Method for improving voyage precision of unmanned aerial vehicle through wind estimation

Abstract

The invention discloses a method for improving the voyage precision of an unmanned aerial vehicle through wind estimation. The method comprises a flight condition energy consumption statistics module, a battery energy calculation module and a voyage calculation and landing energy calculation module. According to the method for improving the voyage precision of the unmanned aerial vehicle through wind estimation, in a windy environment, flight voyage and landing residual energy prediction which is much more accurate than that of a traditional method can be provided, and by providing more reliable endurance prediction, the risk of task failure or aircraft loss caused by accidental battery exhaustion is reduced; the voyage estimation is more stable, so that more optimized or bold task planning is allowed, a more reliable basis can be provided for decisions such as whether to continue tasks or trigger return voyage in flight, and the voyage and energy prediction precision is improved. The unmanned aerial vehicle is expected to be deployed under the condition that the unmanned aerial vehicle is difficult to operate due to too high risk or too large uncertainty in the past.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle (UAV) flight management systems, and in particular to a method for improving the range accuracy of a UAV by estimating wind speed. Background Art

[0002] The UAV Flight Management System is a comprehensive system framework and technical solution for the safe, efficient, and large-scale integration of unmanned aerial vehicles (UAVs) into the national airspace system, particularly low-altitude airspace (typically below 400 feet / 120 meters above ground level).

[0003] In the prior art, the Chinese patent application number CN202410189677.8 discloses a multi-UAV coverage search planning method and system based on estimated range. The method includes: initial planning processing: task area setting, conversion to obtain a convex polygon area; coverage parameter solution, obtaining coverage parameters such as heading field of view size, radial field of view size, aerial photography interval and flight strip interval; coverage task allocation: flight strip direction determination; multi-machine area allocation, estimating the total length of the straight segment and turning track of the UAV, and combining the estimated range to allocate task sub-areas to each UAV; coverage track planning: sequence track point solution, through the intersection detection of the straight segment track and the convex polygon, cyclic expansion and solution to obtain the sequence track points; multi-machine task starting point determination, combining the estimated range to determine the starting point of each UAV task, and planning the coverage search track of each UAV.

[0004] For example, in the prior art, the Chinese patent application number CN202110912128.5 discloses a range-adjusted UAV trajectory planning method based on the Dubins path. According to actual mission requirements, the terminal heading angle constraint is considered, and a feasible trajectory based on the Dubins path is quickly generated. By considering the UAV kinematic constraint information and range information, a segmented compensation strategy is adopted to adjust the range of the straight line segment in the Dubins path. The time consistency constraint in the UAV trajectory planning process is converted into a fixed range problem, so that the range of multiple UAVs is consistent, and after the range adjustment, each UAV still maintains the flight direction of the straight segment, guiding multiple UAVs to arrive at the target position at the same time, that is, achieving the temporal and spatial consistency of multiple UAVs.

[0005] Combined with the above materials, it can be seen that drones in the existing technology generally rely on ground speed to estimate the drone's flight range and landing energy. However, in actual use, there are variable wind fields during flight. Due to the influence of wind force, the accuracy of the drone's flight range and landing energy estimation will be affected (for example, at a given ground speed, flying against the wind consumes significantly more energy than flying with the wind. Therefore, under windy conditions, traditional range and landing energy estimation may have large errors, which may lead to mission failure, drone loss or even safety risks). Summary of the Invention

[0006] The purpose of the present invention is to provide a method for improving the range accuracy of a UAV by wind estimation, so as to solve the problem of errors in the flight range and landing energy of a UAV raised in the above background technology.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for improving the range accuracy of a UAV through wind estimation, comprising a flight condition energy consumption statistics module, a battery energy calculation module, and a range calculation and landing energy calculation module, characterized in that:

[0008] The flight condition energy consumption statistics module includes:

[0009] Define flight condition types. Aircraft flight missions include six states: ground holding, takeoff and climb, enroute flight, air stop, descent, and landing.

[0010] Statistical analysis of energy consumption in various operating conditions: Match different flight conditions to the current flight speed during flight, calculate the energy consumption over a certain flight distance, and obtain a gradually converging energy consumption estimation curve.

[0011] Saving and loading historical energy consumption data: After landing, the aircraft can save the power consumption data collected from each flight in the form of a file on the internal storage device. Before the next flight, this file can be loaded to obtain historical statistical data to calculate the range and landing power consumption, thus predicting risks in advance.

[0012] Power consumption data correction, including single flight power consumption data correction and historical flight power consumption data correction;

[0013] The battery energy calculation module includes:

[0014] Initial battery SOC acquisition: The aircraft communicates with the BMS through a private protocol to obtain the SOC value in the initial state;

[0015] Life correction: Map the SOC read before takeoff to battery energy based on the battery capacity, and then correct the battery energy from the theoretical value to the actual value based on the battery cycle life curve;

[0016] Temperature correction: Use the temperature correction curve to correct the battery energy value obtained in the previous step to compensate for the changes in battery capacity under high and low temperature conditions. The final correction result is used as the battery energy;

[0017] The flight range calculation and landing energy calculation module includes:

[0018] Range calculation: Before takeoff, flight data is sent to the aircraft in the form of waypoints. After obtaining the waypoint information, the aircraft will fly according to the waypoint position and the set waypoint speed. After knowing the current aircraft position and waypoint information, the distance to be flown can be calculated. ;

[0019] Altitude compensation for take-off and landing points: When receiving flight data, the drone also receives the attribute information of the take-off and landing fields. When flying in areas with complex terrain, the altitude of the take-off and landing airports often differs. In this case, it is necessary to compensate for the altitude difference between the take-off and landing airports to improve the accuracy of the calculation of the distance to be flown.

[0020] Calculate the remaining range energy consumption. Through the flight data mentioned in the range calculation, the flight conditions of the aircraft in each section can be obtained. The energy required for the remaining range can be calculated by combining the mileage and time required for each flight condition with the statistical power consumption of each condition. ;

[0021] The remaining energy and SOC conversion converts the remaining battery energy into the SOC value through the mapping relationship between battery energy and SOC, which is used to indicate whether the battery energy can support the flight mission.

[0022] Preferably, the state of the aircraft is divided into the following three states:

[0023] Vertical flight: horizontal flight speed is less than 0.5m / s, vertical flight speed is greater than 0.5m / s;

[0024] Cruise: horizontal flight speed is greater than 4.0m / s, vertical flight speed is less than 0.5m / s;

[0025] Hovering: The horizontal flight speed is less than 0.5m / s, and the vertical flight speed is less than 0.5m / s.

[0026] Preferably, the calculation process of the energy consumption estimation curve is: ,in is the average energy consumption obtained over a period of time. is the instantaneous energy consumption, is the instantaneous speed, For statistical time.

[0027] Preferably, the temperature correction curve is drawn by testing the ratio of the nominal capacity to the actual capacity of the battery under typical operating conditions at different temperatures.

[0028] Preferably, The calculation method is: ,in To be flown miles, is the distance between the current waypoint and the next waypoint, is the current waypoint number, is the number of waypoints, The distance between the drone and the current waypoint.

[0029] Preferably, , that is, total energy consumption = the sum of energy consumption under different working conditions, where For working conditions Power consumption, For working conditions Miles to be flown.

[0030] Preferably, the remaining energy of the drone when it lands during the conversion between the remaining energy and SOC = the battery energy before takeoff - the energy consumed during flight, that is, ,in Provides landing energy for drone batteries. is the initial energy of the battery before the drone takes off, The energy consumed by the drone during flight.

[0031] Preferably, the method comprises the following steps:

[0032] S1. Characterization and preprocessing of wind field data;

[0033] S2, derive the airspeed vector from the ground speed and wind field data;

[0034] S3, wind field corrected flight mode power consumption.

[0035] Preferably, the ground speed vector of the UAV in step S2 is , airspeed vector With wind speed vector There is the following vector relationship between them: , so the airspeed vector can be calculated by the following formula , in this relationship Given by GPS or route information, From the processed wind field data, the goal is to calculate ;

[0036] The mathematical formula for airspeed calculation is:

[0037] , the ground speed is , ground speed direction is , wind speed is , wind direction is , let the relative angle .

[0038] Preferably, in step S3, when calculating When the airspeed effect is introduced, the flight time As an intermediate variable, the conversion between the two is realized: ,in is the flight time of the aircraft in this segment. Knowing the flight time in the segment, the relative air displacement of the drone in this segment can be calculated ,in is calculated based on wind field data under working conditions To set the airspeed at which the drone needs to fly, use Replace the original , the rest of the calculation process remains the same, that is, the accuracy of range estimation and landing energy estimation is improved by introducing airspeed.

[0039] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0040] 1. Improved accuracy: Especially in windy environments, it can provide far more accurate predictions of flight range and landing remaining energy than traditional methods.

[0041] 2. Enhanced safety: By providing more reliable endurance predictions, the risk of mission failure or vehicle loss due to unexpected battery depletion is reduced.

[0042] 3. Improved mission planning and efficiency: More robust range estimates allow for more optimized or aggressive mission planning, while also providing a more reliable basis for in-flight decisions such as whether to continue a mission or trigger a return.

[0043] 4. Optimize resource management: More accurate energy predictions help better manage the battery life cycle.

[0044] 5. Expanding application scenarios: Improved range and energy prediction accuracy is expected to enable the deployment of drones in conditions that were previously difficult to operate due to high risk or uncertainty (for example, operating in complex terrain with variable wind conditions, or performing longer-distance missions), thereby expanding the operating envelope and application range of drones, such as conducting continuous surveillance missions in areas of known wind shear. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 A schematic diagram of data changes for the correction of historical flight power consumption data according to the present invention;

[0046] Figure 2 It is a schematic flow chart of the overall operation steps of the present invention. DETAILED DESCRIPTION

[0047] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0048] Example 1: Please refer to Figure 1 , this embodiment provides a method for improving the range accuracy of a UAV by wind estimation:

[0049] Range estimation

[0050] Range estimation is divided into three modules:

[0051] 1. Flight energy consumption statistics

[0052] a. Define flight condition types

[0053] In the past, multi-rotor drones were often in the following stages during enroute flight missions: ground waiting, takeoff and climb, enroute flight, airborne stop, descent, and landing. Each of these states often has different names and definitions due to different drone manufacturers / software providers. Therefore, it is more intuitive to directly distinguish flight conditions according to the performance status of the aircraft. The aircraft status can be roughly divided into the following three states:

[0054] Vertical flight: horizontal flight speed is less than 0.5m / s, vertical flight speed is greater than 0.5m / s

[0055] Vertical climb: The vertical flight speed value is positive, and the altitude increases in the direction

[0056] Vertical descent: The vertical flight speed is negative and the altitude is decreasing.

[0057] Cruise: horizontal flight speed is greater than 4.0m / s, vertical flight speed is less than 0.5m / s

[0058] Hovering: Horizontal flight speed is less than 0.5m / s, vertical flight speed is less than 0.5m / s

[0059] b. Statistics of energy consumption in each working condition

[0060] During flight, the energy consumption over a certain distance / time is calculated based on the current flight speed and different flight conditions. A gradually converging energy consumption estimation curve is obtained. The specific calculation process is as follows: (On hover: ),in is the average energy consumption obtained over a period of time. is the instantaneous energy consumption (if it is an electric drone, , 、 are the voltage and current when the drone is working), is the instantaneous speed, To ensure the accuracy of statistical data, the aircraft must meet a certain flight distance / time under various flight conditions before statistical data can be used. This flight distance / time parameter is set based on experience.

[0061] c. Saving and loading historical energy consumption data

[0062] After landing, the drone can save the power consumption data collected from each flight in the form of a file on the internal storage device. Before the next flight, this file can be loaded to obtain historical statistical data to calculate the flight range / landing power consumption and predict risks in advance.

[0063] d. Power consumption data correction

[0064] Single-flight power consumption data correction: Because the payload, environment, and mounted equipment of each UAV flight may vary, long-term statistical data, while more representative, may not be applicable to a single flight. Therefore, it is necessary to collect power consumption statistics for each UAV flight mission. When the collected power consumption data meets the convergence index, the data from this flight can be switched to estimate the range / landing power.

[0065] Correction of historical flight power consumption data: During the lifecycle of a drone, its powertrain output characteristics, energy utilization efficiency, and device power consumption under the same operating conditions change slowly. Therefore, a time "window" needs to be set to dynamically update this slowly changing data. The specific diagram is shown below. By setting the update time interval, valid and expired power consumption data are introduced and discarded.

[0066] 2. Battery energy calculation

[0067] a. Initial battery SOC acquisition

[0068] The drone communicates with the BMS through a private protocol to obtain the SOC value in the initial state.

[0069] b. Lifespan correction

[0070] The SOC read before takeoff is mapped to battery energy based on the battery capacity. Then, based on the battery cycle life curve, the battery energy is corrected from the theoretical value to the actual value (if life correction is already available in the BMS, the battery energy calculated from the SOC can be directly used).

[0071] Cycle life curve: Usually provided by the manufacturer or drawn by testing the ratio of the battery's nominal capacity to its actual capacity after a specified number of cycles.

[0072] c. Temperature correction

[0073] The battery energy value obtained in the previous step is corrected using the temperature correction curve to compensate for changes in battery capacity under high / low temperature conditions. The final correction result is used as the battery energy.

[0074] Temperature correction curve: usually provided by the manufacturer or drawn by testing the ratio of the nominal capacity to the actual capacity of the battery under typical operating conditions at different temperatures.

[0075] 3. Calculation of flight distance and landing energy

[0076] a. Voyage calculation

[0077] Before takeoff, the flight data is sent to the drone in the form of waypoints. After obtaining the waypoint information, the drone will fly according to the waypoint position and the set waypoint speed. After knowing the current drone position and waypoint information, the distance to be flown can be calculated. , the calculation method is: ,in To be flown miles, is the distance between the current waypoint (the point the drone is flying to) and the next waypoint, is the current waypoint number, is the number of waypoints, The distance between the drone and the current waypoint.

[0078] b. Altitude compensation for take-off and landing points

[0079] When receiving flight data, drones also receive the attribute information of the take-off and landing airports. When flying in areas with complex terrain, the altitude of the take-off airport and the landing airport are often inconsistent. In this case, it is necessary to compensate for the altitude difference between the take-off and landing airports to improve the accuracy of calculating the distance to be flown.

[0080] c. Calculate the remaining range energy consumption

[0081] The flight data mentioned in a) can be used to obtain the flight conditions of the aircraft in each flight segment. The energy required for the remaining flight range can be calculated by combining the mileage / time required for each flight condition and the power consumption of each condition. ,have , that is, total energy consumption = the sum of energy consumption under different working conditions, where For working conditions Power consumption, For working conditions Miles to be flown.

[0082] d. Residual energy and SOC conversion

[0083] The remaining energy of the drone after landing = the battery energy before takeoff - the energy consumed during flight, that is, ,in Provides landing energy for drone batteries. is the initial energy of the battery before the drone takes off, The energy consumed by the drone during flight; the remaining battery energy is converted into an SOC value through the mapping relationship between battery energy and SOC, which is used to indicate whether the battery energy can support the flight mission.

[0084] Example 2: Please refer to Figure 2 , this embodiment provides a method for improving the range accuracy of a UAV by wind estimation:

[0085] 1. Characterization and preprocessing of wind field data

[0086] a. Assumptions and requirements for inputting wind farm data

[0087] This proposal assumes that wind field data differs from traditional three-dimensional grid data sets. Traditional wind field data grid points (defined by longitude, latitude, and altitude) are associated with a wind vector (including wind speed and direction). The wind field data relied upon in this paper is real-time wind speed estimation data output by the [Multi-rotor UAV Wind Field Estimation Method] (hereinafter referred to as the "wind estimation algorithm"). This is the wind field that is sensed and affected by the drone in real time, and is more real-time and direct than traditional wind field data.

[0088] b. Interpolation of wind field data on the flight embankment

[0089] Since the pre-acquired wind field data is usually discrete, and the output frequency of the wind estimation algorithm is usually different from the calculation frequency of the module for calculating the distance to be flown / consumed energy, an interpolation method is needed to obtain the wind conditions at any time; spatial interpolation algorithms (such as linear interpolation, inverse distance weighted interpolation, etc.) can be used to estimate the wind speed at fixed intervals or specific times. and wind direction The accuracy of the interpolation also depends on the density and quality of the original wind field data.

[0090] 2. Derivation of airspeed vector from ground speed and wind field data

[0091] a. Vector relationship

[0092] UAV ground speed vector , airspeed vector With wind speed vector There is the following vector relationship between them:

[0093]

[0094] Therefore, the airspeed vector can be calculated as follows:

[0095]

[0096] In this relationship, Usually given by GPS or route information (target ground speed), From the processed wind field data, the goal is to calculate .

[0097] b. Mathematical formula for airspeed calculation

[0098] Assuming the ground speed is known , ground speed direction (track angle) , wind speed , and wind direction (refers to the direction of wind source, which is consistent with the definition of meteorological wind direction), let the relative angle , then:

[0099]

[0100] 3. Power consumption of wind field corrected flight mode

[0101] Calculate the distance to be flown in [Distance Calculation and Landing Energy Calculation] - [Distance Calculation] Calculation method: middle, is the flight distance of the aircraft in this section. Now we need to introduce the effect of airspeed, which can be expressed as flight time. As an intermediate variable, the conversion between the two is realized:

[0102]

[0103] in is the flight time of the aircraft in this segment (since the waypoint speed is set according to the ground speed, the flight time of the drone in this segment can be calculated in advance). If the flight time in the segment is known, the relative air displacement of the drone in this segment can be calculated. :

[0104]

[0105] in is calculated based on wind field data under working conditions To set the airspeed at which the drone needs to fly, use Replace the original , the rest of the calculation process remains the same, and the accuracy of range estimation / landing energy estimation can be improved by introducing airspeed.

[0106] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for improving the range accuracy of a UAV by wind estimation, comprising a flight condition energy consumption statistics module, a battery energy calculation module, and a range calculation and landing energy calculation module, characterized in that: The flight condition energy consumption statistics module includes: Define flight condition types. Aircraft flight missions include six states: ground holding, takeoff and climb, enroute flight, air stop, descent, and landing. Statistical analysis of energy consumption in various operating conditions: Match different flight conditions to the current flight speed during flight, calculate the energy consumption over a certain flight distance, and obtain a gradually converging energy consumption estimation curve. Saving and loading historical energy consumption data: After landing, the aircraft can save the power consumption data collected from each flight in the form of a file on the internal storage device. Before the next flight, this file can be loaded to obtain historical statistical data to calculate the range and landing power consumption, thus predicting risks in advance. Power consumption data correction, including single flight power consumption data correction and historical flight power consumption data correction; The battery energy calculation module includes: Initial battery SOC acquisition: The aircraft communicates with the BMS through a private protocol to obtain the SOC value in the initial state; Life correction: Map the SOC read before takeoff to battery energy based on the battery capacity, and then correct the battery energy from the theoretical value to the actual value based on the battery cycle life curve; Temperature correction: Use the temperature correction curve to correct the battery energy value obtained in the previous step to compensate for the changes in battery capacity under high and low temperature conditions. The final correction result is used as the battery energy; The flight range calculation and landing energy calculation module includes: Range calculation: Before takeoff, flight data is sent to the aircraft in the form of waypoints. After obtaining the waypoint information, the aircraft will fly according to the waypoint position and the set waypoint speed. After knowing the current aircraft position and waypoint information, the distance to be flown can be calculated. ; Altitude compensation for take-off and landing points: When receiving flight data, the drone also receives the attribute information of the take-off and landing fields. When flying in areas with complex terrain, the altitude of the take-off and landing airports often differs. In this case, it is necessary to compensate for the altitude difference between the take-off and landing airports to improve the accuracy of the calculation of the distance to be flown. Calculate the remaining range energy consumption. Through the flight data mentioned in the range calculation, the flight conditions of the aircraft in each section can be obtained. The energy required for the remaining range can be calculated by combining the mileage and time required for each flight condition with the statistical power consumption of each condition. ; The remaining energy and SOC conversion converts the remaining battery energy into the SOC value through the mapping relationship between battery energy and SOC, which is used to indicate whether the battery energy can support the flight mission.

2. The method for improving the range accuracy of a UAV by wind estimation according to claim 1, characterized in that: The aircraft status is divided into the following three states: Vertical flight: horizontal flight speed is less than 0.5m / s, vertical flight speed is greater than 0.5m / s; Cruise: horizontal flight speed is greater than 4.0m / s, vertical flight speed is less than 0.5m / s; Hovering: The horizontal flight speed is less than 0.5m / s, and the vertical flight speed is less than 0.5m / s.

3. The method for improving the range accuracy of a UAV by wind estimation according to claim 1, characterized in that: The calculation process of the energy consumption estimation curve is: ,in is the average energy consumption obtained over a period of time. is the instantaneous energy consumption, is the instantaneous speed, For statistical time.

4. The method for improving the range accuracy of a UAV by wind estimation according to claim 1, characterized in that: The temperature correction curve is drawn by testing the ratio of the nominal capacity to the actual capacity of the battery under typical operating conditions at different temperatures.

5. The method for improving the range accuracy of a UAV by wind estimation according to claim 1, characterized in that: The calculation method is: ,in To be flown miles, is the distance between the current waypoint and the next waypoint, is the current waypoint number, is the number of waypoints, The distance between the drone and the current waypoint.

6. The method for improving the range accuracy of a UAV by wind estimation according to claim 1, characterized in that: , that is, total energy consumption = the sum of energy consumption under different working conditions, where For working conditions Power consumption, For working conditions Miles to be flown.

7. The method for improving the range accuracy of a UAV by wind estimation according to claim 1, characterized in that: The remaining energy of the drone when it lands in the conversion between remaining energy and SOC = battery energy before takeoff - energy consumed during flight, i.e. ,in Provides landing energy for drone batteries. is the initial energy of the battery before the drone takes off, The energy consumed by the drone during flight.

8. The method for improving the range accuracy of a UAV by wind estimation according to claim 1, characterized in that: The following steps are involved: S1. Characterization and preprocessing of wind field data; S2, derive the airspeed vector from the ground speed and wind field data; S3, wind field corrected flight mode power consumption.

9. The method for improving the range accuracy of a UAV by wind estimation according to claim 8, characterized in that: The ground speed vector of the UAV in step S2 , airspeed vector With wind speed vector There is the following vector relationship between them: , so the airspeed vector can be calculated by the following formula , in this relationship Given by GPS or route information, From the processed wind field data, the goal is to calculate ; The mathematical formula for airspeed calculation is: , the ground speed is , ground speed direction is , wind speed is , wind direction is , let the relative angle .

10. The method for improving the range accuracy of a UAV by wind estimation according to claim 8, characterized in that: In step S3, when calculating When the airspeed effect is introduced, the flight time As an intermediate variable, the conversion between the two is realized: ,in is the flight time of the aircraft in this segment. Knowing the flight time in the segment, the relative air displacement of the drone in this segment can be calculated ,in is calculated based on wind field data under working conditions To set the airspeed at which the drone needs to fly, use Replace the original , the rest of the calculation process remains the same, that is, the accuracy of range estimation and landing energy estimation is improved by introducing airspeed.

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