the two vectors to obtain a result M, if M <0, the barrier area is on the right side of the route, and executing the route operation required by right turn; if M >0, the obstacle region is to the left of the course, at which point the course operation required for the left turn needs to be performed. The new route obtained by judging, steering and optimizing the two routes of the unmanned aerial vehicle A1 and A2 is shown in FIG. 12.

By spatially disposing the safety spacer around the obstacle, the probability of multiple unmanned aerial vehicles coming too close in flight can be reduced. However, there are a plurality of non-flying areas spaced closely above the city, resulting in a situation where the local flyable airspace is narrower, as shown in fig. 13. In this case there may be two problems: firstly, in the case of setting a safety interval area as shown in fig. 14, due to the situation that the safety interval areas are overlapped between the no-fly zones (obstacles), the left turn route cannot be adjusted, secondly, because the area is used as a key airspace, more unmanned aerial vehicles fly in the area at the same time, and when no reasonable measures are taken, the risk that the unmanned aerial vehicles fly too close in a narrow airspace is high, as shown in fig. 13.

S202: the design of the equally divided channels in the narrow area is proposed, the unmanned aerial vehicle can only fly unidirectionally in different channels, and meanwhile, different channels are distributed to the unmanned aerial vehicle according to different positions of the unmanned aerial vehicle, where the unmanned aerial vehicle enters the narrow area, so that each channel can be reasonably utilized. After adopting the channel isolation, unmanned aerial vehicles with different flight directions in a narrow airspace are divided from space. The maneuvering performance of the unmanned aerial vehicle needs to be considered when dividing the channel. On the other hand, unmanned aerial vehicles with different speeds should be divided on different channels as much as possible, so that the unmanned aerial vehicles can be ensured to be smooth in high efficiency when running in the channels. The flight channels of the unmanned aerial vehicle arranged in the narrow space are as shown in fig. 15, on the basis of determining the performance of the unmanned aerial vehicle running in the urban space, different channel numbers can be set according to the width of the narrow space, and when the route of the unmanned aerial vehicle is generated, the local route of the unmanned aerial vehicle can be determined by adopting a method for defining a fixed channel for the unmanned aerial vehicle.

After the modification of the route of the unmanned aerial vehicle based on the urban environment under the condition of considering the operation of the multiple unmanned aerial vehicles, a front-back comparison chart is shown in fig. 16, wherein fig. 16 (1) is a route diagram generated based on RRT direct planning, and fig. 16 (2) is a route diagram based on the modification of the urban environment under the condition of considering the operation of the multiple unmanned aerial vehicles.

The embodiment also provides a city low altitude unmanned aerial vehicle route planning device, including:

at least one processor;

at least one memory for storing at least one program;

the at least one program, when executed by the at least one processor, causes the at least one processor to implement the method of fig. 17.

The urban low-altitude unmanned aerial vehicle route planning device provided by the embodiment of the application can be used for executing the method for planning the urban low-altitude unmanned aerial vehicle route, and any combination of the embodiments of the method can be executed, so that the method has the corresponding functions and beneficial effects.

Embodiments of the present application also disclose a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The computer instructions may be read from a computer-readable storage medium by a processor of a computer device, and executed by the processor, to cause the computer device to perform the method shown in fig. 17.

The embodiment also provides a storage medium which stores instructions or programs for executing the urban low-altitude unmanned aerial vehicle route planning method provided by the embodiment of the method, and when the instructions or programs are operated, the steps can be implemented by any combination of the embodiment of the method, so that the method has the corresponding functions and beneficial effects.

In some alternative embodiments, the functions/acts noted in the block diagrams may occur out of the order noted in the operational illustrations. For example, two blocks shown in succession may in fact be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved. Furthermore, the embodiments presented and described in the flowcharts of the present application are provided by way of example in order to provide a more thorough 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 changed, and in which sub-operations described as part of a larger operation are performed independently.

Furthermore, while the application is described in the context of functional modules, it should be appreciated that, unless otherwise indicated, one or more of the described functions and/or features may be integrated in a single physical device and/or software module or one or more functions and/or features may be implemented in separate physical devices or software modules. It will also be appreciated that a detailed discussion of the actual implementation of each module is not necessary to an understanding of the present application. Rather, the actual implementation of the various functional modules in the apparatus disclosed herein will be apparent to those skilled in the art from consideration of their attributes, functions and internal relationships. Accordingly, one of ordinary skill in the art can implement the application as set forth in the claims without undue experimentation. It is also to be understood that the specific concepts disclosed are merely illustrative and are not intended to be limiting upon the scope of the application, which is to be defined in the appended claims and their full scope of equivalents.

The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.

Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.

More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.

It is to be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.

In the foregoing description of the present specification, reference has been made to the terms "one embodiment/example", "another embodiment/example", "certain embodiments/examples", and the like, means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

While embodiments of the present application have been shown and described, it will be understood by those of ordinary skill in the art that: many changes, modifications, substitutions and variations may be made to the embodiments without departing from the spirit and principles of the application, the scope of which is defined by the claims and their equivalents.

While the preferred embodiment of the present application has been described in detail, the present application is not limited to the above embodiments, and various equivalent modifications and substitutions can be made by those skilled in the art without departing from the spirit of the present application, and these equivalent modifications and substitutions are intended to be included in the scope of the present application as defined in the appended claims.

Claims

1. The city low-altitude unmanned aerial vehicle route planning method is characterized by comprising single unmanned aerial vehicle route planning and route modification adjustment considering simultaneous flight of multiple unmanned aerial vehicles;

acquiring a start point and an end point of flight and barrier information;

optimizing the preliminary course to obtain a final course;

2. The method for planning a route of a low-altitude unmanned aerial vehicle according to claim 1, wherein the determining the route search area according to the start point and the end point comprises:

setting the length and width of the minimum search area;

3. The urban low-altitude unmanned aerial vehicle route planning method according to claim 1, wherein the preliminary route is obtained by:

a3, performing collision detection from the X-rand to the X-new, if the collision detection result is no collision, adding the X-new node serving as a child node of the X-near into a tree, and continuing to scatter point search in an aviation searching area; if the collision detection result is that a collision exists, the node generation fails, X-new is deleted, and scattering point searching is performed again;

4. A method for planning an urban low-altitude unmanned aerial vehicle according to claim 3, wherein said optimizing the preliminary course to obtain the final course comprises:

5. The method for planning a route of an urban low-altitude unmanned aerial vehicle according to claim 4, wherein the performing, on the two line segments, route collision detection and route optimization according to a preset step size iteration comprises:

gradually advancing the selected detection points on the two line segments according to a preset step length, connecting the two detection points and performing collision detection,

until collision is detected, returning to the last collision detection point and recording;

6. The method for planning a course of an urban low-altitude unmanned aerial vehicle according to claim 1, wherein when the course of the unmanned aerial vehicle bypasses the obstacle region, designing a safety interval according to the course of the course, and modifying the course according to the safety interval to avoid course collision between the courses of unmanned aerial vehicles with different courses, comprises:

if the detected obstacle area is positioned at the left side of the route, modifying the obstacle information according to the safety interval, and modifying the route according to the new obstacle information;

7. The method for planning an air course of a low-altitude unmanned aerial vehicle according to claim 6, wherein the detecting the obstacle region according to the course of the air course is located on the left or right side of the air course, comprising:

acquiring two points in a straight line route before passing through an obstacle area, namely a point= (px, py) and a point B= (qx, qy) on a subsequent route;

acquiring a point in the obstacle region to be passed as a point c= (lx, ly);

8. The method of claim 1, wherein the detecting of the existence of a narrow flyable area, if so, sets a plurality of channels according to the width of the narrow airspace and the maneuvering characteristics of the unmanned aerial vehicle,

according to unmanned aerial vehicle's course, for entering narrow unmanned aerial vehicle that can fly the district and distribute fixed channel, include:

9. An urban low-altitude unmanned aerial vehicle route planning device, which is characterized by comprising:

at least one processor;

at least one memory for storing at least one program;

the at least one program, when executed by the at least one processor, causes the at least one processor to implement the method of any one of claims 1-8.

10. A computer readable storage medium, in which a processor executable program is stored, characterized in that the processor executable program is for performing the method according to any of claims 1-8 when being executed by a processor.