Mountainous area unmanned aerial vehicle route design method and system

Abstract

This invention relates to the field of unmanned aerial vehicle (UAV) technology, and more particularly to a method and system for designing UAV flight paths in mountainous areas. Based on the UAV's total weight and the vertical velocity components of the UAV at each moment within the total flight time, this invention determines the power required for the UAV to overcome gravity at each moment within the total flight time. Based on the power required to overcome gravity and air resistance at each moment within the total flight time, an energy consumption model for the UAV is constructed. The terrain elevation function, minimum safe airspace constraint, flight altitude limit constraint, and upper and lower velocity bounds are uniformly coupled into the trajectory optimization model, and the target flight path of the UAV is obtained by solving the trajectory optimization model. This invention can effectively obtain UAV flight paths with low total energy consumption, continuous and smooth trajectories, and suitability for complex mountainous environments.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) technology, and in particular to a method and system for designing UAV flight routes in mountainous areas. Background Technology

[0002] Unmanned Aerial Vehicle (UAV) technology is increasingly being used in scenarios such as material delivery and emergency rescue in mountainous areas. Compared to flat areas, mountainous regions have significantly undulating terrain and narrow airspace, requiring UAVs to perform frequent three-dimensional maneuvers during missions, resulting in a substantial increase in energy consumption. Especially in long-distance transportation in mountainous areas, energy constraints have become a key bottleneck restricting the execution of UAV missions. More importantly, insufficient endurance can also cause UAVs to lose control or even crash during the return phase, resulting in serious economic losses.

[0003] Specifically, in mountainous drone transportation scenarios, drones mainly face three interrelated core challenges, all of which are directly related to energy consumption: First, frequent changes in vertical altitude during flight require the power system to continuously output additional power to overcome terrain undulations, significantly increasing energy consumption compared to flight at a fixed altitude; second, mountain transportation is mostly a long-distance scenario, and energy consumption during the cruise phase continues to accumulate, further exacerbating the pressure on endurance; third, the complex terrain and unstable communication conditions in mountainous areas make it difficult to rely on continuous manual remote control, necessitating the use of onboard autonomous navigation systems to complete global path planning and real-time obstacle avoidance, while the maneuvering operations during navigation and obstacle avoidance further increase the energy consumption burden.

[0004] To address the aforementioned challenges, existing research mainly falls into two categories, but both have significant limitations and fail to effectively solve the energy consumption constraints of long-distance flight in mountainous terrain. The first category focuses on UAV route planning methods for complex terrain. These methods prioritize route accessibility, safety, and mission adaptability. They typically utilize 3D terrain models, obstacle models, or multi-objective optimization models to avoid complex terrains such as peaks, ridges, and valleys, while also considering factors like path length, flight time, and mission coverage. However, this type of method has a core weakness: most studies still prioritize the shortest path and flight feasibility as primary optimization objectives, treating energy consumption only as a post-event evaluation indicator or a simplified cost item. It doesn't deeply participate in the route generation process, resulting in planned routes that, while having shorter geometric distances, may exceed energy consumption limits due to frequent climbs and descents. This makes them unsuitable for energy-constrained long-distance flight missions in mountainous terrain and may even lead to safety risks such as return-to-base failures and mission interruptions.

[0005] The second category is energy-efficient UAV flight optimization technology. This type of technology focuses on the relationship between UAV flight status and power consumption. By establishing flight energy consumption models, it estimates the energy requirements under different flight speeds, attitudes, or mission conditions. Specifically, it can be divided into power models based on theoretical derivation and energy consumption estimation models based on empirical data, effectively improving the precision of energy consumption characterization. However, the limitation of this type of technology is that it is mostly used independently of the three-dimensional flight path planning process in mountainous areas. Existing energy consumption models are mostly decoupled from spatial location and mountainous terrain, making it difficult to accurately reflect the real energy consumption changes caused by frequent three-dimensional maneuvers such as climbing, descending, and detouring in mountainous areas. It cannot provide accurate energy consumption constraints for flight path planning and cannot fundamentally solve the problem of insufficient endurance of UAVs in mountainous areas.

[0006] In summary, existing methods have failed to meet the requirements for long-distance flight of UAVs in energy-constrained scenarios. Therefore, in complex mountainous environments, how to achieve flight path planning with the lowest total energy consumption for UAVs while meeting constraints such as safe flight and path accessibility has become an important research direction that urgently needs to be addressed in the field of UAV mountain applications. It is also the key to breaking through the endurance bottleneck of mountain UAVs and improving mission execution efficiency and safety. Summary of the Invention

[0007] Therefore, the technical problem to be solved by the present invention is to overcome the shortcomings of existing UAV mountain route planning and energy consumption optimization methods being disconnected, and energy consumption models being decoupled from mountain terrain, which cannot meet the requirements of safe and efficient long-distance flight in mountainous areas under energy-constrained scenarios.

[0008] To address the aforementioned technical problems, this invention provides a method for designing unmanned aerial vehicle (UAV) flight routes in mountainous areas, comprising: Based on the total weight of the drone and the vertical velocity component of the drone at each moment during the total flight time, determine the power required for the drone to overcome gravity at each moment during the total flight time. An energy consumption model for the UAV is constructed based on the power required to overcome gravity and air resistance at various moments during its total flight time. Based on the UAV's energy consumption model, digital elevation data of mountainous areas, coordinates of the UAV's mission start and end points, UAV platform parameters, and flight constraint parameters, an objective function is constructed with the goal of minimizing the total energy consumed by the UAV to complete the flight mission. Constraints are set to obtain a trajectory optimization model. Solving the trajectory optimization model yields the UAV's target flight path.

[0009] Preferably, the formula for determining the power required by the UAV to overcome gravity at each moment within the total flight time, based on the UAV's total weight and the vertical velocity component of the UAV at each moment within the total flight time, is as follows: , in, For drones at all times The power required to overcome gravity Indicates time, For a moment The vertical velocity component of the drone. The total weight of the drone. For the quality of drones, This is the acceleration due to gravity.

[0010] Preferably, the formula for the power required by the UAV to overcome air resistance at various moments during the total flight time is: , in, For drones at all times The power required to overcome air resistance Indicates time, air density, The drag coefficient of the drone. This refers to the windward area of ​​the drone. For a moment The speed of the drone.

[0011] Preferably, the method for constructing an energy consumption model for the UAV based on the power required to overcome gravity and the power required to overcome air resistance at various moments during the total flight time includes: The sum of the power required to overcome gravity and the power required to overcome air resistance at each moment during the total flight time of the drone is taken as the mechanical power of the drone at each moment during the total flight time. The ratio of the mechanical power to the equivalent efficiency constant at each moment during the total flight time of the drone is taken as the battery-side power of the drone at each moment during the total flight time. An energy consumption model for the drone is constructed based on the battery power at various points during its total flight time.

[0012] Preferably, the energy consumption model of the UAV is as follows: , in, The total energy consumed by the drone to complete its flight mission. Total flight time For drones at all times Battery-side power, Indicates the time.

[0013] Preferably, the method for obtaining the target flight path of the UAV by solving the trajectory optimization model includes: The total flight time is mapped to a normalized time interval, and several discrete nodes are set within the normalized time interval. The trajectory optimization model is transformed into a finite-dimensional nonlinear programming problem. Solving the finite-dimensional nonlinear programming problem yields the target flight path of the UAV.

[0014] Preferably, the method for transforming the trajectory optimization model into a finite-dimensional nonlinear programming problem includes: The trajectory optimization model is discretized, and the objective function is transformed into a weighted sum of the battery-side power of the UAV at each discrete node. The constraints are also transformed into constraints corresponding to each discrete node, resulting in a finite-dimensional nonlinear programming problem.

[0015] Preferably, the method for solving finite-dimensional nonlinear programming problems is the sequential quadratic programming method.

[0016] Preferably, the constraints include: The drone is required to start from a preset starting point and reach a preset destination at the end of the total flight time. The difference between the flight altitude of the drone at each moment during the total flight time and the ground elevation at the corresponding horizontal coordinate at that moment shall not be less than the preset minimum safe airspace distance and shall not be greater than the preset maximum allowable altitude above ground. The speed of the drone at any point during the total flight time is constrained to be no less than the preset minimum flight speed and no greater than the preset maximum flight speed.

[0017] This invention also provides a UAV flight path design system for mountainous areas, comprising: The partial power acquisition module is used to determine the power required for the drone to overcome gravity at each moment during the total flight time, based on the drone's total weight and the vertical velocity component of the drone at each moment during the total flight time. The energy consumption model building module is used to build an energy consumption model for the UAV based on the power required to overcome gravity and the power required to overcome air resistance at various moments during the total flight time. The flight path acquisition module is used to construct an objective function and set constraints based on the UAV's energy consumption model, digital elevation data of mountainous areas, coordinates of the UAV's mission start and end points, UAV platform parameters, and flight constraint parameters, with the goal of minimizing the total energy consumed by the UAV to complete the flight mission. This results in a trajectory optimization model, which is then solved to obtain the UAV's target flight path.

[0018] Compared with the prior art, the above-described technical solution of the present invention has the following advantages: This invention discloses a method and system for designing UAV flight paths in mountainous areas. It deeply couples the UAV's three-dimensional flight state in mountainous terrain with real-time energy consumption changes, directly converting the undulations of the mountainous terrain into a trajectory height profile. Based on the altitude change rate, it obtains the real-time vertical velocity component. Based on the UAV's total weight and the vertical upward velocity component during flight, it accurately calculates the power required to overcome gravity at each moment. Furthermore, it combines this with the power required to overcome air resistance to construct an energy consumption model that is in real-time correlated with the flight state. This allows the energy consumption calculation to accurately reflect the instantaneous power changes caused by frequent ascents and descents in mountainous areas. Simultaneously, this application utilizes this refined energy consumption model... With the model as the core, this application combines digital elevation data of mountainous areas, mission start and end points, platform parameters, and flight constraints to construct a trajectory optimization model with the goal of minimizing total energy consumption. This allows the planned flight path to no longer only pursue the geometric shortest distance, but to fully adapt to the undulating characteristics of mountainous terrain. Under the premise of meeting terrain safety, altitude restrictions, and speed constraints, it significantly reduces the energy consumption of long-distance mountain flights, effectively alleviating the insufficient endurance, return difficulties, and flight safety risks caused by battery energy constraints. Thus, it truly meets the actual needs of safe, efficient, and low-energy flight of UAVs in mountainous material delivery and emergency rescue scenarios under energy-limited conditions. Attached Figure Description

[0019] To make the content of this invention easier to understand, the invention will be further described in detail below with reference to specific embodiments and accompanying drawings, wherein:

[0020] Figure 1 This is a flowchart illustrating a method for designing unmanned aerial vehicle (UAV) flight routes in mountainous areas according to the present invention.

[0021] Figure 2 This is a map showing the drone delivery route.

[0022] Figure 3 This is a schematic diagram of the force analysis of a point mass.

[0023] Figure 4 These are 3D flight path diagrams for four different terrain scenarios. Figure 4 (a) in the image is a 3D view of the flight path for the first terrain scenario. Figure 4 (b) is a 3D map of the flight path for the second terrain scenario. Figure 4 (c) in the image is a 3D map of the flight path for the third terrain scenario. Figure 4 (d) is a 3D map of the flight path for the fourth terrain scenario.

[0024] Figure 5 These are top-down flight path views for four different terrain scenarios. Figure 5 (a) in the image is a top-down view of the flight path for the first terrain scenario. Figure 5 (b) is a top-down view of the flight path for the second terrain scenario. Figure 5(c) in the image is a top-down view of the flight path for the third terrain scenario. Figure 5 (d) in the image is a top-down view of the flight path for the fourth terrain scenario.

[0025] Figure 6 This is a vertical profile of the flight path for the first terrain scenario.

[0026] Figure 7 This is a vertical profile of the flight path for the second terrain scenario.

[0027] Figure 8 This is a vertical profile of the flight path for the third terrain scenario.

[0028] Figure 9 This is a vertical profile of the flight path for the fourth terrain scenario.

[0029] Figure 10 It is a sensitivity analysis of different quality parameters in four scenarios. Figure 10 (a) in the figure compares the total energy consumption in various scenarios under different drone masses. Figure 10 (b) in the figure shows a comparison of flight distances in different scenarios under different drone masses.

[0030] Figure 11 This is a topographic elevation map of the Huangshan Scenic Area.

[0031] Figure 12 This is a 3D map of the flight routes in the Huangshan Scenic Area.

[0032] Figure 13 This is an aerial view of the flight path in the Huangshan Scenic Area.

[0033] Figure 14 This is a cross-sectional view of route 1.

[0034] Figure 15 This is a cross-sectional view of route 2.

[0035] Figure 16 This is a cross-sectional view of Route 1 with a height limit of 300 meters.

[0036] Figure 17 This is a cross-sectional view of Route 2 with a height restriction of 300 meters. Detailed Implementation

[0037] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand and implement the present invention. However, the embodiments described are not intended to limit the present invention.

[0038] Reference Figure 1 As shown, this embodiment provides a method for designing UAV flight paths in mountainous areas, including: like Figure 2 As shown, Figure 2This is a schematic diagram of the drone delivery route.

[0039] In this embodiment, the flight path of the UAV in the mountainous area is defined in a three-dimensional space, and the flight process of the UAV is described in a three-dimensional Cartesian coordinate system.

[0040] Let the ground coordinate system be O-xyz, where the x-axis and y-axis lie in the horizontal plane, and the z-axis points vertically upwards. The UAV at time... spatial location Represented as , Indicates time, , The velocity vector of the UAV is the total flight time. Defined as , To monitor drones at all times spatial location The derivative of the velocity magnitude Recorded as The drone must complete the cargo delivery task within the total flight time.

[0041] In a spatial rectangular coordinate system, the three-dimensional coordinates of a given starting point are: The three-dimensional coordinates of the endpoint are ,in, , , These are the x, y, and z coordinates of the preset starting point, respectively. , , The x, y, and z coordinates are the preset endpoint, respectively, and the decision variable is the trajectory of the drone from the starting point to the endpoint. .

[0042] Treating the UAV as a flying point mass in three-dimensional space, and under quasi-steady-state flight conditions, an instantaneous mechanical power model for the UAV is established based on the force relationship of thrust, gravity, and air resistance acting on the UAV. This instantaneous mechanical power includes the power required to overcome gravity and the power required to overcome air resistance. Combining this with the overall propulsion efficiency, the mechanical power is converted into battery-side power, and by integrating over the entire flight time interval, the total energy consumption expression for the UAV to complete its mission is obtained.

[0043] like Figure 3 As shown, Figure 3 This is a schematic diagram of the force analysis of a point mass. To simplify the analysis, it is assumed that the UAV maintains quasi-steady-state flight during its flight, with relatively small acceleration, and the influence of inertial force on energy consumption is negligible. At any given moment... The drone is subjected to thrust ,gravity air resistance The three forces acting approximately satisfy the force equilibrium relationship, which can be determined by Newton's second law. ,Right now , The combined force acting on the drone.

[0044] The instantaneous power of a drone comes from the work done by thrust on velocity, expressed as: Substituting the force balance relationship into the above equation, we can see that the power consumption of the drone can be decomposed into the sum of the power required to overcome gravity and air resistance.

[0045] S1: Based on the total weight of the drone and the vertical velocity components of the drone at each moment during the total flight time, determine the power required for the drone to overcome gravity at each moment during the total flight time; The power required to overcome gravity depends only on the vertical component of the velocity, and can be expressed as: , in, The total weight of the drone (including payload) is expressed in Newtons (N). For drones at all times The power required to overcome gravity Indicates time, It is a unit vector in the vertical direction. For a moment The vertical velocity component of the drone (climbing or descending speed), unit: meters per second (m / s). The total weight of the drone. For the quality of drones, This is the acceleration due to gravity.

[0046] S2: Based on the power required to overcome gravity and air resistance at various moments during the total flight time, construct an energy consumption model for the UAV. The air resistance experienced by an aircraft is proportional to the air density, the aircraft's frontal area, and the square of its speed. The power required to overcome air resistance is equal to the product of resistance and speed. Therefore, the formula for the power required to overcome air resistance at various moments during the total flight time of a UAV is: , in, For drones at all times The power required to overcome air resistance Indicates time, The tangent vector is the unit velocity. Air density, unit: kilograms per cubic meter (kg / m³). Let be the drag coefficient of the drone, dimensionless. The windward area of ​​the drone is expressed in square meters (m²). For a moment The speed of the drone.

[0047] The method for constructing an energy consumption model for a UAV based on the power required to overcome gravity and air resistance at various moments during its total flight time includes: The sum of the power required to overcome gravity and the power required to overcome air resistance at each moment during the total flight time of the drone can be taken as the mechanical power of the drone at each moment during the total flight time, as shown in the formula: , in, For drones at all times Mechanical power.

[0048] In electric propulsion systems, the battery output power needs to be converted into thrust power through the motor, ESC, and propeller, and the two can be correlated through the overall propulsion efficiency. To maintain model identifiability and the solvability of the optimization problem, this invention adopts the equivalent efficiency assumption, treating the battery-side power as... It is approximately expressed as the ratio of the mechanical power to the equivalent efficiency constant at each moment during the total flight time of the UAV, and the formula is: ,in, The present invention uses the equivalent efficiency constant to represent this transformation process at the task scale.

[0049] In electric propulsion systems, the battery output power needs to be converted into thrust power through components such as the motor, electronic speed controller, and propeller. The two can be correlated through overall propulsion efficiency. To ensure the model's identifiability and the solvability of the optimization problem, this invention adopts the equivalent efficiency assumption, converting the battery-side power at each moment... It is approximately expressed as the ratio of the mechanical power to the equivalent efficiency constant at each moment throughout the entire flight mission of the UAV, and the formula is: This equivalent efficiency constant is used to uniformly characterize the overall energy transfer efficiency of converting battery-output electrical energy into the mechanical power required for flight via components such as motors and propellers.

[0050] An energy consumption model for the drone is constructed based on the battery power at various points during its total flight time. The drone's energy consumption model is as follows: , in, The total energy consumed by the drone to complete its flight mission. Total flight time For drones at all times Battery-side power, Indicates the time.

[0051] The energy consumption model for UAVs proposed in this invention can continuously characterize the impact of flight trajectory and speed distribution on energy consumption without distinguishing between flight phases, and is applicable to the problem of energy-saving flight path optimization for UAVs in mountainous areas.

[0052] S3: Based on the UAV's energy consumption model, digital elevation data of mountainous areas, coordinates of the UAV's mission start and end points, UAV platform parameters, and flight constraint parameters, to minimize the total energy consumed by the UAV in completing the flight mission. To achieve the objective, an objective function is constructed, and constraints are set to obtain a trajectory optimization model. Solving the trajectory optimization model yields the target flight path of the UAV.

[0053] In this embodiment, specifically, the digital elevation data of the mountainous area is the ground elevation at various horizontal positions in the mountainous area, and the parameters of the UAV platform include the total weight of the UAV, drag coefficient, frontal area and propulsion efficiency; the flight constraint parameters include the minimum safe clearance distance, the maximum upper limit of the maximum takeoff altitude and the upper and lower limits of the flight speed.

[0054] Based on the energy consumption model, the problem of designing energy-saving flight routes for UAVs in mountainous areas is formulated as a continuous-time trajectory optimization problem.

[0055] In this embodiment, preferably, the constraints include: Start point constraint and end point constraint: Constrain the drone to start from a preset start point and reach a preset end point when the total flight time ends; Terrain safety constraints and flight altitude constraints: The difference between the flight altitude of the UAV at each moment during the total flight time and the ground elevation at the corresponding horizontal coordinate at that moment shall not be less than the preset minimum safe airspace distance and shall not be greater than the preset maximum allowable altitude above the ground. This invention introduces a terrain elevation function Indicates at the horizontal coordinate point The altitude of the location. To ensure that the UAV maintains a sufficient safe distance from the ground surface (and near-ground obstacles), while complying with relevant airspace management and regulations regarding flight altitude, the following altitude constraints are given: , in, To ensure the minimum safe clearance distance, For drones at all times Flight altitude For a moment Corresponding horizontal coordinates The ground elevation at that location For a moment Corresponding to the x-coordinate in the horizontal coordinate system, For a moment Corresponding to the y-coordinate in the horizontal coordinate system, This is the maximum allowed ground clearance (AGL) limit.

[0056] Flight speed constraint: Constrains the speed of the UAV at any time during the total flight time to be no less than the preset minimum flight speed and no more than the preset maximum flight speed, in order to ensure that the flight trajectory is physically feasible and meets flight safety requirements; By imposing upper and lower bounds on the magnitude of the velocity, we ensure that the trajectory is physically feasible and meets flight safety requirements. The dynamic constraints are then: , in, For minimum flight speed, For maximum flight speed, For drones at all times The speed constraint is used to ensure the feasibility and safety of the flight process. The drone can autonomously decide to use different speeds in different locations, decelerating when climbing, accelerating when descending, and maintaining a moderate speed when circling.

[0057] This invention incorporates terrain elevation, minimum safe airspace, flight altitude limit and speed constraints into a unified optimization framework, making it difficult to simultaneously consider flight safety, path feasibility and smoothness.

[0058] Since the trajectory equation is a parameterized three-dimensional curve, the total energy consumption can be represented in functional form, and the corresponding trajectory optimization model is: .

[0059] In this embodiment, preferably, the method for obtaining the target flight path of the UAV by solving the trajectory optimization model includes: The total flight time is mapped to a normalized time interval, and several discrete nodes are set within the normalized time interval. The trajectory optimization model is transformed into a finite-dimensional nonlinear programming problem. Solving the finite-dimensional nonlinear programming problem yields the target flight path of the UAV.

[0060] Methods for transforming trajectory optimization models into finite-dimensional nonlinear programming problems include: The trajectory optimization model is discretized, and the objective function is transformed into a weighted sum of the battery-side power of the UAV at each discrete node. The constraints are also transformed into constraints corresponding to each discrete node, resulting in a finite-dimensional nonlinear programming problem.

[0061] This invention discretizes the continuous-time trajectory optimization model, mapping the original flight time interval to a normalized time interval, and setting multiple discrete nodes within the normalized time interval. The continuous trajectory is parameterized using piecewise interpolation, so that the continuous trajectory is characterized by the coordinates of a finite number of trajectory nodes and the total flight time. Furthermore, the total energy consumption integral objective function is discretized into a weighted sum of power at each discrete node using numerical integration rules, and terrain safety constraints, flight altitude restrictions, and speed constraints are transformed into a finite number of inequality constraints at each discrete node, thereby transforming the continuous-time trajectory optimization problem into a finite-dimensional nonlinear programming problem.

[0062] In this embodiment, preferably, the method for solving the finite-dimensional nonlinear programming problem is the sequential quadratic programming method.

[0063] For the finite-dimensional nonlinear programming problem obtained after discretization, a sequential quadratic programming method based on the trust region concept is employed for solution. In each iteration, a second-order approximation is performed on the objective function near the current solution, and the constraints are linearized to first order, thus forming a quadratic programming subproblem with trust region constraints. The search direction is obtained by solving the quadratic programming subproblem, and the trajectory nodes and total flight time are iteratively updated using an adaptive update strategy for the trust region radius until the preset optimality and constraint feasibility conditions are met.

[0064] The system outputs the optimal 3D flight path that satisfies the origin-end point constraints, terrain safety constraints, flight altitude restrictions, and speed constraints, and provides the corresponding trajectory node sequence, flight altitude change profile, total flight time, and total energy consumption estimates. The output path prioritizes traversing valleys, saddles, or relatively flat areas, avoiding unnecessary large climbs and descents, thereby achieving energy-efficient flight.

[0065] This invention improves the stability and efficiency of route solving under complex nonlinear constraints by transforming the continuous-time trajectory optimization problem into time normalization, trajectory discretization, and finite-dimensional nonlinear programming, and combining it with the trust-region sequence quadratic programming method.

[0066] In complex mountainous environments, existing technologies struggle to effectively balance the energy consumption trade-off between "directly traversing peaks" and "circling along valleys and saddles" for UAVs, often resulting in flight paths with high energy consumption or local instability. To address this, this invention provides a UAV flight path planning method for mountainous areas that considers energy consumption and terrain constraints. This method first establishes a unified UAV flight energy consumption model, incorporating the power required to overcome gravity and air resistance into the same energy consumption expression. Based on this, it combines the mountainous terrain elevation function, minimum safe airspace constraints, flight altitude constraints, and speed upper and lower limits to construct a continuous-time trajectory optimization model with the objective of minimizing the total energy consumed by the UAV to complete its flight mission. Furthermore, the continuous-time trajectory optimization model is discretized using a direct discretization method, and iteratively solved using a sequential quadratic programming method based on the trust region concept, thereby obtaining a three-dimensional UAV flight path that satisfies safety constraints and has low total energy consumption.

[0067] This invention enables drones to prioritize navigating valleys, saddles, or relatively flat areas in complex mountainous environments, reducing unnecessary climbs and additional energy consumption caused by directly crossing peaks, thereby effectively reducing total flight energy consumption. This invention is applicable not only to artificially constructed mountainous scenarios but also to real, complex mountainous environments such as the Huangshan Scenic Area, demonstrating good engineering applicability and promotional value.

[0068] Based on Example 1, Example 2 aims to evaluate the applicability of the energy consumption model in different mountainous environments. This invention artificially constructs four representative simulated terrain scenarios to simulate undulating mountainous terrain. The experimental area is set as a 15km × 15km rectangular region, with the UAV's starting and ending coordinates at (0.0, 0.0, 100.0) m and (15000.0, 15000.0, 200.0) m respectively, simulating a long-distance cross-ridge transport mission. The aircraft's physical constraints are set as follows: speed range v ∈ [10, 20] m / s, and a flight altitude that is always at least 30m above the terrain surface for safety margin. The UAV selected is the DJI FlyCart30, with a weight of 30kg, a maximum payload of 30kg in dual-battery mode, and a maximum empty range of 28km. The simulation only applies the minimum airspace constraint, without setting an upper altitude limit (i.e., not considering altitude restrictions). This setting is used to eliminate the influence of altitude restrictions, making the change in flight path altitude mainly driven by energy consumption costs, thereby highlighting the model's energy-saving effect. The terrain function is a superposition of multiple two-dimensional Gaussian functions. Each Gaussian function corresponds to a mountain peak or ridge. The position, height and width are controlled by adjusting the parameters. The parameters are shown in Table 1. Table 1 shows the parameter settings for four simulated mountain scenes.

[0069] Table 1

[0070] Gaussian function The expression is: , in, It is the first The height of each mountain peak It is the first The center coordinates of each mountain peak; standard deviation Control the first The width of each mountain peak; the larger the standard deviation, the gentler the mountain. n is the total number of mountain peaks, e is the natural constant, x is the horizontal projection of the drone's current position, and y is the horizontal projection of the drone's current position, all in meters.

[0071] The Trust-Region Sequence Quadratic Programming (TQP) framework is used to solve the discretized trajectory optimization model, such as... Figure 4 As shown, Figure 4 Three-dimensional flight path diagrams for four terrain scenarios. Figure 4 (a) in the image is a 3D view of the flight path for the first terrain scenario. Figure 4 (b) in the image is a 3D view of the flight path for the second terrain scenario. Figure 4 (c) in the image is a 3D map of the flight path for the third terrain scenario. Figure 4 (d) in the figure is a 3D map of the flight path for the fourth terrain scenario.

[0072] like Figure 5 As shown, Figure 5 A top-down view of the flight path for four terrain scenarios. Figure 5 (a) in the image is a top-down view of the flight path for the first terrain scenario. Figure 5 (b) in the image is a top-down view of the flight path for the second terrain scenario. Figure 5 (c) in the image is a top-down view of the flight path for the third terrain scenario. Figure 5 (d) in the image is the top-down view of the flight path for the fourth terrain scenario.

[0073] like Figure 6 , Figure 7 , Figure 8 , Figure 9 As shown, Figure 6 This is a vertical cross-section of the flight path for the first terrain scenario. Figure 7 This is a vertical profile of the flight path for the second terrain scenario. Figure 8 This is a vertical profile of the flight path for the third terrain scenario. Figure 9 This is a vertical profile of the flight path for the fourth terrain scenario.

[0074] From a 3D terrain perspective, all four scenarios exhibit varying degrees of mountain undulation and terrain constraints, effectively simulating flight challenges in typical mountainous environments. The corresponding flight path overhead results demonstrate that the algorithm can adaptively adjust its flight path based on terrain distribution, rationally selecting detour or crossing strategies while ensuring safety margins, thus avoiding high-risk areas.

[0075] Judging from the trajectory characteristics, the drone did not choose to simply rise in altitude to pass over the high obstacle area, but instead bypassed the peak along the gentle slope terrain, planning a flight trajectory that was close to a straight line. It only made slight directional adjustments at the edge of the high obstacle and did not exhibit large-scale detours, demonstrating the algorithm's efficient avoidance capability of obstacles on one side.

[0076] The vertical profiles of the four scenarios show that the planned flight paths exhibit smooth overall altitude changes, without unnecessary sharp ascents or descents, demonstrating the effective constraint of the energy consumption model on the cost of vertical motion. The drone flight paths are all precisely planned to traverse lower areas between mountain peaks, rather than simply ascending in a straight line. Figure 6 , Figure 7 , Figure 8 , Figure 9 As shown in the figure, these profile features consistently demonstrate that the UAV can autonomously choose lower passages between peaks in mountainous terrain of varying complexity, ensuring safety margins while avoiding unnecessary ascents, thus showcasing the algorithm's precise obstacle avoidance capabilities.

[0077] In this third embodiment, the total mass of the UAV is used as a sensitive parameter to compare and analyze the optimal flight path under different mass configurations in the same artificial mountain scenario. The total mass of the UAV is set to 40 kg, 45 kg, 50 kg, and 55 kg, corresponding to different payload mass conditions. Using the method of this invention, energy-saving flight path planning is performed for each set of mass parameters, and the total energy consumption, flight distance, and changes in flight path morphology under different masses are statistically analyzed.

[0078] like Figure 10 As shown, Figure 10 Sensitivity analysis was performed for different quality parameters in four scenarios. Figure 10 (a) in the figure shows a comparison of the total energy consumption in various scenarios under different drone masses. Figure 10 (b) in the figure shows the comparison of flight distances in different scenarios under different drone masses.

[0079] Experimental results show that as the total mass of the UAV increases, the total energy consumption of the planned flight path steadily increases. Meanwhile, with a larger mass, the overall flight path tends to choose a path with a gentler altitude change but a slightly longer horizontal range to avoid the additional energy consumption caused by the high-cost vertical climb. Although there are differences in local path selection under different mass conditions, the overall flight path structure remains continuous and stable, indicating that the method of this invention can adaptively adjust the flight path planning strategy according to load changes and reasonably balance range length and flight altitude under different load conditions, thereby maintaining good physical consistency and robustness.

[0080] This fourth example selects the core area of ​​Huangshan Scenic Area in Huangshan District, Huangshan City, Anhui Province, China as a real mountain test scenario. The terrain in this area is undulating and complex, with the main peak, Lotus Peak, reaching an altitude of 1864.8 m and the lowest point being about 400 m, resulting in a relative height difference of over 1400 m. This makes it suitable as a typical test environment for UAV path planning in complex mountainous areas.

[0081] First, based on ASTER GDEM V3 digital elevation data and combined with ArcGIS tools, digital elevation model data for the core area of ​​Huangshan Scenic Area was obtained. The obtained data adopted the UTM 50N projection coordinate system, with a spatial resolution of 90m, a raster size of 1496 rows × 1300 columns, and an elevation range of 400 m to 1864.8 m. Subsequently, based on this digital elevation model (such as...), Figure 11 As shown, construct a realistic three-dimensional mountain terrain model.

[0082] To simulate actual delivery tasks, two transportation scenarios were set up: the first scenario involves transporting supplies to a mountaintop hotel, starting from Tangkou Town and ending at Xihai Hotel, corresponding to route 1; the second scenario involves delivering emergency supplies, starting from Kuzhuxi Village and ending at a campsite, corresponding to route 2. Further, two flight altitude limits were set at 120 m and 300 m to compare the route planning results under different altitude restrictions. Using the method of this invention, safe, feasible, and energy-efficient three-dimensional flight routes were obtained in both scenarios.

[0083] like Figure 12 , Figure 13 , Figure 14 , Figure 15 , Figure 16 , Figure 17 As shown, Figure 12 A 3D map of flight routes in the Huangshan Scenic Area. Figure 13 This is an aerial view of the flight path in the Huangshan Scenic Area. Figure 14 This is a cross-sectional view of route 1. Figure 15 This is a cross-sectional view of route 2. Figure 16 This is a cross-sectional view showing the 300-meter height restriction for route 1. Figure 17This is a cross-sectional view of the route 2 with a height limit of 300 meters.

[0084] Experimental results show that under both height restrictions, the overall spatial orientation of the planned flight routes is largely consistent, actively avoiding high-altitude areas and prioritizing travel along saddles, valleys, and relatively gentle ridgelines between mountains, rather than directly crossing high-altitude peaks. The resulting flight routes are continuous and smooth in planar projection, without significant turns or oscillations; in vertical profiles, both ascent and descent are relatively gentle, and the flight altitude consistently meets the safety clearance constraint of 30 m above the terrain surface. At a height restriction of 120 m, the total distance of route 1 is 10528.81 m, and the total distance of route 2 is 10179.93 m; at a height restriction of 300 m, the total distance of route 1 is 10460.78 m, and the total distance of route 2 is 9984.86 m. This demonstrates that the method of this invention has good applicability and engineering application value in real, complex mountain environments.

[0085] This fifth embodiment provides a UAV route design system for mountainous areas, including: The partial power acquisition module is used to determine the power required for the drone to overcome gravity at each moment during the total flight time, based on the drone's total weight and the vertical velocity component of the drone at each moment during the total flight time. The energy consumption model building module is used to build an energy consumption model for the UAV based on the power required to overcome gravity and the power required to overcome air resistance at various moments during the total flight time. The flight path acquisition module is used to construct an objective function and set constraints based on the UAV's energy consumption model, digital elevation data of mountainous areas, coordinates of the UAV's mission start and end points, UAV platform parameters, and flight constraint parameters, with the goal of minimizing the total energy consumed by the UAV to complete the flight mission. This results in a trajectory optimization model, which is then solved to obtain the UAV's target flight path.

[0086] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application 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.

[0087] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. 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... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0088] 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 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0089] 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 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0090] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A mountainous region unmanned aerial vehicle route design method, characterized in that, include: Based on the total weight of the drone and the vertical velocity component of the drone at each moment during the total flight time, determine the power required for the drone to overcome gravity at each moment during the total flight time. An energy consumption model for the UAV is constructed based on the power required to overcome gravity and air resistance at various moments during its total flight time. Based on the UAV's energy consumption model, digital elevation data of mountainous areas, coordinates of the UAV's mission start and end points, UAV platform parameters, and flight constraint parameters, an objective function is constructed with the goal of minimizing the total energy consumed by the UAV to complete the flight mission. Constraints are set to obtain a trajectory optimization model. Solving the trajectory optimization model yields the UAV's target flight path.

2. The mountain area unmanned aerial vehicle route design method according to claim 1, characterized in that, The formula for determining the power required by the drone to overcome gravity at each moment within the total flight time, based on the drone's total weight and the vertical velocity component of the drone at each moment within the total flight time, is as follows: ， wherein is the time at which the UAV the power required to overcome gravity, denotes the time, is the time at which the UAV is the vertical velocity component of the UAV, is the total weight of the UAV, is the mass of the UAV, is the acceleration due to gravity.

3. The method for designing UAV flight routes in mountainous areas according to claim 1, characterized in that, The formula for the power required for a drone to overcome air resistance at various moments during its total flight time is: ， in, For drones at all times The power required to overcome air resistance Indicates time, air density, The drag coefficient of the drone. This refers to the windward area of ​​the drone. For a moment The speed of the drone.

4. The method for designing UAV flight routes in mountainous areas according to claim 1, characterized in that, The method for constructing an energy consumption model for a UAV based on the power required to overcome gravity and air resistance at various moments during its total flight time includes: The sum of the power required to overcome gravity and the power required to overcome air resistance at each moment during the total flight time of the drone is taken as the mechanical power of the drone at each moment during the total flight time. The ratio of the mechanical power to the equivalent efficiency constant at each moment during the total flight time of the drone is taken as the battery-side power of the drone at each moment during the total flight time. An energy consumption model for the drone is constructed based on the battery power at various points during its total flight time.

5. The method for designing UAV flight routes in mountainous areas according to claim 4, characterized in that, The energy consumption model for drones is as follows: ， in, The total energy consumed by the drone to complete its flight mission. Total flight time For drones at all times Battery-side power, Indicates the time.

6. The method for designing UAV flight routes in mountainous areas according to claim 5, characterized in that, Methods for obtaining the target flight path of a UAV by solving the trajectory optimization model include: The total flight time is mapped to a normalized time interval, and several discrete nodes are set within the normalized time interval. The trajectory optimization model is transformed into a finite-dimensional nonlinear programming problem. Solving the finite-dimensional nonlinear programming problem yields the target flight path of the UAV.

7. The method for designing UAV flight routes in mountainous areas according to claim 6, characterized in that, Methods for transforming trajectory optimization models into finite-dimensional nonlinear programming problems include: The trajectory optimization model is discretized, and the objective function is transformed into a weighted sum of the battery-side power of the UAV at each discrete node. The constraints are also transformed into constraints corresponding to each discrete node, resulting in a finite-dimensional nonlinear programming problem.

8. The method for designing UAV flight routes in mountainous areas according to claim 6, characterized in that, The method for solving finite-dimensional nonlinear programming problems is the sequential quadratic programming method.

9. The method for designing UAV flight routes in mountainous areas according to claim 1, characterized in that, The constraints include: The drone is required to start from a preset starting point and reach a preset destination at the end of the total flight time. The difference between the flight altitude of the drone at each moment during the total flight time and the ground elevation at the corresponding horizontal coordinate at that moment shall not be less than the preset minimum safe airspace distance and shall not be greater than the preset maximum allowable altitude above ground. The speed of the drone at any point during the total flight time is constrained to be no less than the preset minimum flight speed and no greater than the preset maximum flight speed.

10. A flight path design system for unmanned aerial vehicles (UAVs) in mountainous areas, characterized in that, include: The partial power acquisition module is used to determine the power required for the drone to overcome gravity at each moment during the total flight time, based on the drone's total weight and the vertical velocity component of the drone at each moment during the total flight time. The energy consumption model building module is used to build an energy consumption model for the UAV based on the power required to overcome gravity and the power required to overcome air resistance at various moments during the total flight time. The flight path acquisition module is used to construct an objective function and set constraints based on the UAV's energy consumption model, digital elevation data of mountainous areas, coordinates of the UAV's mission start and end points, UAV platform parameters, and flight constraint parameters, with the goal of minimizing the total energy consumed by the UAV to complete the flight mission. This results in a trajectory optimization model, which is then solved to obtain the UAV's target flight path.

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