CN113008238B - Unmanned plane collaborative reconnaissance path planning method based on information sharing - Google Patents
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Abstract
The invention discloses an unmanned aerial vehicle collaborative reconnaissance path planning method based on information sharing, which 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 dividing a reconnaissance area into a plurality of subareas, determining the area center of each subarea 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 one unmanned plane; aiming at each splitting scheme, judging the information sharing relation of unmanned aerial vehicles, determining the information sharing points of each group of unmanned aerial vehicles according to the principle of minimum total energy consumption, and enabling the unmanned aerial vehicles to share information with each other at the information sharing points and determine the users of the service; and reserving an unmanned aerial vehicle path scheme with the minimum total energy consumption, generating a new complete path from the current complete path through genetic operation, 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 collaborative reconnaissance path planning method based on information sharing.
Background
In the reconnaissance field, unmanned aerial vehicle reconnaissance is adopted, so that the unmanned aerial vehicle reconnaissance has economical efficiency and safety compared with unmanned aerial vehicle reconnaissance. By carrying the equipment such as the high-resolution camera and the sensor, the unmanned aerial vehicle can obtain accurate reconnaissance information, and meanwhile, the energy of the unmanned aerial vehicle is greatly consumed when carrying the equipment to fly, so that the reduction of the energy consumption of the unmanned aerial vehicle is another important target on the premise of ensuring that the unmanned aerial vehicle completes reconnaissance on a specified target, and reasonable path planning is required to be carried out on the unmanned aerial vehicle so as to reduce the motion energy consumption of the unmanned aerial vehicle and improve reconnaissance efficiency.
Two processes that must be considered in unmanned aerial vehicle path planning are that unmanned aerial vehicle acquires target information and unmanned aerial vehicle transmits information to users, and when unmanned aerial vehicle provides reconnaissance service for the multiuser, traditional information transmission mode is that unmanned aerial vehicle transmits information to data center, data center distributes information to each user through the network, and the path planning problem at this moment is usually modeled as problems such as travel provider (TSP) problem or Vehicle Routing (VRP). When there is no data center or the channel between the data center and the user is interfered, the unmanned aerial vehicle needs to directly distribute the information to the corresponding user according to the information demand of the user. When user information demands coincide, that is, when a plurality of users need the same target information, the adoption of a traditional path planning method can lead to the storage of the needed part information of a plurality of users by a single unmanned aerial vehicle at the moment, and then the situations that the single unmanned aerial vehicle accesses the plurality of users, the plurality of unmanned aerial vehicles accesses one user and the like are generated, so that the redundant access of the unmanned aerial vehicle to the users and the repeated route between the unmanned aerial vehicles are caused, and when the distance between the users is far, the energy consumption of each unmanned aerial vehicle can be greatly wasted.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides an unmanned aerial vehicle collaborative reconnaissance path planning method based on information sharing.
The specific technical scheme of the invention is as follows: an unmanned aerial vehicle collaborative reconnaissance path planning method based on information sharing comprises the following steps:
step 1, dividing a reconnaissance area into a plurality of subareas according to an area equalization principle, wherein the center of each area is used as an information sharing point;
step 2, constructing a ground target point network and an information sharing point as an undirected graph G (V, E), upsilon i E V represents the vertex in the undirected graph, e (V i ，v j ) E represents the edges of two adjacent vertices; wherein the method comprises the steps ofIs denoted as target point, +.>Wherein the vertices of ∈1 represent predetermined information sharing points, +.>The middle vertex represents the user; u (u) i E U represents an unmanned aerial vehicle for reconnaissance of the target point;
step 3, calculating a complete path covering all target points in the VT by using a genetic algorithm, and determining the maximum iteration times;
step 4, carrying out integer splitting on the complete path obtained in the step 3, and generating a plurality of different splitting schemes according to the number of unmanned aerial vehicles, wherein 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 relation of the unmanned aerial vehicle according to the information demand relation condition of the target point and the user in the unmanned aerial vehicle path, grouping the unmanned aerial vehicles according to the information sharing relation, and determining the information sharing points 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, carrying out 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 relation of the unmanned aerial vehicle according to the information demand relation condition of the target point and the user in the unmanned aerial vehicle path, grouping the unmanned aerial vehicles according to the information sharing relation, 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, determining a target access sequence path (u i )＝(vt i，1 ，vt i，2 …vt i，end )，u i Represent unmanned plane, ut i，end E VT represents unmanned plane u i The last target point accessed before information sharing calculates hovering energy consumption h (upsilon) i ) Flight energy consumption w (v) i ，υ j )；
Step 5.2, determining a user set UR which is required to provide information with the unmanned aerial vehicle according to the target point accessed by each unmanned aerial vehicle i ＝(υr 1 ，υr 2 …υr k ) Represents unmanned plane u i Storing information required by users in a collection, wherein upsilonr k E VR represents a user;
step 5.3, randomly selecting one unmanned plane from unmanned planes which do not incorporate the information sharing group to establish an information sharing group SR α = (ui) determining a user set SUR in relation to all unmanned aerial vehicle presence information provision within the information sharing group α ＝(υr i …υr j )；
Step 5.4, selecting SUR in the remaining unmanned aerial vehicle α UR with aggregate intersection relationship i Corresponding unmanned aerial vehicle incorporating current information sharing group SR α Updating SUR α ；
Step 5.5, repeating the step 5.4 until the UR and SUR corresponding to each unmanned aerial vehicle remain α There is no intersection;
step 5.6, traversing the predetermined information sharing points us i E, VS, selecting all unmanned aerial vehicles in the information sharing group to be driven by vt i，end To vs i The point of minimum total flight energy consumption, i.eAs information sharing point IS within the information sharing group α The method comprises the steps of carrying out a first treatment on the surface of the And IS IS combined with α The information sharing method comprises the steps that the information sharing method is incorporated into each unmanned aerial vehicle path in a group, at the moment, each unmanned aerial vehicle in the information sharing group has all information of service users of the unmanned aerial vehicle, and then information distribution is carried out on the users who distribute services for the unmanned aerial vehicles;
and 5.6, judging whether unmanned aerial vehicles which are not incorporated into the information sharing group exist or not, and if the remaining unmanned aerial vehicles exist, repeating the steps 5.2 to 5.5.
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 a reconnaissance service for multiple users through a path planning method based on information sharing; according to the invention, all information required by the user of the single unmanned aerial vehicle service is shared and completed by using unmanned aerial vehicle information, so that only one unmanned aerial vehicle is required to provide information for one user, the problem of multi-user redundant access is avoided, and the total energy consumption of multiple unmanned aerial vehicles is reduced.
Drawings
Fig. 1 is a schematic diagram of a collaborative 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 according to the present invention.
Fig. 3 is a schematic diagram of the relationship between total energy consumption and the number of sensors of the multi-unmanned aerial vehicle according to the present invention.
Fig. 4 is a schematic diagram of the relationship between total energy consumption and the number of users of the multi-unmanned aerial vehicle according to the present invention.
Fig. 5 is a schematic diagram of the relationship between total energy consumption and user demand information of multiple unmanned aerial vehicles according to the present invention.
Detailed Description
An unmanned plane collaborative reconnaissance path planning method based on information sharing is disclosed in fig. 1, a plurality of unmanned planes are cooperated to reconnaissance a target point, then the unmanned plane shares information with other unmanned planes at a preset information interaction point, and after all information required by a service user is completed, the unmanned plane will go to the vicinity of the user to send information. On the premise of considering flight and hover energy consumption, the total energy consumption of the multiple unmanned aerial vehicles is reduced by carrying out path planning on each unmanned aerial vehicle, and the method comprises the following specific steps:
step 1, dividing a reconnaissance area into a plurality of subareas according to an area equalization principle, wherein the center of each area is used as an information sharing point;
step 2, constructing a ground target point network and an information sharing point into an undirected graph, G (V, E), V i E V represents the vertex in the undirected graph, e (V i ，v j ) E represents the edges of two adjacent vertices. Wherein the method comprises the steps ofIs denoted as target point, +.>Wherein the vertices of ∈1 represent predetermined information sharing points, +.>The middle vertex represents the user; u (u) i The e U represents the drone that scouts the target point.
The unmanned aerial vehicle path planning aims at enabling multiple unmanned aerial vehicles to traverse all target points, simultaneously minimizing total energy consumption of the multiple unmanned aerial vehicles, and energy consumption components of the unmanned aerial vehicles mainly comprise flight energy consumption and hovering energy consumption. The flight energy consumption is specifically as follows:
w(v i ，v j )＝Q·l
wherein Q is energy consumed by the unmanned aerial vehicle in flight unit length, and l is the flight length of the unmanned aerial vehicle;
the energy consumption for hovering is as follows:
h(v i )＝p(h)·ht i
wherein p (h) represents unmanned aerial vehicle hover power, ht i Indicating that unmanned plane is in v i Hover time of the dot;
when unmanned aerial vehicle near field communication, unmanned aerial vehicle communication energy consumption compares its motion energy consumption and can neglect, therefore the total energy consumption mainly comprises flight energy consumption and energy consumption of hovering, specifically:
wherein path (u) i ) Representing unmanned plane u i Track of E i Representing unmanned plane u i E represents the total energy consumption of the multiple unmanned aerial vehicles.
For unmanned aerial vehicle reconnaissance process, useRepresenting the reconnaissance relation between the unmanned plane and the target point, namely:
by usingThe relationship of information sharing between unmanned aerial vehicles is represented:
when unmanned aerial vehicle mutual information, can only transmit its information that has gathered to other unmanned aerial vehicles, its constraint condition represents:
when the unmanned aerial vehicle reconnaissance task is finished, all information in the information set of the service user is obtained through a direct acquisition and information sharing mode, and constraint conditions are expressed as follows:
by v max Representing the maximum speed of the unmanned aerial vehicle, the constraint conditions are expressed as:
to sum up, the problem is to carry out path planning to multiple unmanned aerial vehicles under the condition of an information collection sharing mechanism, so that the total energy consumption of the unmanned aerial vehicle system is minimum, and formalized description is as follows:
step 3, calculating a complete path covering all target points in the VT by using a genetic algorithm, and simultaneously determining the maximum iteration times;
step 4, carrying out integer splitting on the complete path obtained in the step 3, and generating a plurality of different splitting schemes according to the number of unmanned aerial vehicles, wherein 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 relation of the unmanned aerial vehicle according to the information demand relation condition of the target point and the user in the unmanned aerial vehicle path, grouping the unmanned aerial vehicles according to the information sharing relation, 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, determining a target access sequence path (ui) = (vt) of each unmanned aerial vehicle according to one of the split schemes obtained in step 4 i，1 ，vt i，2 …vt i，end )，u i Representing unmanned plane, vt i，end E VT represents unmanned plane u i The last target point accessed before the information sharing. Calculating hover energy consumption h (v) i ) Flight energy consumption w (v) i ，v j )。
Step 5.2, determining a user set UR related to unmanned aerial vehicle information provision according to the target point visited by each unmanned aerial vehicle i ＝(vr i ，vr 2 …vr k ) Represents unmanned plane u i Storing information required by users in a collection, wherein vr k E VR represents a user.
Step 5.3 absence of the non-incorporated information sharing groupSelecting one unmanned plane from the unmanned planes to establish an information sharing group SR α ＝(u i ) Determining a user set SUR in relation to all unmanned aerial vehicle presence information provision in the information sharing group α ＝(v i …vr j )。
Step 5.4, selecting SUR in the remaining unmanned aerial vehicle α UR with aggregate intersection relationship i Corresponding unmanned aerial vehicle incorporating current information sharing group SR α Updating SUR α 。
Step 5.5, repeating the step 5.4 until the UR and SUR corresponding to each unmanned aerial vehicle remain α There is no intersection.
Step 5.6, traversing the preset information sharing points vs i E, VS, selecting all unmanned aerial vehicles in the information sharing group to be driven by vt i，end To vs i The point of minimum total flight energy consumption, i.eAs information sharing point IS within the information sharing group α The method comprises the steps of carrying out a first treatment on the surface of the And IS IS combined with α And the information sharing group is integrated into each unmanned plane path in the group, each unmanned plane of the information sharing group has all information of service users of the unmanned planes, and then the unmanned planes are distributed with users for information distribution.
And 5.6, judging whether unmanned aerial vehicles which are not incorporated into the information sharing group exist or not, and if the remaining unmanned aerial vehicles exist, repeating the steps 5.2 to 5.5.
And 6, reserving the path scheme of the unmanned aerial vehicle with the minimum total energy consumption, carrying out 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.
Examples:
as shown in fig. 1, the method for planning the collaborative reconnaissance path of the multi-user unmanned aerial vehicle based on information sharing is applied to a specific example, so as to ensure that the total energy consumption of the multi-unmanned aerial vehicle is minimum, and the specific application is as follows:
assuming that the sensors are randomly distributed within a 5km x 5km scout area, the default number of sensors is 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, and the user information coincidence ratio Ior represents the proportion of the information needed by the multiple users to the total requirement information of the users, and the default value is 30%. The flying 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 herein presented as ISCR (Information Sharing based Cooperative Reconnaissance) the present example contrast algorithm is SWIS (Executing Information Service Without Information Sharing) using the MTSP algorithm instead of the drone information sharing strategy.
Fig. 2 shows the energy alignment per unmanned in SWIS and ISCR methods at default parameter settings. Compared with SWIS, each unmanned aerial vehicle in the ISCR has lower energy consumption, but unmanned aerial vehicles with higher energy consumption are poorer in meeting the time-efficiency requirement of users, and because the unmanned aerial vehicles are used for carrying own user reconnaissance tasks and are used for carrying out reconnaissance tasks of other users too much, the ISCR method is not suitable for scenes with higher information time-efficiency requirement.
Fig. 3 shows the trend of the total energy consumption of multiple unmanned aerial vehicles along with the number of the scout targets, the rising trend of the total energy consumption of the multiple unmanned aerial vehicles in the SWIS is accelerated along with the increase of the number of the scout targets, the rising trend of the energy consumption in the ISCR is not greatly changed, and meanwhile, the total energy consumption of the multiple unmanned aerial vehicles in the ISCR is always smaller than the total energy consumption of the unmanned aerial vehicles in the SWIS.
Fig. 4 shows the trend of the total energy consumption of multiple unmanned aerial vehicles along with the change of the number of users, the total energy consumption of the multiple unmanned aerial vehicles in the ISCR is always smaller than the total energy consumption of the unmanned aerial vehicles in the SWIS along with the increase of the number of users, meanwhile, the rising trend of the total energy consumption of the multiple unmanned aerial vehicles in the SWIS is obviously accelerated, the rising trend of the ISCR is stable, and the invention is more suitable for saving the total energy consumption of the multiple unmanned aerial vehicles along with the increase of the number of users served by the unmanned aerial vehicles.
Fig. 5 shows the trend of the total energy consumption of multiple unmanned aerial vehicles along with the change of the information coincidence rate, and the total energy consumption of the multiple unmanned aerial vehicles in the ISCR is always smaller than the total energy consumption of the unmanned aerial vehicles in the SWIS along with the increase of the information coincidence rate, meanwhile, the trend of the total energy consumption of the multiple unmanned aerial vehicles in the SWIS is obviously accelerated, the trend of the rise of the ISCR is gradually slowed down, and the fact that the greater the information coincidence degree of the user demands is, the invention is more suitable for saving the total energy consumption of the multiple unmanned aerial vehicles.
Claims (2)
1. The unmanned aerial vehicle collaborative reconnaissance path planning method based on information sharing is characterized by comprising the following steps of:
step 1, dividing a reconnaissance area into a plurality of subareas according to an area equalization principle, wherein the center of each area is used as an information sharing point;
step 2, constructing a ground target point network and an information sharing point into an undirected graph, G (V, E), V i E V represents the vertex in the undirected graph, e (V i ，v j ) E represents the edges of two adjacent vertices; wherein the method comprises the steps ofIs denoted as the target point,wherein the vertices of ∈1 represent predetermined information sharing points, +.>The middle vertex represents the user; u (u) i E U represents an unmanned aerial vehicle for reconnaissance of the target point;
step 3, calculating a complete path covering all target points in the VT by using a genetic algorithm, and determining the maximum iteration times;
step 4, carrying out integer splitting on the complete path obtained in the step 3, and generating a plurality of different splitting schemes according to the number of unmanned aerial vehicles, wherein 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 relation of the unmanned aerial vehicle according to the information demand relation condition of the target point and the user in the unmanned aerial vehicle path, grouping the unmanned aerial vehicles according to the information sharing relation, and determining the information sharing points of each group of unmanned aerial vehicles according to the principle of minimum total energy consumption;
the method comprises the following specific steps:
step 5.1, determining a target access sequence path (u i )＝(vt i，1 ，vt i，2 …vt i，end )，u i Representing unmanned plane, vt i，end E VT represents unmanned plane u i The last target point accessed before information sharing, the hover energy consumption h (v i ) Flight energy consumption w (v) i ，v j )；
Step 5.2, determining a user set UR which is required to provide information with the unmanned aerial vehicle according to the target point accessed by each unmanned aerial vehicle i ＝(vr 1 ，vr 2 …vr k ) Represents unmanned plane u i Storing information required by users in a collection, wherein vr k E VR represents a user;
step 5.3, randomly selecting one unmanned plane from unmanned planes which do not incorporate the information sharing group to establish an information sharing group SR α ＝(u i ) Determining a user set SUR in relation to all unmanned aerial vehicle presence information provision in the information sharing group α ＝(vr i …vr j )；
Step 5.4, selecting SUR in the remaining unmanned aerial vehicle α UR with aggregate intersection relationship i Corresponding unmanned aerial vehicle incorporating current information sharing group SR α Updating SUR α ；
Step 5.5, repeating the step 5.4 until the UR and SUR corresponding to each unmanned aerial vehicle remain α There is no intersection;
step 5.6, traversing the preset information sharing points vs i E, VS, selecting all unmanned aerial vehicles in the information sharing group to be driven by vt i，end To vs i The point of minimum total flight energy consumption, i.eAs information sharing point IS within the information sharing group α The method comprises the steps of carrying out a first treatment on the surface of the And IS IS combined with α Incorporated into each unmanned plane path in the group, where each unmanned plane of the information sharing group has all the information of its service user, and then distributes service to the unmanned planesInformation distribution is carried out on users of the system;
step 5.6, judging whether unmanned aerial vehicles which are not incorporated into the information sharing group exist or not, and repeating the steps 5.2 to 5.5 if the rest unmanned aerial vehicles exist;
and 6, reserving the path scheme of the unmanned aerial vehicle with the minimum total energy consumption, carrying out 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 information sharing-based multi-user-oriented unmanned aerial vehicle collaborative reconnaissance method according to claim 1, wherein the flight energy consumption determined in step 5.1 is specifically:
ω(v i ,v j )＝Q·l
wherein Q is energy consumed by the unmanned aerial vehicle in flight unit length, and l is the flight length of the unmanned aerial vehicle;
the energy consumption for hovering is as follows:
h(v i )＝p(h)·ht i
wherein p (h) represents unmanned aerial vehicle hover power, ht i Indicating that unmanned plane is in v i Hover time of the dot;
when unmanned aerial vehicle near field communication, unmanned aerial vehicle communication energy consumption compares its motion energy consumption and neglects, therefore the total energy consumption mainly comprises flight energy consumption and energy consumption of hovering, specifically does:
wherein path (u) i ) Representing unmanned plane u i Track of E i Representing unmanned plane u i E represents the total energy consumption of the multiple unmanned aerial vehicles.
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