An unmanned aerial vehicle trajectory optimization method based on obstacle perception

Abstract

The present application relates to a kind of unmanned aerial vehicle trajectory optimization method based on obstacle perception, belong to unmanned aerial vehicle path planning technical field, comprising the following steps: S1: the initial collection trajectory of unmanned aerial vehicle is discretized, and for the trajectory segment blocked by single obstacle after discretization, edge obstacle avoidance and flyover obstacle avoidance method is used to generate feasible trajectory;S2: under the condition of multiple obstacles, according to the number and height of obstacle, more feasible trajectories are obtained by using obstacle avoidance method, and the feasible trajectory is optimized;S3: according to the principle that the total energy consumption of unmanned aerial vehicle to complete task is minimum, the flight speed and trajectory of unmanned aerial vehicle are optimized, and the optimal flight speed and flight trajectory of unmanned aerial vehicle under the condition of obstacle are obtained.The present application is simple to implement, applicable to the feasible trajectory optimization of unmanned aerial vehicle under the condition of multiple obstacles, and has high practical value.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of unmanned aerial vehicle path planning, and relates to an unmanned aerial vehicle trajectory optimization method based on obstacle perception. BACKGROUND

[0002] With the rapid development of Internet of Things technology, unmanned aerial vehicles, as a new information and energy transmission platform, have shown great application potential in many fields. Unmanned aerial vehicles can not only serve as mobile base stations or relay nodes to enhance the coverage and reliability of ground communication networks, but also provide wireless energy transmission services for Internet of Things devices in remote or hard-to-reach areas, thereby extending the working life of these devices.

[0003] However, to fully utilize unmanned aerial vehicles in wireless energy supply communication networks, a series of technical challenges must be addressed, particularly the design and optimization of unmanned aerial vehicle flight trajectories. Although existing research has some guiding significance for reducing the flight energy consumption of unmanned aerial vehicles to assist ground node communication in Internet of Things, there are still deficiencies. Most research does not fully consider the flight speed of unmanned aerial vehicles and the impact of obstacles on flight trajectories, and usually relies on probabilistic models to simulate visible and invisible links between nodes. In actual scenarios, whether facing a single obstacle or multiple obstacles, the choice of bypassing or flying over the obstacle needs to be considered comprehensively, and simply pursuing the shortest path often cannot achieve the optimal solution. Therefore, how to consider the optimization of obstacle avoidance and flight energy consumption in flight path planning simultaneously has become a key problem. SUMMARY

[0004] In view of this, the purpose of the present application is to provide an unmanned aerial vehicle trajectory optimization method based on obstacle perception, which avoids obstacles on the flight path segment of the unmanned aerial vehicle, reduces the flight energy consumption of the unmanned aerial vehicle, and reduces the total energy consumption of the unmanned aerial vehicle serving the entire area.

[0005] To achieve the above purpose, the present application provides the following technical solutions:

[0006] An unmanned aerial vehicle trajectory optimization method based on obstacle perception, comprising the following steps:

[0007] S1: discretizing the initial collected trajectory of the unmanned aerial vehicle, and generating feasible trajectories by using edge obstacle avoidance and flyover obstacle avoidance methods for the trajectory segment blocked by a single obstacle after discretization;

[0008] S2: under the condition of multiple obstacles, obtaining more feasible trajectories by using an obstacle avoidance method according to the number and height of the obstacles, and optimizing the feasible trajectories;

[0009] S3: jointly optimizing the flight speed and trajectory of the unmanned aerial vehicle according to the principle of minimizing the total energy consumption of the unmanned aerial vehicle to complete the task, and obtaining the optimal flight speed and flight trajectory of the unmanned aerial vehicle under the condition of obstacles.

[0010] Furthermore, the discretization processing of the initial acquisition trajectory of the UAV includes:

[0011] Consider a wireless communication system consisting of K IoT nodes and a drone. In this system, the drone collects data from the IoT nodes. For nodes with insufficient power, it first replenishes their power via radio frequency before collecting data. A three-dimensional coordinate system is established with the drone's starting point as the origin. The set of drones and IoT nodes is defined as S = {s0, s1, s2, ..., s...}. k ,...,s K}, where s0 represents the drone, s k This represents the k-th IoT node, k = 1, 2, ..., K. The problem of planning the flight path of the drone collecting data from each IoT node is transformed into a traveling salesman problem (TSM) traversing K cities from the starting point, establishing the shortest path optimization problem as follows:

[0012]

[0013] In the formula, X0 represents the starting point coordinates of the UAV, X... k Represents the coordinates of the k-th IoT node;

[0014] The shortest path optimization problem is solved using a genetic algorithm to obtain the shortest initial trajectory of the UAV traversing K IoT nodes and the corresponding data collection order S. q ;

[0015] Based on the drone's starting point, the location of the IoT node, and the obtained data collection sequence S q The initial trajectory is discretized into K segments, that is, the trajectory between every two IoT nodes from the drone's starting point to the K IoT nodes is a segment.

[0016] Furthermore, for the k-th segment of the discrete trajectory, if this segment contains a single obstacle... The initial trajectory is then optimized into a feasible trajectory through the following steps:

[0017] S11: Remove obstacles Projected onto the xy plane, assuming The vertex coordinates are respectively Transform it into a convex n-gon with the shortest moving distance, where n = 3, 4, ...;

[0018] S12: Edge obstacle avoidance path planning method: Assuming the convex polygon with the shortest travel distance is a convex n1 (3≤n1≤n) polygon, the coordinates of each vertex are represented as follows: The hovering point Q of the drone above node k k =(x k,y k ,z k The hovering point Q above node k+1 k+1 (x k+1 ,y k+1 ,z k+1 );

[0019] Let the feasible path segment from node k to node k+1 of the UAV be defined as an undirected graph G(V,E), where V is the set of hovering points of the UAV and the vertices of the convex n1-sided polygon in this segment, denoted as... E is a set indicating whether there is a feasible path between any two vertices, denoted as: like Then it represents the i1th vertex. To the j1-th vertex There are directed road segments between them; if Then it means There are obstacles in the undirected graph; Dijkstra's algorithm is applied to find the shortest path for the drone to bypass the obstacle plane from node k to node k+1. and the edge of the obstacle Two-dimensional coordinates

[0020] calculate With hovering point Q k and hovering point Q k+1 Positional proportions on a two-dimensional plane:

[0021]

[0022] Where t represents the distance from hovering point Q k to hover point Q k+1 The relative distance ratio; using the ratio t to interpolate the points z-axis coordinate, get 3D coordinates And record it in set W k middle;

[0023] S13: Obstacle Avoidance Path Planning Method: Assuming the drone starts from hovering point Q k to hover point Q k+1 Path segment Q k →Q k+1 Obstacles Obscuring; Q k Q k+1 and Project onto the xy plane, and solve the system of equations for path segment Q. k →Q k+1 The equation of a straight line and obstacles Solve for the equations of the sides and the coordinates of the intersection points; check if the intersection points are located at... Within the boundary range: if the intersection point is within the boundary range, then record the intersection point as a valid intersection point of the path segment; otherwise, discard it and do not record it; through calculation, obtain the two-dimensional intersection points that meet the conditions. and Obstacles height Add the z-axis coordinates of the two-dimensional intersection points to generate the corresponding three-dimensional coordinates. and These two 3D points represent the key points on the shortest path over the obstacle, and are recorded in set W. k middle;

[0024] S14: From set W k Extract each point one by one, and compare each point with the hovering point Q. k Hover point Q k+1 This forms a feasible path from hovering point Q. k to hover point Q k+1 A feasible path.

[0025] Furthermore, step S11 specifically includes the following steps:

[0026] S111: For vertices Calculate its relationship with other vertices The angle between the vector formed and the x-axis Among them, i=1,2,…,n, j=i+1,…,n,1,2,…,i-1:

[0027]

[0028] in, It is through the vertex A unit vector parallel to the x-axis, a vector in and These are the vertices. and vertex The coordinates; when and hour, when and hour, In other cases angle Record to set θ k middle;

[0029] S112: When the vertex Complete with all other vertices After calculating the included angle, from the set θ k Select the smallest included angle. And the corresponding vertex j, which will form the vertex with the smallest included angle. Record to set δ k middle;

[0030] S113: If j > i, then set the current vertex... Update to minimum corresponding vertex and set θ k Clear the value to zero, then return to S111 to continue calculating the angle between the current vertex and other vertices; if j < i, proceed to step S114;

[0031] S114: After the calculation is complete, set δ k Connect the vertices in sequence to obtain the convex polygon with the shortest moving distance.

[0032] Furthermore, in step S2, under conditions with multiple obstacles, more feasible trajectories are obtained based on the number and height of the obstacles, and the feasible trajectories are optimized. This specifically includes the following steps:

[0033] S21: Use the method in step S11 to remove the m-th obstacle in the path segment. Convert to the convex n with the shortest moving distance m polygon, n m =3,4,…, and record the obstacles. The coordinates of the vertex with the shortest movement distance

[0034] S22: Using the edge obstacle avoidance path planning method described in step S12, find the shortest path for the UAV to bypass the obstacle plane from node k to node k+1 and the edge points of the obstacle. And record it in set W k middle;

[0035] S23: Assume the highest obstacle in the path segment is... Considering only the highest obstacle, the obstacle-avoidance path planning method described in step S13 is used to obtain the obstacle-avoidance path. trajectory Next, check the path segments. and Are there other obstacles in the path? There is an intersection, where and If the path segment does not intersect with other obstacles, the obstacle avoidance problem with M obstacles is simplified to a single obstacle avoidance problem; if the path segment intersects with obstacles... If there is an intersection, then the plan should simultaneously avoid obstacles. and The trajectory of , where b∈M and a≠b;

[0036] S24: Path optimization: From set W k After extracting the point sequence for each feasible path, connect the points on each path sequentially to form a sequence starting from the hovering point Q. k to hover point Q k+1 The feasible paths are determined; for all feasible path segments, path optimization is performed, and the Fibonacci filtering method for straightening the obtained paths is as follows:

[0037]

[0038] in, and It is a path point on path segment k; if path segment arrive If the path does not intersect with any obstacles, then remove the previous path points. By connecting the optimized path points, we obtain the path from hovering point Q. k to hover point Q k+1 Feasible paths;

[0039] S25: By calculating the energy consumption of each feasible flight path segment, the path with the lowest energy consumption is selected as the flight path for that segment; the flight energy consumption of the UAV consists of three parts: The UAV flies at a constant horizontal speed of υ for a time of T. υ Energy consumed per hour Its flight power is: P υ =P(τ) c ,F y ,υ); When flying horizontally, the pitch angle τ of the UAV c =0°, longitudinal air friction resistance F y =0, τ is included during pitch flight. c and F y The value is used to calculate flight power; energy consumption of the UAV during acceleration. P acc =P(τ) c ,F y ,υ t ) represents the propulsion power of the UAV during acceleration / deceleration, υ t This refers to the flight speed during the drone's acceleration phase; the acceleration time from 0 to υ is... 'a' represents acceleration, and the energy consumption of the drone during the deceleration phase. Equal to the acceleration phase, that is: The path selection method is defined as the problem of minimizing the flight energy consumption of UAVs:

[0040]

[0041] stC1:X min ≤x t ≤X max

[0042] C2: Y min ≤y t ≤Y max

[0043] C3:Z min ≤z t ≤Z max

[0044] C4: 0 ≤ a ≤ a max

[0045] C5:

[0046] C6: -t c,max ≤t c ≤t c,max

[0047] In the formula, X min X max Y min and Y max It is the limitation of the flight area in the xy plane, Z min and Z max The limitation is the flight altitude of the drone, x t y t and z t The three-dimensional coordinates of the UAV at time t, a max This is the maximum acceleration of the drone, z(x) t ,y t ) is the path point in (x t ,y t The height at (,0) The obstacle is in (x) t ,y t The height at (0), t c,max C1-C3 represents the maximum pitch angle of the UAV, C5 represents the feasible flight space of the UAV, and C5 represents the maximum pitch angle of the UAV if (x t ,y t If there are obstacles on the coordinates, the height of the drone must be greater than the height of the obstacles.

[0048] Furthermore, step S23 involves planning and obstacle avoidance simultaneously. and The steps for determining the trajectory are as follows:

[0049] S231: After height-based optimization, if path segment Q k →Q k+1 Obstacles and If there is obstruction, then obstacles need to be considered. and In the case of obstacle avoidance path planning described in step S13, the coordinates of the two obstacles to be overcome are calculated. and And record these waypoints in set W k middle;

[0050] S232: Using the edge obstacle avoidance path planning method described in step S12, represent the set of hovering points and waypoints as follows: Dijkstra's algorithm is applied again to calculate the shortest path for the UAV to bypass the obstacle from node k to node k+1; at the same time, the edge points of flying over or bypassing the obstacle are recorded, and the optimal path point is added to the set W. k middle.

[0051] Furthermore, step S3 specifically includes the following steps:

[0052] During path optimization, flight speed and trajectory are jointly optimized, and the data transmission volume of the completed nodes is taken as a constraint to establish a problem of minimizing the total energy consumption of the UAV, as shown below:

[0053]

[0054] stC1-C6

[0055] C7: 0 ≤ υ[t] ≤ V max

[0056] C8:

[0057] C9:

[0058] in, This indicates the total energy consumption of the drone. V represents the energy consumed by the drone when it hovers above the k-th IoT node for communication, υ[t] represents the speed of the drone at time t, and V max ρ is the maximum speed of the drone. k,t This represents the signal-to-noise ratio between the drone and the k-th IoT node. R is the signal-to-noise ratio threshold that ensures successful data transmission. k,t =Blog2(1+ρ k,t ) represents the data transmission rate between the drone and the k-th IoT node, B represents the channel bandwidth, and Tk Q represents the communication time between the drone and node k. k This is the amount of data that the k-th node needs to upload;

[0059] The problem of minimizing total energy consumption is solved by iterative optimization algorithm to obtain the optimal flight speed and the optimal flight trajectory for the UAV to avoid obstacles.

[0060] The beneficial effects of this invention are as follows: when some nodes in a wireless sensor network lack sufficient energy to transmit data, the drone can act as a mobile power transmitter to wirelessly power the nodes and receive the data transmitted by the nodes, thus restoring the network to normal operation. This invention, while completing data transmission for all IoT nodes, jointly optimizes the drone's trajectory and flight speed, reducing the drone's flight energy consumption and the total energy consumption for the entire area served by the drone.

[0061] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0062] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein:

[0063] Figure 1 A schematic diagram of a model for trajectory optimization of drones in urban environments;

[0064] Figure 2 This is the vector representation of the obstacle avoidance algorithm based on vector rotation angle;

[0065] Figure 3 A graphical representation of an obstacle avoidance algorithm based on vector rotation angle;

[0066] Figure 4 This diagram illustrates the feasible path selection for a drone when a single obstacle exists in the path segment.

[0067] Figure 5 This is the initial distribution map of multiple obstacles in the path segment;

[0068] Figure 6 This is a schematic diagram of the feasible UAV path selection based on altitude optimization within a path segment. Detailed Implementation

[0069] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0070] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0071] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the invention. However, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the invention.

[0072] like Figure 1 As shown, consider a wireless communication system consisting of K IoT nodes and a drone. In this system, the drone collects data from the IoT nodes. For nodes with insufficient power, it first replenishes their power via radio frequency before collecting data. A three-dimensional coordinate system is established with the drone's starting point as the origin. The set of drones and IoT nodes is defined as S = {s0, s1, s2, ..., s}. k ,…,s K}, where s0 represents the drone, s k This represents the k-th IoT node, where k = 1, 2, ..., K. The problem of planning the flight path for the drone to collect data from each IoT node is transformed into a Traveling Salesman Problem (TSP) that traverses K cities from the starting point. The shortest path optimization problem is then established as follows:

[0073]

[0074] In the formula, X0 represents the starting point coordinates of the UAV, X... k Let S represent the coordinates of the k-th IoT node. A genetic algorithm is used to solve this problem, yielding the shortest initial trajectory for the UAV to traverse the k IoT nodes and the corresponding data collection order S. q .

[0075] Since there may be obstacles on the shortest initial path, the drone needs to avoid these obstacles while flying. Therefore, the shortest path needs to be further processed to obtain a feasible flight trajectory for the drone.

[0076] Based on the drone's starting point, the location of the IoT node, and the obtained data collection sequence S q The initial trajectory is discretized into K segments, that is, the trajectory between every two IoT nodes from the drone's starting point to the K IoT nodes is a segment.

[0077] For the k-th segment of the discrete trajectory, if there is a single obstacle in that segment... The initial trajectory is then optimized into a feasible trajectory using the following method:

[0078] S11: Remove obstacles Projected onto the xy plane, such as Figure 2 As shown, obstacles The vertex coordinates are respectively Transform it into a convex n-gon with the shortest moving distance, where n = 3, 4, ... . The specific steps are as follows:

[0079] S111: For vertices Calculate its relationship with other vertices The angle between the vector formed and the x-axis

[0080]

[0081] in, It is through the vertex A unit vector parallel to the x-axis, a vector in here and These are the vertices. and vertex The coordinates. When and hour, when and hour, In other cases angle Record to set θ k middle.

[0082] S112: When the vertex Complete with all other vertices After calculating the included angle, from the set θ k Select the smallest included angle. And the corresponding vertex j, which will form the vertex with the smallest included angle. Record to set δ k middle.

[0083] S113: If j > i, then set the current vertex... Update to minimum corresponding vertex and set θ k Clear the value to zero, then return to S11 to continue calculating the angle between the current vertex and other vertices; if j < i, proceed to the next step S14.

[0084] S114: After the calculation is completed, as follows Figure 3 As shown, the set δ k Connect the vertices in sequence to obtain the convex polygon with the shortest moving distance.

[0085] S12: Edge obstacle avoidance path planning method: such as Figure 4 As shown, assuming the convex polygon with the shortest travel distance is a convex n1 (3≤n1≤n) polygon, the coordinates of each vertex are represented as follows: The hovering point Q of the drone above node k k =(x k ,y k ,z k The hovering point Q above node k+1 k+1 (x k+1 ,y k+1 ,z k+1 ).

[0086] Let the feasible path segment from node k to node k+1 of the UAV be defined as an undirected graph G(V,E), where V is the set of hovering points of the UAV and the vertices of the convex n1-sided polygon in this segment, denoted as... E is a set indicating whether there is a feasible path between any two vertices, denoted as: like Then it represents the i1th vertex. To the j1-th vertex There are directed road segments between them; if Then it means There are obstacles in the undirected graph. Applying Dijkstra's algorithm, we can find the shortest path for the drone to bypass the obstacle plane from node k to node k+1. and the edge of the obstacle Two-dimensional coordinates

[0087] calculate With hovering point Q k and hovering point Q k+1 Positional proportions on a two-dimensional plane:

[0088]

[0089] Where t represents the distance from hovering point Q k to hover point Q k+1 The relative distance ratio. Calculate the point using the ratio t through interpolation. z-axis coordinate, get 3D coordinates And record it in set W k middle.

[0090] S13: Obstacle Avoidance Path Planning Method: Assuming the drone starts from hovering point Q k to hover point Q k+1 Path segment Q k →Q k+1 Obstacles Obscuring. Q k Q k+1 and Project onto the xy plane, and solve the system of equations for path segment Q. k →Q k+1 The equation of a straight line and obstacles Given the equations of the sides, solve for the coordinates of the intersection point. Check if the intersection point is located within... Within the boundary range: if the intersection point is within the boundary range, then record the intersection point as a valid intersection point of the path segment; otherwise, discard it and do not record it. Through calculation, two-dimensional intersection points that meet the conditions are obtained. and Obstacles height Add the z-axis coordinates of the two-dimensional intersection points to generate the corresponding three-dimensional coordinates. and These two 3D points represent the key points on the shortest path over the obstacle, and are recorded in set W. k middle.

[0091] S14: From set W k Extract each point one by one, and compare each point with the hovering point Q. k Hover point Q k+1 This forms a feasible path from hovering point Q. k to hover point Q k+1 A feasible path.

[0092] like Figure 5 As shown, for the k-th segment of a discrete trajectory, if this segment contains M obstacles... The initial trajectory is then optimized into a feasible trajectory using the following method:

[0093] S21: Remove the m-th obstacle in the path segment using the method described in step S11. Convert to the convex n with the shortest moving distance m polygon, nm =3,4,…, and record the obstacles. The coordinates of the vertex with the shortest movement distance

[0094] S22: Using the edge obstacle avoidance path planning method in step S12, find the shortest path for the UAV to bypass the obstacle plane from node k to node k+1 and the edge points of the obstacle. And record it in set W k middle.

[0095] S23: Assume the highest obstacle in the path segment is... Considering only the highest obstacle, the obstacle avoidance path planning method in step S13 is used to obtain the obstacle avoidance path. trajectory Next, check the path segments. and Are there other obstacles in the path? ( and There is an intersection. If the path segment has no intersection with other obstacles, the obstacle avoidance problem with M obstacles can be simplified to a single obstacle avoidance problem; if the path segment intersects with obstacles... If there is an intersection between (b∈M and a≠b), then the following steps should be followed to plan simultaneous obstacle avoidance. and trajectory:

[0096] S231: As Figure 6 As shown, after height-based optimization, if path segment Q... k →Q k+1 Obstacles and If there is obstruction, then obstacles need to be considered. and In this case, the obstacle avoidance path planning method in step S13 is used to calculate the coordinates of the points where the two obstacles are to be overcome. and And record these waypoints in set W k middle.

[0097] S232: Apply the edge obstacle avoidance path planning method from step S12, and represent the set of hovering points and waypoints as follows: Dijkstra's algorithm is applied again to calculate the shortest path for the UAV to bypass the obstacle from node k to node k+1; at the same time, the edge points of flying over or bypassing the obstacle are recorded, and the optimal path point is added to the set W. k middle.

[0098] S24: Path optimization. From set W k After extracting the point sequence for each feasible path, connect the points on each path sequentially to form a sequence starting from the hovering point Q. k to hover point Q k+1 The feasible paths are determined. For all feasible path segments, path optimization is performed to further reduce the energy consumption of the path segments. The Fibonacci filtering method for straightening the obtained paths is as follows:

[0099]

[0100] in, and It is a path point on path segment k. If path segment arrive If the path points do not intersect with any obstacles, then previous path points can be removed. This straightens the path. By connecting the optimized path points, the path from the hovering point Q is obtained. k to hover point Q k+1 A feasible path.

[0101] S26: By calculating the energy consumption of each feasible flight path segment, the path with the lowest energy consumption is selected as the flight path for that segment. The flight energy consumption of a UAV mainly consists of three parts. The UAV flies at a constant horizontal speed of υ for a time of T. υ Energy consumed per hour Its flight power is: P υ =P(τ) c ,F y ,υ); When flying horizontally, the pitch angle τ of the UAV c =0°, longitudinal air friction resistance F y =0, while τ needs to be included during pitch flight. c and F y The value is used to calculate flight power. Energy consumption of the drone during acceleration. P acc =P(τ) c ,F y ,υ t ) represents the propulsion power of the UAV during acceleration / deceleration, υ t This refers to the flight speed during the drone's acceleration phase; the acceleration time from 0 to υ is... 'a' represents acceleration, and the energy consumption of the drone during the deceleration phase. Equal to the acceleration phase, that is: We define the above path selection method as the problem of minimizing the energy consumption of UAV flight:

[0102]

[0103] st X min ≤x t ≤X max (6)

[0104] Y min ≤y t ≤Y max (7)

[0105] Z min ≤z t ≤Z max (8)

[0106] 0≤a≤a max (9)

[0107]

[0108] -t c,max ≤t c ≤t c,max (11)

[0109] In the formula, X min X max Y min and Y max It is the limitation of the flight area in the xy plane, Z min and Z max The limitation is the flight altitude of the drone, x t y t and z t The three-dimensional coordinates of the UAV at time t, a max This is the maximum acceleration of the drone, z(x) t ,y t ) is the path point in (x t ,y t The height at (,0) The obstacle is in (x) t ,y t The height at (0), t c,max Let be the maximum pitch angle of the UAV. Equations (6)-(8) represent the feasible flight space of the UAV, and equation (10) indicates that if (x) t ,y t If there are obstacles at the coordinates, the drone's height must be greater than the obstacle's height. By solving this optimization problem, the optimal trajectory under the aforementioned obstacle avoidance mode can be obtained.

[0110] Through the above calculations, the optimal flight path can be selected based on the height characteristics and relative position of obstacles, thereby minimizing energy consumption while ensuring mission completion.

[0111] In the path optimization process, to reduce the total energy consumption of the UAV, the flight speed of the UAV on the path segment can be further optimized. Adjusting the flight speed directly affects the UAV's propulsion energy consumption, and changes in propulsion energy consumption, in turn, affect the choice of flight trajectory. Therefore, it is necessary to jointly optimize the flight speed and flight trajectory to minimize the overall energy consumption. By taking the data transmission volume of the completion node as a constraint, the total energy consumption minimization problem of the UAV is established, as shown below:

[0112]

[0113] st(6)-(11) (13)

[0114] 0≤υ[t]≤V max (14)

[0115]

[0116] in, This indicates the total energy consumption of the drone. V represents the energy consumed by the drone when it hovers above the k-th IoT node for communication, υ[t] represents the speed of the drone at time t, and V max ρ is the maximum speed of the drone. k,t This represents the signal-to-noise ratio between the drone and the k-th IoT node. R is the signal-to-noise ratio threshold that ensures successful data transmission. k,t =Blog2(1+ρ k,t ) represents the data transmission rate between the drone and the k-th IoT node, B represents the channel bandwidth, and T k Q represents the communication time between the drone and node k. k It is the amount of data that the k-th node needs to upload.

[0117] The problem of minimizing total energy consumption is solved by iterative optimization algorithm to obtain the optimal flight speed and the optimal flight trajectory for the UAV to avoid obstacles.

[0118] In the above embodiments, the reference to "this embodiment" in the specification indicates that a specific feature, structure, or characteristic described in connection with the embodiment is included in at least some embodiments, but not necessarily all embodiments. Multiple appearances of "this embodiment" do not necessarily refer to the same embodiment.

[0119] In the above embodiments, although the invention has been described in conjunction with specific embodiments thereof, many substitutions, modifications, and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory structures (e.g., dynamic RAM (DRAM)) may be used with the embodiments discussed. The embodiments of the invention are intended to cover all such substitutions, modifications, and variations falling within the broad scope of the appended claims.

[0120] This embodiment also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the methods in this embodiment.

[0121] This embodiment also provides an electronic terminal, including: a processor and a memory;

[0122] The memory is used to store computer programs, and the processor is used to execute the computer programs stored in the memory to cause the terminal to perform any of the methods in this embodiment.

[0123] As will be understood by those skilled in the art, the computer-readable storage medium described in this embodiment allows for the implementation of all or part of the steps in the above method embodiments by computer program-related hardware. The aforementioned computer program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0124] The electronic terminal provided in this embodiment includes a processor, a memory, a transceiver, and a communication interface. The memory and the communication interface are connected to the processor and the transceiver and complete communication between them. The memory is used to store computer programs, the communication interface is used to perform communication, and the processor and the transceiver are used to run the computer programs, so that the electronic terminal performs the steps of the above method.

[0125] In this embodiment, the memory may include random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage device.

[0126] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0127] This invention can be used in a wide range of general-purpose or special-purpose computing system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices, etc.

[0128] This invention can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. This invention can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0129] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for optimizing the trajectory of a UAV based on obstacle perception, characterized in that: Includes the following steps: S1: Discretize the initial trajectory collected by the UAV, and for the trajectory segment blocked by a single obstacle after discretization, use edge obstacle avoidance and fly-over obstacle avoidance methods to generate a feasible trajectory. S2: Under conditions with multiple obstacles, based on the number and height of the obstacles, use obstacle avoidance methods to obtain more feasible trajectories and optimize the feasible trajectories; S3: Based on the principle of minimizing the total energy consumption of the drone to complete the mission, jointly optimize the drone's flight speed and trajectory to obtain the optimal flight speed and trajectory of the drone under obstacle conditions. The discretization of the initial acquisition trajectory of the UAV includes: Considering A wireless communication system consisting of an IoT node and a drone. In this system, the drone collects data from the IoT nodes, replenishes the power of nodes with insufficient power via radio frequency before continuing data collection; a three-dimensional coordinate system is established with the drone's starting point as the origin; the set of the drone and the IoT nodes is defined as... ,in It refers to drones. It indicates the first One IoT node, The problem of planning the flight trajectory of drones collecting data from various IoT nodes is transformed into traversing from the starting point... The Traveling Salesman Problem for each city can be transformed into a shortest path optimization problem: In the formula, This indicates the coordinates of the drone's starting point. Indicates the first The coordinates of each IoT node; The shortest path optimization problem is solved using a genetic algorithm, resulting in the UAV traversal... Shortest initial trajectory and corresponding data collection sequence for each IoT node ; Based on the drone's starting point, the location of the IoT node, and the sequence of data collection. Discretize the initial trajectory as The segment, that is, from the drone's starting point to... Between each pair of IoT nodes, the trajectory between any two nodes constitutes a segment. For the discrete trajectory If the segment contains a single obstacle The initial trajectory is then optimized into a feasible trajectory through the following steps: S11: Remove obstacles Projected to Plane, assuming The vertex coordinates are respectively , , , Convert it into a convex shape with the shortest travel distance. polygon, ; S12: Edge obstacle avoidance path planning method: Assuming the convex polygon with the shortest travel distance is convex... polygon, The coordinates of each vertex are represented as , , , Drones at the nodes hover point above At the node hover point above ; Take the drone from the node To the node A feasible path segment is defined as an undirected graph. ,in, The drone hovered at this point and the convex The set of vertices of a polygon is denoted as , It is a set indicating whether there is a feasible path between vertices, represented as ;like Then it means the first vertices To the vertices There are directed road segments between them; if , then it means There are obstacles between them; in this undirected graph, the application... The algorithm finds the drone from the node To the node The shortest path around the obstacle plane and the edge of the obstacle Two-dimensional coordinates ; calculate With hovering point and hovering point Positional proportions on a two-dimensional plane: in, Indicates from the hovering point to hover point The relative distance ratio; using the ratio Interpolation calculation points of Axis coordinates ,get 3D coordinates And record it in the set. middle; S13: Obstacle Avoidance Path Planning Method: Assuming the drone starts from the hovering point to hover point path segment Obstacles To cover; to shield , and Projected to Plane, connecting path segments The equation of a straight line and obstacles Solve for the equations of the sides and the coordinates of the intersection points; check if the intersection points are located at... Within the boundary range: if the intersection point is within the boundary range, then record the intersection point as a valid intersection point of the path segment; otherwise, discard it and do not record it; through calculation, obtain the two-dimensional intersection points that meet the conditions. and Obstacles height Add two-dimensional intersections In the axial coordinate system, the corresponding three-dimensional coordinates are generated. and These two 3D points represent key points on the shortest path over obstacles and are recorded in a set. middle; S14: From the set Extract each point one by one, and compare each point with the hover point. Hovering point To form a feasible path, starting from the hovering point to hover point A feasible path.

2. The UAV trajectory optimization method based on obstacle perception according to claim 1, characterized in that: Step S11 specifically includes the following steps: S111: For vertices Calculate its relationship with other vertices The vector formed by Angle between axes ,in , : in, It is through the vertex and A unit vector parallel to the axis, a vector ,in , , and These are the vertices. and vertex The coordinates; when and hour, ;when and hour, In other cases, ; make the included angle Record to collection middle; S112: When the vertex Complete with all other vertices After calculating the included angle, from the set Select the smallest included angle. and corresponding vertices And will form the vertex with the smallest included angle. Record to collection middle; S113: If Then the current vertex Update to minimum corresponding vertex and the set Clear to zero, then return to S111 to continue calculating the angle between the current vertex and other vertices; if Then proceed to step S114; S114: After the calculation is complete, set Connect the vertices in sequence to obtain the convex polygon with the shortest moving distance.

3. The UAV trajectory optimization method based on obstacle perception according to claim 1, characterized in that: Step S2 describes obtaining more feasible trajectories under multi-obstacle conditions based on the number and height of obstacles using obstacle avoidance methods, and optimizing these feasible trajectories. Specifically, this includes the following steps: S21: Using the method in step S11, the first segment in the path segment... One obstacle Convert to the convex shape with the shortest travel distance polygon, And record the obstacles. The coordinates of the vertex with the shortest movement distance , , , ; S22: Using the edge obstacle avoidance path planning method described in step S12, find the drone's starting point from the node. To the node The shortest path around the obstacle plane and the point passing through the edge of the obstacle. , , , , , And record it in the set. middle; S23: Assume the highest obstacle in the path segment is... , Considering only the highest obstacle, the obstacle-avoidance path planning method described in step S13 is used to obtain the obstacle-avoidance path. trajectory Next, check the path segments. and Is it related to other obstacles in the path? There is an intersection, where If the path segment does not intersect with other obstacles, then... The obstacle avoidance problem involving multiple obstacles is simplified to a single obstacle avoidance problem; if the path segment and the obstacle... If there is an intersection, then the plan should simultaneously avoid obstacles. and The trajectory, in which ; S24: Path tuning: from set After extracting the point sequence for each feasible path, connect the points on each path sequentially to form a sequence starting from the hovering point. to hover point The feasible paths are determined; for all feasible path segments, path optimization is performed, and the Fibonacci filtering method for straightening the obtained paths is as follows: in, and It is a path segment On the path point; if the path segment arrive If the path does not intersect with any obstacles, then remove the previous path points. By connecting the optimized path points, we can obtain the path from the hovering point. to hover point Feasible paths; S25: By calculating the energy consumption of each feasible flight path segment, the path with the lowest energy consumption is selected as the flight path for that segment; the flight energy consumption of the UAV consists of three parts: Among them, the drone flies at a constant horizontal speed of Flight time is Energy consumed per hour Its flight power is: When flying horizontally, the pitch angle of the drone Longitudinal air friction resistance During pitching flight, it carries in and The value is used to calculate flight power; energy consumption of the UAV during acceleration. , The propulsion power of the drone during acceleration / deceleration. This refers to the flight speed during the drone's acceleration phase, from 0 to... The acceleration time is , Energy consumption of the drone during the deceleration phase in order to accelerate. Equal to the acceleration phase, that is: The path selection method is defined as a problem of minimizing the flight energy consumption of UAVs. C1： C2： C3： C4： C5： C6： In the formula, , , and The flight area is in The limitations of the plane, and It is the limitation of the drone's flight altitude. , and It is a drone The three-dimensional coordinates at time [time]. That is the maximum acceleration of the drone. For path points in The height of the location It is an obstacle. The height of the location C1-C3 represents the maximum pitch angle of the drone, C1-C3 represents the feasible flight space of the drone, and C5 represents the maximum pitch angle of the drone. If there are obstacles on the coordinates, the drone's height must be greater than the height of the obstacles.

4. The UAV trajectory optimization method based on obstacle perception according to claim 3, characterized in that: Step S23 describes the simultaneous planning and obstacle avoidance. and The steps for determining the trajectory are as follows: S231: After height-based optimization, if the path segment Obstacles and If there is obstruction, then obstacles need to be considered. and In the case of obstacle avoidance path planning described in step S13, the coordinates of the two obstacles to be overcome are calculated. , , and And record these waypoints in a set. middle; S232: Using the edge obstacle avoidance path planning method described in step S12, represent the set of hovering points and waypoints as follows: Apply again Algorithm, calculates drone from node To the node Find the shortest path around the obstacle; simultaneously record the edge points where the obstacle is crossed or bypassed, and add the optimal path point to the set. middle.

5. The UAV trajectory optimization method based on obstacle perception according to claim 4, characterized in that: Step S3 specifically includes the following steps: During path optimization, flight speed and trajectory are jointly optimized, and the data transmission volume of the completed nodes is taken as a constraint to establish a problem of minimizing the total energy consumption of the UAV, as shown below: C1-C6 C7： C8： C9： in, This indicates the total energy consumption of the drone. Indicates that the drone is in The energy consumed when an IoT node hovers above another node to communicate. Indicates that drones are in The speed of time, That is the maximum speed of the drone. Indicates drones and the first Signal-to-noise ratio of each IoT node It is the signal-to-noise ratio threshold that ensures successful data transmission. It is a drone and the first Data transmission rate between IoT nodes Indicates channel bandwidth. For drones and nodes Communication time, It is the first The amount of data that each node needs to upload; The problem of minimizing total energy consumption is solved by iterative optimization algorithm to obtain the optimal flight speed and the optimal flight trajectory for the UAV to avoid obstacles.

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