CN112180723B - Unmanned aerial vehicle task planning method and device based on energy analysis - Google Patents
Unmanned aerial vehicle task planning method and device based on energy analysis Download PDFInfo
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Abstract
本发明公开了一种基于能量分析的无人机任务规划方法及装置，涉及无人机技术领域，主要目的在于对无人机进行电量预估，并对返航时间进行决策，以提高无人机在执行任务时的效率并实现安全返航。所述方法包括：获取无人机当前的位置坐标、飞行速度、飞行加速度以及能量消耗率；利用飞行速度及飞行加速度，匹配运动模式及持续时间；根据能量消耗模型、能量权重矩阵、时间信息矩阵及能量消耗率和持续时间，得到已消耗电量；根据电池容量及已消耗电量，得到电池余量；根据位置坐标及预设的返航运动模式，计算返航所需能量，并与电池余量进行对比，若电池余量不超过返航所需能量，结束当前任务并返航。本发明适用于基于能量分析的无人机任务规划。
The invention discloses an unmanned aerial vehicle mission planning method and device based on energy analysis, and relates to the technical field of unmanned aerial vehicles. Efficiency while performing missions and achieve a safe return home. The method includes: acquiring the current position coordinates, flight speed, flight acceleration and energy consumption rate of the UAV; using the flight speed and flight acceleration to match the motion pattern and duration; according to the energy consumption model, the energy weight matrix, the time information matrix and the energy consumption rate and duration to obtain the consumed power; according to the battery capacity and consumed power, the remaining battery capacity is obtained; according to the position coordinates and the preset return-to-home motion mode, the energy required for returning to home is calculated and compared with the remaining battery capacity , if the remaining battery power does not exceed the energy required to return home, end the current mission and return home. The present invention is suitable for UAV mission planning based on energy analysis.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicles, in particular to an unmanned aerial vehicle task planning method and device based on energy analysis.
Background
Many rotor unmanned aerial vehicle is one of recent research focus, relies on its outstanding low-altitude low-speed stable flight ability and nimble maneuvering ability, and it is striking splendid in fields such as aerial photograph, patrols and examines, military reconnaissance. However, most of the current unmanned aerial vehicles still use a lithium battery pack, and due to the nonlinearity of the discharge characteristic of the lithium battery, it is difficult to obtain the current state of charge through an effective and accurate measurement method. For the unmanned aerial vehicle in the task execution, the inaccuracy of the remaining power may cause the unmanned aerial vehicle to have some decision errors, such as misjudgment of early return of the power or crash caused by power exhaustion.
At present, the existing electric quantity measurement generally comprises two methods, namely, the current discharge degree is judged according to the discharge curve of the battery through the change of the battery voltage, and although the current discharge degree is subjected to calibration processing, the method has low precision and can not meet the control requirement. Another method is to measure the current and time to calculate the power consumption during this time, but because of the complexity of the battery itself, the accuracy is low even if the data is corrected by calibration.
Disclosure of Invention
In view of the above, the invention provides an unmanned aerial vehicle mission planning method and device based on energy analysis, and mainly aims to estimate electric quantity of an unmanned aerial vehicle through a regression method, and provide a method for calculating the energy consumption condition of the unmanned aerial vehicle on line and making a decision on return flight time. Let unmanned aerial vehicle in task execution process, can maximize efficiency and safe back navigation.
According to one aspect of the invention, an unmanned aerial vehicle mission planning method based on energy analysis is provided, and comprises the following steps:
acquiring the current position coordinate, the flight speed, the flight acceleration and the energy consumption rate of the unmanned aerial vehicle;
matching corresponding motion patterns and the duration of the motion patterns by using the flying speeds and the flying accelerations, wherein the flying speeds and the flying accelerations have mapping relations with the motion patterns;
obtaining the consumed electric quantity of the unmanned aerial vehicle according to a pre-established energy consumption model, an energy weight matrix, a time information matrix, the energy consumption rate and the duration;
obtaining battery allowance according to the acquired battery capacity and the consumed electric quantity;
calculating the energy required by the unmanned aerial vehicle for returning according to the position coordinates and a preset returning motion mode;
and comparing the battery allowance with the energy required by the return voyage, and controlling the unmanned aerial vehicle to finish the current task and return voyage if the battery allowance does not exceed the energy required by the return voyage.
Further, according to the position coordinate and the preset return flight motion mode, the required energy of unmanned aerial vehicle return flight is calculated, and the method comprises the following steps:
calculating the return distance according to a preset return distance formula and the position coordinate;
matching corresponding return voyage speed and energy consumption rate according to the preset return voyage mode;
processing the return distance and the return speed according to a preset return duration formula to obtain return duration;
and processing the return duration time and the energy consumption rate according to a preset return energy formula to obtain the energy required by the return of the unmanned aerial vehicle.
Optionally, before obtaining the consumed electric quantity of the drone according to the pre-established energy consumption model, the energy weight matrix, the time information matrix, and the energy consumption rate and the duration, the method further includes:
and establishing a least square regression model, and performing parameter optimization on the energy consumption model by using the least square regression model.
Optionally, before obtaining the current position coordinates, the flight speed, the flight acceleration, and the energy consumption rate of the drone, the method further includes:
dividing the motion mode of the unmanned aerial vehicle;
acquiring the flying speed, the flying acceleration, the duration and the energy consumption rate in each motion mode;
establishing a time matrix according to the duration of each motion mode;
and establishing an energy weight matrix according to the energy consumption rate of each motion mode.
Optionally, after the energy weight matrix is established according to the energy consumption rate of each motion mode, the method further includes:
acquiring the energy consumption rate of the flight controller;
and establishing an energy consumption model according to the energy weight matrix, the time information matrix and the energy consumption rate of the flight controller.
Further, the obtaining of the battery remaining capacity according to the acquired battery capacity and the consumed power includes:
acquiring the relative real capacity of the battery;
obtaining the available capacity of the battery according to the real capacity of the battery and a preset proportion parameter;
determining a difference between the battery available capacity and the consumed power as a battery remaining capacity of the drone.
According to two aspects of the invention, an unmanned aerial vehicle mission planning device based on energy analysis is provided, which comprises:
the acquiring unit is used for acquiring the current position coordinate, the flight speed, the flight acceleration and the energy consumption rate of the unmanned aerial vehicle;
the matching unit is used for matching a corresponding motion mode and the duration of the motion mode by utilizing the flying speed and the flying acceleration, wherein the flying speed and the flying acceleration have a mapping relation with the motion mode;
the first processing unit is used for obtaining the consumed electric quantity of the unmanned aerial vehicle according to a pre-established energy consumption model, an energy weight matrix, a time information matrix, the energy consumption rate and the duration;
the second processing unit is used for obtaining the battery allowance according to the acquired battery capacity and the consumed electric quantity;
the calculating unit is used for calculating the energy required by the return flight of the unmanned aerial vehicle according to the position coordinates and a preset return flight motion mode;
and the decision unit is used for comparing the battery allowance with the energy required by the return voyage, and controlling the unmanned aerial vehicle to finish the current task and return voyage if the battery allowance does not exceed the energy required by the return voyage.
Further, the calculation unit includes:
the calculation module is used for calculating the return distance according to a preset return distance formula and the position coordinate;
the matching module is used for matching corresponding return speed and energy consumption rate according to the preset return motion mode;
the first processing module is used for processing the return distance and the return speed according to a preset return duration formula to obtain return duration;
and the second processing module is used for processing the return flight duration and the energy consumption rate according to a preset return flight energy formula to obtain the energy required by the return flight of the unmanned aerial vehicle.
Further, the apparatus further comprises:
and the optimization unit is used for establishing a least square regression model and performing parameter optimization on the energy consumption model by using the least square regression model.
Further, the apparatus further comprises:
the division unit is used for dividing the motion mode of the unmanned aerial vehicle;
the acquisition unit is used for acquiring the flying speed, the flying acceleration, the duration and the energy consumption rate in each motion mode;
the establishing unit is used for establishing a time matrix according to the duration of each motion mode;
the establishing unit is specifically further configured to establish an energy weight matrix according to the energy consumption rate of each motion mode.
Further, the obtaining unit is specifically further configured to obtain an energy consumption rate of the flight controller;
the establishing unit is further used for establishing an energy consumption model according to the energy weight matrix, the time information matrix and the energy consumption rate of the flight controller.
Further, the processing unit includes:
the acquisition module is used for acquiring the relative real capacity of the battery;
the third processing module is used for obtaining the available capacity of the battery according to the real capacity of the battery and a preset proportion parameter;
a determination unit configured to determine a difference between the available battery capacity and the consumed power as a remaining battery capacity of the drone.
According to a third aspect of the present invention, there is provided a storage medium having at least one executable instruction stored therein, the executable instruction causing a processor to perform the steps of: acquiring the current position coordinate, the flight speed, the flight acceleration and the energy consumption rate of the unmanned aerial vehicle; matching corresponding motion patterns and the duration of the motion patterns by using the flying speeds and the flying accelerations, wherein the flying speeds and the flying accelerations have mapping relations with the motion patterns; obtaining the consumed electric quantity of the unmanned aerial vehicle according to a pre-established energy consumption model, an energy weight matrix, a time information matrix, the energy consumption rate and the duration; obtaining battery allowance according to the acquired battery capacity and the consumed electric quantity; calculating the energy required by the unmanned aerial vehicle for returning according to the position coordinates and a preset returning motion mode; and comparing the battery allowance with the energy required by the return voyage, and controlling the unmanned aerial vehicle to finish the current task and return voyage if the battery allowance does not exceed the energy required by the return voyage.
According to a fourth aspect of the present invention, there is provided a computer device comprising a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus, and the memory is used for storing at least one executable instruction, and the executable instruction causes the processor to perform the following steps: acquiring the current position coordinate, the flight speed, the flight acceleration and the energy consumption rate of the unmanned aerial vehicle; matching corresponding motion patterns and the duration of the motion patterns by using the flying speeds and the flying accelerations, wherein the flying speeds and the flying accelerations have mapping relations with the motion patterns; obtaining the consumed electric quantity of the unmanned aerial vehicle according to a pre-established energy consumption model, an energy weight matrix, a time information matrix, the energy consumption rate and the duration; obtaining battery allowance according to the acquired battery capacity and the consumed electric quantity; calculating the energy required by the unmanned aerial vehicle for returning according to the position coordinates and a preset returning motion mode; and comparing the battery allowance with the energy required by the return voyage, and controlling the unmanned aerial vehicle to finish the current task and return voyage if the battery allowance does not exceed the energy required by the return voyage.
Compared with the prior art that the current discharging degree is judged through the change of the battery voltage according to the discharging curve of the battery, the unmanned aerial vehicle task planning method and the device based on energy analysis have the advantages that the current position coordinate, the flight speed, the flight acceleration and the energy consumption rate of the unmanned aerial vehicle are obtained; matching corresponding motion patterns and the duration of the motion patterns by using the flying speeds and the flying accelerations, wherein the flying speeds and the flying accelerations have mapping relations with the motion patterns; obtaining the consumed electric quantity of the unmanned aerial vehicle according to a pre-established energy consumption model, an energy weight matrix, a time information matrix, the energy consumption rate and the duration; obtaining battery allowance according to the acquired battery capacity and the consumed electric quantity; calculating the energy required by the unmanned aerial vehicle for returning according to the position coordinates and a preset returning motion mode; and comparing the battery allowance with the energy required by the return voyage, and controlling the unmanned aerial vehicle to finish the current task and return voyage if the battery allowance does not exceed the energy required by the return voyage. Therefore, the electric quantity of the unmanned aerial vehicle can be estimated through a regression method, the energy consumption condition of the unmanned aerial vehicle can be calculated on line, and the decision-making method for the return flight time is provided. Let unmanned aerial vehicle in task execution process, can maximize efficiency and safe back navigation.
The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
fig. 1 shows a flowchart of a method for planning a mission of an unmanned aerial vehicle based on energy analysis according to an embodiment of the present invention;
fig. 2 is a flowchart illustrating another unmanned aerial vehicle mission planning method based on energy analysis according to an embodiment of the present invention;
fig. 3 shows an energy analysis-based unmanned aerial vehicle mission planning program diagram provided by an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an unmanned aerial vehicle mission planning apparatus based on energy analysis according to an embodiment of the present invention;
fig. 5 shows a physical structure diagram of a computer device according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
As described in the background art, at present, the existing electric quantity measurement generally includes two methods, one is to judge the current discharge degree through the change of the battery voltage according to the discharge curve of the battery, and although the method is subjected to calibration processing, the accuracy of the method is very low, and the control requirement is far from being met. Another method is to measure the current and time to calculate the power consumption during this time, but because of the complexity of the battery itself, the accuracy is low even if the data is corrected by calibration.
In order to solve the above problem, an embodiment of the present invention provides an unmanned aerial vehicle mission planning method based on energy analysis, as shown in fig. 1, the method includes:
101. and acquiring the current position coordinate, the flight speed, the flight acceleration and the energy consumption rate of the unmanned aerial vehicle.
The current position coordinate of the unmanned aerial vehicle is the coordinate of the current position of the unmanned aerial vehicle in an inertial coordinate system established by taking the starting point of the unmanned aerial vehicle as the origin of coordinates; the flight speed and the flight acceleration refer to the flight speed and the flight acceleration of the unmanned aerial vehicle flying in a certain motion mode, and specific values of the flight speed and the flight acceleration can be detected by a sensor preset on the unmanned aerial vehicle; the energy consumption rate can be the energy consumption rate of the unmanned aerial vehicle flying in a certain motion mode, the specific value of the energy consumption rate can be obtained by manually analyzing a flash log, downloading the log through a Mallink, and obtaining the power consumption in different motion modes in a CURRENT module, so that the energy consumption rate is obtained through calculation.
102. Matching a corresponding movement pattern and a duration of the movement pattern using the flight speed and the flight acceleration.
Wherein the motion pattern may include: hovering mode, vertical rising mode, accelerating flying mode, uniform speed flying mode, decelerating flying mode, vertical falling mode, etc. The flying speed and the flying acceleration may have a mapping relationship with the motion pattern. The duration of the movement pattern may be a duration of a continuous flight of the unmanned aerial vehicle in a certain movement pattern, and may be specifically obtained from a flight status log according to the movement pattern. Specifically, after the flying speed and the flying acceleration of the unmanned aerial vehicle are obtained, the corresponding motion mode can be matched, and the corresponding duration time is obtained in the flight state log according to the motion mode.
103. And obtaining the consumed electric quantity of the unmanned aerial vehicle according to a pre-established energy consumption model, an energy weight matrix, a time information matrix, the energy consumption rate and the duration.
Specifically, after the current energy consumption rate and the duration of the unmanned aerial vehicle are obtained, the energy consumption model can be used for processing to obtain the current consumed electric quantity of the unmanned aerial vehicle. Thereby realizing rapid calculation of the current alreadyThe power consumption provides data basis for the motion decision of the unmanned aerial vehicle. Mapping the acceleration and speed conditions of the current unmanned aerial vehicle with the known motion pattern, and judging the motion pattern p of the current unmanned aerial vehicle through online matchingmnAnd calculating the duration t under the corresponding motion mode according to the sampling periodmnWith tmnSubstituting the input variables into the established energy consumption model to calculate the consumed electric quantity of the current unmanned aerial vehicle, wherein the consumed electric quantity can be CconsumedMeaning that C can be calculated by the method of accumulation due to real-time nature of power consumptionconsumed：
Cconsumed(k)＝Cconsumed(k-1)+wmntmn
Wherein: k is 1, 2, 3 … ….
104. And obtaining the battery allowance according to the acquired battery capacity and the consumed electric quantity.
The battery capacity can be the battery capacity that unmanned aerial vehicle carried the battery, obtains after having consumed the electric quantity, can calculate the battery capacity and the difference of having consumed the electric quantity to can obtain the residual capacity of current battery, promptly the battery surplus is in order to provide the data basis for follow-up unmanned aerial vehicle's motion decision.
105. And calculating the energy required by the unmanned aerial vehicle for returning according to the position coordinates and a preset returning motion mode.
Specifically, the return distance can be calculated according to a preset return distance formula and the position coordinate, the corresponding return speed and the energy consumption rate are matched according to a preset return motion mode, the return distance and the return speed are processed according to a preset return duration formula to obtain the return duration, and the return duration and the energy consumption rate are processed according to a preset return energy formula to obtain the energy required by the unmanned aerial vehicle for return. Therefore, the energy required by the return flight of the unmanned aerial vehicle can be obtained more conveniently and rapidly, and data support is provided for the decision of the follow-up task of the unmanned aerial vehicle.
106. And comparing the battery allowance with the energy required by the return voyage, and controlling the unmanned aerial vehicle to finish the current task and return voyage if the battery allowance does not exceed the energy required by the return voyage.
Specifically, the battery allowance and the energy required for returning voyage can be compared in real time, and when the battery allowance is equal to the energy required for returning voyage, the unmanned aerial vehicle is controlled to end the current task to return voyage. According to the embodiment of the invention, the battery surplus is compared with the energy required by return voyage, and the return voyage is carried out when the battery surplus reaches the energy required by the return voyage, so that frequent return voyage of the unmanned aerial vehicle can be avoided, the possibility of forced landing in the midway due to insufficient energy is reduced, and the efficiency of completing tasks is improved to the maximum extent.
The invention provides an unmanned aerial vehicle mission planning method based on energy analysis, which can obtain the current position coordinate, flight speed, flight acceleration and energy consumption rate of an unmanned aerial vehicle; matching a corresponding motion mode and the duration time of the motion mode by using the flight speed and the flight acceleration, and obtaining the consumed electric quantity of the unmanned aerial vehicle according to a pre-established energy consumption model, an energy weight matrix, a time information matrix, the energy consumption rate and the duration time; obtaining battery allowance according to the acquired battery capacity and the consumed electric quantity; calculating the energy required by the unmanned aerial vehicle for returning according to the position coordinates and a preset returning motion mode; and comparing the battery allowance with the energy required by the return voyage, and controlling the unmanned aerial vehicle to finish the current task and return voyage if the battery allowance does not exceed the energy required by the return voyage. Therefore, the electric quantity of the unmanned aerial vehicle can be estimated through a regression method, the energy consumption condition of the unmanned aerial vehicle can be calculated on line, and the decision-making method for the return flight time is provided. Let unmanned aerial vehicle in task execution process, can maximize efficiency and safe back navigation.
The embodiment of the invention provides another unmanned aerial vehicle mission planning method based on energy analysis, as shown in fig. 2, the method comprises the following steps:
201. dividing the motion mode of the unmanned aerial vehicle; acquiring the flying speed, the flying acceleration, the duration and the energy consumption rate in each motion mode; establishing a time matrix according to the duration of each motion mode; and establishing an energy weight matrix according to the energy consumption rate of each motion mode.
Wherein, the different energy consumption rate corresponding to each motion mode can be represented as wmnThe running time of the corresponding mode is correspondingly denoted as tmn. Accordingly, an unmanned aerial vehicle motion pattern information matrix can be established, and the unmanned aerial vehicle motion pattern information matrix can be defined as:
wherein: p denotes unmanned aerial vehicle movement pattern information, P11……PmnIndicating different motion patterns.
The energy weight matrix may be defined according to different obtained energy consumption rates, and the energy weight matrix may be specifically defined as:
wherein: w represents the energy consumption rate, W11……wmnRepresenting the rate of energy consumption in different motion modes.
The time information matrix may be specifically expressed as
Wherein T represents a time information matrix, T11……tmnIndicating the duration of flight in different motion modes.
For the embodiment of the present invention, after the step 201, the method further includes: acquiring the energy consumption rate of the flight controller; and establishing an energy consumption model according to the energy weight matrix, the time information matrix and the energy consumption rate of the flight controller.
The energy consumption model may specifically be:
f (w, t), w is the weight, and t is the time. Namely:
F＝w11t11+w12t12+…+wmntmn+w0(t11+t12+…+tmn)
wherein: f may represent the sum of the energy consumptions during the flight in the various modes of motion, w0The electric quantity consumption of the unmanned aerial vehicle flight controller in the flight process is represented, and the electric quantity consumption is small, so that the electric quantity consumption can be regarded as a small constant and obtained through an unmanned aerial vehicle flight controller in a loop simulation experiment. This part is relatively small for the power consumed by the drone actuator, but due to the longer duration, and in general a part of the larger power consumption, the omission process may not be done for increased accuracy. For the above formula, it can be abbreviated as:
written as a matrix norm is expressed as:
F＝||WTT+w0T||1
wherein: i | · | purple wind1Representing the L1 norm of the matrix.
For the embodiment of the present invention, after the energy consumption model is established according to the energy weight matrix, the time information matrix, and the energy consumption rate of the flight controller, the method further includes: and establishing a least square regression model, and performing parameter optimization on the energy consumption model by using the least square regression model.
Specifically, the energy consumption values of the unmanned aerial vehicle in different motion modes are recorded in the flight log, and the energy consumption values are recorded in the flight logmnExpressed, the matrix form is:
to obtain more accurate weights, a least squares regression model is built for model E above:
the matrix paradigm can be:
wherein: i | · | purple windFRepresenting the F-norm of the matrix.
And continuously optimizing and iterating the least square regression model by using the acquired data to obtain an energy weight matrix W closer to a true value, so as to obtain a more accurate energy consumption model F.
202. And acquiring the current position coordinate, the flight speed, the flight acceleration and the energy consumption rate of the unmanned aerial vehicle.
This step is the same as step 101 shown in fig. 1, and is not described herein again.
203. Matching a corresponding movement pattern and a duration of the movement pattern using the flight speed and the flight acceleration.
This step is the same as step 102 shown in fig. 1, and is not described herein again.
204. And obtaining the consumed electric quantity of the unmanned aerial vehicle according to a pre-established energy consumption model, an energy weight matrix, a time information matrix, the energy consumption rate and the duration.
This step is the same as step 103 shown in fig. 1, and is not described herein again.
205. And obtaining the battery allowance according to the acquired battery capacity and the consumed electric quantity.
This step is the same as step 104 shown in fig. 1, and will not be described herein again.
For the embodiment of the present invention, as shown in fig. 2, the step 205 may specifically include: acquiring the relative real capacity of the battery; obtaining the available capacity of the battery according to the real capacity of the battery and a preset proportion parameter; determining a difference between the battery available capacity and the consumed power as a battery remaining capacity of the drone.
In a practical application scenario, the nominal capacity of the battery can be defined as CnominalIt should be noted that the nominal capacity of the battery does not reflect the discharge capacity of the battery completely and truly. The embodiment of the invention introduces another variable CrealTo represent a relatively true value of the electrical quantity. Specifically, since the temperature has a large influence on the discharge capacity of the lithium battery, in the task preparation stage, the battery is completely discharged at the current temperature and the discharge voltage, and the amount of electricity discharged by the battery in the process is recorded as the consideration of the actual discharge capacity of the battery. It can be seen that this value is a variable, i.e. the C measured at different temperatures of the batteryrealThere may be some difference. However, although each measurement can be calibrated to obtain a more accurate CrealBut periodically to C in view of battery life and durabilityrealThe measurement is performed sufficiently to meet the task accuracy requirements.
Definition C0Represents the available energy of the drone battery because it is unsafe to take full battery capacity as an energy consideration given the aging of the battery and the randomness and contingency of errors during the mission. To avoid this risk, let C0＝95％CrealAs available energy for the drone battery.
Defining the battery residual quantity as CSOCThe calculation formula of the battery residual capacity may include:
CSOC＝C0-Cconsumed。
206. and calculating the energy required by the unmanned aerial vehicle for returning according to the position coordinates and a preset returning motion mode.
This step is the same as step 105 shown in fig. 1, and is not described herein again.
For the embodiment of the present invention, as shown in fig. 2, the step 206 may specifically include: calculating the return distance according to a preset return distance formula and the position coordinate; matching corresponding return voyage speed and energy consumption rate according to the preset return voyage mode; processing the return distance and the return speed according to a preset return duration formula to obtain return duration; and processing the return duration time and the energy consumption rate according to a preset return energy formula to obtain the energy required by the return of the unmanned aerial vehicle.
Specifically, the current position coordinate of the unmanned aerial vehicle in the inertial coordinate system is represented as (x, y, z), and the return distance is calculated according to the current position coordinate, where the return distance formula includes:
wherein, S represents the return journey distance, and x, y represent unmanned aerial vehicle horizontal axis, ordinate on the horizontal plane respectively.
According to a preset return-to-air motion mode, corresponding return-to-air speed and energy consumption rate can be matched, in an actual application scene, an appropriate return-to-air speed can be selected according to the return-to-air distance, and the return-to-air duration formula comprises the following steps:
tmn＝S/v
wherein, tmnIndicating the duration of the return journey, S the return distance, v the return speed.
And processing the duration and the energy consumption rate according to a preset return energy formula to obtain the energy required by the return of the unmanned aerial vehicle. The return energy formula may include:
Creturn＝(wmn+w0)tmn
wherein: creturnRepresenting the energy, w, required for the unmanned aerial vehicle to returnmnAnd representing the energy consumption rate corresponding to the return motion mode of the unmanned aerial vehicle.
207. And comparing the battery allowance with the energy required by the return voyage, and controlling the unmanned aerial vehicle to finish the current task and return voyage if the battery allowance does not exceed the energy required by the return voyage.
This step is the same as step 106 shown in fig. 1, and will not be described herein again.
The invention provides an unmanned aerial vehicle mission planning method based on energy analysis, which can obtain the current position coordinate, flight speed, flight acceleration and energy consumption rate of an unmanned aerial vehicle; matching a corresponding motion mode and the duration time of the motion mode by using the flight speed and the flight acceleration, and obtaining the consumed electric quantity of the unmanned aerial vehicle according to a pre-established energy consumption model, an energy weight matrix, a time information matrix, the energy consumption rate and the duration time; obtaining battery allowance according to the acquired battery capacity and the consumed electric quantity; calculating the energy required by the unmanned aerial vehicle for returning according to the position coordinates and a preset returning motion mode; and comparing the battery allowance with the energy required by the return voyage, and controlling the unmanned aerial vehicle to finish the current task and return voyage if the battery allowance does not exceed the energy required by the return voyage. Therefore, the electric quantity of the unmanned aerial vehicle can be estimated through a regression method, the energy consumption condition of the unmanned aerial vehicle can be calculated on line, and the decision-making method for the return flight time is provided. Let unmanned aerial vehicle in task execution process, can maximize efficiency and safe back navigation.
Further, as a specific implementation of fig. 1, an embodiment of the present invention provides an unmanned aerial vehicle mission planning apparatus based on energy analysis, as shown in fig. 3, the apparatus includes: an obtaining unit 31, a matching unit 32, a first processing unit 33, a second processing unit 34, a calculating unit 35 and a decision unit 36.
The acquiring unit 31 may be configured to acquire a current position coordinate, a flight speed, a flight acceleration, and an energy consumption rate of the unmanned aerial vehicle;
the matching unit 32 may be configured to match a corresponding motion pattern and a duration of the motion pattern by using the flying speed and the flying acceleration, where the flying speed and the flying acceleration have a mapping relationship with the motion pattern;
the first processing unit 33 may be configured to obtain the consumed electric quantity of the unmanned aerial vehicle according to a pre-established energy consumption model, an energy weight matrix, a time information matrix, the energy consumption rate, and the duration;
the second processing unit 34 may be configured to obtain a remaining battery capacity according to the acquired battery capacity and the consumed power;
the calculating unit 35 may be configured to calculate energy required by the unmanned aerial vehicle for returning according to the position coordinates and a preset return motion mode;
the decision unit 36 may be configured to compare the battery remaining amount with the energy required for return voyage, and control the unmanned aerial vehicle to end the current task and return voyage if the battery remaining amount does not exceed the energy required for return voyage.
Further, the calculating unit 35 includes:
the calculating module 351 may be configured to calculate the return distance according to a preset return distance formula and the position coordinate;
the matching module 352 may be configured to match corresponding return travel speeds and energy consumption rates according to the preset return travel motion mode;
the first processing module 353 is configured to process the return distance and the return speed according to a preset return duration formula to obtain a return duration;
the second processing module 354 may be configured to process the return distance, the return speed, and the energy consumption rate according to a preset return energy formula, so as to obtain energy required by the return of the unmanned aerial vehicle.
Further, the apparatus further comprises:
the optimization unit 37 may be configured to establish a least squares regression model, and perform parameter optimization on the energy consumption model using the least squares regression model.
Further, the apparatus further comprises:
a dividing unit 38, which may be configured to divide the motion pattern of the drone;
an acquisition unit 39, which can be used to acquire the flying speed, flying acceleration, duration and energy consumption rate in each motion mode;
a building unit 310, configured to build a time matrix according to the duration of each motion pattern;
the establishing unit 310 is further specifically configured to establish an energy weight matrix according to the energy consumption rate of each motion pattern.
Further, the obtaining unit 31 may be specifically configured to obtain an energy consumption rate of the flight controller;
the establishing unit 310 may be further configured to establish an energy consumption model according to the energy weight matrix, the time information matrix, and the energy consumption rate of the flight controller.
Further, the second processing unit 34 includes:
an obtaining module 341, configured to obtain a relative real capacity of the battery;
the third processing module 342 may be configured to obtain the available capacity of the battery according to the real capacity of the battery and a preset ratio parameter;
a determining module 343, configured to determine a difference between the available battery capacity and the consumed power as a remaining battery capacity of the drone.
It should be noted that other corresponding descriptions of the functional modules involved in the unmanned aerial vehicle mission planning apparatus based on energy analysis provided in the embodiment of the present invention may refer to the corresponding descriptions of the method shown in fig. 1, and are not described herein again.
Based on the method shown in fig. 1, correspondingly, an embodiment of the present invention further provides a storage medium, where at least one executable instruction is stored in the storage medium, and the executable instruction causes a processor to perform the following steps: acquiring the current position coordinate, the flight speed, the flight acceleration and the energy consumption rate of the unmanned aerial vehicle; matching corresponding motion patterns and the duration of the motion patterns by using the flying speeds and the flying accelerations, wherein the flying speeds and the flying accelerations have mapping relations with the motion patterns; obtaining the consumed electric quantity of the unmanned aerial vehicle according to a pre-established energy consumption model, an energy weight matrix, a time information matrix, the energy consumption rate and the duration; obtaining battery allowance according to the acquired battery capacity and the consumed electric quantity; calculating the energy required by the unmanned aerial vehicle for returning according to the position coordinates and a preset returning motion mode; and comparing the battery allowance with the energy required by the return voyage, and controlling the unmanned aerial vehicle to finish the current task and return voyage if the battery allowance does not exceed the energy required by the return voyage.
Based on the above embodiments of the method shown in fig. 1 and the apparatus shown in fig. 4, the embodiment of the present invention further provides a computer device, as shown in fig. 5, including a processor (processor)41, a communication Interface (communication Interface)42, a memory (memory)43, and a communication bus 44. Wherein: the processor 41, the communication interface 42, and the memory 43 communicate with each other via a communication bus 44. A communication interface 44 for communicating with network elements of other devices, such as clients or other servers. The processor 41 is configured to execute a program, and may specifically execute relevant steps in the above-described unmanned aerial vehicle mission planning method embodiment based on energy analysis. In particular, the program may include program code comprising computer operating instructions. The processor 41 may be a central processing unit CPU or a Specific Integrated circuit asic (application Specific Integrated circuit) or one or more Integrated circuits configured to implement an embodiment of the invention.
The terminal comprises one or more processors, which can be the same type of processor, such as one or more CPUs; or may be different types of processors such as one or more CPUs and one or more ASICs. And a memory 43 for storing a program. The memory 43 may comprise a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory. The program may specifically be adapted to cause the processor 41 to perform the following operations: acquiring the current position coordinate, the flight speed, the flight acceleration and the energy consumption rate of the unmanned aerial vehicle; matching a corresponding motion pattern and a duration of the motion pattern using the flight speed and the flight acceleration; obtaining the consumed electric quantity of the unmanned aerial vehicle according to a pre-established energy consumption model, an energy weight matrix, a time information matrix, the energy consumption rate and the duration; obtaining battery allowance according to the acquired battery capacity and the consumed electric quantity; calculating the energy required by the unmanned aerial vehicle for returning according to the position coordinates and a preset returning motion mode; and comparing the battery allowance with the energy required by the return voyage, and controlling the unmanned aerial vehicle to finish the current task and return voyage if the battery allowance does not exceed the energy required by the return voyage.
By the technical scheme, the current position coordinate, the flight speed, the flight acceleration and the energy consumption rate of the unmanned aerial vehicle can be acquired; matching a corresponding motion pattern and a duration of the motion pattern using the flight speed and the flight acceleration; obtaining the consumed electric quantity of the unmanned aerial vehicle according to a pre-established energy consumption model, an energy weight matrix, a time information matrix, the energy consumption rate and the duration; obtaining battery allowance according to the acquired battery capacity and the consumed electric quantity; calculating the energy required by the unmanned aerial vehicle for returning according to the position coordinates and a preset returning motion mode; and comparing the battery allowance with the energy required by the return voyage, and controlling the unmanned aerial vehicle to finish the current task and return voyage if the battery allowance does not exceed the energy required by the return voyage. Therefore, the electric quantity of the unmanned aerial vehicle is estimated through a regression method, the energy consumption condition of the unmanned aerial vehicle is calculated on line, and the return flight time is decided. Let unmanned aerial vehicle in task execution process, can maximize efficiency and safe back sailing
It will be apparent to those skilled in the art that the modules or steps of the present invention described above may be implemented by a general purpose computing device, they may be centralized on a single computing device or distributed across a network of multiple computing devices, and alternatively, they may be implemented by program code executable by a computing device, such that they may be stored in a storage device and executed by a computing device, and in some cases, the steps shown or described may be performed in an order different than that described herein, or they may be separately fabricated into individual integrated circuit modules, or multiple ones of them may be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. An unmanned aerial vehicle mission planning method based on energy analysis is characterized by comprising the following steps:
acquiring the current position coordinate, the flight speed, the flight acceleration and the energy consumption rate of the unmanned aerial vehicle;
matching corresponding motion patterns and the duration of the motion patterns by using the flying speeds and the flying accelerations, wherein the flying speeds and the flying accelerations have mapping relations with the motion patterns;
obtaining the consumed electric quantity of the unmanned aerial vehicle according to a pre-established energy consumption model, an energy weight matrix, a time information matrix, the energy consumption rate and the duration;
obtaining battery allowance according to the acquired battery capacity and the consumed electric quantity;
calculating the energy required by the unmanned aerial vehicle for returning according to the position coordinates and a preset returning motion mode;
the required energy of unmanned aerial vehicle back journey of calculation includes:
calculating the return distance according to a preset return distance formula and the position coordinate;
matching corresponding return voyage speed and energy consumption rate according to the preset return voyage mode;
processing the return distance and the return speed according to a preset return duration formula to obtain return duration;
processing the return duration time and the energy consumption rate according to a preset return energy formula to obtain the energy required by the return of the unmanned aerial vehicle;
and comparing the battery allowance with the energy required by the return voyage, and controlling the unmanned aerial vehicle to finish the current task and return voyage if the battery allowance does not exceed the energy required by the return voyage.
2. The method of claim 1, wherein before obtaining the consumed power of the drone according to the pre-established energy consumption model, the energy weight matrix, the time information matrix, and the energy consumption rate and the duration, the method further comprises:
and establishing a least square regression model, and performing parameter optimization on the energy consumption model by using the least square regression model.
3. The method of claim 1, wherein prior to obtaining the current position coordinates, flight speed, flight acceleration, and energy consumption rate of the drone, the method further comprises:
dividing the motion mode of the unmanned aerial vehicle;
acquiring the flying speed, the flying acceleration, the duration and the energy consumption rate in each motion mode;
establishing a time matrix according to the duration of each motion mode;
and establishing an energy weight matrix according to the energy consumption rate of each motion mode.
4. The method of claim 3, wherein after the establishing the energy weight matrix according to the energy consumption rate of each motion pattern, the method further comprises:
acquiring the energy consumption rate of the flight controller;
and establishing an energy consumption model according to the energy weight matrix, the time information matrix and the energy consumption rate of the flight controller.
5. The method of claim 1, wherein obtaining the remaining battery capacity according to the obtained battery capacity and the consumed power comprises:
acquiring the relative real capacity of the battery;
obtaining the available capacity of the battery according to the real capacity of the battery and a preset proportion parameter;
determining a difference between the battery available capacity and the consumed power as a battery remaining capacity of the drone.
6. An unmanned aerial vehicle mission planning device based on energy analysis, characterized by comprising:
the acquiring unit is used for acquiring the current position coordinate, the flight speed, the flight acceleration and the energy consumption rate of the unmanned aerial vehicle;
the matching unit is used for matching a corresponding motion mode and the duration of the motion mode by utilizing the flying speed and the flying acceleration, wherein the flying speed and the flying acceleration have a mapping relation with the motion mode;
the first processing unit is used for obtaining the consumed electric quantity of the unmanned aerial vehicle according to a pre-established energy consumption model, an energy weight matrix, a time information matrix, the energy consumption rate and the duration;
the second processing unit is used for obtaining the battery allowance according to the acquired battery capacity and the consumed electric quantity;
the calculating unit is used for calculating the energy required by the return flight of the unmanned aerial vehicle according to the position coordinates and a preset return flight motion mode;
the decision unit is used for comparing the battery allowance with the energy required by the return voyage, and controlling the unmanned aerial vehicle to end the current task and return voyage if the battery allowance does not exceed the energy required by the return voyage;
wherein the computing unit comprises:
the calculation module is used for calculating the return distance according to a preset return distance formula and the position coordinate;
the matching module is used for matching corresponding return speed and energy consumption rate according to the preset return motion mode;
the first processing module is used for processing the return distance and the return speed according to a preset return duration formula to obtain return duration;
and the second processing module is used for processing the return flight duration and the energy consumption rate according to a preset return flight energy formula to obtain the energy required by the return flight of the unmanned aerial vehicle.
7. A storage medium having a computer program stored thereon, the storage medium having at least one executable instruction stored therein, the executable instruction causing a processor to perform operations corresponding to the energy analysis based unmanned aerial vehicle mission planning method of any one of claims 1-5.
8. A computer device comprising a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other via the communication bus, and the memory is used for storing at least one executable instruction, which causes the processor to perform operations corresponding to the energy analysis-based unmanned aerial vehicle mission planning method according to any one of claims 1-5.
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| CN113110072B (en) * | 2021-06-15 | 2021-09-03 | 深圳联和智慧科技有限公司 | Unmanned aerial vehicle residual power real-time monitoring method and system based on smart lamp post |
| CN113741512A (en) * | 2021-08-03 | 2021-12-03 | 扬州郁金光子技术有限公司 | Unmanned aerial vehicle laser navigation system and method |
| CN114527782A (en) * | 2021-11-19 | 2022-05-24 | 嘉兴恒创电力设计研究院有限公司 | Unmanned aerial vehicle flight path planning method and system based on power grid map |
| CN114137426B (en) * | 2021-11-30 | 2024-04-09 | 广州极飞科技股份有限公司 | Residual electric quantity estimation method, device, equipment and storage medium |
| CN114720887A (en) * | 2022-03-29 | 2022-07-08 | 上海沃兰特航空技术有限责任公司 | Airplane endurance estimation method and device, electronic equipment and storage medium |
| CN114967761B (en) * | 2022-07-29 | 2022-11-01 | 广东省农业科学院植物保护研究所 | Intelligent control method and system for operation of plant protection unmanned aerial vehicle |
| CN115265549B (en) * | 2022-09-27 | 2022-12-27 | 季华实验室 | Unmanned aerial vehicle path planning method and device and electronic equipment |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104062588A (en) * | 2013-03-18 | 2014-09-24 | 日电（中国）有限公司 | Device and method used for estimating remaining power of electric vehicle |
| CN107885225A (en) * | 2014-07-16 | 2018-04-06 | 深圳市大疆创新科技有限公司 | Electronic unmanned plane and its intelligent power guard method |
| CN109976164A (en) * | 2019-04-25 | 2019-07-05 | 南开大学 | A kind of energy-optimised vision covering method for planning track of multi-rotor unmanned aerial vehicle |
| CN109991997A (en) * | 2018-01-02 | 2019-07-09 | 华北电力大学 | An efficient and energy-saving UAV power line patrol scheme in smart grid |
| CN110348595A (en) * | 2019-05-31 | 2019-10-18 | 南京航空航天大学 | A kind of unmanned plane mixed propulsion system energy management-control method based on flying quality |
| US10539621B2 (en) * | 2017-08-02 | 2020-01-21 | Total Solar International | Method and apparatus for identifying a battery model |
| CN110730933A (en) * | 2018-08-23 | 2020-01-24 | 深圳市大疆创新科技有限公司 | Unmanned aerial vehicle return control method and equipment and unmanned aerial vehicle |
Family Cites Families (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107238388B (en) * | 2017-05-27 | 2018-02-23 | 合肥工业大学 | Multiple no-manned plane task is distributed and trajectory planning combined optimization method and device |
| JP2019073056A (en) * | 2017-10-12 | 2019-05-16 | 株式会社トプコン | Unmanned aircraft control device, unmanned aircraft, data processing device, unmanned aircraft control method and program for control of unmanned aircraft |
| US11117567B2 (en) * | 2018-06-26 | 2021-09-14 | Toyota Motor Engineering & Manufacturing North America, Inc. | Real time trajectory optimization for hybrid energy management utilizing connected information technologies |
| CN108931741A (en) * | 2018-09-18 | 2018-12-04 | 深圳市格瑞普智能电子有限公司 | Method and system for monitoring remaining power of battery pack |
| US20200264236A1 (en) * | 2019-02-15 | 2020-08-20 | Johnson Controls Technology Company | Building management system with remaining battery energy estimation for wireless devices |
| CN110275546B (en) * | 2019-07-31 | 2022-05-10 | 河海大学常州校区 | A UAV formation search and task scheduling method |
| CN110675035B (en) * | 2019-09-06 | 2022-05-06 | 三峡大学 | Charging scheduling method for UAV laser energy supply cluster based on real-time energy consumption detection |
| CN110766159B (en) * | 2019-09-29 | 2022-08-30 | 南京理工大学 | Task allocation method for multi-UAV service edge calculation based on improved genetic algorithm |
-
2020
- 2020-09-15 CN CN202010964567.6A patent/CN112180723B/en not_active Expired - Fee Related
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104062588A (en) * | 2013-03-18 | 2014-09-24 | 日电（中国）有限公司 | Device and method used for estimating remaining power of electric vehicle |
| CN107885225A (en) * | 2014-07-16 | 2018-04-06 | 深圳市大疆创新科技有限公司 | Electronic unmanned plane and its intelligent power guard method |
| US10539621B2 (en) * | 2017-08-02 | 2020-01-21 | Total Solar International | Method and apparatus for identifying a battery model |
| CN109991997A (en) * | 2018-01-02 | 2019-07-09 | 华北电力大学 | An efficient and energy-saving UAV power line patrol scheme in smart grid |
| CN110730933A (en) * | 2018-08-23 | 2020-01-24 | 深圳市大疆创新科技有限公司 | Unmanned aerial vehicle return control method and equipment and unmanned aerial vehicle |
| CN109976164A (en) * | 2019-04-25 | 2019-07-05 | 南开大学 | A kind of energy-optimised vision covering method for planning track of multi-rotor unmanned aerial vehicle |
| CN110348595A (en) * | 2019-05-31 | 2019-10-18 | 南京航空航天大学 | A kind of unmanned plane mixed propulsion system energy management-control method based on flying quality |
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