Unmanned aerial vehicle path planning method and system for complex urban environment

Abstract

The invention discloses an unmanned aerial vehicle path planning method and system for a complex urban environment, and the method comprises the steps: collecting urban environment data, and obtaining the building distribution, public facility arrangement, average pedestrian flow and other information in a city; and respectively calculating an obstacle risk, an injury risk, a property loss risk and a weather influence existing in the urban environment, and finally obtaining a path of the unmanned aerial vehicle through an optimization algorithm. The method provided by the invention comprehensively considers the risks of obstacle collision, personal injury, property loss, weather influence and the like possibly caused by the operation of the unmanned aerial vehicle in the urban environment, is convenient to calculate, facilitates measurement or estimation of parameters, can quickly determine the path scheme of the unmanned aerial vehicle, and improves the working efficiency. Technical guarantee is provided for safe work of the unmanned aerial vehicle in a complex urban environment, energy conservation, improvement of the service life and improvement of the working efficiency.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle (UAV) planning and control, and in particular to a UAV path planning method and system for complex urban environments. Background Art

[0002] With the development of drone technology, drones are increasingly used in urban environments, including logistics distribution, emergency rescue, infrastructure inspection, environmental monitoring, etc. However, urban environments are often very complex, with tall buildings, crisscrossing roads, dense traffic, strong radio interference, and many other challenges. This complexity places higher demands on drone path planning.

[0003] Existing research has focused on obstacle avoidance and path optimization of drones, but the planned scenarios are often very simplified, ignoring the complexity of the urban environment. Summary of the invention

[0004] Purpose of the invention: In order to overcome the shortcomings of the above-mentioned prior art, the purpose of the present invention is to propose a UAV path planning method and system for complex urban environments, which uses data such as urban building distribution, pedestrian flow, weather conditions, etc. as basic information to calculate the optimal path for UAV flight, provide theoretical support and technical guarantee for the safe and efficient operation of UAVs, ensure that UAVs can complete tasks such as logistics distribution and emergency rescue, reduce safety hazards, and improve work efficiency and operational safety levels.

[0005] Technical solution: To solve the above technical problems, the technical solution adopted by the present invention is: In a first aspect, the present invention provides a method for UAV path planning in a complex urban environment, the method comprising the following steps: Step (1) collecting environmental information of the working area of ​​the drone; wherein the environmental information at least includes obstacle information and pedestrian information; Step (2) dividing the UAV working area into three-dimensional grids; Step (3) constructing a UAV operation risk calculation model; Step (4) improves the cost function in the A* path planning algorithm based on the total risk of UAV operations in each grid, and plans the operation path of the UAV through the improved A* path planning algorithm.

[0006] As a further optimization scheme of the present invention, in step (3), the expression of the UAV operation risk calculation model is: Q(i)=α1* T i1 +α2* T i2 +α3* T i3 +α4* T i4 Where Q(i) is the total risk of drone operations in the i-th grid, T i1 、T i2 、T i3 、T i4 are the obstacle risk, direct injury risk, indirect injury risk, and property loss risk in the i-th grid, respectively. α1, α2, α3, and α4 are T i1 、T i2 、T i3 、T i4 The proportion is α1+α2+α3+α4=1.

[0007] As a further optimization scheme of the present invention, in step (4), the improved cost function is: f(i)= Q(i)+ h(i) Where f(i) represents the total cost consumed by the drone at the i-th grid, h(i)=0.2d i +0.8 Q(i)*d i , Q(i) represents the total risk of drone operations in the i-th grid, d i Represents the Euclidean distance from the geometric center point of the i-th grid to the end point of the UAV operation.

[0008] As a further optimization solution of the present invention, the calculation method of the obstacle risk in the i-th grid is: T i1 =(V obstacle / V block )* N obstacle *(e -0.5d / (d 2 +0.1))+1.1 Where V obstacle is the volume of the obstacle in the grid, V block is the volume of the grid, N obstacle is the number of obstacles in the grid, and d is the Euclidean distance from the UAV to the geometric center of the grid.

[0009] As a further optimization scheme of the present invention, the calculation method of the direct injury risk in the i-th grid is: T i2 =P crush * P impact * D injury P crush =1*10 -4 *e t / 3600 P impact =(S uav / S work )*ρ people*K t

[0010] Where P crush is the probability of the human-machine falling failure, P impact is the probability of a drone crashing and hitting a pedestrian, D injury is the damage directly caused by the drone, t is the estimated total working time of the drone, S uav is the vertical projection area of ​​the UAV in the grid, S work is the plane area of ​​the UAV working area, ρ people is the pedestrian density in the plane of the UAV working area, K t is the preset weather coefficient, K u is the material coefficient of the drone, m is the mass of the drone, S manhit is the collision area between the UAV and the pedestrian, H is the flight altitude of the UAV, H0 is the average height of obstacles in the working area of ​​the UAV, and β is the preset adjustment coefficient.

[0011] As a further optimization scheme of the present invention, the calculation method of the indirect damage risk in the i-th grid is: T i3 =P crush * N injury * D object P crush =1*10 -4 *e t / 3600

[0012] Where P crush is the probability of a human-machine crash, N injury is the number of obstacles that collide with the drone in the grid, D object is the magnitude of the damage indirectly caused by the drone, t is the estimated total working time of the drone, K u is the material coefficient of the drone, γ is the preset adjustment factor, m is the mass of the drone, v is the flight speed of the drone, S objecthit is the collision area between the drone and the obstacle.

[0013] As a further optimization scheme of the present invention, the calculation method of the property loss risk in the i-th grid is: Where γ1 and γ2 are preset adjustment coefficients, m is the mass of the UAV, v is the flight speed of the UAV, g is the acceleration of gravity, and H is the flight altitude of the UAV.

[0014] In a second aspect, the present invention further proposes a UAV path planning system for complex urban environments, the system comprising: Environmental information collection module, used to collect environmental information of the drone's working area; The grid division module is used to divide the UAV working area into three-dimensional grids; Positioning module, used to obtain the location information of the drone; Model building module, used to build a risk calculation model for drone operations; The path planning module is used to improve the cost function in the A* path planning algorithm based on the total risk of UAV operations in each grid, and plan the operation path of the UAV through the improved A* path planning algorithm.

[0015] In a third aspect, the present invention further proposes a computer-readable storage medium storing one or more programs, wherein the one or more programs include instructions, and when the instructions are executed by a computing device, the computing device executes the method as described above.

[0016] In a fourth aspect, the present invention also proposes an electronic device, comprising one or more processors, one or more memories and one or more programs, wherein the one or more programs are stored in the one or more memories and are configured to be executed by the one or more processors, and the one or more programs include instructions for executing the method as described above.

[0017] Beneficial effects: The method for UAV path planning in complex urban environments proposed by the present invention fully considers many factors such as urban building distribution, pedestrian flow, weather conditions, etc.; the method is easy to calculate, and the parameters are easy to measure or estimate, and the optimal path of the UAV can be quickly determined. Effective path planning can help UAVs avoid obstacles, comply with airspace restrictions, ensure flight safety and mission reliability, improve the work efficiency of UAVs, and promote innovation and development in urban logistics, traffic management, public safety, environmental monitoring and other fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a method flow chart of an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to make the content of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0020] Considering the risks of drones in complex urban environments is the key to ensuring the efficient completion of missions, especially in cities with many pedestrians and buildings. Drone path planning is related to many factors such as urban building distribution, pedestrian flow, weather conditions, etc. This paper proposes a drone path planning method for complex urban environments, which can provide theoretical support and technical guarantee for the safe and efficient operation of drones.

[0021] like Figure 1 As shown, the present invention proposes a UAV path planning method for complex urban environments, and the specific steps are as follows: (1) Collecting basic environmental information of the UAV’s operating area; wherein the environmental information at least includes information about obstacles (including buildings, public facilities, etc.) and pedestrians.

[0022] (2) Divide the UAV working area into three-dimensional grids and calculate the risks of each UAV operation in a single grid: (2.1) The calculation method of obstacle risk is: T1=(V obstacle / V block )* N obstacle *(e -0.5d / (d 2 +0.1))+1.1 Where V obstacle is the volume of the obstacle in the grid, V block is the volume of the grid, N obstacle is the number of obstacles in the grid, and d is the Euclidean distance from the UAV to the geometric center of the grid.

[0023] (2.2) The calculation method for direct injury risk is: T2=P crush * P impact * D injury P crush =1*10 -4 *e t / 3600 P impact =(S uav / S work )*ρ people *K t

[0024] Where P crush is the probability of the human-machine falling failure, P impact is the probability of a drone crashing and hitting a pedestrian, D injury is the damage directly caused by the drone, t is the estimated total working time of the drone, Suav is the vertical projection area of ​​the UAV in the grid, S work is the working area of ​​the UAV, ρ people is the pedestrian density in the UAV working area, K t is the preset weather coefficient, K u is the material coefficient of the drone, m is the mass of the drone, S manhit is the collision area between the UAV and the pedestrian, H is the flight altitude of the UAV, H0 is the average height of obstacles in the working area, and β is the preset adjustment coefficient.

[0025] It should be noted here that the vertical projection area S of the drone in the grid in the present invention is uav The expression is S uav =πr uav 2 , r uav represents the radius of the drone; the collision area between the drone and the pedestrian S manhit The expression is S manhit =π(r uav +r human ) 2 , r human Indicates the pedestrian radius.

[0026] (2.3) The calculation method for indirect harm risk is: T3=P crush * N injury * D object P crush =1*10 -4 *e t / 3600

[0027] Where D object is the magnitude of the damage indirectly caused by the drone, t is the estimated total working time of the drone, N injury is the number of obstacles that collide with the drone in the grid, K u is the material coefficient of the drone, γ is the preset adjustment factor, m is the mass of the drone, v is the flight speed of the drone, S objecthit is the collision area between the drone and the obstacle.

[0028] (2.4) The calculation method of property loss risk is:

[0029] Where γ1 and γ2 are preset adjustment coefficients, m is the mass of the drone, v is the flight speed of the drone, and g is 9.8 m / s 2 , H is the flight altitude of the UAV.

[0030] (3) Construct a calculation model for the total risk of drone operations within a single grid. The specific expression is: Q(i)=α1* T i1 +α2* T i2 +α3* T i3 +α4* T i4 Where Q(i) is the total risk of drone operations in the i-th grid, T i1 , T i2 , T i3 , T i4 are the obstacle risk, direct injury risk, indirect injury risk, and property loss risk in the i-th grid, respectively. α1, α2, α3, and α4 are T i1 , T i2 , T i3 , T i4 The proportion is α1+α2+α3+α4=1.

[0031] (4) The operation path of the UAV is planned using the cost-A* algorithm. The cost-A* algorithm here refers to an improved path planning algorithm obtained by improving the cost function in the A* path planning algorithm based on the total risk of the UAV operation in each grid.

[0032] Specifically, the improved cost function is: f(i)= Q(i)+ h(i) Where f(i) represents the total cost consumed by the drone at the i-th grid, h(i)=0.2d i +0.8 Q(i)*d i , Q(i) represents the total risk of drone operations in the i-th grid, d i Represents the Euclidean distance from the geometric center point of the i-th grid to the end point of the UAV operation. Example

[0033] The drone is to perform a regional reconnaissance mission in a street area in the city. The street area is a straight line with a length of 50m and a width of 5m. It is divided into a three-dimensional grid of 5m×5m×5m. The mass of the drone is 2kg and the material of the drone is aluminum alloy K u The value is 1.5, S uav =0.16πm 2 , speed v is 5m / s. Find the optimal path for the drone to perform this task.

[0034] (1) Obtain information about the distribution of obstacles (including buildings, public facilities, etc.) and calculate the average building height H0 = 20 m and the pedestrian density ρ in this area. people=0.7 person / m 2 . Street area S work =250m 2 、N number of objects that collide injury =3. Pedestrian impact area S manhit =0.3136πm 2 、The impact area of ​​the object S objecthit =0.16πm 2 .

[0035] (2) Calculate the risks of each drone operation within a single grid: (2.1) Obstacle risk calculation Take a 3D air mass near a drone as an example, where there are 3 obstacles:

[0036] (2.2) Calculation of direct injury risk

[0037]

[0038] (2.3) Calculation of indirect damage risk Assume the number of colliding objects is 3:

[0039] (2.4) Calculation of property loss risk

[0040]

[0041] (2.5) Calculation of the total risk of drone operations within this grid Pick:

[0042] .

[0043] (3) Based on the total risk of UAV operations in each grid, the cost function in the A* path planning algorithm is improved. The UAV operation path is planned using the improved A* path planning algorithm to obtain the optimal path.

[0044] Specifically, the improved cost function is: f(i)= Q(i)+ h(i) Where f(i) represents the total cost consumed by the drone at the i-th grid, h(i)=0.2d i +0.8 Q(i)*d i , Q(i) represents the total risk of drone operations in the i-th grid, di Represents the Euclidean distance from the geometric center point of the i-th grid to the end point of the UAV operation.

[0045] Obviously, the above embodiments are merely examples for the purpose of clear explanation, and are not intended to limit the use. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the use methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the invention.

[0046] Based on the same technical solution, the present invention also discloses a computer-readable storage medium storing one or more programs, wherein the one or more programs include instructions, which, when executed by a computing device, enable the computing device to execute the above-mentioned drone path planning method for complex urban environments.

[0047] Based on the same technical solution, the present invention also discloses a computing device, including one or more processors, one or more memories and one or more programs, wherein the one or more programs are stored in the one or more memories and are configured to be executed by the one or more processors, and the one or more programs include instructions for executing the above-mentioned drone path planning method for complex urban environments.

[0048] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0049] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0050] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0051] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

Claims

1. A UAV path planning method for complex urban environments, characterized in that: The method comprises the following steps: Step (1) collecting environmental information of the working area of ​​the UAV; wherein the environmental information at least includes obstacle information and pedestrian information; Step (2) divide the UAV working area into three-dimensional grids; Step (3) constructing a UAV operation risk calculation model; Step (4) improves the cost function in the A* path planning algorithm based on the total risk of UAV operations in each grid, and plans the operation path of the UAV through the improved A* path planning algorithm.

2. The method for UAV path planning in a complex urban environment according to claim 1, characterized in that: In step (3), the expression of the UAV operation risk calculation model is: Q(i)=α1* T i1 +α2* T i2 +α3* T i3 +α4* T i4， Where Q(i) is the total risk of drone operations in the i-th grid, T i1 , T i2 , T i3 , T i4 are the obstacle risk, direct injury risk, indirect injury risk, and property loss risk in the i-th grid, respectively. α1, α2, α3, and α4 are T i1 , T i2 , T i3 , T i4 The proportion is α1+α2+α3+α4=1.

3. The method for UAV path planning in a complex urban environment according to claim 1, characterized in that: In step (4), the improved cost function is: f(i)= Q(i)+ h(i), Where f(i) represents the total cost consumed by the drone at the i-th grid, h(i)=0.2d i +0.8 Q(i)*d i , Q(i) represents the total risk of drone operations in the i-th grid, d i Represents the Euclidean distance from the geometric center point of the i-th grid to the end point of the UAV operation.

4. The method for UAV path planning in a complex urban environment according to claim 2, characterized in that: The calculation method of the obstacle risk in the i-th grid is: T i1 =(V obstacle / V block )* N obstacle *(e -0.5d / (d 2 +0.1))+1.1, Where V obstacle is the volume of the obstacle in the grid, V block is the volume of the grid, N obstacle is the number of obstacles in the grid, and d is the Euclidean distance from the UAV to the geometric center of the grid.

5. The method for UAV path planning in a complex urban environment according to claim 2, characterized in that: The calculation method of the direct injury risk in the i-th grid is: T i2 =P crush * P impact * D injury， P crush =1*10 -4 *And t / 3600， P impact =(S uav / S work )*r people *K t， ， Where P crush is the probability of the human-machine falling failure, P impact is the probability of a drone crashing and hitting a pedestrian, D injury is the damage directly caused by the drone, t is the estimated total working time of the drone, S uav is the vertical projection area of ​​the UAV in the grid, S work is the plane area of ​​the UAV working area, ρ people is the pedestrian density in the plane of the UAV working area, K t is the preset weather coefficient, K u is the material coefficient of the drone, m is the mass of the drone, S manhit is the collision area between the drone and the pedestrian, H is the flight altitude of the drone, H0 is the average height of obstacles in the working area of ​​the drone, and α and β are both preset adjustment coefficients.

6. The method for UAV path planning in a complex urban environment according to claim 2, characterized in that: The calculation method of the indirect damage risk in the i-th grid is: T i3 =P crush * N injury * D object， P crush =1*10 -4 *And t / 3600， ， Where P crush is the probability of a human-machine crash, N injury is the number of obstacles that collide with the drone in the grid, D object is the magnitude of the damage indirectly caused by the drone, t is the estimated total working time of the drone, K u is the material coefficient of the drone, γ is the preset adjustment factor, m is the mass of the drone, v is the flight speed of the drone, S objecthit is the collision area between the drone and the obstacle.

7. The method for UAV path planning in a complex urban environment according to claim 2, characterized in that: The calculation method of the property loss risk in the i-th grid is: ， Where γ1 and γ2 are preset adjustment coefficients, m is the mass of the UAV, v is the flight speed of the UAV, g is the acceleration of gravity, and H is the flight altitude of the UAV.

8. A UAV path planning system for complex urban environments, characterized in that: The system comprises: Environmental information collection module, used to collect environmental information of the drone's working area; The grid division module is used to divide the UAV working area into three-dimensional grids; Positioning module, used to obtain the location information of the drone; Model building module, used to build a risk calculation model for drone operations; The path planning module is used to improve the cost function in the A* path planning algorithm based on the total risk of UAV operations in each grid, and plan the operation path of the UAV through the improved A* path planning algorithm.

9. A computer-readable storage medium storing one or more programs, wherein the one or more programs include instructions, characterized in that: When the instructions are executed by a computing device, the computing device is caused to perform the method according to any one of claims 1 to 7.

10. An electronic device, characterized in that: The method comprises one or more processors, one or more memories and one or more programs, wherein the one or more programs are stored in the one or more memories and are configured to be executed by the one or more processors, and the one or more programs include instructions for executing the method as claimed in any one of claims 1 to 7.

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