CN113485429A - Route optimization method and device for air-ground cooperative traffic inspection - Google Patents

Route optimization method and device for air-ground cooperative traffic inspection Download PDF

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CN113485429A
CN113485429A CN202110837997.6A CN202110837997A CN113485429A CN 113485429 A CN113485429 A CN 113485429A CN 202110837997 A CN202110837997 A CN 202110837997A CN 113485429 A CN113485429 A CN 113485429A
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inspection
path
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CN113485429B (en
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徐丽
朱默宁
朱武
罗贺
王国强
靳鹏
蒋儒浩
马滢滢
张歆悦
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Anhui Youyun Intelligent Technology Co ltd
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Abstract

The invention provides a path optimization method and device for air-ground cooperative traffic inspection, and relates to the technical field of path planning. Firstly, acquiring inspection task data of multiple unmanned aerial vehicles and vehicles in cooperation; then, based on the data of the routing inspection task, constructing an open-ground cooperative path model which can start from a plurality of sites, does not need to return to the starting site and can visit the same routing inspection point for a plurality of times by taking the minimum completion time of the whole routing inspection task as a target; and finally, acquiring an optimal path planning scheme of the vehicle and unmanned aerial vehicle cooperative inspection point target based on inspection task data, the air-ground cooperative path model and the modular cause algorithm. The invention carries out traffic inspection through the cooperation of the multiple unmanned aerial vehicles and the vehicle, realizes the cooperative completion of traffic inspection tasks by the vehicle carried unmanned aerial vehicles when facing the traffic inspection work required by a long distance, and improves the working efficiency.

Description

Route optimization method and device for air-ground cooperative traffic inspection
Technical Field
The invention relates to the technical field of path planning, in particular to a path optimization method and device for air-ground cooperative traffic inspection.
Background
Adopt unmanned aerial vehicle to carry out traffic and patrol and examine the operation and replace traditional artifical mode of patrolling and examining gradually, along with the development image acquisition technique of science and technology is becoming mature day by day, solve gradually and use fixed camera to lead to having the problem of keeping watch on the blind area.
However, the cruising mileage of the unmanned aerial vehicle is not large enough, so that when the unmanned aerial vehicle faces traffic inspection work required by a long distance, due to cruising mileage constraint, the unmanned aerial vehicle formation is required to go and go many times or a plurality of unmanned aerial vehicles are required to cooperate, and the inspection efficiency is lower.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects of the prior art, the invention provides a path optimization method and a path optimization device for air-ground cooperative traffic inspection, and solves the technical problem of low inspection efficiency when an unmanned aerial vehicle inspection method faces traffic inspection work with remote requirements.
(II) technical scheme
In order to achieve the purpose, the invention is realized by the following technical scheme:
in a first aspect, the invention provides a path optimization method for air-ground cooperative traffic inspection, which comprises the following steps:
s1, acquiring inspection task data of the cooperation of multiple unmanned aerial vehicles and vehicles;
s2, constructing an open-ground cooperative path model which can start from a plurality of sites, does not need to return to a starting site and can visit the same inspection point for a plurality of times by taking the minimum completion time of the whole inspection task as a target based on the inspection task data;
and S3, acquiring an optimal path planning scheme of the vehicle and unmanned aerial vehicle cooperative inspection point target based on the inspection task data, the air-ground cooperative path model and the modular cause algorithm.
Preferably, the patrol task data includes:
coordinates of the inspection point target;
the number of stations, station numbers and station coordinates of the vehicles and the unmanned aerial vehicles;
unmanned aerial vehicle number, unmanned aerial vehicle flight speed and unmanned aerial vehicle endurance time;
vehicle number and vehicle travel speed.
Preferably, the air-ground collaborative path model includes:
the objective function of the air-ground collaborative path model is expressed by formula (1):
Figure BDA0003177827440000021
wherein l and m are inspection point target numbers, and N is a node set; k is a vehicle number, and K is a vehicle set;
Figure BDA0003177827440000022
the running time from the node l to the node m of the vehicle with the number k is obtained;
Figure BDA0003177827440000023
whether the vehicle with the number k selects a path from the node l to the node m or not is a decision variable; the objective function represents load balancing, minimizing the return time of the last vehicle and minimizing the overall task completion time.
Preferably, the constraints of the air-ground collaborative path model are expressed by formulas (2) to (10):
Figure BDA0003177827440000031
Figure BDA0003177827440000032
Figure BDA0003177827440000033
Figure BDA0003177827440000034
Figure BDA0003177827440000035
Figure BDA0003177827440000036
Figure BDA0003177827440000037
Figure BDA0003177827440000038
Figure BDA0003177827440000039
wherein the content of the first and second substances,
equation (2) indicates that each node is visited at least once;
equation (3) represents ensuring that each vehicle must depart from the warehouse;
formula (4) indicates that each vehicle must return to the warehouse after completing the task;
equation (5) represents the flow conservation constraint;
equation (6) represents that the variable is associated with the arrival time in equation (6);
formula (7) shows that the flight time of each unmanned aerial vehicle cannot exceed the maximum duration of the unmanned aerial vehicle;
equation (8) indicates that if drone d transmits from point i and collects at point n, then points i and n must be assigned to vehicle k, i.e., on the path of the vehicle;
the equations (9) to (10) represent decision variable value constraints;
d is unmanned aerial vehicle number, and D is unmanned aerial vehicle set; l, m, N and p are node numbers, N is a node set, and T is a routing inspection point target set; k is a vehicle number, and K is a vehicle set;
Figure BDA00031778274400000310
whether the vehicle numbered k selects a path from node l to node m for the decision variable,
Figure BDA0003177827440000041
whether the unmanned aerial vehicle with the number d selects a path which starts from the node l to reach the node m and is converged with the vehicle with the number d at the node n is a decision variable;
Figure BDA0003177827440000042
whether a vehicle with the number k selects a path from the station with the number e to the inspection point target l is a decision variable;
Figure BDA0003177827440000043
whether a vehicle with the number k selects a path from the inspection point target q to the station with the number e is a decision variable;
Figure BDA0003177827440000044
the running time from the node l to the node m of the vehicle with the number k is obtained;
Figure BDA0003177827440000045
the arrival time at node i for the vehicle numbered k,
Figure BDA0003177827440000046
the arrival time of the vehicle with the number k to the node l;
Figure BDA0003177827440000047
the flight time of the unmanned aerial vehicle numbered d from the node l to the patrol point target m,
Figure BDA0003177827440000048
the arrival time of the drone at node m numbered d,
Figure BDA0003177827440000049
the arrival time of the unmanned aerial vehicle with the number d to the node n is shown;
Figure BDA00031778274400000410
whether the vehicle numbered k selects a path from node h to node l for the decision variable,
Figure BDA00031778274400000411
whether the vehicle with the number k selects a path from the node n to the node i is a decision variable; cdThe unmanned aerial vehicle numbered d can execute the maximum flight duration of the patrol task.
Preferably, the acquiring of the optimal path planning scheme of the vehicle and unmanned aerial vehicle cooperative inspection point target based on the inspection task data, the air-ground cooperative path model and the modular cause algorithm includes:
s301, encoding a chromosome air-ground cooperative traffic inspection path initial planning scheme by using chromosomes with unequal length in a chromosome encoding mode;
s302, generating an initial routing inspection path planning scheme set of the cooperative traffic inspection of the vehicle and the unmanned aerial vehicle;
s303, optimizing the initial routing inspection path planning scheme set by adopting a modular factorial algorithm, and obtaining an optimal path planning scheme of the vehicle and unmanned aerial vehicle cooperative routing inspection point target.
Preferably, the generating of the initial routing inspection path planning scheme set for vehicle and unmanned aerial vehicle cooperative traffic inspection includes:
s302a, randomly arranging the numbers of the inspection target points to generate a 2 nd line of chromosomes, dividing the arrangement into | K | each segment of which is added with 1 site number in front and back, wherein the K-th segment of chromosomes corresponds to the path of a K-th vehicle, and | K | is the number of the empty-ground cooperative associations;
s302b, sequentially taking out 2 node numbers from the front to the back of the kth chromosome each time, taking point targets corresponding to the 2 node numbers as 2 focuses of an ellipse, and taking the cruising ability of the unmanned aerial vehicle as a long axis to construct a 'maximum cruising range';
s302c, if only 1 target point exists in the maximum cruising range, writing the number of the target point below the number of the previous target point; if more than 1 target point in the 'maximum cruising range', randomly selecting 1 target number to write below the previous target number; if no target exists in the maximum cruising range, writing '-1' below the number of the previous target, repeating the operation until the 2 nd last position of the chromosome of the segment and writing '-1' below the last 1 position;
s302d, adding a vehicle number to the I K section chromosome on the first line of the chromosome according to the vehicle number;
s302e, repeating the steps S302 b-S302 d for | K | times to obtain a scheme of the cooperative traffic inspection path of each vehicle and the unmanned aerial vehicle; and each vehicle and the unmanned aerial vehicle cooperate with the traffic inspection path scheme to form an initial inspection path planning scheme set.
Preferably, the optimizing the initial routing plan set by using the modular factorial algorithm to obtain the optimal routing plan of the vehicle and unmanned aerial vehicle cooperative routing inspection point target includes:
s303a, setting execution parameters of the modular factorial algorithm, wherein the execution parameters comprise maximum iteration times and cross probability; calculating the fitness value of the initial path planning scheme of the air-ground cooperative traffic inspection by taking the target function of the air-ground cooperative path model as a fitness function, wherein the calculation formula is as follows:
Figure BDA0003177827440000061
s303b, selecting 2 schemes for descendant genetic operations according to the fitness value of the initial path planning scheme of the air-ground cooperative traffic inspection, wherein the probability of selecting the scheme with the smaller fitness value is higher;
s303c, creating a new candidate solution for the selected 2 solutions in an initial solution mixing mode; wherein the initial scheme mixing comprises:
step 1: dividing 2 initial path planning schemes into | K | segments according to the number of the space-ground cooperative associations;
step 2: performing cross operation on the initial path planning scheme, judging whether the paths visited by the vehicles in the air-ground cooperative complex with the same number have the same routing inspection point targets, and if so, exchanging the routing inspection point targets visited by the unmanned aerial vehicle starting from the routing inspection point target;
and step 3: repeating the step 2 according to the number | K | of the space-ground cooperative complexes to complete the cross operation of all the segments;
and 4, step 4: splicing the | K | segments to form a complete space and ground cooperative routing inspection path planning scheme;
s303d, selecting a sub-scheme with a large adaptability value from the candidate schemes, and performing cross operation according to the cross probability;
s303e, selecting a good sub-scheme to carry out a path planning scheme set to replace the original poor parent path planning scheme;
s303f, repeating the steps S303 c-S303 e until the current iteration number is equal to the maximum iteration number, and terminating the operation to obtain an optimized routing inspection path planning scheme set;
303g, selecting the routing inspection path planning scheme with the minimum fitness value from the optimized routing inspection path planning scheme set as an optimal path planning scheme.
In a second aspect, the present invention provides a path optimization device for air-ground cooperative traffic inspection, including:
the data acquisition module is used for acquiring the inspection task data of the cooperation of the multiple unmanned aerial vehicles and the vehicles;
the model construction module is used for constructing an open-ground cooperative path model which can start from a plurality of sites, does not need to return to a starting site and can visit the same inspection point for multiple times based on the inspection task data by taking the minimum completion time of the whole inspection task as a target;
and the optimal path planning scheme acquisition module is used for acquiring an optimal path planning scheme of the vehicle and unmanned aerial vehicle cooperative inspection point target based on the inspection task data, the air-ground cooperative path model and the modular cause algorithm.
In a third aspect, the present invention provides a computer-readable storage medium storing a computer program for path optimization for air-ground cooperative traffic inspection, wherein the computer program causes a computer to execute the path optimization method for air-ground cooperative traffic inspection 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 a path optimization method for air-ground cooperative traffic inspection as described above.
(III) advantageous effects
The invention provides a path optimization method and device for air-ground cooperative traffic inspection. Compared with the prior art, the method has the following beneficial effects:
firstly, acquiring inspection task data of multiple unmanned aerial vehicles and vehicles in cooperation; then, based on the data of the routing inspection task, constructing an open-ground cooperative path model which can start from a plurality of sites, does not need to return to the starting site and can visit the same routing inspection point for a plurality of times by taking the minimum completion time of the whole routing inspection task as a target; and finally, acquiring an optimal path planning scheme of the vehicle and unmanned aerial vehicle cooperative inspection point target based on inspection task data, the air-ground cooperative path model and the modular cause algorithm. The invention carries out traffic inspection through the cooperation of the multiple unmanned aerial vehicles and the vehicle, realizes the cooperative completion of traffic inspection tasks by the vehicle carried unmanned aerial vehicles when facing the traffic inspection work required by a long distance, and improves the working efficiency. Meanwhile, the optimization method provided by the embodiment of the invention uses a modular factorial algorithm to solve, and can rapidly obtain the optimal path planning scheme of the air-ground collaborative path model.
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 path optimization method for air-ground cooperative traffic inspection according to an embodiment of the present invention;
FIG. 2 is a schematic representation of chromosome form;
FIG. 3 is a schematic diagram of the chromosome mapping path shown in FIG. 2;
FIG. 4 is a schematic diagram of road network constraints;
fig. 5 is a schematic diagram of the initial path planning scheme performing the crossover operation.
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 derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the application provides a route optimization method and a route optimization device for air-ground cooperative traffic inspection, solves the technical problem of low inspection efficiency when an unmanned aerial vehicle inspection method faces traffic inspection work with remote requirements, achieves the purpose that a vehicle carries an unmanned aerial vehicle to cooperatively finish a traffic inspection task, and improves work efficiency.
In order to solve the technical problems, the general idea of the embodiment of the application is as follows:
in the prior art, the task of completing traffic inspection is only completed by the unmanned aerial vehicle, but the endurance mileage of the unmanned aerial vehicle is limited, and the task of long-distance inspection is difficult to complete. The embodiment of the invention can cooperate the vehicle and the unmanned aerial vehicle to complete the long-distance traffic inspection and greatly reduce the completion time of the inspection task. Meanwhile, in the embodiment of the invention, the heterogeneous air-ground cooperative traffic inspection path planning problem considering the road network constraint is optimized, the working time for completing the traffic inspection task can be obviously shortened, and the modular cause Algorithm (MA) can effectively reduce the operation time for obtaining the optimal path optimization scheme for the air-ground cooperative traffic inspection.
In order to better understand the technical solution, the technical solution will be described in detail with reference to the drawings and the specific embodiments.
The embodiment of the invention provides a path optimization method for air-ground cooperative traffic inspection, which comprises the following steps as shown in figure 1:
s1, acquiring inspection task data of the cooperation of multiple unmanned aerial vehicles and vehicles;
s2, constructing an open-ground cooperative path model which can start from a plurality of sites, does not need to return to a starting site and can visit the same inspection point for a plurality of times based on inspection task data by taking the minimum completion time of the whole inspection task as a target;
and S3, acquiring an optimal path planning scheme of the vehicle and unmanned aerial vehicle cooperative inspection point target based on inspection task data, the air-ground cooperative path model and the modular cause algorithm.
The embodiment of the invention cooperatively carries out traffic inspection through the multiple unmanned aerial vehicles and the vehicle, and realizes cooperative completion of traffic inspection tasks by the vehicle carried unmanned aerial vehicles when the traffic inspection work meeting the remote requirement is carried out, thereby improving the work efficiency. Meanwhile, the optimization method provided by the embodiment of the invention uses a modular factorial algorithm to solve, and can rapidly obtain the optimal path planning scheme of the air-ground collaborative path model.
The following describes the implementation process of the embodiment of the present invention in detail:
in step S1, acquiring patrol task data in which multiple drones and vehicles cooperate, the specific implementation process is as follows:
the computer acquires the task data of patrolling and examining in coordination with the vehicle by a plurality of unmanned aerial vehicles, and the task data of patrolling and examining includes:
coordinates of the inspection point target; the number of stations, station numbers and station coordinates of the vehicles and the unmanned aerial vehicles; unmanned aerial vehicle number, unmanned aerial vehicle flight speed and unmanned aerial vehicle endurance time of the unmanned aerial vehicle; the vehicle number of the vehicle and the vehicle running speed.
In step S2, an open-air cooperative path model that can start from multiple sites, does not need to return to the starting site, and can visit the same inspection point multiple times is constructed based on the inspection task data to minimize the completion time of the entire inspection task, and the specific implementation process is as follows:
the objective function of the air-ground collaborative path model is expressed by formula (1):
Figure BDA0003177827440000111
wherein l and m are inspection point target numbers, and N is a node set; k is a vehicle number, and K is a vehicle set;
Figure BDA0003177827440000112
the running time from the node l to the node m of the vehicle with the number k is obtained;
Figure BDA0003177827440000113
whether the vehicle with the number k selects a path from the node l to the node m or not is a decision variable; the target function represents load balance, so that the return time of the last vehicle is minimized, the completion time of the whole task is minimized, and the target completion inspection task gap time of each inspection point is ensured to be small.
The constraints of the air-ground collaborative path model are expressed by formulas (2) to (10):
Figure BDA0003177827440000114
Figure BDA0003177827440000115
Figure BDA0003177827440000116
Figure BDA0003177827440000117
Figure BDA0003177827440000118
Figure BDA0003177827440000119
Figure BDA00031778274400001110
Figure BDA00031778274400001111
Figure BDA00031778274400001112
wherein the content of the first and second substances,
equation (2) indicates that each node is visited at least once;
equation (3) represents ensuring that each vehicle must depart from the warehouse;
formula (4) indicates that each vehicle must return to the warehouse after completing the task;
equation (5) represents the flow conservation constraint;
equation (6) represents that the variable is associated with the arrival time in equation (6);
formula (7) shows that the flight time of each unmanned aerial vehicle cannot exceed the maximum duration of the unmanned aerial vehicle;
equation (8) indicates that if drone d transmits from point i and collects at point n, then points i and n must be assigned to vehicle k, i.e., on the path of the vehicle;
equations (9) to (10) represent decision variable value constraints.
D is unmanned aerial vehicle number, and D is unmanned aerial vehicle set; l, m, N and p are node numbers, N is a node set, and T is a routing inspection point target set; k is a vehicle number, and K is a vehicle set;
Figure BDA0003177827440000121
whether the vehicle numbered k selects a path from node l to node m for the decision variable,
Figure BDA0003177827440000122
whether the unmanned aerial vehicle with the number d selects a path which starts from the node l to reach the node m and is converged with the vehicle with the number d at the node n is a decision variable;
Figure BDA0003177827440000123
whether a vehicle with the number k selects a path from the station with the number e to the inspection point target l is a decision variable;
Figure BDA0003177827440000124
whether a vehicle with the number k selects a path from the inspection point target q to the station with the number e is a decision variable;
Figure BDA0003177827440000125
whether the running time from the node l to the node m is selected for the vehicle with the number k;
Figure BDA0003177827440000126
the arrival time at node i for the vehicle numbered k,
Figure BDA0003177827440000127
the arrival time of the vehicle with the number k to the node l;
Figure BDA0003177827440000128
for unmanned aerial vehicle with number dThe time of flight from node l to the patrol point target m,
Figure BDA0003177827440000129
the arrival time of the drone at node m numbered d,
Figure BDA00031778274400001210
the arrival time of the unmanned aerial vehicle with the number d to the node n is shown;
Figure BDA00031778274400001211
whether the vehicle numbered k selects a path from node h to node l for the decision variable,
Figure BDA00031778274400001212
whether the vehicle with the number k selects a path from the node n to the node i is a decision variable; cdThe unmanned aerial vehicle numbered d can execute the maximum flight duration of the patrol task.
Flight time of unmanned aerial vehicle numbered d in the above formula from node l to node m
Figure BDA0003177827440000131
The calculation formula of (a) is as follows:
Figure BDA0003177827440000132
in the formula, vdThe flight speed of the unmanned aerial vehicle numbered d; x is the number oflIs the abscissa of node l, ylIs the ordinate, x, of node lmIs the abscissa, y, of node mmIs the ordinate of node m.
The length of time for the vehicle numbered k to travel from node l to node m
Figure BDA0003177827440000133
The calculation formula of (a) is as follows:
Figure BDA0003177827440000134
in the formula, vkThe running speed of the vehicle numbered k; x is the number oflIs the abscissa of node l, ylIs the ordinate, x, of node lmIs the abscissa, y, of node mmIs the ordinate of node m.
In step S3, an optimal path planning scheme for the vehicle and unmanned aerial vehicle cooperative inspection point target is obtained based on the inspection task data, the air-ground cooperative path model, and the cause algorithm. The specific implementation process is as follows:
s301, encoding is carried out on the chromosome in the space-ground cooperative traffic inspection path initial planning scheme by using chromosome encoding modes with different lengths. The method comprises the following specific steps:
the chromosome of the chromosome in the unequal-length chromosome coding mode represents an air-ground cooperative traffic inspection path initial scheme, the number of chromosome lines is variable, the first line of the chromosome is formed by the vehicle-machine combination number formed by vehicles and unmanned aerial vehicles, the second line of the chromosome is formed by the inspection point target numbers accessed by the vehicles, and the second line to the last line of the chromosome are formed by the inspection point target numbers accessed by the unmanned aerial vehicles. The chromosome form is shown in FIG. 2.
The chromosomes shown in FIG. 2 represent: and the two vehicles and the two unmanned aerial vehicles cooperatively finish the routing inspection task. Vehicle slave station D with number 11Go to the inspection point target 5 and the inspection point target 3 in turn to be inspected, and finally return to the station D2Number 1 unmanned aerial vehicle slave station D1Go out and go to patrol and examine point target 1 and patrol and examine, then go to patrol and examine point target 5 and join with the vehicle that is numbered 1, go to patrol and examine point target 3 with the vehicle that is numbered 1 together after, go to patrol and examine point target 5 after, go to patrol and examine point target 7 after, return to website D at last2. Vehicle slave station D with number 22Go to the inspection point target 4, the inspection point target 6 and the inspection point target 2 in sequence after starting to perform inspection, and finally return to the station D2Unmanned aerial vehicle slave station D with number 22Go to the inspection point target 7 for inspection, then converge with the vehicle with the number 2 at the inspection point target 4, and then go to the inspection point target 2, the inspection point target 6, the inspection point target 9 and the inspection point in sequenceThe target 8 is patrolled, then is converged with the vehicle at the patrol point target 6, then goes to the patrol point target 2 together with the vehicle, and finally returns to the station D together3. The chromosome mapping path shown in FIG. 2 is shown in FIG. 3.
S302, generating an initial routing inspection path planning scheme set for cooperative traffic inspection of vehicles and unmanned planes, which comprises the following specific steps:
s302a, randomly arranging the numbers of the patrol target points to generate a 2 nd line of chromosomes, dividing the arrangement into | K | each segment of which is respectively added with 1 site number at the front and the back, wherein the K-th segment of chromosomes corresponds to the path of the K-th vehicle, and | K | is the number of the empty-ground cooperative associations.
S302b, sequentially taking out 2 node numbers from the front to the back of the kth chromosome, taking the point targets corresponding to the 2 node numbers as 2 focuses of the ellipse, and taking the cruising ability of the unmanned aerial vehicle as the major axis to construct the maximum cruising range.
S302c, if only 1 target point exists in the maximum cruising range, writing the number of the target point below the number of the previous target point; if more than 1 target point in the 'maximum cruising range', randomly selecting 1 target number to write below the previous target number; if there is no object in the "maximum endurance range", then write "-1" under the previous object number, repeat the above operation until the 2 nd last bit of the segment chromosome, and write "-1" under the last 1 bit.
S302d, adding a vehicle number to the I K section chromosome on the first line of the chromosome according to the vehicle number;
and S302e, repeating the steps S302 b-S302 d for | K | times, and obtaining the scheme of the cooperative traffic inspection path of each vehicle and the unmanned aerial vehicle. And each vehicle and the unmanned aerial vehicle cooperate with the traffic inspection path scheme to form an initial inspection path planning scheme set.
It should be noted that, in the process of generating the initial routing inspection path planning scheme set, it is considered that the vehicle needs to travel along the road network in the routing inspection process, and the unmanned aerial vehicle is not restricted by the road network. The road network constraints are shown in fig. 4. Meanwhile, in the specific implementation process, the planning schemes in the initial patrol route planning scheme set do not necessarily satisfy the constraint conditions of the air-ground collaborative path model, so that it is necessary to perform constraint check on each chromosome in the initial patrol route planning scheme set and delete chromosomes that do not satisfy the constraint conditions.
S303, optimizing the initial routing inspection path planning scheme set by adopting a modular factorial algorithm to obtain an optimal path planning scheme of the vehicle and unmanned aerial vehicle cooperative routing inspection point target, which specifically comprises the following steps:
s303a, setting execution parameters of the modulo-factorial algorithm, where the execution parameters include a maximum iteration number and a crossover probability (in the embodiment of the present invention, the maximum iteration number is 500, and the crossover probability is 0.7); calculating the fitness value of the initial path planning scheme of the air-ground cooperative traffic inspection by taking the target function of the air-ground cooperative path model as a fitness function, wherein the calculation formula is as follows:
Figure BDA0003177827440000151
s303b, selecting 2 schemes for offspring genetic operation according to the fitness value of the initial path planning scheme of the air-ground cooperative traffic inspection and the roulette mechanism, wherein the probability of selecting the scheme with smaller fitness value is higher;
s303c, creating a new candidate solution for the selected 2 solutions in an initial solution mixing mode; wherein the initial scheme mixing comprises:
step 1: dividing 2 initial path planning schemes into | K | segments according to the number of the space-ground cooperative associations;
step 2: performing cross operation on the initial path planning scheme, judging whether the paths visited by the vehicles in the air-ground cooperative complex with the same number have the same routing inspection point targets, and if so, exchanging the routing inspection point targets visited by the unmanned aerial vehicle starting from the routing inspection point target, as shown in fig. 5;
and step 3: repeating the step 2 according to the number | K | of the space-ground cooperative complexes to complete the cross operation of all the segments;
and 4, step 4: and splicing the | K | sections to form a complete space and ground cooperative routing inspection path planning scheme.
S303d, selecting a sub-scheme with a large adaptability value from the candidate schemes, and performing cross operation according to the cross probability;
s303e, selecting a good sub-scheme to carry out a path planning scheme set to replace the original poor parent path planning scheme;
and S303f, repeating the steps S303 c-S303 e until the iteration termination condition is reached, the current iteration number is equal to the maximum iteration number, and terminating the operation to obtain an optimized routing inspection path planning scheme set.
S303g, selecting the inspection path planning scheme with the minimum fitness value from the optimized inspection path planning scheme set as the optimal path planning scheme.
The embodiment of the invention also provides a path optimization device for air-ground cooperative traffic inspection, which comprises:
the data acquisition module is used for acquiring the inspection task data of the cooperation of the multiple unmanned aerial vehicles and the vehicles;
the model construction module is used for constructing an open-ground cooperative path model which can start from a plurality of sites, does not need to return to a starting site and can visit the same inspection point for multiple times based on the inspection task data by taking the minimum completion time of the whole inspection task as a target;
and the optimal path planning scheme acquisition module is used for acquiring an optimal path planning scheme of the vehicle and unmanned aerial vehicle cooperative inspection point target based on the inspection task data, the air-ground cooperative path model and the modular cause algorithm.
It can be understood that the path optimization device for air-ground cooperative traffic inspection provided by the embodiment of the present invention corresponds to the path optimization method for air-ground cooperative traffic inspection, and the explanations, examples, and beneficial effects of the relevant contents thereof may refer to the corresponding contents in the path optimization method for air-ground cooperative traffic inspection, and are not described herein again.
An embodiment of the present invention further provides a computer-readable storage medium storing a computer program for path optimization for air-ground cooperative traffic inspection, where the computer program causes a computer to execute the method for path optimization for air-ground cooperative traffic inspection 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 a path optimization method for air-ground cooperative traffic inspection as described above.
In summary, compared with the prior art, the method has the following beneficial effects:
1. the embodiment of the invention cooperatively carries out traffic inspection through the multiple unmanned aerial vehicles and the vehicle, and realizes cooperative completion of traffic inspection tasks by the vehicle carried unmanned aerial vehicles when the traffic inspection work meeting the remote requirement is carried out, thereby improving the work efficiency.
2. The optimization method provided by the embodiment of the invention uses a modular factorial algorithm to solve, and can rapidly obtain the optimal path planning scheme of the air-ground collaborative path model.
3. The method provided by the embodiment of the invention solves the problem that a united body formed by the vehicle and the unmanned aerial vehicle starts from a plurality of stations, the inspection point target can be accessed for a plurality of times, the vehicle considers the path optimization method of the road network constraint in the traffic inspection process, and the obtained optimal path planning scheme is more in line with the actual application scene.
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 an … …" does not exclude the presence of other identical elements 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, but 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 (10)

1. A path optimization method for air-ground cooperative traffic inspection is characterized by comprising the following steps:
s1, acquiring inspection task data of the cooperation of multiple unmanned aerial vehicles and vehicles;
s2, constructing an open-ground cooperative path model which can start from a plurality of sites, does not need to return to a starting site and can visit the same inspection point for a plurality of times by taking the minimum completion time of the whole inspection task as a target based on the inspection task data;
and S3, acquiring an optimal path planning scheme of the vehicle and unmanned aerial vehicle cooperative inspection point target based on the inspection task data, the air-ground cooperative path model and the modular cause algorithm.
2. The method for optimizing a route for air-ground cooperative traffic inspection according to claim 1, wherein the inspection task data includes:
coordinates of the inspection point target;
the number of stations, station numbers and station coordinates of the vehicles and the unmanned aerial vehicles;
unmanned aerial vehicle number, unmanned aerial vehicle flight speed and unmanned aerial vehicle endurance time;
vehicle number and vehicle travel speed.
3. The air-ground cooperative traffic inspection path optimization method according to claim 1, wherein the air-ground cooperative path model includes:
the objective function of the air-ground collaborative path model is expressed by formula (1):
Figure FDA0003177827430000011
wherein l and m are inspection point target numbers, and N is a node set; k is a vehicle number, and K is a vehicle set;
Figure FDA0003177827430000012
the running time from the node l to the node m of the vehicle with the number k is obtained;
Figure FDA0003177827430000013
whether the vehicle with the number k selects a path from the node l to the node m or not is a decision variable; the objective function represents load balancing, minimizing the return time of the last vehicle and minimizing the overall task completion time.
4. The method for optimizing the path of the air-ground cooperative traffic inspection according to claim 1, wherein the constraints of the air-ground cooperative path model are expressed by equations (2) to (10):
Figure FDA0003177827430000021
Figure FDA0003177827430000022
Figure FDA0003177827430000023
Figure FDA0003177827430000024
Figure FDA0003177827430000025
Figure FDA0003177827430000026
Figure FDA0003177827430000027
Figure FDA0003177827430000028
Figure FDA0003177827430000029
wherein the content of the first and second substances,
equation (2) indicates that each node is visited at least once;
equation (3) represents ensuring that each vehicle must depart from the warehouse;
formula (4) indicates that each vehicle must return to the warehouse after completing the task;
equation (5) represents the flow conservation constraint;
equation (6) represents that the variable is associated with the arrival time in equation (6);
formula (7) shows that the flight time of each unmanned aerial vehicle cannot exceed the maximum duration of the unmanned aerial vehicle;
equation (8) indicates that if drone d transmits from point i and collects at point n, then points i and n must be assigned to vehicle k, i.e., on the path of the vehicle;
the equations (9) to (10) represent decision variable value constraints;
d is unmanned aerial vehicle number, and D is unmanned aerial vehicle set; l, m, N and p are node numbers, N is a node set, and T is a routing inspection point target set; k is a vehicle number, and K is a vehicle set;
Figure FDA0003177827430000031
whether the vehicle numbered k selects a path from node l to node m for the decision variable,
Figure FDA0003177827430000032
whether the unmanned aerial vehicle with the number d selects a path which starts from the node l to reach the node m and is converged with the vehicle with the number d at the node n is a decision variable;
Figure FDA0003177827430000033
whether a vehicle with the number k selects a path from the station with the number e to the inspection point target l is a decision variable;
Figure FDA0003177827430000034
whether a vehicle with the number k selects a path from the inspection point target q to the station with the number e is a decision variable;
Figure FDA0003177827430000035
the running time from the node l to the node m of the vehicle with the number k is obtained;
Figure FDA0003177827430000036
the arrival time at node i for the vehicle numbered k,
Figure FDA0003177827430000037
the arrival time of the vehicle with the number k to the node l;
Figure FDA0003177827430000038
the flight time of the unmanned aerial vehicle numbered d from the node l to the patrol point target m,
Figure FDA0003177827430000039
the arrival time of the drone at node m numbered d,
Figure FDA00031778274300000310
the arrival time of the unmanned aerial vehicle with the number d to the node n is shown;
Figure FDA00031778274300000311
whether the vehicle numbered k selects a path from node h to node l for the decision variable,
Figure FDA00031778274300000312
whether the vehicle with the number k selects a path from the node n to the node i is a decision variable; cdThe unmanned aerial vehicle numbered d can execute the maximum flight duration of the patrol task.
5. The method for optimizing the path for air-ground cooperative traffic inspection according to any one of claims 1 to 4, wherein the obtaining of the optimal path planning scheme for the vehicle and unmanned aerial vehicle cooperative inspection point target based on the inspection task data, the air-ground cooperative path model and the modular cause algorithm comprises:
s301, encoding a chromosome air-ground cooperative traffic inspection path initial planning scheme by using chromosomes with unequal length in a chromosome encoding mode;
s302, generating an initial routing inspection path planning scheme set of the cooperative traffic inspection of the vehicle and the unmanned aerial vehicle;
s303, optimizing the initial routing inspection path planning scheme set by adopting a modular factorial algorithm, and obtaining an optimal path planning scheme of the vehicle and unmanned aerial vehicle cooperative routing inspection point target.
6. The method for optimizing a path in air-ground cooperative traffic inspection according to claim 5, wherein generating an initial inspection path planning scheme set for vehicle and unmanned aerial vehicle cooperative traffic inspection comprises:
s302a, randomly arranging the numbers of the inspection target points to generate a 2 nd line of chromosomes, dividing the arrangement into | K | each segment of which is added with 1 site number in front and back, wherein the K-th segment of chromosomes corresponds to the path of a K-th vehicle, and | K | is the number of the empty-ground cooperative associations;
s302b, sequentially taking out 2 node numbers from the front to the back of the kth chromosome each time, taking point targets corresponding to the 2 node numbers as 2 focuses of an ellipse, and taking the cruising ability of the unmanned aerial vehicle as a long axis to construct a 'maximum cruising range';
s302c, if only 1 target point exists in the maximum cruising range, writing the number of the target point below the number of the previous target point; if more than 1 target point in the 'maximum cruising range', randomly selecting 1 target number to write below the previous target number; if no target exists in the maximum cruising range, writing '-1' below the number of the previous target, repeating the operation until the 2 nd last position of the chromosome of the segment and writing '-1' below the last 1 position;
s302d, adding a vehicle number to the I K section chromosome on the first line of the chromosome according to the vehicle number;
s302e, repeating the steps S302 b-S302 d for | K | times to obtain a scheme of the cooperative traffic inspection path of each vehicle and the unmanned aerial vehicle; and each vehicle and the unmanned aerial vehicle cooperate with the traffic inspection path scheme to form an initial inspection path planning scheme set.
7. The method for optimizing the path for air-ground cooperative traffic inspection according to claim 5, wherein the optimizing the initial inspection path planning scheme set by using a modular factorial algorithm to obtain the optimal path planning scheme for the vehicle and unmanned aerial vehicle cooperative inspection point target comprises:
s303a, setting execution parameters of the modular factorial algorithm, wherein the execution parameters comprise maximum iteration times and cross probability; calculating the fitness value of the initial path planning scheme of the air-ground cooperative traffic inspection by taking the target function of the air-ground cooperative path model as a fitness function, wherein the calculation formula is as follows:
Figure FDA0003177827430000051
s303b, selecting 2 schemes for descendant genetic operations according to the fitness value of the initial path planning scheme of the air-ground cooperative traffic inspection, wherein the probability of selecting the scheme with the smaller fitness value is higher;
s303c, creating a new candidate solution for the selected 2 solutions in an initial solution mixing mode; wherein the initial scheme mixing comprises:
step 1: dividing 2 initial path planning schemes into | K | segments according to the number of the space-ground cooperative associations;
step 2: performing cross operation on the initial path planning scheme, judging whether the paths visited by the vehicles in the air-ground cooperative complex with the same number have the same routing inspection point targets, and if so, exchanging the routing inspection point targets visited by the unmanned aerial vehicle starting from the routing inspection point target;
and step 3: repeating the step 2 according to the number | K | of the space-ground cooperative complexes to complete the cross operation of all the segments;
and 4, step 4: splicing the | K | segments to form a complete space and ground cooperative routing inspection path planning scheme;
s303d, selecting a sub-scheme with a large adaptability value from the candidate schemes, and performing cross operation according to the cross probability;
s303e, selecting a good sub-scheme to carry out a path planning scheme set to replace the original poor parent path planning scheme;
s303f, repeating the steps S303 c-S303 e until the current iteration number is equal to the maximum iteration number, and terminating the operation to obtain an optimized routing inspection path planning scheme set;
303g, selecting the routing inspection path planning scheme with the minimum fitness value from the optimized routing inspection path planning scheme set as an optimal path planning scheme.
8. The utility model provides a route optimization device that air-ground is patrolled and examined in coordination with traffic which characterized in that includes:
the data acquisition module is used for acquiring the inspection task data of the cooperation of the multiple unmanned aerial vehicles and the vehicles;
the model construction module is used for constructing an open-ground cooperative path model which can start from a plurality of sites, does not need to return to a starting site and can visit the same inspection point for multiple times based on the inspection task data by taking the minimum completion time of the whole inspection task as a target;
and the optimal path planning scheme acquisition module is used for acquiring an optimal path planning scheme of the vehicle and unmanned aerial vehicle cooperative inspection point target based on the inspection task data, the air-ground cooperative path model and the modular cause algorithm.
9. A computer-readable storage medium storing a computer program for path optimization for air-ground cooperative traffic inspection, wherein the computer program causes a computer to execute the path optimization method for air-ground cooperative traffic inspection according to any one of claims 1 to 7.
10. 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 method for path optimization of air-ground cooperative traffic inspection according to any one of claims 1 to 7.
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