CN114020005B - Flight path planning method and system for multi-unmanned aerial vehicle collaborative inspection distribution network line - Google Patents
Flight path planning method and system for multi-unmanned aerial vehicle collaborative inspection distribution network line Download PDFInfo
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Abstract
The application provides a track planning method and a system for a multi-unmanned aerial vehicle collaborative inspection distribution network line, and relates to the technical field of path planning. The method is based on the information of the distribution network lines to be patrolled and examined, the number and the number of unmanned aerial vehicles, the information of the tarmac and the weight of the distribution network lines, and the sum of the weights of the distribution network lines to be patrolled and examined by the unmanned aerial vehicles is maximized as an optimization target to construct a team orientation problem model; and solving the team orientation problem model to obtain an optimal path for autonomous inspection of the multi-unmanned aerial vehicle distribution network. According to the application, the number of the interval days of each distribution network line from the last inspection is taken as the weight of the line target, modeling and solving are carried out based on the weight, so that the interval time problem between two inspection of the distribution network line is considered, the distribution network line can be effectively prevented from being inspected for a long time, the finishing quality of the inspection task is improved, and the potential safety hazard is reduced.
Description
Technical Field
The application relates to the technical field of path planning, in particular to a track planning method and system for a multi-unmanned-plane collaborative inspection distribution network line.
Background
The distribution network is taken as an important component of the power system, is used for receiving electric energy from a power transmission network or a regional power plant, and then is distributed to various users in situ or step by step according to voltage by a distribution facility, and is directly connected with the power supply users, so that the safe and stable operation of the distribution line plays a vital role in the safety of the whole power supply system. The distribution network line has the characteristic of wide points and multiple faces, and the bottom line, the insulator tower pole and other hardware fittings are easily damaged by natural and human factors such as lightning, snow and illegal construction, so that the distribution network line needs to be periodically subjected to inspection work.
In recent years, with the rapid development of unmanned aerial vehicle technology, unmanned aerial vehicles are widely applied in various fields such as electric power, traffic, logistics, agricultural plant protection and the like due to the characteristics of flexibility, portability, capability of reaching places where personnel cannot reach or cannot reach conveniently. Especially in the electric power inspection field, unmanned aerial vehicle has been widely used in electric power inspection business activity as novel platform of patrolling and examining. At present, when using unmanned aerial vehicle to carry out distribution network inspection, mainly rely on inspection personnel manual control unmanned aerial vehicle to shoot the image of distribution network circuit and circuit. Under the manual inspection mode, inspection personnel need to control the unmanned aerial vehicle to fly to a proper position according to experience and technology, and adjust the cloud deck to take a picture to the distribution network line and the distribution network line at a proper angle, which puts forward higher requirements for the inspection personnel. In order to better exert the efficacy of unmanned aerial vehicles, a technology for autonomous inspection of multiple unmanned aerial vehicle distribution networks needs to be studied.
However, the existing unmanned aerial vehicle distribution network autonomous inspection technology generally focuses on optimizing the total flight path of the unmanned aerial vehicle under the endurance constraint of the unmanned aerial vehicle during track planning, and minimizes the path distance, so that more distribution network lines can be inspected as much as possible, part of distribution network lines are not inspected for a long time due to track planning, the inspection task is low in finishing quality, and certain potential safety hazards exist in the distribution network lines.
Disclosure of Invention
(one) solving the technical problems
Aiming at the defects of the prior art, the application provides a track planning method and a track planning system for a multi-unmanned aerial vehicle collaborative inspection distribution network line, which solve the technical problem that the existing unmanned aerial vehicle distribution network autonomous inspection technology is low in quality when an inspection task is executed.
(II) technical scheme
In order to achieve the above purpose, the application is realized by the following technical scheme:
in a first aspect, the present application provides a track planning method for a multi-unmanned aerial vehicle collaborative inspection distribution network line, the method comprising:
s1, acquiring information of a distribution network line to be inspected, the number and the number of unmanned aerial vehicles, information of an apron and the weight of the distribution network line, wherein the weight of the distribution network line is the number of days of interval between the distribution network line and the last inspected line;
s2, constructing a team orientation problem model by maximizing the sum of weights of the distribution network lines to be inspected by the unmanned aerial vehicle based on the distribution network line information to be inspected, the number and the number of the unmanned aerial vehicles, the apron information and the weights of the distribution network lines;
and S3, solving the team directional problem model to obtain an optimal path for autonomous inspection of the multi-unmanned aerial vehicle distribution network.
Preferably, the team oriented problem model includes an objective function and constraints;
the objective function is expressed by equation (1):
wherein: i, j is the number of the distribution network to be inspected; w (w) i The weight of the distribution network line i; x is x ij For the value of the binary decision variable, when the unmanned aerial vehicle patrols and examines the distribution network line j after finishing the distribution network line i, x ij =1, otherwise x ij ＝0。
Preferably, the constraint condition includes:
t p ≤T max (6)
wherein:
l i the length to be flown when the unmanned aerial vehicle patrols and examines the distribution network line i is represented; t represents the set of all the distribution network lines to be inspected; 0 and n+1 represent the position numbers of the start point and the end point of the unmanned aerial vehicle respectively; a represents a starting point, an ending point and a set formed by all network lines to be inspected of the unmanned aerial vehicle; p represents the unmanned aerial vehicle inspection task path; t (T) max Representing the maximum travel time of the unmanned aerial vehicle; v 1 The flying speed between the network lines is distributed at the two ends of the unmanned aerial vehicle; v 2 The method comprises the following steps that the flight speed of the unmanned aerial vehicle when the unmanned aerial vehicle patrols and examines the distribution network is used; t is t P Representing the total flight time of the flight path P of the unmanned aerial vehicle; u (u) i ,u j The sequence of the target i and the target j in the unmanned plane path is respectively;
the unmanned aerial vehicle is represented by a slave distribution network (2)The time when the line i flies to the distribution network line j and the inspection of the distribution network line j is completed; the formula (3) represents task execution time corresponding to the unmanned aerial vehicle inspection task path P; equation (4) indicates that the unmanned aerial vehicle must start from the starting point and finally return to the ending point; equation (5) indicates that each distribution network line can only be inspected at most once; formula (6) is a constraint of the endurance of the unmanned aerial vehicle; equations (7) and (8) avoid the sub-paths; equation (9) is the value of the binary decision variable, and x is when the unmanned aerial vehicle patrols and examines the distribution network line j after finishing the distribution network line i ij =1, otherwise x ij ＝0。
Preferably, the S3 includes:
s301, setting a chromosome coding mode and setting execution parameters of a single parent genetic algorithm;
s302, initializing a population according to a set chromosome coding mode, an execution parameter of a single parent genetic algorithm and a set of distribution network lines to be patrolled and examined to obtain an initial population;
s303, calculating the fitness value of all individuals in the initial population to obtain the fitness value of the initial population;
s304, selecting a temporary population from the parent population;
s305, generating 2 random mutant fragment selection points i and j and a mutant fragment insertion position p;
s306, mutating each individual in the temporary population by using four mutation operators, and adding the individual obtained by each mutation and the original individual in the temporary population into a offspring population;
s307, acquiring fitness of the child population, comparing the fitness with the fitness value of the parent population, and if the fitness of the child population is better than the fitness value of the parent population, replacing the parent population by the child population, otherwise, reserving the parent population;
s308, selecting an individual with the largest fitness value from the parent population, and marking the individual as an optimal solution;
s309, updating the current iteration times, judging whether the maximum iteration times are reached, if so, outputting an optimal solution, otherwise, returning to the step S304, wherein the optimal solution is the code of the optimal task allocation scheme.
Preferably, the step S302 includes:
s302a, randomly arranging numbers in a set of to-be-inspected distribution network lines to obtain a sequence H;
s302b, randomly setting K-1 break points according to the number K of the unmanned aerial vehicles, so that the sequence H is divided into K sections, and the distribution network line which each unmanned aerial vehicle should check is determined.
S302c, repeating the steps S302a-S302b according to a preset population scale to obtain an initial population, wherein the initial population comprises a plurality of task allocation schemes, the task allocation schemes comprise task execution sequences and corresponding site numbers of each unmanned aerial vehicle in the plurality of unmanned aerial vehicles, and the task execution sequences comprise numbers of to-be-inspected distribution network lines through which the unmanned aerial vehicles pass in sequence.
Preferably, the step S304 includes:
s304a, selecting 5 unselected individuals from the parent population by using roulette.
S304b, finding out the individual with the highest fitness value from the 5 newly selected individuals, storing the individual with the highest fitness value into the temporary population, and repeating the steps S304 a-S304 b until all the individuals in the parent population are bet-selected by the roulette wheel.
Preferably, in the step S306, four mutation operations are performed on each individual in the temporary population with four mutation operators, the four mutation operations including: mutation operator swapinsert, flipinsert, lslideinsert, rslideinsert;
wherein:
mutation of mutation operator swapinsert is: exchanging the sequence numbers of positions i and j, exchanging the sequence numbers of positions i+1 and j-1, and then inserting the fragment of positions i to j into the insertion position p;
the mutation process of the mutation operator flip insert is as follows: reversing the sequence numbers in segments i through j-1 and then inserting the segment at positions i through j-1 into insertion position p;
the mutation process of the mutation operator lslidainsert is as follows: the sequence numbers in i to j are circularly shifted to the left by one position, the sequence numbers of the positions i+1 and j-1 are exchanged, and then the fragment of the positions i to j is inserted into the insertion position p;
the mutation process of the mutation operator rslidainsert is as follows: the sequence numbers in i through j are cyclically shifted to the right by one position, the sequence numbers of positions i+1 and j-1 are swapped, and then the fragment of positions i through j is inserted into the insertion position p.
In a second aspect, the present application provides a track planning system for a multi-unmanned aerial vehicle collaborative inspection distribution network line, the system comprising:
the data acquisition module is used for acquiring the information of the distribution network lines to be inspected, the number and the number of the unmanned aerial vehicles, the information of the air apron and the weights of the distribution network lines, wherein the weights of the distribution network lines are the number of days of interval between the distribution network lines and the last inspection;
the model construction module is used for constructing a team orientation problem model by maximizing the sum of the weights of the distribution network lines to be inspected by the unmanned aerial vehicle based on the distribution network line information to be inspected, the number and the number of the unmanned aerial vehicles, the apron information and the weights of the distribution network lines;
and the model solving module is used for solving the team directional problem model and acquiring an optimal path for autonomous inspection of the multi-unmanned aerial vehicle distribution network.
In a third aspect, the present application provides a computer readable storage medium storing a computer program for track planning of a multi-unmanned aerial vehicle collaborative inspection distribution network line, wherein the computer program causes a computer to execute the track planning method of the multi-unmanned aerial vehicle collaborative inspection distribution network line as described above.
In a fourth aspect, the present application 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 a track planning method for performing a multi-drone collaborative inspection distribution network line as described above.
(III) beneficial effects
The application provides a track planning method and a system for a multi-unmanned aerial vehicle collaborative inspection distribution network line. Compared with the prior art, the method has the following beneficial effects:
the method comprises the steps of obtaining information of a distribution network line to be inspected, the number and the number of unmanned aerial vehicles, information of an apron and the weight of the distribution network line, wherein the weight of the distribution network line is the number of days of interval between the distribution network line and the last inspected line; based on the information of the distribution network lines to be patrolled and examined, the number and the number of unmanned aerial vehicles, the information of the air apron and the weight of the distribution network lines, the sum of the weights of the distribution network lines to be patrolled and examined by the unmanned aerial vehicles is maximized as an optimization target to construct a team orientation problem model; and solving the team orientation problem model to obtain an optimal path for autonomous inspection of the multi-unmanned aerial vehicle distribution network. According to the application, the number of the interval days of each distribution network line from the last inspection is taken as the weight of the line target, modeling and solving are carried out based on the weight, so that the interval time problem between two inspection of the distribution network line is considered, the distribution network line can be effectively prevented from being inspected for a long time, the finishing quality of the inspection task is improved, and the potential safety hazard is reduced.
Drawings
In order to more clearly illustrate the embodiments of the application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a block diagram of a track planning method for a multi-unmanned aerial vehicle collaborative inspection distribution network line according to an embodiment of the present application;
FIG. 2 is a diagram showing a chromosome coding scheme according to an embodiment of the present application;
FIG. 3 is a schematic diagram of a mutation process of a mutation operator swapinsert in an embodiment of the present application;
FIG. 4 is a schematic diagram of a mutation process of a mutation operator flip in an embodiment of the present application;
FIG. 5 is a schematic diagram of a mutation process of a mutation operator lslidainsert according to an embodiment of the present application;
fig. 6 is a schematic diagram of a mutation process of the mutation operator rslidainsert in an embodiment of the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application are clearly and completely described, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The embodiment of the application solves the technical problem of lower quality of the traditional distribution network line inspection method by providing the track planning method and the system for the multi-unmanned aerial vehicle collaborative inspection distribution network line, realizes taking the number of interval days of each distribution network line distance inspected last as the weight of the line target, optimizes the inspection path of the unmanned aerial vehicle, furthest plays the cruising ability of the unmanned aerial vehicle, and improves the quality of the completion of the inspection task.
The technical scheme in the embodiment of the application aims to solve the technical problems, and the overall thought is as follows:
the main work of distribution network inspection comprises line inspection and distribution network line inspection, wherein the distribution network line inspection needs to photograph key components such as insulators, wires and the like on a distribution network line. The number of distribution network lines of the distribution network is large, the distribution network lines are different in variety and wide in distribution, and the unmanned aerial vehicle is limited by the cruising ability, so that only part of the distribution network lines can be inspected by single flight. Meanwhile, all distribution network lines need to be periodically inspected to ensure the normal operation of key components. How to furthest exert the unmanned aerial vehicle endurance in single flight and patrol and examine as many distribution network lines as possible, make most distribution network lines two time interval between patrol and examine approximately the same simultaneously, be unmanned aerial vehicle distribution network autonomous patrol and examine the main problem that needs to solve. However, in the prior art, the problem of the interval time between two inspection is not considered, so that part of network wiring lines are not inspected for a long time, the inspection task is low in finishing quality, and certain potential safety hazards exist. In the embodiment of the application, the problem of path optimization when a plurality of unmanned aerial vehicles carry out inspection on the distribution network lines of the distribution network is researched. Firstly, abstracting a distribution network line as a line target, secondly, taking the number of interval days of each distribution network line distance which is inspected last as the weight of the line target, then modeling a problem by using a team orientation problem, and finally, solving the model by using an improved single parent genetic algorithm to obtain an optimal path for autonomous inspection of the multi-unmanned aerial vehicle distribution network, thereby improving the finishing quality of the inspection task.
In order to better understand the above technical solutions, the following detailed description will refer to the accompanying drawings and specific embodiments.
The embodiment of the application provides a track planning method for a multi-unmanned aerial vehicle collaborative inspection distribution network line, which is shown in fig. 1 and comprises the following steps of S1 to S3:
s1, acquiring information of a distribution network line to be inspected, the number and the number of unmanned aerial vehicles, information of an apron and the weight of the distribution network line, wherein the weight of the distribution network line is the number of days of interval between the distribution network line and the last inspected line;
s2, constructing a team orientation problem model by maximizing the sum of weights of the distribution network lines to be inspected by the unmanned aerial vehicle based on the distribution network line information to be inspected, the number and the number of the unmanned aerial vehicles, the apron information and the weights of the distribution network lines;
and S3, solving the team directional problem model to obtain an optimal path for autonomous inspection of the multi-unmanned aerial vehicle distribution network.
According to the embodiment of the application, the number of interval days from the last inspection of each distribution network line is used as the weight of the distribution network line, the team orientation problem is used for modeling the problem, and then the improved single parent genetic algorithm is used for solving the model, so that the optimal path for autonomous inspection of the multi-unmanned aerial vehicle distribution network is obtained. The embodiment of the application focuses on the problem of the interval time between two inspection of the distribution network line, and can effectively prevent the distribution network line from being inspected for a long time, thereby improving the finishing quality of the inspection task and reducing the potential safety hazard.
The implementation process of the embodiment of the present application is described in detail below:
in step S1, the information of the distribution network lines to be inspected, the number and the number of the unmanned aerial vehicles, the information of the apron and the weights of the distribution network lines are obtained, wherein the weights of the distribution network lines are the number of days of interval between the distribution network lines and the last inspection. The specific implementation process is as follows:
the network line information to be inspected comprises: distribution network line number, length and coordinates.
The apron information includes: the number of the tarmac and the number and the coordinates of the positions of the tarmac are obtained.
In the implementation process, 0 and n+1 are used to represent the starting point and the end point of the unmanned aerial vehicle respectively, the set t= {1, …, i, …, N } is the set of all the distribution network lines to be inspected, and the set formed by the starting point and the end point of the unmanned aerial vehicle and all the distribution network lines to be inspected is a= {0,1, …, N, n+1}. Weight w of distribution network line i i And (i epsilon T) indicating that the weight indicates the time interval that the distribution network line i is inspected last time, and the larger the weight is, the longer the time interval is, the higher the priority of the distribution network line being inspected is, and the unmanned aerial vehicle needs to be arranged to inspect the distribution network line i as soon as possible. L for length of distribution network line i i (i e T) represents the distance that the drone needs to fly when checking the distribution network line i. Let p= {0, i, …, j, n+1}, i, j e T denote the path of the inspection task performed by the unmanned aerial vehicle, which must start from the start point to perform the inspection task and finally return to the end point after the task is performed. The unmanned aerial vehicle starts from the starting point 0, selects part of network lines to carry out inspection, returns to the terminal point n+1 after finishing the inspection task, and the total duration from take-off to landing of the unmanned aerial vehicle cannot exceed the endurance limit T of the unmanned aerial vehicle max . The unmanned aerial vehicle is provided with an automatic obstacle avoidance device and a stability augmentation device, has automatic obstacle avoidance capability and certain wind resistance, and has very small path deviation relative to the total flight path length due to wind force influence or obstacle avoidance, so that the path deviation can be ignored.
In step S2, a team orientation problem model is constructed with the maximum sum of the weights of the network distribution lines to be patrolled and examined by the unmanned aerial vehicle as an optimization target based on the network distribution line information to be patrolled and examined, the number and the number of the unmanned aerial vehicles, the apron information and the weights of the network distribution lines. The specific implementation process is as follows:
the team oriented problem model includes objective functions and constraints. The objective function is expressed by equation (1):
wherein: i, j is the number of the distribution network to be inspected; w (w) i The weight of the distribution network line i; x is x ij For the value of the binary decision variable, when the unmanned aerial vehicle patrols and examines the distribution network line j after finishing the distribution network line i, x ij =1, otherwise x ij ＝0。
The constraint conditions include:
t p ≤T max (6)
wherein:
l i the length to be flown when the unmanned aerial vehicle patrols and examines the distribution network line i is represented; t represents the set of all the distribution network lines to be inspected; 0 and n+1 represent the position numbers of the start point and the end point of the unmanned aerial vehicle respectively; a represents a starting point, an ending point and a set formed by all network lines to be inspected of the unmanned aerial vehicle; p represents the unmanned aerial vehicle inspection task path; t (T) max Representing the maximum travel time of the unmanned aerial vehicle; v 1 The flying speed between the network lines is distributed at the two ends of the unmanned aerial vehicle; v 2 The method comprises the following steps that the flight speed of the unmanned aerial vehicle when the unmanned aerial vehicle patrols and examines the distribution network is used; t is t P Representing the total flight time of the flight path P of the unmanned aerial vehicle; u (u) i ,u j The sequence of the target i and the target j in the unmanned plane path is respectively;
the formula (2) represents the time when the unmanned aerial vehicle flies from the distribution network line i to the distribution network line j and completes the inspection of the distribution network line j; the formula (3) represents task execution time corresponding to the unmanned aerial vehicle inspection task path P; equation (4) indicates that the unmanned aerial vehicle must start from the starting point and finally return to the ending point; equation (5) indicates that each distribution network line can only be inspected at most once; formula (6) is a constraint of the endurance of the unmanned aerial vehicle; equations (7) and (8) avoid the sub-paths; equation (9) is the value of the binary decision variable, and x is when the unmanned aerial vehicle patrols and examines the distribution network line j after finishing the distribution network line i ij =1, otherwise x ij ＝0。
In step S3, the team directional problem model is solved, and an optimal path for autonomous inspection of the multi-unmanned aerial vehicle distribution network is obtained. The specific implementation process is as follows:
in the embodiment of the application, a single parent genetic algorithm is adopted to solve the team oriented problem model as an example. The specific process is as follows:
s301, setting a chromosome coding mode and setting execution parameters of a single parent genetic algorithm, wherein the method specifically comprises the following steps:
the chromosome coding mode is as follows:
the method comprises the steps of encoding a chromosome by adopting an integer encoding method based on a task point serial number and a breakpoint setting method, namely, representing one chromosome by using two vectors, wherein the first vector is a random arrangement of all target numbers, and the second vector is a randomly set breakpoint position. One chromosome represents one possible path planning scheme for a team oriented problem model.
The chromosomal representation as depicted in fig. 2: the 1 st unmanned aerial vehicle is from the air park, the air park that returns its departure behind the line task 8, 4, 3, 1 of patrolling in proper order, the 2 nd unmanned aerial vehicle is from the air park, the air park that returns its departure behind the line task 2, 9, 10 of patrolling in proper order, the 3 rd unmanned aerial vehicle is from the air park, the air park that returns its departure behind the line task 5, 6, 7 of patrolling in proper order.
The execution parameters of the genetic algorithm comprise population size N, maximum iteration times T and contemporary iteration times T.
S302, initializing a population according to a set chromosome coding mode, an execution parameter of a single parent genetic algorithm and a set of distribution network lines to be patrolled and examined to obtain an initial population. The method specifically comprises the following steps:
s302a, randomly arranging numbers in a set of to-be-inspected distribution network lines to obtain a sequence H;
s302b, randomly setting K-1 break points according to the number K of the unmanned aerial vehicles, so that the sequence H is divided into K sections, and the distribution network line which each unmanned aerial vehicle should check is determined.
S302c, repeating the steps S302a-S302b according to a preset population scale to obtain an initial population, wherein the initial population comprises a plurality of task allocation schemes, the task allocation schemes comprise task execution sequences and corresponding site numbers of each unmanned aerial vehicle in the plurality of unmanned aerial vehicles, and the task execution sequences comprise numbers of to-be-inspected distribution network lines through which the unmanned aerial vehicles pass in sequence.
It should be noted that, the individuals in the initial population all meet the constraint condition of the team-oriented problem model, and the initial population is the first generation parent population.
S303, calculating the fitness value of all individuals in the initial population to obtain the fitness value of the initial population, wherein the fitness value is specifically as follows:
in the embodiment of the application, the sum of the weights of the distribution network lines patrolled by the unmanned aerial vehicle is used as an optimization target, so that the fitness value takes the sum f (x) of the weights as a fitness function of a genetic algorithm. The larger the f (x) number, the higher the fitness is indicated by the chromosome. The fitness function f (x) is calculated according to the following formula, and when the fitness value of the individuals in the population is calculated, namely:
adding the fitness values of all individuals to obtain the fitness value of the initial population;
s304, selecting a temporary population from parent populations, wherein the temporary population is specifically:
s304a, selecting 5 unselected individuals from the parent population by using roulette.
S304b, finding out the individual with the highest fitness value from the 5 newly selected individuals, storing the individual with the highest fitness value into the temporary population, and repeating the steps S304 a-S304 b until all the individuals in the parent population are bet-selected by the roulette wheel.
S305, generating 2 random mutant fragment selection points i and j and a mutant fragment insertion position p;
s306, mutating each individual in the temporary population by using four mutation operators, namely, swapins ert and flipinsert, lslideinsert, rslideinsert, and adding the individual obtained by each mutation and the original individual in the temporary population into a child population;
wherein:
the mutation process of the mutation operator swapinsert can be described as: the sequence numbers of positions i, j are swapped, the sequence numbers of positions i +1 and j-1 are swapped, and then the fragment of positions i to j is inserted into the insertion position p. Fig. 3 shows the process of mutation operator swapinsert mutation.
The mutation process of the mutation operator flip insert can be described as: the sequence numbers in fragments i through j-1 are reversed and then the fragment at positions i through j-1 is inserted into insertion position p. Fig. 4 shows the process of mutation operator flip-insert mutation.
The mutation process of the mutation operator lslidainsert can be described as: the sequence numbers in i through j are cyclically shifted to the left by one position, the sequence numbers of positions i+1 and j-1 are swapped, and then the fragment of positions i through j is inserted into the insertion position p. Fig. 5 shows the process of mutation by the mutation operator lslidainsert.
The mutation process of the mutation operator rslidainsert can be described as: the sequence numbers in i through j are cyclically shifted to the right by one position, the sequence numbers of positions i+1 and j-1 are swapped, and then the fragment of positions i through j is inserted into the insertion position p. Fig. 6 shows the process of mutation by the mutation operator rslidainsert.
S307, acquiring the fitness of the child population, comparing the fitness with the fitness value of the parent population, and if the fitness of the child population is better than the fitness value of the parent population, replacing the parent population by the child population, otherwise, reserving the parent population.
And S308, selecting an individual with the largest fitness value from the parent population, and marking the individual as an optimal solution.
S309, updating the current iteration times, judging whether the maximum iteration times are reached, if so, outputting an optimal solution, otherwise, returning to the step S304, wherein the optimal solution is the code of the optimal task allocation scheme.
The embodiment of the application also provides a track planning system of the multi-unmanned aerial vehicle collaborative inspection distribution network line, which comprises the following steps:
the data acquisition module is used for acquiring the information of the distribution network lines to be inspected, the number and the number of the unmanned aerial vehicles, the information of the air apron and the weights of the distribution network lines, wherein the weights of the distribution network lines are the number of days of interval between the distribution network lines and the last inspection;
the model construction module is used for constructing a team orientation problem model by maximizing the sum of the weights of the distribution network lines to be inspected by the unmanned aerial vehicle based on the distribution network line information to be inspected, the number and the number of the unmanned aerial vehicles, the apron information and the weights of the distribution network lines;
and the model solving module is used for solving the team directional problem model and acquiring an optimal path for autonomous inspection of the multi-unmanned aerial vehicle distribution network.
It may be understood that, the track planning system for the multi-unmanned aerial vehicle collaborative inspection distribution network line provided by the embodiment of the application corresponds to the track planning method for the multi-unmanned aerial vehicle collaborative inspection distribution network line, and the explanation, the examples, the beneficial effects and other parts of the relevant content can refer to the corresponding content in the track planning method for the multi-unmanned aerial vehicle collaborative inspection distribution network line, which are not repeated herein.
The embodiment of the application also provides a computer readable storage medium which stores a computer program for the track planning of the multi-unmanned aerial vehicle collaborative inspection distribution network line, wherein the computer program enables a computer to execute the track planning method of the multi-unmanned aerial vehicle collaborative inspection distribution network line.
The embodiment of the application also provides electronic equipment, which comprises:
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 a track planning method for performing a multi-drone collaborative inspection distribution network line as described above.
In summary, compared with the prior art, the method has the following beneficial effects:
1. according to the embodiment of the application, the number of interval days from the last inspection of each distribution network line is used as the weight of the distribution network line, the team orientation problem is used for modeling the problem, and then the improved single parent genetic algorithm is used for solving the model, so that the optimal path for autonomous inspection of the multi-unmanned aerial vehicle distribution network is obtained. The embodiment of the application focuses on the problem of the interval time between two inspection of the distribution network line, and can effectively prevent the distribution network line from being inspected for a long time, thereby improving the finishing quality of the inspection task and reducing the potential safety hazard.
2. The embodiment of the application designs four mutation operators, avoids the complexity of parameter setting, simplifies algorithm operation, and improves the calculation efficiency.
3. According to the embodiment of the application, when the unmanned aerial vehicle flies from the distribution network line i to the distribution network line j and the inspection of the distribution network line j is completed, the difference between the flight speed of the unmanned aerial vehicle between the distribution network lines at the two ends and the flight speed of the unmanned aerial vehicle when the unmanned aerial vehicle inspects the distribution network is considered, and the practical scene is more met.
It should be noted that, in this document, from the description of the above embodiments, those skilled in the art may clearly understand that each embodiment may be implemented by means of software plus a necessary general hardware platform. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.
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. Moreover, 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 one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above embodiments are only for illustrating the technical solution of the present application, and are not limiting; although the application 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 scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application.
Claims (7)
1. A track planning method for a multi-unmanned aerial vehicle collaborative inspection distribution network line is characterized by comprising the following steps:
s1, acquiring information of a distribution network line to be inspected, the number and the number of unmanned aerial vehicles, information of an apron and the weight of the distribution network line, wherein the weight of the distribution network line is the number of days of interval between the distribution network line and the last inspected line;
s2, constructing a team orientation problem model by maximizing the sum of weights of the distribution network lines to be inspected by the unmanned aerial vehicle based on the distribution network line information to be inspected, the number and the number of the unmanned aerial vehicles, the apron information and the weights of the distribution network lines; wherein the team oriented problem model includes an objective function and a constraint condition;
the objective function is expressed by equation (1):
wherein: i, j is the number of the distribution network to be inspected; w (w) i The weight of the distribution network line i; x is x ij For the value of the binary decision variable, when the unmanned aerial vehicle patrols and examines the distribution network line j after finishing the distribution network line i, x ij =1, otherwise x ij =0; s3, solving the team directional problem model to obtain an optimal path for autonomous inspection of the multi-unmanned aerial vehicle distribution network, wherein the method comprises the following steps:
s301, setting a chromosome coding mode and setting execution parameters of a single parent genetic algorithm;
s302, initializing a population according to a set chromosome coding mode, an execution parameter of a single parent genetic algorithm and a set of distribution network lines to be patrolled and examined to obtain an initial population; comprising the following steps:
s302a, randomly arranging numbers in a set of to-be-inspected distribution network lines to obtain a sequence H;
s302b, randomly setting K-1 break points according to the number K of the unmanned aerial vehicles, so that the sequence H is divided into K sections, and determining a distribution network line which each unmanned aerial vehicle should check;
s302c, repeating the steps S302a-S302b according to a preset population scale to obtain an initial population, wherein the initial population comprises a plurality of task allocation schemes, the task allocation schemes comprise task execution sequences and corresponding site numbers of each unmanned aerial vehicle in the plurality of unmanned aerial vehicles, and the task execution sequences comprise numbers of to-be-inspected distribution network lines through which the unmanned aerial vehicles pass in sequence;
s303, calculating the fitness value of all individuals in the initial population to obtain the fitness value of the initial population;
s304, selecting a temporary population from the parent population;
s305, generating 2 random mutant fragment selection points i and j and a mutant fragment insertion position p;
s306, mutating each individual in the temporary population by using four mutation operators, and adding the individual obtained by each mutation and the original individual in the temporary population into a offspring population;
s307, acquiring fitness of the child population, comparing the fitness with the fitness value of the parent population, and if the fitness of the child population is better than the fitness value of the parent population, replacing the parent population by the child population, otherwise, reserving the parent population;
s308, selecting an individual with the largest fitness value from the parent population, and marking the individual as an optimal solution;
s309, updating the current iteration times, judging whether the maximum iteration times are reached, if so, outputting an optimal solution, otherwise, returning to the step S304, wherein the optimal solution is the code of the optimal task allocation scheme.
2. The method for planning a track of a multi-unmanned aerial vehicle cooperative inspection distribution network line according to claim 1, wherein the constraint condition comprises:
t p ≤T max (6)
wherein:
l i the length to be flown when the unmanned aerial vehicle patrols and examines the distribution network line i is represented; t represents the set of all the distribution network lines to be inspected; 0 and n+1 represent the position numbers of the start point and the end point of the unmanned aerial vehicle respectively; a represents a starting point, an ending point and a set formed by all network lines to be inspected of the unmanned aerial vehicle; p represents the unmanned aerial vehicle inspection task path; t (T) max Representing the maximum travel time of the unmanned aerial vehicle; v 1 The flying speed between the network lines is distributed at the two ends of the unmanned aerial vehicle; v 2 The method comprises the following steps that the flight speed of the unmanned aerial vehicle when the unmanned aerial vehicle patrols and examines the distribution network is used; t is t P Representing the total flight time of the flight path P of the unmanned aerial vehicle; u (u) i ,u j The sequence of the target i and the target j in the unmanned plane path is respectively;
the formula (2) represents the time when the unmanned aerial vehicle flies from the distribution network line i to the distribution network line j and completes the inspection of the distribution network line j; the formula (3) represents task execution time corresponding to the unmanned aerial vehicle inspection task path P; equation (4) indicates that the unmanned aerial vehicle must start from the starting point and finally return to the ending point; the maximum number of each distribution network line is represented by (5)Can only be inspected once; formula (6) is a constraint of the endurance of the unmanned aerial vehicle; equations (7) and (8) avoid the sub-paths; equation (9) is the value of the binary decision variable, and x is when the unmanned aerial vehicle patrols and examines the distribution network line j after finishing the distribution network line i ij =1, otherwise x ij ＝0。
3. The method for planning a track of a multi-unmanned aerial vehicle cooperative inspection distribution network line according to claim 1, wherein S304 comprises:
s304a, selecting 5 unselected individuals from the parent population by using roulette;
s304b, finding out the individual with the highest fitness value from the 5 newly selected individuals, storing the individual with the highest fitness value into the temporary population, and repeating the steps S304 a-S304 b until all the individuals in the parent population are bet-selected by the roulette wheel.
4. The method for path planning for a multi-unmanned aerial vehicle collaborative inspection distribution network line according to claim 1, wherein in S306, each individual in a temporary population is subjected to four variations with four mutation operators, the four variations comprising: mutation operator swapinsert, flipinsert, lslideinsert, rslideinsert;
wherein:
mutation of mutation operator swapinsert is: exchanging the sequence numbers of positions i and j, exchanging the sequence numbers of positions i+1 and j-1, and then inserting the fragment of positions i to j into the insertion position p;
the mutation process of the mutation operator flip insert is as follows: reversing the sequence numbers in segments i through j-1 and then inserting the segment at positions i through j-1 into insertion position p;
the mutation process of the mutation operator lslidainsert is as follows: the sequence numbers in i to j are circularly shifted to the left by one position, the sequence numbers of the positions i+1 and j-1 are exchanged, and then the fragment of the positions i to j is inserted into the insertion position p;
the mutation process of the mutation operator rslidainsert is as follows: the sequence numbers in i through j are cyclically shifted to the right by one position, the sequence numbers of positions i+1 and j-1 are swapped, and then the fragment of positions i through j is inserted into the insertion position p.
5. A track planning system for a multi-unmanned aerial vehicle collaborative inspection distribution network line, the system comprising:
the data acquisition module is used for acquiring the information of the distribution network lines to be inspected, the number and the number of the unmanned aerial vehicles, the information of the air apron and the weights of the distribution network lines, wherein the weights of the distribution network lines are the number of days of interval between the distribution network lines and the last inspection;
the model construction module is used for constructing a team orientation problem model by maximizing the sum of the weights of the distribution network lines to be inspected by the unmanned aerial vehicle based on the distribution network line information to be inspected, the number and the number of the unmanned aerial vehicles, the apron information and the weights of the distribution network lines; wherein the team oriented problem model includes an objective function and a constraint condition;
the objective function is expressed by equation (1):
wherein: i, j is the number of the distribution network to be inspected; w (w) i The weight of the distribution network line i; x is x ij For the value of the binary decision variable, when the unmanned aerial vehicle patrols and examines the distribution network line j after finishing the distribution network line i, x ij =1, otherwise x ij =0; the model solving module is used for solving the team directional problem model to obtain an optimal path for autonomous inspection of the multi-unmanned aerial vehicle distribution network, and comprises the following steps:
s301, setting a chromosome coding mode and setting execution parameters of a single parent genetic algorithm;
s302, initializing a population according to a set chromosome coding mode, an execution parameter of a single parent genetic algorithm and a set of distribution network lines to be patrolled and examined to obtain an initial population; comprising the following steps:
s302a, randomly arranging numbers in a set of to-be-inspected distribution network lines to obtain a sequence H;
s302b, randomly setting K-1 break points according to the number K of the unmanned aerial vehicles, so that the sequence H is divided into K sections, and determining a distribution network line which each unmanned aerial vehicle should check;
s302c, repeating the steps S302a-S302b according to a preset population scale to obtain an initial population, wherein the initial population comprises a plurality of task allocation schemes, the task allocation schemes comprise task execution sequences and corresponding site numbers of each unmanned aerial vehicle in the plurality of unmanned aerial vehicles, and the task execution sequences comprise numbers of to-be-inspected distribution network lines through which the unmanned aerial vehicles pass in sequence;
s303, calculating the fitness value of all individuals in the initial population to obtain the fitness value of the initial population;
s304, selecting a temporary population from the parent population;
s305, generating 2 random mutant fragment selection points i and j and a mutant fragment insertion position p;
s306, mutating each individual in the temporary population by using four mutation operators, and adding the individual obtained by each mutation and the original individual in the temporary population into a offspring population;
s307, acquiring fitness of the child population, comparing the fitness with the fitness value of the parent population, and if the fitness of the child population is better than the fitness value of the parent population, replacing the parent population by the child population, otherwise, reserving the parent population;
s308, selecting an individual with the largest fitness value from the parent population, and marking the individual as an optimal solution;
s309, updating the current iteration times, judging whether the maximum iteration times are reached, if so, outputting an optimal solution, otherwise, returning to the step S304, wherein the optimal solution is the code of the optimal task allocation scheme.
6. A computer-readable storage medium, characterized in that it stores a computer program for track planning of a multi-unmanned aerial vehicle cooperative inspection distribution network line, wherein the computer program causes a computer to execute the track planning method of a multi-unmanned aerial vehicle cooperative inspection distribution network line according to any one of claims 1 to 4.
7. 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 a track planning method for performing the multi-drone co-patrol distribution network route of any one of claims 1-4.
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