Energy-saving flight path planning methods, devices, equipment and media for low-altitude electric aircraft

Abstract

This application relates to the field of low-altitude electric aircraft trajectory planning technology, and discloses a method, apparatus, equipment, and medium for energy-saving trajectory planning of low-altitude electric aircraft. The method includes: acquiring mission information and real-time information of the aircraft to be analyzed; inputting the real-time coordinates from the real-time information into a preset three-dimensional environment model to obtain environmental information; inputting the real-time information and the environmental information into a preset aircraft energy consumption dynamics model to obtain energy consumption; dividing multiple initial paths based on the starting and ending coordinates in the mission information; and determining the final trajectory based on the energy consumption corresponding to the initial paths. The technical solution provided by this application can solve the problems in low-altitude electric aircraft trajectory planning caused by the failure to integrate multi-source atmospheric data and the optimization objective's focus on the shortest path while neglecting energy optimization, leading to inaccurate energy consumption prediction and unstable endurance under spatiotemporal dynamic wind fields.

Description

Technical Field

[0001] This application relates to the field of trajectory planning technology for low-altitude electric aircraft, and in particular to an energy-saving trajectory planning method, device, equipment and medium for low-altitude electric aircraft. Background Technology

[0002] With the rapid development of the low-altitude economy, low-altitude electric aircraft are increasingly being used in logistics, emergency rescue, aerial inspection, and passenger commuting. However, most of the trajectory planning technologies for low-altitude electric aircraft focus on "shortest path" or "shortest time" as their core optimization objectives, neglecting the dynamic characteristics of the atmospheric environment. This leads to excessive energy consumption, insufficient range, and even mission failure due to sudden atmospheric disturbances during actual flight, severely restricting their large-scale and commercial application.

[0003] Specifically, the relevant technologies have the following shortcomings: First, they fail to fully utilize favorable wind fields in the atmosphere to reduce energy consumption, and also fail to effectively avoid the additional energy consumption caused by unfavorable wind fields, resulting in low energy utilization. Second, trajectory planning is mostly offline static planning, which cannot respond to real-time changes in the atmospheric environment. Once a sudden change in the atmospheric environment occurs, it is difficult to adjust the trajectory in time, which can easily lead to excessive energy consumption and flight safety hazards. Third, a precise mapping relationship between aircraft energy consumption and atmospheric environmental parameters has not been established, resulting in large errors in energy consumption prediction and difficulty in ensuring the stability of flight endurance. Fourth, multiple constraints such as energy consumption, safety, and timeliness have not been comprehensively balanced, which can easily lead to problems such as insufficient trajectory compliance or failure to meet mission timeliness.

[0004] Therefore, how to solve the problem of inaccurate energy consumption prediction and unstable endurance under spatiotemporal dynamic wind fields in the trajectory planning of low-altitude electric aircraft due to the failure to integrate multi-source atmospheric data and the optimization of the target focusing on the shortest path while ignoring energy optimization has become a technical problem that needs to be solved. Summary of the Invention

[0005] This application provides a method, apparatus, equipment, and medium for energy-saving trajectory planning of low-altitude electric aircraft. It solves the problems caused by factors such as the failure to integrate multi-source atmospheric data in trajectory planning of low-altitude electric aircraft, and the fact that the optimization process focuses on the shortest path while ignoring energy optimization, resulting in inaccurate energy consumption prediction and unstable endurance in spatiotemporal dynamic wind fields.

[0006] To achieve the above objectives, the main technical solutions adopted in this application include: In a first aspect, embodiments of this application provide an energy-saving trajectory planning method for low-altitude electric aircraft, the method comprising: Obtain the mission information and real-time information of the aircraft to be analyzed, and input the real-time coordinates in the real-time information into a preset three-dimensional environment model to obtain environmental information; The real-time information and the environmental information are input into a preset aircraft energy consumption dynamics model to obtain energy consumption; Multiple initial paths are defined based on the starting and ending coordinates in the task information; The final trajectory is determined based on the energy consumption corresponding to the initial path.

[0007] This embodiment provides an energy-saving trajectory planning method for low-altitude electric aircraft. It acquires mission information and real-time information of the aircraft to be analyzed, inputting the real-time coordinates into a preset three-dimensional environment model to obtain environmental information including parameters such as wind speed, air density, temperature, and air pressure. Then, the real-time information and the acquired environmental information are input into a preset aircraft energy consumption dynamics model, which calculates the energy consumption under the current state. Based on this, multiple initial paths are generated by introducing intermediate guiding points, based on the starting and ending coordinates from the mission information, to expand the search space and avoid local optima. Finally, the final trajectory is determined by selecting the optimal path based on the energy consumption of each initial path. This embodiment effectively integrates multi-source atmospheric data, using energy consumption as the core indicator for trajectory optimization, solving the problems of inaccurate energy consumption prediction and unstable endurance under spatiotemporal dynamic wind fields, and significantly improving the dynamic response capability and energy utilization efficiency of low-altitude electric aircraft trajectory planning.

[0008] In one implementation, the step of inputting the real-time information and the environmental information into a preset aircraft energy consumption dynamics model to obtain an energy consumption model includes: The relative incoming flow velocity is determined based on the attenuation of the wind speed component in the environmental information on the velocity component in the real-time information. Based on the air density in the environmental information, the rotor parameters in the real-time information, the preset thrust coefficient, and the relative incoming flow velocity, the rotor aerodynamic thrust is determined using a preset rotor thrust model. The rotor induced velocity is determined based on the preset rotor induced velocity model, the rotor aerodynamic thrust, the air density, and the rotor parameters. Energy consumption is obtained based on the rotor aerodynamic thrust, rotor induced velocity, air density, rotor parameters, and fuselage data in the real-time information.

[0009] This embodiment determines the relative incoming flow velocity based on the attenuation of the real-time velocity component caused by the environmental wind speed component. Then, combining air density, rotor rotation area, and a preset thrust coefficient, it calculates the rotor aerodynamic thrust using a rotor thrust model. Based on this, it uses a rotor induced velocity model to solve for the rotor induced velocity using rotor aerodynamic thrust, air density, and rotor area. Finally, it integrates four instantaneous power components—climb power, induced power, exhaust drag power, and rotor drag power—along the analysis path over a time span determined by the relative incoming flow velocity. This embodiment achieves unified modeling and layer-by-layer quantification of the environmental wind field, rotor aerodynamic characteristics, and fuselage drag effects. It accurately reflects the instantaneous power consumption required by the aircraft to generate thrust and overcome various drags in dynamic wind environments, and accurately accumulates the total energy demand along the path, providing a high-fidelity calculation basis for aircraft trajectory energy management and performance evaluation under complex weather conditions.

[0010] In one embodiment, obtaining energy consumption based on the rotor aerodynamic thrust, the rotor induced velocity, the air density, the rotor parameters, and the fuselage data in the real-time information includes: The climb power is determined by multiplying the rotor aerodynamic thrust by the climb rate in the fuselage data. The induced power is determined by the product of the rotor aerodynamic thrust and the rotor induced velocity; The air density, the relative incoming flow velocity, the fuselage drag coefficient and the fuselage frontal area in the fuselage data are input into a preset waste resistance power model to determine the waste resistance power; The rotor type drag power coefficient, rotor radius, rotor angular velocity, rotor rotation area and air density in the rotor parameters are input into the preset rotor type drag power model to determine the rotor type drag power; The instantaneous power is determined by summing the climb power, the induced power, the waste drag power, and the rotor drag power. Energy consumption is determined by integrating the instantaneous power over time.

[0011] This embodiment determines the instantaneous power of the aircraft by summing climb power, induced power, waste drag power, and rotor drag power. Climb power is determined based on rotor aerodynamic thrust and climb speed; induced power is determined based on rotor aerodynamic thrust and induced speed; waste drag power is determined based on air density, relative incoming flow velocity, fuselage drag coefficient, and fuselage frontal area; and rotor drag power is determined based on rotor drag power coefficient, rotor radius, rotor angular velocity, rotor rotation area, and air density. Energy consumption is obtained by integrating the instantaneous power over time on the path segment to be analyzed. The obtained energy consumption is used to estimate battery power demand and range energy output, and is an important quantitative basis for path planning and energy management.

[0012] In one implementation, the step of dividing multiple initial paths based on the start and end coordinates of the task information includes: Obtain the energy consumption corresponding to the straight-line distance from the real-time coordinates to the endpoint coordinates, determine the estimated energy consumption, and construct an energy consumption heuristic function based on the ratio of the estimated energy consumption to the preset energy consumption coefficient. The energy consumption heuristic function, the spatial restrictions, safety intervals, and task timeliness in the task information, as well as the battery inventory in the real-time information, are used as constraints to perform random sampling and obtain multiple initial paths.

[0013] This embodiment optimizes the process with energy consumption as the core focus. First, it calculates the estimated energy consumption using the straight-line distance between real-time coordinates and the destination, compensating for energy conversion and transfer losses, making the heuristic cost closer to real-world energy requirements. Then, it integrates the energy consumption heuristic function with multiple constraints such as airspace limitations, safety intervals, mission timeliness, and battery availability. Random sampling is performed within the initial three-dimensional search space to generate multiple initial paths that simultaneously satisfy conditions such as flight altitude, no-fly zone avoidance, horizontal and vertical safety intervals, attitude angle limits, total energy consumption not exceeding available battery energy and safety margin, and mission deadline. This embodiment directly incorporates energy consumption into the heuristic cost, effectively guiding the path search towards the energy-optimal solution. It generates a set of candidate paths that balance energy saving, safety, and mission timeliness from the initial planning stage, significantly improving the feasibility and practicality of path planning for power-constrained UAVs in complex environments.

[0014] In one implementation, the initial path is obtained as follows: An exploration circle is constructed using a preset radius and random sampling points that satisfy the constraints as the center. For any sampling point, all nodes in a preset random tree that fall within the exploration circle to which the sampling point belongs are determined as the set of candidate parent nodes corresponding to the sampling point. The energy consumption corresponding to the path is determined by the path from the candidate parent node in the candidate parent node set to the center of the circle; Based on the energy consumption corresponding to the path, determine the parent node of any sampling point, and repeat the above steps until the preset conditions are met to obtain the initial path corresponding to any sampling point.

[0015] This embodiment uses a random tree with the starting point as the root node to randomly sample within a three-dimensional search space. An exploration circle is constructed with the sampling point as the center and a preset neighborhood radius. All random tree nodes within this circle form a set of candidate parent nodes. Then, using an aircraft energy consumption dynamics model, the energy consumption corresponding to the path from each candidate parent node to the sampling point is calculated. The parent node is selected based on the criterion of minimum energy consumption, and a connection is established. This process is iterated until the random tree is connected to the endpoint, generating the initial path. This embodiment directly uses energy consumption as the optimization guide for random tree growth, actively avoiding high-energy-consuming wind fields and no-fly zones during the search process. It avoids the problem of inaccurate energy prediction caused by focusing solely on the shortest path, significantly improving the global optimality and energy efficiency of trajectory planning in dynamic atmospheric environments, and ensuring the stability of the low-altitude electric aircraft's endurance and the robustness of trajectory planning.

[0016] In one embodiment, the method further includes: If any real-time indicator is detected to be greater than a preset value, the final trajectory is locally corrected to obtain the corrected final trajectory.

[0017] This embodiment continuously monitors real-time indicators during the aircraft's mission along the planned trajectory. If any real-time indicator exceeds a preset safety threshold, a local trajectory correction is immediately triggered. The correction employs a rolling time-domain optimization method, setting the rolling time-domain window length to 30-60 seconds. Only the trajectory segment within the current time-domain window is adjusted, while the original scheme outside the window remains unchanged. Optimization aims to minimize local energy consumption within the window, and requires that the corrected trajectory fully satisfy the constraints again. This embodiment achieves agile response and low-cost local repair to exceed-limit risks, avoiding the accumulation of danger while preserving the overall mission plan framework. It restores constraint satisfaction in a way that minimizes additional energy consumption, ensuring the corrected trajectory is physically reachable, flight safe, and mission feasible. Thus, it achieves the most energy-efficient real-time trajectory maintenance capability under safety constraints in a dynamic environment.

[0018] In one implementation, real-time indicators include: sudden gust wind speed, temperature change, and air pressure change.

[0019] In this embodiment, during the flight path execution, real-time monitoring of sudden gusts, temperature changes, and air pressure changes is continuously performed. When a sudden gust reaches or exceeds 8 m / s, or a temperature change within 10 minutes exceeds 5°C, or an air pressure change within 10 minutes exceeds 5 hPa, it is considered an environmental abrupt event. These sudden environmental changes can significantly alter the aircraft's aerodynamic characteristics and energy consumption levels, potentially causing the original flight path to no longer meet energy consumption or safety constraints. Therefore, once any of the above real-time indicators is detected to exceed the corresponding preset threshold, a local correction to the current final flight path is immediately triggered to ensure the feasibility and safety of the remaining flight distance.

[0020] Secondly, embodiments of this application provide an apparatus for energy-saving trajectory planning of low-altitude electric aircraft, the apparatus comprising: The information processing unit is used to acquire mission information and real-time information of the aircraft to be analyzed, input the real-time coordinates in the real-time information into a preset three-dimensional environment model, and acquire environmental information. An energy consumption calculation unit is used to input the real-time information and the environmental information into a preset aircraft energy consumption dynamics model to obtain energy consumption. The path planning unit is used to divide multiple initial paths based on the starting point coordinates and ending point coordinates in the task information; The trajectory determination unit is used to determine the final trajectory based on the energy consumption corresponding to the initial path.

[0021] Thirdly, embodiments of this application provide a computer device, including: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes these computer instructions to perform the energy-saving flight path planning method for low-altitude electric aircraft described above.

[0022] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer instructions, which are used to cause a computer to execute the energy-saving flight path planning method for low-altitude electric aircraft described in any of the above-mentioned embodiments. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the specific embodiments of this application or the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0024] Figure 1 A flowchart illustrating an energy-saving trajectory planning method for low-altitude electric aircraft provided in this application embodiment; Figure 2 A flowchart of step S31 provided in an embodiment of this application; Figure 3 A flowchart of step S371 provided in an embodiment of this application; Figure 4 A flowchart of step S51 provided in an embodiment of this application; Figure 5 A flowchart of step S531 provided in an embodiment of this application; Figure 6 A block diagram of an energy-saving flight path planning device for a low-altitude electric aircraft provided in this application embodiment; Figure 7 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0026] With the rapid development of the low-altitude economy, low-altitude electric aircraft are increasingly being used in logistics, emergency rescue, aerial inspection, and passenger commuting. However, most of the trajectory planning technologies for low-altitude electric aircraft focus on "shortest path" or "shortest time" as their core optimization objectives, neglecting the dynamic characteristics of the atmospheric environment. This leads to excessive energy consumption, insufficient range, and even mission failure due to sudden atmospheric disturbances during actual flight, severely restricting their large-scale and commercial application.

[0027] Specifically, the relevant technologies have the following shortcomings: First, they fail to fully utilize favorable wind fields in the atmosphere to reduce energy consumption, and also fail to effectively avoid the additional energy consumption caused by unfavorable wind fields, resulting in low energy utilization. Second, trajectory planning is mostly offline static planning, which cannot respond to real-time changes in the atmospheric environment. Once a sudden change in the atmospheric environment occurs, it is difficult to adjust the trajectory in time, which can easily lead to excessive energy consumption and flight safety hazards. Third, a precise mapping relationship between aircraft energy consumption and atmospheric environmental parameters has not been established, resulting in large errors in energy consumption prediction and difficulty in ensuring the stability of flight endurance. Fourth, multiple constraints such as energy consumption, safety, and timeliness have not been comprehensively balanced, which can easily lead to problems such as insufficient trajectory compliance or failure to meet mission timeliness.

[0028] In summary, the current technical problem to be solved is how to address the issues of inaccurate energy consumption prediction and unstable endurance under spatiotemporal dynamic wind fields caused by the failure to integrate multi-source atmospheric data and the focus on shortest path optimization while neglecting energy optimization in the trajectory planning of low-altitude electric aircraft.

[0029] To address the aforementioned technical problems, according to an embodiment of this application, an embodiment of an energy-saving trajectory planning method for low-altitude electric aircraft is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0030] This embodiment provides an energy-saving trajectory planning method for low-altitude electric aircraft. Figure 1 A flowchart illustrating an energy-saving trajectory planning method for low-altitude electric aircraft provided in this application embodiment is shown below. Figure 1 As shown, the process includes the following steps: Step S1: Obtain the mission information and real-time information of the aircraft to be analyzed, input the real-time coordinates from the real-time information into the preset three-dimensional environment model, and obtain the environmental information.

[0031] Specifically, the process involves inputting mission information (starting point coordinates, ending point coordinates, mission time window, maximum permissible flight altitude, no-fly zone layer, etc.) and real-time information (such as aircraft model parameters like rotor parameters, fuselage data, battery remaining charge status (SOC), real-time coordinates, etc.). The real-time coordinates from the real-time information are then input into a pre-defined 3D environment model to obtain environmental information. The data sources for the 3D environment model include multi-source atmospheric data, specifically covering Weather Research and Forecasting Model (WRF) numerical forecast data, ground meteorological station data, airborne micro-electro-mechanical system (MEMS) sensor data, and 5G meteorological edge node data. The acquisition and fusion of atmospheric environmental data specifically includes the following steps: WRF numerical forecast data provides 3D wind field, temperature, and air pressure forecast information for the next 1-2 hours, with a spatial resolution ≤100m and a temporal resolution ≤5 minutes; airborne MEMS sensors, including pitot tubes, inertial measurement units (IMUs), and thermo-hygrometers, provide real-time atmospheric parameters during the aircraft's flight.

[0032] Outlier removal and missing value completion were performed on multi-source atmospheric data. Outlier removal adopted the 3σ criterion, removing data exceeding the mean ± 3 standard deviations. Missing value completion used the Kriging interpolation algorithm, filling in missing data based on surrounding valid data. A weighted fusion algorithm was used to fuse the multi-source atmospheric data, with a weight coefficient of 0.5 for airborne sensor data, 0.3 for WRF numerical weather prediction data, and a combined weight coefficient of 0.2 for ground meteorological station data and 5G meteorological edge node data. A three-dimensional spatiotemporal grid was constructed after fusion. This forms a spatiotemporally consistent three-dimensional environment model, expressed as: in: , , These are the wind speed components in the x, y, and z directions, respectively; Here, is air density; T is atmospheric temperature; P is atmospheric pressure; x and y are horizontal spatial coordinates; z is vertical height coordinate; and t is time. Inputting real-time coordinates into a preset 3D environment model outputs the corresponding location and real-time thermodynamic parameters such as wind speed, air density, temperature, and air pressure.

[0033] Step S3: Input real-time information and environmental information into the preset aircraft energy consumption dynamics model to obtain energy consumption.

[0034] Specifically, by inputting real-time and environmental information into a pre-defined aircraft energy consumption dynamics model, the energy consumption per unit time or unit distance under the current flight state can be accurately calculated, providing a high-fidelity energy consumption benchmark for trajectory planning and online correction. The energy consumption dynamics model can reflect in real time the comprehensive impact of environmental factors such as atmospheric wind field, temperature, and air pressure, as well as changes in aircraft attitude and speed, on energy consumption. This significantly improves the dynamic response capability and quantification accuracy of energy consumption prediction, ensuring that subsequent parent node selection, global energy consumption constraint verification, and local correction optimization are all based on reliable energy consumption assessment.

[0035] Step S5: Divide multiple initial paths based on the starting point and ending point coordinates in the task information.

[0036] Specifically, based on the starting and ending coordinates, multiple intermediate guiding points are introduced into the task space. These points are combined in different ways to generate a set of polylines or curves connecting the starting and ending points, forming multiple initial paths. This embodiment effectively expands the diversity of the path search space, avoids premature convergence to local extrema in subsequent optimizations, and allows paths to better bypass obstacles and no-fly zones, improving the robustness of the overall planning and the quality of the solution.

[0037] Step S7: Determine the final trajectory based on the energy consumption corresponding to the initial path.

[0038] Specifically, the final track consists of several waypoints, and the spacing between waypoints can be dynamically adjusted according to the flight speed. The parameters of each waypoint include: longitude λ, latitude φ, altitude z, flight speed v, and expected arrival time t. The waypoint sequence format follows the MAVLink protocol.

[0039] Based on the energy consumption calculation results, a full-process estimated energy consumption report is generated. The estimated energy consumption report includes: total energy consumption. (Energy consumption determined based on the final flight path), remaining battery power consumption Energy saving ratio compared to the shortest path The document includes information on atmospheric environmental risk levels and corresponding response recommendations, as well as energy consumption distribution across different route segments. The formula for calculating the energy-saving ratio δ is as follows: in, This represents the energy consumption corresponding to the shortest path (i.e., straight-line distance) from the starting coordinates to the ending coordinates. (Energy saving ratio) refers to the percentage of energy saved by the aircraft flying along the "final trajectory" planned by the method of this application, compared to flying along the "shortest path (i.e., the straight-line spatial distance between two points)". The energy saving ratio is used to quantitatively evaluate the energy saving optimization effect of the trajectory planning scheme of this application. (Total energy consumption) refers to the total estimated energy consumption of the aircraft to be analyzed during its flight from the starting point coordinates to the ending point coordinates along the planned "final trajectory". This is calculated by the aircraft energy consumption dynamics model after comprehensively evaluating factors such as environmental wind field, fuselage drag and rotor aerodynamic characteristics. (Remaining battery energy) refers to the total available energy that the aircraft's power battery system is expected to have remaining when it reaches the destination coordinates, based on the initial battery capacity and after deducting the total energy consumption required to fly along the "final flight path". It is used to assess the endurance safety margin of the flight mission.

[0040] This embodiment provides an energy-saving trajectory planning method for low-altitude electric aircraft. It acquires mission information and real-time information of the aircraft to be analyzed, inputting the real-time coordinates into a preset three-dimensional environment model to obtain environmental information including parameters such as wind speed, air density, temperature, and air pressure. Then, the real-time information and the acquired environmental information are input into a preset aircraft energy consumption dynamics model, which calculates the energy consumption under the current state. Based on this, multiple initial paths are generated by introducing intermediate guiding points, based on the starting and ending coordinates from the mission information, to expand the search space and avoid local optima. Finally, the final trajectory is determined by selecting the optimal path based on the energy consumption of each initial path. This embodiment effectively integrates multi-source atmospheric data, using energy consumption as the core indicator for trajectory optimization, solving the problems of inaccurate energy consumption prediction and unstable endurance under spatiotemporal dynamic wind fields, and significantly improving the dynamic response capability and energy utilization efficiency of low-altitude electric aircraft trajectory planning.

[0041] Figure 2 The flowchart for step S31 provided in the embodiments of this application may include the following steps: Step S31: Determine the relative incoming flow velocity based on the attenuation of the wind speed component in the environmental information to the velocity component in the real-time information.

[0042] Specifically, the environmental information provides the decomposition of wind speed components along the x, y, and z directions. , , These decomposition quantities are the attenuation amounts caused to the velocity components in the real-time information. The velocity components are decomposed along the coordinate direction into... , , Then, subtract the attenuation in the corresponding direction (i.e., the wind speed component) from the decomposed value of the velocity component. , , The relative incoming velocity components after attenuation by ambient wind in each direction are obtained. Its representation is as follows: Step S33: Determine the rotor aerodynamic thrust based on the air density in the environmental information, the rotor parameters in the real-time information, the preset thrust coefficient, and the relative incoming flow velocity through the preset rotor thrust model.

[0043] Specifically, in determining the relative incoming flow velocity Based on this, combined with air density from environmental information The rotor aerodynamic thrust can be calculated using a pre-defined rotor thrust model by taking into account the inherent rotor rotation area and the preset thrust coefficient of the aircraft's rotor. This rotor thrust model comprehensively considers the environmental atmospheric characteristics and the rotor's dynamic effects in the relative airflow. The expression is: in, air density; This refers to the rotor rotation area in the rotor parameters; This is the thrust coefficient, with a preset value between 0.05 and 0.15 based on the rotor's aerodynamic characteristics. Rotor aerodynamic thrust. The net aerodynamic force that reflects the upward or pull direction exerted by the rotor on the aircraft is directly used to balance gravity and provide acceleration.

[0044] Step S35: Determine the rotor induced velocity based on the preset rotor induced velocity model, rotor aerodynamic thrust, air density, and rotor parameters.

[0045] Specifically, based on the determined rotor aerodynamic thrust Combined with air density And the rotor rotation area in the rotor parameters The rotor induced velocity can be further calculated. Induced velocity is the velocity of the induced flow field formed when the rotor generates thrust and accelerates the air downwards. The magnitude of the rotor induced velocity is directly obtained from the rotor induced velocity model, and its expression is as follows: in, For rotor aerodynamic thrust, air density, The rotor's induced velocity is directly proportional to the square root of the thrust and inversely proportional to the square roots of the air density and rotor area. It represents the degree to which the rotor needs to accelerate the air to generate that thrust. The greater the thrust and the smaller the rotor disk area, the more violently the airflow needs to be accelerated.

[0046] Step S37: Obtain energy consumption based on rotor aerodynamic thrust, rotor induced velocity, air density, rotor parameters, and fuselage data in real-time information.

[0047] Specifically, the climb power is determined by multiplying the rotor aerodynamic thrust by the climb velocity, and the induced power is determined by multiplying it by the induced velocity. The waste drag power is obtained by substituting air density, relative incoming flow velocity, fuselage drag coefficient, and frontal area into a pre-defined waste drag power model. Then, the rotor drag power coefficient, rotor radius, rotor angular velocity, rotor rotation area, and air density are input into the rotor drag power model to calculate the rotor drag power. The instantaneous power is obtained by summing these four parameters. Finally, the instantaneous power is integrated over time along the path to be analyzed, and the time span for each segment is determined by the path distance and relative incoming flow velocity, thus accumulating the energy consumption for that path.

[0048] This embodiment determines the relative incoming flow velocity based on the attenuation of the real-time velocity component caused by the environmental wind speed component. Then, combining air density, rotor rotation area, and a preset thrust coefficient, it calculates the rotor aerodynamic thrust using a rotor thrust model. Based on this, it uses a rotor induced velocity model to solve for the rotor induced velocity using rotor aerodynamic thrust, air density, and rotor area. Finally, it integrates four instantaneous power components—climb power, induced power, exhaust drag power, and rotor drag power—along the analysis path over a time span determined by the relative incoming flow velocity. This embodiment achieves unified modeling and layer-by-layer quantification of the environmental wind field, rotor aerodynamic characteristics, and fuselage drag effects. It accurately reflects the instantaneous power consumption required by the aircraft to generate thrust and overcome various drags in dynamic wind environments, and accurately accumulates the total energy demand along the path, providing a high-fidelity calculation basis for aircraft trajectory energy management and performance evaluation under complex weather conditions.

[0049] Figure 3 The flowchart for step S371 provided in the embodiments of this application may include the following steps: Step S371: Determine the climb power based on the product of the rotor aerodynamic thrust and the climb speed in the fuselage data.

[0050] Specifically, in determining the rotor aerodynamic thrust Then, combined with the climb rate provided in the fuselage data... The climbing power can be obtained by multiplying the two, and its expression is as follows: in, For rotor aerodynamic thrust, This represents the climb rate obtained from the aircraft's data. Climb power quantifies the energy expenditure required for an aircraft to overcome gravity and gain gravitational potential energy in the vertical direction. Climb power reflects the power demand for changing altitude, making the aircraft's instantaneous power... The composition is more precise.

[0051] Step S372: Determine the induced power based on the product of the rotor aerodynamic thrust and the rotor induced velocity.

[0052] Specifically, the force exerted by the rotor of the aircraft to be analyzed on the airflow (rotor aerodynamic thrust) is: The airflow moves at a velocity in the direction of the force. Motion, the work done on the airflow per unit time is called motion. Therefore, based on rotor aerodynamic thrust With rotor induced velocity The induced power can be determined by the product of these two factors. Its representation is as follows: The induced power depends on the magnitude of the rotor aerodynamic thrust and the induced velocity obtained by the airflow under the action of the rotor aerodynamic thrust.

[0053] Step S373: Input the air density, relative incoming flow velocity, fuselage drag coefficient and fuselage frontal area from the fuselage data into the preset exhaust resistance power model to determine the exhaust resistance power.

[0054] Specifically, exhaust drag power describes the power consumed by an aircraft to overcome "parasitic drag" caused by airflow viscosity, pressure differences, etc., as it moves through the air. The form of exhaust drag power is: Among them, parasitic resistance The representation is as follows: Substituting the resistance into the power formula yields the waste resistance power model, which is expressed as follows: in, air density; The drag coefficient is determined by factors such as fuselage shape, surface condition, and angle of attack. In the exhaust drag power model, its range is set to 0.1–0.3. The frontal area of ​​the fuselage is generally taken as the maximum cross-sectional area or the orthographic projection area of ​​the fuselage, representing the main geometric dimension of the fuselage blocking the incoming flow. The relative incoming flow velocity is denoted as . The exhaust resistance power model shows that exhaust resistance power is proportional to the cube of the relative incoming flow velocity. Therefore, parasitic power consumption increases sharply when the aircraft is flying at high speed, which becomes a key constraint in this embodiment.

[0055] Step S374: Input the rotor drag power coefficient, rotor radius, rotor angular velocity, rotor rotation area and air density from the rotor parameters into the preset rotor drag power model to determine the rotor drag power.

[0056] Specifically, the rotor drag power coefficient, rotor radius, rotor angular velocity, rotor rotation area, and air density are taken as inputs and substituted into a preset rotor drag power model to obtain the rotor drag power. Its representation is as follows: in, air density; The power drag coefficient for rotor blades is typically set between 0.01 and 0.03. The rotor radius; The rotor's rotational area; The rotor angular velocity is the rotor drag power, which is the power consumption that the rotor must expend to overcome drag when rotating. It directly reflects the rotor aerodynamic efficiency. The greater the rotor drag power, the more energy the aircraft loses to overcome drag, thus affecting hovering performance and endurance.

[0057] Step S375: Determine the instantaneous power by summing the climb power, induced power, exhaust drag power, and rotor drag power.

[0058] Specifically, instantaneous power It is determined by the sum of four terms: climb power, induced power, waste drag power, and rotor drag power, and its expression is as follows: in, It is climb power, which represents the power consumed by an aircraft in the vertical direction to change altitude and overcome gravity; It is induced power, which comes from the induced drag that the rotor needs to overcome when generating lift. It is closely related to the rotor disk load and flight conditions. It is waste drag power, used to overcome the parasitic drag generated by non-lifting components such as the fuselage and landing gear; This refers to rotor drag power, which is the power consumed by the rotor blades to overcome their own airfoil drag during rotation. By summing these four power components according to their corresponding values ​​under actual flight conditions, the total power input required by the aircraft at that instant can be obtained. It is a key basis for assessing power system requirements, energy consumption, and range.

[0059] Step S376: Determine the energy consumption based on the integral of the instantaneous power over time.

[0060] Specifically, the energy consumption of the analyzed path can be determined by integrating the instantaneous power over time. The formula is as follows: in, Indicates the first Energy consumption corresponding to each path segment; Instantaneous power, i.e., power gained during ramp-up Induced power waste resistance power and rotor type resistance power The summation is obtained; and These represent the start and end times of the path segment to be analyzed. When the spacecraft travels along the path to be analyzed from the first... The point moves to the first... At each point, the time span Δt between the start and end times is determined based on the ratio of the three-dimensional spatial distance of the path segment to be analyzed to the actual flight speed of the aircraft. ), which can convert instantaneous power By performing time integration over this time span, the energy consumption of the path segment to be analyzed can be obtained.

[0061] Energy consumption is directly used to estimate the battery power requirements and range energy output of an aircraft under complex trajectories, and is an important quantitative basis for path planning and energy management.

[0062] This embodiment determines the instantaneous power of the aircraft by summing climb power, induced power, waste drag power, and rotor drag power. Climb power is determined based on rotor aerodynamic thrust and climb speed; induced power is determined based on rotor aerodynamic thrust and induced speed; waste drag power is determined based on air density, relative incoming flow velocity, fuselage drag coefficient, and fuselage frontal area; and rotor drag power is determined based on rotor drag power coefficient, rotor radius, rotor angular velocity, rotor rotation area, and air density. Energy consumption is obtained by integrating the instantaneous power over time on the path segment to be analyzed. The obtained energy consumption is used to estimate battery power demand and range energy output, and is an important quantitative basis for path planning and energy management.

[0063] Figure 4 The flowchart provided for embodiments of this application, which divides multiple initial paths based on the starting and ending coordinates of task information, may include the following steps: Step S51: Obtain the energy consumption corresponding to the straight-line distance from the real-time coordinates to the endpoint coordinates, determine the estimated energy consumption, and construct an energy consumption heuristic function based on the ratio of the estimated energy consumption to the preset energy consumption coefficient.

[0064] Specifically, relevant heuristic functions typically come at the cost of distance, such as Euclidean or Manhattan distance. However, for power-constrained drones, the quality of a path after incorporating multi-source atmospheric data depends not only on geometric length but also on actual energy consumption. Therefore, an energy-consumption-oriented heuristic function is constructed as a constraint on the energy-optimal path. The current node is obtained from real-time information. Real-time coordinates And based on the endpoint coordinates in the task information The distance between the two points is calculated as follows: This represents the straight-line distance from the real-time coordinates to the endpoint coordinates. And determine... The corresponding energy consumption is used as the estimated energy consumption. Estimated energy consumption is the energy required to travel from real-time coordinates to the destination coordinates.

[0065] However, the above-mentioned estimated energy consumption does not take into account the various losses during energy conversion, transmission, and driving processes, and its value is often underestimated. To make the heuristic function value closer to the actual possible cost, a preset energy consumption coefficient is introduced. The constructed energy consumption heuristic function is as follows: in, For energy consumption estimates based on straight-line distance, This is a preset energy consumption coefficient. The intuitive meaning of the energy consumption heuristic function is: when considering the energy consumption coefficient... Under the premise that the energy consumption is [value], the "equivalent energy cost" required to travel from the current node to the destination along a straight path. For example, the estimated energy consumption is [value]. ,and Then the actual need may be about The energy required to complete a straight-line motion is determined by the energy consumed. The energy consumption heuristic function can serve as a constraint to effectively guide subsequent path planning to find the path with the lowest energy consumption. It is applicable to various energy-sensing navigation and planning tasks.

[0066] Step S53: Using the energy consumption heuristic function, spatial restrictions, safety intervals, task timeliness, and battery inventory in real-time information as constraints, random sampling is performed to obtain multiple initial paths.

[0067] Specifically, constraints may include requirements regarding flight altitude. ,in The maximum permissible flight altitude as specified in the mission information, for example: Simultaneously, all points on the flight path must not enter the no-fly zone layer entered in the mission information. Added safety constraints may include: horizontal safety separation from other aircraft during flight. Vertical safety interval ; aircraft attitude angle The added energy consumption constraints may include: the total energy consumption of the trajectory must meet the following requirements. ;in The total usable energy of the battery, , For battery capacity, This is the battery's rated voltage. Initial SOC of the battery; For safety margin, a value of 0.8 is used. Added time constraints may include: the total flight time must meet... ;in This refers to the task start time within the time window of the task information. This refers to the task deadline in the task time window. Let this be the time it takes for the spacecraft to reach its destination. Based on this, an initial three-dimensional search space is constructed. Random sampling is performed within the initialized three-dimensional search space to obtain multiple initial paths.

[0068] This embodiment optimizes the process with energy consumption as the core focus. First, it calculates the estimated energy consumption using the straight-line distance between real-time coordinates and the destination, compensating for energy conversion and transfer losses, making the heuristic cost closer to real-world energy requirements. Then, it integrates the energy consumption heuristic function with multiple constraints such as airspace limitations, safety intervals, mission timeliness, and battery availability. Random sampling is performed within the initial three-dimensional search space to generate multiple initial paths that simultaneously satisfy conditions such as flight altitude, no-fly zone avoidance, horizontal and vertical safety intervals, attitude angle limits, total energy consumption not exceeding available battery energy and safety margin, and mission deadline. This embodiment directly incorporates energy consumption into the heuristic cost, effectively guiding the path search towards the energy-optimal solution. It generates a set of candidate paths that balance energy saving, safety, and mission timeliness from the initial planning stage, significantly improving the feasibility and practicality of path planning for power-constrained UAVs in complex environments.

[0069] Figure 5A flowchart illustrating a method for obtaining any initial path according to an embodiment of this application is provided. The process specifically includes the following steps: Step S531: Using random sampling points that satisfy the constraints as the center, construct an exploration circle with a preset domain radius.

[0070] Specifically, the starting and ending coordinates of the spacecraft are obtained based on the mission information, and a random tree is initialized (a random tree is a tree-shaped data structure that grows gradually through repeated "random sampling + optimal connection". The root is fixed as the starting point and is the only source of the backtracking path (i.e., the starting coordinate). Each node of the random tree represents a spatial coordinate in the initialized three-dimensional search space, and the edge represents a feasible connection between two spatial coordinates, with a cost (i.e., energy consumption). In each iteration, a sampling point is randomly generated in the initialized three-dimensional search space. (When generating a random sampling point in each iteration, the coordinates of the sampling point are first substituted into the energy consumption heuristic function.) The estimated remaining energy consumption from the sampling point to the endpoint is calculated, and then added to the known energy consumption from the starting point to the sampling point to obtain the "estimated total energy consumption". A validity check is then performed: if the estimated total energy consumption is greater than the total usable battery energy (i.e., triggering the energy consumption limit), or if the sampling point violates spatial restrictions such as altitude, no-fly zone, or safety interval, the point is discarded and random sampling is performed again until a valid coordinate point that fully meets the verification is obtained. Then, using the valid coordinate point as the center, an exploration circle is constructed with a preset neighborhood radius, and the local neighborhood range is determined using the exploration circle. The point is then added to the tree according to the principle of minimizing energy consumption; therefore, the tree's growth direction is driven by random sampling, allowing for a probabilistic and complete exploration of the entire space. Except for the starting point coordinates, each sampling point has one and only one parent node, ensuring that there is only one acyclic path from the starting point to any node. Simultaneously, the maximum number of iterations and the neighborhood radius are set, with the neighborhood radius ranging from 50 to 100 meters.

[0071] Step S533: For any sampling point, determine all nodes in the preset random tree that fall within the exploration circle to which the sampling point belongs, and use them as the candidate parent node set corresponding to the sampling point.

[0072] Specifically, for any sampling point obtained through random sampling during the planning process, all nodes of the exploration circle are searched and determined in the constructed random tree with the starting point coordinates as the root node. The spatial distance of these nodes to the sampling point is less than or equal to the preset neighborhood radius. All the searched nodes together constitute the candidate parent node set corresponding to the sampling point.

[0073] Step S535: Determine the energy consumption corresponding to the path from the candidate parent node in the candidate parent node set to the center of the circle.

[0074] Specifically, for each candidate parent node in the candidate parent node set, the position of the candidate parent node is the starting point and the position of the sampling point (i.e. the center of the exploration circle) is the ending point. A path is constructed along the straight line connecting the two. Then, the drag and wind field effects that the aircraft needs to overcome to fly along this path are determined by combining the aircraft energy consumption dynamics model, so as to obtain the energy consumption corresponding to the path. The energy consumption will be used as the basis for comparison when selecting the optimal parent node from all candidate parent nodes.

[0075] Step S537: Determine the parent node of any sampling point based on the energy consumption of the path, and repeat the above steps until the preset conditions are met to obtain the initial path corresponding to any sampling point.

[0076] Specifically, for any sampling point generated in the current iteration, after calculating the energy consumption value of the path segment corresponding to each candidate parent node in the candidate parent node set, the energy consumption of the path corresponding to each candidate parent node is further compared, and the candidate parent node with the smallest energy consumption is selected as the unique parent node of the sampling point, and the corresponding parent-child connection edge is established in the random tree. Subsequently, the process of re-random sampling in the search space, searching for neighboring candidate parent nodes, calculating energy consumption, and selecting the optimal parent node is returned to be executed. This process is repeated iteratively until the preset termination condition is reached: that is, the number of iterations reaches the set maximum number of iterations, or a node in the random tree has been successfully connected to the endpoint coordinates, thus forming a complete connected path from the starting coordinates to the endpoint coordinates, which serves as an initial path.

[0077] This embodiment uses a random tree with the starting point as the root node to randomly sample within a three-dimensional search space. An exploration circle is constructed with the sampling point as the center and a preset neighborhood radius. All random tree nodes within this circle form a set of candidate parent nodes. Then, using an aircraft energy consumption dynamics model, the energy consumption corresponding to the path from each candidate parent node to the sampling point is calculated. The parent node is selected based on the criterion of minimum energy consumption, and a connection is established. This process is iterated until the random tree is connected to the endpoint, generating the initial path. This embodiment directly uses energy consumption as the optimization guide for random tree growth, actively avoiding high-energy-consuming wind fields and no-fly zones during the search process. It avoids the problem of inaccurate energy prediction caused by focusing solely on the shortest path, significantly improving the global optimality and energy efficiency of trajectory planning in dynamic atmospheric environments, and ensuring the stability of the low-altitude electric aircraft's endurance and the robustness of trajectory planning.

[0078] In one alternative embodiment, the method further includes: If any real-time indicator is detected to be greater than the preset value, the final trajectory is locally corrected to obtain the corrected final trajectory.

[0079] Specifically, during the mission executed by the aircraft along the planned trajectory, real-time indicators are continuously monitored. If any real-time indicator is detected to exceed a preset safety threshold, a local correction is immediately triggered on the current final trajectory. The local trajectory correction employs a rolling time-domain optimization method, setting the length of the rolling time-domain window to 30–60 seconds. The correction operation only adjusts the trajectory segments within the current time-domain window; the trajectories outside the window remain unchanged. The optimization process aims to minimize local energy consumption within the time-domain window, and the corrected trajectory must fully satisfy the constraints again to obtain the corrected final trajectory.

[0080] This embodiment continuously monitors real-time indicators during the aircraft's mission along the planned trajectory. If any real-time indicator exceeds a preset safety threshold, a local trajectory correction is immediately triggered. The correction employs a rolling time-domain optimization method, setting the rolling time-domain window length to 30-60 seconds. Only the trajectory segment within the current time-domain window is adjusted, while the original scheme outside the window remains unchanged. Optimization aims to minimize local energy consumption within the window, and requires that the corrected trajectory fully satisfy the constraints again. This embodiment achieves agile response and low-cost local repair to exceed-limit risks, avoiding the accumulation of danger while preserving the overall mission plan framework. It restores constraint satisfaction in a way that minimizes additional energy consumption, ensuring the corrected trajectory is physically reachable, flight safe, and mission feasible. Thus, it achieves the most energy-efficient real-time trajectory maintenance capability under safety constraints in a dynamic environment.

[0081] In one alternative implementation, real-time indicators include: sudden gust wind speed, temperature change, and air pressure change.

[0082] In this embodiment, during the flight path execution, real-time monitoring of sudden gusts, temperature changes, and air pressure changes is continuously performed. When a sudden gust reaches or exceeds 8 m / s, or a temperature change within 10 minutes exceeds 5°C, or an air pressure change within 10 minutes exceeds 5 hPa, it is considered an environmental abrupt event. These sudden environmental changes can significantly alter the aircraft's aerodynamic characteristics and energy consumption levels, potentially causing the original flight path to no longer meet energy consumption or safety constraints. Therefore, once any of the above real-time indicators is detected to exceed the corresponding preset threshold, a local correction to the current final flight path is immediately triggered to ensure the feasibility and safety of the remaining flight distance.

[0083] This embodiment takes an electric multi-rotor logistics aircraft as an example, and the specific implementation of this solution is as follows: Obtain basic mission information and input the flight mission information: starting coordinates (116.4074°E, 39.9042°N, 50m), ending coordinates (116.4574°E, 39.9042°N, 50m), mission time window [09:00:00, 09:30:00], maximum permissible flight altitude. =120m, the no-fly zone is a residential area between the start and end points (boundary coordinates: 116.4274°E-116.4374°E, 39.8942°N-39.9142°N, altitude 0-120m), and real-time information: aircraft model parameters (rotor radius 0.35m, number of rotors 6, rotor rotation area S=2.31m², fuselage weight 9.2kg), battery parameters (capacity 50Ah, rated voltage 22.8V, initial SOC=80%), total usable battery energy. =50×22.8×0.8=912Wh.

[0084] Atmospheric environmental data were collected and fused, incorporating multi-source atmospheric data: WRF numerical forecast data (for the next 2 hours, spatial resolution 100m, temporal resolution 5 minutes), ground meteorological station data (near-surface wind speed 2m / s, easterly wind direction, temperature 25℃, air pressure 1013hPa), airborne MEMS sensor data (real-time monitoring of wind speed, temperature, and humidity during flight), and 5G meteorological edge node data (transmission latency 80ms). The data underwent preprocessing (outlier removal and missing value completion), and a weighted fusion algorithm was used (airborne sensor weight 0.5, WRF data weight 0.3, and other weights 0.2). This fusion process then constructed a 3D environmental model, obtaining parameters such as the 3D wind field and air density within the area from the starting point to the endpoint. The wind speed within the flight area was 3m / s, and the air density ρ = 1.225kg / m³.

[0085] Step 3: Input real-time information and environmental information into the preset aircraft energy consumption dynamics model: Assuming aircraft speed information =10m / s, climbing speed =0m / s (horizontal flight), thrust coefficient =0.1, fuselage drag coefficient =0.2, fuselage frontal area =0.5m², rotor-type drag power coefficient =0.02, rotor angular velocity Ω=50rad / s; relative inflow velocity = =7m / s (East wind, aircraft flying westward, headwind); The aerodynamic thrust of the rotor is T = 0.5 × 1.225 × 2.31 × 0.1 × 7² ≈ 7.02 N; Rotor induced velocity ≈1.11m / s; waste resistance power = ×1.225×0.2×0.5×7³≈34.3W; Rotor-type power resistance ×1.225×0.02×2.31×0.35×50³≈189.6W; Instantaneous power P = 7.02 × (0 + 1.11) + 34.3 + 189.6 ≈ 231.7 W; Energy consumption: The flight path is divided into 10 segments, each 500m long. The flight time is t = 500 / 10 = 50s. The energy consumption of each segment is... =231.7 × 50 = 11585 J = 3.22 Wh, the energy consumption corresponding to the total trajectory. =10×3.22=32.2Wh.

[0086] Multi-constraint trajectory optimization is performed with the objective of minimizing the energy consumption corresponding to the total trajectory. The comprehensive constraints (altitude ≤ 120m, no entry into no-fly zones, horizontal safety separation ≥ 50m, total energy consumption ≤ 912 × 0.8 = 729.6Wh, flight time ≤ 30min) are considered. An improved random tree (number of iterations) is employed. =1000, neighborhood radius The optimal flight path was determined using a radius of 80m and an energy efficiency coefficient η=1.0. After optimization, the flight path bypassed the no-fly zone and utilized crosswind areas to reduce the impact of headwinds. The total energy consumption was optimized to 27.4Wh, representing a 15% energy saving compared to the shortest path (32.2Wh). The flight time was 25 minutes, meeting the mission timeliness requirements. During the flight, atmospheric data updates were received in real time. When the aircraft reached the mid-flight altitude (116.4374°E, 39.9042°N, 80m altitude), a sudden gust of wind (9m / s, exceeding the 8m / s abrupt change standard) was detected, triggering dynamic replanning. A rolling time-domain optimization method (30s window, 500m flight distance) was used to locally correct the flight path, bypassing the gust area. After correction, the energy consumption of the local path segment increased by 2.1Wh, and the total energy consumption became 29.5Wh, still meeting the energy consumption constraint. The replanning response time was 0.8s, ensuring flight safety and optimal energy efficiency.

[0087] Based on the acquired final flight path, a waypoint sequence (12 waypoints in total, spaced 500m apart, conforming to the MavLink protocol) is generated and sent to the flight control system. An energy efficiency report is generated: total energy consumption 29.5Wh, remaining battery energy consumption 912-29.5=882.5Wh, energy saving rate 15%, atmospheric environmental risk level medium, and the response recommendation is "maintain the current flight path and continuously monitor atmospheric changes". At the same time, the flight path, energy consumption distribution, and gust area location are visualized on the GCS for operator monitoring.

[0088] Accordingly, please refer to Figure 6 A block diagram of an energy-saving trajectory planning method device for low-altitude electric aircraft provided in this application embodiment, the device comprising: The information processing unit 101 is used to acquire mission information and real-time information of the aircraft to be analyzed, input the real-time coordinates in the real-time information into a preset three-dimensional environment model, and acquire environmental information.

[0089] The energy consumption calculation unit 103 is used to input the real-time information and the environmental information into a preset aircraft energy consumption dynamics model to obtain energy consumption.

[0090] The path planning unit 105 is used to divide multiple initial paths based on the starting point coordinates and ending point coordinates in the task information.

[0091] The trajectory determination unit 107 is used to determine the final trajectory based on the energy consumption corresponding to the initial path.

[0092] In some alternative implementations, the energy consumption calculation unit 103 includes: The relative incoming flow velocity is determined based on the attenuation of the wind speed component in the environmental information to the velocity component in the real-time information.

[0093] The aerodynamic thrust of the rotor is determined by using a preset rotor thrust model based on the air density in the environmental information, the rotor parameters in the real-time information, the preset thrust coefficient, and the relative incoming flow velocity.

[0094] The rotor induced velocity is determined based on the preset rotor induced velocity model, rotor aerodynamic thrust, air density, and rotor parameters.

[0095] Energy consumption is obtained based on rotor aerodynamic thrust, rotor induced velocity, air density, rotor parameters, and fuselage data in real-time information.

[0096] In some alternative implementations, the energy consumption calculation unit 103 includes: The climb power is determined by multiplying the rotor aerodynamic thrust by the climb rate in the fuselage data.

[0097] The induced power is determined by the product of the rotor aerodynamic thrust and the rotor induced velocity.

[0098] The air density, fuselage drag coefficient from the fuselage data, and fuselage frontal area are input into a preset exhaust drag power model to determine the exhaust drag power.

[0099] Input the rotor type drag power coefficient, rotor radius, rotor angular velocity, rotor rotation area and air density from the rotor parameters into the preset rotor type drag power model to determine the rotor type drag power.

[0100] The instantaneous power is determined by summing the climb power, induced power, exhaust drag power, and rotor drag power.

[0101] Energy consumption is determined by integrating instantaneous power over time.

[0102] In some alternative implementations, the path planning unit 105 includes: Obtain the energy consumption corresponding to the straight-line distance from the real-time coordinates to the endpoint coordinates, determine the estimated energy consumption, and construct an energy consumption heuristic function based on the ratio of the estimated energy consumption to the preset energy consumption coefficient.

[0103] The energy consumption heuristic function, spatial constraints, safety intervals, task timeliness, and battery inventory in real-time information are used as constraints to perform random sampling and obtain multiple initial paths.

[0104] In some alternative implementations, the path planning unit 105 includes: An exploration circle is constructed using a preset radius and random sampling points that satisfy the constraints as the center. For any sampling point, determine all nodes in the preset random tree that fall within the exploration circle to which the sampling point belongs, and use them as the set of candidate parent nodes corresponding to the sampling point. Determine the energy consumption of the path from the candidate parent node in the candidate parent node set to the center of the circle; Based on the energy consumption corresponding to the path, determine the parent node of any sampling point, and repeat the above steps until the preset conditions are met to obtain the initial path corresponding to any sampling point.

[0105] In some alternative implementations, real-time indicators include: sudden gust wind speed, temperature change, and air pressure change.

[0106] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0107] In this embodiment, the energy-saving trajectory planning method device for low-altitude electric aircraft is presented in the form of functional units. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0108] Please see Figure 7 , Figure 7 This application provides a schematic diagram of the structure of a computer device, as shown in the embodiment of the present application. Figure 7As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 7 Take a processor 10 as an example.

[0109] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0110] The memory 20 stores instructions executable by at least one processor 10 to cause at least one processor 10 to perform the method shown in the above embodiments.

[0111] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0112] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0113] The computer device also includes a communication interface 30 for communicating with other devices or communication networks.

[0114] This application also provides a computer-readable storage medium. The methods described in this application can be implemented in hardware or firmware, or implemented as recordable on a storage medium, or implemented as computer code downloaded over a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and subsequently stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the methods shown in the above embodiments are implemented.

[0115] The apparatus and units described in the above embodiments can be implemented by a computer chip or physical entity, or by a product with a certain function. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or any combination of these devices.

[0116] For ease of description, the above devices are described separately by function as various units. Of course, in implementing this application, the functions of each unit can be implemented in one or more software and / or hardware.

[0117] Those skilled in the art will understand that the embodiments of this application can be provided as methods or apparatus. 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.

[0118] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatuses, and devices 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 process. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0119] 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.

[0120] 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.

[0121] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0122] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the apparatus embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0123] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

[0124] Although embodiments of this application have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of this application, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for energy-saving trajectory planning of low-altitude electric aircraft, characterized in that, The method includes: Obtain the mission information and real-time information of the aircraft to be analyzed, and input the real-time coordinates in the real-time information into a preset three-dimensional environment model to obtain environmental information; The real-time information and the environmental information are input into a preset aircraft energy consumption dynamics model to obtain energy consumption; Multiple initial paths are defined based on the starting and ending coordinates in the task information; The final trajectory is determined based on the energy consumption corresponding to the initial path.

2. The method according to claim 1, characterized in that, The step of inputting the real-time information and the environmental information into a preset aircraft energy consumption dynamics model to obtain an energy consumption model includes: The relative incoming flow velocity is determined based on the attenuation of the wind speed component in the environmental information on the velocity component in the real-time information. Based on the air density in the environmental information, the rotor parameters in the real-time information, the preset thrust coefficient, and the relative incoming flow velocity, the rotor aerodynamic thrust is determined using a preset rotor thrust model. The rotor induced velocity is determined based on the preset rotor induced velocity model, the rotor aerodynamic thrust, the air density, and the rotor parameters. Energy consumption is obtained based on the rotor aerodynamic thrust, rotor induced velocity, air density, rotor parameters, and fuselage data in the real-time information.

3. The method according to claim 2, characterized in that, The process of obtaining energy consumption based on the rotor aerodynamic thrust, rotor induced velocity, air density, rotor parameters, and fuselage data in real-time information includes: The climb power is determined by multiplying the rotor aerodynamic thrust by the climb rate in the fuselage data. The induced power is determined by the product of the rotor aerodynamic thrust and the rotor induced velocity; The air density, the relative incoming flow velocity, the fuselage drag coefficient and the fuselage frontal area in the fuselage data are input into a preset waste resistance power model to determine the waste resistance power; The rotor type drag power coefficient, rotor radius, rotor angular velocity, rotor rotation area and air density in the rotor parameters are input into the preset rotor type drag power model to determine the rotor type drag power; The instantaneous power is determined by summing the climb power, the induced power, the waste drag power, and the rotor drag power. Energy consumption is determined by integrating the instantaneous power over time.

4. The method according to claim 1, characterized in that, The process of dividing multiple initial paths based on the starting and ending coordinates of the task information includes: Obtain the energy consumption corresponding to the straight-line distance from the real-time coordinates to the endpoint coordinates, determine the estimated energy consumption, and construct an energy consumption heuristic function based on the ratio of the estimated energy consumption to the preset energy consumption coefficient. The energy consumption heuristic function, the spatial restrictions, safety intervals, and task timeliness in the task information, as well as the battery inventory in the real-time information, are used as constraints to perform random sampling and obtain multiple initial paths.

5. The method according to claim 4, characterized in that, The method for obtaining any initial path is as follows: An exploration circle is constructed using a preset radius and random sampling points that satisfy the constraints as the center. For any sampling point, all nodes in a preset random tree that fall within the exploration circle to which the sampling point belongs are determined as the set of candidate parent nodes corresponding to the sampling point. The energy consumption corresponding to the path is determined by the path from the candidate parent node in the candidate parent node set to the center of the circle; Based on the energy consumption corresponding to the path, determine the parent node of any sampling point, and repeat the above steps until the preset conditions are met to obtain the initial path corresponding to any sampling point.

6. The method according to claim 1, characterized in that, The method further includes: If any real-time indicator is detected to be greater than a preset value, the final trajectory is locally corrected to obtain the corrected final trajectory.

7. The method according to claim 6, characterized in that, Real-time indicators include: sudden gust wind speed, temperature change, and air pressure change.

8. A method and apparatus for energy-saving trajectory planning of low-altitude electric aircraft, characterized in that, The device includes: The information processing unit is used to acquire mission information and real-time information of the aircraft to be analyzed, input the real-time coordinates in the real-time information into a preset three-dimensional environment model, and acquire environmental information. An energy consumption calculation unit is used to input the real-time information and the environmental information into a preset aircraft energy consumption dynamics model to obtain energy consumption. The path planning unit is used to divide multiple initial paths based on the starting point coordinates and ending point coordinates in the task information; The trajectory determination unit is used to determine the final trajectory based on the energy consumption corresponding to the initial path.

9. A computer device, characterized in that, include: A memory and a processor are interconnected, the memory stores computer instructions, and the processor executes the computer instructions to perform the energy-saving trajectory planning method for low-altitude electric aircraft as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the energy-saving flight path planning method for low-altitude electric aircraft as described in any one of claims 1 to 7.

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