CN113762594A - Path planning method and device for vehicle-mounted machine to cooperatively deliver post-disaster rescue goods and materials - Google Patents

Path planning method and device for vehicle-mounted machine to cooperatively deliver post-disaster rescue goods and materials Download PDF

Info

Publication number
CN113762594A
CN113762594A CN202110839638.4A CN202110839638A CN113762594A CN 113762594 A CN113762594 A CN 113762594A CN 202110839638 A CN202110839638 A CN 202110839638A CN 113762594 A CN113762594 A CN 113762594A
Authority
CN
China
Prior art keywords
vehicle
disaster
unmanned aerial
path
point
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202110839638.4A
Other languages
Chinese (zh)
Other versions
CN113762594B (en
Inventor
罗贺
朱默宁
王国强
胡笑旋
靳鹏
夏维
马华伟
唐奕城
张歆悦
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Hefei University of Technology
Original Assignee
Hefei University of Technology
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Hefei University of Technology filed Critical Hefei University of Technology
Priority to CN202110839638.4A priority Critical patent/CN113762594B/en
Publication of CN113762594A publication Critical patent/CN113762594A/en
Application granted granted Critical
Publication of CN113762594B publication Critical patent/CN113762594B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G06Q10/047—Optimisation of routes or paths, e.g. travelling salesman problem
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/12—Computing arrangements based on biological models using genetic models
    • G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G06Q10/083—Shipping
    • G06Q10/0835—Relationships between shipper or supplier and carriers
    • G06Q10/08355—Routing methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/26—Government or public services
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/40—Business processes related to the transportation industry
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

Landscapes

  • Business, Economics & Management (AREA)
  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Economics (AREA)
  • Human Resources & Organizations (AREA)
  • Theoretical Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Strategic Management (AREA)
  • Tourism & Hospitality (AREA)
  • General Physics & Mathematics (AREA)
  • Marketing (AREA)
  • General Business, Economics & Management (AREA)
  • Biophysics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Development Economics (AREA)
  • General Health & Medical Sciences (AREA)
  • Operations Research (AREA)
  • Evolutionary Biology (AREA)
  • Quality & Reliability (AREA)
  • Primary Health Care (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Artificial Intelligence (AREA)
  • Data Mining & Analysis (AREA)
  • Physiology (AREA)
  • Genetics & Genomics (AREA)
  • Educational Administration (AREA)
  • Biomedical Technology (AREA)
  • Computational Linguistics (AREA)
  • Game Theory and Decision Science (AREA)
  • Evolutionary Computation (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention provides a path planning method and a path planning device for vehicle-mounted equipment to cooperatively distribute post-disaster rescue goods and relates to the field of task allocation. The invention provides a path planning method for vehicle-machine cooperative distribution of post-disaster rescue goods and materials, which comprises the steps of obtaining disaster data and a plurality of complex data for distributing post-disaster rescue goods and materials vehicles and an unmanned aerial vehicle, and constructing a vehicle-machine cooperative path problem model with divisible requirements by taking the shortest total time for reaching all disaster people gathering points as a target based on the disaster data and the complex data for distributing the post-disaster rescue goods and materials vehicles and the unmanned aerial vehicle; and acquiring an optimal distribution path based on the disaster data, the union data composed of the plurality of vehicles and the unmanned aerial vehicle, the vehicle-machine cooperation path problem model and the cultural genetic algorithm. The invention realizes that the complex formed by a plurality of vehicles and unmanned aerial vehicles can jointly complete the delivery task of rescue goods and materials, can improve the delivery efficiency and can quickly and fully complete the goods and materials delivery task.

Description

Path planning method and device for vehicle-mounted machine to cooperatively deliver post-disaster rescue goods and materials
Technical Field
The invention relates to the technical field of task allocation, in particular to a path planning method and device for vehicle-mounted machine collaborative distribution of post-disaster rescue goods and materials.
Background
After an earthquake disaster occurs, collapse of buildings, traffic interruption, damage to life line engineering facilities and the like can be caused. Thus. After a disaster occurs, logistics business is urgently needed, and clean water, food and emergency drugs are provided for people in the disaster area under the environment that infrastructure such as roads and the like are damaged, so that people in the disaster area can be rescued at the highest speed.
In traditional post-disaster rescue, vehicles are generally used for material distribution, but basic facilities such as roads and the like after disaster are possibly damaged to different degrees, so that the distribution efficiency of materials is influenced.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects of the prior art, the invention provides a path planning method and a path planning device for vehicle-mounted machine cooperative delivery of post-disaster rescue goods and materials, and solves the technical problem of low delivery efficiency of the existing goods and materials.
(II) technical scheme
In order to achieve the purpose, the invention is realized by the following technical scheme:
in a first aspect, the present invention provides a path planning method for vehicle-mounted equipment to cooperatively deliver post-disaster rescue goods, where the method includes:
s1, acquiring disaster data and a plurality of data of a complex formed by vehicles for delivering post-disaster rescue goods and unmanned aerial vehicles;
s2, constructing a vehicle-machine cooperation path problem model with separable requirements by taking the shortest total time for reaching all disaster people gathering points as a target based on the disaster-stricken data and the data of a complex formed by a plurality of vehicles for delivering post-disaster rescue goods and unmanned aerial vehicles;
and S3, acquiring an optimal distribution path based on the disaster data, the union data composed of a plurality of vehicles for distributing the post-disaster rescue goods and the unmanned aerial vehicle, the vehicle-machine cooperation path problem model and the cultural genetic algorithm.
Preferably, the data of the complex formed by the plurality of vehicles for distributing the post-disaster relief supplies and the unmanned aerial vehicle comprises:
unmanned aerial vehicle's information and vehicle information, unmanned aerial vehicle's information includes: unmanned aerial vehicle number, unmanned aerial vehicle flight speed, unmanned aerial vehicle bearing capacity and unmanned aerial vehicle endurance of the unmanned aerial vehicle; the vehicle information includes a vehicle number and a vehicle running speed.
Preferably, the demand-separable vehicle machine cooperation path problem model includes an objective function, as shown in formula (1):
Figure BDA0003178329530000021
wherein i is the number of the disaster people gathering points, and T is the disaster people gathering point set; m is the unmanned aerial vehicle number, and U is the unmanned aerial vehicle set; n is a vehicle number, and V is a vehicle set;
Figure BDA0003178329530000022
the number of packages sent to the disaster people gathering point i by the unmanned aerial vehicle with the number m,
Figure BDA0003178329530000023
the number of packages which are sent to the disaster people gathering point i by the vehicle with the number n;
Figure BDA0003178329530000024
the time when the unmanned plane with the number m reaches the disaster people gathering point i,
Figure BDA0003178329530000025
the time when the vehicle with the number n reaches the disaster people gathering point i is taken as the time; and the objective function represents that the total time for reaching all the disaster people gathering points is shortest.
Preferably, the demand-separable vehicle-mounted machine cooperation path problem model comprises constraint conditions, and is expressed by formulas (2) to (21):
Figure BDA0003178329530000031
Figure BDA0003178329530000032
Figure BDA0003178329530000033
Figure BDA0003178329530000034
Figure BDA0003178329530000035
Figure BDA0003178329530000036
Figure BDA0003178329530000037
Figure BDA0003178329530000038
Figure BDA0003178329530000039
Figure BDA00031783295300000310
Figure BDA00031783295300000311
Figure BDA00031783295300000312
Figure BDA00031783295300000313
Figure BDA00031783295300000314
Figure BDA00031783295300000315
Figure BDA00031783295300000316
Figure BDA00031783295300000317
Figure BDA00031783295300000318
Figure BDA00031783295300000319
Figure BDA0003178329530000041
wherein formula (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; formula (5) flow conservation constraint; variable of formula (6)
Figure BDA0003178329530000042
Associated with the arrival time in equation (6), sub-paths that do not contain a warehouse are prevented; equation (7) indicates that if drone m transmits from point i and collects at point k, then points i and k must be assigned to vehicle n, i.e., on the path of the vehicle; equations (8) and (9) ensure that vehicle n and drone m are time coordinated when drone m is launched from point i, noting that drone m and vehicle n may leave base at different times, these constraints will force vehicle n and drone m to reach point i at the same time; when vehicle n and drone m meet at point k, equations (10) and (11) represent time coordination, and these constraints will force vehicle n and drone m to reach node k at the same time; equations (8) - (11) assume that if drone m is launched from vehicle n at point i, they cannot meet at point i, i.e. the drone cannot be launched multiple times from the same point; formula (12) and formula (13) show that assuming that the drone m is launched from the vehicle n at point i and meets the vehicle at point k after visiting point j, formula (12) constrains the time for the drone to reach point j, and formula (13) constrains the time for the drone to reach point k; formula (14) solves the endurance constraint of the drone, where TmaxThe maximum duration of the unmanned aerial vehicle; equation (15) defines the departure times of the vehicle and drone; equations (16) - (17) ensure that the node can only be serviced if the vehicle and drone are accessing the node; equation (18) ensures that the maximum load per drone does not exceedIts capacity CU(ii) a Equation (19) requires that all the requirements of each point be met; the values of the decision variables are defined by formulas (20) to (21);
i is a node number, T is a disaster citizen gathering point set, and N is a node set; m is the unmanned aerial vehicle number, and U is the unmanned aerial vehicle set; n is a vehicle number, and V is a vehicle set;
Figure BDA0003178329530000051
if the k point is a warehouse, the decision variable represents whether the unmanned plane with the number m selects a path which starts from the node i to reach the disaster people gathering point j and returns to the warehouse;
Figure BDA0003178329530000052
whether the vehicle with the number n selects a path from the node i to the disaster people gathering point j or not is a decision variable;
Figure BDA0003178329530000053
as a decision variable, the vehicle with the number n starts from the warehouse 0 and reaches a path of the disaster people gathering point i;
Figure BDA0003178329530000054
as a decision variable, the path from the disaster people gathering point i to the warehouse L +1 is numbered n;
Figure BDA0003178329530000055
a path from node j to node k for vehicle number n;
Figure BDA0003178329530000056
time from node i to node j for vehicle number n;
Figure BDA0003178329530000057
the arrival time of the vehicle with the number n to the node j;
Figure BDA0003178329530000058
number n of vehicleThe arrival time of the vehicle at node i;
Figure BDA0003178329530000059
a path from the node h to the node i of the vehicle with the number n is a decision variable;
Figure BDA00031783295300000510
a path from the node k to the node l of the vehicle with the number n is a decision variable;
Figure BDA00031783295300000511
the arrival time of the unmanned aerial vehicle with the number m to the node i;
Figure BDA00031783295300000512
the arrival time of the unmanned aerial vehicle with the number m to the node k;
Figure BDA00031783295300000513
the arrival time of the vehicle with the number n to the node k;
Figure BDA00031783295300000514
the arrival time of the unmanned aerial vehicle with the number m to the node j is shown;
Figure BDA00031783295300000515
the time from the node i to the disaster people gathering point j of the unmanned aerial vehicle with the number m is shown;
Figure BDA00031783295300000516
the time from a disaster people gathering point i to a node k is the unmanned aerial vehicle with the number m; t ismaxThe maximum duration of the unmanned aerial vehicle;
Figure BDA00031783295300000517
the time when the unmanned aerial vehicle with the number m departs from the warehouse 0;
Figure BDA00031783295300000518
time of departure from warehouse 0 for vehicle number n;
Figure BDA00031783295300000519
the number of packages sent to the disaster people gathering point j by the unmanned aerial vehicle with the number m; q. q.sjThe demand of the disaster people gathering point j is obtained; cUIs the load-bearing capacity of the vehicle; m is a sufficiently large positive integer.
5. The path planning method for vehicle-machine cooperative delivery of post-disaster rescue goods and materials according to any one of claims 1 to 4, wherein the obtaining of the optimal delivery path based on the disaster-stricken data, the data of a complex formed by a plurality of vehicles for delivering post-disaster rescue goods and unmanned aerial vehicles, the vehicle-machine cooperative path problem model, and the cultural genetic algorithm comprises:
s301, setting a coding rule;
s302, according to the coding rule, completing initialization of the population according to the following 5 steps to obtain an initial distribution path set:
s302a, firstly, randomly arranging the numbers of all point targets to generate the 1 st line of chromosomes, then dividing the arrangement into K sections, and adding 10 to the front and the back of each section to represent a warehouse, wherein the kth section of chromosomes corresponds to the path of the kth vehicle;
s302b, taking out 2 target numbers from the front to the back of the kth chromosome each time, taking the point targets corresponding to the 2 numbers as 2 focuses of the ellipse, and taking the cruising ability T of the unmanned aerial vehiclemaxIs a long shaft, structure TmaxAn ellipse;
s302c, if TmaxIf only 1 point target exists in the ellipse, writing the number of the point target under the number of the previous target; if T ismaxIf more than 1 point target in the ellipse, randomly selecting 1 target number to be written below the previous target number; if T ismaxIf no target exists in the ellipse, writing-1 under the number of the previous target, repeating the operation until the 2 nd position of the last chromosome of the segment is reached, and writing-1 under the last 1 st position;
s302d, repeating the steps S302 b-S302 c for K times, and obtaining the path of each vehicle and unmanned aerial vehicle combined body;
s302e, repeating the steps S302 a-S302 d according to the preset population scale to obtain an initial distribution path set;
s303, optimizing the initial distribution path set by adopting an improved cultural genetic algorithm to obtain an optimal distribution path.
Preferably, the optimizing the initial distribution path set by using an improved cultural genetic algorithm to obtain an optimal distribution path includes:
s303a, obtaining the population size N of the culture gene algorithmP；
S303b, designing a calculation method of the fitness value based on the objective function of the demand-separable vehicle-mounted device collaborative path problem model, as shown in formula (22):
Figure BDA0003178329530000071
s303c, selecting 2 schemes for offspring genetic operation according to the fitness value of the vehicle-machine cooperative traffic patrol initial path planning scheme and a roulette mechanism, wherein the probability of selecting the scheme with smaller fitness value is larger;
s303d, creating a new candidate solution for the selected 2 solutions in an initial solution mixing mode;
s303e, selecting a sub-scheme with a large adaptability value from the candidate schemes to carry out cross operation;
s303f, selecting a good sub-scheme to carry out a path planning scheme set to replace the original poor parent path planning scheme;
s303g, repeating the steps S303 d-S303 f until reaching the condition of ending iteration, and when MA continues T2And when the system is not updated, the algorithm is automatically terminated, and a path planning scheme with the optimal fitness value in the set is output to serve as an optimal distribution path, wherein T is the number of the disaster people gathering points.
Preferably, the initial protocol mixing comprises:
step 1: dividing 2 initial path planning schemes into | K | sections according to the number of the vehicle-machine cooperative complex;
step 2: performing cross operation on the initial path planning scheme, judging whether the paths visited by the vehicles in the vehicle-machine cooperative combination with the same number have the same patrol point target, and if so, exchanging the patrol point targets visited by the unmanned aerial vehicle starting from the patrol point target;
and step 3: repeating the step 2 according to the number | K | of the vehicle-machine cooperative complexes to complete the cross operation of all the sections;
and 4, step 4: and splicing the | K | sections to form a complete vehicle-machine cooperative distribution path planning scheme.
In a second aspect, the present invention provides a path planning device for vehicle-mounted devices to cooperatively deliver post-disaster rescue materials, including:
the data acquisition device is used for acquiring disaster data and a plurality of union data which are used for distributing rescue goods and materials after a disaster and are formed by the unmanned aerial vehicle;
the model building device is used for building a vehicle-machine cooperative path problem model with separable requirements by taking the shortest total time for reaching all disaster people gathering points as a target based on the disaster-stricken data and the data of a complex formed by a plurality of vehicles for delivering post-disaster rescue goods and unmanned aerial vehicles;
and the distribution path acquisition device is used for acquiring an optimal distribution path based on the disaster data, the complex data composed of a plurality of vehicles for distributing the post-disaster rescue goods and materials and the unmanned aerial vehicle, the vehicle-mounted machine cooperation path problem model and the cultural genetic algorithm.
In a fourth aspect, the present invention provides a computer-readable storage medium storing a computer program for path planning for vehicle-machine cooperative distribution of post-disaster rescue goods, wherein the computer program enables a computer to execute the method for path planning for vehicle-machine cooperative distribution of post-disaster rescue goods 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 planning method for car-machine coordinated distribution of post-disaster rescue assets as described above.
(III) advantageous effects
The invention provides a path planning method and device for vehicle-mounted machine cooperative distribution of post-disaster rescue goods and materials. Compared with the prior art, the method has the following beneficial effects:
the invention provides a path planning method for vehicle-machine cooperative distribution of post-disaster rescue goods and materials, which comprises the steps of obtaining disaster data and a plurality of complex data for distributing post-disaster rescue goods and materials vehicles and an unmanned aerial vehicle, and constructing a vehicle-machine cooperative path problem model with divisible requirements by taking the shortest total time for reaching all disaster people gathering points as a target based on the disaster data and the complex data for distributing the post-disaster rescue goods and materials vehicles and the unmanned aerial vehicle; and acquiring an optimal distribution path based on the disaster data, the union data composed of the plurality of vehicles and the unmanned aerial vehicle, the vehicle-machine cooperation path problem model and the cultural genetic algorithm. The invention realizes that the complex formed by a plurality of vehicles and unmanned aerial vehicles can jointly complete the delivery task of rescue goods and materials, can improve the delivery efficiency and can quickly and fully complete the goods and materials delivery task.
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 planning method for vehicle-mounted equipment to cooperatively deliver post-disaster rescue goods and materials according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of the chromosome mapping paths shown in Table 1.
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 path planning method and a path planning device for vehicle-machine cooperative delivery of post-disaster rescue goods and materials, solves the technical problem of low delivery efficiency of the existing goods and materials, achieves the effect that a complex formed by a plurality of vehicles and an unmanned aerial vehicle jointly completes the delivery task of the rescue goods and materials, and improves the delivery efficiency of the goods and materials.
In order to solve the technical problems, the general idea of the embodiment of the application is as follows:
in the prior art, the vehicle machine is not researched to be cooperatively applied to post-disaster rescue material distribution, vehicles are generally used for material distribution in traditional post-disaster rescue, but basic facilities such as post-disaster roads and the like are possibly damaged to different degrees, and the distribution efficiency of materials is influenced. The distribution task of accomplishing goods and materials with vehicle and unmanned aerial vehicle in coordination is applied to earthquake disaster back scene, and the vehicle not only is as unmanned aerial vehicle's platform of taking off and land, can carry out the goods and materials distribution simultaneously, because unmanned aerial vehicle receives bearing capacity's restriction transportation goods and materials limited, can revisit many times and satisfy the demand of disaster people focus, full play car machine in coordination with the advantage of transportation mode in solving the last kilometer distribution of rescue goods and materials, extended the application mode of unmanned aerial vehicle in earthquake disaster back scene.
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 planning method for vehicle-machine cooperative distribution of post-disaster rescue goods and materials, as shown in fig. 1, the method comprises the following steps of S1-S3:
s1, acquiring disaster data and a plurality of data of a complex formed by vehicles for delivering post-disaster rescue goods and unmanned aerial vehicles;
s2, constructing a vehicle-machine cooperation path problem model with separable requirements by taking the shortest total time for reaching all disaster people gathering points as a target based on the disaster-stricken data and the data of a complex formed by a plurality of vehicles for delivering post-disaster rescue goods and unmanned aerial vehicles;
and S3, acquiring an optimal distribution path based on the disaster data, the union data composed of a plurality of vehicles for distributing the post-disaster rescue goods and the unmanned aerial vehicle, the vehicle-machine cooperation path problem model and the cultural genetic algorithm.
The embodiment of the invention realizes that the complex formed by a plurality of vehicles and unmanned aerial vehicles jointly completes the delivery task of the rescue goods and materials, can improve the delivery efficiency and can complete the goods and materials delivery task quickly and fully.
The following describes the implementation process of the embodiment of the present invention in detail:
in step S1, disaster data and data of a plurality of complexes formed by vehicles for delivering post-disaster rescue goods and unmanned aerial vehicles are obtained, and the specific implementation process is as follows:
the method comprises the steps that a computer obtains disaster-affected data and a plurality of data used for distributing complex formed by post-disaster rescue goods and materials vehicles and an unmanned aerial vehicle, wherein the disaster-affected data comprise requirements and coordinates of disaster people gathering points; the data of a complex formed by a plurality of vehicles for delivering the post-disaster rescue goods and the unmanned aerial vehicles comprises information of the unmanned aerial vehicles and vehicle information, and in the specific implementation process, warehouse coordinates of the rescue goods are required to be acquired.
The information of the unmanned aerial vehicle includes: unmanned aerial vehicle's unmanned aerial vehicle serial number, unmanned aerial vehicle flying speed, unmanned aerial vehicle bearing capacity and unmanned aerial vehicle duration.
The vehicle information includes a vehicle number and a vehicle travel speed.
In step S2, based on the disaster data and the data of the complex formed by the post-disaster relief material vehicles and the unmanned aerial vehicles, a vehicle-machine cooperation path problem model with separable requirements is constructed with the shortest total time to reach all disaster people gathering points as a target, and the specific implementation process is as follows:
the objective function of the vehicle machine cooperation path problem model with separable requirements is shown as formula (1):
Figure BDA0003178329530000121
wherein i is the number of the disaster people gathering points, and T is the disaster people gathering point set; m is the unmanned aerial vehicle number, and U is the unmanned aerial vehicle set; n is a vehicle number, and V is a vehicle set;
Figure BDA0003178329530000122
the number of packages sent to the disaster people gathering point i by the unmanned aerial vehicle with the number m,
Figure BDA0003178329530000123
the number of packages which are sent to the disaster people gathering point i by the vehicle with the number n;
Figure BDA0003178329530000124
the time when the unmanned plane with the number m reaches the disaster people gathering point i,
Figure BDA0003178329530000125
the time when the vehicle with the number n reaches the disaster people gathering point i is taken as the time; and the objective function represents that the total time for reaching all the disaster people gathering points is shortest. The constraint conditions of the vehicle-machine collaborative path problem model are expressed by (2) to (21):
Figure BDA0003178329530000126
Figure BDA0003178329530000127
Figure BDA0003178329530000128
Figure BDA0003178329530000129
Figure BDA00031783295300001210
Figure BDA00031783295300001211
Figure BDA00031783295300001212
Figure BDA00031783295300001213
Figure BDA0003178329530000131
Figure BDA0003178329530000132
Figure BDA0003178329530000133
Figure BDA0003178329530000134
Figure BDA0003178329530000135
Figure BDA0003178329530000136
Figure BDA0003178329530000137
Figure BDA0003178329530000138
Figure BDA0003178329530000139
Figure BDA00031783295300001310
Figure BDA00031783295300001311
Figure BDA00031783295300001312
wherein formula (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; formula (5) flow conservation constraint; variable of formula (6)
Figure BDA00031783295300001313
Associated with the arrival time in equation (6), this also prevents sub-paths that do not contain a warehouse; equation (7) indicates that if drone m transmits from point i and collects at point k, then points i and k must be assigned to vehicle n, i.e., on the path of the vehicle; equations (8) and (9) ensure that vehicle n and drone m are time coordinated when drone m is launched from point i, noting that drone m and vehicle n may leave base at different times, these constraints will force vehicle n and drone m to reach point i at the same time; when vehicle n and drone m meet at point k, equations (10) and (11) time coordinate them. These constraints will force vehicle n and drone m to reach node k simultaneously. Equations (8) - (11) assume that if drone m is launched from vehicle n at point i, they cannot meet at point i, which means that the drone cannot be launched multiple times from the same point; equations (12) and (13) show that suppose drone m is launched from vehicle n at point i, and after visiting point j, at point k and at point kThe vehicles converge, the formula (12) restricts the time of the unmanned plane reaching the j point, and the formula (13) restricts the time of the unmanned plane reaching the k point; formula (14) solves the endurance constraint of the drone, where TmaxThe maximum duration of the unmanned aerial vehicle; equation (15) defines the departure times of the vehicle and drone; equations (16) - (17) ensure that the node can only be serviced if the vehicle and drone are accessing the node; equation (18) ensures that the maximum load per drone does not exceed its capacity CU(ii) a Equation (19) requires that all the requirements of each point be met; equations (20) to (21) define the values of the decision variables.
i is a node number, T is a disaster citizen gathering point set, and N is a node set; m is the unmanned aerial vehicle number, and U is the unmanned aerial vehicle set; n is a vehicle number, and V is a vehicle set;
Figure BDA0003178329530000141
the decision variable is used for indicating whether the unmanned aerial vehicle with the number m selects a path which starts from the node i to reach the disaster people gathering point j and meets the vehicle with the number m at the point k, and if the point k is a warehouse, the decision variable indicates whether the unmanned aerial vehicle with the number m selects a path which starts from the node i to reach the disaster people gathering point j and returns to the warehouse;
Figure BDA0003178329530000142
a decision variable represents whether the vehicle with the number n selects a path from the node i to the disaster people gathering point j;
Figure BDA0003178329530000143
the decision variable represents the path from the warehouse 0 to the disaster people gathering point i of the vehicle with the number n;
Figure BDA0003178329530000144
as a decision variable, the path from the disaster people gathering point i to the warehouse L +1 is numbered n;
Figure BDA0003178329530000145
a path from node j to node k for vehicle number n;
Figure BDA0003178329530000146
time from node i to node j for vehicle number n;
Figure BDA0003178329530000147
the arrival time of the vehicle with the number n to the node j;
Figure BDA0003178329530000148
the arrival time of the vehicle with the number n to the node i;
Figure BDA0003178329530000149
a path from the node h to the node i of the vehicle with the number n is a decision variable;
Figure BDA00031783295300001410
a path from the node k to the node l of the vehicle with the number n is a decision variable;
Figure BDA0003178329530000151
the arrival time of the unmanned aerial vehicle with the number m to the node i;
Figure BDA0003178329530000152
the arrival time of the unmanned aerial vehicle with the number m to the node k;
Figure BDA0003178329530000153
the arrival time of the vehicle with the number n to the node k;
Figure BDA0003178329530000154
the arrival time of the unmanned aerial vehicle with the number m to the node j is shown;
Figure BDA0003178329530000155
the time from the node i to the disaster people gathering point j of the unmanned aerial vehicle with the number m is shown;
Figure BDA0003178329530000156
the time from a disaster people gathering point i to a node k is the unmanned aerial vehicle with the number m; t ismaxFor maximum endurance of unmanned aerial vehicleA duration;
Figure BDA0003178329530000157
the time when the unmanned aerial vehicle with the number m departs from the warehouse 0;
Figure BDA0003178329530000158
time of departure from warehouse 0 for vehicle number n;
Figure BDA0003178329530000159
the number of packages sent to the disaster people gathering point j by the unmanned aerial vehicle with the number m; q. q.sjThe demand of the disaster people gathering point j is obtained; cUIs the load-bearing capacity of the vehicle; m is a sufficiently large positive integer.
In step S3, an optimal distribution path is obtained based on the disaster data, the data of a complex formed by a plurality of vehicles for distributing post-disaster rescue goods and unmanned aerial vehicles, the vehicle-machine cooperation path problem model, and the cultural genetic algorithm, and the specific implementation process is as follows:
s301, setting a coding rule, and coding disaster-suffered data and data of a complex formed by a plurality of vehicles for delivering post-disaster rescue goods and unmanned aerial vehicles, wherein the coding rule is shown in the following table 1:
Figure BDA00031783295300001510
from the cultural genetic algorithm (MA) chromosome coding shown in table 1, it can be seen that there are two vehicles and two drones performing the task. The chromosomal codes shown in table 1 represent: the 1# vehicle starts from the warehouse and then sequentially goes to the 1# point target and the 3# point target to deliver the rescue goods, the 1# unmanned aerial vehicle starts from the warehouse and then previously delivers the rescue goods to the 2# point target, then goes to the 4# point target to deliver the rescue goods after the 1# point target is converged with the vehicle, then goes to the 4# point target to deliver the rescue goods after the 3# point target is converged with the vehicle, and finally returns to the warehouse; the 2# vehicle departs from the warehouse and then sequentially goes to the 5# point target, the 6# point target and the 7# point target to deliver the rescue goods, the 2# unmanned aerial vehicle departs from the warehouse and then previously delivers the rescue goods to the 8# point target, then the 5# point target and the vehicle converge and then go to the 8# point target to deliver the rescue goods, then the 6# point target and the vehicle converge and then go to the 7# point target to deliver the rescue goods, then the 7# point target and the vehicle converge, and finally the vehicles return to the warehouse together. Table 1 a schematic diagram of the corresponding path of the chromosomal code is shown in fig. 2.
S302, according to the coding rule, completing initialization of the population according to the following 5 steps:
s302a, firstly, randomly arranging the numbers of all point targets to generate the 1 st line of chromosomes, then dividing the arrangement into K sections, and adding 1 '0' to the front and the back of each section to represent a warehouse, wherein the kth section of chromosomes corresponds to the path of the kth vehicle.
S302b, taking out 2 target numbers from the front to the back of the kth chromosome each time, taking the point targets corresponding to the 2 numbers as 2 focuses of the ellipse, and taking the cruising ability T of the unmanned aerial vehiclemaxFor the major axis, construct "TmaxOval ".
S302c, if "TmaxIf only 1 point target exists in the ellipse, the number of the point target is written below the number of the previous target; if "TmaxIf more than 1 point target in the ellipse is selected, 1 target number is randomly selected and written below the previous target number; if "TmaxEllipse "without object, write" -1 "under the previous object number, repeat the above operation until the 2 nd last position of the segment chromosome, and write" -1 "under the last 1 position.
S302d, repeating the steps S302 b-S302 c for K times, and obtaining the path of each vehicle and unmanned plane Combination (Combination of vehicle and line).
S302e, repeating the steps S302a to S302d according to the preset population size, and obtaining an initial distribution path set.
S303, optimizing the initial distribution path set by adopting an improved cultural genetic algorithm to obtain an optimal distribution path. The method specifically comprises the following steps:
s303a, obtaining the population size N of the culture gene algorithmP。
The embodiment of the invention is not provided withThe maximum cycle number of the MA algorithm is determined because the embodiment of the invention designs an adaptive algorithm termination mechanism, and when the MA is continuous T2And when the updating is not performed, the algorithm is automatically terminated, and the current optimal solution is output as the optimal solution of the problem. The number of the T disaster people gathering points is set in such a way that the efficiency of the algorithm can be improved and the algorithm is guaranteed to converge to an optimal solution, namely, the running time of the algorithm is dynamically adjusted according to the problem scale. The cultural genetic algorithm generally sets fixed iteration times, such as 100 times or 500 times, but when the problem scale is small, the problem can be converged quickly without iteration for multiple times; when the problem is large in scale, 500 times may not have obtained the optimal solution. The efficiency and the performance of the culture genetic algorithm can be effectively submitted by the aid of the method.
S303b, designing a calculation method of the fitness value based on the objective function of the demand-separable vehicle-mounted device collaborative path problem model, as shown in formula (22):
Figure BDA0003178329530000171
s303c, selecting 2 schemes for offspring genetic operation according to the fitness value of the vehicle-machine cooperative traffic patrol initial path planning scheme and a roulette mechanism, wherein the probability of selecting the scheme with smaller fitness value is larger;
s303d, 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 | sections according to the number of the vehicle-machine cooperative complex;
step 2: performing cross operation on the initial path planning scheme, judging whether the paths visited by the vehicles in the vehicle-machine cooperative combination with the same number have the same patrol point target, and if so, exchanging the patrol point targets visited by the unmanned aerial vehicle starting from the patrol point target;
and step 3: repeating the step 2 according to the number | K | of the vehicle-machine cooperative complexes to complete the cross operation of all the sections;
and 4, step 4: and splicing the | K | sections to form a complete vehicle-machine cooperative distribution path planning scheme.
S303e, selecting a sub-scheme with a large adaptability value from the candidate schemes to carry out cross operation;
s303f, selecting a good sub-scheme to carry out a path planning scheme set to replace the original poor parent path planning scheme;
s303g, repeating the steps S303 d-S303 f until reaching the condition of ending iteration, and when MA continues T2The next time there is no update, the algorithm automatically terminates. And outputting the path planning scheme with the optimal fitness value in the set as the optimal delivery path.
The embodiment of the invention also provides a path planning device for vehicle-machine cooperative distribution of post-disaster rescue goods and materials, which comprises:
the data acquisition device is used for acquiring disaster data and a plurality of union data which are used for distributing rescue goods and materials after a disaster and are formed by the unmanned aerial vehicle;
the model building device is used for building a vehicle-machine cooperative path problem model with separable requirements by taking the shortest total time for reaching all disaster people gathering points as a target based on the disaster-stricken data and the data of a complex formed by a plurality of vehicles for delivering post-disaster rescue goods and unmanned aerial vehicles;
and the distribution path acquisition device is used for acquiring an optimal distribution path based on the disaster data, the complex data composed of a plurality of vehicles for distributing the post-disaster rescue goods and materials and the unmanned aerial vehicle, the vehicle-mounted machine cooperation path problem model and the cultural genetic algorithm.
It can be understood that the path planning apparatus for vehicle-machine cooperative distribution of post-disaster relief supplies provided in the embodiment of the present invention corresponds to the path planning method for vehicle-machine cooperative distribution of post-disaster relief supplies, and explanations, examples, and beneficial effects of relevant contents thereof may refer to corresponding contents in the path planning method for vehicle-machine cooperative distribution of post-disaster relief supplies, which are not described herein again.
The embodiment of the present invention further provides a computer-readable storage medium, which stores a computer program for path planning for vehicle-machine cooperative distribution of post-disaster rescue goods, where the computer program enables a computer to execute the above-mentioned path planning method for vehicle-machine cooperative distribution of post-disaster rescue goods.
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 planning method for car-machine coordinated distribution of post-disaster rescue assets as described above.
In summary, compared with the prior art, the method has the following beneficial effects:
the embodiment of the invention realizes that the complex formed by a plurality of vehicles and unmanned aerial vehicles jointly completes the distribution task of the rescue goods and materials, can improve the distribution efficiency, and can complete the goods and materials distribution task quickly and fully.
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 planning method for vehicle-mounted equipment to cooperatively deliver post-disaster rescue goods is characterized by comprising the following steps:
s1, acquiring disaster data and a plurality of data of a complex formed by vehicles for delivering post-disaster rescue goods and unmanned aerial vehicles;
s2, constructing a vehicle-machine cooperation path problem model with separable requirements by taking the shortest total time for reaching all disaster people gathering points as a target based on the disaster-stricken data and the data of a complex formed by a plurality of vehicles for delivering post-disaster rescue goods and unmanned aerial vehicles;
and S3, acquiring an optimal distribution path based on the disaster data, the union data composed of a plurality of vehicles for distributing the post-disaster rescue goods and the unmanned aerial vehicle, the vehicle-machine cooperation path problem model and the cultural genetic algorithm.
2. The method for planning a path for vehicle-machine cooperative delivery of post-disaster relief materials according to claim 1, wherein the data of the complex consisting of the plurality of vehicles for delivering post-disaster relief materials and the unmanned aerial vehicle comprises:
unmanned aerial vehicle's information and vehicle information, unmanned aerial vehicle's information includes: unmanned aerial vehicle number, unmanned aerial vehicle flight speed, unmanned aerial vehicle bearing capacity and unmanned aerial vehicle endurance of the unmanned aerial vehicle; the vehicle information includes a vehicle number and a vehicle running speed.
3. The method for planning the path for the cooperative distribution of the post-disaster rescue goods and materials by the vehicle-mounted machine according to claim 1, wherein the problem model of the cooperative path of the vehicle-mounted machine with separable requirements comprises an objective function, as shown in formula (1):
Figure FDA0003178329520000011
wherein i is the number of the disaster people gathering points, and T is the disaster people gathering point set; m is the unmanned aerial vehicle number, and U is the unmanned aerial vehicle set; n is a vehicle number, and V is a vehicle set;
Figure FDA0003178329520000021
the number of packages sent to the disaster people gathering point i by the unmanned aerial vehicle with the number m,
Figure FDA0003178329520000022
the number of packages which are sent to the disaster people gathering point i by the vehicle with the number n;
Figure FDA0003178329520000023
the time when the unmanned plane with the number m reaches the disaster people gathering point i,
Figure FDA0003178329520000024
the time when the vehicle with the number n reaches the disaster people gathering point i is taken as the time; and the objective function represents that the total time for reaching all the disaster people gathering points is shortest.
4. The method for planning the path for the cooperative distribution of the post-disaster rescue goods and materials by the vehicle-mounted machine according to claim 1, wherein the vehicle-mounted machine cooperative path problem model with separable requirements comprises constraint conditions, and is expressed by formulas (2) to (21):
Figure FDA0003178329520000025
Figure FDA0003178329520000026
Figure FDA0003178329520000027
Figure FDA0003178329520000028
Figure FDA0003178329520000029
Figure FDA00031783295200000210
Figure FDA00031783295200000211
Figure FDA00031783295200000212
Figure FDA00031783295200000213
Figure FDA00031783295200000214
Figure FDA00031783295200000215
Figure FDA00031783295200000216
Figure FDA00031783295200000217
Figure FDA0003178329520000031
Figure FDA0003178329520000032
Figure FDA0003178329520000033
Figure FDA0003178329520000034
Figure FDA0003178329520000035
Figure FDA0003178329520000036
Figure FDA0003178329520000037
wherein formula (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; formula (5) flow conservation constraint; variable of formula (6)
Figure FDA0003178329520000038
Associated with the arrival time in equation (6), sub-paths that do not contain a warehouse are prevented; equation (7) shows that if drone m transmits from point i and collects at point k, points i and k must be dividedAllocating a vehicle n, i.e. on the path of the vehicle; equations (8) and (9) ensure that vehicle n and drone m are time coordinated when drone m is launched from point i, noting that drone m and vehicle n may leave base at different times, these constraints will force vehicle n and drone m to reach point i at the same time; when vehicle n and drone m meet at point k, equations (10) and (11) represent time coordination, and these constraints will force vehicle n and drone m to reach node k at the same time; equations (8) - (11) assume that if drone m is launched from vehicle n at point i, they cannot meet at point i, i.e. the drone cannot be launched multiple times from the same point; formula (12) and formula (13) show that assuming that the drone m is launched from the vehicle n at point i and meets the vehicle at point k after visiting point j, formula (12) constrains the time for the drone to reach point j, and formula (13) constrains the time for the drone to reach point k; formula (14) solves the endurance constraint of the drone, where TmaxThe maximum duration of the unmanned aerial vehicle; equation (15) defines the departure times of the vehicle and drone; equations (16) - (17) ensure that the node can only be serviced if the vehicle and drone are accessing the node; equation (18) ensures that the maximum load per drone does not exceed its capacity CU(ii) a Equation (19) requires that all the requirements of each point be met; the values of the decision variables are defined by formulas (20) to (21);
i is a node number, T is a disaster citizen gathering point set, and N is a node set; m is the unmanned aerial vehicle number, and U is the unmanned aerial vehicle set; n is a vehicle number, and V is a vehicle set;
Figure FDA0003178329520000041
if the k point is a warehouse, the decision variable represents whether the unmanned plane with the number m selects a path which starts from the node i to reach the disaster people gathering point j and returns to the warehouse;
Figure FDA0003178329520000042
whether the vehicle with the number n selects a path from the node i to the disaster people gathering point j or not is a decision variable;
Figure FDA0003178329520000043
as a decision variable, the vehicle with the number n starts from the warehouse 0 and reaches a path of the disaster people gathering point i;
Figure FDA0003178329520000044
as a decision variable, the path from the disaster people gathering point i to the warehouse L +1 is numbered n;
Figure FDA0003178329520000045
a path from node j to node k for vehicle number n;
Figure FDA0003178329520000046
time from node i to node j for vehicle number n;
Figure FDA0003178329520000047
the arrival time of the vehicle with the number n to the node j;
Figure FDA0003178329520000048
the arrival time of the vehicle with the number n to the node i;
Figure FDA0003178329520000049
a path from the node h to the node i of the vehicle with the number n is a decision variable;
Figure FDA00031783295200000410
a path from the node k to the node l of the vehicle with the number n is a decision variable;
Figure FDA00031783295200000411
the arrival time of the unmanned aerial vehicle with the number m to the node i;
Figure FDA00031783295200000412
the arrival time of the unmanned aerial vehicle with the number m to the node k;
Figure FDA00031783295200000413
the arrival time of the vehicle with the number n to the node k;
Figure FDA00031783295200000414
the arrival time of the unmanned aerial vehicle with the number m to the node j is shown;
Figure FDA00031783295200000415
the time from the node i to the disaster people gathering point j of the unmanned aerial vehicle with the number m is shown;
Figure FDA00031783295200000416
the time from a disaster people gathering point i to a node k is the unmanned aerial vehicle with the number m; t ismaxThe maximum duration of the unmanned aerial vehicle;
Figure FDA00031783295200000417
the time when the unmanned aerial vehicle with the number m departs from the warehouse 0;
Figure FDA0003178329520000051
time of departure from warehouse 0 for vehicle number n;
Figure FDA0003178329520000052
the number of packages sent to the disaster people gathering point j by the unmanned aerial vehicle with the number m; q. q.sjThe demand of the disaster people gathering point j is obtained; cUIs the load-bearing capacity of the vehicle; m is a sufficiently large positive integer.
5. The path planning method for vehicle-machine cooperative delivery of post-disaster rescue goods and materials according to any one of claims 1 to 4, wherein the obtaining of the optimal delivery path based on the disaster-stricken data, the data of a complex formed by a plurality of vehicles for delivering post-disaster rescue goods and unmanned aerial vehicles, the vehicle-machine cooperative path problem model, and the cultural genetic algorithm comprises:
s301, setting a coding rule;
s302, according to the coding rule, completing initialization of the population according to the following 5 steps to obtain an initial distribution path set:
s302a, firstly, randomly arranging the numbers of all point targets to generate the 1 st line of chromosomes, then dividing the arrangement into K sections, and adding 10 to the front and the back of each section to represent a warehouse, wherein the kth section of chromosomes corresponds to the path of the kth vehicle;
s302b, taking out 2 target numbers from the front to the back of the kth chromosome each time, taking the point targets corresponding to the 2 numbers as 2 focuses of the ellipse, and taking the cruising ability T of the unmanned aerial vehiclemaxIs a long shaft, structure TmaxAn ellipse;
s302c, if TmaxIf only 1 point target exists in the ellipse, writing the number of the point target under the number of the previous target; if T ismaxIf more than 1 point target in the ellipse, randomly selecting 1 target number to be written below the previous target number; if T ismaxIf no target exists in the ellipse, writing-1 under the number of the previous target, repeating the operation until the 2 nd position of the last chromosome of the segment is reached, and writing-1 under the last 1 st position;
s302d, repeating the steps S302 b-S302 c for K times, and obtaining the path of each vehicle and unmanned aerial vehicle combined body;
s302e, repeating the steps S302 a-S302 d according to the preset population scale to obtain an initial distribution path set;
s303, optimizing the initial distribution path set by adopting an improved cultural genetic algorithm to obtain an optimal distribution path.
6. The path planning method for vehicle-machine cooperative distribution of post-disaster rescue materials according to claim 5, wherein the step of optimizing the initial distribution path set by using an improved cultural genetic algorithm to obtain an optimal distribution path comprises the following steps:
s303a, obtaining the population size N of the culture gene algorithmP；
S303b, designing a calculation method of the fitness value based on the objective function of the demand-separable vehicle-mounted device collaborative path problem model, as shown in formula (22):
Figure FDA0003178329520000061
s303c, selecting 2 schemes for offspring genetic operation according to the fitness value of the vehicle-machine cooperative traffic patrol initial path planning scheme and a roulette mechanism, wherein the probability of selecting the scheme with smaller fitness value is larger;
s303d, creating a new candidate solution for the selected 2 solutions in an initial solution mixing mode;
s303e, selecting a sub-scheme with a large adaptability value from the candidate schemes to carry out cross operation;
s303f, selecting a good sub-scheme to carry out a path planning scheme set to replace the original poor parent path planning scheme;
s303g, repeating the steps S303 d-S303 f until reaching the condition of ending iteration, and when MA continues T2And when the system is not updated, the algorithm is automatically terminated, and a path planning scheme with the optimal fitness value in the set is output to serve as an optimal distribution path, wherein T is the number of the disaster people gathering points.
7. The path planning method for vehicle-machine cooperative distribution of post-disaster rescue materials according to claim 5, wherein the initial scheme mixing comprises:
step 1: dividing 2 initial path planning schemes into | K | sections according to the number of the vehicle-machine cooperative complex;
step 2: performing cross operation on the initial path planning scheme, judging whether the paths visited by the vehicles in the vehicle-machine cooperative combination with the same number have the same patrol point target, and if so, exchanging the patrol point targets visited by the unmanned aerial vehicle starting from the patrol point target;
and step 3: repeating the step 2 according to the number | K | of the vehicle-machine cooperative complexes to complete the cross operation of all the sections;
and 4, step 4: and splicing the | K | sections to form a complete vehicle-machine cooperative distribution path planning scheme.
8. The utility model provides a path planning device of car machine collaborative distribution rescue goods and materials after calamity which characterized in that, the device includes:
the data acquisition device is used for acquiring disaster data and a plurality of union data which are used for distributing rescue goods and materials after a disaster and are formed by the unmanned aerial vehicle;
the model building device is used for building a vehicle-machine cooperative path problem model with separable requirements by taking the shortest total time for reaching all disaster people gathering points as a target based on the disaster-stricken data and the data of a complex formed by a plurality of vehicles for delivering post-disaster rescue goods and unmanned aerial vehicles;
and the distribution path acquisition device is used for acquiring an optimal distribution path based on the disaster data, the complex data composed of a plurality of vehicles for distributing the post-disaster rescue goods and materials and the unmanned aerial vehicle, the vehicle-mounted machine cooperation path problem model and the cultural genetic algorithm.
9. A computer-readable storage medium storing a computer program for path planning for vehicle-machine cooperative distribution of post-disaster relief goods, wherein the computer program causes a computer to execute the method for path planning for vehicle-machine cooperative distribution of post-disaster relief goods 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 a path planning method for cooperative distribution of post-disaster relief supplies by a car machine according to any of claims 1-7.
CN202110839638.4A 2021-07-23 2021-07-23 Route planning method and device for vehicle-machine collaborative distribution post-disaster rescue materials Active CN113762594B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110839638.4A CN113762594B (en) 2021-07-23 2021-07-23 Route planning method and device for vehicle-machine collaborative distribution post-disaster rescue materials

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110839638.4A CN113762594B (en) 2021-07-23 2021-07-23 Route planning method and device for vehicle-machine collaborative distribution post-disaster rescue materials

Publications (2)

Publication Number Publication Date
CN113762594A true CN113762594A (en) 2021-12-07
CN113762594B CN113762594B (en) 2023-07-07

Family

ID=78787886

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110839638.4A Active CN113762594B (en) 2021-07-23 2021-07-23 Route planning method and device for vehicle-machine collaborative distribution post-disaster rescue materials

Country Status (1)

Country Link
CN (1) CN113762594B (en)

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115602306A (en) * 2022-12-17 2023-01-13 苏州维伟思医疗科技有限公司（Cn） Multi-mode AED (automated guided Equipment) scheduling method, device and equipment and readable storage medium
WO2023166615A1 (en) * 2022-03-02 2023-09-07 日本電信電話株式会社 Material supply assistance device, method, and program
CN116822761A (en) * 2023-03-14 2023-09-29 北京人人平安科技有限公司 Air-ground integrated rescue vehicle path optimization method for forest fire prevention
CN117350445A (en) * 2023-11-16 2024-01-05 广西桂冠电力股份有限公司 Intelligent emergency command system and method based on artificial intelligence

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2688016A1 (en) * 2012-07-18 2014-01-22 The Boeing Company Mission re-planning for coordinated multivehicle
CN110782086A (en) * 2019-10-24 2020-02-11 山东师范大学 Rescue vehicle distribution path optimization method and system with unmanned aerial vehicle
CN111352417A (en) * 2020-02-10 2020-06-30 合肥工业大学 Rapid generation method of heterogeneous multi-unmanned aerial vehicle cooperative path
LU102400A1 (en) * 2019-08-06 2021-02-09 Nanjing Seawolf Ocean Tech Co Ltd Path planning method and system for unmanned surface vehicle based on improved genetic algorithm
CN112906968A (en) * 2021-03-01 2021-06-04 广东安恒电力科技有限公司 Cable transportation path planning method based on culture gene wolf optimization algorithm
CN113139678A (en) * 2021-04-02 2021-07-20 长沙理工大学 Unmanned aerial vehicle-vehicle combined distribution path optimization method and model construction method thereof

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2688016A1 (en) * 2012-07-18 2014-01-22 The Boeing Company Mission re-planning for coordinated multivehicle
LU102400A1 (en) * 2019-08-06 2021-02-09 Nanjing Seawolf Ocean Tech Co Ltd Path planning method and system for unmanned surface vehicle based on improved genetic algorithm
CN110782086A (en) * 2019-10-24 2020-02-11 山东师范大学 Rescue vehicle distribution path optimization method and system with unmanned aerial vehicle
CN111352417A (en) * 2020-02-10 2020-06-30 合肥工业大学 Rapid generation method of heterogeneous multi-unmanned aerial vehicle cooperative path
CN112906968A (en) * 2021-03-01 2021-06-04 广东安恒电力科技有限公司 Cable transportation path planning method based on culture gene wolf optimization algorithm
CN113139678A (en) * 2021-04-02 2021-07-20 长沙理工大学 Unmanned aerial vehicle-vehicle combined distribution path optimization method and model construction method thereof

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
JIANQIANG LI 等: "A Memetic Path Planning Algorithm for Unmanned Air/Ground Vehicle Cooperative Detection Systems", IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING *
MONING ZHU 等: "Optimization Dubins Path of Multiple UAVs for Post-Earthquake Rapid-Assessment", APPLIED SCIENCES *
李少波;宋启松;李志昂;张星星;柘龙炫;: "遗传算法在机器人路径规划中的研究综述", 科学技术与工程, no. 02 *

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2023166615A1 (en) * 2022-03-02 2023-09-07 日本電信電話株式会社 Material supply assistance device, method, and program
CN115602306A (en) * 2022-12-17 2023-01-13 苏州维伟思医疗科技有限公司（Cn） Multi-mode AED (automated guided Equipment) scheduling method, device and equipment and readable storage medium
CN116822761A (en) * 2023-03-14 2023-09-29 北京人人平安科技有限公司 Air-ground integrated rescue vehicle path optimization method for forest fire prevention
CN116822761B (en) * 2023-03-14 2024-02-02 北京人人平安科技有限公司 Air-ground integrated rescue vehicle path optimization method for forest fire prevention
CN117350445A (en) * 2023-11-16 2024-01-05 广西桂冠电力股份有限公司 Intelligent emergency command system and method based on artificial intelligence
CN117350445B (en) * 2023-11-16 2024-03-29 广西桂冠电力股份有限公司 Intelligent emergency command system and method based on artificial intelligence

Also Published As

Publication number Publication date
CN113762594B (en) 2023-07-07

Similar Documents

Publication Publication Date Title
CN113762594A (en) Path planning method and device for vehicle-mounted machine to cooperatively deliver post-disaster rescue goods and materials
CN107977739B (en) Method, device and equipment for optimizing logistics distribution path
CN110264062B (en) Distributed multi-AGV dynamic task allocation and path planning method and system thereof
US8768614B2 (en) Increasing throughput for carpool assignment matching
CN110782086B (en) Rescue vehicle distribution path optimization method and system with unmanned aerial vehicle
US20130158869A1 (en) Preserving assigned carpools after a cancellation
US20060003823A1 (en) Dynamic player groups for interest management in multi-character virtual environments
CN113139678A (en) Unmanned aerial vehicle-vehicle combined distribution path optimization method and model construction method thereof
CN110097218B (en) Unmanned commodity distribution method and system in time-varying environment
CN113885555A (en) Multi-machine task allocation method and system for power transmission line dense channel routing inspection
CN113962481B (en) Resource allocation method and device for emergency materials and server
CN113762593B (en) Unmanned aerial vehicle emergency material distribution method and device after earthquake disaster
CN116720642A (en) Method and system for optimizing path of cooperative distribution of vehicle and unmanned aerial vehicle
Wu et al. Autonomous Last‐Mile Delivery Based on the Cooperation of Multiple Heterogeneous Unmanned Ground Vehicles
CN113741418B (en) Method and device for generating cooperative paths of heterogeneous vehicle and machine formation
CN114611794A (en) Vehicle-machine cooperative pick-and-place path optimization method and system based on sub-heuristic algorithm
CN111950768B (en) Site selection-distribution method and system based on bacterial foraging algorithm and ant colony algorithm
CN114020005A (en) Flight path planning method and system for cooperative inspection of multiple unmanned aerial vehicles and distribution network lines
CN112787833B (en) Method and device for deploying CDN (content delivery network) server
CN109598443B (en) Mission planning method and machine-readable storage medium for vehicle in dynamic environment
Wang et al. Simulation of multi-agent based cybernetic transportation system
CN114862065B (en) Social work task planning method and device, electronic equipment and storage medium
CN115564117A (en) Vehicle-machine cabinet cooperative distribution path optimization method and system
CN115755953A (en) Unmanned aerial vehicle task planning method and system based on comprehensive empowerment and storage medium
CN112149921A (en) Large-scale electric logistics vehicle path planning method and system and charging planning method

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant