A UAV path planning method and system for complex urban environments

Abstract

This invention discloses a method and system for drone path planning in complex urban environments. The method includes: collecting urban environmental data, acquiring information such as the distribution of buildings, the arrangement of public facilities, and average pedestrian traffic; calculating the risks of obstacles, injuries, property damage, and weather effects in the urban environment; and finally obtaining the drone's path through an optimization algorithm. The method provided by this invention comprehensively considers the risks that drones may cause in urban environments, such as obstacle collisions, personal injury, property damage, and weather effects. It is also convenient to calculate, and the parameters are easy to measure or estimate, enabling rapid determination of the drone's path. This provides technical support for the safe operation of drones in complex urban environments, energy conservation, improved operational lifespan, and increased work efficiency.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) planning and control, and specifically to a UAV path planning method and system for complex urban environments. Background Technology

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

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

[0004] Purpose of the invention: In order to overcome the shortcomings of the prior art, the purpose of this invention is to propose a method and system for drone path planning in complex urban environments. Using data such as urban building distribution, pedestrian flow, and weather conditions as basic information, the method calculates the optimal flight path for drones, providing theoretical support and technical guarantee for the safe and efficient operation of drones. This ensures that drones can complete tasks such as logistics delivery and emergency rescue, reduce safety hazards, and improve work efficiency and operational safety.

[0005] Technical solution: To solve the above technical problems, the technical solution adopted by the present invention is as follows:

[0006] In a first aspect, the present invention proposes a method for unmanned aerial vehicle (UAV) path planning in complex urban environments, the method comprising the following steps:

[0007] Step (1) Collect environmental information of the UAV's working area; wherein the environmental information includes at least obstacle information and pedestrian information;

[0008] Step (2) Divide the working area of ​​the UAV into a three-dimensional mesh;

[0009] Step (3) Construct a risk calculation model for drone operations;

[0010] Step (4) 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.

[0011] As a further optimization of the present invention, in step (3), the expression of the UAV operation risk calculation model is:

[0012] Q(i) = α1 * T i1+α2* T i2 +α3* T i3 +α4* T i4

[0013] In the formula, Q(i) represents the total risk of drone operations within the i-th grid, and T... i1 、T i2 、T i3 、T i4 Let α1, α2, α3, and α4 represent the risks of obstacles, direct harm, indirect harm, and property damage within the i-th grid, respectively, and let T be the risk of T. i1 、T i2 、T i3 、T i4 The proportion is α1+α2+α3+α4=1.

[0014] As a further optimization of the present invention, in step (4), the improved cost function is:

[0015] f(i) = Q(i) + h(i)

[0016] In the formula, f(i) represents the total cost consumed by the drone at the i-th grid, and h(i) = 0.2d i +0.8 Q(i)*d i Q(i) represents the total risk of drone operations within the i-th grid, d i This represents the Euclidean distance from the geometric center of the i-th grid to the endpoint of the UAV operation.

[0017] As a further optimization of the present invention, the method for calculating the obstacle risk within the i-th grid is as follows:

[0018] T i1 =(V obstacle / V block )* N obstacle *(e -0.5d / (d 2 +0.1))+1.1

[0019] In the formula, V obstacle V is the volume of the obstacle within the grid. block N is the volume of the grid. obstacle d represents the number of obstacles in the grid, and d is the Euclidean distance between the drone and the geometric center of the grid.

[0020] As a further optimization of the present invention, the method for calculating the direct damage risk within the i-th grid is as follows:

[0021] T i2 =P crush * P impact* D injury

[0022] P crush =1*10 -4 *e t / 3600

[0023] P impact =(S uav / S work )*ρ people *K t

[0024]

[0025] In the formula, P crush The probability of a human-machine interface crashing, P impact D represents the probability that a drone crashes and hits a pedestrian. injury The magnitude of damage directly caused by the drone, t represents the estimated total operating time of the drone, and S represents the total operating time of the drone. uav S represents the vertical projected area of ​​the drone within the grid. work ρ is the planar area of ​​the drone's working area. people K represents the pedestrian density within the drone's working area. t K is the preset weather coefficient. u S is the material coefficient of the drone, m is the mass of the drone, and S is the mass of the drone. manhit H represents the impact area between the drone and the pedestrian, H represents the flight altitude of the drone, H0 represents the average height of obstacles within the drone's working area, and β represents the preset adjustment coefficient.

[0026] As a further optimization of the present invention, the method for calculating the indirect damage risk within the i-th grid is as follows:

[0027] T i3 =P crush * N injury * D object

[0028] P crush =1*10 -4 *e t / 3600

[0029]

[0030] In the formula, P crush The probability N of a human-machine interface crashing or malfunctioning. injury D represents the number of obstacles that collide with the drone within the grid. object The magnitude of damage indirectly caused by the drone, t represents the estimated total operating time of the drone, and K represents the amount of damage caused indirectly by the drone. uγ is the material coefficient of the drone, m is the mass of the drone, v is the flight speed of the drone, and S is the mass of the drone. objecthit This represents the impact area between the drone and the obstacle.

[0031] As a further optimization of the present invention, the method for calculating the property loss risk within the i-th grid is as follows:

[0032]

[0033] In the formula, 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 gravitational acceleration, and H is the flight altitude of the UAV.

[0034] Secondly, this invention also proposes a UAV path planning system for complex urban environments, the system comprising:

[0035] The environmental information acquisition module is used to collect environmental information about the drone's operating area.

[0036] The mesh generation module is used to generate a three-dimensional mesh for the working area of ​​the UAV.

[0037] The positioning module is used to obtain the location information of the drone;

[0038] The model building module is used to build a risk calculation model for drone operations;

[0039] 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 within each grid, and to plan the operation path of the UAV using the improved A* path planning algorithm.

[0040] Thirdly, the present invention also provides a computer-readable storage medium for storing one or more programs, said one or more programs including instructions that, when executed by a computing device, cause the computing device to perform the method described above.

[0041] Fourthly, 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 configured to be executed by the one or more processors, and the one or more programs include instructions for performing the methods described above.

[0042] Beneficial Effects: This invention proposes a drone path planning method for complex urban environments, fully considering numerous factors such as urban building distribution, pedestrian traffic, and weather conditions. The method is computationally convenient, and its parameters are easy to measure or estimate, enabling rapid determination of the optimal drone path. Effective path planning can help drones avoid obstacles, comply with airspace restrictions, ensure flight safety and mission reliability, improve drone efficiency, and promote innovation and development in urban logistics, traffic management, public safety, and environmental monitoring. Attached Figure Description

[0043] Figure 1 This is a flowchart of a method according to an embodiment of the present invention. Detailed Implementation

[0044] To make the content of this invention easier to understand, the invention will be further described in detail below with reference to specific embodiments.

[0045] Considering the risks that drones pose in complex urban environments is crucial to ensuring the efficient completion of missions, especially given the large number of pedestrians and buildings in cities. Drone path planning is related to many factors such as urban building distribution, pedestrian traffic, and weather conditions. This invention proposes a drone path planning method for complex urban environments, which can provide theoretical support and technical assurance for the safe and efficient operation of drones.

[0046] like Figure 1 As shown, the present invention proposes a UAV path planning method for complex urban environments, the specific steps of which are as follows:

[0047] (1) Collect basic environmental information of the working area of ​​the UAV; wherein the environmental information includes at least obstacle information (including buildings, public facilities, etc.) and pedestrian information.

[0048] (2) Divide the UAV's working area into a three-dimensional mesh, and calculate the risks of each UAV operation within a single mesh:

[0049] (2.1) The method for calculating obstacle risk is as follows:

[0050] T1=(V obstacle / V block )* N obstacle *(e -0.5d / (d 2 +0.1))+1.1

[0051] In the formula V obstacle V is the volume of the obstacle within the grid. block N is the volume of the mesh. obstacle d represents the number of obstacles in the grid, and d is the Euclidean distance between the drone and the geometric center of the grid.

[0052] (2.2) The calculation method for direct injury risk is as follows:

[0053] T2=P crush * P impact * D injury

[0054] P crush =1*10 -4 *e t / 3600

[0055] P impact =(S uav / S work )*ρ people *K t

[0056]

[0057] In the formula P crush The probability of a human-machine interface crashing, P impact D represents the probability that a drone crashes and hits a pedestrian. injury The magnitude of damage directly caused by the drone, t represents the estimated total operating time of the drone, and S represents the total operating time of the drone. uav S represents the vertical projected area of ​​the drone within the grid. work ρ is the area of ​​the drone's working area. people K represents the pedestrian density in the drone's operating area. t K is the preset weather coefficient. u S is the material coefficient of the drone, m is the mass of the drone, and S is the mass of the drone. manhit H represents the impact area between the drone and the pedestrian, H represents the drone's flight altitude, H0 represents the average height of obstacles within the working area, and β represents the preset adjustment coefficient.

[0058] It should be noted that the vertical projected area S of the UAV within the grid in this invention... uav The expression is S uav =πr uav 2 , r uav The radius of the drone; the impact area S between the drone and the pedestrian. manhit The expression is S manhit =π(r uav +r human ) 2 , r human Indicates the pedestrian radius.

[0059] (2.3) The calculation method for indirect injury risk is as follows:

[0060] T3=P crush * Ninjury * D object

[0061] P crush =1*10 -4 *e t / 3600

[0062]

[0063] In the formula D object The magnitude of damage indirectly caused by the drone, t represents the estimated total operating time of the drone, and N represents the total operating time of the drone. injury K represents the number of obstacles within the grid that collide with the drone. u γ is the material coefficient of the drone, m is the mass of the drone, v is the flight speed of the drone, and S is the mass of the drone. objecthit This represents the impact area between the drone and the obstacle.

[0064] (2.4) The calculation method for property loss risk is as follows:

[0065]

[0066] In the formula, γ1 and γ2 are preset adjustment coefficients, m is the mass of the UAV, v is the flight speed of the UAV, and g is taken as 9.8 m / s. 2 H represents the flight altitude of the drone.

[0067] (3) Construct a calculation model for the total risk of UAV operations within a single grid, the specific expression of which is:

[0068] Q(i) = α1 * T i1 +α2* T i2 +α3* T i3 +α4* T i4

[0069] In the formula, Q(i) represents the total risk of drone operations within the i-th grid, and T... i1 、T i2 、T i3 、T i4 Let α1, α2, α3, and α4 represent the risks of obstacles, direct harm, indirect harm, and property damage within the i-th grid, respectively, and let T be the risk of T. i1 、T i2 、T i3 、T i4 The proportion is α1+α2+α3+α4=1.

[0070] (4) The operation path of the UAV is planned by the cost-A* algorithm. Here, the cost-A* algorithm refers to the improved path planning algorithm obtained by improving the cost function in the A* path planning algorithm based on the total risk of UAV operation in each grid.

[0071] Specifically, the improved cost function is:

[0072] f(i) = Q(i) + h(i)

[0073] In the formula, f(i) represents the total cost consumed by the drone at the i-th grid, and h(i) = 0.2d i +0.8 Q(i)*d i Q(i) represents the total risk of drone operations within the i-th grid, d i This represents the Euclidean distance from the geometric center of the i-th grid to the endpoint of the UAV operation. Example

[0074] A drone is to perform a reconnaissance mission on a straight street in a city. The street is 50m long and 5m wide, divided into a 3D grid of 5m x 5m x 5m sections. The drone has a mass of 2kg and is made of aluminum alloy. u The value is 1.5, S uav =0.16πm 2 The speed v is 5 m / s. Find the optimal path for the UAV to perform this task.

[0075] (1) Obtain information related to the distribution of obstacles (including buildings, public facilities, etc.), calculate the average building height H0=20m in this street, and the pedestrian density ρ in this area. people =0.7 people / m 2 Street area S work =250m 2 The number of colliding objects N injury =3. Pedestrian impact area S manhit =0.3136πm 2 Impact area S of the object objecthit =0.16πm 2 .

[0076] (2) Calculate the risks of each UAV operation within a single grid:

[0077] (2.1) Obstacle risk calculation

[0078] Taking a three-dimensional air mass near a drone as an example, there are 3 obstacles:

[0079]

[0080] (2.2) Calculation of direct injury risk

[0081]

[0082]

[0083] (2.3) Calculation of indirect injury risk

[0084] Assume the number of colliding objects is 3:

[0085]

[0086] (2.4) Calculation of property loss risk

[0087]

[0088]

[0089] (2.5) Calculation of total risk of UAV operation within this grid

[0090] Pick:

[0091] .

[0092] (3) Based on the total risk of UAV operation in each grid, the cost function in the A* path planning algorithm is improved, and the operation path of the UAV is planned by the improved A* path planning algorithm to obtain the optimal path.

[0093] Specifically, the improved cost function is:

[0094] f(i) = Q(i) + h(i)

[0095] In the formula, f(i) represents the total cost consumed by the drone at the i-th grid, and h(i) = 0.2d i +0.8 Q(i)*d i Q(i) represents the total risk of drone operations within the i-th grid, d i This represents the Euclidean distance from the geometric center of the i-th grid to the endpoint of the UAV operation.

[0096] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the usage. Those skilled in the art can make other variations or modifications based on the above description. It is neither necessary nor possible to exhaustively list all possible uses here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

[0097] Based on the same technical solution, the present invention also discloses a computer-readable storage medium for storing one or more programs, the one or more programs including instructions that, when executed by a computing device, cause the computing device to perform the above-described UAV path planning method for complex urban environments.

[0098] 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 configured to be executed by the one or more processors, and the one or more programs include instructions for executing the above-described UAV path planning method for complex urban environments.

[0099] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied 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.

[0100] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0101] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0102] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

Claims

1. A method for unmanned aerial vehicle (UAV) path planning in complex urban environments, characterized in that, The method includes the following steps: Step (1) Collect environmental information of the UAV's working area; wherein the environmental information includes at least obstacle information and pedestrian information; Step (2) Divide the working area of ​​the UAV into a three-dimensional mesh; Step (3) Construct a risk calculation model for drone operations; Step (4) 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 using the improved A* path planning algorithm; In step (3), the expression for the UAV operation risk calculation model is: Q(i)=α1* T i1 +α2* T i2 +α3* T i3 +α4* T i4 , In the formula, Q(i) represents the total risk of drone operations within the i-th grid, and T... i1 、T i2 、T i3 、T i4 Let α1, α2, α3, and α4 represent the risks of obstacles, direct harm, indirect harm, and property damage within the i-th grid, respectively, and let T be the risk of T. i1 、T i2 、T i3 、T i4 The proportion is α1+α2+α3+α4=1; In step (4), the improved cost function is: f(i) = Q(i) + h(i), In the formula, f(i) represents the total cost consumed by the drone at the i-th grid, and h(i) = 0.2d i +0.8 Q(i)*d i Q(i) represents the total risk of drone operations within the i-th grid, d i This represents the Euclidean distance from the geometric center of the i-th grid to the endpoint of the UAV operation; The method for calculating the obstacle risk within the i-th grid is as follows: T i1 =(V obstacle / V block )* N obstacle *(e -0.5d / (d 2 +0.1))+1.1, In the formula, V obstacle V is the volume of the obstacle within the grid. block N is the volume of the grid. obstacle d represents the number of obstacles in the grid, and d is the Euclidean distance between the UAV and the geometric center of the grid. The method for calculating the direct damage risk within the i-th grid is as follows: 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 , ， In the formula, P crush The probability of a human-machine interface crashing, P impact D represents the probability that a drone crashes and hits a pedestrian. injury The magnitude of damage directly caused by the drone, t represents the estimated total operating time of the drone, and S represents the total operating time of the drone. uav S represents the vertical projected area of ​​the drone within the grid. work ρ is the planar area of ​​the drone's working area. people K represents the pedestrian density within the drone's working area. t K is the preset weather coefficient. u S is the material coefficient of the drone, m is the mass of the drone, and S is the mass of the drone. manhit H represents the collision area between the drone and the pedestrian, H represents the flight altitude of the drone, H0 represents the average height of obstacles within the drone's working area, and α and β are both preset adjustment coefficients. The method for calculating the indirect damage risk within the i-th grid is as follows: T i3 =P crush * N injury * D object , P crush =1*10 -4 *And t / 3600 , ， In the formula, P crush The probability N of a human-machine interface crashing or malfunctioning. injury D represents the number of obstacles that collide with the drone within the grid. object The magnitude of damage indirectly caused by the drone, t represents the estimated total operating time of the drone, and K represents the amount of damage caused indirectly by the drone. u γ is the material coefficient of the drone, m is the mass of the drone, v is the flight speed of the drone, and S is the mass of the drone. objecthit The impact area between the drone and the obstacle; The method for calculating the property loss risk within the i-th grid is as follows: ， In the formula, 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 gravitational acceleration, and H is the flight altitude of the UAV.

2. A system applying the UAV path planning method for complex urban environments as described in claim 1, characterized in that, The system includes: The environmental information acquisition module is used to collect environmental information about the drone's operating area. The mesh generation module is used to generate a three-dimensional mesh for the working area of ​​the UAV. The positioning module is used to obtain the location information of the drone; The model building module is 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 within each grid, and to plan the operation path of the UAV using the improved A* path planning algorithm.

3. A computer-readable storage medium storing one or more programs, said one or more programs comprising instructions, characterized in that, When the instruction is executed by the computing device, it causes the computing device to perform the method as described in claim 1.

4. An electronic device, characterized in that, It includes 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 configured to be executed by the one or more processors, and the one or more programs include instructions for performing the method of claim 1.

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