CN110645988A - Unmanned aerial vehicle path planning method based on optimal service life - Google Patents

Unmanned aerial vehicle path planning method based on optimal service life Download PDF

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CN110645988A
CN110645988A CN201910961053.2A CN201910961053A CN110645988A CN 110645988 A CN110645988 A CN 110645988A CN 201910961053 A CN201910961053 A CN 201910961053A CN 110645988 A CN110645988 A CN 110645988A
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CN110645988B (en
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刘贵云
彭德鸣
欧阳海滨
蒋文俊
彭百豪
张杰钊
唐冬
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Guangzhou University
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Abstract

The invention relates to an unmanned aerial vehicle path planning method based on optimal service life, which comprises the following steps: s1, clustering nodes which can communicate with each other within a preset distance; s2, taking the node which is closest to the centroid position and has the highest residual energy in the cluster members as the cluster head of the cluster in the information acquisition process of the current round; taking a cluster head and an independent node which is not clustered as a task object; and S3, performing unified path planning on all nodes in the task object by using a dynamic planning algorithm to obtain an optimal path with the optimal service life for the unmanned aerial vehicle flight. The invention not only effectively optimizes the flight path of the unmanned aerial vehicle, but also further shortens the information life of the node and ensures the real-time property of information acquisition.

Description

Unmanned aerial vehicle path planning method based on optimal service life
Technical Field
The invention relates to the technical field of unmanned aerial vehicle path design, in particular to an unmanned aerial vehicle path planning method based on optimal service life.
Background
Nowadays, the development of the unmanned aerial vehicle technology is mature day by day, and the application of the unmanned aerial vehicle technology is more and more extensive, including the fields of military affairs, industry, traffic, agriculture and the like. Meanwhile, the wireless sensor network communication is increasingly applied to scenes such as information acquisition, environmental data monitoring and the like by people due to the outstanding advantages of large-scale characteristics, self-organization, reliability and the like. The wireless sensor network is matched with the unmanned aerial vehicle, unified collection, monitoring and analysis are carried out on information of each task area, and a large amount of time, manpower and material resources can be saved.
Under the situation of the above-mentioned information collection, the route design of the unmanned aerial vehicle often can use environment and task conditions such as energy, load as the purpose, and the life-span of the information often can be neglected, has just also led to guaranteeing the information of each node of gathering, is as far as possible real-time and up-to-date. Therefore, many scholars and researchers have taken the lead to propose and clarify the definition of information lifetime before solving the problem related to information lifetime: the sum of the data transmission time of an Unmanned Aerial Vehicle (UAV) at a certain node and the time consumed by the UAV in the process of flying to the next node is the information life of the node. All aspects of consideration are based on the definition of the information life, the consumption of the information life of each node is effectively reduced, and therefore the freshness and the real-time performance of collected information are ensured.
If a scene with large space and rich conditions exists, a plurality of unmanned aerial vehicles are required to cooperatively complete tasks, and the service life of the acquired information is ensured to be consumed as little as possible, so that scientific and efficient flight path planning needs to be performed, and the consumption cost of unmanned aerial vehicle flight and wireless sensor network operation is reduced as much as possible.
The existing dynamic programming algorithm and genetic algorithm can be suitable for the scene, and the unmanned aerial vehicle needs to traverse all random nodes by utilizing the dynamic programming algorithm and the genetic algorithm, so that the convergence speed is low, the iteration times are more, and the running time is long.
Disclosure of Invention
Aiming at the problem that the unmanned aerial vehicle needs to traverse all random nodes in the prior art, the invention provides an unmanned aerial vehicle path planning method based on the optimal service life.
The specific scheme of the application is as follows:
an unmanned aerial vehicle path planning method based on optimal service life comprises the following steps:
s1, clustering nodes which can communicate with each other within a preset distance;
s2, taking the node which is closest to the centroid position and has the highest residual energy in the cluster members as the cluster head of the cluster in the information acquisition process of the current round; taking a cluster head and an independent node which is not clustered as a task object;
and S3, performing unified path planning on all nodes in the task object by using a dynamic planning algorithm to obtain an optimal path with the optimal service life for the unmanned aerial vehicle flight.
Preferably, step S1 includes:
judging the distance dis between every two wireless sensor nodesi,j(vi,vj∈V+) And if so, clustering, and recording the wireless sensor nodes R which can be clustered with each other by using an array.
Preferably, R ═ 20 m.
Preferably, step S3 includes:
s31, correcting the data uploading time to obtain the data uploading time of each cluster
Figure BDA0002228916000000021
S32, uploading data of the current node in the task object
Figure BDA0002228916000000022
Plus the time consumed by the unmanned aerial vehicle flying from the current node to the next node
Figure BDA0002228916000000023
Obtaining current node consumption time, adding the current node consumption time to known path consumption time, and taking a next node corresponding to the minimum sum of the current node consumption time and the known path consumption time as a next node of the optimal path;
and S33, taking the next node of the optimal path as the current node, and repeatedly executing the step S32 to obtain the optimal path with the optimal service life AoI.
Preferably, in step S32, the current minimum path consumption time calculation formula in each stage is:
Figure BDA0002228916000000031
the calculated optimal path is a shortest Hamiltonian loop starting from the first node v1Terminating at the initial node v0The calculated optimal life AoI is:
Figure BDA0002228916000000032
preferably, in step S2,
when the distance between the unmanned aerial vehicle and the communication node is smaller than a critical value, the energy consumption of the node is calculated as shown in formula (3):
E_use＝E_elec*bit+E_fs*bit*(distance)2(3)
when the distance between the unmanned aerial vehicle and the communication node is greater than or equal to the critical value, the energy consumption of the node is calculated as shown in formula (4):
E_use＝E_elec*bit+E_mp*bit*(distance)4(4)
wherein E _ use is consumed energy, E _ elec is energy transmitted by each bit, E _ fs is energy dissipated by each bit when the E _ fs is smaller than a critical distance, E _ mp is energy dissipated by each bit when the E _ mp is larger than the critical distance, and distance is the distance between a node performing communication and the unmanned aerial vehicle;
all nodes capable of being selected in the phase
Figure BDA0002228916000000033
Distance d from current local nodei,jCalculating according to the formula (5):
Figure BDA0002228916000000034
wherein, the prefixes of Node _ x and Node _ y in the formula are respectively the abscissa and the ordinate of the points i and j;
and respectively calculating the energy consumption and the distance from the node member in each cluster to the center of mass according to the formulas (3) to (4) and the formula (5), so that the selection of the cluster head can accord with the flight characteristics of the UAV.
Preferably, the data upload time of each cluster in step S31 is
Figure BDA0002228916000000035
α(i)Is the number of members of the cluster.
Preferably, step S3 is preceded by: a rotation of cluster heads, the rotation of cluster heads comprising: selecting a cluster head according to a threshold value T (n), wherein each cluster member node generates a random number, and if the random number is smaller than the threshold value T (n), the node is selected as a cluster head node of the cluster in the current round of information acquisition; the random number ranges from 0 to 1;
Figure BDA0002228916000000041
wherein r is the current round number of information acquisition, p is the ratio of the number of cluster heads in the network to the total number of nodes, G is the node set of non-elected cluster heads in the nearest 1/p round, EcurrIs the remaining energy of the node, EaverIs the average energy of the nodes after the end of each round, dtoMSDistance of cluster head to center of mass, dmaxThe maximum distance to the centroid among all nodes in the current cluster.
Compared with the prior art, the invention has the following beneficial effects:
the method includes clustering nodes which can communicate with each other within a preset distance into clusters, and then taking the node which is closest to a centroid position and has the highest residual energy in the cluster members as a cluster head of the cluster in the information acquisition process of the current round; and then, a dynamic planning algorithm is utilized to carry out uniform path planning on all nodes in the task object, so that the flight path of the unmanned aerial vehicle is effectively optimized, the information service life of the nodes is further shortened, and the real-time property of information acquisition is ensured.
Drawings
Fig. 1 is a schematic flow chart of a method for planning a route of an unmanned aerial vehicle with an optimal service life according to the present embodiment;
FIG. 2 is a weighted directed multi-segment graph according to this embodiment;
FIG. 3 is an initial random distribution diagram of sense nodes for a MATLAB simulation process.
Fig. 4 is a diagram of planning an optimal path by a conventional DP algorithm.
Fig. 5 is a graph AoI illustrating the information life of each node of the optimal path planned by the existing DP algorithm.
FIG. 6 is a DP algorithm path planning diagram for cluster optimization improvement.
Fig. 7 is a graph of the remaining energy of each cluster member.
Fig. 8 is a diagram of service life of information of each cluster node of the DP algorithm after cluster optimization.
Detailed Description
Referring to fig. 1, an unmanned aerial vehicle path planning method based on optimal lifetime includes:
s1, clustering nodes which can communicate with each other within a preset distance; specifically, step S1 includes:
judging the distance dis between every two wireless sensor nodesi,j(vi,vj∈V+) And if so, clustering, and recording the wireless sensor nodes R which can be clustered with each other by using an array. And R is 20 m. Re-counting and sorting the related parameters after the nodes are clustered, including the number of clusters and the sequence index (marked as Group ═ G ═ sequence ═ G)1,G2,G3,...,Gn}), the number of individual nodes not clustered and the sequence label (keeping the original label), the number of members in each cluster and the sequence label (keeping the original label)Holding the original label), the coordinate locations of the members within each cluster, and so on. After the related parameters (including cluster member labels, coordinates and the like) of the clusters are generated, the centroid position coordinates of each cluster are calculated according to the coordinates of the members in each cluster and a centroid calculation method. And marks the sequential labels of their corresponding clusters on the centroid (e.g., Center)1Is G1The center of mass of); and then recording all task objects needing dynamic planning by using an array Member, wherein the task objects comprise clusters and unclustered individual nodes.
S2, taking the node which is closest to the centroid position and has the highest residual energy in the cluster members as the cluster head of the cluster in the information acquisition process of the current round; taking a cluster head and an independent node which is not clustered as a task object;
after clustering, performing timing rotation of cluster heads (communication nodes preferentially selected from cluster members) in the clusters, and performing unified management, detection and operation on each cluster outside the clusters. Therefore, step S3 is preceded by: a rotation of cluster heads, the rotation of cluster heads comprising: selecting a cluster head according to a threshold value T (n), wherein each cluster member node generates a random number, and if the random number is smaller than the threshold value T (n), the node is selected as a cluster head node of the cluster in the current round of information acquisition; the random number ranges from 0 to 1;
wherein r is the current round number of information acquisition, p is the ratio of the number of cluster heads in the network to the total number of nodes, G is the node set of non-elected cluster heads in the nearest 1/p round, EcurrIs the remaining energy of the node, EaverIs the average energy of the nodes after the end of each round, dtoMSDistance of cluster head to center of mass, dmaxThe maximum distance to the centroid among all nodes in the current cluster.
Clustering is to save consumption cost of nodes or an unmanned aerial vehicle, and also needs to consider consumption of node energy, so as to perform cluster head rotation as mentioned above, it is necessary to select a node with the highest residual energy and the closest distance to the centroid (i.e., the best condition) among members of each cluster as a cluster head, and perform communication as the cluster head when the unmanned aerial vehicle arrives at the cluster to acquire information.
Wherein, when the distance between the unmanned aerial vehicle and the communication node is less than the critical value, the energy consumption of the node is calculated as shown in formula (3):
E_use＝E_elec*bit+E_fs*bit*(distance)2 (3)
when the distance between the unmanned aerial vehicle and the communication node is greater than or equal to the critical value, the energy consumption of the node is calculated as shown in formula (4):
E_use＝E_elec*bit+E_mp*bit*(distance)4 (4)
wherein E _ use is consumed energy, E _ elec is energy transmitted by each bit, E _ fs is energy dissipated by each bit when the E _ fs is smaller than a critical distance, E _ mp is energy dissipated by each bit when the E _ mp is larger than the critical distance, and distance is the distance between a node performing communication and the unmanned aerial vehicle;
all nodes capable of being selected in the phase
Figure BDA0002228916000000061
Distance d from current local nodei,jCalculating according to the formula (5):
Figure BDA0002228916000000062
wherein, the prefixes of Node _ x and Node _ y in the formula are respectively the abscissa and the ordinate of the points i and j;
and respectively calculating the energy consumption and the distance from the node member in each cluster to the center of mass according to the formulas (3) to (4) and the formula (5), so that the selection of the cluster head can accord with the flight characteristics of the UAV.
After clustering, the unmanned aerial vehicle does not need to traverse all nodes, and an object needing to execute a task is converted into a set of partial non-clustered individual nodes and cluster heads of clustered nodes from all nodes. Therefore, the path of the unmanned aerial vehicle is shortened, and the service life of the information of the nodes is effectively optimized.
And S3, performing unified path planning on all nodes in the task object by using a dynamic planning algorithm to obtain an optimal path with the optimal service life for the unmanned aerial vehicle flight.
In the present embodiment, step S3 includes:
s31, correcting the data uploading time to obtain the data uploading time of each cluster
Figure BDA0002228916000000071
The data upload time of each cluster in step S31 is
Figure BDA0002228916000000072
α(i)Is the number of members of the cluster.
S32, uploading data of the current node in the task object
Figure BDA0002228916000000073
Plus the time consumed by the unmanned aerial vehicle flying from the current node to the next node
Figure BDA0002228916000000074
Obtaining current node consumption time, adding the current node consumption time to known path consumption time, and taking a next node corresponding to the minimum sum of the current node consumption time and the known path consumption time as a next node of the optimal path;
in step S32, at each stage, the current minimum path consumption time (path _ cost) is calculated as:
Figure BDA0002228916000000075
the calculated optimal path is a shortest Hamiltonian loop starting from the first node v1Terminating at the initial node v0The calculated optimal lifetime AoI (i.e., path consumption time) is:
Figure BDA0002228916000000076
and S33, taking the next node of the optimal path as the current node, and repeatedly executing the step S32 to obtain the optimal path with the optimal service life AoI.
Examples are: as shown in fig. 2, the shortest path between the two points a and D is found:
if F (i) represents the shortest distance from the point A to the point i (arbitrary point), there is
F(A)＝0；
F(B)＝5，F(B2)＝2；
F(C1)＝min{F(B1)+3}＝8；
F(C2)＝min{F(B1)+2，F(B2)+7}＝7；
F(C3)＝min{F(B2)+4}＝6；
F(D)＝min{F(C1)+4，F(C2)+3,F(C3)+5}＝10.
As can be seen from the above, the whole problem process is divided into four stages to be solved, and the numerical calculation of each stage is only related to the numerical value of the previous stage, so that the recurrence is carried out until the target overall optimal solution is obtained.
When the unmanned aerial vehicle carries out information acquisition for multiple rounds, the energy of the original cluster head may be excessively consumed during communication, in order to avoid that the node selected as the cluster head dies too early and affects the life cycle of the network, the cluster head selection and the rotation of each cluster need to be carried out again during each round of information acquisition. Therefore, step S3 is preceded by: a rotation of cluster heads, the rotation of cluster heads comprising: selecting a cluster head according to a threshold value T (n), wherein each cluster member node generates a random number, and if the random number is smaller than the threshold value T (n), the node is selected as a cluster head node of the cluster in the current round of information acquisition; the random number ranges from 0 to 1;
Figure BDA0002228916000000081
wherein r is the current round number of information acquisition, p is the ratio of the number of cluster heads in the network to the total number of nodes, G is the node set of non-elected cluster heads in the nearest 1/p round, EcurrIs the remaining energy of the node, EaverIs the average energy of the nodes after the end of each round, dtoMSDistance of cluster head to center of mass, dmaxThe maximum distance to the centroid among all nodes in the current cluster.
The dynamic programming algorithm recurs the following specific steps (pseudo code):
the method comprises the following steps: input sensor node network G ═ V+ε) and all system parameters (v, h, β, P)m,σ2)；
Step two: calculating the time eta consumed by information acquisition between every two nodesi,j(i≠j，vi,vj∈M+)；
Step three:
Figure BDA0002228916000000082
step IV: finally, the optimum life AoI is calculated according to the formula (4-2) and recorded as(starting from v in M1)；
Step five: finding member nodes of current optimal path
Step (c): planning the optimal path by recursion forward
Figure BDA0002228916000000085
Starting from a nodeEnd at node v0；
Step (c): outputting the optimal path
MATLAB simulation procedure and results
The dynamic programming algorithm simulation based on the energy and centroid distance clustering optimization has the following basic parameters:
TABLE 1-1 Cluster optimized improved dynamic programming algorithm simulation parameter settings
Figure BDA0002228916000000091
In addition, the data uploading rate and the data uploading time of each node are consistent, so that the data uploading time is not calculated additionally in order to simplify the model, and the data uploading time is directly set to be a uniform numerical value tx. Similarly, the initial nodes are randomly distributed as shown in FIG. 3.
Then, before cluster optimization, an existing dynamic planning algorithm ((DP)) is simulated to plan an optimal path at the moment, and the node information life at the moment is calculated according to the proposed idea methods of 'maximum-life optimization' and 'average-life optimization' according to the formulas (7) to (11). This facilitates the comparison of the paths before and after optimization, and also facilitates the comparison of the node information lifetimes AoI before and after optimization.
Therein, 1, "maximum-life optimization" path planning
According to the definition of the information life, the total time spent in the flight process of the unmanned aerial vehicle is equal to the information life acquired by the unmanned aerial vehicle at the first node, namely the information life of the node is the maximum. Therefore, the first node v can be considered to start from the information life of the node1The information lifetime of (a) is calculated as:
Figure BDA0002228916000000101
at this time, a specific algorithm is utilized to reasonably minimize the value in the path planning, and the path obtained at this time is the optimal path obtained under the thought method.
2. "average-life optimized" path planning
From equation (12) for the total average information lifetime, one can derive:
Figure BDA0002228916000000102
Figure BDA0002228916000000103
for ease of explanation, the way is describedFrom the ith node v in the path1To Mth node vMThe weighted information lifetime of the collected information is defined as:
Figure BDA0002228916000000104
similarly, (9) the formula satisfies the condition:
Figure BDA0002228916000000105
from the above formula, the total average life of the collected information in the flight path is also equal to the average life of the information of the first node:
Figure BDA0002228916000000106
optimizing the total average information lifetime is equivalent to optimizing the information lifetime of the first node.
Then the average lifetime of the first node information is substituted as follows according to equation (9):
the results of the simulation of the existing dynamic programming algorithm and the calculation of the node information life are shown in fig. 4 and 5, respectively.
Next, cluster optimization is performed, and nodes within the communication radius, that is, nodes capable of communicating with each other, are divided into a cluster, which is marked yellow. And then calculating the mass centers of all the clusters according to a mass center calculation method, marking the mass centers in sequence, and marking the mass centers to be black. Next, the cluster member node closest to the centroid position within the cluster is calculated, and the cluster head is selected (only the first round of information acquisition) and marked in red. And then, referring to the principle steps of dynamic planning, and planning the path according to the pseudo code of the dynamic planning algorithm after clustering optimization. As shown in fig. 6, compared with the route before optimization shown in fig. 4, the route after the clustering optimization has a shorter obvious distance, a lower complexity of the route, and a significant simplification effect.
As shown in FIG. 7, the cluster selected from the Group1 and 2The head is respectively a node v5And v8Therefore, after a round of information acquisition, the 5 th node and the 8 th node generate energy consumption, and the consumed energy is different because the distances between the two nodes and the mass centers of the clusters to which the two nodes belong are different.
Node information lifetimes for the same cluster member are considered equal because clustering is done. The node life of each individual unit (including individual nodes and clusters) after clustering is shown in fig. 8. Compared with AoI (see fig. 5) before optimization, the maximum AoI value (the first section node AoI value) in the flight path or the AoI value of each stage is obviously reduced. The information life of the first section of the node is reduced by nearly 50%. Therefore, the path planning effectively shortens the service life of the node information and ensures that the acquired instant data information is fresh enough.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims (8)

1. An unmanned aerial vehicle path planning method based on optimal service life is characterized by comprising the following steps:
s1, clustering nodes which can communicate with each other within a preset distance;
s2, taking the node which is closest to the centroid position and has the highest residual energy in the cluster members as the cluster head of the cluster in the information acquisition process of the current round; taking a cluster head and an independent node which is not clustered as a task object;
and S3, performing unified path planning on all nodes in the task object by using a dynamic planning algorithm to obtain an optimal path with the optimal service life for the unmanned aerial vehicle flight.
2. The optimal-life-based unmanned aerial vehicle path planning method according to claim 1, wherein step S1 includes:
judging the distance dis between every two wireless sensor nodesi,j(vi,vj∈V+) And if so, clustering, and recording the wireless sensor nodes R which can be clustered with each other by using an array.
3. The optimal-life-based unmanned aerial vehicle path planning method of claim 2, wherein R-20 m.
4. The optimal-life-based unmanned aerial vehicle path planning method according to claim 1, wherein step S3 includes:
s31, correcting the data uploading time to obtain the data uploading time of each cluster
Figure FDA0002228915990000011
S32, uploading data of the current node in the task object
Figure FDA0002228915990000012
Plus the time consumed by the unmanned aerial vehicle flying from the current node to the next node
Figure FDA0002228915990000013
Obtaining current node consumption time, adding the current node consumption time to known path consumption time, and taking a next node corresponding to the minimum sum of the current node consumption time and the known path consumption time as a next node of the optimal path;
and S33, taking the next node of the optimal path as the current node, and repeatedly executing the step S32 to obtain the optimal path with the optimal service life AoI.
5. The optimal-life-based unmanned aerial vehicle path planning method according to claim 4, wherein in step S32, the current minimum path consumption time calculation formula in each stage is as follows:
Figure FDA0002228915990000014
the calculated optimal path is a shortest Hamiltonian loop starting from the first node v1Terminating at the initial node v0The calculated optimal life AoI is:
Figure FDA0002228915990000021
6. the optimal lifetime-based unmanned aerial vehicle path planning method of claim 1, wherein in step S2,
when the distance between the unmanned aerial vehicle and the communication node is smaller than a critical value, the energy consumption of the node is calculated as shown in formula (3):
E_use＝E_elec*bit+E_fs*bit*(distance)2 (3)
when the distance between the unmanned aerial vehicle and the communication node is greater than or equal to the critical value, the energy consumption of the node is calculated as shown in formula (4):
E_use＝E_elec*bit+E_mp*bit*(distance)4 (4)
wherein E _ use is consumed energy, E _ elec is energy transmitted by each bit, E _ fs is energy dissipated by each bit when the E _ fs is smaller than a critical distance, E _ mp is energy dissipated by each bit when the E _ mp is larger than the critical distance, and distance is the distance between a node performing communication and the unmanned aerial vehicle;
all selectable nodes v of the phasej
Figure FDA0002228915990000022
Distance d from current local nodei,jCalculating according to the formula (5):
wherein, the prefixes of Node _ x and Node _ y in the formula are respectively the abscissa and the ordinate of the points i and j;
and respectively calculating the energy consumption and the distance from the node member in each cluster to the center of mass according to the formulas (3) to (4) and the formula (5), so that the selection of the cluster head can accord with the flight characteristics of the UAV.
7. The optimal-life-based unmanned aerial vehicle path planning method according to claim 1, wherein the data uploading time of each cluster in step S31 is
Figure FDA0002228915990000024
α(i)Is the number of members of the cluster.
8. The optimal-life-based unmanned aerial vehicle path planning method according to claim 1, wherein step S3 is preceded by: a rotation of cluster heads, the rotation of cluster heads comprising: selecting a cluster head according to a threshold value T (n), wherein each cluster member node generates a random number, and if the random number is smaller than the threshold value T (n), the node is selected as a cluster head node of the cluster in the current round of information acquisition; the random number ranges from 0 to 1;
Figure FDA0002228915990000031
wherein r is the current round number of information acquisition, p is the ratio of the number of cluster heads in the network to the total number of nodes, G is the node set of non-elected cluster heads in the nearest 1/p round, EcurrIs the remaining energy of the node, EaverIs the average energy of the nodes after the end of each round, dtoMSDistance of cluster head to center of mass, dmaxThe maximum distance to the centroid among all nodes in the current cluster.
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