CN111144827A - Unmanned aerial vehicle cruising path dynamic planning method based on historical gain estimation - Google Patents
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Abstract
The invention belongs to the technical field of unmanned aerial vehicles, and relates to a dynamic planning method for a cruising path of an unmanned aerial vehicle based on historical gain estimation. The method comprises the following steps: s1) calculating the historical gain of each region; s2) calculating the regional benefit score of the next hop by combining the distance factors; s3) designing a dynamic planning method for the unmanned aerial vehicle path. The method comprises the steps of calculating historical gains of all areas according to historical data obtained by the fact that the unmanned aerial vehicle visits all the areas, and calculating benefit scores of all the areas in the next-hop cruise. Compared with the traditional unmanned aerial vehicle ferrying method, the method is more flexible, so that the aim of dynamically acquiring the next-hop optimal path in the cruising process of the unmanned aerial vehicle is fulfilled, and the comprehensive performance is improved.
Description
Technical Field
The invention belongs to the technical field of unmanned aerial vehicles, and relates to a dynamic planning method for a cruising path of an unmanned aerial vehicle based on historical gain estimation.
Background
The unmanned aerial vehicle is a new sensing device, and because the unmanned aerial vehicle has the characteristics of small volume, flexible and free movement in a three-dimensional space, convenient use, suitability for complex environments and strong viability, the unmanned aerial vehicle and related technologies thereof are favored by countries all over the world, are widely concerned about and used in various fields, and are often used in industries such as agriculture, weather, emergency rescue and disaster relief. Particularly, in an emergency environment, due to the reason that the geographic position of the mobile node is not uniformly distributed, the mobile node moves, the node fails and the like, a plurality of disconnected sub-networks in the mobile ad hoc network, namely, a network segmentation phenomenon occurs. The connectivity of the network is very important in disaster area rescue, military communication and other aspects, and concerns about the safety of people's lives and properties, so how to connect a plurality of disconnected subnets is very important to ensure the connectivity of the whole network. When a plurality of disconnected subnetworks occur in the mobile ad hoc network, the realization of the mutual communication of nodes in different subnetworks through the unmanned aerial vehicle is one of effective solutions to the problem.
Unmanned aerial vehicle flight involves many technologies, but most importantly, the problem of path planning for unmanned aerial vehicles. The planning of the path is an important component of an unmanned aerial vehicle control strategy, and the reasonable planning of the cruise path plays an important role in reducing the length of the flight path and improving the endurance performance. In the existing researches, because many scenes utilizing the unmanned aerial vehicle are driven based on tasks at the present stage, the motion trail of the unmanned aerial vehicle is artificially planned in advance, the unmanned aerial vehicle only needs to move according to the planned trail, the current position state of the unmanned aerial vehicle is only considered in many existing methods, and the characteristic that the unmanned aerial vehicle at the present moment is driven based on the tasks is not fully considered, so that the optimal object for next hop transmission is difficult to find.
Therefore, the designed historical gain estimation-based unmanned aerial vehicle cruising path dynamic planning method suitable for the multiple target nodes has great significance for auxiliary data transmission of the unmanned aerial vehicle.
Disclosure of Invention
In view of this, the present invention provides a method for dynamically planning a cruising path of an unmanned aerial vehicle based on historical gain estimation. The unmanned aerial vehicle can acquire historical data when accessing each area, gains obtained by analyzing the historical data attributes of the unmanned aerial vehicle represent data increment obtained in unit time, such as the weight and number of express delivered by communities and the number of message packages in a network. The unmanned aerial vehicle obtains the historical gain according to the region and calculates according to the position distance of the unmanned aerial vehicle at the current moment to obtain the regional benefit, and the next hop target region is reasonably selected. According to the method, when the unmanned aerial vehicle carries out path planning, the area with the highest area benefit is selected as the next hop, and therefore the optimal path is dynamically obtained in the cruising process of the unmanned aerial vehicle.
In order to achieve the purpose, the invention provides the following technical scheme:
an unmanned aerial vehicle cruising path dynamic planning method based on historical gain estimation comprises the following steps:
step 1) calculating the historical gain of each region;
step 2) calculating the benefit score of the next hop destination region by combining the distance factors;
and 3) designing a dynamic planning method for the unmanned aerial vehicle path.
Further, the step 1) specifically comprises the following steps:
and step 11) after the unmanned aerial vehicle interacts with one region, the unmanned aerial vehicle makes a decision on the central node of the next hop region according to the collected historical data. And the unmanned aerial vehicle calculates the historical gain of each region by combining the relative weight distributed by the data attribute according to the historical data obtained by visiting the regions. The historical data and the classification (attribute) thereof can be defined differently for different application scenarios. For example: in the application scene of carrying out article delivery with unmanned aerial vehicle, the number of the goods of delivery can regard as historical data, can classify the goods according to the emergency degree that the goods need deliver. Each class is given a respective weight. Thus, the weighted sum of all the goods and their corresponding weights can be counted and used as the historical gain of the area for a period of time. For another example: in a scene that an unmanned aerial vehicle is used as a ferry node to complete carrying and forwarding of messages between different areas in a delay tolerant network, the number of message packets can be used as historical data, the message packets can be classified according to the importance, the emergency degree, the size and the like of the message packets, and different weights are given to the message packets of each grade. And counting the weighted sum of the message packets of all the grades as the historical gain of the region for a period of time. The historical gain may be calculated by the following formula:
arrival time t of jth cruise r regionjThe data volume of the ith type data received at the moment is niEach class of data is weighted by ωi；
Step 12) calculating the average gain of the previous m cruising times according to the historical gains of all the areas, wherein the average gain can be expressed as:
further the step 2) comprises the following steps:
step 21) calculating benefit scores of all regions of the next-hop cruise by combining the distances from the unmanned aerial vehicle to all the regions at the moment after the unmanned aerial vehicle obtains the historical gains; unmanned plane at tkSelecting the next hop destination area according to the area benefit at the moment, and at tkThe requirements of other areas cannot be obtained at any moment, so the average gain estimation t obtained by the previous m times of cruising obtained in the step 1 is adoptedkThe gain in the region of time r can be expressed as:
wherein t isk-1D (f, r) is the time of the previous visit to the r area, and d is the linear distance from the unmanned plane to the r area; v is the average cruising speed;
step 22) additionally needs to take into account t on the dronekThe cruise gain for the r region calculated from the region data stored at the time can be expressed as:
step 23) the benefit epsilon (r) of each cruise r region can be calculated by the following formula:
further, the step 3) specifically comprises the following steps:
step 31) in the cruising starting stage of the unmanned aerial vehicle, due to the lack of historical information, the uploading requirement of the central node of each area cannot be obtained, so in the starting stage, the linear distance d between the areas is firstly usedijAnd calculating a shortest path as a weight, taking the shortest path as a cruise path at the beginning stage of ferry, wherein the average gain of each historical data of each area needs to be calculated and the record needs to be updated during the cruising period of the unmanned aerial vehicle except that the required historical data acquisition of each area is completed.
And 32) when the online decision is started, the ferry node uses the information collected in the starting stage as an initial value, calculates epsilon (r) according to the step 2), selects the area with the maximum epsilon (r) as the next hop destination area of the unmanned aerial vehicle, and repeats the process until the cruise is finished.
The invention has the beneficial effects that: according to the invention, the historical data attribute of the unmanned aerial vehicle is calculated, and the historical gain, distance factors and the like of each region are calculated according to the weight distributed by the data attribute to jointly calculate the benefit score. Compared with the traditional unmanned aerial vehicle cruising method, the method is more flexible, the unmanned aerial vehicle cruising process can dynamically obtain the optimal path, and the service efficiency of the unmanned aerial vehicle is improved.
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For the purposes of promoting a better understanding of the objects, aspects and advantages of the invention, reference will now be made to the following detailed description taken in conjunction with the accompanying drawings in which:
FIG. 1 is a schematic flow chart of a method for implementation
FIG. 2 shows the whole environment for planning the cruising path of the unmanned aerial vehicle
Detailed Description
Preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
The invention provides a method for dynamically planning an unmanned aerial vehicle path based on message priority, which is shown in a schematic flow chart of a method provided for implementation as shown in figure 1. Fig. 2 shows the whole environment of the cruise path planning of the unmanned aerial vehicle. According to the path planning of the historical cruising data of the unmanned aerial vehicle, the unmanned aerial vehicle cruises among a plurality of areas. The method comprises the following steps: step 1) calculating the historical gain of each region; step 2) calculating the benefit score of the next hop destination region by combining the distance factors; and 3) designing a dynamic planning method for the unmanned aerial vehicle path.
Further, the step 1) specifically comprises the following steps:
and step 11) after the unmanned aerial vehicle interacts with one region, the unmanned aerial vehicle makes a decision on the central node of the next hop region according to the collected historical data. And the unmanned aerial vehicle calculates the historical gain of each region by combining the relative weight distributed by the data attribute according to the historical data obtained by visiting the regions. The historical data and the classification (attribute) thereof can be defined differently for different application scenarios. For example: in the application scene of carrying out article delivery with unmanned aerial vehicle, the number of the goods of delivery can regard as historical data, can classify the goods according to the emergency degree that the goods need deliver. Each class is given a respective weight. Thus, the weighted sum of all the goods and their corresponding weights can be counted and used as the historical gain of the area for a period of time. For another example: in a scene that an unmanned aerial vehicle is used as a ferry node to complete carrying and forwarding of messages between different areas in a delay tolerant network, the number of message packets can be used as historical data, the message packets can be classified according to the importance, the emergency degree, the size and the like of the message packets, and different weights are given to the message packets of each grade. And counting the weighted sum of the message packets of all the grades as the historical gain of the region for a period of time. The historical gain may be calculated by the following formula:
arrival time t of jth cruise r regionjClass i received at timeData amount of data is niEach class of data is weighted by ωi；
Step 12) calculating the average gain of the previous m cruising times according to the historical gains of all the areas, wherein the average gain can be expressed as:
further the step 2) comprises the following steps:
step 21) calculating benefit scores of all regions of the next-hop cruise by combining the distances from the unmanned aerial vehicle to all the regions at the moment after the unmanned aerial vehicle obtains the historical gains; unmanned plane at tkSelecting the next hop destination area according to the area benefit at the moment, and at tkThe requirements of other areas cannot be obtained at any moment, so the average gain estimation t obtained by the previous m times of cruising obtained in the step 1 is adoptedkThe gain in the region of time r can be expressed as:
wherein t isk-1D (f, r) is the time of the previous visit to the r area, and d is the linear distance from the unmanned plane to the r area; v is the average cruising speed;
step 22) additionally needs to take into account t on the dronekThe cruise gain for the r region calculated from the region data stored at the time can be expressed as:
step 23) the benefit epsilon (r) of each cruise r region can be calculated by the following formula:
further, the step 3) specifically comprises the following steps:
step 31) unmanned plane cruise start phase due to lack of historyInformation, the uploading requirement of each area center node can not be obtained, so in the initial stage, the linear distance between the areas is firstly used
And calculating a shortest path as a weight, taking the shortest path as a cruise path at a cruise starting stage, and calculating an average gain of historical data of each area and updating a record, except for finishing the necessary historical data acquisition of each area during the cruise of the unmanned aerial vehicle.
And 32) when the online decision is started, the ferry node uses the information collected in the starting stage as an initial value, calculates epsilon (r) according to the step 2), selects the area with the maximum epsilon (r) as the next hop destination area of the unmanned aerial vehicle, and repeats the process until the cruise is finished.
Through carrying out above step, can realize at the unmanned aerial vehicle region of jumping of the dynamic selection of in-process that cruises, and then realize that unmanned aerial vehicle cruises the in-process dynamic acquireing optimum route, improve comprehensive properties's purpose.
Finally, the above embodiments are only intended to illustrate the technical solutions of the present invention and not to limit the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all of them should be covered by the claims of the present invention.
Claims (4)
1. An unmanned aerial vehicle cruising path dynamic planning method based on historical gain estimation is characterized in that: the method comprises the following steps:
s1) calculating the historical gain of each region;
s2) calculating the regional benefit score of the next hop by combining the distance factors;
s3) designing a dynamic planning method for the unmanned aerial vehicle path.
2. The method for dynamically planning the cruising path of the unmanned aerial vehicle based on historical gain estimation according to claim 1, wherein: in step S1, the step of calculating the historical gain of each region includes the steps of:
step S11) after the unmanned aerial vehicle interacts with an area, the unmanned aerial vehicle makes a decision on the central node of the next hop area according to the collected historical data. And the unmanned aerial vehicle calculates the historical gain of each region by combining the relative weight distributed by the data attribute according to the historical data obtained by visiting the regions. The historical data and the classification (attribute) thereof can be defined differently for different application scenarios. For example: in the application scene of carrying out article delivery with unmanned aerial vehicle, the number of the goods of delivery can regard as historical data, can classify the goods according to the emergency degree that the goods need deliver. Each class is given a respective weight. Thus, the weighted sum of all the goods and their corresponding weights can be counted and used as the historical gain of the area for a period of time. For another example: in a scene that an unmanned aerial vehicle is used as a ferry node to complete carrying and forwarding of messages between different areas in a delay tolerant network, the number of message packets can be used as historical data, the message packets can be classified according to the importance, the emergency degree, the size and the like of the message packets, and different weights are given to the message packets of each grade. And counting the weighted sum of the message packets of all the grades as the historical gain of the region for a period of time. The historical gain may be calculated by the following formula:
wherein the arrival time t of the jth cruise r regionjThe data volume of the ith type data received at the moment is niEach class of data is weighted by ωi；
Step S12) calculating the average gain of the last m cruising times according to the historical gains of the regions, wherein the average gain can be expressed as:
3. the method for dynamically planning the cruising path of the unmanned aerial vehicle based on historical gain estimation according to claim 1, wherein: in step S2, the calculating the regional benefit score of the next hop according to the distance factor includes the following steps:
step S21), calculating benefit scores of each region of the next-hop cruise by combining the distances from the unmanned aerial vehicle to each region at the moment after the unmanned aerial vehicle obtains the historical gains; unmanned plane at tkSelecting the next hop destination area according to the area benefit at the moment, and at tkThe requirements of other areas cannot be obtained at any moment, so the average gain estimation t obtained by the previous m times of cruising obtained in the step 1 is adoptedkThe gain in the region of time r can be expressed as:
wherein t isk-1D (f, r) is the time of the previous visit to the r area, and d is the linear distance from the unmanned plane to the r area; v is the average cruising speed;
step S22) additionally needs to take into account t on the dronekThe cruise gain for the r region calculated from the region data stored at the time can be expressed as:
step S23) the benefit epsilon (r) for each r region can be calculated by the following formula:
4. the method for dynamically planning the cruising path of the unmanned aerial vehicle based on historical gain estimation according to claim 1, wherein: in step S3, the method for designing a dynamic path plan of an unmanned aerial vehicle includes the following steps:
step (ii) of31) In the cruise starting stage of the unmanned aerial vehicle, due to the lack of historical information, the uploading requirement of the central node of each area cannot be obtained, so that in the starting stage, the linear distance between the areas is firstly used
And calculating a shortest path as a weight, taking the shortest path as a cruise path at the beginning stage of ferry, wherein the average gain of each historical data of each area needs to be calculated and the record needs to be updated during the cruising period of the unmanned aerial vehicle except that the required historical data acquisition of each area is completed.
Step 32) when the online decision is started, the ferry node uses the information collected in the starting stage as an initial value, calculates the obtained epsilon (r) according to the step SS2), selects the area with the largest epsilon (r) as the next hop destination area of the unmanned aerial vehicle, and repeats the process until the cruise is finished.
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