CN114355987A - Unmanned aerial vehicle formation reconstruction method based on elastic importance - Google Patents
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Abstract
The invention provides an unmanned aerial vehicle formation reconstruction method based on elastic importance, which comprises the following specific steps: step (1): acquiring relative positions and motion information of all unmanned aerial vehicles in the current formation, and obtaining a topological structure of the formation according to a communication relation in the current formation; step (2): the task-oriented performance and the task elasticity of the system are evaluated by combining the task requirements of the formation of the multiple unmanned aerial vehicles; and (3): evaluating the elastic importance of the unmanned aerial vehicles at all nodes according to the control optimization model; and (4): designing a reconstruction target structure of a plurality of unmanned aerial vehicle formations based on the elastic importance; and (5): and (4) controlling the optimization model according to the same parameters as those in the step (3) to determine the motion trail of the unmanned aerial vehicle. Aiming at a multi-unmanned aerial vehicle formation system, the invention provides an importance degree evaluation mode of an unmanned aerial vehicle, which can be used for quantitatively measuring the contribution degree of unmanned aerial vehicles with different nodes to the system elasticity; on the basis, the invention provides a determination mode of the reconstruction target structure of the formation of the multiple unmanned aerial vehicles in the face of disturbance, and the elastic promotion of the formation of the multiple unmanned aerial vehicles is realized by combining a control optimization model.
Description
(I) in the field of technology
The invention relates to the field of reconstruction recovery of a multi-unmanned aerial vehicle system, in particular to an evaluation method of unmanned aerial vehicle elasticity importance and a guidance mode of a reconstruction target.
(II) background of the invention
With the continuous improvement of the unmanned aerial vehicle technology, the unmanned aerial vehicle cluster is more and more widely applied in various fields such as detection, monitoring and battle. The formation of multiple drones can face various interferences such as special weather influence, random or deliberate attack by enemies and the like in the task execution process, and the interferences can cause the failure of a certain node in UAVs to further influence the task execution. Meanwhile, the system is damaged due to faults of the components of the unmanned aerial vehicle, communication interruption and the like. In addition, the particularity of the multi-unmanned aerial vehicle system makes the problem of repairing the fault of the multi-unmanned aerial vehicle system very difficult. Therefore, the task execution capacity and fault repair problem of multi-drone formation are widely concerned.
The flexibility of the novel cluster unmanned aerial vehicle system is taken as one of important design targets, namely, the capability of recovering to complete tasks after adaptive reconstruction after damage or performance degradation of part of unmanned aerial vehicles/stations/links. The elasticity of the unmanned aerial vehicle system is taken as a new quality characteristic, namely, the elasticity is closely related to the single general quality characteristic of the composition structure of the unmanned aerial vehicle, such as reliability, maintainability and the like, and is also closely related to the survivability/vulnerability of the system level and the specific reconstruction and repair strategy.
The existing unmanned aerial vehicle reconstruction recovery technology mostly searches reasonable control parameters under different targets, and the reasonability of a target structure is rarely concerned. Meanwhile, each node in the multi-unmanned aerial vehicle system has a large difference in the overall elastic contribution degree of the system, and how to quantitatively measure the contribution and guide the reconstruction recovery process becomes very important.
Disclosure of the invention
The invention aims to provide an evaluation method of unmanned aerial vehicle node importance, which quantificationally measures contribution degrees of unmanned aerial vehicles at different nodes to system elasticity, determines a reconstruction target structure of multi-unmanned aerial vehicle formation according to the contribution degrees, and further combines a control optimization model to realize the improvement of the multi-unmanned aerial vehicle formation system elasticity.
In order to achieve the purpose, the invention provides a multi-unmanned aerial vehicle formation reconstruction method based on elastic importance, which comprises the following specific steps:
step (1): acquiring relative positions and motion information of all unmanned aerial vehicles in the current formation, and obtaining a topological structure of the formation according to a communication relation in the current formation;
step (2): determining a task-oriented system performance and elasticity calculation mode by combining task requirements of a plurality of unmanned aerial vehicles in formation;
and (3): evaluating the elastic importance of the unmanned aerial vehicles at all nodes according to the control optimization model;
and (4): designing a reconstruction target structure of a plurality of unmanned aerial vehicle formations based on the elastic importance;
and (5): and (4) controlling the optimization model according to the same parameters as those in the step (3) to determine the motion trail of the unmanned aerial vehicle.
In the step (1), the relative position and the communication relation of the unmanned aerial vehicles are obtained, and the position and the communication relation between different unmanned aerial vehicles are mapped into an undirected and authorized network model G (V, E). And V ═ V1，v2，…，vNIn which v isiRepresenting a node in the network, wherein the actual physical meaning of the node is an unmanned aerial vehicle in a formation; e is used to represent the communication and relative position relationship between different drones and can be represented as a matrix:
if eijWhere M represents a constant other than 0, then it represents drone node viAnd vjThe distance between the two groups is M; if eijInfinity then denotes the drone node viAnd vjThere is no communication relationship between them. When i ═ j, e ij0 has no practical meaning.
In step (2), the expression mode of the system performance is oriented to specific tasks, such as the performance of multi-unmanned aerial vehicle formation in the joint investigation task, namelyIt can be measured in terms of the equivalent area observed by the formation:
wherein Si(t) is the area detectable by the drone at node i at time t. The performance can be normalized as follows,
wherein S (t) is the actual performance of the formation of multiple unmanned planes, and S (t)0) For the performance of multiple unmanned aerial vehicles in formation at the initial moment, namely the target, p (t) is a performance index required in the method. Determining a calculation mode of the elasticity measure of the unmanned aerial vehicle system based on the formation performance change, wherein the calculation mode is shown as the following formula:
in the formula, Ptarget(t) represents a target performance level for the formation, P (t) represents an actual performance level for the formation, tdAnd trThe reconstruction starting time and the reconstruction finishing time are respectively, R is the elasticity level of the unmanned aerial vehicle formation expressed in the reconstruction process, and the reconstruction method aims to find out a reasonable target structure and a reasonable motion track, so that the performance expression in the recovery process is best, and the R is maximized.
Wherein the calculation of the elastic importance in step (3) is also dependent on the change in the performance curve and the recovery process is repeated. The performance of a formation of multiple drones is a function of the state of each drone, then viThe impact of whether the unmanned aerial vehicle recovers or not on the system performance can be calculated as:
if xi(t) ═ 1 denotes that the drone at node i is working normally, xi(t) ═ 0 indicates that the drone at node i is in a failed state. The total performance recovered by the drone system may be calculated as:
based on the performance change of the system, calculating the elastic importance of the unmanned aerial vehicle at different nodes:
because the index considers the reconstruction recovery process, namely the overall performance recovery level, the recovery importance I of all the unmanned aerial vehicles can be comparediAnd analyzing the system recovery. Max { I }iAnd i is 1,2, n represents the unmanned plane with the largest influence on the recoverability of the unmanned plane system. The performance change of a certain unmanned aerial vehicle is obtained according to a control optimization model, at this time, the target structure is the current unmoved formation structure, and the specific description of the model will be specifically set forth in the explanation of step (5).
In the step (4), the specific steps of determining the formation reconstruction target structure by using the elastic importance degree are as follows:
step 1: finding nodes with elastic importance smaller than node vjAll the nodes in the set are sorted from small to large according to the index to form a set omega ═ { v ═ vm|Im＜IjAnd m ∈ {1,2, …, n }.
Step 2: if omega is not equal to phi, searching a node v in the set omegakSatisfy Ik＝min{Im|vm∈Ω}.
Step 3: judging node vkUnmanned aerial vehicle of moves to destroyed node vjWhether it can become an effective repair mode, if so, the moving mode is a repair scheme; otherwise, removing the node v from the set omegakAnd returns to Step 1.
Step 4: if the set is empty, the current formation structure is the target structure, and the target structure determination process is finished.
Meanwhile, whether the SteD3 can be a reasonable recovery scheme or not needs to judge that the movement can not cause topology division, namely whether the unmanned aerial vehicle is in a cluster or not is judged, and the distance factor can not effectively communicate with other unmanned aerial vehicles in the formation, so that the unmanned aerial vehicle can not participate in the execution of tasks. An example of the specific judgment is shown in fig. 1.
And (3) determining a reasonable motion track by combining the constraints of the communication distance and the safety distance on the basis of the three-dimensional motion model and taking the maximized elasticity as a target, wherein the control optimization model used in the step (5) is the same as that in the step (3). Assuming that the number of drones in formation is N, the in-flight position characteristic of the ith drone is given by:
wherein
Position coordinates, v, for describing the ith droneiRepresenting its flight speed, gammaiHexix-iThe flight path angle and the course angle of the unmanned aerial vehicle are calculated according to the following formula:
wherein the thrust force TiLoad factor niAnd an inclination angle thetaiRepresenting outer loop variables and selected as control input for each drone, DiRepresents the aerodynamic resistance, miIs the weight of the ith vehicle, and g is the acceleration of gravity. Consider a non-linear system that describes the motion of the ith drone in a standard fashion, which can be expressed as:
wherein the state variable is
Controlling input parameters
Including thrust force TiLoad factor niAnd an inclination angle thetai. Thus, the mathematical model of the multi-drone formation system herein may be described in the form of a composite state space as:
the state of the formation structure may be represented as X ═ X1，x2，…，xN]T∈R6*NContinuously controlling the input factors
Given a set of successive control inputs U and an initial state X (0) ═ X0At any time T e (0, T)]The states formed can be uniquely determined as:
the specific optimization model is as follows
max R
s.t the following constraints need to be satisfied:
Dsafe≤di，j≤Dcomm
wherein Δ x (t) ═ xi(T)-xm(T)，Δy(T)＝yi(T)-ym(T) and Δ z (T) zi(T)-zm(T) represents the difference in position between the drone at node i and the formation center for m, which is {1, …, N }.
Representing the position coordinates of the unmanned aerial vehicle at the node i relative to the formation center m after the repair process is finished,
distance between unmanned aerial vehicles at any two nodes i and j, DsafeAnd DcommRespectively representing a safe collision avoidance distance and a maximum communication distance. The recovery process of the formation structure can be obtained by solving the optimization model.
The invention provides an elastic importance degree based on performance change curves of a plurality of unmanned aerial vehicle formation combined with the quality characteristic of elasticity, so as to quantitatively measure the influence of different unmanned aerial vehicles on the formation elasticity in the recovery process. The importance is then used to guide the recovery process of the formation, and unlike the research aspect of the traditional formation control mode, the text focuses more on determining a reasonable target structure. The method can play a good role in the analysis of the formation of the multiple unmanned aerial vehicles and the guidance of the recombination process.
(IV) description of the drawings
FIG. 1 is an example of determining whether a target structure is feasible
FIG. 2 is a common formation topology
FIG. 3 is a graph of performance change under different drone breaches
FIG. 4 is a calculation result of the importance of elasticity
FIG. 5 shows a three-guided target reconstruction structure of an unmanned aerial vehicle
FIG. 6 is a diagram of the trajectory of the unmanned aerial vehicle under three kinds of guidance
FIG. 7 is a graph comparing performance under three guidelines
(V) detailed description of the preferred embodiments
For the purpose of promoting a better understanding of those skilled in the art, reference will now be made in detail to the exemplary embodiments of the present invention, examples of which are illustrated in the accompanying drawings. It is worthy to note that the following description includes specific details to aid understanding, but these details are to be considered exemplary only. Moreover, descriptions of well-known functions and constructions are omitted for clarity and conciseness.
Step (1): the relative position information of each unmanned aerial vehicle in the current formation is obtained and is shown in table 1, and the topological structure of the formation obtained according to the communication relationship in the current formation is shown in fig. 2, which is the most common triangular formation structure.
TABLE 1 relative position information
Step (2): investigation task oriented expression mode for describing unmanned aerial vehicle formation performance by equivalent area
Wherein Si(t) is the area detectable by the drone at node i at time t. The properties were then normalized as follows:
wherein S (t) is the actual performance of the formation of the unmanned aerial vehicles, S (t)0) And forming a formation for the unmanned aerial vehicles to perform the performance of the targets at the initial moment. Determining a calculation mode of the elasticity measure of the unmanned aerial vehicle system based on the performance expression mode, wherein the calculation mode is shown as the following formula:
wherein, Ptarget(t) represents a target performance level for the formation, P (t) represents an actual performance level for the formation, tdAnd trRespectively reconstruction start and endR is the elasticity level exhibited by the formation of drones during the reconstruction process, and the specific performance change is set forth in step (3).
And (3): the injection fault disables the unmanned aerial vehicles at different positions, the formation structure is not changed as a reconstruction target, and a performance change curve is obtained by means of a control optimization model with maximized elasticity, as shown in fig. 3. In particular, the UAV5 does not participate in the analysis process, but only participates in the reconstruction process to complement the V-shaped structure, due to structural requirements. The elastic importance of each drone is then calculated by means of the following formula:
because the index considers the reconstruction recovery process, namely the overall performance recovery level, the recovery importance I of all the unmanned aerial vehicles can be comparediAnd analyzing the system recovery. The calculation results are shown in fig. 4.
And (4): according to the corresponding implementation steps, the reconstructed target structure under the method is found after the UAV1 fails. Meanwhile, the corresponding target structure obtained under the condition of no guidance and random guidance is compared to verify the effectiveness of the method, and the specific structure is shown in fig. 5.
And (5): according to the control optimization model, reasonable power parameters are obtained, the unmanned aerial vehicle is moved to a proper position, and the moving tracks of the unmanned aerial vehicle under the three target structures are shown in fig. 6.
In this case, through the elasticity importance to different node unmanned aerial vehicle having confirmed reasonable target structure, further optimized the reconsitution of many unmanned aerial vehicle formations, obtained better recovery effect, finally realized the elasticity promotion of formation. The recovery effect pairs under different guidance modes are shown in fig. 7.
Claims (3)
1. An unmanned aerial vehicle formation reconstruction method based on elastic importance is characterized by comprising the following steps:
step (1): acquiring relative positions and motion information of all unmanned aerial vehicles in the current formation, and obtaining a topological structure of the formation according to a communication relation in the current formation;
step (2): the task-oriented performance and the task elasticity of the system are evaluated by combining the task requirements of the formation of the multiple unmanned aerial vehicles;
and (3): evaluating the elastic importance of the unmanned aerial vehicles at all nodes according to the control optimization model;
and (4): designing a reconstruction target structure of a plurality of unmanned aerial vehicle formations based on the elastic importance;
and (5): and (4) controlling the optimization model according to the same parameters as those in the step (3) to determine the motion trail of the unmanned aerial vehicle.
2. Step (3) of the method according to claim 1, characterized in that:
the performance of a formation of multiple drones is a function of the state of each drone, then viThe impact of whether the unmanned aerial vehicle recovers or not on the system performance can be calculated as:
if xi(t) ═ 1 denotes that the drone at node i is working normally, xi(t) ═ 0 indicates that the drone at node i is in a failed state. The total performance recovered by the drone system may be calculated as:
based on the performance change of the system, calculating the elastic importance of the unmanned aerial vehicle at different nodes:
because the index considers the reconstruction recovery process, namely the overall performance recovery level, the recovery importance I of all the unmanned aerial vehicles can be comparediAnd analyzing the system recovery.Max { I }iAnd i is 1,2, n represents the unmanned plane with the largest influence on the recoverability of the unmanned plane system.
3. Step (3) of the method according to claim 1, characterized in that:
the specific steps of determining the formation reconstruction target structure by using the elastic importance degree are as follows:
step 1: finding nodes with elastic importance smaller than node vjAll the nodes in the set are sorted from small to large according to the index to form a set omega ═ { v ═ vm|Im＜IjAnd m ∈ {1, 2.., n }.
Step 2: if omega is not equal to phi, searching a node v in the set omegakSatisfy Ik＝min{Im|vm∈Ω}.
Step 3: judging node vkUnmanned aerial vehicle of moves to destroyed node vjWhether it can become an effective repair mode, if so, the moving mode is a repair scheme; otherwise, removing the node v from the set omegakAnd returns to Step 1.
Step 4: if the set is empty, the current formation structure is the target structure, and the target structure determination process is finished.
Meanwhile, whether the Step3 can be a reasonable recovery scheme or not needs to judge that the movement can not cause topology division, namely whether the unmanned aerial vehicle is out of group or not is judged, and the situation that the unmanned aerial vehicle cannot participate in task execution due to the fact that distance factors cannot effectively communicate with other unmanned aerial vehicles in the formation is prevented.
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