An unmanned aerial vehicle operation path planning method considering air-ground cooperative risk

Abstract

The application discloses a kind of unmanned aerial vehicle operation path planning methods considering air-ground collaborative risk, facing unmanned aerial vehicle city operation scene, belong to unmanned aerial vehicle path planning technical field.First, the planning area is finely modeled using grid method, and the airspace grid environment is constructed;Then, according to the building position and operation requirement, the protection zone is set in the calibration planning area, and the unmanned aerial vehicle operation risk in the grid is quantified by air collision risk and ground impact risk index;After that, the distribution demand point, logistics network point, protection zone position information and unmanned aerial vehicle performance parameters are obtained;Finally, under the condition of meeting interval restriction and unmanned aerial vehicle performance constraint, the A* algorithm is used to search the optimal flight path with the minimum flight path cost as the objective function, thereby generating the optimal flight path set.The application realizes efficient unmanned aerial vehicle operation path planning under the premise of fine unmanned aerial vehicle operation risk assessment.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) route network planning technology, and specifically to a UAV operation path planning method that takes into account the risks of air-ground cooperation. Background Technology

[0002] In urban low-altitude environments, UAV operations face two main risks: air-to-air collision risk and ground-to-ground collision risk. Currently, route planning typically uses ground-to-ground collision risk as the cost function, employing heuristic algorithms to find the path with the minimum cost and path length. However, there is a chain of events between air-to-air and ground-to-ground collisions; for example, air-to-air collision risk is generally a higher-level event than ground-to-ground collision. Furthermore, maintaining the distance between the UAV and obstacles usually involves modeling fixed protected areas, which is detrimental to efficient airspace utilization. Therefore, there is an urgent need to propose a more efficient and precise UAV route planning method that considers air-to-ground coordination risks. Summary of the Invention

[0003] The purpose of this invention is to address the shortcomings of the prior art by providing a UAV operation path planning method that considers the risks of air-ground cooperation. This method constructs the path with the lowest operational risk between take-off and landing points based on requirements, and incorporates mid-air collision risk assessment during path planning to maintain safe distances from obstacles. This solves the problems described in the background section.

[0004] The technical solution to achieve the purpose of this invention is as follows:

[0005] A method for planning the operational path of a UAV that considers the risks of air-ground cooperation includes the following steps:

[0006] Step S1: Use the grid method to create a 3D model of the planned area and construct a grid environment for the operation of low-altitude UAVs;

[0007] Step S2: Delineate a low-altitude obstacle protection zone within the planning area. For each three-dimensional grid in the planning area, introduce a grid hazard level that combines aerial collision risk and ground impact risk to quantitatively describe the operational risk of UAVs in the grid.

[0008] Step S3: Obtain information on delivery demand points, logistics network points, protected area locations, and drone performance parameters, such as maximum turning angle, minimum turning buffer distance, and minimum collision interval;

[0009] Step S4: Considering the constraints of complex low-altitude environment and UAV performance, with the objective function of minimizing route cost, the improved A* algorithm based on cost function is used to search for the optimal route between all target points and generate the optimal route set.

[0010] Furthermore, step S1 specifically includes:

[0011] Determine the low-altitude operating area of ​​the UAV, perform three-dimensional grid modeling and encoding of the selected area, represent it with coordinates (x, y, z), and use the center of each grid as a candidate waypoint.

[0012] Furthermore, step S2 specifically includes:

[0013] Step S21: Within the UAV's operating airspace, special areas such as buildings and restricted airspace are complex. It is necessary to delineate protection zones for each building. The protection zone is a three-dimensional area surrounded by the smallest circumscribed cylinder. The grid airspace is also divided based on whether it is occupied.

[0014] Step S22: Construct the minimum circumscribed cylinder collision protection zone for the UAV, and calculate the collision range R between each grid cell and its surrounding cells. detec The collision probabilities of N obstacles with radius r. Each event is defined as an independent event, and the total probability of an aerial collision is calculated accordingly.

[0015]

[0016] Step S23: Calculate the ground risk of falling for each grid cell, S(x,y,z)=[S d ,S p ,S n ], where [S d ,S p ,S n These correspond to the risks of personal injury, property damage, and noise impact, respectively.

[0017] Step S24: Calculate the danger level coefficient for each grid cell: r(x,y,z)=P C (x,y,z)·S(x,y,z).

[0018] Furthermore, step S3 specifically includes:

[0019] Step S31: Based on the population density and distribution characteristics of residential buildings, office buildings, delivery outlets, and protected areas within the selected area, obtain the takeoff coordinates A of the UAV. s = (x0, y0, z0) and landing coordinates A e =(x e ,y e ,z e );

[0020] Step S32: Obtain the performance parameters of the logistics drone, such as: maximum turning angle, maximum climb angle, maximum descent angle, flight altitude range, minimum turning buffer distance, and minimum collision interval.

[0021] Furthermore, step S4 specifically includes:

[0022] Step S41: Suppose the path passes through a point A. k =(x k ,y k ,z k ), calculate the cost of the journey:

[0023]

[0024] Step S42: Calculate the risk cost:

[0025]

[0026] In the formula, δ is the risk penalty coefficient, and r i For grid hazard level, l i This represents the distance the drone travels within the corresponding grid.

[0027] Step S43: Calculate the route search cost function, let g(k) = ω1C L (k)+ω2C D (k), where C L (k) and C D (k) represent the starting point A. s To the current point A k The actual voyage cost and the risk cost, where ω1 and ω2 are both weighting coefficients;

[0028] Step S44: Calculate the heuristic function, taking the current point and the endpoint A as the reference points. e =(x e ,y e ,z e The distance to () is used as a heuristic function:

[0029]

[0030] Step S45: Using the minimization of route cost as the objective function, the improved A* algorithm based on the cost function is used to search for the optimal UAV operating path.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] 1. The present invention proposes a UAV operation path planning method that considers air-ground cooperation risks. It fully considers the air-ground cooperation risk accident chain, uses the probability of mid-air collision as the inducing factor of crash-to-ground collision accidents, and thus plans the operation path with the minimum cooperation risk.

[0033] 2. This invention uses the probability of mid-air collision as the benchmark for maintaining distance from obstacles, maximizing the use of airspace capacity and improving operational efficiency. Attached Figure Description

[0034] Figure 1 This is a flowchart illustrating the overall implementation of a UAV operation path planning method that considers air-ground cooperation risks according to the present invention.

[0035] Figure 2 This is a schematic diagram illustrating the construction of a three-dimensional geographic environment and protected area according to the present invention.

[0036] Figure 3 This is a flowchart of the path search algorithm of the present invention. Detailed Implementation

[0037] The present invention is described below based on embodiments, but the invention is not limited to these embodiments. In the detailed description of the invention below, certain specific details are described in detail. Those skilled in the art will fully understand the invention even without these details. To avoid obscuring the essence of the invention, well-known methods, processes, flows, elements, and circuits are not described in detail.

[0038] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described below with reference to the accompanying drawings.

[0039] This invention proposes a method for planning the operational path of unmanned aerial vehicles (UAVs) that considers the risks of air-ground cooperation, such as... Figure 1 As shown, the specific steps are as follows:

[0040] Step S1: Determine the low-altitude operating area of ​​the drone, such as... Figure 2 As shown, the selected area is modeled and encoded in three dimensions using a grid, represented by coordinates (x, y, z), and the center of each grid is used as a candidate waypoint.

[0041] Step S2: Establish a low-altitude obstacle protection zone within the planned area. For each three-dimensional grid within the planned area, introduce a grid hazard level that combines aerial collision risk and ground impact risk to quantitatively describe the operational risk of UAVs within the grid. This includes the following steps:

[0042] Step S21: Within the UAV's operating airspace, special areas such as buildings and restricted airspace are complex. It is necessary to delineate protection zones for each building. The protection zone is a three-dimensional area enclosed by the smallest circumscribed cylinder. Furthermore, the grid airspace is delineated based on whether it is occupied.

[0043]

[0044] Step S22: Construct the minimum circumscribed cylinder collision protection zone for the UAV, and calculate the collision range R between each grid cell and its surrounding cells. detec The collision probabilities of N obstacles with radius r. Each event is defined as an independent event, and the total probability of an aerial collision is calculated accordingly.

[0045]

[0046] in, Let be the three-dimensional probability integral of the UAV trajectory error in each obstacle collision zone, where the relative distance Δd follows a given formula. Let h be a three-dimensional Gaussian distribution with mean and variance.

[0047] Step S23: Calculate the ground risk of falling for each grid cell: S(x,y,z)=[S d ,S p ,S n ], [S d ,S p ,S n These correspond to the risks of personal injury, property damage, and noise impact, respectively.

[0048] S d (x,y,z)=p death ·V population (x,y,z), p death V represents the fatality rate caused by the impact. population (x,y,z) represents the population density in the corresponding grid cell;

[0049] in, μ = 3.0467, σ = 0.76023,

[0050] S n (x,y,z)=L×V population (x,y,z), L s =80db, This is the noise attenuation index.

[0051] Step S24: Calculate the danger level coefficient for each grid cell: r(x,y,z)=P C (x,y,z)·S(x,y,z).

[0052] Step S3: Obtain information on delivery demand points, logistics network points, protected area locations, and drone performance parameters, such as maximum turning angle, minimum turning buffer distance, and minimum collision interval; specifically, this includes the following steps:

[0053] Step S31: Based on the population density and distribution characteristics of residential buildings, office buildings, delivery outlets, and protected areas within the selected area, obtain the takeoff coordinates A of the UAV. s = (x0, y0, z0) and landing coordinates A e =(x e ,y e ,z e);

[0054] Step S32: Obtain the performance parameter constraints of the logistics drone:

[0055] Maximum turning angle: adjacent waypoints (x i-1 ,y i-1 ,z i-1 ),(x i ,y i ,z i ),(x i+1 ,y i+1 ,z i+1 Turning angle between The turning angle of the logistics drone must not exceed the maximum turning angle. Must meet:

[0056]

[0057] (1) Maximum climb angle: Climb angle c i The maximum climb angle c of the logistics drone cannot be exceeded. max It must meet the following requirements:

[0058]

[0059] (2) Maximum descent angle: descent angle d i The maximum descent angle of the logistics drone cannot be exceeded. max It must meet the following requirements:

[0060]

[0061] (3) Flight altitude range: Due to the limitations on the service ceiling of drones and the influence of urban low-altitude airspace management policies, the flight altitude range of logistics drones is limited. i It should not exceed the maximum allowable height H. max Not lower than the minimum allowable height H min It must meet the following requirements:

[0062] H min ≤z i ≤H max

[0063] (4) Minimum Turning Buffer Distance: The UAV needs a certain buffer distance before turning to ensure safe cornering. Assume the coordinates of two adjacent turning points on the flight path are (x i ,y i ,z i ),(x j ,y j ,z j By default, it flies in a straight line between turning points, and the distance between adjacent turning points is greater than the minimum turning buffer distance. minIt must meet the following requirements:

[0064]

[0065] (5) Minimum spacing distance: The UAV is in the range of R detec The collision probabilities of N obstacles with radius r. All are not less than a fixed value of 0.05:

[0066]

[0067] Step S4: Considering the limitations of the complex low-altitude environment and the performance constraints of the UAV, and taking the minimization of route cost as the objective function, an improved A* algorithm based on the cost function is used to search for the optimal route between all target points, generating an optimal route set; specifically including the following steps:

[0068] Step S41: Suppose the path passes through a point A. k =(x k ,y k ,z k ), calculate the cost of the journey:

[0069]

[0070] Step S42: Calculate the risk cost:

[0071]

[0072] In the formula, δ is the risk penalty coefficient, and r i For grid hazard level, l i This represents the distance the drone travels within the corresponding grid.

[0073] Step S43: Calculate the route search cost function, let g(k) = ω1C L (k)+ω2C D (k), where C L (k) and C D (k) represent the starting point A. s To the current point A k The actual voyage cost and the risk cost, where ω1 and ω2 are both weighting coefficients;

[0074] Step S44: Calculate the heuristic function. Using the current point A... k With destination A e =(x e ,y e ,z e The distance to () is used as a heuristic function:

[0075]

[0076] Step S45: Using minimizing route cost as the objective function, the improved A* algorithm based on the cost function is used to search for the optimal UAV operating path, setting the starting point A... s Add the node to the Open list, check if it's empty. If not empty, select the node with the lowest cost in the list, remove it from the Open list, and add it to the Close list. Then check if the node is the destination. If not, repeat the previous step. If it is the destination, store it as the optimal path and optimal cost. Figure 3 As shown.

[0077] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for planning the operational path of a UAV that considers the risks of air-ground cooperation, characterized in that: Includes the following steps: Step S1: Use a grid method to create a 3D model of the planned area, constructing a low-altitude UAV operating grid environment, determining the range of the UAV's low-altitude operating area, and performing 3D grid modeling and encoding on the selected area, using coordinates... This indicates that the center of each grid cell is used as an alternative waypoint; Step S2: Establish a low-altitude obstacle protection zone within the planned area. For each three-dimensional grid within the planned area, introduce a grid hazard level that combines aerial collision risk and ground impact risk to quantitatively describe the operational risk of UAVs within the grid; specifically including: Step S21: Within the airspace where the UAV operates, a protection zone needs to be delineated for each building. The protection zone is a three-dimensional area surrounded by the smallest circumscribed cylinder, and the grid airspace needs to be delineated to determine whether it is occupied. Step S22: Construct the minimum circumscribed cylinder collision protection zone for the UAV, and calculate the collision distance between each grid cell and its surrounding cells. The collision probabilities of N obstacles with radius r. Each event is defined as an independent event for calculating the total probability of aerial collision: Step S23: Calculate the ground risk of each grid cell falling. ,in These correspond to the risks of personal injury, property damage, and noise impact, respectively. Step S24: Calculate the grid hazard level for each grid cell: ; Step S3: Obtain information on delivery demand points, logistics network points, protected area locations, and drone performance parameters; Step S4: Considering the limitations of the complex low-altitude environment and the performance constraints of the UAV, and taking the minimization of route cost as the objective function, an improved cost function-based approach is adopted. The algorithm searches for the optimal route between all target points and generates a set of optimal routes; specifically, it includes: Step S41: Suppose the path passes through a certain point. Calculate the cost of the journey: Step S42: Calculate the risk cost: In the formula, This is the penalty coefficient for the degree of danger. The grid hazard level for the corresponding grid. This represents the distance the drone travels within the corresponding grid. Step S43: Calculate the route search cost function, let... ;in, and Representing the starting point To the current point The actual cost of the flight and the cost of the danger. and All are weighted coefficients; Step S44: Calculate the heuristic function, using the current point and landing coordinates. Distance is used as a heuristic function: ； Step S45: Using minimizing route cost as the objective function, an improved cost function-based approach is adopted. The algorithm searches for the optimal drone flight path.

2. The UAV operation path planning method considering air-ground cooperation risks according to claim 1, characterized in that, Step S3 specifically includes: Step S31: Based on the population density and distribution characteristics of the selected area, obtain the takeoff coordinates of the UAV. Landing coordinates ; Step S32: Obtain the performance parameters of the logistics drone: maximum turning angle, maximum climb angle, maximum descent angle, flight altitude range, and minimum collision interval.

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