CN113260012B - Unmanned aerial vehicle cluster topology control method based on position track prediction - Google Patents
Unmanned aerial vehicle cluster topology control method based on position track prediction Download PDFInfo
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Abstract
The invention relates to the technical field of unmanned aerial vehicle cluster communication, in particular to an unmanned aerial vehicle cluster topology control method based on position track prediction, which directly takes the low transmission power consumption and high robustness as targets from the cluster networking communication dimension to control the cluster topology and has greater optimization and improvement effect on the overall network comprehensive performance; the unmanned aerial vehicle node predicts the position tracks of other surrounding nodes, can acquire the group topology information at the next moment in the future in advance, and can maintain the optimized topology for a longer time and make the topology more stable through key node selection and connected region division; the unmanned aerial vehicle node can input the future change trend of the cluster topology into the control system by sensing the cluster topology information at the next moment in advance, so that the control system can carry out formation configuration pre-control in advance, and communication and control are linked.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicle cluster communication, in particular to an unmanned aerial vehicle cluster topology control method based on position track prediction.
Background
The unmanned aerial vehicle cluster networking system is characterized in that a plurality of unmanned aerial vehicles form a multi-node cluster communication network through a specific networking protocol (usually distributed ad hoc network), and network nodes interact service messages through the cluster network under a specific cluster topology to jointly complete specified tasks.
The change condition of the cluster topology of the unmanned aerial vehicle cluster networking system has important influence on the performance of the whole network, such as network connectivity, throughput, data forwarding success rate, transmission delay and the like. In the traditional unmanned aerial vehicle cluster topological control, cluster formation among unmanned aerial vehicles is singly controlled from the viewpoint of unmanned aerial vehicle cluster formation configuration control (such as formation consistency control and the like), and the aspect of cluster networking communication performance is basically not considered, so that the improvement of cluster network performance does not achieve an ideal effect, and the comprehensive network performance such as network connectivity, data forwarding success rate, transmission delay and the like needs to be optimized and improved.
Patent document No. CN103268102B discloses a task, communication and topology interleaved unmanned aerial vehicle cluster cooperative control method, which includes the following steps: establishing an association function of tasks, communication and topology of the unmanned aerial vehicle; establishing an interactive communication number strategy set, a task information matrix strategy set and a relative distance strategy set among the interactive communication unmanned aerial vehicles of the unmanned aerial vehicles, and determining a winning matrix and related constraints; acquiring the interactive communication number of the unmanned aerial vehicles, the task information transmission quantity and the relative distance between the unmanned aerial vehicles, wherein the interactive communication number strategy set, the task information matrix strategy set and the relative distance strategy between the interactive communication unmanned aerial vehicles meet preset requirements; and controlling the unmanned aerial vehicle cluster according to the number of the unmanned aerial vehicle interactive communication meeting the preset requirement, the task information transmission quantity and the relative distance between the unmanned aerial vehicles. However, the technical scheme disclosed in the patent document still cannot control the distance, the configuration and the like between the unmanned aerial vehicles, and does not play a great role in promoting the overall performance of the survivability of the cluster network.
Therefore, the existing control mode of the unmanned aerial vehicle cluster still has a space which needs to be optimized and improved, the structural control of the whole cluster is difficult to be accurate and flexible, the requirement is difficult to be met in response to the rapid response of special conditions, meanwhile, the power consumption of the current unmanned aerial vehicle cluster control is high, and the control continuity and the reliability of the unmanned aerial vehicle cluster are reduced. These are all the parts that need to be improved, should optimize unmanned aerial vehicle cluster control mode, satisfy the control demand of unmanned aerial vehicle cluster development under this, so need provide more reasonable technical scheme, solve the not enough among the prior art.
Disclosure of Invention
In order to solve the defects of the prior art mentioned in the above, the invention provides a position trajectory prediction-based unmanned aerial vehicle cluster topology control method, a communication unit is set as a communication unit, the communication unit takes a main key node and an auxiliary key node as network communication points, and control information is transferred to other communication nodes in the communication area, so that link setting of unmanned aerial vehicle cluster communication is optimized, communication load is reduced, the main key node and the auxiliary key node bear main power consumption, power consumption of other communication nodes is greatly reduced, sustainable control performance of the whole unmanned aerial vehicle cluster is improved, and flexibility and safety of unmanned aerial vehicle cluster control are also improved.
In order to achieve the purpose, the invention specifically adopts the technical scheme on the aspect of structural improvement that:
a position trajectory prediction-based unmanned aerial vehicle cluster topology control method comprises the following steps:
acquiring the current position of each node in the unmanned aerial vehicle cluster network to form an initial cluster communication topology network; acquiring the position track of each node at the next moment to form a group communication topology network at the next moment;
dividing an unmanned aerial vehicle cluster communication topological network into a plurality of connected areas, wherein each connected area comprises a plurality of nodes, and determining a minimum spanning tree and optimally adjusting the transmitting power of each node in each connected area;
selecting a plurality of key nodes from the nodes of each connected region, wherein each connected region at least comprises one key node, the key nodes are communicated with other nodes in the connected region, and the key nodes are communicated with the key nodes of the adjacent connected regions.
According to the topology control method, the unmanned aerial vehicle is used as a communication node of the topology network, the communication network of the unmanned aerial vehicle cluster is divided into a plurality of communication areas which are communicated with one another, a key node is arranged in each communication area and used for a leading node of communication, main communication transceiving tasks are undertaken, and each node in each communication area is coordinated, in the process, the transmitting power consumption required by each node in each communication area can be reduced, so that the purpose of saving energy consumption is achieved, and the overall cruising performance of the unmanned aerial vehicle cluster can be improved; the topological structure of the communication network at the next moment can be known in advance by predicting the position track of the unmanned aerial vehicle group, so that the condition that the unmanned aerial vehicle group communication network deals with abnormal communication can be greatly improved, the stable reliability of the unmanned aerial vehicle group communication network can be greatly improved, and the safety of the unmanned aerial vehicle group is improved.
Further, the present invention performs overall control by the topology structure of the current node position and the topology structure of the node of the unmanned aerial vehicle position at the next moment, and can be implemented in various ways when determining the topology structure of the current node position, without being limited uniquely, where optimization is performed and one of the feasible options is presented: the acquiring the current position of each node in the unmanned aerial vehicle cluster network to form an initial cluster communication topology network comprises:
each unmanned aerial vehicle node periodically sends a network maintenance message, wherein the network maintenance message at least comprises node ID, position and TTL (Time To Live) information; meanwhile, each unmanned aerial vehicle node receives network maintenance information of surrounding nodes to determine initial positions and hop count information of the surrounding nodes, and then an initial group communication topology network is formed. When the scheme is adopted, the key nodes are taken as the center, the positions, the distances and the like of the nodes around the key nodes can be determined in real time, and the completeness and the stability of an initial group communication topological structure are ensured; and the group communication topology at the next moment can be predicted conveniently in time.
Still further, the manner of predicting the next-time group communication topology adopted in the present invention may be various, and is not limited uniquely, and the optimization is performed here to give one of the feasible options:
the position track of each node at the next moment is obtained to form a group communication topology network at the next moment; the method comprises the following steps:
establishing a state equation of the key nodes and the surrounding nodes, wherein the state equation at least comprises a position state quantity, a speed state quantity and a state transition matrix, establishing an observation equation for the distance measurement of the received network maintenance message, and completing the prediction of the position track at the next moment through the state equation and the observation equation.
Furthermore, the invention divides the area of the unmanned aerial vehicle cluster communication topology network, the connected area sends communication information to the adjacent connected area as a whole, the transmission power consumption can be reduced, each area is in charge of communication coordination task by key nodes, the key nodes are communicated with the nodes in the connected area and the adjacent connected area, the best key node can be determined by various modes, and the optimization is carried out and one feasible selection is taken out: the unmanned aerial vehicle cluster communication topological network is divided into a plurality of connected areas, a key node is selected in each connected area, and the process of selecting the key node specifically comprises the following steps:
determining an input factor: distributing nodes in a communication area, measuring the distance between each node and nodes in a one-hop and two-hop range around the node, and defining the range by taking each node as a center through a distance mean value to determine the number of the nodes in the defined range; determining the farthest distance that the node can reach the node through multiple times of jumping, and quantifying the number of the nodes and the farthest distance to be used as an input factor;
determining a key node: and according to an input factor determination method, input factors corresponding to each node in the initial group communication topology network and the next moment group communication topology network are compared after averaging the two input factors of each node, and the averaged value comprises the node with the largest number and the farthest arrival distance as a key node of the communication area.
Furthermore, the defined range is defined by drawing a circle by taking the node as the center of a circle and taking the average value of the distances between the two-hop nodes as the radius.
The key nodes are selected according to the mode, the nodes with the strongest communication capacity in the communication area can be used as the key nodes, and the information interaction capacity in the communication area is improved.
Still further, in the whole unmanned aerial vehicle communication topology network, each node belongs to only one connected region, and performs communication interaction with one key node, and performs the connected region division on each node, specifically including the following division process:
the key node sends a region division message to surrounding adjacent nodes, wherein the region division message at least comprises key node identification, time information, TTL information and local node address information;
after receiving the region division message, the adjacent node returns a region division confirmation message to the key node, wherein the region division confirmation message at least comprises a key node mark, time information, TTL information, local node address information and key point address information.
By adopting the method to divide the node areas, each node is effectively divided into the most appropriate communication area, and the communication convenience and efficiency are improved.
Further, in the node division process, if a node may be located in the communication range of two key nodes at the same time, a connected region where one of the key nodes is located is selected according to the relationship between the node and the two key nodes, specifically, the connected region may be selected as follows: if the adjacent nodes receive two or more zone division messages, the hop count and the distance reaching each key node are calculated, the key node with the least hop count and the closest distance is selected to return zone division confirmation messages, and the zone division confirmation messages are added into the connected zone where the key nodes are located.
Furthermore, in each communication area, a tree structure is used for connection communication between different nodes, the simpler the tree structure is, the higher the communication efficiency is, and the smaller the communication delay is, the nodes on the tree structure in the invention bear certain communication tasks, the key nodes bear main communication control coordination tasks, and unnecessary power loss can be reduced on the premise of meeting communication requirements by adjusting the transmitting power of each node on the tree structure, specifically, the invention can adopt the following optimized scheme: the determining the minimum spanning tree in each connected region and optimizing and adjusting the transmitting power of each node comprises the following steps:
and (3) taking the transmitting power consumption and the node residual energy as variables, giving corresponding weights to the two variables to generate a double-objective function, and determining a minimum spanning tree by using an ant colony algorithm based on multi-objective improvement.
Still further, the minimum spanning tree is generated specifically according to the following processing steps:
s01: initializing parameters including the number of ants and the maximum iteration cycle number, and creating an initialized spanning tree set;
s02: selecting 1 ant from all ants, and randomly selecting a node;
s03: the ants move to the next node according to the state transfer rule function, the node is recorded, and the edge which passes through is subjected to local pheromone updating;
s04: cycling in the manner of S03 until the ant has gone through all nodes, and a solution is generated;
s05: if the solution produced by the ant is non-dominant to the previously created spanning tree set, adding the ant to the set, and deleting the solution dominated by the ant in the set; calculating a double-objective function value, and if the value is minimum, replacing a current value; otherwise, performing global pheromone updating on each solution in the current set;
s06: repeating S02-S05, re-determining that one ant has finished walking all nodes and updating the spanning tree set and the global pheromone until all ants are traversed;
s07: and repeating S02-S06 until the maximum iteration cycle number is reached, and finally obtaining the updated local minimum spanning tree.
Still further, the optimizing and adjusting the transmission power of each node comprises: and each node adjusts the self transmitting power according to the minimum spanning tree, and the transmitting distance meets the transmission radius of the node with the highest requirement on the coverage range on the spanning tree. In such a way, the purpose of realizing the minimum power consumption on the premise of meeting the requirement of the communication distance can be achieved.
Further, in order to improve the communication efficiency of the communication link, simplify the structure of the communication link, and simplify the invalid link, the duplicate link, and the like, one of the following feasible options may be specifically adopted: and taking a union set of the corresponding connection relations of the minimum spanning trees of each node, deleting each one-way connection link, and after all the links in the minimum spanning trees are bidirectional links, overlapping each minimum spanning tree to form an optimized unmanned aerial vehicle cluster topological structure.
Compared with the prior art, the invention has the beneficial effects that:
the invention directly uses the cluster networking communication dimension, takes low transmission power consumption and high robustness (residual energy value) as the target to control the cluster topology, and has great optimization and improvement effect on the overall network comprehensive performance; the unmanned aerial vehicle node predicts the position tracks of other surrounding nodes, can acquire the group topology information at the next moment in the future in advance, and can maintain the optimized topology for a longer time and make the topology more stable through key node selection and connected region division; the unmanned aerial vehicle node can input the future change trend of the cluster topology into the control system by sensing the cluster topology information at the next moment in advance, so that the control system can carry out formation configuration pre-control in advance, and communication and control are linked.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only show some embodiments of the present invention, and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is an overall schematic diagram of the topology control of the unmanned aerial vehicle cluster.
Fig. 2 is a schematic flow chart of the division of the connected region for each node.
FIG. 3 is a diagram illustrating steps for generating a minimum spanning tree.
Detailed Description
The invention is further explained below with reference to the drawings and the specific embodiments.
It should be noted that the description of the embodiments is provided to help understanding of the present invention, but the present invention is not limited thereto. Specific structural and functional details disclosed herein are merely illustrative of example embodiments of the invention. This invention may, however, be embodied in many alternate forms and should not be construed as limited to the embodiments set forth herein.
Examples
Aiming at the problems of high power consumption, large communication delay and the like of the unmanned aerial vehicle cluster communication topological network in the prior art, the unmanned aerial vehicle cluster communication control method is optimized in the embodiment, so that the problems in the prior art can be solved, the communication efficiency of the unmanned aerial vehicle cluster is improved, the power consumption is reduced, and the time for stably maintaining the topology of the unmanned aerial vehicle cluster is prolonged.
Specifically, the technical solution disclosed in this embodiment is:
as shown in fig. 1, a method for controlling topology of an unmanned aerial vehicle cluster based on position trajectory prediction includes:
s01: acquiring the current position of each node in the unmanned aerial vehicle cluster network to form an initial cluster communication topology network; acquiring the position track of each node at the next moment to form a group communication topology network at the next moment;
s02: dividing an unmanned aerial vehicle cluster communication topological network into a plurality of connected areas, wherein each connected area comprises a plurality of nodes, and determining a minimum spanning tree and optimally adjusting the transmitting power of each node in each connected area;
s03: selecting a plurality of key nodes from the nodes of each connected region, wherein each connected region at least comprises one key node, the key nodes are communicated with other nodes in the connected region, and the key nodes are communicated with the key nodes of the adjacent connected regions.
Preferably, in this embodiment, a kalman filtering algorithm may be used to predict the position trajectory of the peripheral node and the next time of the node, so as to obtain the predicted group topology information at the next time.
According to the topology control method, the unmanned aerial vehicle is used as a communication node of the topology network, the communication network of the unmanned aerial vehicle cluster is divided into a plurality of communication areas which are communicated with one another, a key node is arranged in each communication area and used for a leading node of communication, main communication transceiving tasks are undertaken, and each node in each communication area is coordinated, in the process, the transmitting power consumption required by each node in each communication area can be reduced, so that the purpose of saving energy consumption is achieved, and the overall cruising performance of the unmanned aerial vehicle cluster can be improved; the topological structure of the communication network at the next moment can be known in advance by predicting the position track of the unmanned aerial vehicle group, so that the condition that the unmanned aerial vehicle group communication network deals with abnormal communication can be greatly improved, the stable reliability of the unmanned aerial vehicle group communication network can be greatly improved, and the safety of the unmanned aerial vehicle group is improved.
In this embodiment, the topology structure of the current node position and the topology structure of the node of the unmanned aerial vehicle position at the next moment are used for overall control, and the topology structure of the current node position can be determined in multiple ways, which are not limited uniquely, and this embodiment is optimized and one of the feasible options is adopted: the acquiring the current position of each node in the unmanned aerial vehicle cluster network to form an initial cluster communication topology network comprises:
each unmanned aerial vehicle node periodically sends a network maintenance message, wherein the network maintenance message at least comprises node ID, position and TTL (Time To Live) information; meanwhile, each unmanned aerial vehicle node receives network maintenance information of surrounding nodes to determine initial positions and hop count information of the surrounding nodes, and then an initial group communication topology network is formed. When the scheme is adopted, the key nodes are taken as the center, the positions, the distances and the like of the nodes around the key nodes can be determined in real time, and the completeness and the stability of an initial group communication topological structure are ensured; and the group communication topology at the next moment can be predicted conveniently in time.
The manner of predicting the group communication topology at the next time in this embodiment may be various, and is not limited to these, and this embodiment optimizes and adopts one of the feasible options:
the position track of each node at the next moment is obtained to form a group communication topology network at the next moment; the method comprises the following steps:
establishing a state equation of the key nodes and the surrounding nodes, wherein the state equation at least comprises a position state quantity, a speed state quantity and a state transition matrix, establishing an observation equation for the distance measurement of the received network maintenance message, and completing the prediction of the position track at the next moment through the state equation and the observation equation.
In this embodiment, the process of establishing the state equation and the observation equation is as follows: the state equation takes the position, the speed, the acceleration and the like of the unmanned aerial vehicle as state vectors, a state transition matrix is established according to a kinematics model of the unmanned aerial vehicle, and the state vector at the current moment is converted and updated into the state vector at the next moment, so that the establishment of the state equation is completed.
The state equation is:
wherein, X is a state vector (including time deviation, time deviation ratio, position, speed of the drone):
Φ k,k-1 for the transition matrix:
wherein, T is the update period (2 seconds);
Γ k-1 for the system noise driven array:
wherein, T is the update period (2 seconds);
W k the system excitation noise sequence has the following characteristics:
wherein Q is k System noise variance matrix:
wherein, the noise corresponding to W1-W5 is clock frequency noise, phase noise, and speed error noise in X, Y, Z three directions; assuming that the error is zero mean normal distribution and the variance distribution is delta2 w1To delta2 w5。
The observation equation is established by taking measured data (data for distance measurement by receiving network maintenance messages of other nodes) as an observation matrix and combining the state vector and observation noise.
The observation equation is:
the linearized observation matrix obtained from the above observation equation is:
wherein the content of the first and second substances,V k to measure the noise sequence, the following characteristics are satisfied:
wherein the content of the first and second substances,R k for measuring the variance matrix of the noise sequence, equal to σ2 TOA+σ2 jitter ，σTOAFor standard deviation of time of arrival measurements, σ jitter Is the standard deviation of the transmitter jitter.
In this embodiment, area division is performed on an unmanned aerial vehicle cluster communication topology network, a communication area sends communication information to an adjacent communication area as a whole, transmission power consumption can be reduced, each area is responsible for a communication coordination task by a key node, the key node is in communication with nodes in the communication area and adjacent communication areas, the best key node can be determined in multiple ways, and the present embodiment is optimized and adopts one of the feasible options: the unmanned aerial vehicle cluster communication topological network is divided into a plurality of connected areas, a key node is selected in each connected area, and the process of selecting the key node specifically comprises the following steps:
determining an input factor: distributing nodes in a communication area, measuring the distance between each node and nodes in a one-hop and two-hop range around the node, and defining the range by taking each node as a center through a distance mean value to determine the number of the nodes in the defined range; meanwhile, the farthest distance that the node can reach the node through multiple jumps is determined, the number of the nodes and the farthest distance are quantized (the number of the nodes N and the farthest distance D are weighted to obtain input factors, the weighting coefficients are 0.7 and 0.3 respectively) and the input factors are expressed by specific numerical values obtained after weighting, and specific examples are as follows:
and measuring the distance between a certain node and the nodes in the range of one hop and two hops around the certain node to obtain the average distance (assumed to be 2 km). A circular range is defined by taking the node as a center and 2km as a radius, and the number of the nodes (assumed to be 10) in the range is obtained; and simultaneously determining the farthest distance (assumed to be 8 km) which can be reached by the node through multi-time jumping, and quantifying the number of the nodes which is 10 and the farthest distance 8km through weighting coefficients (0.7 and 0.3): 10 × 0.7+8 × 0.3=9.4, and the quantization result (9.4) is used as an input factor.
Determining a key node: and according to an input factor determination method, input factors corresponding to each node in the initial group communication topology network and the next moment group communication topology network are compared after averaging the two input factors of each node, and the averaged value comprises the node with the largest number and the farthest arrival distance as a key node of the communication area.
Preferably, the defined range is defined by drawing a circle with the node as the center of the circle and the average distance value of the two-hop nodes as the radius.
The key nodes are selected according to the mode, the nodes with the strongest communication capacity in the communication area can be used as the key nodes, and the information interaction capacity in the communication area is improved.
Preferably, the present embodiment may select a plurality of key nodes by using a key node selection algorithm based on the predicted location trajectory in combination with the link state.
In the whole unmanned aerial vehicle communication topology network, each node belongs to only one connected region, and performs communication interaction with one key node, and divides each node into the connected regions, as shown in fig. 2, specifically including the following division processes:
s01: the key node sends a region division message to surrounding adjacent nodes, wherein the region division message at least comprises key node identification, time information, TTL information and local node address information;
s02: after receiving the region division message, the adjacent node returns a region division confirmation message to the key node, wherein the region division confirmation message at least comprises a key node mark, time information, TTL information, local node address information and key point address information.
Preferably, the node areas are divided by adopting the mode, each node is effectively divided into the most appropriate communication area, and the convenience degree and the communication efficiency of communication are improved.
Preferably, in the node division process, if a node may be located in the communication range of two key nodes at the same time, a connected region where one of the key nodes is located is selected according to the relationship between the node and the two key nodes, specifically, the connected region may be selected as follows:
s03: if the adjacent nodes receive two or more zone division messages, the hop count and the distance reaching each key node are calculated, the key node with the least hop count and the closest distance is selected to return zone division confirmation messages, and the zone division confirmation messages are added into the connected zone where the key nodes are located.
In this embodiment, in each communication area, a tree structure is used to perform connection communication between different nodes, the simpler the tree structure is, the higher the communication efficiency is, the smaller the communication delay is, nodes on the tree structure in this embodiment undertake a certain communication task, key nodes undertake a main communication control coordination task, and by adjusting the transmission power of each node on the tree structure, unnecessary power loss can be reduced on the premise of meeting communication requirements, specifically, the following optimized scheme can be adopted in this embodiment: the determining the minimum spanning tree in each connected region and optimizing and adjusting the transmitting power of each node comprises the following steps:
and (3) taking the transmitting power consumption and the node residual energy as variables, giving corresponding weights to the two variables to generate a double-objective function, and determining a minimum spanning tree by using an ant colony algorithm based on multi-objective improvement.
Preferably, as shown in fig. 3, the minimum spanning tree is generated according to the following processing steps:
s01: initializing parameters including the number of ants and the maximum iteration cycle number, and creating an initialized spanning tree set;
s02: selecting 1 ant from all ants, and randomly selecting a node;
s03: the ants move to the next node according to the state transfer rule function, the node is recorded, and the edge which passes through is subjected to local pheromone updating;
s04: cycling in the manner of S03 until the ant has gone through all nodes, and a solution is generated;
s05: if the solution produced by the ant is non-dominant to the previously created spanning tree set, adding the ant to the set, and deleting the solution dominated by the ant in the set; calculating a double-objective function value, and if the value is minimum, replacing a current value; otherwise, performing global pheromone updating on each solution in the current set;
s06: repeating S02-S05, re-determining that one ant has finished walking all nodes and updating the spanning tree set and the global pheromone until all ants are traversed;
s07: and repeating S02-S06 until the maximum iteration cycle number is reached, and finally obtaining the updated local minimum spanning tree.
In this embodiment, the optimizing and adjusting the transmission power of each node includes: and each node adjusts the self transmitting power according to the minimum spanning tree, and the transmitting distance meets the transmission radius of the node with the highest requirement on the coverage range on the spanning tree. In such a way, the purpose of realizing the minimum power consumption on the premise of meeting the requirement of the communication distance can be achieved.
In order to improve the communication efficiency of the communication link, simplify the structure of the communication link, and simplify the invalid link, the duplicate link, and the like, one of the following feasible options may be specifically adopted: and taking a union set of the corresponding connection relations of the minimum spanning trees of each node, deleting each one-way connection link, and after all the links in the minimum spanning trees are bidirectional links, overlapping each minimum spanning tree to form an optimized unmanned aerial vehicle cluster topological structure.
The above embodiments are just exemplified in the present embodiment, but the present embodiment is not limited to the above alternative embodiments, and those skilled in the art can obtain other various embodiments by arbitrarily combining with each other according to the above embodiments, and any other various embodiments can be obtained by anyone in light of the present embodiment. The above detailed description should not be construed as limiting the scope of the present embodiments, which should be defined in the claims, and the description should be used for interpreting the claims.
Claims (9)
1. A position trajectory prediction-based unmanned aerial vehicle cluster topology control method is characterized by comprising the following steps:
acquiring the current position of each node in the unmanned aerial vehicle cluster network to form an initial cluster communication topology network; acquiring the position track of each node at the next moment to form a group communication topology network at the next moment;
dividing an unmanned aerial vehicle cluster communication topological network into a plurality of connected areas, wherein each connected area comprises a plurality of nodes, and determining a minimum spanning tree and optimally adjusting the transmitting power of each node in each connected area;
selecting a plurality of key nodes from the nodes of each communication area, wherein each communication area at least comprises one key node, the key nodes are communicated with other nodes in the communication area, and the key nodes are communicated with the key nodes of the adjacent communication areas;
the process of selecting the key node specifically comprises the following steps:
determining an input factor: distributing nodes in a communication area, measuring the distance between each node and nodes in a one-hop and two-hop range around the node, and defining the range by taking each node as a center through a distance mean value to determine the number of the nodes in the defined range; determining the farthest distance that the node can reach the node through multiple times of jumping, and quantifying the number of the nodes and the farthest distance to be used as an input factor;
determining a key node: and according to an input factor determination method, input factors corresponding to each node in the initial group communication topology network and the next moment group communication topology network are compared after averaging the two input factors of each node, and the averaged value comprises the node with the largest number and the farthest arrival distance as a key node of the communication area.
2. The method according to claim 1, wherein the obtaining a current location of each node in the drone swarm network to form an initial swarm communication topology network comprises:
each unmanned aerial vehicle node periodically sends a network maintenance message, wherein the network maintenance message at least comprises node ID, position and TTL information; meanwhile, each unmanned aerial vehicle node receives network maintenance information of surrounding nodes to determine initial positions and hop count information of the surrounding nodes, and then an initial group communication topology network is formed.
3. The method for controlling topology of unmanned aerial vehicle fleet based on location trajectory prediction according to claim 1, wherein the location trajectory of each node at the next moment is obtained to form a group communication topology network at the next moment; the method comprises the following steps:
establishing a state equation of the key nodes and the surrounding nodes, wherein the state equation at least comprises a position state quantity, a speed state quantity and a state transition matrix, establishing an observation equation for the distance measurement of the received network maintenance message, and completing the prediction of the position track at the next moment through the state equation and the observation equation.
4. The method for controlling topology of unmanned aerial vehicle cluster based on location trajectory prediction as claimed in claim 1, wherein each node belongs to only one connected area, specifically comprising the following partitioning process:
the key node sends a region division message to surrounding adjacent nodes, wherein the region division message at least comprises key node identification, time information, TTL information and local node address information;
after receiving the region division message, the adjacent node returns a region division confirmation message to the key node, wherein the region division confirmation message at least comprises a key node mark, time information, TTL information, local node address information and key point address information.
5. The unmanned aerial vehicle cluster topology control method based on location trajectory prediction as claimed in claim 4, wherein if two or more area division messages are received by the neighboring node, the hop count and distance to each key node are calculated, and the key node with the least hop count and the closest distance is selected to return an area division confirmation message and join the connected area where the key node is located.
6. The method for controlling topology of unmanned aerial vehicle cluster based on location and trajectory prediction as claimed in claim 1, wherein the minimum spanning tree is determined in each connected region and the transmitting power of each node is optimally adjusted, comprising:
and (3) taking the transmitting power consumption and the node residual energy as variables, giving corresponding weights to the two variables to generate a double-objective function, and determining a minimum spanning tree by using an ant colony algorithm based on multi-objective improvement.
7. The method for unmanned aerial vehicle fleet topology control based on location trajectory prediction as claimed in claim 6, wherein the minimum spanning tree is generated specifically according to the following processing steps:
s01: initializing parameters including the number of ants and the maximum iteration cycle number, and creating an initialized spanning tree set;
s02: selecting 1 ant from all ants, and randomly selecting a node;
s03: the ants move to the next node according to the state transfer rule function, the node is recorded, and the edge which passes through is subjected to local pheromone updating;
s04: cycling in the manner of S03 until the ant has gone through all nodes, and a solution is generated;
s05: if the solution produced by the ant is non-dominant to the previously created spanning tree set, adding the ant to the set, and deleting the solution dominated by the ant in the set; calculating a double-objective function value, and if the value is minimum, replacing a current value; otherwise, performing global pheromone updating on each solution in the current set;
s06: repeating S02-S05, re-determining that one ant has finished walking all nodes and updating the spanning tree set and the global pheromone until all ants are traversed;
s07: and repeating S02-S06 until the maximum iteration cycle number is reached, and finally obtaining the updated local minimum spanning tree.
8. The method for controlling topology of unmanned aerial vehicle cluster based on location trajectory prediction as claimed in claim 1, 6 or 7, wherein said optimizing and adjusting the transmission power of each node comprises: and each node adjusts the self transmitting power according to the minimum spanning tree, and the transmitting distance meets the transmission radius of the node with the highest requirement on the coverage range on the spanning tree.
9. The method for controlling topology of unmanned aerial vehicle fleet based on location trajectory prediction according to claim 1, 6 or 7, wherein: and taking a union set of the corresponding connection relations of the minimum spanning trees of each node, deleting each one-way connection link, and after all the links in the minimum spanning trees are bidirectional links, overlapping each minimum spanning tree to form an optimized unmanned aerial vehicle cluster topological structure.
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| CN115278699B (en) * | 2022-06-17 | 2024-01-16 | 深圳市人工智能与机器人研究院 | Multi-agent coverage control method and device under communication maintaining constraint |
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