CN106203697A - A kind of paths planning method during Unmanned Aerial Vehicle Data collection - Google Patents
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Abstract
The invention discloses the paths planning method during a kind of Unmanned Aerial Vehicle Data is collected, for the shortage of data problem caused due to unmanned plane energy constraint in method of data capture based on unmanned plane, paths planning method during providing a kind of Unmanned Aerial Vehicle Data to collect, consider the barrier being likely to occur at any time in the abnormal data and environment that sensing node may collect at any time, dynamically plan the navigation route of unmanned plane, finally making unmanned plane in the case of energy constraint, the data collected have the data value of maximum.
Description
Technical Field
The invention relates to a path planning method in an unmanned aerial vehicle data collection process.
Background
Compared with the main data collection method in the current sensor network, the data collection method based on the unmanned aerial vehicle has great advantages. First, this approach is very similar in form to the mobile sink node-based data collection approach, so it can completely avoid the "energy hole problem". Meanwhile, the method separates data acquisition and data collection in different space dimensions, thereby avoiding the problem of limited sink movement and the problem of limited application scene. However, most of the existing drones are powered by batteries, and the flight time is very short, if a severe flight environment is encountered, for example: when the aircraft flies, the number of obstacles is large, the wind resistance is too large, and the like, the navigation time is greatly reduced, and the scale of the existing sensor network is larger and larger. Therefore, if a single unmanned aerial vehicle is used for collecting data of all the data sensing nodes in the sensing network, a large amount of data loss is caused.
Although the data collection method based on mutual cooperation of multiple unmanned aerial vehicles can solve the problem of data loss caused by energy limitation of the unmanned aerial vehicles, the method has great disadvantages. Firstly, the network cost is greatly increased by increasing a plurality of unmanned aerial vehicles for data collection, secondly, the existing method for collecting data by the cooperation of a plurality of unmanned aerial vehicles mostly assumes that the influence of the surrounding environment on the unmanned aerial vehicles is little or even none, but the method cannot be achieved in the actual environment. Therefore, the data collection method based on mutual cooperation of multiple unmanned aerial vehicles is difficult to apply in reality.
Disclosure of Invention
In view of the problems or defects in the prior art, the invention aims to provide a path planning method in the data collection process of an unmanned aerial vehicle, aiming at the problem of data loss caused by energy limitation of the unmanned aerial vehicle in the data collection method based on the unmanned aerial vehicle, comprehensively considering abnormal data possibly collected at any time by a sensing node and obstacles possibly appearing at any time in the environment, and dynamically planning the navigation route of the unmanned aerial vehicle, so that the collected data has the maximum data value under the condition that the energy of the unmanned aerial vehicle is limited.
In order to achieve the purpose, the invention adopts the following technical scheme:
a path planning method in an unmanned aerial vehicle data collection process comprises the following steps:
acquiring the physical position of each data sensing node arranged in a sensor network;
step two, data collection nodes are loaded on the unmanned aerial vehicle, the data collection nodes are UAV nodes, data collection task queues DCTQ are arranged in the UAV nodes, the data collection task queues DCTQ comprise a plurality of data collection tasks, and each data collection task represents the collection of data of one data sensing node in a sensor network;
step three: calculating the collection benefit corresponding to each task in the data collection task queue DCTQ, finding out the task with the maximum collection benefit in the data collection task queue DCTQ, and taking the data perception node corresponding to the task as the target node of data collection;
step four: the unmanned aerial vehicle flies towards the target node according to the physical position of the target node for data collection, if the unmanned aerial vehicle encounters an obstacle in the course of flying, the third step is returned, if the unmanned aerial vehicle does not encounter the obstacle in the course of flying, the unmanned aerial vehicle reaches the target node, the data of the target node is collected, and the completed tasks in the data collection task queue DCTQ are deleted;
step five: and if the energy of the unmanned aerial vehicle is sufficient and the data acquisition task of the whole area is not completed, returning to the step three to obtain a target node for next data acquisition until the energy consumption of the unmanned aerial vehicle is completed or the data acquisition task of the whole area is completed, and returning the unmanned aerial vehicle to the service station.
Specifically, the method for collecting the collection benefit corresponding to each task in the task queue DCTQ in the step three includes:
for the ith task in the data collection task queue DCTQ, the corresponding collection benefit calculation formula is as follows:
wherein,representing the probability of occurrence of anomalous data during data collection, CiRepresents the data value, P, corresponding to the ith taskUAVRepresenting the real-time location of the UAV node,data perception node n for representing data to be collected in ith taskiPosition of, | | PUAV-Pni| | represents UAV node and data perception node niThe linear distance of (a) is,a mathematical expectation representing a direct input-output ratio of the node performing the ith task; function(s)Means that if the UAV node finishes the ith task, the maximum value of the input-output ratio of all tasks in the UAV node task queue is shown, thereforeIt represents the mathematical expectation of the indirect maximum in-out ratio corresponding to the execution of the ith task.
Specifically, in the step one, the physical position of each data sensing node in the sensor network is obtained by manually recording when the data sensing node in the sensor network is deployed, or by using a node positioning technology.
Compared with the prior art, the invention has the following technical effects:
1. the invention selects the node with the highest input-output ratio under the current condition for the unmanned aerial vehicle node as the destination to collect data. Therefore, compared with the prior art, the invention can better utilize the energy of the unmanned aerial vehicle and collect more data with value.
2. With the increase of the total initialization energy of the unmanned aerial vehicle nodes, the data collected by the unmanned aerial vehicle nodes can more quickly approach to the total data value in the network. Before the data collected by the unmanned aerial vehicle nodes approach the total data value in the network, the unmanned aerial vehicle nodes select the task with the highest input-output ratio to execute each time the task is executed, and the prior art always selects the task with the least time to execute, so that the unmanned aerial vehicle nodes can collect more valuable data in a general view.
The embodiments of the invention will be explained and explained in further detail with reference to the drawings and the detailed description.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a schematic diagram of outdoor environment data set data fields;
FIG. 3 is a comparison of total values of TSP algorithm data under different occurrence probabilities of obstacles;
FIG. 4 is a comparison of total data values of TSP and DG algorithms under different initial energies of the UAVs;
Detailed Description
According to the above technical solution, referring to fig. 1, the path planning method in the data collection process of the unmanned aerial vehicle of the present invention includes the following steps:
acquiring the physical position of each data sensing node arranged in a sensor network; the specific acquisition method is manual recording when data sensing nodes in the sensor network are deployed, or acquisition by utilizing a node positioning technology.
And step two, carrying data collection nodes, namely UAV nodes on the unmanned aerial vehicle, wherein the UAV nodes are provided with data collection task queues DCTQs, the data collection task queues DCTQs comprise a plurality of data collection tasks, and each data collection task represents the collection of data of one data perception node in the sensing network.
Step three: and calculating the collection benefit corresponding to each task in the data collection task queue DCTQ, finding out the task with the maximum collection benefit in the data collection task queue DCTQ at the moment, and taking the data perception node corresponding to the task as a target node for data collection. For the ith task in the data collection task queue DCTQ, the corresponding collection benefit can be calculated according to the following formula:
wherein,representing the probability of occurrence of anomalous data during data collection, CiRepresents the data value, P, corresponding to the ith taskUAVRepresenting the real-time location of the UAV node,data perception node n for representing data to be collected in ith taskiPosition of, | | PUAV-Pni| | represents UAV node and data perception node niThe linear distance of (a), and therefore,representing the mathematical expectation of the node to perform the direct input-output ratio of the ith task. Function(s)Means that if the UAV node finishes the ith task, the maximum value of the input-output ratio of all tasks in the UAV node task queue is shown, thereforeIt represents the mathematical expectation of the indirect maximum in-out ratio corresponding to the execution of the ith task. Therefore, equation (1) generally represents the sum of the mathematical expectations of the direct input-output ratio and the maximum indirect input-output ratio resulting from performing a data collection task, i.e., collecting data for a node.
The invention introduces the concept of "Collection Gain" CG. The CG is physically expressed, and the UAV node collects the sum of the direct input-output ratio and the mathematical expectation of the maximum indirect input-output ratio brought by the data of one node under the current condition. It directly reflects the energy usage efficiency of the UAV node. Thus, if a data collection task corresponds to a higher CG value, the UAV node considers the task to be of higher priority.
The above steps can be represented by the following codes:
step four: the unmanned aerial vehicle flies towards the target node of the data collection according to the physical position of the target node of the data collection, if the unmanned aerial vehicle encounters an obstacle in the flying process, the unmanned aerial vehicle returns to the step three to find out the task with the maximum collection benefit in the data collection task queue DCTQ again, if the unmanned aerial vehicle does not encounter the obstacle in the flying process, the unmanned aerial vehicle arrives at the target node of the data collection, the data of the target node is collected, the completed task in the data collection task queue DCTQ is deleted, and the task with the maximum collection benefit in the data collection task queue DCTQ is deleted.
Step five: and if the energy of the unmanned aerial vehicle is sufficient and the data acquisition task in the whole area is not completed, returning to the step three to obtain a target node of next data acquisition, deleting the task with the maximum collection benefit in the data acquisition task queue DCTQ in the step four by the target node of the next data acquisition, recalculating the task with the maximum collection benefit in the obtained data acquisition task queue DCTQ until the energy consumption of the unmanned aerial vehicle is completed or the data acquisition task in the whole area is completed, and returning the unmanned aerial vehicle to the service station.
For the path planning problem of node data collection, the physical significance lies in that: when the UAV node is at the beginning of data collection work, when the UAV node meets an obstacle and changes an existing navigation route, and when the UAV node receives an abnormal perception data collection request and finishes a certain data collection task, the UAV node selects a data collection target node according to a data collection task queue DCTQ thereof. The target node for collecting the UAV node data should satisfy the following characteristics: the current position of the UAV node is close to the current position; the node has a large data value; after collecting the data of the node, the next target node is closer to the node.
Simulation experiment
1. Simulation parameter introduction method for unmanned aerial vehicle data collection method
First, the data set used by the invention is the data collected by 41 data sensing nodes deployed in the site of great wall of north table of elm town between 12 months 1 and 12 months 20 and 2015 years. The bottom layer of the 41 nodes adopts a CC2530 hardware structure and is responsible for collecting the temperature value of 15cm depth inside the soil of the great wall site. The data acquisition range is between-20 ℃ and 40 ℃. The specific data format is shown in fig. 2:
the first field Nodeid represents the node number corresponding to the node for collecting the Data, the second field SenseTime represents the time for collecting the Data by the node, the third field StoreTime represents the time for storing the Data by the database, and the fourth field Data represents the soil internal temperature value collected by the node with the depth of 15 cm.
With an application error of 0.01, the data critical nodes in the network and their corresponding coverage areas are used. And before the node data collection it has acquired all the information of the data critical nodes, as well as the geographical location of all the data aware nodes in the network. According to the situation in the real environment, the simulation assumes that the node consumes 0.02 unit of energy per unit of sailing length, and always runs at a constant speed of 1.6 unit of sailing length in unit time during sailing. The node is assumed to consume energy between 0.02 and 0.04 unit energy per obstacle avoidance in the data collection process, and the range of deviation from the flight path is within 2 unit lengths. It is assumed herein that the monitoring environment to be involved is generally in a steady state. Therefore, the data sensing node does not frequently acquire the abnormal sensing data, and the abnormal degree of the acquired abnormal sensing data is not very high, so that the data value range of the abnormal sensing data in the simulation is set to be between 5 and 10, and the probability of the abnormal sensing data is 10%.
2. Analysis of algorithm simulation results
When the UAV nodes respectively carry out path planning by utilizing the DG algorithm and the TSP algorithm provided by the text in the data collection process, the total data value of the finally collected data of the UAV nodes is compared. Because a random function is introduced to characterize the probability of abnormal data and obstacles in the simulation process, the simulation program is run 20 times according to the result in each case, and the average value of all the running results is taken as the final result to be displayed.
Fig. 3 is a comparison graph of the total value of data corresponding to data collected by UAV nodes when the probabilities of encountering obstacles are different between the DG algorithm and the TSP algorithm in the data collection process. The purple red curve and the blue curve in the figure are final simulation results of the UAV node by using a DG algorithm and a TSP algorithm respectively. From the overall view of the graph, the data collected by the UAV nodes will have a lower total value when the probability of the occurrence of the obstacle increases. When the probability of the obstacle is increased to a certain value, the total value of the data collected by the UAV node approaches 0, that is, the UAV node will collect almost no data at this time. The reason for this is two reasons: first, when the probability of an obstacle increases, the UAV node may consume too much energy to avoid the obstacle. From the data collection perspective alone, this energy consumption is wasted and the real available energy for data collection is dramatically reduced, thus reducing the overall final data value based on that approach. Secondly, when the probability of the appearance of the obstacle is increased, the UAV node frequently deviates from the current data collection route due to avoiding the obstacle, so that the UAV node can go too much to "make no use of the route", consume too much energy, and finally reduce the effective energy for data collection, so that the total value of the collected data is reduced.
It is also clear from fig. 3 that the DG algorithm always outperforms the TSP-like algorithm in most cases. This is because the core idea of TSP-like algorithms is to let the data of the nodes be collected as early as possible. Therefore, in the path planning of the UAV nodes, the nodes close to the current position are always selected as the destination to collect data, and there is no concept of data value in the data collection process. The core idea of the DG algorithm is to select the node with the highest input-output ratio in the current situation for the UAV node, and collect data as a destination. Therefore, compared with a TSP algorithm DG algorithm, the method can better utilize the energy of the algorithm and collect more 'valuable' data. However, when the probability of the occurrence of an obstacle increases to a certain amount, the TSP algorithm is superior to the DG algorithm. This is because, when the probability of occurrence of an obstacle is very high, the UAV node frequently deviates from the flight path, and thus there is a possibility that the destination of data collection may be changed, and the motion state of the UAV node approaches a random state. Thus, UAV nodes will only "happen" to be in the vicinity of a node. At this time, the TSP class algorithm collects the data of the node. For the DG algorithm, if the node happens to be the data critical node that has not been collected, then the data for that node is collected. When the node is a data trivial node, the DG algorithm collects abnormal data unless the data trivial node collects the abnormal data, and if the abnormal data is normal data, the DG algorithm can ignore the abnormal data. In summary, when the probability of the occurrence of the obstacle increases, the probability that the DG algorithm "happens" to meet the node capable of collecting data is very small, and at the moment, almost no data can be collected by the UAV node using the DG algorithm, so that the TSP-type algorithm is better than the DG algorithm when the probability of the occurrence of the obstacle increases to a certain amount.
Fig. 4 is a comparison graph of the total value of the finally collected data when the nodes have different initialization energies and routes are planned by using algorithms of the DG algorithm and the TSP class, respectively. The purple red curve in the figure is the simulation result of the DG algorithm, and the blue curve in the figure is the simulation result of the TSP algorithm. As is apparent from the figure, as the total energy for UAV node initialization increases, the total data value of the data collected using both algorithms increases. When the total energy of the UAV node is increased to a certain value mu, the data collected by the two algorithms approach to the total data value omega in the network, and at the moment, when the initial energy of the UAV node is continuously increased, the results of the two algorithms are basically kept unchanged around omega. (in the present simulation environment, the theoretical data worth sum Ω in the network is 42). This is because neither the DG algorithm nor the TSP algorithm can guarantee that the UAV node can complete all the data collection tasks in its own task queue when the total energy of the UAV node is small. When the total energy of the UAV node becomes larger, the "ability" of the UAV node to perform the task is strengthened, so the total value of the data finally collected becomes higher. But before the energy of the UAV node is less than mu, the DG algorithm is obviously superior to the TSP algorithm under the same initial total energy. This is because, before the energy is less than μ, although neither of them has the ability to completely execute the tasks in the task queue, the DG algorithm executes the task with the highest input-output ratio by selecting the task at each time of executing the task, and the TSP-like algorithm always selects the task with the least time-saving ratio to execute, so that the DG algorithm collects more valuable data as a whole.
Claims (3)
1. A path planning method in an unmanned aerial vehicle data collection process is characterized by comprising the following steps:
acquiring the physical position of each data sensing node arranged in a sensor network;
step two, data collection nodes are loaded on the unmanned aerial vehicle, the data collection nodes are UAV nodes, data collection task queues DCTQ are arranged in the UAV nodes, the data collection task queues DCTQ comprise a plurality of data collection tasks, and each data collection task represents the collection of data of one data sensing node in a sensor network;
step three: calculating the collection benefit corresponding to each task in the data collection task queue DCTQ, finding out the task with the maximum collection benefit in the data collection task queue DCTQ, and taking the data perception node corresponding to the task as the target node of data collection;
step four: the unmanned aerial vehicle flies towards the target node according to the physical position of the target node for data collection, if the unmanned aerial vehicle encounters an obstacle in the course of flying, the third step is returned, if the unmanned aerial vehicle does not encounter the obstacle in the course of flying, the unmanned aerial vehicle reaches the target node, the data of the target node is collected, and the completed tasks in the data collection task queue DCTQ are deleted;
step five: and if the energy of the unmanned aerial vehicle is sufficient and the data acquisition task of the whole area is not completed, returning to the step three to obtain a target node for next data acquisition until the energy consumption of the unmanned aerial vehicle is completed or the data acquisition task of the whole area is completed, and returning the unmanned aerial vehicle to the service station.
2. The method for planning a path in the data collection process of an unmanned aerial vehicle according to claim 1, wherein the collection benefit corresponding to each task in the data collection task queue DCTQ is calculated in step three by the following specific method:
for the ith task in the data collection task queue DCTQ, the corresponding collection benefit calculation formula is as follows:
wherein,representing the probability of occurrence of anomalous data during data collection, CiRepresents the data value, P, corresponding to the ith taskUAVRepresenting the real-time location of the UAV node,data perception node n for representing data to be collected in ith taskiIs, | Ρ | |UAV—Ρni| | represents UAV node and data perception node niThe linear distance of (a) is,a mathematical expectation representing a direct input-output ratio of the node performing the ith task; function(s)Means that if the UAV node finishes the ith task, the maximum value of the input-output ratio of all tasks in the UAV node task queue is shown, thereforeIt represents the mathematical expectation of the indirect maximum in-out ratio corresponding to the execution of the ith task.
3. The method for planning a path in the unmanned aerial vehicle data collection process according to claim 1, wherein the physical location of each data-aware node in the sensor network is obtained in the first step, and the specific obtaining method is manual recording when the data-aware nodes in the sensor network are deployed, or obtaining by using a node positioning technology.
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