CN110645988B - Unmanned aerial vehicle path planning method based on optimal service life - Google Patents
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Abstract
The application 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 a node which is closest to the mass center position and has the highest residual energy in the cluster members as a cluster head of the cluster in the process of collecting the information of the round; taking the cluster head and the single nodes which are not clustered as task objects; and S3, carrying out unified path planning on all nodes in the task object by utilizing a dynamic planning algorithm to obtain an optimal path of the optimal service life of the unmanned aerial vehicle. The method not only effectively optimizes the flight path of the unmanned aerial vehicle, but also further shortens the information service life of the nodes and ensures the real-time property of information acquisition.
Description
Technical Field
The application 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, unmanned aerial vehicle technology is developed gradually, and the unmanned aerial vehicle technology is applied more and more widely, including the fields of military, industry, traffic, agriculture and the like. Meanwhile, 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 to uniformly collect, monitor and analyze the information of each task area, so that a large amount of time, manpower and material resources can be saved.
Under the situation of information collection, the path design of the unmanned aerial vehicle aims at energy, load and other environmental and task conditions, the service life of the information is ignored, and the collected information of each node cannot be guaranteed as much as possible in real time and up to date. Therefore, before solving the problems about information lifetime, many scholars and researchers have first proposed and elucidated the definition of information lifetime clearly: the sum of the data transmission time of the 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 service life of the node. The consideration of all aspects starts from the definition of the information service life, so that the consumption of the information service life of each node is effectively reduced, and the freshness and the real-time performance of the acquired information are ensured.
If a scene with larger space and rich conditions exists, a plurality of unmanned aerial vehicles are required to cooperatively complete tasks, the service life of acquired information is ensured to be consumed as little as possible, then scientific and efficient flight path planning is required to be made, 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 as the unmanned aerial vehicle needs to traverse all random nodes by utilizing the dynamic programming algorithm and the genetic algorithm, 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 application provides an unmanned aerial vehicle path planning method based on 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 a node which is closest to the mass center position and has the highest residual energy in the cluster members as a cluster head of the cluster in the process of collecting the information of the round; taking the cluster head and the single nodes which are not clustered as task objects;
and S3, carrying out unified path planning on all nodes in the task object by utilizing a dynamic planning algorithm to obtain an optimal path of the optimal service life of the unmanned aerial vehicle.
Preferably, step S1 comprises:
judging distance dis between every two wireless sensor nodes i,j (v i ,v j ∈V + ) If the communication radius is smaller than or equal to the preset communication radius, clustering, and recording the wireless sensor nodes R which can be clustered mutually by using an array.
Preferably, r=20m.
Preferably, step S3 includes:
s31, correcting the data uploading time to obtain the data uploading time of each cluster
S32, uploading time of data of current node in task objectPlus the time consumed by the unmanned aerial vehicle to fly from the current node to the next node->Obtaining the current node consumption time, adding the current node consumption time with the known path consumption time, and taking the next node with the minimum sum of the current node consumption time and the known path consumption time as the 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, in each stage, the current minimum path consumption time calculation formula is:
calculated outThe optimal path is a shortest hamiltonian loop, which starts at the first node v 1 Terminating at the initial node v 0 The calculated optimal lifetime AoI is:
preferably, in step S2, the process of the present application,
when the distance between the unmanned plane and the communication node is smaller than a critical value, the energy consumption calculation of the node is shown as a formula (3):
E_use＝E_elec*bit+E_fs*bit*(distance) 2 (3)
when the distance between the unmanned plane and the communication node is greater than or equal to a critical value, the energy consumption calculation of the node is shown as a 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 greater than the critical distance, and distance is the distance between a node for communication and the unmanned aerial vehicle;
all the nodes capable of selecting in the stageDistance d from current node i,j Calculated according to formula (5):
the node_x and node_y prefixes in the formula are the abscissa and the ordinate of the points i and j respectively;
according to formulas (3) to (4) and (5), energy consumption and calculation of the distance between the node members in each cluster and the mass center are respectively carried out, so that cluster head election can accord with the flight characteristics of the UAV.
Preferably, the data upload time per cluster in step S31 isα (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 T (n), wherein each cluster member node generates a random number, and if the random number is smaller than the threshold T (n), the node is selected as the cluster head node of the cluster in the round information acquisition; the random number ranges from 0 to 1;
wherein r is the current number of rounds of information acquisition, p is the ratio of the number of cluster heads to the total number of nodes in the network, G is the node set of the unselected cluster heads in the latest 1/p rounds, E curr For the remaining energy of the node E aver For the average energy of the nodes after each round has ended, d toMS D is the distance from the cluster head to the mass center max Is the maximum distance to the centroid among all nodes in the current cluster.
Compared with the prior art, the application has the following beneficial effects:
the method comprises the steps of clustering nodes which can communicate with each other within a preset distance into clusters, and then taking the node which is closest to the mass center position and has the highest residual energy in cluster members as a cluster head of the cluster in the process of collecting information of the round in the formed clusters; and then, carrying out unified path planning on all nodes in the task object by utilizing a dynamic planning algorithm, 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 the acquired information is ensured.
Drawings
Fig. 1 is a schematic flow chart of an unmanned aerial vehicle path planning method of optimal lifetime of the present embodiment;
FIG. 2 is a weighted directed multi-segment diagram of the present embodiment;
FIG. 3 is an initial random distribution diagram of sensing nodes for a MATLAB simulation process.
Fig. 4 is a diagram of an optimal path planned by the conventional DP algorithm.
Fig. 5 is a graph AoI of the information lifetime of each node of the optimal path planned by the conventional DP algorithm.
Fig. 6 is a path planning diagram of a clustering optimization enhancement DP algorithm.
Fig. 7 is a graph of energy remaining for each cluster member.
Fig. 8 is a graph of the information lifetime 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 service life includes:
s1, clustering nodes which can communicate with each other within a preset distance; specifically, step S1 includes:
judging distance dis between every two wireless sensor nodes i,j (v i ,v j ∈V + ) If the communication radius is smaller than or equal to the preset communication radius, clustering, and recording the wireless sensor nodes R which can be clustered mutually by using an array. R=20m. The related parameters after node clustering are reclassified and arranged, including the number of clusters and sequence labels (marked as group= { G 1 ,G 2 ,G 3 ,...,G n -number and sequential labels of individual nodes not clustered (holding the original labels), number and sequential labels of members within each cluster (holding the original labels), coordinate positions of members within each cluster, etc. After the related parameters (including cluster member numbers, 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 the centroid calculation method. And marking the centroid with the sequential label of its corresponding cluster (e.g. Center 1 Is G 1 Is a centroid of (2); all task objects which need to be dynamically planned are recorded by a group number Member, and the task objects comprise clusters and individual nodes which are not clustered.
S2, taking a node which is closest to the mass center position and has the highest residual energy in the cluster members as a cluster head of the cluster in the process of collecting the information of the round; taking the cluster head and the single nodes which are not clustered as task objects;
after clustering, cluster heads (communication nodes which are preferentially selected in cluster members) are periodically rotated in the clusters, and unified management, detection and operation are carried out 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 T (n), wherein each cluster member node generates a random number, and if the random number is smaller than the threshold T (n), the node is selected as the cluster head node of the cluster in the round information acquisition; the random number ranges from 0 to 1;
wherein r is the current number of rounds of information acquisition, p is the ratio of the number of cluster heads to the total number of nodes in the network, G is the node set of the unselected cluster heads in the latest 1/p rounds, E curr For the remaining energy of the node E aver For the average energy of the nodes after each round has ended, d toMS D is the distance from the cluster head to the mass center max Is the maximum distance to the centroid among all nodes in the current cluster.
The clustering is to save the consumption cost of the nodes or the unmanned aerial vehicle, and also needs to consider the consumption of the energy of the nodes, so the cluster head rotation mentioned above needs to select one node with the highest residual energy and the closest distance to the mass center (namely, the optimal condition) from the members of each cluster as the cluster head, and the node is used as the cluster head to communicate when the unmanned aerial vehicle arrives at the cluster to collect information.
When the distance between the unmanned plane and the communication node is smaller than a critical value, the energy consumption calculation of the node is shown as a formula (3):
E_use＝E_elec*bit+E_fs*bit*(distance) 2 (3)
when the distance between the unmanned plane and the communication node is greater than or equal to a critical value, the energy consumption calculation of the node is shown as a 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 greater than the critical distance, and distance is the distance between a node for communication and the unmanned aerial vehicle;
all the nodes capable of selecting in the stageDistance d from current node i,j Calculated according to formula (5):
the node_x and node_y prefixes in the formula are the abscissa and the ordinate of the points i and j respectively;
according to formulas (3) to (4) and (5), energy consumption and calculation of the distance between the node members in each cluster and the mass center are respectively carried out, so that cluster head election can accord with the flight characteristics of the UAV.
After clustering, the drone does not need to traverse all nodes, and the objects needed to perform tasks are converted from all nodes to partially non-clustered individual nodes, and to a set of cluster heads of clustered nodes. Therefore, the path of the unmanned aerial vehicle is shortened, and the information service life of the nodes is effectively optimized.
And S3, carrying out unified path planning on all nodes in the task object by utilizing a dynamic planning algorithm to obtain an optimal path of the optimal service life of the unmanned aerial vehicle.
In this embodiment, step S3 includes:
s31, correcting the data uploading time to obtain the data uploading time of each clusterThe data upload time per cluster in step S31 is +.>α (i) Is the number of members of the cluster.
S32, uploading data of current node in task objectTimePlus the time consumed by the unmanned aerial vehicle to fly from the current node to the next node->Obtaining the current node consumption time, adding the current node consumption time with the known path consumption time, and taking the next node with the minimum sum of the current node consumption time and the known path consumption time as the next node of the optimal path;
in step S32, in each stage, the current minimum path-consuming time (path_cost) is calculated as:
the calculated optimal path is a shortest hamiltonian loop, which starts at the first node v 1 Terminating at the initial node v 0 The calculated optimal lifetime AoI (i.e., path elapsed time) is:
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: as shown in fig. 2, the shortest path between the first and last points a and D is found:
let F (i) be the shortest distance from point A to point i (any 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.
From the above, the whole problem process is divided into four stages, and the numerical calculation of each stage is only related to the numerical value of the previous stage, so that the iteration is performed until the target overall optimal solution is obtained.
When unmanned aerial vehicle carries out information acquisition for many times, the energy of former cluster head can be too much consumed when the communication, in order to avoid the node that is selected as the cluster head to die too early, influence network life cycle, need carry out cluster head selection and rotation again to each cluster when each round information acquisition. Therefore, before step S3, it further includes: a rotation of cluster heads, the rotation of cluster heads comprising: selecting a cluster head according to a threshold T (n), wherein each cluster member node generates a random number, and if the random number is smaller than the threshold T (n), the node is selected as the cluster head node of the cluster in the round information acquisition; the random number ranges from 0 to 1;
wherein r is the current number of rounds of information acquisition, p is the ratio of the number of cluster heads to the total number of nodes in the network, G is the node set of the unselected cluster heads in the latest 1/p rounds, E curr For the remaining energy of the node E aver For the average energy of the nodes after each round has ended, d toMS D is the distance from the cluster head to the mass center max Is the maximum distance to the centroid among all nodes in the current cluster.
The dynamic programming algorithm recursively comprises the following specific steps (pseudo codes):
step (1): input sensor node network g= (V) + Epsilon) and all system parameters (v, h, beta, P) m ,σ 2 )；
Step (2): calculating the time eta consumed by information acquisition between every two nodes i,j (i≠j，v i ,v j ∈M + )；
Step (3):
step (4): finally, the optimal lifetime AoI is calculated according to the formula (4-2) and is recorded as(starting from v in M) 1 )；
Step (5): member node for finding current optimal path
Step (6): forward recursion planning optimal pathStarting at node->Ending at node v 0 ；
Step (7): output of optimal path
MATLAB simulation process and results
The dynamic programming algorithm simulation based on energy and centroid distance clustering optimization comprises the following basic parameters:
table 1-1 clustering optimization of improved dynamic programming algorithm simulation parameter settings
In addition, the data uploading speed and time of each node are consistent, so that in order to simplify the model, the data uploading time is not calculated any more and is directly set as a unified value t x . Similarly, the initial node random distribution is shown in fig. 3.
Then, before cluster optimization, the existing dynamic programming algorithm ((DP)) is simulated, the optimal path at the moment is planned, and the node information service life at the moment is calculated according to formulas (7) to (11) according to the proposed thought method of 'maximum-service life optimization' and 'average-service life optimization'. This facilitates the comparison of the paths before and after the optimization and the comparison of the node information lives AoI before and after the optimization.
Wherein 1, "max-life optimization" path planning
According to the definition of the information service life, it can be known that the total time spent in the flight process of the unmanned aerial vehicle is equivalent to the information service life acquired by the unmanned aerial vehicle at the first node, namely the information service life of the node is the largest. It is possible to consider the first node v starting from the information lifetime of this node 1 The information lifetime calculation of (1) is:
at this time, a specific algorithm is utilized to reasonably minimize the numerical value in path planning, and then the obtained path is the optimal path obtained by the thought method.
2. "average-life optimization" path planning
From equation (12) for the total average information lifetime:
for ease of illustration, the slave i node v in the path 1 To the Mth node v M The service life of the acquired information is defined as:
and similarly, the formula (9) meets the condition:
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:optimizing the total average information lifetime is equivalent to optimizing the information lifetime of the first node.
The average lifetime of the first node information is substituted as:
simulation and node information lifetime calculation results of the conventional dynamic programming algorithm are shown in fig. 4 and 5, respectively.
Next, cluster optimization is performed, and nodes within the communication radius, i.e. capable of communicating with each other, are divided into a cluster, which is marked yellow. And then calculating the mass centers of all clusters according to a mass center calculating method, and marking the mass centers as black according to sequential marks. Next, the cluster member node closest to the centroid position within the cluster is calculated, selected as the cluster head (only for the first round of information acquisition), and marked as red. And then, referring to the principle steps of dynamic planning, path planning is carried out according to the pseudo codes of the dynamic planning algorithm after clustering optimization. As shown in fig. 6, compared with the path diagram 4 before optimization, the path after clustering optimization has obviously shortened distance, reduced complexity and obvious simplifying effect.
As shown in fig. 7, since the cluster heads selected from the clusters Group1 and Group2 are respectively the nodes v 5 And v 8 Therefore, through one round of information acquisition, the 5 th and 8 th nodes generate energy consumption, and the consumed energy is different due to the fact that the distances between the two nodes and the mass centers of the clusters to which the two nodes belong are different.
Because clustering has passed, the node information lifetimes of the same cluster members are considered equal. The node life of each individual unit after clustering (including individual nodes and clusters) is shown in fig. 8. Compared to AoI (see fig. 5) before optimization, there was a significant reduction in either the maximum AoI value (first segment node AoI value) or AoI value for each stage in the flight path. Wherein the first segment node information lifetime is reduced by even more 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 examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.
Claims (6)
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 a node which is closest to the mass center position and has the highest residual energy in the cluster members as a cluster head of the cluster in the process of collecting the information of the round; taking the cluster head and the single nodes which are not clustered as task objects;
s3, carrying out unified path planning on all nodes in the task object by utilizing a dynamic planning algorithm to obtain an optimal path of the optimal service life of the unmanned aerial vehicle;
the step S3 comprises the following steps:
s31, correcting the data uploading time to obtain the data uploading time of each cluster
S32, uploading time of data of current node in task objectPlus the time consumed by the unmanned aerial vehicle to fly from the current node to the next node->Obtaining the current node consumption time, adding the current node consumption time with the known path consumption time, and taking the next node with the minimum sum of the current node consumption time and the known path consumption time as the next node of the optimal path;
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;
in step S32, in each stage, the current optimal path consumption time calculation formula is:
the calculated optimal path is a shortest hamiltonian loop, which starts at the first node v 1 Terminating at the initial node v 0 The calculated optimal lifetime AoI is:
2. the optimal lifetime-based unmanned aerial vehicle path planning method of claim 1, wherein step S1 comprises:
judging distance dis between every two wireless sensor nodes i，j If the communication radius is smaller than or equal to the preset communication radius, clustering, and recording the wireless sensor nodes R which can be clustered mutually by using an array.
3. The optimal lifetime-based unmanned aerial vehicle path planning method of claim 2, wherein R = 20m.
4. The unmanned aerial vehicle path planning method according to claim 1, wherein in step S2, when the distance between the unmanned aerial vehicle and the communication node is smaller than a threshold value, the energy consumption calculation of the node is as shown in formula (3):
E_use＝E_elec*bit+E_fs*bit*(distance) 2 (3)
when the distance between the unmanned plane and the communication node is greater than or equal to a critical value, the energy consumption calculation of the node is shown as a 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 greater than the critical distance, and distance is the distance between a node for communication and the unmanned aerial vehicle;
all the nodes capable of being selectedDistance d from current node i，j Calculated according to formula (5):
the node_x and node_y prefixes in the formula are the abscissa and the ordinate of the points i and j respectively;
according to formulas (3) to (4) and (5), energy consumption and calculation of the distance between the node members in each cluster and the mass center are respectively carried out, so that cluster head election can accord with the flight characteristics of the UAV.
5. The optimal lifetime-based unmanned aerial vehicle path planning method of claim 1, wherein the data upload time for each cluster in step S31 isα (i) Membership to the clusterOrder (1).
6. The optimal lifetime-based unmanned aerial vehicle path planning method of claim 1, further comprising, prior to step S3: a rotation of cluster heads, the rotation of cluster heads comprising: selecting a cluster head according to a threshold T (n), wherein each cluster member node generates a random number, and if the random number is smaller than the threshold T (n), the node is selected as the cluster head node of the cluster in the round information acquisition; the random number ranges from 0 to 1;
wherein r is the current number of rounds of information acquisition, p is the ratio of the number of cluster heads to the total number of nodes in the network, G is the node set of the unselected cluster heads in the latest 1/p rounds, E curr For the remaining energy of the node E aver For the average energy of the nodes after each round has ended, d toMS D is the distance from the cluster head to the mass center max Is the maximum distance to the centroid among all nodes in the current cluster.
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