CN113759959B - Unmanned aerial vehicle path planning method and device for emergency material distribution - Google Patents

Unmanned aerial vehicle path planning method and device for emergency material distribution Download PDF

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CN113759959B
CN113759959B CN202110839626.1A CN202110839626A CN113759959B CN 113759959 B CN113759959 B CN 113759959B CN 202110839626 A CN202110839626 A CN 202110839626A CN 113759959 B CN113759959 B CN 113759959B
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罗贺
靳鹏
张歆悦
朱默宁
王国强
胡笑旋
马华伟
唐奕城
夏维
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Hefei University of Technology
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Abstract

The invention provides an unmanned aerial vehicle path planning method and device for emergency material distribution, and relates to the technical field of path planning. According to the invention, the heterogeneous unmanned aerial vehicle is used for distributing materials for disaster relief points from a plurality of different stations, so that the total time for completing distribution tasks can be effectively shortened. Meanwhile, time window limitation is added, the rescue points of the rescue task in emergency are preferentially distributed, and a material distribution route is accurately determined, so that the arrangement of the rescue route is more reasonable.

Description

Unmanned aerial vehicle path planning method and device for emergency material distribution
Technical Field
The invention relates to the technical field of path planning, in particular to an unmanned aerial vehicle path planning method and device for emergency material distribution.
Background
Natural disasters such as earthquake and the like can cause great threat to the lives of people, and how to distribute emergency materials to needed hands in the first time is about the life safety of people. Because unmanned aerial vehicle can not receive the restriction of topography factor, can carry out the goods and materials distribution to the earthquake post-disaster area that the manpower is difficult to reach, unmanned aerial vehicle distribution has been applied to earthquake post-disaster goods and materials distribution work gradually, can effectively solve the delivery demand of post-disaster goods and materials. After an earthquake disaster occurs, emergency points which urgently need emergency materials are more, the number of unmanned aerial vehicles which can be used for rescuing material distribution after the disaster is limited, in order to provide emergency materials to the emergency points as early as possible, the distribution path of the unmanned aerial vehicles needs to be optimized, and the emergency materials are distributed to a specified place in the minimum flight duration under the constraint of cruising ability.
However, in the unmanned aerial vehicle distribution method in the prior art, the homogeneous unmanned aerial vehicle distributes to the disaster relief point under the constraint of cruising ability, and a single station has difficulty in meeting large-scale requirements, and an unreasonable mission path planning scheme results in that materials cannot be distributed to the disaster relief point in time, that is, the distribution path generated by the unmanned aerial vehicle distribution method in the prior art results in too long distribution time.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects of the prior art, the invention provides an unmanned aerial vehicle path planning method and device for emergency material distribution, and solves the technical problem that the distribution time is too long due to the distribution path generated by the unmanned aerial vehicle distribution method in the prior art.
(II) technical scheme
In order to realize the purpose, the invention is realized by the following technical scheme:
in a first aspect, the present invention provides an unmanned aerial vehicle path planning method for emergency material distribution, the method including:
s1, disaster relief point information, a plurality of site information and heterogeneous unmanned aerial vehicle information are obtained;
s2, constructing a multi-station multi-unmanned aerial vehicle distribution model with time windows by taking the minimum flight time of the unmanned aerial vehicle as a target based on disaster relief point information, multiple station information and heterogeneous unmanned aerial vehicle information;
and S3, solving the multi-station multi-unmanned aerial vehicle distribution model with the time window to obtain an optimal task path planning scheme.
Preferably, the multi-station multi-drone distribution model with time windows includes an objective function, which is expressed by formula (1):
Figure GDA0003758896840000021
wherein i and j are node numbers, and V is a set of all nodes; h is the unmanned aerial vehicle number, and H is the unmanned aerial vehicle set;
Figure GDA0003758896840000022
the flight time from node i to node j of the unmanned aerial vehicle numbered h;
Figure GDA0003758896840000023
a path from the node i to the node j of the unmanned aerial vehicle with the serial number h is used as a decision variable;
flight time of unmanned aerial vehicle with number h from node i to node j
Figure GDA0003758896840000024
Calculated by the following formula:
Figure GDA0003758896840000025
wherein, vi h The flight speed of the unmanned aerial vehicle numbered h; x is the number of i Is the abscissa, y, of node i i Is the ordinate of the node i; x is a radical of a fluorine atom j Is the abscissa, y, of node j j Is the ordinate of node j.
3. The unmanned aerial vehicle path planning method for emergency material delivery of claim 1, wherein the multi-site multi-unmanned aerial vehicle delivery model with time windows further comprises constraints expressed by equations (3) to (11):
Figure GDA0003758896840000031
Figure GDA0003758896840000032
Figure GDA0003758896840000033
Figure GDA0003758896840000034
Figure GDA0003758896840000035
Figure GDA0003758896840000036
Figure GDA0003758896840000037
Figure GDA0003758896840000038
Figure GDA0003758896840000039
wherein:
formula (3) indicates that each disaster point is visited only once;
formula (4) represents the balance constraint of the entrance and exit of each disaster citizen point
Equation (5) indicates that each drone is used only once
Formulas (6) to (7) represent the relationship between the time when each unmanned aerial vehicle reaches the disaster point and the service starting time of the disaster point;
equation (7) indicates that the drone must provide service within the service time window of the disaster point
Equation (8) shows that the drone must provide service within the service time window of the disaster point
Formulas (9) to (10) represent elimination of sub-paths, and guarantee that the flight time of the unmanned aerial vehicle cannot exceed the maximum endurance time of the unmanned aerial vehicle;
equation (11) represents a decision variable constraint;
l, i and j are numbers of disaster relief points, and V is a set of all nodes; d is an unmanned aerial vehicle station set, and N is a disaster relief point set; h is the unmanned aerial vehicle number, and H is the unmanned aerial vehicle set;
Figure GDA0003758896840000041
the unmanned plane numbered h has a flight time after visiting the disaster relief point j,
Figure GDA0003758896840000042
the unmanned plane with the number h flies for a long time after visiting the disaster relief point i,
Figure GDA0003758896840000043
the flying time length after the unmanned plane with the number h visits the disaster relief point r is S h The duration of the unmanned aerial vehicle numbered h; e.g. of the type i The earliest service starting time for the disaster relief point i; l i Starting service time for the latest disaster relief point i;
Figure GDA0003758896840000044
the time when the unmanned aerial vehicle with the serial number h arrives at the disaster relief point i is counted;
Figure GDA0003758896840000045
the time when the unmanned aerial vehicle with the number h reaches the disaster relief point j is counted;
Figure GDA0003758896840000046
the time for the unmanned aerial vehicle with the number h to reach the beginning of the service of the disaster relief point i is counted; se i The time for the unmanned aerial vehicle to reach the disaster relief point i to complete the task is set;
Figure GDA0003758896840000047
a path from the node i to the node j of the unmanned aerial vehicle with the serial number h is used as a decision variable;
Figure GDA0003758896840000048
a path, numbered h, of the unmanned aerial vehicle from the node l to the disaster relief point i is a decision variable;
Figure GDA0003758896840000049
a path, with the number h, of the unmanned aerial vehicle from the disaster relief point i to the node j is a decision variable;
Figure GDA00037588968400000410
a path from the node r to the node i of the unmanned aerial vehicle with the serial number h is used as a decision variable;
Figure GDA00037588968400000411
the flight time from node i to node j of the unmanned aerial vehicle numbered h; m is a positive integer.
Preferably, the S3 includes:
s301, acquiring an initial mission path planning scheme set of an unmanned aerial vehicle distribution path based on disaster point information, multiple station information, heterogeneous unmanned aerial vehicle information and a multi-station multi-unmanned aerial vehicle distribution model with time windows;
s302, optimizing the generated initial task path planning scheme set through an improved genetic algorithm with the introduction of a subsection crossover operator and a dynamic insertion operator, and thus obtaining an optimal task path planning scheme for one or more disaster relief point distribution services of the unmanned aerial vehicle.
Preferably, the S301 includes:
s301a, setting a coding rule;
s301b, generating an initial task path planning scheme set based on the encoding rule, wherein the method comprises the following steps:
step 1: randomly arranging the disaster relief points in the disaster relief point set N r Forming a first line code of a chromosome;
step 2: for permutation N r Randomly selecting an unmanned aerial vehicle from the set H for access by each client in the set H to form a second row code of the chromosome;
and step 3: select disaster relief that visits according to unmanned aerial vehicle serial numberThe points are arranged in a non-descending order according to the earliest starting access time of the time window of the disaster relief point, so that a disaster relief point sequence Ro accessed by the unmanned aerial vehicle is obtained k ；
And 4, step 4: adding station numbers corresponding to the unmanned aerial vehicles into the frontmost part and the rearmost part of the sequence of the disaster relief points visited by each unmanned aerial vehicle to represent the starting points of the unmanned aerial vehicles, and obtaining the sequence R of the disaster relief points visited by each unmanned aerial vehicle k ；
And 5: according to the preset population scale N p And (5) repeating the steps 1-4 to obtain an initial population.
Preferably, the S302 includes:
s302a, setting execution parameters of an improved genetic algorithm and taking a formula (12) as a fitness function to calculate the fitness value of each initial task path planning scheme, wherein the execution parameters comprise maximum iteration times and cross probability;
Figure GDA0003758896840000051
s302b, selecting 2 different path planning schemes from the initial mission path planning scheme set by a roulette selection method, and performing cross operation by using a segmentation cross operator according to cross probability to obtain 2 path planning schemes, wherein the scheme with the smaller fitness value has the higher selection probability, and the method comprises the following steps:
s302c, performing dynamic insertion operation on the path planning scheme obtained in the step S302b to obtain 2 new path planning schemes;
and S302d, repeating the steps S302b-S302c until the preset maximum iteration times is reached, finding the task path planning scheme with the minimum fitness function value from the updated task path planning scheme set, and obtaining the optimal task path planning scheme for the unmanned aerial vehicle to carry out one or more disaster relief point distribution services.
Preferably, the S302b includes:
step 1: selecting two chromosomes from the initial mission path planning scheme set as parent chromosomes through a roulette selection method;
step 2: segmenting the parent chromosomes according to the numbers of the unmanned aerial vehicles, wherein each segmented chromosome represents a task path planning scheme of one unmanned aerial vehicle;
and step 3: performing single-point crossing operation on one segment of the two chromosomes;
and 4, step 4: repeating the step 3 according to the number | H | of the unmanned aerial vehicles until the cross operation of all the segments is completed, and merging the segments of the chromosomes according to the number of the unmanned aerial vehicles to obtain sub-chromosomes;
and/or
The S302c includes:
step 1: if the disaster relief point set N is not distributed vc If not, turning to the step 2; otherwise, outputting a task path planning scheme;
step 2: randomly selecting an unmanned aerial vehicle H from the unmanned aerial vehicle set H;
and step 3: judging whether the unmanned aerial vehicle h meets endurance constraints, and turning to the step 2 if the unmanned aerial vehicle h violates the constraints; otherwise, turning to the step 4;
and 4, step 4: from N using equation (13) vc Selecting a disaster relief point c;
Figure GDA0003758896840000071
wherein e is c The earliest service starting time for the disaster relief point c;
Figure GDA0003758896840000072
the time when the unmanned plane numbered h arrives at the disaster relief point c,
Figure GDA0003758896840000073
the unmanned aerial vehicle numbered h reaches the disaster relief point c from the disaster relief point u;
and 5: checking whether the inserted disaster relief point c meets the constraint condition, and if so, inserting the disaster relief point c into the current planned path R h And from the set N vc Deleting the disaster relief point c, and turning to the step 4; otherwise, turning to step 6;
step 6: and deleting the unmanned plane H from the unmanned plane set H, and turning to the step 1.
In a second aspect, the present invention provides an unmanned aerial vehicle path planning apparatus for emergency material distribution, the apparatus comprising:
the information acquisition module is used for acquiring disaster relief point information, a plurality of site information and heterogeneous unmanned aerial vehicle information;
the model building model is used for building a multi-station multi-unmanned aerial vehicle distribution model with time windows on the basis of disaster relief point information, multi-station information and heterogeneous unmanned aerial vehicle information and with the aim of minimizing the flight time of the unmanned aerial vehicle;
and the solving model is used for solving the multi-station multi-unmanned aerial vehicle distribution model with the time window to obtain an optimal task path planning scheme.
In a third aspect, the present invention provides a computer readable storage medium storing a computer program for unmanned aerial vehicle path planning for emergency material delivery, wherein the computer program causes a computer to execute the unmanned aerial vehicle path planning method for emergency material delivery as described above.
In a fourth aspect, the present invention provides an electronic device comprising:
one or more processors;
a memory; and
one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs comprising instructions for performing the drone path planning method for emergency material delivery as described above.
(III) advantageous effects
The invention provides an unmanned aerial vehicle path planning method and device for emergency material distribution. Compared with the prior art, the method has the following beneficial effects:
according to the invention, the heterogeneous unmanned aerial vehicle starts from a plurality of different stations to supply materials to disaster relief points, so that the total time for completing distribution tasks can be effectively shortened. Meanwhile, time window limitation is added, the rescue points of the rescue task in emergency are preferentially distributed, and a material distribution route is accurately determined, so that the arrangement of the rescue route is more reasonable.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a block diagram of a method for planning a route of an unmanned aerial vehicle for emergency material distribution according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a chromosome form;
fig. 3 is a schematic diagram of an initial mission path planning plan generating process, fig. 3 (a) is a schematic diagram of a change process of a chromosome in the initial mission path planning plan generating process, and fig. 3 (b) is a schematic diagram of a path corresponding to fig. 3 (a).
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are clearly and completely described, and it is obvious that the described embodiments are a part of the embodiments of the present invention, but not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without inventive step based on the embodiments of the present invention, are within the scope of protection of the present invention.
The embodiment of the application provides an unmanned aerial vehicle path planning method and device for emergency material distribution, solves the technical problem that the distribution path generated by the unmanned aerial vehicle distribution method in the prior art can cause overlong distribution time, realizes distribution service for a plurality of disaster relief points by starting from a plurality of different sites by using a heterogeneous unmanned aerial vehicle, and shortens the distribution time.
In order to solve the technical problems, the general idea of the embodiment of the application is as follows:
in the prior art, in unmanned aerial vehicle distribution, a homogeneous unmanned aerial vehicle distributes to disaster relief points under the constraint of cruising ability, a single station has difficulty in meeting large-scale requirements, an unreasonable task path planning scheme may violate the time window constraint of the disaster relief points, and the time for reaching the disaster relief points is delayed. Aiming at the problems in the prior art, the embodiment of the invention provides an unmanned aerial vehicle path planning method for emergency material distribution.
In order to better understand the technical scheme, the technical scheme is described in detail in the following with reference to the attached drawings of the specification and specific embodiments.
The embodiment of the invention provides an unmanned aerial vehicle path planning method for emergency material distribution, which comprises the following steps:
s1, obtaining disaster relief point information, a plurality of station information and heterogeneous unmanned aerial vehicle information;
s2, constructing a multi-station multi-unmanned aerial vehicle distribution model with time windows by taking the minimum flight time of the unmanned aerial vehicle as a target based on disaster relief point information, multiple station information and heterogeneous unmanned aerial vehicle information;
and S3, solving a multi-station multi-unmanned aerial vehicle distribution model with a time window to obtain an optimal task path planning scheme.
According to the embodiment of the invention, the heterogeneous unmanned aerial vehicle starts from a plurality of different stations to supply materials to disaster relief points, so that the total time for completing distribution tasks can be effectively shortened. Meanwhile, time window limitation is added, the rescue points of the rescue task in emergency are preferentially distributed, and a material distribution route is accurately determined, so that the arrangement of the rescue route is more reasonable.
The following is a detailed description of the implementation of the embodiments of the present invention:
in step S1, disaster relief point information, multiple site information, and heterogeneous unmanned aerial vehicle information are obtained, and the specific implementation process is as follows:
the computer obtains disaster relief point information, a plurality of site information and heterogeneous unmanned aerial vehicle information.
The disaster relief point information includes the needs and coordinates of the disaster relief points.
The plurality of site information includes a site number, site coordinates, and a site number.
Heterogeneous unmanned aerial vehicle information includes unmanned aerial vehicle's serial number, unmanned aerial vehicle airspeed and unmanned aerial vehicle duration.
In step S2, based on the disaster relief point information, the multiple site information, and the heterogeneous unmanned aerial vehicle information, a multiple-site unmanned aerial vehicle distribution model with a time window is constructed with the minimum unmanned aerial vehicle flight duration as a target, and the specific implementation process is as follows:
the objective function of the multi-station multi-unmanned aerial vehicle distribution model with the time window is expressed by formula (1):
Figure GDA0003758896840000111
wherein i and j are node numbers, and V is a set of all nodes; h is the unmanned aerial vehicle number, and H is the unmanned aerial vehicle set;
Figure GDA0003758896840000112
the flight time from node i to node j of the unmanned aerial vehicle numbered h;
Figure GDA0003758896840000113
and d, as a decision variable, the unmanned aerial vehicle numbered h reaches the path of the node j from the node i.
Flight time of unmanned aerial vehicle with number h from node i to node j
Figure GDA0003758896840000114
Calculated by the following formula:
Figure GDA0003758896840000115
wherein, vi h The flight speed of the unmanned aerial vehicle numbered h; x is the number of i Is the abscissa, y, of node i i Is the ordinate of the node i;
x j is the abscissa, y, of node j j Is the ordinate of node j.
The constraint conditions of the multi-station multi-unmanned aerial vehicle distribution model with the time window are expressed by formulas (3) to (11):
Figure GDA0003758896840000116
Figure GDA0003758896840000117
Figure GDA0003758896840000118
Figure GDA0003758896840000119
Figure GDA00037588968400001110
Figure GDA00037588968400001111
Figure GDA0003758896840000121
Figure GDA0003758896840000122
Figure GDA0003758896840000123
wherein:
formula (3) indicates that each victim point is visited only once;
formula (4) represents the balance constraint of the entrance and exit of each disaster citizen point
Equation (5) indicates that each drone is used only once
Formulas (6) to (7) represent the relationship between the time when each unmanned aerial vehicle reaches the disaster point and the service starting time of the disaster point;
equation (7) shows that the drone must provide service within the service time window of the disaster point
Equation (8) indicates that the drone must provide service within the service time window of the disaster point
Formulas (9) to (10) represent elimination of sub-paths, and guarantee that the flight duration of the unmanned aerial vehicle cannot exceed the maximum duration of the unmanned aerial vehicle;
equation (11) represents a decision variable constraint.
l, i and j are numbers of disaster relief points, and V is a set of all nodes; d is an unmanned aerial vehicle station set, and N is a disaster relief point set; h is the unmanned aerial vehicle number, and H is the unmanned aerial vehicle set;
Figure GDA0003758896840000124
the unmanned plane numbered h has a flight time after visiting the disaster relief point j,
Figure GDA0003758896840000125
the unmanned plane with the number h flies for a long time after visiting the disaster relief point i,
Figure GDA0003758896840000126
the flying time length after the unmanned aerial vehicle with the number h visits the disaster relief point r, S h The duration of the unmanned aerial vehicle numbered h; e.g. of a cylinder i The earliest service starting time for a disaster relief point i; l. the i Starting service time for the latest disaster relief point i;
Figure GDA0003758896840000127
the time when the unmanned aerial vehicle with the serial number h arrives at the disaster relief point i is counted;
Figure GDA0003758896840000128
the time when the unmanned aerial vehicle with the number h reaches the disaster relief point j is counted;
Figure GDA0003758896840000129
the time when the unmanned aerial vehicle with the number h reaches the disaster relief point i is the service starting time; se i The time for the unmanned aerial vehicle to reach the disaster relief point i to complete the task is set;
Figure GDA0003758896840000131
a path from the node i to the node j of the unmanned aerial vehicle with the serial number h is used as a decision variable;
Figure GDA0003758896840000132
a path, numbered h, of the unmanned aerial vehicle from the node l to the disaster relief point i is a decision variable;
Figure GDA0003758896840000133
a path, with the number h, of the unmanned aerial vehicle from the disaster relief point i to the node j is a decision variable;
Figure GDA0003758896840000134
a path from the node r to the node i of the unmanned aerial vehicle with the serial number h is used as a decision variable;
Figure GDA0003758896840000135
the flight time from node i to node j of the unmanned aerial vehicle numbered h; m is a large positive integer.
In step S3, the multi-station multi-unmanned aerial vehicle distribution model with the time window is solved, and the optimal mission path planning scheme is specifically implemented as follows:
s301, acquiring an initial mission path planning scheme set of an unmanned aerial vehicle distribution path based on disaster point information, multiple site information, heterogeneous unmanned aerial vehicle information and a multi-site multi-unmanned aerial vehicle distribution model with time windows. The method specifically comprises the following steps:
s301a, setting an encoding rule as follows:
a chromosome represents an initial mission path planning scheme, the chromosome adopts an integer coding mode and is composed of two lines, disaster relief points visited by the unmanned aerial vehicles form a first line of the chromosome, and the unmanned aerial vehicles form a second line of the chromosome through numbers. The chromosomal pattern is shown in FIG. 2:
the chromosomes shown in FIG. 1 represent: the unmanned aerial vehicle with the number of 1 accesses a disaster relief point 3, a disaster relief point 4, a disaster relief point 5, a disaster relief point 6 and a disaster relief point 7; the unmanned aerial vehicle numbered 2 accesses the disaster relief point 1, the disaster relief point 2, the disaster relief point 8, and the disaster relief point 9.
S301b, generating an initial task path planning scheme set according to the encoding rule, wherein the method comprises the following steps:
step 1: randomly arranging the disaster relief points in the disaster relief point set N r Forming a first line code of a chromosome;
step 2: for permutation N r Randomly selecting an unmanned aerial vehicle from the set H for access by each client in the set H to form a second row code of the chromosome;
and step 3: selecting the accessed disaster relief points according to the numbers of the unmanned aerial vehicles, and arranging the accessed disaster relief points in a non-descending order according to the earliest starting access time of the time windows of the disaster relief points, thereby obtaining a disaster relief point sequence Ro accessed by the unmanned aerial vehicles k ；
And 4, step 4: adding station numbers corresponding to the unmanned aerial vehicles at the forefront and the rearmost of the sequence of the disaster relief points accessed by each unmanned aerial vehicle to represent the starting points of the unmanned aerial vehicles, and obtaining the sequence R of the disaster relief points accessed by each unmanned aerial vehicle k ；
And 5: according to the preset population scale N p And (5) repeating the steps 1-4 to obtain an initial task path planning scheme set.
The initial mission path planning scheme generation process is shown in fig. 3:
the chromosome representation shown in FIG. 3 (a): the unmanned aerial vehicle with the number of 1 accesses a disaster relief point 3, a disaster relief point 4, a disaster relief point 5, a disaster relief point 6 and a disaster relief point 7; the unmanned aerial vehicle numbered 2 accesses the disaster relief point 1, the disaster relief point 2, the disaster relief point 8, and the disaster relief point 9. And adjusting the sequence of the disaster relief points according to the non-descending sequence of the earliest starting access time of the time window of the disaster relief points, wherein the unmanned aerial vehicle with the number of 1 sequentially accesses a disaster relief point 6, a disaster relief point 5, a disaster relief point 4, a disaster relief point 3 and a disaster relief point 7, and the unmanned aerial vehicle with the number of 2 sequentially accesses a disaster relief point 8, a disaster relief point 1, a disaster relief point 9 and a disaster relief point 2. And adding station numbers corresponding to the unmanned aerial vehicle at the forefront and the rearmost of the task sequence to obtain a complete initial task path planning scheme, starting from the station with the number of 10, the unmanned aerial vehicle with the number of 1 sequentially completes distribution tasks of a disaster relief point 6, a disaster relief point 5, a disaster relief point 4, a disaster relief point 3 and a disaster relief point 7, finally returning to the station with the number of 10, starting from the station with the number of 11, the unmanned aerial vehicle with the number of 2 sequentially completes distribution tasks of a disaster relief point 8, a disaster relief point 1, a disaster relief point 9 and a disaster relief point 2, and finally returning to the station with the number of 11. The path diagram is shown in fig. 3 (b).
In a specific implementation process, the planning schemes in the initial mission path planning scheme set do not necessarily all satisfy the constraint conditions of the multi-site multi-unmanned aerial vehicle distribution model with time windows, so it is necessary to perform constraint check on each chromosome in the initial mission path planning scheme set and delete chromosomes that do not satisfy the constraint conditions.
S302, optimizing the generated initial task path planning scheme set by introducing a segment crossing operator and an improved genetic algorithm of a dynamic insertion operator, so as to obtain an optimal task path planning scheme for one or more disaster relief point delivery services of the unmanned aerial vehicle, wherein the optimal task path planning scheme comprises the following steps:
s302a, setting execution parameters of a genetic algorithm, such as maximum iteration times, cross probability and the like; calculating the fitness value of each initial task path planning scheme by taking a formula (12) as a fitness function;
Figure GDA0003758896840000151
s302b, selecting 2 different path planning schemes from the initial mission path planning scheme set by a roulette selection method, and performing cross operation by using a segmentation cross operator according to cross probability to obtain 2 path planning schemes, wherein the scheme with the smaller fitness value has the higher selection probability, and the method comprises the following steps:
step 1: selecting two chromosomes from the initial mission path planning scheme set as parent chromosomes through a roulette selection method;
step 2: segmenting the parent chromosomes according to the numbers of the unmanned aerial vehicles, wherein each segmented chromosome represents a task path planning scheme of one unmanned aerial vehicle;
and step 3: performing single-point crossing operation on one segment of the two chromosomes;
and 4, step 4: and (4) repeating the step (3) according to the number | H | of the unmanned planes until the cross operation of all the segments is completed, and merging the segments of the chromosomes according to the numbers of the unmanned planes to obtain the sub chromosomes.
S302c, performing dynamic insertion operation on the path planning scheme obtained in the step S302b to obtain 2 new path planning schemes;
step 1: if the disaster relief point set N is not distributed vc If not, turning to the step 2; otherwise, outputting a task path planning scheme;
step 2: randomly selecting an unmanned aerial vehicle H from the unmanned aerial vehicle set H;
and step 3: judging whether the unmanned aerial vehicle h meets endurance constraints, and turning to the step 2 if the unmanned aerial vehicle h violates the constraints; otherwise, turning to the step 4;
and 4, step 4: from N using equation (13) vc Selecting a disaster relief point c;
Figure GDA0003758896840000161
wherein e is c The earliest service starting time for the disaster relief point c;
Figure GDA0003758896840000162
the time when the unmanned plane numbered h arrives at the disaster relief point c,
Figure GDA0003758896840000163
the unmanned aerial vehicle numbered h reaches the disaster relief point c from the disaster relief point u;
and 5: checking whether the inserted disaster relief point c meets the constraint condition, and if so, inserting the disaster relief point c into the current planned path R h And from the set N vc Deleting the disaster relief point c, and turning to the step 4; otherwise, turning to step 6;
step 6: and deleting the unmanned plane H from the unmanned plane set H, and turning to the step 1.
And S302d, repeating the steps S302b-S302c until the preset maximum iteration times is reached, finding the task path planning scheme with the minimum fitness function value from the updated task path planning scheme set, and obtaining the optimal task path planning scheme for the unmanned aerial vehicle to carry out one or more disaster relief point distribution services.
The embodiment of the invention also provides an unmanned aerial vehicle path planning device for emergency material distribution, which comprises:
the information acquisition module is used for acquiring disaster relief point information, a plurality of site information and heterogeneous unmanned aerial vehicle information;
the model building model is used for building a multi-station multi-unmanned aerial vehicle distribution model with time windows on the basis of disaster relief point information, multi-station information and heterogeneous unmanned aerial vehicle information and with the aim of minimizing the flight time of the unmanned aerial vehicle;
and the solving model is used for solving the multi-station multi-unmanned aerial vehicle distribution model with the time window to obtain an optimal task path planning scheme.
It can be understood that the unmanned aerial vehicle path planning device for emergency material distribution provided by the embodiment of the present invention corresponds to the unmanned aerial vehicle path planning method for emergency material distribution, and the explanation, examples, and beneficial effects of the relevant contents thereof may refer to the corresponding contents in the unmanned aerial vehicle path planning method for emergency material distribution, which are not described herein again.
An embodiment of the present invention further provides a computer-readable storage medium storing a computer program for unmanned aerial vehicle path planning for emergency material distribution, where the computer program causes a computer to execute the method for unmanned aerial vehicle path planning for emergency material distribution as described above.
An embodiment of the present invention further provides an electronic device, including:
one or more processors;
a memory; and
one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs comprising instructions for performing the drone path planning method for emergency material delivery as described above.
In summary, compared with the prior art, the method has the following beneficial effects:
according to the embodiment of the invention, the heterogeneous unmanned aerial vehicle starts from a plurality of different stations to supply materials to disaster relief points, so that the total time for completing distribution tasks can be effectively shortened. Meanwhile, time window limitation is added, the rescue points of the rescue task in emergency are preferentially distributed, and a material distribution route is accurately determined, so that the arrangement of the rescue route is more reasonable.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising a … …" does not exclude the presence of another identical element in a process, method, article, or apparatus that comprises the element.
The above examples are only intended to illustrate the technical solution of the present invention, and not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (8)

1. An unmanned aerial vehicle path planning method for emergency material distribution, the method comprising:
s1, disaster relief point information, a plurality of site information and heterogeneous unmanned aerial vehicle information are obtained;
s2, constructing a multi-station multi-unmanned aerial vehicle distribution model with time windows by taking the minimum flight time of the unmanned aerial vehicle as a target based on disaster relief point information, multiple station information and heterogeneous unmanned aerial vehicle information;
s3, solving a multi-station multi-unmanned aerial vehicle distribution model with time windows to obtain an optimal task path planning scheme;
the multi-station multi-unmanned aerial vehicle distribution model with the time window comprises an objective function and a constraint condition;
the objective function is expressed using equation (1):
Figure FDA0003758896830000011
wherein, i and j are node numbers, and V is a set of all nodes; h is the unmanned aerial vehicle number, and H is the unmanned aerial vehicle set;
Figure FDA0003758896830000012
the flight time from node i to node j of the unmanned aerial vehicle numbered h;
Figure FDA0003758896830000013
a path from the node i to the node j of the unmanned aerial vehicle with the serial number h is used as a decision variable;
flight time of unmanned aerial vehicle with number h from node i to node j
Figure FDA0003758896830000015
Calculated by the following formula:
Figure FDA0003758896830000014
wherein, vi h The flight speed of the unmanned aerial vehicle numbered h; x is a radical of a fluorine atom i Is the abscissa, y, of node i i Is the ordinate of the node i; x is the number of j Is the abscissa, y, of node j j Is the ordinate of the node j;
the constraints are expressed by equations (3) to (11):
Figure FDA0003758896830000021
Figure FDA0003758896830000022
Figure FDA0003758896830000023
Figure FDA0003758896830000024
Figure FDA0003758896830000025
Figure FDA0003758896830000026
Figure FDA0003758896830000027
Figure FDA0003758896830000028
Figure FDA0003758896830000029
wherein:
formula (3) indicates that each victim point is visited only once;
formula (4) represents the balance constraint of the entrance and exit of each disaster citizen point
Equation (5) indicates that each drone is used only once
Formulas (6) to (7) represent the relationship between the time when each unmanned aerial vehicle reaches the disaster point and the service starting time of the disaster point;
equation (7) indicates that the drone must provide service within the service time window of the disaster point
Equation (8) indicates that the drone must provide service within the service time window of the disaster point
Formulas (9) to (10) represent elimination of sub-paths, and guarantee that the flight duration of the unmanned aerial vehicle cannot exceed the maximum duration of the unmanned aerial vehicle;
equation (11) represents a decision variable constraint;
l, i and j are numbers of disaster relief points, and V is a set of all nodes; d is an unmanned aerial vehicle station set, and N is a disaster relief point set; h is the unmanned aerial vehicle number, and H is the unmanned aerial vehicle set;
Figure FDA0003758896830000031
the unmanned plane numbered h has a flight time after visiting the disaster relief point j,
Figure FDA0003758896830000032
the unmanned plane with the number h has a flight time after visiting the disaster relief point i,
Figure FDA0003758896830000033
the flying time length after the unmanned aerial vehicle with the number h visits the disaster relief point r, S h The duration of the unmanned aerial vehicle numbered h; e.g. of a cylinder i The earliest service starting time for a disaster relief point i; l i Starting service time for the latest disaster relief point i;
Figure FDA0003758896830000034
the time when the unmanned aerial vehicle with the number h arrives at the disaster relief point i is counted;
Figure FDA0003758896830000035
the time when the unmanned aerial vehicle with the number h reaches the disaster relief point j is counted;
Figure FDA0003758896830000036
the time when the unmanned aerial vehicle with the number h reaches the disaster relief point i is the service starting time; se i The time for the unmanned aerial vehicle to reach the disaster relief point i to complete the task is set;
Figure FDA0003758896830000037
a path from the node i to the node j of the unmanned aerial vehicle with the serial number h is used as a decision variable;
Figure FDA0003758896830000038
a path, numbered h, of the unmanned aerial vehicle from the node l to the disaster relief point i is a decision variable;
Figure FDA0003758896830000039
a path, with the number h, of the unmanned aerial vehicle from the disaster relief point i to the node j is a decision variable;
Figure FDA00037588968300000310
a path from the node r to the node i of the unmanned aerial vehicle with the serial number h is used as a decision variable;
Figure FDA00037588968300000311
the flight time from node i to node j of the unmanned aerial vehicle numbered h; m is a positive integer.
2. The unmanned aerial vehicle path planning method for emergency material delivery of claim 1, wherein the S3 comprises:
s301, acquiring an initial mission path planning scheme set of an unmanned aerial vehicle distribution path based on disaster point information, multiple station information, heterogeneous unmanned aerial vehicle information and a multi-station multi-unmanned aerial vehicle distribution model with time windows;
s302, optimizing the generated initial task path planning scheme set by introducing a segment crossing operator and an improved genetic algorithm of a dynamic insertion operator, so as to obtain an optimal task path planning scheme for one or more disaster relief point delivery services of the unmanned aerial vehicle.
3. The unmanned aerial vehicle path planning method for emergency material distribution of claim 2, wherein the S301 comprises:
s301a, setting a coding rule;
s301b, generating an initial task path planning scheme set based on the encoding rule, wherein the method comprises the following steps:
step 1: randomly arranging the disaster relief points in the disaster relief point set N r Forming a first line code of a chromosome;
step 2: for permutation N r Randomly selecting an unmanned aerial vehicle from the set H for access by each client in the set H to form a second row code of the chromosome;
and step 3: selecting the accessed disaster relief points according to the numbers of the unmanned aerial vehicles, and arranging the accessed disaster relief points in a non-descending order according to the earliest starting access time of the time windows of the disaster relief points, thereby obtaining a disaster relief point sequence Ro accessed by the unmanned aerial vehicles k ；
And 4, step 4: adding station numbers corresponding to the unmanned aerial vehicles into the frontmost part and the rearmost part of the sequence of the disaster relief points visited by each unmanned aerial vehicle to represent the starting points of the unmanned aerial vehicles, and obtaining the sequence R of the disaster relief points visited by each unmanned aerial vehicle k ；
And 5: according to the preset population scale N p And (5) repeating the steps 1-4 to obtain an initial population.
4. The unmanned aerial vehicle path planning method for emergency material delivery of claim 2, wherein the S302 comprises:
s302a, setting execution parameters of an improved genetic algorithm and taking a formula (12) as a fitness function to calculate the fitness value of each initial task path planning scheme, wherein the execution parameters comprise maximum iteration times and cross probability;
Figure FDA0003758896830000041
s302b, selecting 2 different path planning schemes from the initial mission path planning scheme set by a roulette selection method, and performing cross operation by using a segmentation cross operator according to cross probability to obtain 2 path planning schemes, wherein the scheme with the smaller fitness value has the higher selection probability, and the method comprises the following steps:
s302c, performing dynamic insertion operation on the path planning scheme obtained in the step S302b to obtain 2 new path planning schemes;
and S302d, repeating the steps S302b-S302c until the preset maximum iteration times is reached, finding the task path planning scheme with the minimum fitness function value from the updated task path planning scheme set, and obtaining the optimal task path planning scheme for the unmanned aerial vehicle to carry out one or more disaster relief point distribution services.
5. An unmanned aerial vehicle path planning method for emergency material delivery according to claim 2, wherein the S302b comprises:
step 1: selecting two chromosomes from the initial mission path planning scheme set as parent chromosomes through a roulette selection method;
step 2: segmenting the parent chromosomes according to the numbers of the unmanned aerial vehicles, wherein each segmented chromosome represents a task path planning scheme of one unmanned aerial vehicle;
and step 3: performing single-point crossing operation on one segment of the two chromosomes;
and 4, step 4: repeating the step 3 according to the number | H | of the unmanned planes until the cross operation of all the segments is completed, and merging the segmented chromosomes according to the number of the unmanned planes to obtain sub-chromosomes;
and/or
The S302c includes:
step 1: if the disaster relief points are not distributedSet N vc If not, turning to the step 2; otherwise, outputting a task path planning scheme;
step 2: randomly selecting an unmanned aerial vehicle H from the unmanned aerial vehicle set H;
and step 3: judging whether the unmanned aerial vehicle h meets endurance constraints, and turning to the step 2 if the unmanned aerial vehicle h violates the constraints; otherwise, turning to the step 4;
and 4, step 4: from N using equation (13) vc Selecting a disaster relief point c;
Figure FDA0003758896830000061
wherein e is c The earliest service starting time for the disaster relief point c;
Figure FDA0003758896830000062
the time when the unmanned plane with the number h arrives at the disaster relief point c,
Figure FDA0003758896830000063
the unmanned aerial vehicle numbered h reaches the disaster relief point c from the disaster relief point u;
and 5: checking whether the inserted disaster relief point c meets the constraint condition, and if so, inserting the disaster relief point c into the current planned path R h And from the set N vc Deleting the disaster relief point c, and turning to the step 4; otherwise, turning to step 6;
step 6: and deleting the unmanned plane H from the unmanned plane set H, and turning to the step 1.
6. An unmanned aerial vehicle path planning device to emergent material delivery, its characterized in that, the device includes:
the information acquisition module is used for acquiring disaster relief point information, a plurality of site information and heterogeneous unmanned aerial vehicle information;
the model building model is used for building a multi-station multi-unmanned aerial vehicle distribution model with time windows on the basis of disaster relief point information, multi-station information and heterogeneous unmanned aerial vehicle information and with the aim of minimizing the flight time of the unmanned aerial vehicle;
the solving model is used for solving a multi-station multi-unmanned aerial vehicle distribution model with time windows to obtain an optimal task path planning scheme;
the multi-station multi-unmanned aerial vehicle distribution model with the time window comprises an objective function and a constraint condition;
the objective function is expressed using equation (1):
Figure FDA0003758896830000071
wherein i and j are node numbers, and V is a set of all nodes; h is the unmanned aerial vehicle number, and H is the unmanned aerial vehicle set;
Figure FDA0003758896830000072
the flight time from node i to node j of the unmanned aerial vehicle numbered h;
Figure FDA0003758896830000073
a path from the node i to the node j of the unmanned aerial vehicle with the serial number h is used as a decision variable;
flight time of unmanned aerial vehicle with number h from node i to node j
Figure FDA0003758896830000074
Calculated by the following formula:
Figure FDA0003758896830000075
wherein, vi h The flight speed of the unmanned aerial vehicle numbered h; x is a radical of a fluorine atom i Is the abscissa, y, of node i i Is the ordinate of the node i; x is the number of j Is the abscissa, y, of node j j Is the ordinate of the node j;
the constraints are expressed by equations (3) to (11):
Figure FDA0003758896830000076
Figure FDA0003758896830000077
Figure FDA0003758896830000078
Figure FDA0003758896830000079
Figure FDA00037588968300000710
Figure FDA00037588968300000711
Figure FDA00037588968300000712
Figure FDA00037588968300000713
Figure FDA00037588968300000714
wherein:
formula (3) indicates that each victim point is visited only once;
the formula (4) expresses the balance constraint of the entrance and the exit of each disaster point
Equation (5) indicates that each drone is used only once
Formulas (6) to (7) represent the relationship between the time when each unmanned aerial vehicle reaches the disaster point and the service starting time of the disaster point;
equation (7) indicates that the drone must provide service within the service time window of the disaster point
Equation (8) indicates that the drone must provide service within the service time window of the disaster point
Formulas (9) to (10) represent elimination of sub-paths, and guarantee that the flight time of the unmanned aerial vehicle cannot exceed the maximum endurance time of the unmanned aerial vehicle;
equation (11) represents a decision variable constraint;
l, i and j are numbers of disaster relief points, and V is a set of all nodes; d is an unmanned aerial vehicle station set, and N is a disaster relief point set; h is the unmanned aerial vehicle number, and H is the unmanned aerial vehicle set;
Figure FDA0003758896830000081
the unmanned aerial vehicle numbered h has a flight time after visiting the disaster relief point j,
Figure FDA0003758896830000082
the unmanned plane with the number h flies for a long time after visiting the disaster relief point i,
Figure FDA0003758896830000083
the flying time length after the unmanned aerial vehicle with the number h visits the disaster relief point r, S h The duration of the unmanned aerial vehicle numbered h; e.g. of the type i The earliest service starting time for a disaster relief point i; l i Starting service time for the latest disaster relief point i;
Figure FDA0003758896830000084
the time when the unmanned aerial vehicle with the serial number h arrives at the disaster relief point i is counted;
Figure FDA0003758896830000085
the time when the unmanned aerial vehicle with the number h reaches the disaster relief point j is counted;
Figure FDA0003758896830000086
the time when the unmanned aerial vehicle with the number h reaches the disaster relief point i is the service starting time; se i The time for the unmanned aerial vehicle to reach the disaster relief point i to complete the task is set;
Figure FDA0003758896830000087
a path from the node i to the node j of the unmanned aerial vehicle with the serial number h is used as a decision variable;
Figure FDA0003758896830000088
a path, numbered h, of the unmanned aerial vehicle from the node l to the disaster relief point i is a decision variable;
Figure FDA0003758896830000089
a path, with the number h, of the unmanned aerial vehicle from the disaster relief point i to the node j is a decision variable;
Figure FDA00037588968300000810
a path from the node r to the node i of the unmanned aerial vehicle with the serial number h is used as a decision variable;
Figure FDA00037588968300000811
the flight time from node i to node j of the unmanned aerial vehicle numbered h; m is a positive integer.
7. A computer-readable storage medium storing a computer program for unmanned aerial vehicle path planning for emergency material delivery, wherein the computer program causes a computer to execute the unmanned aerial vehicle path planning method for emergency material delivery according to any one of claims 1 to 5.
8. An electronic device, comprising:
one or more processors;
a memory; and
one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs comprising instructions for performing the unmanned aerial vehicle path planning method for emergency material delivery of any of claims 1-5.
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