CN113759967B - Multi-unmanned aerial vehicle search task energy equalization method based on pre-estimation - Google Patents
Multi-unmanned aerial vehicle search task energy equalization method based on pre-estimation Download PDFInfo
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Abstract
The invention discloses a multi-unmanned aerial vehicle search task energy equalization method based on pre-estimation, which specifically comprises the following steps of firstly determining an energy consumption model of an unmanned aerial vehicle cluster; then, task energy equalization is distributed for one round of search tasks of the unmanned aerial vehicle, and the specific distribution mode comprises a primary distribution mode and a subdivision mode; the invention provides a decentralized unmanned aerial vehicle cluster search task energy equalization method, which utilizes the unmanned aerial vehicle cluster array type information to estimate the task energy consumption, does not need a centralization algorithm, has good expandability and is suitable for the unmanned aerial vehicle cluster search problem with a certain array type under the connectivity constraint condition; the application mainly provides a task segmentation method and an unmanned aerial vehicle energy consumption model, and the unmanned aerial vehicle energy consumption model is designed by combining the array characteristic of an unmanned aerial vehicle cluster from the task demand.
Description
Technical Field
The invention belongs to the field of unmanned aerial vehicles, relates to an energy distribution technology, and particularly relates to a multi-unmanned aerial vehicle search task energy equalization method based on prediction.
Background
The utility model mainly solves the problem that the energy consumption of each function is too different to influence the whole dead time when the unmanned aerial vehicle group executes the searching task. Aiming at the problem that the unmanned aerial vehicle cluster executes a search task and requires that all machines of the unmanned aerial vehicle cluster are interconnected and communicated, the decentralized energy balancing method for the search task of the unmanned aerial vehicle cluster is provided.
With the rapid development of electronic technology, sensor technology and control technology, the unmanned aerial vehicle technology has made great progress, and the application fields are very wide, including environment monitoring, emergency rescue, agricultural production, forest emergency rescue, regional search and the like; but is limited by the size and battery capacity of the unmanned aerial vehicle, and sensors and professional equipment carried by the unmanned aerial vehicle are often small and have limited performance; in addition, in various application fields such as military reconnaissance, regional measurement and control and the like, harsh requirements such as higher task efficiency, longer dead time and the like are provided for the unmanned aerial vehicle, so that the current unmanned aerial vehicle still faces the challenge that the performance cannot meet the requirements; therefore, in addition to research and development of a new technology to further improve the stand-alone performance of the unmanned aerial vehicle, the adoption of the unmanned aerial vehicle cluster technology is another effective means for improving the efficiency of the unmanned aerial vehicle.
The unmanned aerial vehicle clustering technology is an intelligent technology which maintains a plurality of unmanned aerial vehicles with common task targets to execute tasks orderly in a group form by means of decision means and modern control. The unmanned aerial vehicle cluster technology expands the performance of a single unmanned aerial vehicle and expands the application range of the unmanned aerial vehicle. The intelligent decision-making system can make an intelligent decision autonomously with little human intervention, has the advantages of cooperativity, self-organization, parallelism and the like, and gradually becomes a research hotspot in the field of unmanned aerial vehicles.
There are various ways to improve the dead time of the unmanned aerial vehicle group: 1. the appearance structure of the unmanned aerial vehicle is optimally designed, the pneumatic layout is improved, and the weight of the unmanned aerial vehicle is reduced; 2. a more advanced power system is adopted; 3. the energy density of fuel and battery is improved; 4. applying an energy management system, improving energy usage policies, etc. 0. The method mainly aims at the 4 th approach, and through improving an energy use strategy, the idle time of the unmanned aerial vehicle group is increased.
At present, the energy consumption model research of the unmanned aerial vehicle is mature, the communication energy consumption optimization research is more, the task energy consumption matching research is less, and the decentralized energy consumption optimization research of the unmanned aerial vehicle group is less.
At present, researchers provide a plurality of solutions for energy consumption optimization of unmanned aerial vehicles, and different solutions have different emphasis points, are researched by an emphasis matching method, have the problems of complex algorithm, excessive centralization and the like, and have the emphasis on communication, emphasis on architecture and emphasis on matching. Based on this, a solution is first provided.
Disclosure of Invention
The invention aims to provide a multi-unmanned aerial vehicle search task energy equalization method based on pre-estimation.
The purpose of the invention can be realized by the following technical scheme:
a multi-unmanned aerial vehicle search task energy equalization method based on prediction specifically comprises the following steps:
the method comprises the following steps: determining an energy consumption model of the unmanned aerial vehicle group;
step two: the method comprises the steps that task energy balance distribution is carried out on a round of search tasks of the unmanned aerial vehicle, and the specific distribution mode comprises a primary distribution mode and a subdivision mode;
the method for distributing the task energy equalization according to the subdivision mode comprises the following steps:
s1: dividing the search process of the unmanned aerial vehicle into a plurality of time segments according to the time slot delta; the search process is characterized as a time series Γ (δ,2 δ.);
s2: the time sequence is divided into K subsequences gamma k (kM +1 δ, kM +2 δ.,. K M + M δ), K =1,2, \8230, K, M being the subsequence size, i.e. the search process of the drone is denoted Γ (Γ) 1 ，Γ 2 ，...，Γ K )；
The task is represented as M (r) l j ，Γ)，M(r l j ，Γ k ) Is shown in the search subsequence r k M (r) of time l j ) Task, also task M (r) l j ) In the subsequence Γ k The task segment in (1) is uniquely determined by the radius and the time sequence;
s3: through time series transformation, the task allocation formula is specifically expressed as:
E total (k)＝min{E i (k)|u i ∈U}
in the formula, E i (k) RepresentIn the k subsequence Γ k Rear unmanned plane u i Residual energy of, E total (k) Representing the unmanned aerial vehicle group in a subsequence gamma k The latter overall residual energy, E (r) l j ，Γ k ) Represents task M (r) l j ，Γ k ) The energy consumed is required.
Further, u i Refers to unmanned aerial vehicles; e (r) l j ) Expressed as the energy required for the drone to perform a task, E i Denoted as drone u i The remaining energy.
Further, the specific way of confirming the energy consumption model of the unmanned aerial vehicle group in the step one is as follows:
the search process of a drone is denoted Γ (Γ) 1 、Γ 2 、...、Γ k ) Subsequence r k Contains M time slots delta; by using
Respectively represent unmanned aerial vehicle u i In the subsequence Γ k The speed, acceleration at time t of (a),
indicating unmanned plane u i In subsequence F k Flight energy consumption of;
unmanned plane u i The energy consumption of (c) can be expressed as:
wherein m is unmanned plane u i Mass, Δ e, representing the energy consumed by the unmanned aerial vehicle movement change, c 1 、c 2 Is a constant related to the size of the flying wing of the unmanned aerial vehicle and energy efficiency factors, and g is the gravity acceleration;
in a clear view of the above, it is known that,
the former represents the energy consumption required by the task, and the latter represents the energy consumption of the unmanned aerial vehicle;
since the overall energy level of the drone swarm depends on the energy level of the lowest-powered drone, the overall energy level of the drone swarm is expressed as:
E total ＝min{E i |u i ∈U}
in the formula, E full Indicating the drone battery capacity.
Further, the one-time distribution mode for task energy equalization of the one-round search task of the unmanned aerial vehicle in the second step is specifically as follows:
E total ＝min{E i |u i ∈U}
in the formula (I), the compound is shown in the specification,
represents the task M (r) l j ) To unmanned plane u i ；E total Representing the overall remaining energy of the drone swarm.
The invention has the beneficial effects that:
the invention provides a decentralized unmanned aerial vehicle cluster search task energy equalization method, which utilizes the unmanned aerial vehicle cluster array type information to estimate the task energy consumption, does not need a centralization algorithm, has good expandability and is suitable for the unmanned aerial vehicle cluster search problem with a certain array type under the connectivity constraint condition;
the application mainly provides a task segmentation method and an unmanned aerial vehicle energy consumption model, and the unmanned aerial vehicle energy consumption model is designed by combining the formation characteristic of an unmanned aerial vehicle cluster and starting from the task requirement.
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In order to facilitate understanding for those skilled in the art, the present invention will be further described with reference to the accompanying drawings.
Fig. 1 is a diagram of a multi-drone search matrix;
FIG. 2 is a schematic diagram of task partitioning;
fig. 3 is a schematic diagram of a task allocation scheme based on neighbor transform.
Detailed Description
With the development of technology, unmanned aerial vehicles are highly brilliant in various application fields; for example, in military fields such as search, reconnaissance, and attack operations in dangerous areas, the role of the unmanned aerial vehicle is more and more indispensable; but the resource that is restricted to carrying is limited, and single unmanned aerial vehicle all need consider the energy consumption problem when carrying out all kinds of tasks to improve the task volume that unit energy consumption accomplished. For a formation formed by a plurality of unmanned aerial vehicles, in addition to the problem of capability of a single unmanned aerial vehicle, the energy use condition of the formation as a whole needs to be considered when performing a task. Similar to the "barrel effect" -the amount of water a barrel can hold depends on the board it is shortest, and the dead time for a formation of drones to perform a mission depends on the least energetic drone. Therefore, in order to prolong the dead time of formation of the drones, in addition to optimizing the energy consumption scheme of a single drone, the energy allocation scheme of the whole formation needs to be optimized, so that the energy of each drone in the formation is kept in a relatively balanced state.
How to allocate the energy of each drone on a formation of drones? It is known that when the formation of unmanned aerial vehicles flies in the air, there is no energy channel between the unmanned aerial vehicles, and there is no uniform energy distribution center. Then, how to do can be done to implement energy deployment; the solution proposed in this application is to open up an additional path. Although the energy supply side does not have a dedicated channel, the energy usage, i.e. the demand side, is scalable and does not require a dedicated energy channel. Because the tasks of all the unmanned aerial vehicles in the formation are not completely the same, some unmanned aerial vehicles consume more energy, and some unmanned aerial vehicles consume less energy, for example, under a uniform speed state, the unmanned aerial vehicle with a far flying distance usually consumes more energy than the unmanned aerial vehicle with a short flying distance in the same time. Therefore, the energy consumption of the unmanned aerial vehicles can be regulated and controlled by regulating and controlling different tasks allocated to the unmanned aerial vehicles, so that the energy equalization level of each unmanned aerial vehicle in the formation can be improved.
Based on the method, compared with the method that a single unmanned aerial vehicle search task is not separable, the multiple unmanned aerial vehicles can distribute the search task to each unmanned aerial vehicle to execute and then combine the search tasks;
as shown in fig. 1 to fig. 3, as a first embodiment of the present application, a search task is completed by 3 drones searching side by side;
as can be seen from fig. 1 to 3, based on the "one" matrix type of surrounding search, the search radius of each drone is different, and the formed search path is different, and the path is referred to as a "track" herein; based on this, the j-th round search radius is referred to herein as r l j The task of a track is defined as M (r) l j ) J =1,2, \ 8230; l =1,2, \8230, n; radius r l j The following constraints are satisfied:
SW＝W(n-1)+2L d
W＝min(L c ，2L d )
in the formula, SW is the search width of one round of circumflight, and W is the maximum distance between unmanned aerial vehicles under the constraint of connectivity;
in order to keep the formation stable, the drone must follow the same angular velocity ω j Flying, and the following constraints are to be satisfied:
wherein v is max Is the maximum flying speed of the unmanned aerial vehicle,
and searching the flight radius of the outermost unmanned aerial vehicle for the jth round.
It can be seen that the outermost unmanned aerial vehicle has the fastest flight speed and the longest flight path, while the innermost unmanned aerial vehicle has the slowest flight speed and the shortest flight path. This difference necessarily results in a difference in the energy required to perform each task, given that this energy is denoted as E (r) l j ) Unmanned plane u i The remaining energy is denoted as E i Then, the primary task allocation of the drone energy equalization may be expressed as:
E total ＝min{E i |u i ∈U}
in the formula (I), the compound is shown in the specification,
represent task M (r) l j ) To unmanned plane u i ；E total Representing the overall remaining energy of the drone swarm.
The distribution method distributes a round of search tasks only once, the granularity is large, in fact, a round of search can still be continuously distributed, the distribution is carried out by means of a subdivision mode, and the search process of the unmanned aerial vehicle is divided into a plurality of time segments according to the time slot delta, so that the search process can be characterized as a time sequence gamma (delta, 2 delta. The time sequence is divided into K subsequences gamma k (kM +1 δ, kM +2 δ.,. K M + M δ), K =1,2, \8230, K, M being the subsequence size, i.e. the search process of the drone can be denoted Γ (Γ) 1 ，Γ 2 ，...，Γ K ). Accordingly, task M (r) l j ) Can be represented as M (r) l j ，Γ)，M(r l j ，Γ k ) Is shown in the search subsequence r k M (r) of (M) l j ) Task, or task M (r) l j ) In the subsequence Γ k The task segment in (1) is uniquely determined by the radius and the time sequence, as shown in the figure.
Through time series transformation, the above task allocation formula can be expressed as:
E total (k)＝min{E i (k)|u i ∈U}
wherein E i (k) Is represented in the k subsequence Γ k Rear unmanned plane u i Residual energy of, E total (k) Representing the unmanned aerial vehicle group in a subsequence gamma k The latter overall residual energy, E (r) l j ，Γ k ) Represents task M (r) l j ，Γ k ) The energy consumed is required.
So far, a multi-unmanned aerial vehicle search task allocation model based on energy balance has been established, but a key function allocate in the model is not described in detail; how should this allocation function be designed? One obvious idea is: and allocating the task with the highest energy consumption to the unmanned aerial vehicle with the most abundant energy. Due to the limitation of the formation, although a lot of constraints are brought to the system, such as that each unmanned aerial vehicle must keep operating at the same angular speed, a lot of useful information is provided, such as the position of each unmanned aerial vehicle, the energy consumption level of the task, and the energy consumption of the outermost task is the highest. Therefore, the energy consumption level can be generally balanced only by exchanging the positions of the innermost unmanned aerial vehicle and the outermost unmanned aerial vehicle.
However, the distribution scheme is neglected and adjustedEnergy is also consumed in the process, denoted as E a (k) Indicates acrylate (M (r) l j ，Γ k ),u i ) Energy consumed, unmanned plane u in the above task allocation model i The energy update formula becomes:
according to document 0, a task allocation method for maximizing overall rewards of an unmanned aerial vehicle group is provided, and good performance improvement is obtained in a search-attack task simulation experiment; and document 0 proposes an efficient and energy-saving communication model of an unmanned aerial vehicle and a ground station, which considers the research of network throughput and unmanned aerial vehicle energy consumption, E a (k) The speed, the speed change and the time are related;
obviously, the speed of the unmanned aerial vehicle at the innermost side and the outermost side changes the most, and the farthest distance and the longest consumed time. Therefore, such a task assignment process consumes a lot of energy. This document proposes a scheme of adjacent transformation: from 2 passes (radius) when the subtask sequence k is odd
The track) starts to perform the adjacent conversion, when k is an even number, the adjacent conversion is performed from 1 track (radius r) 1 j The track) begins to make the adjacent transition as shown. Because the distance between adjacent roads is short, the difference of the flight speeds of the unmanned aerial vehicles is small, and therefore, the distribution energy consumption E of the scheme a (k) The value is little, and unmanned aerial vehicle energy consumption changes gently. The method is simple to implement, centralized optimization is not needed, only a small amount of information needs to be exchanged between the unmanned aerial vehicles, and energy consumed by algorithm centralized optimization and a large amount of communication is avoided.
Calculating an energy consumption model of the unmanned aerial vehicle group;
before energy equalization, the energy consumption structure of the unmanned aerial vehicle needs to be researched. In the search task, the energy of the unmanned aerial vehicle is mainly consumed in the processes of flight, detection, communication, calculation and the like. Wherein detection is the same for each drone in the formation and therefore may not be considered. Different search algorithms can affect the calculated amount and the communication amount of each unmanned plane in the formation, thereby affecting the energy consumption. The 'loop-back' search algorithm provided by the invention is a decentralized algorithm, the algorithm is dispersed on each unmanned aerial vehicle, only a small amount of communication is needed, the interactive communication amount of the algorithm is relatively balanced, forwarding is not considered, local maximum calculation inside the unmanned aerial vehicle is realized, and mutual communication is minimized, so that the phenomenon that the algorithm is concentrated on a certain unmanned aerial vehicle to carry out high-load calculation and high-flow communication, and the individual unmanned aerial vehicle consumes excessive energy is avoided. Therefore, the energy equalization in the text only needs to consider the equalization of flight energy consumption.
The unmanned aerial vehicle group performs surrounding flight around the target prior position according to the linear array type, and according to the previous description, the search process of the unmanned aerial vehicle can be expressed as gamma (gamma) 1 、Γ 2 、...、Γ k ) Subsequence r k Containing M slots delta. As used herein
Respectively representing unmanned aerial vehicles u i In the subsequence Γ k The speed and acceleration at time t (t-th time slot) of (1),
indicating unmanned plane u i In the subsequence Γ k The flight energy consumption of. Then, unmanned plane u is an unmanned plane energy model according to the related document 00 i The energy consumption of (c) can be expressed as:
wherein m is unmanned plane u i Mass, Δ e, representing the energy consumed by the unmanned aerial vehicle movement change, c 1 、c 2 Is a constant, related to the size of the flying wing of the unmanned plane and the energy efficiency factor, and g is gravityAcceleration. By the above analysis, it can be seen that
The former represents the energy consumption required by the task, and the latter represents the energy consumption of the unmanned aerial vehicle execution.
Since the overall energy level of the drone swarm depends on the energy level of the lowest-powered drone, the overall energy level of the drone swarm may be expressed as:
E total ＝min{E i |u i ∈U}
in the formula, E full Representing the unmanned aerial vehicle battery capacity;
the following prior art is referred to in this application, and the references are specifically as follows:
[1]Mohiuddin A,Taha T,Zweiri Y H,et al.A Survey of Single and Multi-UAV Aerial Manipulation[J].Unmanned Systems,2020；
[2]Dainelli R,Toscano P,Gennaro S,et al.Recent Advances in Unmanned Aerial Vehicles Forest Remote Sensing—A Systematic Review.Part II:Research Applications[J].Forests,2021,12(4):397；
[3] an intelligent scheduling technology for unmanned aerial vehicle cluster is reviewed in [ J ]. Automation science, 2020,46 (2);
[4]Kim B O,Yun K H,Chang T S,et al.A Preliminary Study on UAV Photogrammetry for the Hyanho Coast Near the Military Reservation Zone,Eastern Coast of Korea[J].Ocean and Polar Research,2017,39(2):159-168；
[5]Boukoberine M N,Zhou Z,Benbouzid M.A critical review on unmanned aerial vehicles power supply and energy management:Solutions,strategies,and prospects[J].Applied Energy,2019,255:113823-；
[6]Hassanalian M,Abdelkefi A.Classifications,applications,and design challenges of drones:A review[J].Progress in Aerospace Sciences,2017,91(may):99-131；
[7]Kalatzis N,Avgeris M,Dechouniotis D,et al.Edge Computing in IoT ecosystems for UAV-enabled Early Fire Detection[C]//2018 IEEE International Conference on Smart Computing(SMARTCOMP).IEEE,2018；
[8]Kim M H,Baik H,Lee S.Resource Welfare Based Task Allocation for UAV Team with Resource Constraints[J].Journal of Intelligent&Robotic Systems,2015,77(3-4):611-627；
[9]MENG HUA,YONGMING HUANG,YI WANG,QINGQINGWU,HAIBO DAI,LYUXI YANG.Energy Optimization for Cellular-Connected Multi-UAV Mobile Edge Computing Systems with Multi-Access Schemes.Journal of Communications and Information Networks,Vol.3,No.4,Dec.2018；
[10]Y.ZENG,R.ZHANG.Energy-efficient UAV communication with trajectory optimization[J].IEEE Transactions onWireless Communications,2017,16(6):3747-3760；
[11]S.JEONG,O.SIMEONE,J.KANG.Mobile edge computing via a UAVmounted cloudlet:Optimization of bit allocation and path planning.IEEE Transactions on Vehicular Technology,2018,67(3):2049-2063；
[12]Prasetia A S,Wai R J,Wen Y L,et al.Mission-Based Energy Consumption Prediction of Multirotor UAV[J].IEEE Access,2019:1-1。
the foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.
Claims (2)
1. A multi-unmanned aerial vehicle search task energy equalization method based on pre-estimation is characterized by comprising the following steps:
the method comprises the following steps: determining an energy consumption model of the unmanned aerial vehicle group;
step two: the method comprises the steps that task energy balance distribution is carried out on a round of search tasks of the unmanned aerial vehicle, and the specific distribution mode comprises a primary distribution mode and a subdivision mode;
the distribution method for balancing the task energy according to the subdivision mode comprises the following steps:
s1: dividing the search process of the unmanned aerial vehicle into a plurality of time segments according to the time slot delta; the search process is characterized as a time series Γ (δ,2 δ.);
s2: the time sequence is subdivided into K subsequences gamma k (kM +1 delta, kM +2 delta, K, M delta), K =1,2, \ 8230, and K, M are subsequence sizes, namely the search process of the unmanned aerial vehicle is represented by gamma (gamma) 1 ，Γ 2 ，...，Γ K )；
The task is represented as M (r) l j ，Γ)，M(r l j ，Γ k ) Is shown in the search subsequence r k M (r) of time l j ) Task, M (r) l j ) Search for radius r for the jth round l j Track search task, j =1,2, \ 8230; l =1,2, \8230;, n; also task M (r) l j ) In the subsequence Γ k The task segment in (1) is uniquely determined by the radius and the time sequence;
s3: through time series transformation, the task allocation formula is specifically expressed as:
E total (k)＝min{E i (k)|u i ∈U}
in the formula, E i (k) Is represented in the k subsequence Γ k Rear unmanned plane u i Residual energy of E total (k) Representing the unmanned aerial vehicle group in a subsequence gamma k The latter overall residual energy, E (r) l j ，Γ k ) Represents task M (r) l j ，Γ k ) The energy that needs to be consumed;
the primary distribution mode for carrying out task energy equalization on the primary search task of the unmanned aerial vehicle in the second step is specifically as follows:
E total ＝min{E i |u i ∈U}
in the formula (I), the compound is shown in the specification,
represents the task M (r) l j ) Allocated to unmanned plane u i ；E total Representing the whole residual energy of the unmanned aerial vehicle group; e (r) l j ) Representing the energy required for the drone to perform the mission, E i Denoted as drone u i The remaining energy.
2. The method for balancing the energy of the search tasks of the multiple unmanned aerial vehicles based on the estimation as claimed in claim 1, wherein the specific manner of confirming the energy consumption model of the unmanned aerial vehicle group in the step one is as follows:
the search process of a drone is denoted Γ (Γ) 1 、Γ 2 、...、Γ k ) Subsequence r k Contains M time slots delta; by using
Respectively representing unmanned aerial vehicles u i In the subsequence Γ k Velocity, acceleration at time t, E i fly (k) Indicating unmanned plane u i In subsequence F k Flight energy consumption of (2);
unmanned u i The energy consumption of (c) can be expressed as:
wherein m is unmanned plane u i Mass, Δ e, representing the energy consumed by the unmanned aerial vehicle movement change, c 1 、c 2 Is a constant related to the size of the flying wing of the unmanned aerial vehicle and energy efficiency factors, and g is the gravity acceleration;
in a clear view of the above, it is known that,
the former represents the energy consumption required by the task, and the latter represents the energy consumption of the unmanned aerial vehicle;
since the overall energy level of the drone swarm depends on the energy level of the lowest-energy drone, the overall energy level of the drone swarm is expressed as:
E total ＝min{E i |u i ∈U)
in the formula, E full Indicating the unmanned aerial vehicle battery capacity.
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