Low-cost UAV trajectory planning method based on noise protection zones

Abstract

This invention discloses a low-cost flight path planning method for unmanned aerial vehicles (UAVs) based on noise protection zones. The method includes the following steps: establishing a total cost objective function based on the sum of the total energy consumption cost and the total noise cost of the UAV's entire flight process; and, based on the total cost objective function and a pre-defined ring-shaped noise protection zone around buildings, planning the optimal UAV flight path at a preset flight altitude that minimizes the total cost while ensuring the UAV avoids the noise protection zone. This invention, combining total energy consumption cost, total noise cost, and Dijkstra's algorithm, and utilizing the setting of noise protection zones around buildings, enables the planning of efficient and safe flight paths for UAVs at preset flight altitudes with minimal noise impact and lowest cost within urban low-altitude airspace.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) path planning technology, and in particular to a low-cost UAV path planning method based on a noise protection zone. Background Technology

[0002] In recent years, with the development of unmanned aerial vehicle (UAV) technology, electric vertical take-off and landing (EVTOL) unmanned aerial vehicles have received widespread attention. They have advantages such as vertical take-off and landing, no need to rely on traditional runway take-off and landing, high mobility, and high efficiency. At the same time, the take-off and landing sites require a small area, saving land resources and having the potential for zero emissions in the future, which is very suitable for the requirements of UAV operation in urban airspace.

[0003] In urban environments, due to the numerous and complex layouts of buildings, UAVs operating in such airspace face dual requirements for both efficiency and safety in their path planning algorithms. The core objective of UAV path planning is to find the optimal flight path from the starting point to the destination while adhering to operational specifications, ensuring the UAV can safely and efficiently complete its designated task. To address this problem, researchers have proposed various algorithmic solutions, which can be broadly categorized into two main types: static path planning and dynamic path planning.

[0004] Static path planning algorithms typically optimize for pre-known and unchanging environmental layouts, while dynamic path planning algorithms can adapt to dynamically changing factors in the environment, such as moving obstacles, thus providing real-time flight path adjustments for UAVs. Urban environments are complex with numerous buildings, and UAVs operating in urban airspace often require efficient path planning algorithms to improve their safety and flight efficiency. UAV path planning refers to finding the optimal flight trajectory from the starting position to the target position while meeting operational requirements, ensuring the successful completion of the UAV's flight mission. Many algorithms have been proposed for UAV path planning, which can be broadly classified into two categories: static path planning algorithms and dynamic path planning algorithms. Static path planning algorithms include: A* algorithm, Dijkstra's algorithm, Voronoi diagram method, fast random number generation algorithm, ant colony optimization, particle swarm optimization, genetic algorithm, etc.

[0005] Dynamic path planning algorithms include ripple diffusion algorithm, artificial potential field method, D* algorithm, etc. Currently, there is much research in the field of UAV path planning, with new algorithms constantly being proposed, improved, and integrated. For example, some researchers have proposed using a grid method combined with an improved A* algorithm, storing cost estimation functions in the grid and using dynamic step size search to plan UAV flight paths. Other literature addresses the issue that the spatial discretization of the grid method significantly increases the number of nodes and links in the model, proposing a ripple diffusion algorithm that comprehensively considers UAV performance constraints. Still other literature proposes new improvement principles for the traditional Voronoi diagram and combines it with the ant colony algorithm to improve the efficiency of path planning. One published paper addresses the problem of local convergence in ant colony algorithms during path planning by proposing an improved ant colony algorithm incorporating the whale algorithm, reducing the difficulty of path search and improving the algorithm's convergence speed. Some literature addresses the problem of local optima in traditional gray wolf optimization algorithms during path planning by employing new strategies such as Cat mapping and introducing nonlinear convergence factors to propose an improved gray wolf optimization algorithm, making path planning faster and more accurate. Some literature addresses the poor performance of the Fast Expanded Random Number (FER) method in 3D path planning by proposing a novel EHT-RRT algorithm based on exploration, heuristics, and transition, resulting in more stable and smoother planned paths. Other literature combines the Smart Droplet algorithm with the Fusion Ant Colony Optimization algorithm, employing a novel node selection strategy to improve algorithm convergence rate and path planning efficiency. Still other literature addresses the traffic congestion problem of urban logistics drones by proposing a two-layer simulated annealing algorithm, improving operational efficiency.

[0006] The aforementioned literature focuses on the path planning problem for unmanned aerial vehicles (UAVs), emphasizing the comprehensive consideration of UAV performance parameters to reduce operational risks. It also improves convergence rate and speed through algorithmic enhancements, thereby improving the efficiency and accuracy of path planning. These methods are used to plan optimal flight trajectories for UAVs.

[0007] However, these studies have limitations in considering the practical impacts of drone operation, particularly neglecting the potential noise issues and operating costs. These factors are crucial in real-world applications; noise can affect social acceptance, while cost directly relates to the economic feasibility of drone deployment.

[0008] The aforementioned literature studies drone path planning, considering drone performance, reducing operational risks, improving algorithm convergence rate and speed, and enhancing the efficiency and accuracy of path planning. It uses algorithms to plan the optimal drone operating path. However, it rarely considers the real-world impacts of drone operation—the noise and operating costs. Whether drone noise meets urban needs and public acceptance is a major issue. Currently, there is increasing attention to drone noise, but research in this area is limited, with most studies simply incorporating noise into drone operating costs in conjunction with other expenses. Summary of the Invention

[0009] The purpose of this invention is to overcome the shortcomings and defects of existing technologies and provide a low-cost drone trajectory planning method based on noise protection zones. It proposes the concept of noise protection zones, which can reduce the noise impact of drones by adding noise protection zones to buildings, thus optimizing drone noise while ensuring drone operational safety. Furthermore, it employs a planning method that combines Dijkstra's algorithm with an improved Dubins algorithm for geometric path planning, introducing a total cost objective function to achieve the optimal drone operating path with the lowest cost and lowest noise.

[0010] A low-cost flight path planning method for UAVs based on noise protection zones includes the following steps:

[0011] A total cost objective function is established based on the sum of the total energy consumption cost and the total noise cost of the entire flight process of the UAV;

[0012] Based on the total cost objective function and the preset ring-shaped noise protection zone of the building, the optimal operating path of the UAV with the minimum total cost at the preset flight altitude is planned, while ensuring that the UAV avoids the noise protection zone.

[0013] The noise protection zone is a ring-shaped area set outside the building below the area through which the drone flies, located between the outer circle of the building and a concentric outer circle outside the outer circle.

[0014] When a drone flies outside a noise protection zone, it has no noise impact on buildings within the noise protection zone. However, when it is on the outer circle of the noise protection zone, it begins to have a noise impact on buildings. The noise impact of the drone on buildings increases with the depth of the drone entering the noise protection zone.

[0015] The width of the annular zone of the noise protection area is determined based on the noise range / distance that the drone's noise affects the building.

[0016] The width of the annular zone of the noise protection area is determined according to the following formula:

[0017]

[0018] In the formula, S represents the width of the annular noise protection zone, and T mi Let A represent the thrust of each motor of the drone, A represent the area of ​​the drone's propeller disk, θ represent the angle between the drone and the observation point, and M represent the thrust of each motor of the drone. t P is the tip Mach number. h To represent the power absorbed, B represents the number of propeller blades, R' represents the propeller radius, and P0 is the reference sound pressure level. m For harmonic order, P SPL The value represents the sound pressure level, and n represents the number of motors in the drone.

[0019] The expression for the total cost objective function is as follows:

[0020] C=δ1ln(E N )+δ2ln(S P In the formula, C represents the total cost objective function, and S... P It is the total noise cost of the entire flight process of the drone, E N It represents the total energy consumption cost of the drone during the entire flight process, where δ1 and δ2 represent weighting coefficients, and ln is a function.

[0021] Wherein, the total noise cost S of the entire flight process of the UAV P The noise cost to those below is calculated using the following formula:

[0022]

[0023] S P，i =S O *PPL i ,

[0024] PP i L = ψ P.i.AVG S d ,

[0025] In the above formula, j represents the number of segments into which the planned UAV flight path is divided, and the population density of each segment is the average population density of that segment; S P，i S represents the noise cost on drone flight segment i. O S represents the noise cost of drones to humans. d The flight segment i is widened to both sides d s The area after, d s ψ represents the horizontal distance between the drone and the person. P.i.AVG PP represents the average population density on drone flight segment i; i L represents the population on drone segment i.

[0026] Among them, the total energy consumption cost E of the entire flight process of the UAV N The expression is as follows:

[0027] E N =E U +E H +E D In the formula, E H E represents the energy cost of a drone during horizontal flight. U With E D These are the energy costs for a drone's vertical takeoff and vertical descent, respectively.

[0028] The process of planning the optimal UAV operating path with the minimum total cost at the preset flight altitude based on the total cost objective function and the preset noise protection zone involves calculating the total energy consumption cost of each flight path between the start and end points of the UAV outside the noise protection zone, and selecting the flight path with the lowest total energy consumption cost as the optimal UAV operating path with the minimum total cost at the preset flight altitude.

[0029] Each flight path consists of horizontally connected straight segments at multiple predetermined flight altitudes between the start and end points of the UAV. These straight segments are tangent to the outer circle of the ring-shaped noise protection zone set outside each building / building complex along the UAV's flight path.

[0030] The algorithm employs Dijkstra's algorithm and an improved Dubins algorithm to plan the optimal UAV operating path with the lowest total cost. When the flight segments of two adjacent noise protection zones intersect, the improved Dubins algorithm is used to smooth the flight segments between the two adjacent noise protection zones.

[0031] The smoothing process for the flight segments of two adjacent noise protection zones includes drawing a tangent line from the starting point of the UAV across the preceding noise protection zone to the following noise protection zone on the same side of the intersecting flight segments, forming a smooth flight segment between the outer circles of the starting point and the following noise protection zone. The straight line of this smooth flight segment is not tangent to the preceding noise protection zone.

[0032] When planning the flight path of the UAV, this invention sets up a noise protection zone and uses a noise protection zone model to reduce the noise impact generated by the UAV operation. The noise cost to the ground population is also considered in the operating cost of the UAV, making the flight path planning of the UAV more comprehensive and effectively reducing the noise impact generated by the UAV operation.

[0033] In the UAV trajectory planning of this invention, an improved Dubins path planning method is proposed to enrich path selection and optimize path smoothing. The improved Dubins path planning provides a richer selection of paths and shortens the UAV's flight distance, thereby improving operational efficiency. At the same time, based on the combination of total energy consumption cost, total noise cost and Dijkstra's algorithm, and based on the setting of noise protection zones for buildings, it plans an efficient and safe operating path for the UAV with the lowest noise impact and lowest cost at a preset flight altitude in urban low-altitude airspace. Attached Figure Description

[0034] Figure 1 This is a flowchart of the low-cost flight path planning method for UAVs based on noise protection zones, as described in this invention.

[0035] Figure 2A , Figure 2B as well as Figure 2C This is a schematic diagram of the invention involving an circumscribed circle on the outer perimeter of a building.

[0036] Figure 3 The diagram shows the calculation of the center and radius of the circumcircle of a building on the XOY plane.

[0037] Figure 4 This is a schematic diagram illustrating the division of the airspace for drone operations into altitude layers.

[0038] Figure 5 A schematic diagram showing the addition of the width S of the noise protection zone to the building model.

[0039] Figure 6 This is a diagram illustrating a drone's flight path through a noise protection zone.

[0040] Figure 7 This is a schematic diagram of a drone flying at a predetermined altitude.

[0041] Figure 8 This diagram illustrates the noise impact on people when a drone is flying at a predetermined altitude.

[0042] Figure 9 This is a schematic diagram of the flight path of a drone bypassing a noise protection zone of a single building.

[0043] Figure 10 This is a diagram illustrating a drone flying from location A to location B, bypassing multiple buildings.

[0044] Figure 11 This is a schematic diagram of the flight path planning for a drone as it passes through two buildings from the starting point to the end point.

[0045] Figure 12 This is a schematic diagram of the smoothing process applied to the selected flight path.

[0046] Figure 13 This is a schematic diagram of the building distribution in the airspace under the drone during the simulation test.

[0047] Figure 14A , Figure 14B This is a comparison of the noise-free, lowest-cost path of this invention among all planned paths with the optimal path obtained according to the A* algorithm. Figure 1 .

[0048] Figure 15A , Figure 15B Figure 2 compares the noise-free lowest-cost path of this invention with the optimal path obtained according to the A* algorithm among all planned paths.

[0049] Figure 16A , Figure 16B This is a comparison of the noise-free, lowest-cost path of this invention among all planned paths with the optimal path obtained according to the A* algorithm. Figure 3 .

[0050] Figure 17A , Figure 17B This is a comparison of the noise-free, lowest-cost path of this invention among all planned paths with the optimal path obtained according to the A* algorithm. Figure 4 . Detailed Implementation

[0051] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0052] This invention addresses the noise problem of drones mentioned in the background art by proposing a noise protection zone. By planning drone routes according to this noise protection zone, low-noise or even noise-free operation of drones can be achieved. Furthermore, since cost determines the economics of drone operation, this invention proposes a total cost objective function that considers energy consumption costs and the noise costs generated by low-altitude drones in urban areas to assess the total cost of the planned route.

[0053] Traditional Dubins geometric path planning seeks the optimal path from the starting point to the target point by continuously linking tangents between two building models. This invention improves upon traditional Dubins by constructing all tangents between each building model and using an improved path optimization method to optimize tangents that do not conform to the actual situation, making the route more efficient and smoother.

[0054] In this invention, "UAV" refers to a vertical takeoff and landing (VTOL) UAV. Flight path planning refers to planning an operational path for a UAV operating within an urban area, where the starting and ending points are known, to meet its operational requirements. The maximum operating altitude of the UAV is no more than 120m; only energy consumption costs are considered during the VTOL phase.

[0055] Please refer to Figure 1 As shown, the low-cost flight path planning method for unmanned aerial vehicles (UAVs) based on noise protection zones according to an embodiment of the present invention includes the following steps: establishing a total cost objective function based on the sum of the total energy consumption cost and the total noise cost of the UAV throughout its flight process; and planning the optimal UAV flight path with the minimum total cost at a preset flight altitude based on the total cost objective function and a preset ring-shaped noise protection zone of buildings, while ensuring that the UAV avoids the noise protection zone.

[0056] The noise protection zone is a ring-shaped area set outside the building below the area where the UAV flies, located between the circumcircle of the building and a concentric outer circle outside the circumcircle. When the UAV flies outside the noise protection zone, it has no noise impact on the building inside the noise protection zone. When it is on the outer circle of the noise protection zone, it begins to have a noise impact on the building. The noise impact of the UAV on the building increases with the depth of the UAV entering the noise protection zone.

[0057] The width of the annular zone of the noise protection area is determined based on the noise range / distance that the drone's noise affects the building.

[0058] In this application, the noise protection zone is established by modeling the city where the UAV operates based on building parameters: First, from the top-down view of the building, a circumcircle is added around its perimeter. The center of this circumcircle is the center of the building, and the distance from this midpoint to the farthest point on the building's boundary is the radius of the circumcircle. See [link to relevant documentation]. Figure 2A , Figure 2B as well as Figure 2C As shown, Figure 2A , Figure 2B This model illustrates the circumcircle surrounding individual buildings with varying shapes. Since the location, height, and other parameters of buildings within a city are known, and their styles and shapes differ significantly, a circumcircle is added around each building from its top-down view. The center of this circumcircle is the center of the building, and the distance from the center point to the farthest point on the building's boundary is the radius of the circumcircle. This model effectively avoids the risk of collisions between drones and buildings. It should be noted that in some cases, there are building clusters within a city where the buildings are very close together. These small clusters can be considered as a single entity. (See...) Figure 2CAs shown, the model is created with the center of the building complex (containing three rectangular buildings in top view) as the center point. The resulting building model is as follows. Figure 2A , Figure 2B as well as Figure 2C As shown, by inserting the aforementioned building model into the coordinate system, the model on the XOY plane is as follows. Figure 3 As shown, the center and radius of the building's circumcircle can be obtained. See [link / reference]. Figure 3 As shown, quadrilateral A1A2A3A4 is the projection of a building or building complex onto the XOY plane, and each point corresponds to a specific coordinate, such as... Figure 3 As shown, circle O0 is the center of the circumcircle of the building or building complex, and r is the radius of the circumcircle. The center coordinates (X0, Y0) and radius r can be obtained from the coordinates of the four vertices of the building or building complex. The noise protection zone is defined as a concentric outer circle outside the circumcircle. The annular area between the circumcircle and the outer circle constitutes the building's noise protection zone. During path or trajectory planning, the UAV at the predetermined flight altitude is directed to fly outside the noise protection zone.

[0059] Since drones fly at a preset altitude, when planning their routes, it is necessary to divide the urban airspace environment in which the drones will operate, assign operating altitude layers to the drones, and plan their flight paths based on the drones' operating altitudes.

[0060] Among these measures, rationally allocating operating altitude layers for drones can effectively improve their operational efficiency and reduce their operating costs. Specifically, this involves acquiring the heights of all buildings within the drone's operating airspace and rationally dividing the airspace into altitude layers. Each altitude layer is defined as a separate altitude interval, as shown in the diagram below. Figure 4 As shown.

[0061] See Figure 4 As shown, when a drone flies at different altitude levels within the same altitude range, the positions of buildings within that altitude range remain unchanged, and the planned path also remains the same. Figure 4 The diagram divides the airspace where drones can operate into four altitude layers: h1, h2, h3, and h4. Altitude layer h0 represents the maximum altitude at which a drone will cause noise pollution to pedestrians. When a drone operates below h0, it will cause noise pollution to pedestrians; when it operates above h0, it will no longer cause noise pollution. The altitudes corresponding to h1, h2, h3, and h4 can be further subdivided according to different cities and regions.

[0062] In this application, the noise range (distance) that affects the building caused by the drone noise, i.e. the width S of the noise protection zone, is calculated and then added to the building model. Based on this application, the optimal drone operation path with the minimum total cost at a preset flight altitude is planned while the drone avoids the noise protection zone.

[0063] This invention proposes noise protection zones, which, by adding noise protection zones to buildings, can significantly reduce the noise impact of drone operations on buildings. The introduction of noise protection zones enables drones to operate without noise and at low cost above a certain altitude, and to operate with low noise and low cost below a certain altitude, thus providing a guarantee for drone operation in urban areas.

[0064] In this invention, the main consideration for drone noise is the operating rotation noise.

[0065] Rotational noise P of a single motor propeller of a drone rel Represented as:

[0066]

[0067] In the formula, ρ0 = 1.225 kg / m 3 This refers to the air density at standard atmospheric sea level, B represents the number of propeller blades on the UAV, R' represents the propeller radius on the UAV, and M... t P is the tip Mach number. h To absorb power, m For harmonic order, P SPL The sound pressure level is represented by P0, the reference sound pressure level is P0, the width of the annular noise protection zone is S (the difference between the radius of the circumscribed circle and the radius of the outer circle), A represents the area of ​​the drone's propeller disk, and T represents the sound pressure level. mi Let θ represent the thrust of each motor of the drone, and θ represent the angle between the drone and the observation point.

[0068] Therefore, in this application, the noise range (distance) that affects the building due to the drone noise, i.e. the width S of the noise protection zone, is calculated as follows;

[0069]

[0070] In the formula, n represents the number of motors in the drone;

[0071] Using the Burgers thrust model, the thrust of each motor of the UAV is obtained as T. mi :

[0072]

[0073] V tip=Π*N*d (5)

[0074] In the formula, V ∞ V represents the airspeed of the drone. tip The speed of the drone's propeller tip is represented by N, the propeller revolutions per second is represented by d, and S is represented by S. b η represents the total area of ​​the propeller blades, η represents the normalized thrust-to-work ratio, ρ represents the air density, and Π represents the calculation model for wingtip velocity in the thrust model.

[0075] The obtained noise protection zone width S is added to the building model, such as... Figure 5 As shown, the center of the building model is point O, the building's center point. r is the radius from point O to the building's circumcircle, and R is the radius from point O to the outer circle of the protected area. The annular area between the building's circumcircle and the outer circle of the protected area constitutes the newly added building noise protection zone. When the drone flies outside the protected area, it has no noise impact on the building. However, when it is on the outer circle of the noise protection zone, it begins to cause noise pollution. Furthermore, the noise level increases with depth within the protected area. A conflict diagram is shown below. Figure 6 As shown, Figure 6 In the diagram, the straight line with an arrow passing through the circular area is the flight path of the drone.

[0076] Energy consumption is a key cost consideration for drones during operation. Drone operation can be divided into three phases: horizontal flight, vertical takeoff, and vertical landing. The battery output power and energy consumption vary during each phase.

[0077] During the vertical takeoff and landing phases of the drone, the thrust generated by the motor is mainly used to overcome its own gravity. During this phase, the battery's output power P... L for:

[0078]

[0079] In the formula, W P W M W ESC These represent the operating efficiency of the drone's propeller, motor, and ESC, respectively; M represents the total weight of the drone; g represents the acceleration due to gravity; and τ is a correction factor.

[0080] When the drone is in a horizontal flight phase, the thrust generated by the motors mainly overcomes air resistance. During this phase of horizontal flight, the battery's output power P... H for:

[0081]

[0082] In the formula, σ represents the windward area, and V HC represents horizontal velocity. D0 denoted by , where represents the zero-lift drag coefficient, and k represents the induced drag factor.

[0083] The battery output power of a drone during operation is related to air density. If the drone is operating at an altitude of H1, its flight altitude is H2, and its travel distance is L, then... Figure 7 As shown, the energy consumption E of the drone during horizontal flight H The expression is:

[0084]

[0085] The density of air is:

[0086]

[0087] ρ0 represents the air density under standard atmospheric conditions, T0 represents the temperature under standard atmospheric conditions, ζ represents the air gas constant, and K represents the density of air. T This represents the temperature gradient below the tropopause; air density varies in different urban areas and at different altitudes. According to the BADA manual, the air density and temperature at standard atmospheric sea level are ρ0 = 1.225 kg / m³. 3 T0 = ​​15℃ / 288.15K. Air density and temperature T decrease with increasing altitude; for every 100 meters increase in altitude, the temperature decreases by 0.6℃. The expression for the decrease in temperature T with increasing altitude is T = T0 - 0.6(H + H). a In the formula, H a H represents altitude, and H represents the operating altitude of the drone. The expression for the change in air density is:

[0088] The drone takes off vertically at a speed of V. U The landing speed is V D The energy consumption E for vertical takeoff and landing is obtained. U With E D The expression is:

[0089]

[0090]

[0091] In the above formula, ρ U , ρ D represents the air density during the drone's vertical takeoff and the air density during the drone's vertical landing, respectively, and t represents the drone's flight time.

[0092] Therefore, the total energy consumption cost E of the UAV proposed in this application during the entire operation process N for:

[0093] E N =E U +E H +E D (14)

[0094] The noise cost model considers the noise impact on people on the ground when the drone operates at a low altitude. Therefore, noise costs must be taken into account during the drone's flight plan. The noise impact of drones decreases as the operating altitude increases, and after a certain altitude, its impact on people on the ground is no longer considered. When drones fly at low altitudes, especially when passing over densely populated areas such as public places, the noise from drones will have a greater impact on people on the ground. The noise impact of drones on people is as follows: Figure 8 As shown. Figure 8 In the diagram, h represents the height of the drone above a person's head, and d represents... s The horizontal distance between a person and a drone.

[0095] Sound propagation approximates spherical diffusion, therefore the noise cost S of drone operation to humans is... O Represented as:

[0096]

[0097] In the formula, ω represents the conversion coefficient from sound intensity to sound level, and S C This represents the reference noise level of the drone.

[0098] Population density is a key factor in measuring the noise impact of drones on people. Assuming the center of the planned route area is the area with the highest population density (busy area), and the population density gradually decreases outwards in a circle from the center, a gravity model is used to better assess the population density distribution within the urban area, as expressed below:

[0099]

[0100] In the above formula, ψ P ψ represents the population density within an area with a radius of r1 from the city center, where r1 represents the distance from the city center. P.AVG Average population density within the entire urban area.

[0101] In subsequent calculations, the planned flight route is divided into j segments, and the population density of each segment is the average population density of that segment. Therefore, the total noise cost is:

[0102] PPL i =ψ P.i.AVG S d (17)

[0103] S P，i =S O *PPLi (18)

[0104]

[0105] In the formula, j represents the number of segments into which the planned UAV flight path is divided, and the population density of each segment is the average population density of that segment; S P，i S represents the noise cost on drone flight segment i. O S represents the noise cost of drones to humans. d The flight segment i is widened to both sides d s The area after, d s ψ represents the horizontal distance between the drone and the person. P.i.AVG PP represents the average population density on drone flight segment i; i L represents the population on drone segment i.

[0106] In this invention, the proposed total cost objective function C is the sum of the energy consumption cost and noise cost of the UAV operation. Using a combination of ln function normalization and a cost model, the expression for the total cost objective function is:

[0107] C=δ1ln(E N )+δ2ln(S P (20)

[0108] This invention is based on a noise protection zone and uses a combination of Dubins path planning and Dijkstra's algorithm to plan the optimal operating path for the UAV with the minimum total cost C.

[0109] Dubins path planning is a widely applicable path planning method, offering smooth and efficient paths. Dijkstra's algorithm is a commonly used method for finding the shortest path within a region based on minimum cost, efficiently determining the lowest-cost path from the starting point to any node within the region. This invention efficiently combines Dijkstra's algorithm with Dubins path planning, enabling the planning of noise-free (or low-noise) and lowest-cost paths for UAVs at various altitude levels.

[0110] To ensure noise-free drone operation, it's necessary to avoid the circular areas of noise protection zones. Applying Dubins path planning principles, the drone's path is constructed using tangents and arcs of a circle. This yields the lowest-cost and shortest route while maintaining safety and minimizing noise impact. Drones should avoid crossing noise protection zones as much as possible; therefore, methods for drones to bypass noise protection zones around individual buildings include... Figure 9 As shown: Figure 9 A schematic diagram of the drone's path is shown. Figure 9The inner circle CEFD is the outer circle of the noise protection zone, with point O as the center, point A as the starting point, and point B as the destination point. Traveling from point A to point B requires detouring around the noise protection zone away from buildings. There are two methods to draw a tangent from a point to the circle, meaning there are two paths from point A to point B: tangent AC → arc CE → tangent EB and tangent AD → arc DF → tangent FB. The line segments from the center of the circle to each tangent point form certain angles; OC forms an angle α with OE, and OD forms an angle β with OF.

[0111] Assuming the distance from point A to the center O of the circle is L1, the distance from point B to the center O of the circle is L2, and the length of the straight line along the AB path is L3, then the calculation model or expression for each path length is as follows:

[0112] The distance from point A to point B via points C and E is:

[0113]

[0114] The distance from point A to point B via points D and F is:

[0115]

[0116] The above method only describes how a drone can bypass a single building. In urban environments, where there are numerous buildings, the diagram above illustrates how a drone can bypass multiple buildings when flying from point A to point B (only the optimal tangent path is retained). Figure 10 As shown. Figure 10 A schematic diagram of Dubins' path planning is shown. Figure 10 The diagram shows three buildings with centers O1, O2, and O3. Each building has two tangent points on its outer circle, C, D, E, F, G, and H. Multiple line segments connect to form the planned flight path from point A to point B, bypassing these buildings. Traditional Dubins methods plan paths based on the shortest distance for the drone. However, considering drone operating costs, this needs improvement. Therefore, this invention uses a geometric method: two tangent lines can be drawn from a point to a circle, and the two circles (see...) Figure 11 As shown, there are four common tangents between the centers O1 and O2. The two ends of each tangent are considered as two nodes. The circle represents the noise protection zone, and the tangent is the flight path of the drone. Tangent paths between the noise protection zones of all buildings are found. Based on the cost model, the cost information of each path is obtained. Finally, Dijkstra's algorithm is used to find the optimal path from the starting point through all nodes to the target point based on the cost information. The improved Dubins path planning method in this application has numerous nodes and paths, greatly enriching the path options. The planning diagram is shown below. Figure 11 As shown. Figure 11 A schematic diagram of the improved Dubins path planning is shown. Figure 11This is just a simple example; a total of four optional paths are planned from the starting point to the end point (the number of optional paths gradually increases as more buildings are added).

[0117] In certain situations, the tangents between two protected area models may intersect, making the drone's flight path uneven and increasing operating costs, necessitating smoothing. For tangents drawn from two different protected area models (tangents on the same side), if the tangents do not intersect, the Dubins method can be used normally for path planning. If the tangents intersect, smoothing is performed, meaning that on that side, a tangent can be drawn directly from the starting point to the next protected area, bypassing the previous protected area. This makes the path smoother, shortens the drone's flight distance, and reduces operating costs. A smoothing diagram is shown below. Figure 12 As shown. Figure 12 The diagram shows an improved Dubins path smoothing process. As shown by the red line in the figure, the line segments where the two boxes intersect are smoothed to form a single line segment.

[0118] Simulation test

[0119] Parameter settings

[0120] The noise protection zone model parameters are shown in Table 1. Since the drone operates in the air and buildings provide some noise insulation, the noise impact of the drone is set at 120 dB. The weighting coefficients in the total cost C are δ1 = 0.75 and δ2 = 0.25.

[0121] Table 1. Noise Protection Zone Model Parameters

[0122]

[0123] Unit conversions for parameters in Table 1: 1 ft = 0.3048 m, 1 dyn / cm 2 =0.1Pa, 1lb =4.4482N.

[0124] The parameters of the energy consumption cost model are shown in Table 2:

[0125] Table 2 Energy Cost Model Parameter Table

[0126]

[0127] The parameters of the noise cost model are shown in Table 3:

[0128] Table 3 Noise Cost Model Parameter Table

[0129]

[0130] Exceeding a certain altitude will not cause noise impact on people on the ground. In this application, the noise cost is defined as 50dB. According to relevant literature, the corresponding flight altitude for this noise cost is 28m.

[0131] Select a region within a city, measuring 800m × 800m, with a starting point coordinate of (43, 724) and an ending point coordinate of (744, 67). Model the buildings within this region (no noise protection zones are included in the building models in the figure) and label them, such as... Figure 13 As shown. Figure 13 A schematic diagram of the building distribution is shown. Each circle in the diagram represents the circumcircle of a building, numbered 1-18 in total. The UAV flight path is from the starting point to the endpoint, passing through the group of buildings or the area where the buildings are located, which are numbered 1-18 in total.

[0132] The parameters of the building are shown in Table 4:

[0133] Table 4 Building Parameters

[0134]

[0135] Path planning

[0136] Based on the division of airspace into flight altitude layers, four altitude layers were identified: 20m, 40m, 60m, and 80m, corresponding to the altitude ranges of (20m, 40m), (40m, 60m), (60m, 80m), and above 80m, respectively. Dijkstra's algorithm and Dubins' path planning algorithm were used to plan the routes for the UAVs operating at each altitude layer.

[0137] In the following description, the red lines in the corresponding figures refer to the flight paths optimized according to the method of the present invention and the optimal path planning obtained according to the A* algorithm.

[0138] (1) When the flight altitude is 20m, remove buildings below this altitude and retain buildings numbered 1, 5, 6, 7, 10, 11, 13, 15, 16, and 18. When the flight altitude is 20m, the noise protection zone S = 8m, S O =72dB. With the building's outer circumcircle and a protected area added, the noise-free lowest-cost path of this invention is compared with the optimal path planning result obtained according to the A* algorithm among all planned paths. Figure 14A , Figure 14B As shown:

[0139] Figure 14A , Figure 14BA comparison chart of path planning results at a 20m height level is shown. The 20m height level is below the height threshold; at this level, the total cost considers both energy and noise costs. The noise cost S of the optimal path is calculated using the method presented in this invention. P =22896dB, energy cost E N =1641.088J, total cost C = 9.79. Noise cost S obtained from the A* algorithm. P =25416dB, energy cost E N =1880.695J, total cost is C=9.92.

[0140] (2) When the flight altitude is 40m, remove buildings below this altitude and retain buildings numbered 1, 7, 11, 13, 16, and 18. When the flight altitude is 40m, calculate the noise protection zone S = 17m according to the method of this invention. Add the protection zone to the outer circle of the buildings. Compare the lowest cost noise-free path among all planned paths with the optimal path planning result obtained according to the A* algorithm. Figure 15A , Figure 15B As shown:

[0141] Figure 15A , Figure 15B A comparison chart of path planning results at a 40m height level is shown. At the 40m height level, there is no noise cost impact; therefore, the total cost only considers energy consumption. The energy cost E of the optimal path is calculated using the method of this invention. N =21280.96J, total cost C=7.47. Energy cost E obtained by A* algorithm. N =23377.33J, total cost is C=7.55.

[0142] (3) When the flight altitude is 60m, remove buildings below this altitude and retain buildings numbered 1, 7, 11, 13, 16, and 18. When the flight altitude is 60m, calculate the noise protection zone S = 26m according to the method of this invention. Add the protection zone to the outer circle of the buildings. Compare the lowest cost noise-free path among all planned paths with the optimal path planning result obtained according to the A* algorithm. Figure 16A , Figure 16B As shown:

[0143] Figure 16A , Figure 16B A comparison chart of path planning results at a height of 60m is shown. At a height of 60m, there is no noise cost impact; therefore, the total cost only considers energy consumption. The energy cost E of the optimal path is calculated using the method of this invention. N =26374.79J Total cost C = 7.64. Energy cost E obtained by A* algorithm. N =29012.33J, total cost is C=7.71.

[0144] (4) When the flight altitude is 80m, remove buildings below this altitude and retain buildings numbered 7, 11, 16, and 18. When the flight altitude is 80m, calculate the noise protection zone S = 34m according to the method of this invention. Add the protection zone to the outer circle of the buildings. Compare the lowest cost noise-free path among all planned paths with the optimal path planning result obtained according to the A* algorithm. Figure 17A , Figure 17B As shown:

[0145] Figure 17A , Figure 17B A comparison chart of path planning results at an 80m height level is shown. At the 80m height level, there is no noise cost impact; therefore, the total cost only considers energy consumption costs. The energy consumption cost E of the optimal path is calculated using the method of this invention. N =31324.97J, total cost C=7.76. Energy cost E obtained by A* algorithm. N =34404.99J, total cost is C=7.84. The optimal path selection and its associated cost at different altitude levels using Dijkstra's algorithm are shown in Table 5 below:

[0146] Table 5. Calculation results of Dijkstra's algorithm

[0147] 20m 184.66 2.51 16410.88 7.28 9.79 40m 188.1 0 21280.96 7.47 7.47 60m 191.79 0 26374.79 7.64 7.64 80m 195.74 0 31324.97 7.76 7.76

[0148] The cost of selecting the optimal path at different height levels using the A* algorithm is shown in Table 6 below:

[0149] Table 6. Calculation results of Algorithm A*

[0150] 20m 184.66 2.54 18806.95 7.38 9.92 40m 188.1 0 23377.33 7.55 7.55 60m 191.79 0 29012.33 7.71 7.71 80m 195.74 0 34404.99 7.84 7.84

[0151] A comparison of the data in Tables 5 and 6 shows that the algorithm proposed in this invention achieves lower costs compared to the A* algorithm. Furthermore, the optimal altitude for UAV operation is 40m, and the minimum cost C for paths at this altitude is 7.47.

[0152] Simulation studies show that the technology or method proposed in this invention can achieve noise-free and low-cost operation at high altitudes in urban airspace, and low-noise and low-cost operation at low altitudes, with short flight times and high efficiency. Specifically, when the weights in the total cost objective cost function are 0.75 and 0.25, the optimal operating altitude for the UAV is 40m, which minimizes noise impact and minimizes cost.

[0153] In summary, the technology or method proposed in this invention establishes noise protection zones and obtains data through a noise protection zone model, thereby minimizing the noise impact of drones on the environment and people. The proposed technology or method improves the Dubins path planning method, enabling path planning based on a comprehensive cost model. Adding nodes optimizes path smoothness, shortens flight paths, and achieves low-cost operation. Furthermore, compared with the A* algorithm, this method highlights its advantages such as comprehensive path search and lower operating costs. The proposed technology or method enriches the factors considered in the cost objective function, making the consideration of drone operating costs more comprehensive and closer to reality. The proposed technology or method not only considers the impact of drones based on noise protection zones on the surrounding environment but also considers the noise impact of drones on ground populations during low-altitude operation in the cost model, making the study of noise impact more comprehensive.

[0154] In summary, the technology or method proposed in this invention is a more practical, safe, and efficient method for planning the flight path of unmanned aerial vehicles (UAVs). It significantly reduces the noise impact of UAVs flying in urban environments and effectively reduces their operating costs, providing innovative solutions and support for the application of UAVs in complex urban spaces.

[0155] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. It will be apparent to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or basic features of the present invention.

[0156] Therefore, the embodiments should be regarded as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, it is intended that all variations falling within the meaning and scope of the equivalents of the claims be included within the invention.

[0157] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A low-cost flight path planning method for UAVs based on noise protection zones, characterized in that, Including the following steps: A total cost objective function is established based on the sum of the total energy consumption cost and the total noise cost of the entire flight process of the UAV; based on the total cost objective function and the preset ring-shaped noise protection zone of the building, the optimal operating path of the UAV with the minimum total cost at the preset flight altitude is planned while the UAV avoids the noise protection zone. The total noise cost of the drone's entire flight process is calculated using the following formula: ， ， ， ， In the above formula, It is the total noise cost of the entire drone flight process. This indicates the number of segments into which the planned drone flight path is divided, with the population density of each segment being the average population density of that segment. Indicates drone flight segment The noise cost, This refers to the noise cost of drones to people. This indicates that segment i is widened to both sides. The area after This indicates the horizontal distance between the drone and the person. Indicates drone flight segment Average population density; Indicates drone flight segment Population size; This represents the conversion factor from sound intensity to sound level. The reference noise of the drone. The height of the drone above a person's head; The width of the annular zone of the noise protection area is determined according to the following formula: ， In the formula, Indicates the width of the ring-shaped noise protection zone. For the thrust of each motor of the drone, This indicates the disk area of ​​the drone's propeller. Indicates the angle between the drone and the observation point. The tip Mach number, To absorb power, Indicates the number of propeller blades. Indicates the propeller radius. For reference sound pressure level, For harmonic sequence, Indicates sound pressure level. This indicates the number of motors in the drone.

2. The low-cost UAV trajectory planning method based on noise protection zones according to claim 1, characterized in that, The noise protection zone is a ring-shaped area set outside the building below the area where the drone flies, located between the circumcircle of the building and a concentric outer circle outside the circumcircle. When the drone flies outside the noise protection zone, it has no noise impact on the building inside the noise protection zone. When it is on the outer circle of the noise protection zone, it begins to have a noise impact on the building. The noise impact of the drone on the building increases with the depth of the drone entering the noise protection zone.

3. The low-cost UAV trajectory planning method based on noise protection zones according to claim 1, characterized in that, The width of the ring-shaped area of ​​the noise protection zone is determined based on the noise range / distance at which the drone's noise affects the building.

4. The low-cost flight path planning method for UAVs based on noise protection zones according to claim 1, characterized in that, Using the Burgers thrust model, the thrust of each motor of the UAV is obtained; ； ； In the formula, Indicates the airspeed of the drone. This indicates the propeller tip speed of the drone. This indicates the number of revolutions per second of the propeller. Indicates the propeller diameter. This represents the total area of ​​the propeller blades. This represents the normalized ratio of work to kinetic energy to thrust. Indicates air density, This refers to the calculation model used to calculate wingtip velocity in the thrust model.

5. The low-cost UAV trajectory planning method based on noise protection zones according to claim 1, characterized in that, The expression for the total cost objective function is as follows: In the formula, This represents the total cost objective function. It is the total energy consumption cost of the drone throughout its entire flight process. , These represent the weighting coefficients, It is a function.

6. The low-cost UAV trajectory planning method based on a noise protection zone according to claim 5, characterized in that, The total energy consumption cost of the entire flight process of the drone The expression is as follows: In the formula, This indicates the energy consumption cost of a drone during horizontal flight. and These are the energy costs for a drone's vertical takeoff and vertical descent, respectively.

7. The low-cost flight path planning method for UAVs based on noise protection zones according to any one of claims 1-6, characterized in that, Based on the total cost objective function and the pre-defined ring-shaped noise protection zone of buildings, the optimal flight path of the UAV at a preset flight altitude is planned while avoiding the noise protection zone. This is done by calculating the total energy cost of each flight path between the start and end points of the UAV outside the noise protection zone, and selecting the flight path with the lowest total energy cost as the optimal flight path at the preset flight altitude. Each flight path consists of multiple straight segments horizontally connected at predetermined flight altitudes between the start and end points of the UAV. The straight segments are tangent to the outer circle of the ring-shaped noise protection zone set outside each building / building complex along the UAV's flight path.

8. The low-cost UAV trajectory planning method based on a noise protection zone according to claim 7, characterized in that, The optimal UAV operating path with the minimum total cost is planned using Dijkstra's algorithm and an improved Dubins algorithm. When the flight segments of two adjacent noise protection zones intersect, the improved Dubins algorithm is used to smooth the flight segments of the two adjacent noise protection zones.

9. The low-cost flight path planning method for UAVs based on noise protection zones according to claim 8, characterized in that, The aforementioned smoothing process for flight segments between two adjacent noise protection zones includes, on the same side of the intersecting flight segments, drawing a tangent line directly from the starting point of the UAV across the preceding noise protection zone to the following noise protection zone, forming a smooth flight segment between the outer circles of the starting point and the following noise protection zone, wherein the straight line of the smooth flight segment is not tangent to the preceding noise protection zone.

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