CN111132258A - Unmanned aerial vehicle cluster cooperative opportunistic routing method based on virtual potential field method - Google Patents

Unmanned aerial vehicle cluster cooperative opportunistic routing method based on virtual potential field method Download PDF

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CN111132258A
CN111132258A CN201911423735.4A CN201911423735A CN111132258A CN 111132258 A CN111132258 A CN 111132258A CN 201911423735 A CN201911423735 A CN 201911423735A CN 111132258 A CN111132258 A CN 111132258A
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雷磊
葛以震
姜阳
王顺章
蔡圣所
张莉涓
宋晓勤
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Nanjing University of Aeronautics and Astronautics
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    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H04W40/22—Communication route or path selection, e.g. power-based or shortest path routing using selective relaying for reaching a BTS [Base Transceiver Station] or an access point
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H04W40/04—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W84/00—Network topologies
    • H04W84/02—Hierarchically pre-organised networks, e.g. paging networks, cellular networks, WLAN [Wireless Local Area Network] or WLL [Wireless Local Loop]
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Abstract

The invention discloses an unmanned aerial vehicle cluster cooperative opportunistic routing method based on a virtual potential field method. The method introduces the concept of virtual force in a virtual potential field method, the virtual force is divided into topological force, navigation force and barrier force again, resultant force is obtained by adding the three force vectors, and the motion state of the unmanned aerial vehicle node is described through the resultant force. And then, designing an unmanned aerial vehicle cluster cooperative opportunistic routing method based on a virtual potential field method, wherein the method takes the node advancing distance, the node virtual force direction and the unmanned aerial vehicle node residual energy as key parameters to be introduced into the selection of the next jump transmitting node. The method comprises three key steps of candidate relay set selection, optimal relay node selection and data forwarding and confirmation. Simulation experiments in a simulation environment based on MATLAB prove that the opportunistic routing method can achieve better network performance in an unmanned aerial vehicle cluster network environment compared with other opportunistic routing methods.

Description

Unmanned aerial vehicle cluster cooperative opportunistic routing method based on virtual potential field method
Technical Field
The invention belongs to the field of wireless networks, and particularly relates to an unmanned aerial vehicle cluster cooperative opportunistic routing method based on a virtual potential field method.
Background
The high mobility of the nodes in the unmanned aerial vehicle cluster brings the results of rapid change of network topology, short link life and the like, so that the routing information in the unmanned aerial vehicle ad hoc network changes frequently. Meanwhile, the network nodes in the unmanned aerial vehicle ad hoc network are easy to destroy, and challenges are provided for the design of a routing protocol. The existing traditional routing protocol applied to a mobile ad hoc network (MANET) and a vehicle ad hoc network (VANET) cannot be well adapted to the unmanned aerial vehicle ad hoc network.
At present, many unmanned aerial vehicle clustering algorithms are researched by using a virtual potential field method, and virtual force provides a key method for researching the speed and the movement direction of the unmanned aerial vehicle. The two most common scenes in unmanned aerial vehicle cluster flight are cluster flight and collaborative obstacle avoidance. The cooperative obstacle avoidance is based on a cluster motion scene, considers the condition that an obstacle exists in a traveling route, and researches the cluster motion of the unmanned aerial vehicle under the condition that the obstacle exists. The virtual potential field method is also called an artificial potential field method and is originally applied to robot navigation related research. The basic idea of the virtual potential field method is to abstract the environment where an agent is located into a virtual potential field, and the agent moves under the action of the force in the virtual potential field. The forces experienced by an intelligent individual can be divided into two categories: attraction and repulsion. And after the attractive force and the repulsive force are superposed, a resultant force is formed, and the individual moves towards the target point under the action of the resultant force. Gravitation F borne by intelligent individual at current position of virtual potential fieldAAnd repulsive force FRRespectively by the gravitational potential field function UAAnd repulsive force potential field function URAnd solving a negative gradient to obtain the target. However, in a large-scale unmanned aerial vehicle cluster scene, because the unmanned aerial vehicle has a fast motion speed, the number of unmanned aerial vehicles in the cluster is large, and the cluster topology structure changes rapidly, so that the network scene is complex and changeable. On the basis of original attractive force and repulsive force, the types of force possibly applied to a single unmanned aerial vehicle in the unmanned aerial vehicle cluster are advancedAnd (3) re-dividing the line, dividing the virtual force received by the line into three parts, namely navigation force, topological force and obstacle force, wherein the three forces are superposed to form a resultant force finally received by the unmanned aerial vehicle, and the resultant force direction is the movement direction of the unmanned aerial vehicle. After the received resultant force is subjected to normalization calculation, the movement speed and the movement direction of the unmanned aerial vehicle are obtained. The method comprises the following specific steps:
(1) topological force
The unmanned aerial vehicle cluster is composed of a large number of unmanned aerial vehicle nodes, and two unmanned aerial vehicles within a certain range can generate interaction force. The interaction force between the unmanned aerial vehicles can be called as 'topological force', the topological force plays a key role in maintaining the topological structure of the cluster on one hand, and on the other hand, the collision accident caused by too close distance between the unmanned aerial vehicles can be effectively avoided. Fig. 1 shows a schematic diagram of topological forces borne by nodes in an unmanned aerial vehicle cluster network. The attractive and repulsive forces between drones can be expressed as:
Figure BSA0000199090900000021
Figure BSA0000199090900000022
wherein k isAAnd kRRespectively is a gravitational gain coefficient and a repulsive gain coefficient, and D (u, v) is the Euclidean distance between the unmanned planes u and v. At this time, for drone u, the topological force it is subjected to may be expressed as:
Figure BSA0000199090900000023
wherein the gravity weight αAAnd repulsive force weight αRSatisfies the following conditions:
Figure BSA0000199090900000024
by adjusting the gravity weight αAAnd repulsive force weight αRThe size relationship between the two can be differentThe optimal topological force effect is obtained under the cluster environment.
(2) Navigation force
When the unmanned aerial vehicle moves, there is a point to which the unmanned aerial vehicle wants to move, and the point may be a certain temporary point in the path planning process or an end point of the whole cluster movement. The navigation force in the virtual force is the force for driving the unmanned aerial vehicle to reach the designated place, and has the function of navigation. In fig. 2, a drone u has planned a motion path, the drone is currently at point P, and its coordinates are P (x)p，yp，zp) This unmanned aerial vehicle knows that should be in the position of P' at self next moment. The navigation force can now be expressed as:
Figure BSA0000199090900000031
in the formula FGFor navigation forces, the direction of the force is the vector direction P to P', kGIs the navigation force gain factor. From the above formula, it can be seen that the navigation force and navigation force gain coefficient is related to the euclidean distance of the starting position. The navigation force is greatest at the beginning of the movement and gradually becomes smaller as it approaches the intermediate temporary point P' in order to form a well stable network topology before proceeding to the next movement. If the unmanned aerial vehicle does not plan the route in advance, the unmanned aerial vehicle can be acted by the navigation force from the terminal point and directly moves to the terminal point.
(3) Force of obstacle
When the unmanned aerial vehicle cluster motion is in an open and barrier-free field, the stress condition of a single unmanned aerial vehicle can be better depicted only by vector superposition of the navigation force and the topological force. However, in a real business scenario, the unmanned aerial vehicle cluster often encounters obstacles such as mountains, buildings and the like during the traveling process. Meanwhile, when the unmanned aerial vehicle cluster is used for military operation, the electromagnetic interference area set by enemy needs to be effectively avoided. In the virtual potential field method, an area threatening a cluster of the unmanned aerial vehicles is abstracted into a geometric obstacle, and a force hindering the cluster of the unmanned aerial vehicles from traveling is defined as an obstacle force, that is, for the unmanned aerial vehicles, the obstacle is located atThe positions can not be close to each other, and the bypassing is needed to be avoided. As shown in fig. 3, once a drone enters the area of an obstacle, the drone is considered to have been destroyed. The distance between the two unmanned planes and the topological force is less than ROptThe repulsion force generated in the process is similar, and the obstacle force acts on the unmanned aerial vehicle in the form of the repulsion force. In the virtual potential field, the set of obstacles around drone u is defined as o (u). Defining the safe distance of the obstacle as RSafeThat is, when the distance between the unmanned aerial vehicle and the center needs to be kept larger than or equal to RSafe. When the unmanned aerial vehicle moves to a position less than R away from the center of the obstacleSafeAnd (4) considering that the unmanned aerial vehicle is damaged. Defining the range of action of the barrier as RSenseThat is, when the unmanned plane moves to a position less than R away from the center of the obstacleSenseWhen the unmanned aerial vehicle moves away from the obstacle, the unmanned aerial vehicle can be driven by repulsive force from the obstacle, and the repulsive force can enable the unmanned aerial vehicle to move away from the obstacle. When the distance between the unmanned aerial vehicle and the center of the obstacle is greater than RSenseWhen the unmanned aerial vehicle is in use, the unmanned aerial vehicle is considered not to be subjected to repulsive force from the obstacle. For a single drone u, it is subjected to a barrier force F from the barrier obObIt can be expressed as follows, and the direction of the obstacle force is the opposite direction from u to the center of the obstacle. The obstacle force suffered by the unmanned aerial vehicle cluster node can be expressed as:
Figure BSA0000199090900000041
in the formula (6), kOAnd D (u, ob) is the distance between the current unmanned aerial vehicle and the center of the obstacle. Then the sum of all barrier forces borne by the unmanned plane u is:
Figure BSA0000199090900000042
(4) resultant force
The three forces are subjected to vector addition, so that the resultant force F of the virtual force finally received by the unmanned aerial vehicle in the virtual potential field can be calculatedRSComprises the following steps:
Figure BSA0000199090900000043
wherein, βG、βTAnd βORespectively, a navigation force weight coefficient, a topology force weight coefficient and an obstacle force weight coefficient. The values of the three coefficients are all between 0 and 1, and their sum is 1, i.e.:
Figure BSA0000199090900000044
in different task scenarios, β may be changedG、βTAnd βOThe ratio relation among the three components can be used for planning the optimal action path for the unmanned aerial vehicle cluster. Virtual force is an imaginary concept, and the magnitude of the force cannot be intuitively felt. By the definition, the resultant force F of the virtual forces finally received by the unmanned aerial vehicle in the virtual potential fieldRSRanging from 0 to positive infinity. The movement speed of the unmanned aerial vehicle is [0, V ]max]Within the interval, VmaxThe maximum value of the unmanned aerial vehicle movement speed. The virtual force applied to the unmanned aerial vehicle needs to be mapped to the corresponding speed. To accomplish this mapping, the magnitude of the virtual force needs to be normalized first. Here the normalization is performed using an arctan function arctan (). The arctangent function arctan () is a kind of inverse trigonometric function, which is defined as the whole number of real numbers R, whose value range is (-pi/2, pi/2). For the unmanned plane u, the mapping relation between the virtual force and the speed received by the unmanned plane u is as follows:
Figure BSA0000199090900000051
disclosure of Invention
Aiming at the characteristics that the cluster network node of the unmanned aerial vehicle moves at a high speed and the cluster topology changes rapidly, the unmanned aerial vehicle cooperative opportunistic routing method based on the virtual potential field method is provided. The method introduces a virtual potential field method into the consideration of the design of a routing protocol, and takes factors such as virtual force, advancing distance, unmanned aerial vehicle node energy and the like as key parameters for selecting the next hop of the routing, and the method adopts the following steps:
step 1: when the period starts, the destination node broadcasts the state information of the destination node in the whole network, and the neighbor nodes exchange the position information of the destination node with each other, so that other nodes can make routing decision. In the candidate relay set selection stage, the nodes carrying data need to perform route discovery. The node may send a Route Request (RREQ) to other nodes within its communication range. At this time, if no other node in the communication range of the node is available for the node to forward data, the node enters a storage-carrying mode, continues to move, and tries to perform route discovery again when waiting for the beginning of the next movement period. If other nodes exist in the communication range of the node, the set formed by the nodes is the candidate relay set of the node. After receiving the routing request, the nodes in the candidate relay set perform priority measurement calculation according to the node states of the nodes. In the previous subsection, a calculation method for comprehensively considering three aspects of node advancing distance, node virtual force and node residual energy is provided. At this time, the node calculates Mar (), Vir () and Ene () according to a defined algorithm, wherein, a final priority measurement result Val () is obtained, the size of a self timer is set according to the value of the Val (), and routing reply is carried out. When the value of Val () is larger, it indicates that the current priority of the node is higher, then the value of the timer set by the node is smaller; if the value of Val () is smaller, the current priority of this node is lower, and it is larger when setting the value of the timer. When the timer of the candidate relay node expires, the node broadcasts a Route Reply (RREP). It should be noted that, according to this routing method, when the forward distance Mar () of the candidate relay node is negative, it indicates that the candidate relay node is opposite to the destination node, and in order to avoid data transmission in the opposite direction, this node will not perform route reply. Similarly, when the remaining energy of a candidate relay node is lower than the preset minimum threshold EminThe node will also give up route replies. And the nodes carrying the data determine priority sequencing according to the received route response and determine the best candidate relay node.
Step 2: for a node to forward data, the node performs priority ordering according to the received route reply. Since the timer value of the candidate relay node having the largest priority metric result is the smallest, the candidate relay node that routes the reply first is generally the best relay node. However, due to the high-speed mobility of the nodes and the rapid variability of the topology of the unmanned aerial vehicle cluster network, a routing loop phenomenon occurs, and data packets are repeatedly transmitted among a plurality of nodes, so that the waste of network resources is caused. In order to avoid the phenomenon of routing loop, the nodes in the candidate relay set need to be filtered. If the nodes in the candidate relay set have participated in data forwarding in the previous path, the nodes which have participated in data forwarding are screened out of the candidate relay set. The checking method can greatly reduce the generation of routing loop phenomenon. And the node to be forwarded selects the node with the highest priority in the filtered candidate relay set for data forwarding. However, in the moving process of the unmanned aerial vehicle cluster, a temporary cluster split may occur due to an obstacle in the scene, and there may be no candidate relay node in the filtered candidate relay set. When the node carrying data has no candidate relay set point, the node enters a storage-carrying mode, continues to move, and waits for the beginning of the next movement period to try the route discovery again.
And step 3: when the node carrying the data finds the best relay node, the data packet will be forwarded to this relay node. The relay node receiving the data packet writes the self node sequence number into the data packet head information, and records the forwarding path that the data packet passes through. For subsequent routing loop verification. After the relay node receives the data packet from the previous hop node, the node repeats the opportunistic routing process to find out the best relay node in the candidate relay set of the node for data forwarding. And when the destination node is in the transmission range of the data carrying node, the data is directly sent to the destination node.
The performance of the unmanned aerial vehicle cluster cooperative opportunistic routing method based on the virtual potential field method is verified in simulation software. The simulation experiment assumes that there are 30 nodes in the unmanned aerial vehicle cluster. One of the nodes is a source node and the other node is a destination node. When each movement period starts, the source node sends a data packet to the destination node, and the data packet is forwarded by other nodes in the unmanned aerial vehicle cluster network and finally received by the destination node. Setting the simulation duration to be 265 motion cycles, wherein the simulation scene is a square area with the side length of 5000m, and two obstacles and a target end point at the upper right corner exist in the scene. The simulation scenario is shown in fig. 4.
Table 1 shows two typical virtual potential field method-based drone cluster cooperative opportunistic routing (VPFC-OR) cases for two different weight coefficient assignments. Fig. 8, 9 and 10 are graphs of two VPFC-OR opportunistic routing algorithms versus hop count based opportunistic routing algorithms (ExOR) and topological link state based opportunistic routing (TLG-OR). As can be seen from fig. 8, the opportunistic routing provided by the method is slightly improved in the performance of the average propagation delay of a single packet, as can be seen from fig. 9, the opportunistic routing provided by the method can effectively reduce the average forwarded times of the single packet, and at the same time, as can be seen from fig. 10, the opportunistic routing provided by the method has a lower normalized residual energy variance of the node. Simulation results show that compared with ExOR and TLG-OR, the unmanned aerial vehicle cluster cooperative opportunistic routing based on the virtual potential field method has the advantage that better network performance can be obtained.
TABLE 1 VPFC-OR routing protocol parameter set
Figure BSA0000199090900000071
Drawings
FIG. 1 is a schematic diagram of topological force stress of unmanned aerial vehicle cluster nodes;
FIG. 2 is a schematic view of navigation force of unmanned aerial vehicle cluster nodes;
FIG. 3 is a schematic diagram of obstacle force of unmanned aerial vehicle cluster nodes;
fig. 4 is a simulation scene of the motion of the cluster of the unmanned aerial vehicle, the topological state of the cluster at the initial time;
FIG. 5 is a basic flow of VPFC-OR opportunistic routing;
FIG. 6 is a schematic of node progression distance;
FIG. 7 is a schematic diagram of a virtual force angle between a candidate relay node and a destination node;
FIG. 8 is a simulated comparison of the average propagation delay of a single packet;
FIG. 9 is a comparison graph of simulation of the average number of times a packet is forwarded;
fig. 10 is a simulation comparison graph of normalized residual energy variance of nodes of the unmanned aerial vehicle.
Detailed Description
The invention is described in further detail below with reference to the figures and examples.
The unmanned aerial vehicle collaborative opportunity network routing method based on the virtual potential field method is realized in a simulation environment built by MATLAB, and the effectiveness of the method is proved through a simulation result. The description of the invention refers to the unmanned aerial vehicle collaborative Opportunistic Routing method Based on the Virtual Potential Field method as VPFC-OR (Virtual-Potential-Field-Based collaborative Opportunistic Routing). FIG. 5 is a basic flow of VPFC-OR opportunistic routing. The specific implementation steps of VPFC-OR are given below:
step 1: candidate relay set selection
The VPFC-OR routing protocol is combined with the specific characteristics of the unmanned aerial vehicle cluster network, and consideration is mainly given to three aspects of node advancing distance, node virtual force direction and node residual energy during candidate relay set priority sequencing.
(1) Node distance of travel
The ultimate goal of opportunistic routing is to forward packets faster and better to the destination node. When the current node selects the optimal relay node, the relative positions of the candidate relay node and the destination node are considered, so that each transmission can advance towards the destination node as much as possible, the hop count of network transmission is reduced, and the overall performance of the network is improved. Fig. 6 is a schematic diagram showing node advance distance.
In fig. 6, it is assumed that the one-hop transmission distance at the source node i is RtxThen there are three candidate relay nodes N within the one-hop range of the source node ii(1)，Ni(2)，Ni(3) Dist (i, d) is defined as the euclidean distance from the source node to the destination node. Candidate relay node N for source node ii(j) In other words, the distance from the source node i is Dist (i, N)i(j) Distance to destination node Dist (N)i(j) D), at this time, the candidate relay node N can be solved through a trigonometric functioni(j) And the angle theta of the destination node d relative to the source node ijThe cosine values of (A) are:
Figure BSA0000199090900000091
θ1is node Ni(1) And an included angle formed by the source node i and the destination node d. N in FIG. 6i' (j) is a candidate relay node Ni(j) Defining a projection point on a connecting line of a source node i and a destination node d to be a candidate relay node Ni(j) Is Dist (i, N)i' (j)). Candidate relay node Ni(j) The advancing distance of (C) is measured using March (N)i(j) Is expressed by the expression:
March(Ni(j))＝Dist(i，Ni(j))·cosθj(12)
available candidate relay node Ni(j) The expression of (a) is:
Figure BSA0000199090900000092
it can be seen that the candidate relay node Ni(1) The forward distance of (1) is from the source node i to NiA distance of' (1). From the equation (13), when the included angle formed by the candidate relay node, the source node and the destination node is greater than 90 degrees, the forward distance is negative, for example, the candidate relay node Ni(3) As shown. If N is to bei(3) As a relay node for the next hop, the packet will be further away from the destination node, which is obviously not a desirable result. When the candidate relay set is actually selected, the candidate relay nodes with the negative advancing distance are firstly screened out of the candidate relay set, so as to prevent the data from being transmitted to the opposite direction of the destination node.To facilitate subsequent calculations, we perform a normalization calculation on the distance traveled. Normalized candidate relay node Ni(j) Advancing distance Mar (N)i(j) ) is:
Figure BSA0000199090900000101
at this time, for node i, its candidate relay set Ni(j) After filtering, the forward distance value range of the normalized nodes in the candidate relay set is [0, 1 ]]。
(2) Nodal virtual force
In the virtual potential field method, an unmanned aerial vehicle node is influenced by three forces, namely navigation force from a destination point, topological force of surrounding neighbor nodes and obstacle force of an obstacle. The three forces are respectively multiplied by the corresponding weight coefficients, and the virtual force F finally borne by the unmanned aerial vehicle node can be obtainedRSIs of the formula, wherein βG、βTAnd βORespectively, a navigation force weight coefficient, a topology force weight coefficient and an obstacle force weight coefficient.
Figure BSA0000199090900000102
When selecting the optimal candidate relay of the node, not only the current position condition of the candidate relay node but also the motion condition of the candidate relay node are considered. As shown in fig. 7, the cluster of drones moves towards the end point. Active node i and relay node N in unmanned aerial vehicle clusteri(1) And Ni(2) The destination node d to which the data is eventually forwarded. The virtual force situation of some nodes in the cluster is shown in fig. 7. N is in the candidate relay set of the source node ii(1) And Ni(2) Two candidate relay nodes, for source node i, node Ni(1) And node Ni(2) The difference of the forward distances is not large, and it is not suitable to select the best relay node only from the forward distance. In this case, the influence of the virtual force needs to be considered when performing the candidate relay node prioritization. The virtual force directly affects the movement direction of the node, for oneIn terms of dynamic topology, if a node carrying data can move towards a destination node, the network hop count can be reduced to a certain extent, and the performance of the network is effectively improved. If the destination node is in a moving state at this time, the relative speed direction between the candidate relay node and the destination node needs to be analyzed. FIG. 7 shows candidate relay nodes N in the UAV clusteri(1)、Ni(2) And the magnitude and the direction of the virtual force borne by the destination node d, the virtual force direction of the candidate relay node j forms an included angle omega with the virtual force direction of the destination node djThe expression of (a) is:
Figure BSA0000199090900000111
from equation (16), ω of the source node i candidate relay set nodejIs in the value range of [0, pi ]]. When ω isjThe smaller the relay node is, the more consistent the virtual force direction of the candidate relay node and the destination node is; when ω isjThe closer to pi, the more opposite the movement direction of the candidate relay node and the destination node. For convenient calculation, normalizing the virtual force included angle between the candidate relay node and the target node:
Figure BSA0000199090900000112
vir (N) for normalized virtual force included anglei(j) Is represented by) has a value range of [0, 1%]. At this time, Vir (N)i(j) Monotonicity of) and ωjIn contrast, i.e. ωjThe smaller, Vir (N)i(j) The larger the candidate relay node is, the higher the priority of the candidate relay node is, and the more likely it is to be the best relay node to forward data.
(3) Node residual energy
The unmanned aerial vehicle cluster network is one of wireless sensor networks and is composed of a plurality of unmanned aerial vehicle nodes carrying transceivers. Influenced by the design of the drone, the drone is usually powered by its onboard battery. The energy possessed by a single drone is limited. When cluster motion is performed, the unmanned aerial vehicle needs to spend a large amount of energy to perform movement and hovering, and meanwhile, needs to consume energy to perform tasks such as routing decision and forwarding data packets. If a certain unmanned aerial vehicle node is used for data forwarding all the time, the unmanned aerial vehicle node can exhaust energy quickly and cannot support the unmanned aerial vehicle node to continuously fly to complete subsequent tasks, so that the unmanned aerial vehicle is damaged, and the overall network performance is affected. Therefore, when the candidate relay nodes are prioritized, the residual energy of the unmanned aerial vehicle needs to be brought into an evaluation standard, and the forwarding task is distributed to the unmanned aerial vehicle nodes with more residual energy as much as possible, so that the duration of the cluster network can be effectively prolonged, the overall load balance of the unmanned aerial vehicle cluster is realized, and the overall network performance is improved.
Defining the energy of a single unmanned aerial vehicle at the initial moment as E0And E can be consumed by a single unmanned aerial vehicle after moving for a unit distancemE is consumed for each data forwardingfThe energy of (a). When the unmanned aerial vehicle cluster moves to the current movement period, defining the energy consumed by the movement as EMEnergy consumed by forwarding a packet is EFThen, the node remaining energy of the drone at this time may be expressed as:
Er＝E0-EM-EF(18)
in order to ensure that the unmanned aerial vehicle node retains enough energy to fly, E is setminMinimum threshold for data forwarding for the drone, i.e. when the node residual energy of the drone in the candidate node set exceeds EminThen, the node performs priority ordering of the candidate relays; if the node residual energy of the unmanned aerial vehicle is less than or equal to EminThe node does not participate in candidate relay node prioritization. For convenient calculation, normalization operation is carried out on the node residual energy of the unmanned aerial vehicle, and the normalized node j residual energy is Ene (N)i(j) Is expressed by the expression:
Figure BSA0000199090900000121
normalized node j residual energy Ene (N)i(j) Is [0, 1 ]]The more node residual energy, Ene (N)i(j) The closer to 1 the value of); the node residual energy is less than or equal to EminWhen, Ene (N)i(j) ) takes 0, at which point the node does not participate in the prioritization of the candidate relay nodes.
Step 2: optimal relay node selection
By integrating the three indexes of the node advancing distance, the node virtual force and the node residual energy, the method provides a priority measurement equation based on the three indexes. For the candidate relay node j of the source node i, the priority metric equation is as follows:
Val(Ni(j))＝γMMar(Ni(j))+γVVir(Ni(j))+γEEne(Ni(j)) (20)
wherein gamma isM、γV、γEThe node advancing distance weight coefficient, the node virtual force weight coefficient and the node residual energy weight coefficient respectively satisfy the following relations:
γM+γV+γE＝1 (21)
the candidate relay nodes can calculate the self priority metric value, route reply is carried out by adopting the coordination method based on the timer or the competition, the source node can sort the priority of the candidate relay centralized nodes according to the route reply information, and the best candidate relay node is selected for data forwarding. In different combat missions, the weighting coefficients of the three indicators can be adjusted for specific combat environments. If the number of hops of data transmission is reduced, the weight coefficient gamma of the node advancing distance can be increasedMThe value of (c), the values of the other two weight coefficients are reduced; if the network topology change randomness is larger, the node virtual force weight coefficient gamma can be improvedVThe occupied proportion; if the energy consumption of the nodes of the unmanned aerial vehicle cluster network is kept as uniform as possible, the residual energy weight coefficient gamma can be increased appropriatelyEThe value of (c). Under different network environments, an optimal coefficient distribution scheme needs to be found, so that the overall performance of the network can be effectively improved.
And step 3: data forwarding and acknowledgement
For a node to forward data, the node performs priority ordering according to the received route reply. Since the timer value of the candidate relay node having the largest priority metric result is the smallest, the candidate relay node that routes the reply first is generally the best relay node. However, due to the high-speed mobility of the nodes and the rapid variability of the topology of the unmanned aerial vehicle cluster network, a routing loop phenomenon occurs, and data packets are repeatedly transmitted among a plurality of nodes, so that the waste of network resources is caused. In order to avoid the phenomenon of routing loop, the nodes in the candidate relay set need to be filtered. If the nodes in the candidate relay set have participated in data forwarding in the previous path, the nodes which have participated in data forwarding are screened out of the candidate relay set. The checking method can greatly reduce the generation of routing loop phenomenon. And the node to be forwarded selects the node with the highest priority in the filtered candidate relay set for data forwarding. However, in the moving process of the unmanned aerial vehicle cluster, a temporary cluster split may occur due to an obstacle in the scene, and there may be no candidate relay node in the filtered candidate relay set. When the node carrying data has no candidate relay set point, the node enters a storage-carrying mode, continues to move, and waits for the beginning of the next movement period to try the route discovery again.
When the node carrying the data finds the best relay node, the data packet will be forwarded to this relay node. The relay node receiving the data packet writes the self node sequence number into the data packet head information, and records the forwarding path that the data packet passes through. For subsequent routing loop verification. After the relay node receives the data packet from the previous hop node, the node repeats the opportunistic routing process to find out the best relay node in the candidate relay set of the node for data forwarding. And when the destination node is in the transmission range of the data carrying node, the data is directly sent to the destination node.
Details not described in the present application are well within the skill of those in the art.

Claims (6)

1. An unmanned aerial vehicle cluster cooperative opportunistic routing method based on a virtual potential field method comprises the following steps:
step 1: candidate relay set selection
When each cluster movement period starts, firstly, whether all data packets in the network are forwarded to a destination node is judged, if all the data packets are received by the destination node, the movement period is not operated, under the normal condition, a new data packet or a data packet which is terminated in the previous period due to the disconnection of a link in the cluster network waits for the next forwarding, meanwhile, when the period starts, the destination node broadcasts the state information of the destination node in the whole network, and the neighbor nodes exchange the position information of the destination node and the neighbor nodes so that other nodes can make routing decision; in the candidate relay set selection stage, a node carrying data needs to perform route discovery, the node sends a Route Request (RREQ) to other nodes in the communication range of the node, at this time, if no other node in the communication range of the node can be used for data forwarding, the node enters a storage-carrying mode, continues to move, tries to perform route discovery again when waiting for the beginning of the next movement period, if other nodes exist in the communication range of the node, the set formed by the nodes is the candidate relay set of the node, after the node in the candidate relay set receives the route request, priority measurement calculation is performed according to the node state of the node, in the last node, a calculation method comprehensively considering three aspects of node advancing distance, node virtual force and node residual energy is given, at this time, the node performs Mar (), calculating Vir () and Ene (), wherein, a final priority measurement result Val () is obtained, the size of a self timer is set according to the size of the value of the Val (), routing reply is carried out, and when the value of the Val () is larger, the higher the current priority of the node is, the smaller the value of the timer set for the node is; if the value of Val () is smaller, the current priority of the node is lower, and the priority is larger when setting the value of the timer, and when the relay node candidate is fixedWhen the timer is overtime, the node broadcasts a Route Reply (RREP), and it should be noted that, according to the routing method, when the forward distance Mar () of the candidate relay node is negative, it indicates that the candidate relay node is opposite to the destination node, and in order to avoid data transmission in the opposite direction, the node will not perform route reply, and similarly, when the remaining energy of a candidate relay node is lower than the preset minimum threshold EminThe node also gives up route reply, and the node carrying data determines priority sequencing according to the received route reply to determine the best candidate relay node;
step 2: optimal candidate relay node selection
For a node to which data is to be forwarded, the node performs priority ranking according to received route responses, and a timer value of a candidate relay node with a maximum priority measurement result is minimum, so the candidate relay node with the first route response is usually the best relay node, but due to high-speed mobility and rapid topology variability of nodes of an unmanned aerial vehicle cluster network, a routing loop phenomenon occurs, a data packet is repeatedly transmitted among a plurality of nodes, waste of network resources is caused, in order to avoid the routing loop phenomenon, the nodes in the candidate relay set need to be filtered, and if the nodes in the candidate relay set participate in data forwarding in a previous path, the nodes which participate in data forwarding are screened out of the candidate relay set, so the verification method can greatly reduce the generation of the routing loop phenomenon, and the node to which data is to be forwarded selects the node with the highest priority in the filtered candidate relay set to perform data forwarding However, in the moving process of the unmanned aerial vehicle cluster, a barrier in a scene may cause the cluster to be briefly split, there may be no candidate relay node in the filtered candidate relay set, and when there is no candidate relay set point in the node carrying data, the node enters a "storage-carrying" mode, continues to move, and tries to find a route again when waiting for the next movement period to start;
and step 3: data forwarding and acknowledgement
When the node carrying the data finds the optimal relay node, the data packet is forwarded to the relay node, the relay node receiving the data packet writes the self node sequence number into the data packet header information, records the forwarding path that the data packet passes through so as to carry out routing loop inspection subsequently, after the relay node receives the data packet from the previous hop node, the node repeats the opportunistic routing process, finds out the optimal relay node in the node candidate relay set for data forwarding, and when the target node is within the transmission range of the node carrying the data, the data is directly sent to the target node.
2. The unmanned aerial vehicle cluster cooperative opportunistic routing method based on the virtual potential field method according to claim 1, characterized in that a specific determination method of the node forward progress in candidate relay cluster point selection is as follows:
the ultimate goal of opportunistic routing is to forward data packets faster and better to the destination node; when the current node selects the optimal relay node, the relative positions of the candidate relay node and the target node are considered, so that each transmission can advance towards the target node as much as possible, the hop count of network transmission is reduced, and the overall performance of the network is improved; suppose that the one-hop transmission distance at source node i is RtxThen there are three candidate relay nodes N within the one-hop range of the source node ii(1)，Ni(2)，Ni(3) Defining Dist (i, d) as the Euclidean distance from the source node to the destination node; candidate relay node N for source node ii(j) In other words, the distance from the source node i is Dist (i, N)i(j) Distance to destination node Dist (N)i(j) D), at this time, the candidate relay node N can be solved through a trigonometric functioni(j) And the angle theta of the destination node d relative to the source node ijThe cosine values of (A) are:
Figure FSA0000199090890000031
θ1is node Ni(1) An included angle formed by the source node i and the destination node d; definition of Ni' (j)) is a candidate relay node Ni(j) On-source nodeDefining a projection point on the connection line of the point i and the destination node d to define a candidate relay node Ni(j) Is Dist (i, N)i' (j)); candidate relay node Ni(j) The advancing distance of (C) is measured using March (N)i(j) Is expressed by the expression:
March(Ni(j))＝Dist(i，Ni(j))·cosθj(2)
available candidate relay node Ni(j) The expression of (a) is:
Figure FSA0000199090890000032
it can be seen that the candidate relay node Ni(1) The forward distance of (1) is from the source node i to NiA distance of' (1); as shown in the formula (3), when the included angle formed by the candidate relay node, the source node and the destination node is greater than 90 degrees, the forward distance is negative, for example, the candidate relay node Ni(3) Shown; if N is to bei(3) As a relay node for the next hop, the packet will be further away from the destination node, which is obviously not a desirable result; when the candidate relay set is actually selected, screening out the candidate relay set from the candidate relay nodes with the negative advancing distance to prevent data from being transmitted to a destination node in the opposite direction; in order to facilitate subsequent calculation, the advancing distance is subjected to normalization calculation; normalized candidate relay node Ni(j) Advancing distance Mar (N)i(j) ) is:
Figure FSA0000199090890000041
at this time, for node i, its candidate relay set Ni(j) After filtering, the forward distance value range of the normalized nodes in the candidate relay set is [0, 1 ]]。
3. The unmanned aerial vehicle cluster cooperative opportunistic routing method based on the virtual potential field method according to claim 1, characterized in that a specific determination method of node virtual force in candidate relay cluster point selection is:
in the virtual potential field method, an unmanned aerial vehicle node is influenced by three forces, namely navigation force from a destination point, topological force of surrounding neighbor nodes and barrier force of a barrier; the three forces are respectively multiplied by the corresponding weight coefficients, and the virtual force F finally borne by the unmanned aerial vehicle node can be obtainedRSIs of the formula, wherein βG、βTAnd βORespectively are a navigation force weight coefficient, a topology force weight coefficient and an obstacle force weight coefficient;
Figure FSA0000199090890000042
when the optimal candidate relay of the node is selected, not only the current position condition of the candidate relay node but also the motion condition of the candidate relay node is considered; defining an active node i and a relay node N in an unmanned aerial vehicle clusteri(1) And Ni(2) A destination node d to which data is to be finally forwarded; n is in the candidate relay set of the source node ii(1) And Ni(2) Two candidate relay nodes, for source node i, node Ni(1) And node Ni(2) The difference of the forward distances is not large, and if the optimal relay node is selected only from the forward distance angle, the optimal relay node is not suitable; at this time, the influence of the virtual force needs to be considered when the priority ranking of the candidate relay nodes is performed; the virtual force directly influences the movement direction of the node, and for a dynamic topology, if the node carrying data can move towards the destination node, the network hop count can be reduced to a certain extent, and the network performance is effectively improved; if the destination node is in a moving state, analyzing the relative speed direction between the candidate relay node and the destination node; virtual force direction included angle omega of candidate relay node j and virtual force direction of destination node djThe expression of (a) is:
Figure FSA0000199090890000051
from equation (6), the source node candidateOmega for selecting relay centralized nodejIs in the value range of [0, pi ]](ii) a When ω isjThe smaller the relay node is, the more consistent the virtual force direction of the candidate relay node and the destination node is; when ω isjWhen the candidate relay node approaches pi, the candidate relay node and the destination node are shown to have approximately opposite movement directions; for convenient calculation, normalizing the virtual force included angle between the candidate relay node and the target node:
Figure FSA0000199090890000052
vir (N) for normalized virtual force included anglei(j) Is represented by) has a value range of [0, 1%](ii) a At this time, Vir (N)i(j) Monotonicity of) and ωjIn contrast, i.e. ωjThe smaller, Vir (N)i(j) The larger the candidate relay node is, the higher the priority of the candidate relay node is, and the more likely it is to be the best relay node to forward data.
4. The unmanned aerial vehicle cluster cooperative opportunistic routing method based on the virtual potential field method according to claim 1, characterized in that the specific determination method of the node residual energy in the candidate relay cluster point selection is:
the unmanned aerial vehicle cluster network is one of wireless sensor networks and consists of a plurality of unmanned aerial vehicle nodes carrying transceivers; the unmanned aerial vehicle is usually powered by a battery carried by the unmanned aerial vehicle under the influence of the design of the unmanned aerial vehicle; the energy possessed by a single drone is limited; when cluster motion is carried out, the unmanned aerial vehicle needs to spend a large amount of energy for moving and hovering, and meanwhile, the unmanned aerial vehicle also needs to consume energy to execute tasks such as routing decision and data packet forwarding; if a certain unmanned aerial vehicle node is used for data forwarding all the time, the unmanned aerial vehicle node can exhaust energy quickly and cannot support the unmanned aerial vehicle node to continuously fly to complete subsequent tasks, so that the unmanned aerial vehicle is damaged, and the overall network performance is influenced; therefore, when the candidate relay nodes are prioritized, the residual energy of the unmanned aerial vehicle needs to be brought into an evaluation standard, and the forwarding task is distributed to the unmanned aerial vehicle nodes with more residual energy as much as possible, so that the duration of the cluster network can be effectively prolonged, the integral load balance of the unmanned aerial vehicle cluster is realized, and the integral network performance is improved;
defining the energy of a single unmanned aerial vehicle at the initial moment as E0And E can be consumed by a single unmanned aerial vehicle after moving for a unit distancemE is consumed for each data forwardingfThe energy of (a); when the unmanned aerial vehicle cluster moves to the current movement period, defining the energy consumed by the movement as EMEnergy consumed by forwarding a packet is EFThen, the node remaining energy of the drone at this time may be expressed as:
Er＝E0-EM-EF(8)
in order to ensure that the unmanned aerial vehicle node retains enough energy to fly, E is setminMinimum threshold for data forwarding for the drone, i.e. when the node residual energy of the drone in the candidate node set exceeds EminThen, the node performs priority ordering of the candidate relays; if the node residual energy of the unmanned aerial vehicle is less than or equal to EminThe node does not participate in the priority ordering of the candidate relay nodes; for convenient calculation, normalization operation is carried out on the node residual energy of the unmanned aerial vehicle, and the normalized node j residual energy is Ene (N)i(j) Is expressed by the expression:
Figure FSA0000199090890000061
normalized node j residual energy Ene (N)i(j) Is [0, 1 ]]The more node residual energy, Ene (N)i(j) The closer to 1 the value of); the node residual energy is less than or equal to EminWhen, Ene (N)i(j) ) takes 0, at which point the node does not participate in the prioritization of the candidate relay nodes.
5. The unmanned aerial vehicle cluster cooperative opportunistic routing method based on the virtual potential field method according to claim 1, characterized in that the specific determination method of the best candidate relay node is:
integrating three indexes of the node advancing distance, the node virtual force and the node residual energy, the method provides a priority measurement equation based on the three indexes; for the candidate relay node j of the source node i, the priority metric equation is as follows:
Val(Ni(j))＝γMMar(Ni(j))+γVVir(Ni(j))+γEEne(Ni(j)) (10)
wherein gamma isM、γV、γEThe node advancing distance weight coefficient, the node virtual force weight coefficient and the node residual energy weight coefficient respectively satisfy the following relations:
γM+γV+γE＝1 (11)
the candidate relay nodes calculate the self priority metric value, route reply is carried out by adopting the above mentioned coordination method based on a timer or competition, the source node can sort the priority of the candidate relay centralized nodes according to the route reply information, and the best candidate relay node is selected for data forwarding; in different combat missions, the weight coefficients of the three indexes can be adjusted according to specific combat environments; if the number of hops of data transmission is reduced, the weight coefficient gamma of the node advancing distance can be increasedMThe value of (c), the values of the other two weight coefficients are reduced; if the network topology change randomness is larger, the node virtual force weight coefficient gamma can be improvedVThe occupied proportion; if the energy consumption of the nodes of the unmanned aerial vehicle cluster network is kept as uniform as possible, the residual energy weight coefficient gamma can be increased appropriatelyEA value of (d); under different network environments, an optimal coefficient distribution scheme needs to be found, so that the overall performance of the network can be effectively improved.
6. The unmanned aerial vehicle cluster cooperative opportunistic routing method based on the virtual potential field method according to claim 1, characterized in that the specific determination method of data forwarding and confirmation is:
for a node to which data is to be forwarded, the node performs priority ordering according to the received route response; since the timer value of the candidate relay node with the largest priority metric result is the smallest, the candidate relay node routing the reply first is usually the best relay node; however, due to the high-speed mobility of nodes and the rapid variability of topology of the unmanned aerial vehicle cluster network, a routing loop phenomenon occurs, and data packets are repeatedly transmitted among a plurality of nodes, so that network resources are wasted; in order to avoid the phenomenon of routing loop, the nodes in the candidate relay set need to be filtered; if the nodes in the candidate relay set participate in data forwarding in the previous path, screening out the candidate relay set from the nodes participating in data forwarding; the checking method can greatly reduce the generation of routing loop phenomenon; the node to be forwarded selects the node with the highest priority in the filtered candidate relay set to forward the data; however, in the moving process of the unmanned aerial vehicle cluster, a short-term cluster split can be caused by obstacles in a scene, and candidate relay nodes may not exist in the filtered candidate relay set; when the node carrying data has no candidate relay set point, the node enters a storage-carrying mode, continues moving, and tries routing discovery again when waiting for the beginning of the next movement period;
when the node carrying the data finds the optimal relay node, the data packet is forwarded to the relay node; the relay node receiving the data packet writes the self node sequence number into the data packet head information and records the forwarding path of the data packet; for subsequent routing loop inspection; after the relay node receives the data packet from the previous hop node, the node repeats the opportunistic routing process to find out the optimal relay node in the candidate relay set of the node for data forwarding; and when the destination node is in the transmission range of the data carrying node, the data is directly sent to the destination node.
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