CN113008238A - Unmanned aerial vehicle cooperative reconnaissance path planning method based on information sharing - Google Patents
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Abstract
The invention discloses an unmanned aerial vehicle cooperative reconnaissance path planning method based on information sharing, belongs to the field of unmanned aerial vehicle path planning, and aims to reduce total energy consumption of multiple unmanned aerial vehicles. The method comprises the steps of equally dividing a reconnaissance area into a plurality of sub-areas, determining the area center of each sub-area as an information sharing point, obtaining a complete path traversing all reconnaissance target points by using a genetic algorithm, and determining a plurality of integer splitting schemes according to the number of the target points in the path so as to obtain a plurality of sub-paths representing an unmanned aerial vehicle; for each splitting scheme, judging the information sharing relation of the unmanned aerial vehicles, determining the information sharing point of each group of unmanned aerial vehicles according to the principle of minimum total energy consumption, and mutually sharing information at the information sharing points by the unmanned aerial vehicles and determining users of the service; and (3) reserving the unmanned aerial vehicle path scheme with the minimum total energy consumption, generating a new complete path by genetic operation on the current complete path, and finally reserving the unmanned aerial vehicle path scheme with the minimum total energy consumption as a final unmanned aerial vehicle path.
Description
Technical Field
The invention belongs to the field of unmanned aerial vehicle path planning, and particularly relates to an unmanned aerial vehicle cooperative reconnaissance path planning method based on information sharing.
Background
In the reconnaissance field, the reconnaissance of the unmanned aerial vehicle has more economical efficiency and safety than the reconnaissance of a manned vehicle. Through carrying on equipment such as high resolution camera, sensor, unmanned aerial vehicle can obtain accurate reconnaissance information, carries on these equipment simultaneously and flies also very big consumption unmanned aerial vehicle's energy, therefore reduces the unmanned aerial vehicle energy consumption and is guaranteeing that it accomplishes another important target under the prerequisite of reconnaissance to the regulation target, and this needs carry out reasonable path planning in order to reduce unmanned aerial vehicle's motion energy consumption to unmanned aerial vehicle, improves reconnaissance efficiency.
Two processes which must be considered when planning the path of the unmanned aerial vehicle are that the unmanned aerial vehicle acquires target information and the unmanned aerial vehicle transmits the information to users, when the unmanned aerial vehicle provides reconnaissance service for multiple users, the traditional information transmission mode is that the unmanned aerial vehicle transmits the information to a data center, the data center distributes the information to each user through a network, and the path planning problem at the moment is generally modeled as a problem of a Traveling Salesman (TSP) or a Vehicle Routing Protocol (VRP). When the data center does not exist or the channel between the data center and the user is not interfered, the unmanned aerial vehicle needs to directly distribute the information to the corresponding user according to the information requirement of the user. When user information demand coincides, that is, a plurality of users need the same target information, the traditional path planning method adopted at the moment can lead a single unmanned aerial vehicle to store the required part information of a plurality of users, and then the conditions that the single unmanned aerial vehicle accesses a plurality of users and a plurality of unmanned aerial vehicles accesses one user and the like are generated, the redundant access of the unmanned aerial vehicle to the users and the route between the unmanned aerial vehicles are repeated, and the energy consumption of each unmanned aerial vehicle can be greatly wasted when the distance between the users is far away.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides an unmanned aerial vehicle cooperative reconnaissance path planning method based on information sharing.
The specific technical scheme of the invention is as follows: an unmanned aerial vehicle cooperative reconnaissance path planning method based on information sharing comprises the following steps:
and 6, reserving the path scheme of the unmanned aerial vehicle with the minimum total energy consumption, performing genetic operation on the complete path generated in the step 3 to generate a new path, adding one to the iteration times, repeating the steps 4 to 6 until the maximum iteration times are reached, and finally taking the path scheme with the minimum total energy consumption as the final path of each unmanned aerial vehicle.
Preferably, in step 5 of the present invention: aiming at the result of each splitting scheme obtained in the step 4, judging the information sharing relationship of the unmanned aerial vehicles according to the information demand relationship condition of the target point and the user in the unmanned aerial vehicle path, grouping the unmanned aerial vehicles according to the information sharing relationship, and determining the information sharing point of each group of unmanned aerial vehicles according to the principle of minimum total energy consumption, wherein the specific steps are as follows:
step 5.1, aiming at one of the splitting schemes obtained in the step 4, determining a target access sequence path (u) of each unmanned aerial vehiclei)＝(vti，1，vti，2…vti，end)，uiDenotes the drone, uti，endE VT denotes drone uiCalculating hovering energy consumption h (upsilon) at the last target point visited before information sharingi) Flight energy consumption w (upsilon)i，υj)；
Step 5.2, determining a user set UR which needs to provide information with the unmanned aerial vehicle according to the target point accessed by each unmanned aerial vehiclei＝(υr1，υr2…υrk) Indicates unmanned plane uiStoring information needed by users in a set, wherein upsilonrkE.g. VR represents the user;
step 5.3, randomly selecting one unmanned aerial vehicle from unmanned aerial vehicles not merged into the information sharing group to establish an information sharing group SRαDetermining a set of users SUR having information provision relations with all drones within the information sharing groupα＝(υri…υrj)；
Step 5.4, selecting and SUR in the rest unmanned aerial vehiclesαUR with set intersection relationshipsiCorresponding unmanned aerial vehicle, incorporating into the current information sharing group SRαUpdating SURα；
Step 5.5, repeating step 5.4 until UR and SUR corresponding to each unmanned aerial vehicle remainαThere is no intersection;
step 5.6, traversing the predetermined information sharing point usiBelongs to VS, and selects all the unmanned aerial vehicles in the information sharing group from vti，endTo vsiAt the point of minimum total flight energy consumption, i.e.
As information sharing points IS within the information sharing groupα(ii) a And use ISαMerge into each unmanned aerial vehicle path in the group, every frame of this information sharing group this momentThe unmanned aerial vehicle has all information of service users thereof, and then distributes the information to the users of the service distributed to the unmanned aerial vehicle;
and 5.6, judging whether unmanned aerial vehicles which are not merged into the information sharing group exist, and repeating the steps 5.2 to 5.5 if the remaining unmanned aerial vehicles exist.
Compared with the prior art, the invention has the remarkable advantages that: the invention aims at minimizing the total energy consumption of multiple unmanned aerial vehicles, and solves the problem of multi-user redundant access when the unmanned aerial vehicle provides reconnaissance service for multiple users through a path planning method based on information sharing; the invention utilizes the information sharing of the unmanned aerial vehicles to complement all information required by the users served by the single unmanned aerial vehicle, ensures that one user only needs one unmanned aerial vehicle to provide information, avoids the problem of multi-user redundant access, and reduces the total energy consumption of multiple unmanned aerial vehicles.
Drawings
Fig. 1 is a schematic view of a cooperative reconnaissance scene of an unmanned aerial vehicle for multiple users.
Fig. 2 is a schematic diagram showing comparison of individual energy consumption of each unmanned aerial vehicle.
Fig. 3 is a schematic diagram of the relationship between the total energy consumption and the number of sensors of the multi-unmanned aerial vehicle.
Fig. 4 is a schematic diagram of the relationship between the total energy consumption and the number of users of the multi-unmanned aerial vehicle.
Fig. 5 is a schematic diagram of a relationship between total energy consumption of multiple unmanned aerial vehicles and coincidence rate of user demand information.
Detailed Description
A method for planning cooperative reconnaissance paths of unmanned aerial vehicles based on information sharing is disclosed, as shown in figure 1, a plurality of unmanned aerial vehicles reconnaissance target points, and then the unmanned aerial vehicles share information with other unmanned aerial vehicles at preset information interaction points, and the unmanned aerial vehicles go to the vicinity of users to send information after complementing all information required by service users of the unmanned aerial vehicles. On the premise of considering flight and hovering energy consumption, the total energy consumption of the multiple unmanned aerial vehicles is reduced by planning the path of each unmanned aerial vehicle, and the method comprises the following specific steps:
The purpose of unmanned aerial vehicle path planning is to enable multiple unmanned aerial vehicles to traverse all target points, and simultaneously enable the total energy consumption of the multiple unmanned aerial vehicles to be minimized, wherein the energy consumption of the unmanned aerial vehicles mainly comprises flight energy consumption and hovering energy consumption. The flight energy consumption specifically is as follows:
w(vi，vj)＝Q·l
q is the energy consumed by the unmanned aerial vehicle in unit flying length, and l is the flying length of the unmanned aerial vehicle;
the hovering energy consumption is as follows:
h(vi)＝p(h)·hti
where p (h) denotes the drone hover power, htiIndicates that the drone is at viThe hover time of the point;
when the unmanned aerial vehicle near field communication, unmanned aerial vehicle communication energy consumption can be neglected compared with its motion energy consumption, therefore the total energy consumption mainly comprises flight energy consumption and hovering energy consumption, specifically:
wherein path (u)i) Represent unmanned plane uiTrack of (E), EiRepresent unmanned plane uiE represents the total energy consumption of the multiple drones.
For unmanned aerial vehicle reconnaissance process, use
Representing the reconnaissance relationship of the drone with the target point, namely:
by using
The relationship representing information sharing between the drones:
when the unmanned aerial vehicle interacts information, the information which is collected by the unmanned aerial vehicle can be transmitted to other unmanned aerial vehicles, and the constraint conditions are as follows:
when the unmanned aerial vehicle reconnaissance task is finished, all information in an information set of a service user is obtained in a direct acquisition and information sharing mode, and constraint conditions are expressed as follows:
by vmaxRepresenting the maximum speed of the drone, the constraint is expressed as:
to sum up, this problem is to carry out route planning to many unmanned aerial vehicles under the information sharing mechanism circumstances of gathering for unmanned aerial vehicle system total energy consumption is minimum, and the formalization is described as:
step 5.1, aiming at one splitting scheme obtained in step 4, determining a target access sequence path (ui) ═ vt of each unmanned aerial vehiclei，1，vti，2…vti，end)，uiIndicating unmanned plane, vti，endE VT denotes drone uiPresence informationThe last destination point visited before the information sharing. Calculating hovering energy consumption h (v)i) Flight energy consumption w (v)i，vj)。
Step 5.2, determining a user set UR in information providing relation with the unmanned aerial vehicles according to the target point visited by each unmanned aerial vehiclei＝(vri，vr2…vrk) Indicates unmanned plane uiStoring information required by a user in a collection, wherein vrkE VR represents the user.
Step 5.3, selecting one unmanned aerial vehicle from unmanned aerial vehicles which are not merged into the information sharing group to establish an information sharing group SRα＝(ui) Determining a set of users SUR having information provision relations with all the drones in the information sharing groupα＝(vi…vrj)。
Step 5.4, selecting and SUR in the rest unmanned aerial vehiclesαUR with set intersection relationshipsiCorresponding unmanned aerial vehicle, incorporating into the current information sharing group SRαUpdating SURα。
Step 5.5, repeating step 5.4 until UR and SUR corresponding to each unmanned aerial vehicle remainαThere is no intersection.
Step 5.6, traversing the preset information sharing point upsilonsiBelongs to VS, and selects all the unmanned aerial vehicles in the information sharing group from vti，endTo vsiAt the point of minimum total flight energy consumption, i.e.
As information sharing points IS within the information sharing groupα(ii) a And use ISαAnd when the unmanned aerial vehicles merge into each unmanned aerial vehicle path in the group, each unmanned aerial vehicle in the information sharing group has all the information of the service users, and then the unmanned aerial vehicles are distributed with users for information distribution.
And 5.6, judging whether unmanned aerial vehicles which are not merged into the information sharing group exist, and repeating the steps 5.2 to 5.5 if the remaining unmanned aerial vehicles exist.
And 6, reserving the path scheme of the unmanned aerial vehicle with the minimum total energy consumption, performing genetic operation on the complete path generated in the step 3 to generate a new path, adding one to the iteration times, repeating the steps 4 to 6 until the maximum iteration times are reached, and finally taking the path scheme with the minimum total energy consumption as the final path of each unmanned aerial vehicle.
Example (b):
as shown in fig. 1, a collaborative reconnaissance path planning method for a multi-user-oriented unmanned aerial vehicle based on information sharing is applied to a specific example to ensure that total energy consumption of the multi-user unmanned aerial vehicle is minimum, and the specific application is as follows:
assume that the sensors are randomly distributed within a 5km x 5km surveillance area, with a default number of sensors of 25. The number of the default unmanned aerial vehicles is 3, the number of the default users is 3, and the demand information set of each user is randomly generated. The multiple users can need the information of the same target, the user information coincidence rate Ior represents the proportion of the information needed by the multiple users in the total user demand information, and the default value is 30%. The flight energy consumption Q of the unmanned aerial vehicle per unit distance is 13.19J/m, and the hovering power p (h) is 220J/s. The method is called ISCR (Information Sharing based Cooperative relaying) in short, the comparison algorithm of the example adopts MTSP algorithm instead of unmanned plane Information Sharing strategy, and is called SWIS (executing Information Service Without Information Sharing).
Fig. 2 shows a comparison of energy consumption per drone in SWIS and ISCR methods at default parameter settings. It can be seen that, compared with the SWIS, each unmanned aerial vehicle in the ISCR has lower energy consumption, but the unmanned aerial vehicle with higher energy consumption has poorer performance in meeting the timeliness requirements of users, because the ISCR method bears the spying tasks of the users of the ISCR and simultaneously bears the spying tasks of other users too much, and is not suitable for scenes with higher requirements on information timeliness.
Fig. 3 shows a trend of total energy consumption of the multiple drones varying with the number of the reconnaissance targets, as the number of the reconnaissance targets increases, a trend of total energy consumption increase of the multiple drones in the SWIS is accelerated, and a trend of energy consumption increase in the ISCR is not large, and meanwhile, the total energy consumption of the multiple drones in the ISCR is always smaller than the total energy consumption of the drones in the SWIS.
Fig. 4 shows a trend of total energy consumption of multiple drones varying with the number of users, as the number of users increases, the total energy consumption of multiple drones in the ISCR is always smaller than that of the drones in the SWIS, and meanwhile, the rising trend of the total energy consumption of multiple drones in the SWIS is accelerated, and the rising trend of the ISCR remains stable, which indicates that the present invention is more suitable for saving the total energy consumption of multiple drones as the number of users served by the drones increases.
Fig. 5 shows a trend of total energy consumption of multiple drones along with information coincidence rate change, and as the information coincidence rate increases, the total energy consumption of the multiple drones in the ISCR is always smaller than that of the drones in the SWIS, and meanwhile, the rising trend of the total energy consumption of the multiple drones in the SWIS is obviously accelerated, and the rising trend of the ISCR gradually slows down, which indicates that the invention is more suitable for saving the total energy consumption of the multiple drones when the coincidence degree of user demand information is larger.
Claims (3)
1. An unmanned aerial vehicle cooperative reconnaissance path planning method based on information sharing is characterized by comprising the following steps:
step 1, dividing a reconnaissance region into a plurality of sub-regions equally according to an area equalization principle, wherein the center of each region is used as an information sharing point;
step 2, constructing the ground target point network and the information sharing point into an undirected graph G (V, E), Vie.V denotes the vertex in the undirected graph, e (V)i，vj) E represents the edge of two adjacent vertexes; wherein
Each vertex in (a) is represented as a target point,
the vertex in (1) represents a predetermined information sharing point,
the middle vertex represents the user; u. ofiE is U and represents the unmanned plane for reconnaissance of the target point;
step 3, calculating a complete path covering all target points in VT by using a genetic algorithm, and determining the maximum iteration times;
step 4, integer splitting is carried out on the complete path obtained in the step 3, a plurality of different splitting schemes are generated according to the number of the unmanned aerial vehicles, each splitting scheme splits the complete path into a plurality of sub-paths, and each sub-path represents a path of one unmanned aerial vehicle;
step 5, aiming at the result of each splitting scheme obtained in the step 4, judging the information sharing relationship of the unmanned aerial vehicles according to the information demand relationship condition of the target point and the user in the unmanned aerial vehicle path, grouping the unmanned aerial vehicles according to the information sharing relationship, and determining the information sharing point of each group of unmanned aerial vehicles according to the principle of minimum total energy consumption;
and 6, reserving the path scheme of the unmanned aerial vehicle with the minimum total energy consumption, performing genetic operation on the complete path generated in the step 3 to generate a new path, adding one to the iteration times, repeating the steps 4 to 6 until the maximum iteration times are reached, and finally taking the path scheme with the minimum total energy consumption as the final path of each unmanned aerial vehicle.
2. The cooperative unmanned aerial vehicle reconnaissance path planning method based on information sharing of claim 1, wherein in step 5: aiming at the result of each splitting scheme obtained in the step 4, judging the information sharing relationship of the unmanned aerial vehicles according to the information demand relationship condition of the target point and the user in the unmanned aerial vehicle path, grouping the unmanned aerial vehicles according to the information sharing relationship, and determining the information sharing point of each group of unmanned aerial vehicles according to the principle of minimum total energy consumption, wherein the specific steps are as follows:
step 5.1, aiming at one of the splitting schemes obtained in the step 4, determining a target access sequence path (u) of each unmanned aerial vehiclei)＝(vti，1，vti，2…vti，end)，uiIndicating unmanned plane, vti，endE VT denotes drone uiCalculating hovering energy consumption h (v) at the last target point visited before information sharingi) Flight energy consumption w (upsilon)i，υj)；
Step 5.2, determining a user set UR which needs to provide information with the unmanned aerial vehicle according to the target point accessed by each unmanned aerial vehiclei＝(υr1，υr2…υrk) Indicates unmanned plane uiStoring information needed by users in a set, wherein upsilonrkE.g. VR represents the user;
step 5.3, randomly selecting one unmanned aerial vehicle from unmanned aerial vehicles not merged into the information sharing group to establish an information sharing group SRα＝(ui) Determining a set of users SUR having information provision relations with all the drones in the information sharing groupα＝(vri…vrj)；
Step 5.4, selecting and SUR in the rest unmanned aerial vehiclesαUR with set intersection relationshipsiCorresponding unmanned aerial vehicle, incorporating into the current information sharing group SRαUpdating SURα；
Step 5.5, repeating step 5.4 until UR and SUR corresponding to each unmanned aerial vehicle remainαThere is no intersection;
step 5.6, traversing the preset information sharing point vsi E to VS, and selecting to enable all unmanned aerial vehicles in the information sharing group to slave vti，endTo vsiAt the point of minimum total flight energy consumption, i.e.
As information sharing points IS within the information sharing groupα(ii) a And use ISαMerging the unmanned planes into paths of all unmanned planes in the group, wherein each unmanned plane of the information sharing group has all information of service users of the unmanned planes at the moment, and then distributing the information for the users of the unmanned planes for distributing service;
and 5.6, judging whether unmanned aerial vehicles which are not merged into the information sharing group exist, and repeating the steps 5.2 to 5.5 if the remaining unmanned aerial vehicles exist.
3. The cooperative reconnaissance method for multi-user-oriented unmanned aerial vehicles based on information sharing as claimed in claim 2, wherein the flight energy consumption determined in step 5.1 is specifically:
w(υi，υj)＝Q·l
q is the energy consumed by the unmanned aerial vehicle in unit flying length, and l is the flying length of the unmanned aerial vehicle;
the hovering energy consumption is as follows:
h(υi)＝p(h)·hti
where p (h) denotes the drone hover power, htiIndicates that the drone is at viThe hover time of the point;
when the unmanned aerial vehicle near field communication, unmanned aerial vehicle communication energy consumption can be neglected compared with its motion energy consumption, therefore the total energy consumption mainly comprises flight energy consumption and hovering energy consumption, specifically:
wherein path (u)i) Represent unmanned plane uiTrack of (E), EiRepresent unmanned plane uiE represents the total energy consumption of the multiple drones.
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