Unmanned aerial vehicle safety path planning method in high-density obstacle environment

Abstract

The invention relates to a safe path planning method for an unmanned aerial vehicle in a high-density obstacle environment, and the method comprises the steps: building a coverage overflow rate quantification model, converting a polygonal obstacle region into a circular coverage set with a minimum overflow rate, and obtaining a circular obstacle set occupied by a minimum flying region; taking the circle center of the circular obstacle set as a node, and constructing a Voronoi graph through triangulation and security screening; searching a feasible path between the starting point and the ending point on the Voronoi diagram according to the motion constraint of the unmanned aerial vehicle, and processing mapping conversion of points not on the diagram to obtain an initial path of the unmanned aerial vehicle; and smoothing the initial path of the unmanned aerial vehicle through curve interpolation, and verifying the safe distance between the smooth path and the obstacle area to obtain the safe path of the unmanned aerial vehicle. The obstacle avoidance safety of the unmanned aerial vehicle can be effectively improved, the distance between the path and the obstacle area is kept longer, and the collision risk is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of unmanned aerial vehicles, and particularly relates to a safe path planning method for unmanned aerial vehicles in a high-density obstacle environment. BACKGROUND

[0002] Under the background of the booming low-altitude economy, unmanned aerial vehicles (UAVs) have shown strong maneuverability and application potential in the fields of agriculture, logistics, urban management and other civil low-altitude airspace. However, the complex scenarios composed of high-density buildings, infrastructure and other obstacles in the urban low-altitude environment pose a serious challenge to the safety and efficiency of UAV path planning. How to enable UAVs to autonomously plan a safe, efficient and low-cost flight route in a dense obstacle environment has become a key problem in optimizing their performance in multiple fields of application.

[0003] In the prior art, mainstream path planning algorithms include A* algorithm, intelligent optimization algorithm and Voronoi diagram method. The A* algorithm realizes path planning through heuristic function design, but its efficiency is limited in complex environments and large-scale problems, and it lacks adaptability to dynamic environments. Intelligent optimization algorithms, such as ant colony algorithm and particle swarm algorithm, have the defects of slow convergence speed, parameter sensitivity, unstable results and inability to guarantee optimality, making it difficult to ensure the safety of the aircraft. Although the Voronoi diagram method can construct a safe flight route network, it has high computational complexity when directly applied to complex environments, and it is easy to produce redundant paths when dealing with planar obstacle areas, affecting the planning efficiency.

[0004] Therefore, it is necessary to improve one or more problems existing in the above-mentioned related technical solutions.

[0005] It should be noted that the information disclosed in the above background section is only used to strengthen the understanding of the background of the present disclosure, and therefore can include information that does not constitute prior art known to those of ordinary skill in the art. SUMMARY

[0006] The purpose of the embodiments of the present disclosure is to provide a safe path planning method for unmanned aerial vehicles in a high-density obstacle environment, thereby at least partially overcoming one or more problems caused by the limitations and defects of the related art.

[0007] In a first aspect, the present application provides a safe path planning method for unmanned aerial vehicles in a high-density obstacle environment, comprising:

[0008] A coverage overflow rate quantization model is established to convert the polygonal obstacle area into a circular coverage set with the minimum overflow rate, and a circular obstacle set with the minimum invasion of the flyable area is obtained;

[0009] The center of the circular obstacle set is taken as a node, and a Voronoi diagram is constructed through triangulation and safety screening;

[0010] According to the UAV motion constraint, a feasible path between the start and end points is searched on the Voronoi diagram, and a mapping conversion of a non-diagram point is processed to obtain an initial path of the UAV;

[0011] The initial path of the UAV is smoothed by curve interpolation, and a safety distance between the smoothed path and the obstacle area is verified to obtain a safe path of the UAV.

[0012] In one possible implementation, the step of establishing a coverage overflow rate quantification model, converting a polygonal obstacle area into a circular coverage set with a minimum overflow rate, and obtaining a circular obstacle set that minimizes the invasion of the flyable area, includes:

[0013] The coverage overflow rate quantification model is established, and the coverage overflow rate is defined as the difference ratio between the circular coverage area and the area of the inscribed square;

[0014] The coverage overflow rate threshold and the maximum radius constraint of the outer coverage circle are set;

[0015] A hierarchical coverage algorithm based on the inscribed square of the circle is used to cover the polygonal obstacle area layer by layer from the boundary until the internal area is covered by a single circumscribed circle, and a circular obstacle set that minimizes the invasion of the flyable area is constructed.

[0016] In one possible implementation, the coverage overflow rate is:

[0017]

[0018] wherein S 圆 is the area of all circles in the circular coverage, and S 正 is the area of the inscribed square of all circles in the circular coverage.

[0019] In one possible implementation, the hierarchical coverage algorithm is:

[0020] The side length of the inscribed square is calculated, the number of squares with a side length of a is determined according to the length of each boundary edge in the Lth layer boundary point set, and each square is used to cover each boundary edge, and the outer circle corresponding to each square is added to the circular coverage area set;

[0021] After covering each boundary edge, a new line segment parallel to the square and each boundary edge is enclosed to form a closed area, and the boundary points of the closed area are calculated;

[0022] The outer circle of the closed area is calculated, and the outer circle of the closed area that falls in the target coverage area is added to the circular coverage area set to obtain the circular obstacle set.

[0023] In one possible implementation, the step of constructing a Voronoi diagram using the centers of the circular obstacle set as nodes through triangulation and safety screening includes:

[0024] Performing triangulation with the centers of the circular obstacle set as nodes to obtain a plurality of first triangles;

[0025] Calculate the intersection points of the first triangle edge and the circular obstacle set edge, and connect the midpoints between the intersection points on the same first triangle edge to obtain a second triangle;

[0026] Deleting the first triangle and connecting each vertex of the second triangle with the centroid to construct an initial Voronoi diagram;

[0027] The initial Voronoi diagram is reconstructed for safety, and the second triangle is deleted to obtain a Voronoi diagram.

[0028] In a possible implementation, the security reconstruction is to perform a security check on the Voronoi diagram through the intersection of the second triangle centroid, the first triangle edge, and the circular obstacle set edge.

[0029] In one possible implementation, the step of performing a security check on the Voronoi diagram using the intersection of the second triangle's centroid, the first triangle's edge, and the circular obstacle set's edge includes:

[0030] If the centroid of the second triangle is within any circle of the circular obstacle set, delete all Voronoi diagram edges connected to the centroid;

[0031] If the midpoint between the intersection points on the same first triangle edge is within any circle of the circular obstacle set, then delete all Voronoi diagram edges connected to the midpoint;

[0032] If the distance between the two corresponding intersection points of the midpoint between the intersection points on the same first triangle edge is less than the preset safety distance, all Voronoi diagram edges connected to the midpoint are deleted.

[0033] In one possible embodiment, the step of searching for a feasible path between the start and end points on the Voronoi diagram based on the UAV motion constraints and processing the mapping conversion of non-graph points to obtain the UAV initial path includes:

[0034] When the starting and ending points are on the Voronoi diagram, the Dijkstra algorithm is used to search for the shortest safe path;

[0035] When the start and end points are not on the Voronoi diagram, the start and end points are mapped to the nearest points on the Voronoi diagram through Dubins curve according to the UAV motion constraint, and then the shortest safe path is searched through Dijkstra algorithm.

[0036] In one possible implementation, the UAV motion constraint includes a minimum turning radius constraint and a heading angle continuity constraint.

[0037] In a second aspect, the present application provides a high-density obstacle environment UAV safe path planning system, which is used to execute the high-density obstacle environment UAV safe path planning method described above, and the system comprises:

[0038] A coverage model construction module is configured to establish a coverage overflow rate quantification model, convert the polygon obstacle area into a circular coverage set with minimum overflow rate, and obtain a circular obstacle set with minimum invasion of flyable area.

[0039] A Voronoi diagram construction module is configured to take the centers of the circular obstacle set as nodes, construct a Voronoi diagram through triangulation and safety screening.

[0040] An initial path generation module is configured to search a feasible path between the start and end points on the Voronoi diagram according to the UAV motion constraint, and process the mapping conversion of non-diagram points to obtain a UAV initial path.

[0041] A path optimization processing module is configured to perform smoothing processing on the UAV initial path through curve interpolation, and verify the safety distance between the smoothed path and the obstacle area to obtain a UAV safe path.

[0042] The technical solution provided by the present application can include the following beneficial effects:

[0043] The high-density obstacle environment UAV safe path planning method provided by the present application can simplify the obstacle area representation by using a circular coverage model, construct a coverage model with minimum overflow rate, effectively improve the UAV obstacle avoidance safety, keep the path and the obstacle area at a farther distance, and reduce the collision risk. A Voronoi diagram construction algorithm based on circular coverage is designed to generate a flyable area skeleton, optimize the path planning calculation efficiency, reduce the redundant path in a complex environment, and improve the algorithm execution speed. The kinematic characteristics of the UAV are fused, and the Dijkstra algorithm is used to generate a path based on the Voronoi skeleton to ensure the optimality and feasibility of the path planning, adapt to the actual flight constraints of the UAV. The initial path is smoothed through a cubic B-spline curve to eliminate sharp turns in the path, improve the flight stability, and reduce the energy consumption and collision risk.

[0044] It should be understood that the foregoing general description and the following detailed description are only exemplary and explanatory, and cannot limit the present disclosure. BRIEF DESCRIPTION OF DRAWINGS

[0045] The accompanying drawings, which are incorporated herein and form a part of the specification, illustrate embodiments consistent with the present disclosure and, together with the description, further serve to explain the principles of the disclosure. It is to be expressly understood, however, that the drawings are only for the purpose of illustration and as an aid to understanding the only embodiments of the application and are not intended as a definition of the limits of the disclosure.

[0046] Figure 1 A flow chart illustrating a method for safe path planning of unmanned aerial vehicles in high-density obstacle environment according to an example embodiment of the present disclosure is shown;

[0047] Figure 2 A detailed flow chart illustrating step S100 of the method for safe path planning of unmanned aerial vehicles in high-density obstacle environment according to an example embodiment of the present disclosure is shown;

[0048] Figure 3 A detailed flow chart illustrating step S200 of the method for safe path planning of unmanned aerial vehicles in high-density obstacle environment according to an example embodiment of the present disclosure is shown;

[0049] Figure 4 A detailed flow chart illustrating step S300 of the method for safe path planning of unmanned aerial vehicles in high-density obstacle environment according to an example embodiment of the present disclosure is shown;

[0050] Figure 5 A detailed flow chart illustrating step S400 of the method for safe path planning of unmanned aerial vehicles in high-density obstacle environment according to an example embodiment of the present disclosure is shown;

[0051] Figure 6 A schematic diagram illustrating a multi-node reciprocal symbiotic radio network model of the method for safe path planning of unmanned aerial vehicles in high-density obstacle environment according to an example embodiment of the present disclosure is shown;

[0052] Figure 7 A comparison schematic diagram illustrating different obstacle zone circular coverage strategies of the method for safe path planning of unmanned aerial vehicles in high-density obstacle environment according to an example embodiment of the present disclosure is shown;

[0053] Figure 8 A schematic diagram illustrating a circular coverage flow of the method for safe path planning of unmanned aerial vehicles in high-density obstacle environment according to an example embodiment of the present disclosure is shown;

[0054] Figure 9 A schematic diagram illustrating a circular coverage of the method for safe path planning of unmanned aerial vehicles in high-density obstacle environment according to an example embodiment of the present disclosure is shown;

[0055] Figure 10 A schematic diagram illustrating a coverage zone disjoint path construction of the method for safe path planning of unmanned aerial vehicles in high-density obstacle environment according to an example embodiment of the present disclosure is shown;

[0056] Figure 11 Fig. 1 shows a schematic diagram of the intersection path construction of the coverage area of the safe path planning method for unmanned aerial vehicles in high-density obstacle environment according to an example embodiment of the present disclosure;

[0057] Figure 12 Fig. 2 shows a schematic diagram of the non-intersection path reconstruction of the coverage area of the safe path planning method for unmanned aerial vehicles in high-density obstacle environment according to an example embodiment of the present disclosure;

[0058] Figure 13 Fig. 3 shows a schematic diagram of the construction of the Voronoi diagram process of the safe path planning method for unmanned aerial vehicles in high-density obstacle environment according to an example embodiment of the present disclosure;

[0059] Figure 14 Fig. 4 shows a schematic diagram of the reference path of the safe path planning method for unmanned aerial vehicles in high-density obstacle environment according to an example embodiment of the present disclosure;

[0060] Figure 15 Fig. 5 shows a schematic diagram of the circular coverage area path result of the safe path planning method for unmanned aerial vehicles in high-density obstacle environment according to an example embodiment of the present disclosure;

[0061] Figure 16 Fig. 6 shows a schematic diagram of the flight collision probability comparison when the safety distance is 5 meters of the safe path planning method for unmanned aerial vehicles in high-density obstacle environment according to an example embodiment of the present disclosure;

[0062] Figure 17 Fig. 7 shows a schematic diagram of the flight collision probability comparison when the safety distance is 10 meters of the safe path planning method for unmanned aerial vehicles in high-density obstacle environment according to an example embodiment of the present disclosure;

[0063] Figure 18 Fig. 8 shows a schematic diagram of the structure of the safe path planning system for unmanned aerial vehicles in high-density obstacle environment according to an example embodiment of the present disclosure. DETAILED DESCRIPTION

[0064] Example implementations will now be described more fully with reference to the accompanying drawings. Example implementations can, however, be implemented in many different forms and should not be construed as limited to the examples set forth herein; rather, these implementations are provided so that this disclosure will be thorough and complete, and will fully convey the scope of example implementations to those skilled in the art. Features, structures or characteristics described can be combined in any suitable manner in one or more implementations.

[0065] In the present example implementation, a safe path planning method for unmanned aerial vehicles in high-density obstacle environment is first provided, which can be applied to a terminal device, such as a mobile terminal, e.g., a mobile phone, a desktop computer, a personal digital assistant, a notebook computer, a tablet computer, a smart watch, etc. Figure 1 With Figure 6As shown in the method can comprise the following steps:

[0066] Step S100: Establish a coverage overflow rate quantification model, convert the polygonal obstacle area into a circular coverage set with minimum overflow rate, and obtain a circular obstacle set with minimized invasion of flyable area.

[0067] Step S200: Take the center of the circular obstacle set as a node, construct a Voronoi diagram through triangulation and safety screening.

[0068] Step S300: Search for a feasible path between start and end points on the Voronoi diagram according to the motion constraints of the UAV, and process the mapping conversion of non-diagram points to obtain an initial path of the UAV.

[0069] Step S400: Smooth the initial path of the UAV through curve interpolation, and verify the safety distance between the smoothed path and the obstacle area to obtain a safe path of the UAV.

[0070] The above method can greatly improve the minimum distance between the path and the obstacle area by combining the circular coverage model with the Voronoi diagram, significantly reduce the collision probability, and improve the obstacle avoidance safety.

[0071] Moreover, the Voronoi diagram construction algorithm based on circular coverage and the Dijkstra search strategy are adopted to greatly improve the calculation efficiency in complex environments. At the same time, the path is smoothed by using a cubic B-spline curve, which eliminates the sharp turning of the initial flight path, reduces the risk of flight collision, and improves the path stability.

[0072] Finally, the circular coverage of the obstacle area and the skeleton generation of the Voronoi diagram are used to effectively solve the problem of redundant path in the traditional Voronoi diagram in complex environments, so that the path is more in line with the safety flight requirements while maintaining the length, providing a practical solution for the safe flight of the UAV in high-density obstacle environment.

[0073] In the following, reference will be made to Figures 1 to 5 The various steps of the above method in the present example embodiment will be described in more detail.

[0074] In step S100, a coverage overflow rate quantification model is established, the polygonal obstacle area is converted into a circular coverage set with minimum overflow rate, and a circular obstacle set with minimized invasion of flyable area is obtained.

[0075] It should be noted that since the subsequent Voronoi diagram generation algorithm is designed for circular obstacle areas, the polygonal obstacle area needs to be covered by circles.

[0076] In one embodiment, as shown in Figure 2As shown, step S100 may include the following sub-steps:

[0077] In step S110 , a coverage overflow rate quantification model is established, and the coverage overflow rate is defined as the difference ratio between the circular coverage area and the inscribed square area.

[0078] It should be noted that in order to reduce the impact of the coverage circle obstacle area on the flyable area, the coverage overflow rate should be as small as possible.

[0079] It can be understood that the coverage overflow rate is:

[0080]

[0081] Among them, S 圆 is the area of ​​all circles in the circle cover, S 正 is the area of ​​all squares inscribed in the circle in the circle cover.

[0082] In step S120 , a coverage overflow rate threshold and a maximum radius constraint of an outer coverage circle are set.

[0083] It should be noted that if Figure 7 As shown in Figure 2, the effects of different coverage overflow rates are compared in the same scenario, namely the uniform coverage strategy and the optimized boundary coverage strategy.

[0084] Figure 7 The coverage solution in (a) occupies a large amount of flyable area, resulting in the inability to obtain a better route;

[0085] So the design is Figure 7 The coverage overflow rate threshold shown in (b) above is used. Considering the impact of the coverage circle radius on the coverage overflow rate, the maximum radius R of the outer coverage circle is set.

[0086] Based on the above analysis, the circular coverage problem of the obstacle area is described as: covering the polygon with the least circle, the coverage overflow rate is less than the threshold b, and the outer coverage circle radius is less than R, and the result is as follows Figure 8 The problem flow chart is shown.

[0087] In step S130, a layered covering algorithm based on inscribed squares in circles is used to cover the polygonal obstacle area layer by layer from the boundary until the inner area is completely covered with a single circumscribed circle, thereby constructing a circular obstacle set that minimizes the encroachment on the flyable area.

[0088] Wherein, the layered coverage algorithm is:

[0089] Calculate the side length of the inscribed square. Based on the length of each boundary edge in the L-th layer boundary point set, determine the number of squares with a side length of a. Cover each boundary edge with a square, and add the outer circle corresponding to each square to the circular coverage area set.

[0090] After covering each boundary edge, a new line segment parallel to each boundary edge is formed to enclose a closed region, and the boundary points of the closed region are calculated;

[0091] The circumcircle of the closed region is calculated, and the circumcircle of the closed region falling in the target coverage region is added to the circular coverage region set to obtain the circular obstacle set.

[0092] It should be noted that the layered coverage algorithm can perform circular coverage on any polygonal region. The boundary coverage of the region determines the overflow rate, and the smaller the coverage circle radius used, the lower the overflow rate. After covering the boundary of the region, the inner region obtained is the same shape as the original region, and the same coverage method can be used to gradually implement boundary coverage, and the inner region gradually shrinks. When the inner region is small enough to be covered by an incircle and an excircle of the region, the algorithm ends.

[0093] As shown in Figure 9 , first, the boundaries of the pentagon are covered with circles of the same radius for the first layer of coverage; the closed figure obtained in the second layer (enclosed by the inner dashed line) maintains the shape of the original pentagon, and since its excircle is within the region, a single circle can be used to cover the inner region.

[0094] Further, the layered coverage algorithm is expressed in algorithm form:

[0095]

[0096] In step S200, the centers of the circles of the circular obstacle set are taken as nodes, and a Voronoi diagram is constructed through triangulation and safety screening.

[0097] It should be noted that after circular coverage of the obstacle region, triangulation is performed with the centers of the coverage circles as the division points to obtain a plurality of triangles, and then flight waypoints are searched on the edges of the triangles to construct a Voronoi diagram, and subsequent pathfinding is performed on the Voronoi diagram. In order to ensure the safety of flight, the waypoints of the Voronoi diagram need to consider the safety distance with the coverage region. The construction and reconstruction of the Voronoi diagram according to whether the circular coverage regions in a triangle intersect will be described below.

[0098] In one embodiment, as shown in Figure 3 , step S200 can include the following sub-steps:

[0099] In step S210, triangulation is performed with the centers of the circles of the circular obstacle set as nodes to obtain a plurality of first triangles.

[0100] It can be understood that, as Figure 10 and Figure 13As shown in (a), triangulation is performed using the center of the covering circle as the segmentation point to obtain multiple triangles.

[0101] In step S220, the intersection points of the first triangle edge and the circular obstacle set edge are calculated, and the midpoints between the intersection points on the same first triangle edge are connected to obtain a second triangle.

[0102] It is understandable that if Figure 10 and Figure 13 As shown in (b), assume that the obstacle area covering circles corresponding to a triangle PKN are CX, CY and CZ, and the centers of the circles are P, K and N respectively.

[0103] First, calculate the intersection of triangle PKN and the three circles. X It intersects with A1 at circle C Y It intersects with A2 at PK, so the two intersection points on PK are A1 and A2. Similarly, we can find the two intersection points B1 and B2 on side PN, and the two intersection points C1 and C2 on side PN. Next, calculate the midpoint of each intersection point. From the two intersection points A1 and A2 on PK, we get the midpoint A; similarly, we can find the two midpoints B and C on PN and PN. At the same time, calculate the centroid O of triangle PKN.

[0104] In step S230 , the first triangle is deleted, and each vertex of the second triangle is connected to the centroid to construct an initial Voronoi diagram.

[0105] It is understandable that if Figure 10 and Figure 13 As shown in (c), connecting the center of gravity O with the three midpoints A, B and C, we get OA, OB and OC, which are the edges of the Voronoi diagram on the triangle PKN.

[0106] In step S240 , the initial Voronoi diagram is reconstructed for safety, and the second triangle is deleted to obtain a Voronoi diagram.

[0107] It should be noted that if Figure 11 and Figure 13 As shown in (d), when constructing the Voronoi diagram in a triangle, it is possible to encounter a situation where the covering circle intersects. In this case, the construction process remains unchanged, and the invalid edges will be deleted in the subsequent security check. Figure 6 As shown in the figure, the three obstacle area covering circles of triangle PKN all intersect. Using the above steps, the edges of the Voronoi diagram are still OA, OB and OC.

[0108] The security reconstruction is to perform a security check on the Voronoi diagram through the intersection of the second triangle centroid, the first triangle edge and the circular obstacle set edge.

[0109] It should be noted that the Voronoi diagram obtained based on the obstacle area provides guidance for subsequent trajectory planning, allowing the UAV to fly from the middle position of the obstacle area, thereby improving flight safety.

[0110] During the construction of the Voronoi diagram, the edges constructed in the triangle may cause the drone to be unable to fly safely: First, the two obstacle area coverage circles intersect, resulting in the midpoint being in the obstacle area, such as Figure 11 A, B and C in the figure; the second is that the midpoint is outside the obstacle area, but the distance between the two coverage circles is less than the safe distance for drone flight. Figure 12 When the drone passes through the obstacle area from point A, the distance between the obstacle circles C1 and C2 is relatively small, making it impossible for the drone to pass safely. Therefore, it is necessary to reconstruct the initially generated Voronoi diagram for safety in the above two situations.

[0111] Specifically:

[0112] In step S241, if the centroid of the second triangle is within any circle of the circular obstacle set, all Voronoi diagram edges connected to the centroid are deleted.

[0113] It is understandable that if Figure 11 As shown, assuming that two adjacent covering circles are C i with C j , the two intersection points calculated during the Voronoi diagram generation process are A i and A j , the corresponding generated edge is OA, where O is the center of gravity of the triangle; if the center of gravity O is in a certain obstacle area covering circle, then delete all Voronoi diagram edges connected to O.

[0114] In step S242, if the midpoint between the intersection points on the same first triangle edge is within any circle of the circular obstacle set, all Voronoi diagram edges connected to the midpoint are deleted.

[0115] It is understandable that if Figure 11 As shown in Figure 3, if the midpoint A is in a certain obstacle area covering circle, then delete all Voronoi diagram edges connected to A.

[0116] In step S243, if the distance between the two corresponding intersection points of the midpoint between the intersection points on the same first triangle side is less than the preset safety distance, all Voronoi diagram edges connected to the midpoint are deleted.

[0117] It is understandable that if Figure 11 As shown, if the two intersection points A corresponding to the midpoint A i and Aj , the distance is less than the set safe flight distance d safe , then delete all Voronoi diagram edges connected to A.

[0118] Furthermore, step S200 is expressed in the form of Algorithm 2:

[0119]

[0120] In step S300, according to the UAV motion constraints, a feasible path between the starting and ending points is searched on the Voronoi diagram, and the mapping conversion of non-graph points is processed to obtain the UAV initial path.

[0121] In one embodiment, Figure 4 As shown, step S300 may include the following sub-steps:

[0122] In step S310, when the starting and ending points are on the Voronoi diagram, the Dijkstra algorithm is used to search for the shortest safe path.

[0123] It should be noted that when the starting point and the end point of the trajectory planning task are located on the skeleton Voronoi diagram, the Dijkstra algorithm can be used directly to find the reference path, such as Figure 14 shown.

[0124] In step S320, when the starting and ending points are not on the Voronoi diagram, the starting and ending points are mapped to the nearest points on the Voronoi diagram using the Dubins curve according to the UAV motion constraints, and then the shortest safe path is searched using the Dijkstra algorithm.

[0125] It should be noted that if the given starting point or end point is not on the Voronoi diagram, the Dubins curve can be used as the starting point or end point to find the point closest to it on the skeleton Voronoi diagram, and then the Dijkstra pathfinding algorithm can be started to obtain the initial route from the starting point to the end point.

[0126] It can be understood that the UAV motion constraints include a minimum turning radius constraint and a heading angle continuity constraint.

[0127] In step S400, the initial path of the UAV is smoothed by curve interpolation, and the safety distance between the smoothed path and the obstacle area is verified to obtain a safe path for the UAV.

[0128] It should be noted that the initial route obtained on the Voronoi diagram is composed of a series of line segments. When the angle between two adjacent line segments is small, the drone cannot turn safely. Therefore, it is necessary to smooth the obtained route. This application uses the cubic B-spline algorithm to smooth the route and obtain the final smoothed flight path, such asFigure 14 The method not only reduces sharp turns in the path, reduces the risk of flight collision, and improves the smoothness of the flight process and flight energy consumption.

[0129] Further, the effect of the unmanned aerial vehicle safe path planning method in a high-density obstacle environment is compared and verified through simulation verification.

[0130] The entire two-dimensional plane is divided into a 300m×300m building area in the experiment. The smoothed Dijkstra algorithm and the A* algorithm are compared in the experiment, and the results are shown in Figure 15 : the red dashed line is the initial best path, the light blue path is the path smoothed by the B-spline curve, and the red solid line represents the best path obtained by the smoothed A* algorithm.

[0131] The experiment randomly selects 50 groups of start-end combinations as test samples and distributes them in the 300m×300m building area. The experiment uses the algorithm (VDS) and the A* algorithm for path planning for each start-end point, records the calculation time of the two algorithms and the shortest distance of each point on the path from the obstacle. The experimental results show that the average calculation time of the algorithm is 1.13×10 -4 -4 seconds, and the average shortest distance from the obstacle is 7.85 meters; the average calculation time of the A* algorithm is 4.40×10 -1 -4 seconds, and the average shortest distance is 1.70 meters.

[0132] Under the scenarios of safety distance settings of 5 meters and 10 meters, Figure 16 and Figure 17 the collision probability of the flight path obtained by using the algorithm and the A* algorithm in the flight process is given, respectively. As shown in Figure 16 , the flight path obtained by the algorithm is greater than 5 meters from the obstacle during the flight of the unmanned aerial vehicle, and will not collide at any position; when the safety distance is 10 meters, the proportion of the path that may collide obtained by the algorithm is less than 10%, as shown in Figure 17 . Under these two scenarios, the average probability of collision of the A* algorithm is relatively high.

[0133] These results show that the algorithm is superior to the A* algorithm in terms of calculation efficiency and path safety, and is more suitable for unmanned aerial vehicle path planning tasks in complex environments.

[0134] Further, in the example embodiment, a high-density obstacle environment unmanned aerial vehicle safe path planning system is also provided. As shown in Figure 18 , the system can include:

[0135] The coverage model construction module is configured to establish a coverage overflow rate quantification model, convert the polygon obstacle region into a circular coverage set with minimum overflow rate, and obtain a circular obstacle set with minimum invasion of flyable region;

[0136] The Voronoi diagram construction module is configured to take the center of the circular obstacle set as a node, construct a Voronoi diagram through triangulation and safety screening, and obtain a Voronoi diagram.

[0137] The initial path generation module is configured to search for a feasible path between start and end points on the Voronoi diagram according to the unmanned aerial vehicle motion constraint, and process mapping conversion of non-diagram points to obtain an initial path of the unmanned aerial vehicle.

[0138] The path optimization processing module is configured to perform smoothing processing on the initial path of the unmanned aerial vehicle through curve interpolation, and verify the safety distance between the smoothed path and the obstacle region to obtain a safe path of the unmanned aerial vehicle.

[0139] As to the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment related to the method, and will not be described in detail here.

[0140] It should be noted that, although several modules or units of the device for action execution are mentioned in the above detailed description, such division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into several modules or units for embodiment. The components shown as modules or units can be or can not be physical units, i.e. can be located in one place, or can be distributed on multiple network units. Some or all of the modules can be selected to achieve the purpose of the present disclosure according to actual needs. Those skilled in the art can understand and implement without creative labor.

[0141] In the exemplary embodiments of the present disclosure, an electronic device is also provided, which can include a processor and a memory for storing executable instructions of the processor. Wherein, the processor is configured to execute the steps of the high-density obstacle environment unmanned aerial vehicle safety path planning method in any one of the above embodiments by executing the executable instructions.

[0142] Those skilled in the art can understand that each aspect of the present application can be implemented as a system, a method or a program product. Therefore, each aspect of the present application can be specifically implemented as a complete hardware embodiment, a complete software embodiment (including firmware, microcode, etc.), or an embodiment combining hardware and software aspects, which can be collectively referred to as "circuitry", "module" or "system" here.

[0143] Through the above description of the embodiments, those skilled in the art can easily understand that the example embodiments described herein can be implemented by software or by software in combination with necessary hardware. Therefore, the technical solutions according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a U disk, a mobile hard disk, etc.) or a network, and includes a plurality of instructions to make a computing device (which can be a personal computer, a server, or a network device, etc.) execute the above-mentioned high-density obstacle environment UAV safe path planning method according to the embodiments of the present disclosure.

[0144] In the example embodiments of the present disclosure, a computer storage medium is also provided, which stores a computer program that can implement the steps of the high-density obstacle environment UAV safe path planning method described in any one of the above embodiments when executed by a processor.

[0145] In some possible embodiments, various aspects of the present application can also be implemented in the form of a computer program product, which includes computer programs or instructions, when the computer program product is run on a terminal device, the computer program code or instructions are used to make the terminal device execute the steps described in the above high-density obstacle environment UAV safe path planning method part of the present specification according to various example embodiments of the present application.

[0146] The program product described above can be written in any combination of one or more programming languages to perform the operations of the present application, including object-oriented programming languages such as Java, C++, etc., and conventional procedural programming languages such as "C" language or similar programming languages. Program code can be executed entirely on a user computing device, partially on a user device, as an independent software package, partially on a user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any kind of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, connected through the Internet by using an Internet service provider).

[0147] The computer software product can be stored in a computer storage medium including a Read-Only Memory (ROM), a Random Access Memory (RAM), a Programmable Read-only Memory (PROM), an Erasable Programmable Read Only Memory (EPROM), a One-time Programmable Read-Only Memory (OTPROM), an Electrically-Erasable Programmable Read-Only Memory (EEPROM), a Compact Disc Read-Only Memory (CD-ROM), or other optical storage, a magnetic storage, a magnetic tape, or any other medium that can be used to carry or store data in the form of instructions or data structures that can be accessed by a computer.

[0148] Other embodiments of the present disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. It is intended that the present disclosure cover any and all variations of the present disclosure that come within the scope of the following claims and their equivalents. It is intended that the specification and examples be considered exemplary only, with the true scope and spirit of the present disclosure being indicated by the following claims.

Claims

1. A safe path planning method for UAV in high-density obstacle environment, characterized by: include: A quantitative model of coverage overflow rate is established to transform polygonal obstacle areas into a circular coverage set with minimum overflow rate, thus obtaining a circular obstacle set that minimizes the encroachment on the flyable area. Taking the centers of the circular obstacle set as nodes, constructing a Voronoi diagram through triangulation and safety screening; According to the UAV motion constraints, a feasible path between the starting and ending points is searched on the Voronoi diagram, and the mapping transformation of non-graph points is processed to obtain the UAV initial path; The initial path of the UAV is smoothed by curve interpolation, and the safe distance between the smoothed path and the obstacle area is verified to obtain the safe path of the UAV.

2. The method for safe path planning of a UAV in a high-density obstacle environment according to claim 1 is characterized in that: The steps of establishing a quantitative model for coverage overflow rate, converting the polygonal obstacle area into a circular coverage set with minimum overflow rate, and obtaining a circular obstacle set that minimizes the encroachment of the flyable area include: A quantitative model for coverage overflow rate is established, and the coverage overflow rate is defined as the ratio of the difference between the circular coverage area and the inscribed square area; Set the coverage overflow rate threshold and the maximum radius constraint of the outer coverage circle; A layered covering algorithm based on inscribed squares in a circle is used to cover the polygonal obstacle area layer by layer from the boundary until the inner area is completely covered with a single circumscribed circle, thus constructing a circular obstacle set that minimizes the encroachment on the flyable area.

3. The method for safe path planning of a UAV in a high-density obstacle environment according to claim 2 is characterized in that: The coverage overflow rate is: Among them, S 圆 is the area of ​​all circles in the circle cover, S 正 is the area of ​​all squares inscribed in the circle in the circle cover.

4. The method for safe path planning of a UAV in a high-density obstacle environment according to claim 2 is characterized in that: The layered coverage algorithm is: Calculate the side length of the inscribed square. Based on the length of each boundary edge in the L-th layer boundary point set, determine the number of squares with a side length of a. Cover each boundary edge with a square, and add the outer circle corresponding to each square to the circular coverage area set. After each boundary edge is covered, a closed area is enclosed by new line segments parallel to the square and each boundary edge, and the boundary points of the closed area are calculated; The outer circle of the closed area is calculated, and the outer circle of the closed area falling in the target coverage area is added to the circular coverage area set to obtain the circular obstacle set.

5. The method for safe path planning of a UAV in a high-density obstacle environment according to claim 1, characterized in that: The step of constructing a Voronoi diagram by triangulation and safety screening with the centers of the circular obstacle set as nodes includes: Performing triangulation with the centers of the circular obstacle set as nodes to obtain a plurality of first triangles; Calculate the intersection points of the first triangle edge and the circular obstacle set edge, and connect the midpoints between the intersection points on the same first triangle edge to obtain a second triangle; Deleting the first triangle and connecting each vertex of the second triangle with the centroid to construct an initial Voronoi diagram; The initial Voronoi diagram is reconstructed for safety, and the second triangle is deleted to obtain a Voronoi diagram.

6. The method for safe path planning of a UAV in a high-density obstacle environment according to claim 5 is characterized in that: The safety reconstruction is to perform a safety check on the Voronoi diagram through the intersection of the second triangle centroid, the first triangle edge and the circular obstacle set edge.

7. The method for safe path planning of a UAV in a high-density obstacle environment according to claim 6, characterized in that: The step of performing a security check on the Voronoi diagram using the intersection of the second triangle's centroid, the first triangle's edge, and the circular obstacle set's edge comprises: If the centroid of the second triangle is within any circle of the circular obstacle set, delete all Voronoi diagram edges connected to the centroid; If the midpoint between the intersection points on the same first triangle edge is within any circle of the circular obstacle set, then delete all Voronoi diagram edges connected to the midpoint; If the distance between the two corresponding intersection points of the midpoint between the intersection points on the same first triangle edge is less than the preset safety distance, all Voronoi diagram edges connected to the midpoint are deleted.

8. The method for safe path planning of a UAV in a high-density obstacle environment according to claim 1, characterized in that: The step of searching for a feasible path between the starting and ending points on the Voronoi diagram according to the UAV motion constraints and processing the mapping conversion of non-graph points to obtain the UAV initial path includes: When the starting and ending points are on the Voronoi diagram, the Dijkstra algorithm is used to search for the shortest safe path; When the starting and ending points are not on the Voronoi diagram, the starting and ending points are mapped to the nearest points on the Voronoi diagram through the Dubins curve according to the UAV motion constraints, and then the shortest safe path is searched through the Dijkstra algorithm.

9. The method for safe path planning of a UAV in a high-density obstacle environment according to claim 8, characterized in that: The UAV motion constraints include a minimum turning radius constraint and a heading angle continuity constraint.

10. A safe path planning system for drones in high-density obstacle environments, characterized by: The system is used to perform the method according to any one of claims 1 to 9, and the system includes: The coverage model construction module is used to establish a quantitative model of coverage overflow rate, transform the polygonal obstacle area into a circular coverage set with the minimum overflow rate, and obtain the circular obstacle set that minimizes the encroachment of the flyable area; A Voronoi diagram construction module, configured to construct a Voronoi diagram by triangulation and safety screening, with the centers of the circular obstacle set as nodes; An initial path generation module is used to search for a feasible path between the starting and ending points on the Voronoi diagram according to the UAV motion constraints, and process the mapping conversion of non-graph points to obtain the UAV initial path; The path optimization processing module is used to smooth the initial path of the UAV through curve interpolation, and verify the safe distance between the smooth path and the obstacle area to obtain the safe path of the UAV.

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