CN115375184A - Garden logistics automatic driving multi-vehicle scheduling method, device, equipment and storage medium - Google Patents

Garden logistics automatic driving multi-vehicle scheduling method, device, equipment and storage medium Download PDF

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CN115375184A
CN115375184A CN202211160104.XA CN202211160104A CN115375184A CN 115375184 A CN115375184 A CN 115375184A CN 202211160104 A CN202211160104 A CN 202211160104A CN 115375184 A CN115375184 A CN 115375184A
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automatic driving
cargo
vehicle
driving
park
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刘磊
郭启翔
雷明星
何薇
晏萌
陈晖�
高宠智
张路玉
胡博伦
屈紫君
李嫩
欧阳辰宇
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Dongfeng Automobile Co Ltd
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Abstract

The invention discloses a park logistics automatic driving multi-vehicle scheduling method, a device, equipment and a storage medium, wherein the method comprises the steps of determining an initial driving route of an automatic driving vehicle according to a road model and a task model by establishing a park road model and a task model of a current park; optimizing the initial driving route according to a preset genetic algorithm to obtain an optimal driving route for driving multiple vehicles; the automatic driving vehicles are controlled to run according to the optimal running route, the vehicle position information of each automatic driving vehicle is monitored in real time, intersection scheduling management is carried out according to the vehicle position information, efficient running of freight transportation can be achieved, congestion at intersections is avoided, the scene requirements of multi-vehicle freight transportation can be met, reasonable route planning of multiple automatic driving vehicles can be carried out under the park logistics scene, fast scheduling of freight transportation is guaranteed, and the speed and efficiency of park logistics automatic driving multi-vehicle scheduling are improved.

Description

Garden logistics automatic driving multi-vehicle scheduling method, device, equipment and storage medium
Technical Field
The invention relates to the technical field of park automatic driving, in particular to a park logistics automatic driving multi-vehicle scheduling method, device, equipment and storage medium.
Background
The goods transportation between storehouses and workshops, between workshops and between storehouses and storehouses is usually required in the closed park, and most of the short-distance and high-frequency logistics transportation depends on manual driving of a driver to transport the goods by a logistics truck; the development of the automatic driving technology enables the logistics in the garden to be unmanned, the automatic driving vehicle is used for replacing the traditional manual driving vehicle, the driver can be released from the boring and mechanical working mode, and the development requirement of the modern and unmanned garden is met.
When unmanned freight transportation in a park is realized by using an automatic driving technology, an operation route and an operation task are planned and scheduled for an automatic driving vehicle through a background or cloud system, and are issued and sent to the automatic driving vehicle for execution; in the existing technical scheme, a one-to-one scheduling scheme is generally adopted, namely a background or cloud system plans a route for a single automatic driving vehicle according to freight transportation requirements to finish transportation of cargos from a starting point to a target point; the logistics in the garden often have the problem that articles in different places need to be transported to different target points at the same time, and obviously, the one-to-one scheduling scheme cannot meet the requirement of high efficiency; therefore, a plurality of automatic driving vehicles are required to be put into the system for cargo transportation, and the background system needs to adopt a one-to-many scheduling scheme to plan a plurality of automatic driving routes simultaneously, so that the reasonable scheduling is realized, and the efficient and orderly cargo transportation is realized; however, in the existing multi-vehicle scheduling scheme, only the planning and scheduling of the multi-vehicle driving route are realized, and the actual situation of the logistics in the garden is not considered; a lot of intersections exist on the garden road, and when a plurality of automatic driving vehicles pass through the same intersection at the same time, the possibility of congestion can occur; for complex cargo transportation operation, the existing one-to-one scheduling scheme has low efficiency and cannot adapt to heavy logistics transportation tasks; in addition, the problem of congestion at the intersection of the garden can not be solved by other multi-vehicle scheduling schemes.
Disclosure of Invention
The invention mainly aims to provide a method, a device, equipment and a storage medium for automatically driving and dispatching multiple vehicles in park logistics, and aims to solve the technical problems that in the prior art, a cargo transportation dispatching scheme is low in efficiency, cannot adapt to heavy logistics transportation tasks, and cannot solve congestion at intersections of parks.
In a first aspect, the invention provides a method for automatically driving and dispatching multiple vehicles in park logistics, which comprises the following steps:
establishing a park road model and a task model of a current park, and determining an initial driving route of an automatic driving vehicle according to the road model and the task model;
optimizing the initial driving route according to a preset genetic algorithm to obtain an optimal driving route for multi-vehicle driving;
and controlling the automatic driving vehicles to run according to the optimal running route, monitoring the vehicle position information of each automatic driving vehicle in real time, and carrying out intersection scheduling management according to the vehicle position information.
Optionally, the establishing a park road model and a task model of the current park, and determining an initial driving route of the autonomous vehicle according to the road model and the task model includes:
analyzing a park road network of a current park to obtain a road section set and an intersection set, and generating a park road model according to the road section set and the intersection set;
acquiring a cargo transportation demand, determining a cargo set, a cargo starting point set and a cargo target point set according to the cargo transportation demand, and generating a task model according to the cargo set, the cargo starting point set and the cargo target point set;
and determining the initial driving route of each automatic driving vehicle for executing the corresponding task according to the road model and the task model.
Optionally, the analyzing the park road network of the current park to obtain a road section set and an intersection set, and generating the park road model according to the road section set and the intersection set includes:
analyzing a park road network of a current park to obtain an analysis result, allocating unique numbers to each road section and each intersection in the park road network according to the analysis result, and counting the total number of each road section and each intersection to obtain a road section set and an intersection set;
and integrating the data in the road section set and the intersection set to generate a park road model.
Optionally, the obtaining a cargo transportation demand, determining a cargo set, a cargo starting point set and a cargo target point set according to the cargo transportation demand, and generating a task model according to the cargo set, the cargo starting point set and the cargo target point set includes:
acquiring a cargo transportation demand, determining the total quantity of cargos according to the cargo transportation demand, allocating a unique cargo number to each cargo, and constructing a cargo set according to the total quantity of the cargos and the cargo number;
acquiring a road section where a starting point of each cargo in the cargo set is located, and constructing a cargo starting point set according to the road section where the starting point is located;
acquiring a road section where a target point of each cargo in the cargo set is located, and constructing a cargo target point set according to the road section where the target point is located;
and generating a task model according to the cargo set, the cargo starting point set and the cargo target point set.
Optionally, the optimizing the initial driving route according to a preset genetic algorithm to obtain an optimal driving route for driving multiple vehicles includes:
randomly coding the driving route of each automatic vehicle in the initial driving route to generate a plurality of individual chromosomes;
forming corresponding initial generation populations of individual chromosomes of the same driving route in each individual chromosome;
and acquiring fitness values of chromosomes of each initial generation population, and optimizing the target chromosomes in each initial generation population according to the fitness values to obtain an optimal driving route for driving multiple vehicles.
Optionally, the obtaining of the fitness value of each chromosome in each initial generation population, and optimizing the target chromosomes in each initial generation population according to the fitness value to obtain the optimal driving route for driving multiple vehicles includes:
acquiring the transport task completion degree and the energy conservation of each individual chromosome in each initial generation population, and determining the fitness value of each individual chromosome according to the transport task completion degree and the energy conservation;
sequencing the fitness values, replacing the individual chromosome with the lowest fitness by the individual chromosome with the highest fitness, performing intersection and variation operation on the individual chromosomes in the current population, and iteratively recalculating the fitness values of the individual chromosomes until a preset iteration number is reached;
and obtaining the optimal individual chromosome with the highest fitness in each current population after iteration, and converting according to each optimal individual chromosome to generate an optimal driving route for driving multiple vehicles.
Optionally, the controlling the automatic driving vehicles to drive according to the optimal driving route, monitoring vehicle position information of each automatic driving vehicle in real time, and performing intersection scheduling management according to the vehicle position information includes:
controlling the automatic driving vehicle to drive in the current park according to the optimal driving route;
the method comprises the steps of acquiring vehicle position information, located road section information and intersection information of a located road section of each automatic driving vehicle in real time, and determining the incoming traffic volume in each intersection buffer area according to the vehicle position information, the located road section information and the intersection information of the located road section;
when the quantity of the coming vehicles is larger than or equal to a preset quantity threshold value, determining a passing sequence according to time mark control of each automatic driving vehicle entering a corresponding intersection buffer area, controlling each automatic driving vehicle to run according to the passing sequence, and deleting the time marks of the target automatic driving vehicles after the target automatic driving vehicles leave the corresponding intersection buffer areas;
and when the amount of the coming vehicles is less than a preset number threshold value, controlling the respective automatic driving vehicles to normally run.
In a second aspect, to achieve the above object, the present invention further provides a campus logistics automatic driving multi-vehicle scheduling device, including:
the route generation module is used for establishing a park road model and a task model of a current park and determining an initial driving route of the automatic driving vehicle according to the road model and the task model;
the route optimization module is used for optimizing the initial driving route according to a preset genetic algorithm to obtain an optimal driving route for driving multiple vehicles;
and the dispatching management module is used for controlling the automatic driving vehicles to run according to the optimal running route, monitoring the vehicle position information of each automatic driving vehicle in real time and carrying out intersection dispatching management according to the vehicle position information.
In order to achieve the above object, a third aspect of the present invention further provides a device for automatically driving and dispatching multiple vehicles in a park logistics, where the device for automatically driving and dispatching multiple vehicles in the park logistics comprises: the system comprises a memory, a processor and a park logistics automatic driving multi-vehicle scheduling program which is stored on the memory and can run on the processor, wherein the park logistics automatic driving multi-vehicle scheduling program is configured to realize the steps of the park logistics automatic driving multi-vehicle scheduling method.
In a fourth aspect, to achieve the above object, the present invention further provides a storage medium, where the storage medium stores a campus logistics automatic driving multi-vehicle scheduling program, and the campus logistics automatic driving multi-vehicle scheduling program, when executed by a processor, implements the steps of the method for scheduling the campus logistics automatic driving multi-vehicle as described above.
The invention provides a method for automatically driving and dispatching multiple vehicles in park logistics, which comprises the steps of establishing a park road model and a task model of a current park, and determining an initial driving route of an automatically-driven vehicle according to the road model and the task model; optimizing the initial driving route according to a preset genetic algorithm to obtain an optimal driving route for driving multiple vehicles; according to optimal route control of traveling the automatic driving vehicle traveles to each automatic driving vehicle's of real time monitoring vehicle position information, according to vehicle position information carries out crossing dispatch management, can provide reasonable planning and dispatching for garden logistics automatic driving vehicle, realizes freight's high-efficient operation, has avoided blocking up at the intersection, can satisfy many cars freight's scene demand, can carry out the reasonable route planning of a plurality of automatic driving vehicles under garden logistics scene, has guaranteed freight's quick dispatch, has promoted the speed and the efficiency of garden logistics automatic driving many cars dispatch.
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FIG. 1 is a schematic diagram of an apparatus architecture of a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a first embodiment of the campus logistics automatic driving multi-vehicle scheduling method according to the present invention;
FIG. 3 is a schematic flow chart of a second embodiment of the campus logistics automatic driving multi-vehicle scheduling method according to the present invention;
FIG. 4 is a schematic flow chart of a third embodiment of the campus logistics automatic driving multi-vehicle scheduling method according to the present invention;
FIG. 5 is a schematic flow chart of a fourth embodiment of the campus logistics automatic driving multi-vehicle scheduling method according to the present invention;
FIG. 6 is a schematic flow chart of a fifth embodiment of the campus logistics automatic driving multi-vehicle scheduling method according to the present invention;
FIG. 7 is a schematic flow chart of a sixth embodiment of the automatic driving multi-vehicle dispatching method for logistics in a park according to the present invention;
fig. 8 is a functional block diagram of the first embodiment of the campus logistics automatic driving multi-vehicle dispatching device.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The solution of the embodiment of the invention is mainly as follows: determining an initial driving route of an automatic driving vehicle according to a road model and a task model by establishing a park road model and the task model of a current park; optimizing the initial driving route according to a preset genetic algorithm to obtain an optimal driving route for driving multiple vehicles; according to the optimal running route control automatic driving vehicles run, vehicle position information of each automatic driving vehicle is monitored in real time, intersection scheduling management is carried out according to the vehicle position information, reasonable planning and scheduling can be provided for garden logistics automatic driving vehicles, efficient operation of freight transportation is achieved, congestion of intersections is avoided, scene requirements of multi-vehicle freight transportation can be met, reasonable route planning of multiple automatic driving vehicles can be carried out under garden logistics scenes, fast scheduling of freight transportation is guaranteed, speed and efficiency of garden logistics automatic driving multi-vehicle scheduling are improved, the technical problems that in the prior art, cargo transportation scheduling schemes are low in efficiency, heavy logistics transportation tasks cannot be adapted, and congestion of garden intersections cannot be solved are solved.
Referring to fig. 1, fig. 1 is a schematic device structure diagram of a hardware operating environment according to an embodiment of the present invention.
As shown in fig. 1, the apparatus may include: a processor 1001, such as a CPU, a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to implement connection communication among these components. The user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., a Wi-Fi interface). The Memory 1005 may be a high-speed RAM Memory or a Non-Volatile Memory (Non-Volatile Memory), such as a disk Memory. The memory 1005 may alternatively be a storage device separate from the processor 1001.
Those skilled in the art will appreciate that the configuration of the apparatus shown in fig. 1 is not intended to be limiting of the apparatus and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components.
As shown in fig. 1, a memory 1005, which is a storage medium, may include an operating device, a network communication module, a user interface module, and a campus logistics automatic driving multi-car scheduler.
The device of the present invention calls the campus logistics automatic driving multi-car scheduler stored in the memory 1005 through the processor 1001, and performs the following operations:
establishing a park road model and a task model of a current park, and determining an initial driving route of an automatic driving vehicle according to the road model and the task model;
optimizing the initial driving route according to a preset genetic algorithm to obtain an optimal driving route for driving multiple vehicles;
and controlling the automatic driving vehicles to run according to the optimal running route, monitoring the vehicle position information of each automatic driving vehicle in real time, and carrying out intersection scheduling management according to the vehicle position information.
The device of the present invention calls the campus logistics automatic driving multi-car scheduler stored in the memory 1005 through the processor 1001, and also performs the following operations:
analyzing a park road network of a current park to obtain a road section set and an intersection set, and generating a park road model according to the road section set and the intersection set;
acquiring a cargo transportation demand, determining a cargo set, a cargo starting point set and a cargo target point set according to the cargo transportation demand, and generating a task model according to the cargo set, the cargo starting point set and the cargo target point set;
and determining the initial driving route of each automatic driving vehicle for executing the corresponding task according to the road model and the task model.
The device of the present invention calls the campus logistics automatic driving multi-car scheduler stored in the memory 1005 through the processor 1001, and also performs the following operations:
analyzing a park road network of a current park to obtain an analysis result, allocating unique numbers to each road section and each intersection in the park road network according to the analysis result, and counting the total number of each road section and each intersection to obtain a road section set and an intersection set;
and integrating the data in the road section set and the intersection set to generate a park road model.
The device of the present invention calls the campus logistics automatic driving multi-car scheduler stored in the memory 1005 through the processor 1001, and also performs the following operations:
acquiring a cargo transportation demand, determining the total quantity of cargos according to the cargo transportation demand, allocating a unique cargo number to each cargo, and constructing a cargo set according to the total quantity of the cargos and the cargo number;
acquiring a road section where a starting point of each cargo in the cargo set is located, and constructing a cargo starting point set according to the road section where the starting point is located;
acquiring a road section where a target point of each cargo in the cargo set is located, and constructing a cargo target point set according to the road section where the target point is located;
and generating a task model according to the cargo set, the cargo starting point set and the cargo target point set.
The device of the present invention calls the campus logistics automatic driving multi-car scheduler stored in the memory 1005 through the processor 1001, and also performs the following operations:
randomly coding the driving route of each automatic vehicle in the initial driving route to generate a plurality of individual chromosomes;
forming corresponding initial generation populations of individual chromosomes of the same driving route in each individual chromosome;
and acquiring fitness values of chromosomes of each initial generation population, and optimizing the target chromosomes in each initial generation population according to the fitness values to obtain an optimal driving route for driving multiple vehicles.
The device of the present invention calls the campus logistics automatic driving multi-car scheduler stored in the memory 1005 through the processor 1001, and also performs the following operations:
acquiring the transport task completion degree and the energy conservation of each individual chromosome in each initial generation population, and determining the fitness value of each individual chromosome according to the transport task completion degree and the energy conservation;
sequencing the fitness values, replacing the individual chromosome with the lowest fitness by the individual chromosome with the highest fitness, performing intersection and variation operation on the individual chromosomes in the current population, and iteratively recalculating the fitness values of the individual chromosomes until a preset iteration number is reached;
and obtaining the optimal individual chromosome with the highest fitness in each current population after iteration, and generating the optimal driving route for multi-vehicle driving according to the transformation of each optimal individual chromosome.
The device of the present invention calls the campus logistics automatic driving multi-car scheduler stored in the memory 1005 through the processor 1001, and also performs the following operations:
controlling the automatic driving vehicle to drive in the current park according to the optimal driving route;
acquiring vehicle position information, road section information and intersection information of a road section of each automatic driving vehicle in real time, and determining the incoming traffic of each intersection buffer area according to the vehicle position information, the road section information and the intersection information of the road section;
when the number of the coming vehicles is larger than or equal to a preset number threshold value, controlling and determining a passing sequence according to the time marks when the respective automatic driving vehicles enter the corresponding intersection buffer areas, controlling the automatic driving vehicles to run according to the passing sequence, and deleting the time marks of the target automatic driving vehicles after the target automatic driving vehicles leave the corresponding intersection buffer areas;
and when the amount of the coming vehicles is smaller than a preset number threshold value, controlling the respective automatic driving vehicles to normally run.
According to the scheme, the initial driving route of the automatic driving vehicle is determined according to the road model and the task model by establishing the park road model and the task model of the current park; optimizing the initial driving route according to a preset genetic algorithm to obtain an optimal driving route for driving multiple vehicles; according to the optimal running route control the automatic driving vehicles run, the vehicle position information of each automatic driving vehicle is monitored in real time, intersection scheduling management is carried out according to the vehicle position information, reasonable planning and scheduling can be provided for garden logistics automatic driving vehicles, efficient operation of freight transportation is achieved, congestion at intersections is avoided, the scene requirements of multi-vehicle freight transportation can be met, reasonable route planning of multiple automatic driving vehicles can be carried out under garden logistics scenes, fast scheduling of freight transportation is guaranteed, and speed and efficiency of garden logistics automatic driving multi-vehicle scheduling are improved.
Based on the hardware structure, the embodiment of the automatic driving multi-vehicle scheduling method for the logistics in the park is provided.
Referring to fig. 2, fig. 2 is a schematic flow diagram of a first embodiment of the method for automatically driving and dispatching multiple vehicles in logistics in a park.
In a first embodiment, the campus logistics automatic driving multi-vehicle scheduling method comprises the following steps:
and S10, establishing a park road model and a task model of the current park, and determining an initial driving route of the automatic driving vehicle according to the road model and the task model.
It should be noted that a park road model corresponding to a park road of a current park and a task model of a vehicle transportation task are established, so that an initial driving route of an automatic driving vehicle can be determined according to the road model and the task model.
And S20, optimizing the initial driving route according to a preset genetic algorithm to obtain an optimal driving route for multi-vehicle driving.
It is understood that the initial driving route may be optimized through a preset genetic algorithm, so that the optimal driving routes of the plurality of autonomous vehicles may be obtained.
And S30, controlling the automatic driving vehicles to drive according to the optimal driving route, monitoring the vehicle position information of each automatic driving vehicle in real time, and carrying out intersection scheduling management according to the vehicle position information.
It should be understood that the automatic driving vehicles can be controlled to run through the optimal running route, so that the vehicle position information of each automatic driving vehicle is monitored in real time, intersection dispatching management can be performed according to the vehicle position information, the position information of the automatic driving vehicles is monitored in real time, and each driving vehicle can be dispatched according to a preset dispatching management scheme when a plurality of vehicles arrive at any intersection.
According to the scheme, the initial driving route of the automatic driving vehicle is determined according to the road model and the task model by establishing the park road model and the task model of the current park; optimizing the initial driving route according to a preset genetic algorithm to obtain an optimal driving route for driving multiple vehicles; according to the optimal running route control the automatic driving vehicles run, the vehicle position information of each automatic driving vehicle is monitored in real time, intersection scheduling management is carried out according to the vehicle position information, reasonable planning and scheduling can be provided for garden logistics automatic driving vehicles, efficient operation of freight transportation is achieved, congestion at intersections is avoided, the scene requirements of multi-vehicle freight transportation can be met, reasonable route planning of multiple automatic driving vehicles can be carried out under garden logistics scenes, fast scheduling of freight transportation is guaranteed, and speed and efficiency of garden logistics automatic driving multi-vehicle scheduling are improved.
Further, fig. 3 is a schematic flow diagram of a second embodiment of the campus logistics automatic driving multi-vehicle scheduling method according to the present invention, and as shown in fig. 3, the second embodiment of the campus logistics automatic driving multi-vehicle scheduling method according to the present invention is provided based on the first embodiment, in this embodiment, the step S10 specifically includes the following steps:
s11, analyzing the park road network of the current park to obtain a road section set and an intersection set, and generating a park road model according to the road section set and the intersection set.
It should be noted that, the park road network of the current park is analyzed, so that road section sets corresponding to different road sections and intersection sets corresponding to different intersections can be obtained, and a park road model can be constructed and generated according to the road section sets and the intersection sets.
S12, acquiring a cargo transportation demand, determining a cargo set, a cargo starting point set and a cargo target point set according to the cargo transportation demand, and generating a task model according to the cargo set, the cargo starting point set and the cargo target point set.
It can be understood that according to a predetermined cargo transportation demand, a corresponding cargo collection, a collection corresponding to a target point and a starting point of cargo transportation, and a task model can be constructed and generated according to the cargo collection, the cargo starting point collection and the cargo target point collection.
Further, the step S12 specifically includes the following steps:
acquiring a cargo transportation demand, determining the total quantity of cargos according to the cargo transportation demand, allocating a unique cargo number to each cargo, and constructing a cargo set according to the total quantity of the cargos and the cargo number;
acquiring a road section where a starting point of each cargo in the cargo set is located, and constructing a cargo starting point set according to the road section where the starting point is located;
acquiring a road section where a target point of each cargo in the cargo set is located, and constructing a cargo target point set according to the road section where the target point is located;
and generating a task model according to the cargo set, the cargo starting point set and the cargo target point set.
It should be understood that, after the cargo transportation demand is obtained, the total number of the cargos can be determined according to the cargo transportation demand, so that a unique cargo number is allocated to each cargo, that is, a unique number is allocated to any one cargo, so as to form a cargo set, for any one cargo, a road section where a starting point of the cargo is located and a road section where a target point of the cargo is located can be determined, so as to form a cargo starting point set and a cargo target point set, so that a task model is generated according to the cargo set, the cargo starting point set and the cargo target point set.
And S13, determining the initial driving route of each automatic driving vehicle for executing the corresponding task according to the road model and the task model.
It is understood that an initial travel route of each driven vehicle from a task start point to a task end point, that is, an initial travel route of each automatically driven vehicle to perform a corresponding task, may be generated from the road model and the task model.
According to the scheme, the park road network of the current park is analyzed to obtain the road section set and the intersection set, and the park road model is generated according to the road section set and the intersection set; acquiring a cargo transportation demand, determining a cargo set, a cargo starting point set and a cargo target point set according to the cargo transportation demand, and generating a task model according to the cargo set, the cargo starting point set and the cargo target point set; and determining initial running routes of the automatic driving vehicles for executing corresponding tasks according to the road model and the task model, rapidly acquiring the running routes of the automatic driving vehicles, providing reasonable planning and scheduling for the automatic driving vehicles in the logistics of the park, and realizing the efficient running of freight transportation.
Further, fig. 4 is a schematic flow diagram of a third embodiment of the campus logistics automatic driving multi-vehicle scheduling method according to the present invention, and as shown in fig. 4, the third embodiment of the campus logistics automatic driving multi-vehicle scheduling method according to the present invention is proposed based on the second embodiment, in this embodiment, the step S11 specifically includes the following steps:
and S111, analyzing the park road network of the current park to obtain an analysis result, allocating unique numbers to each road section and each intersection in the park road network according to the analysis result, counting the total number of each road section and each intersection to obtain a road section set and an intersection set.
It should be noted that, the campus road network of the current campus may be analyzed to obtain a corresponding analysis result, a unique number may be assigned to each road segment in the road network according to the analysis result, a unique number may be assigned to each intersection in the road network, and the total number of each road segment and each intersection may be counted to obtain a road segment set corresponding to the total number of the road segments and obtain an intersection set corresponding to the total number of the intersections, where each road segment corresponds to a different road segment length.
And S112, integrating the data in the road section set and the data in the intersection set to generate a park road model.
It can be understood that, by integrating the data in the road section set and the intersection set, a park road model corresponding to each road section and each intersection can be generated.
According to the scheme, the park road network of the current park is analyzed to obtain an analysis result, unique numbers are distributed to each road section and each intersection in the park road network according to the analysis result, the total number of each road section and each intersection is counted to obtain a road section set and an intersection set, data in the road section set and the intersection set are integrated to generate the park road model, the park road model can be generated, accordingly, the driving route of each automatically-driven vehicle is rapidly obtained, and the speed and the efficiency of automatic multi-vehicle driving scheduling of the park logistics are improved.
Further, fig. 5 is a schematic flow diagram of a fourth embodiment of the campus logistics automatic driving multi-vehicle scheduling method according to the present invention, and as shown in fig. 5, the fourth embodiment of the campus logistics automatic driving multi-vehicle scheduling method according to the present invention is proposed based on the first embodiment, in this embodiment, the step S20 specifically includes the following steps:
and S21, randomly coding the driving routes of all automatic vehicles in the initial driving route to generate a plurality of individual chromosomes.
The driving routes of the automatic vehicles in the initial driving route are randomly encoded, that is, a plurality of chromosomes can be generated according to the self-encoding mode.
In a specific implementation, what road segment is selected when the vehicle passes each intersection may be represented by a 2-bit binary number v. When v =00, not select; when v =01, 1 st link from the counterclockwise direction is indicated; when v =10, it indicates a 2 nd link from the counterclockwise direction; when v =11, 3 rd link from the counterclockwise direction is indicated.
With V = V 1 v 2 …v N Representing the driving route of 1 automatic driving vehicle, wherein the value of N depends on the size of J, and the individual chromosome is defined as V 1 V 2 …V m …V M Denotes a travel route of a plurality of autonomous vehicles, wherein M denotes the number of autonomous vehicles, V m Indicating the travel route of the mth autonomous vehicle.
Generating K chromosomes, i.e. K V, in a randomly coded manner 1 V 2 …V m …V M Forming an initial generation population of the genetic algorithm, wherein the value of K depends on the size of I, and each individual chromosome in the population represents a planning scheme of a plurality of driving routes of the automatic driving vehicle.
And S22, forming corresponding initial generation groups of the individual chromosomes of the same driving route in each individual chromosome.
It will be appreciated that individual chromosomes of the same travel route in each individual chromosome may be grouped into a population, i.e. an initial generation population forming a genetic algorithm.
And S23, obtaining the fitness value of each chromosome in each initial generation population, and optimizing the target chromosomes in each initial generation population according to the fitness value to obtain the optimal driving route for driving multiple vehicles.
It should be understood that the fitness value of each individual chromosome in each initial generation population may be evaluated, and the target chromosomes in each initial generation population may be optimized according to the fitness value, so as to obtain an optimal driving route for driving multiple vehicles.
According to the scheme, the driving routes of all automatic vehicles in the initial driving route are randomly coded to generate a plurality of individual chromosomes; forming corresponding initial generation populations of individual chromosomes of the same driving route in each individual chromosome; the fitness value of each chromosome in each initial generation population is obtained, the target chromosomes in each initial generation population are optimized according to the fitness value, the optimal running route for running of multiple vehicles is obtained, reasonable planning and scheduling can be provided for garden logistics automatic driving vehicles, efficient running of freight transportation is achieved, congestion at intersections is avoided, the scene requirement for multiple vehicle freight transportation can be met, and the speed and efficiency of garden logistics automatic driving multiple vehicle scheduling are improved.
Further, fig. 6 is a schematic flow chart of a fifth embodiment of the campus logistics automatic driving multi-vehicle scheduling method according to the present invention, and as shown in fig. 6, a fifth embodiment of the campus logistics automatic driving multi-vehicle scheduling method according to the present invention is provided based on the fourth embodiment, in this embodiment, the step S23 specifically includes the following steps:
and S231, acquiring the transport task completion degree and the energy saving property of each individual chromosome in each initial generation population, and determining the fitness value of each individual chromosome according to the transport task completion degree and the energy saving property.
It should be noted that after the transportation task completion degree and the energy conservation of each individual chromosome in each initial generation population are obtained, the fitness value of each individual chromosome can be determined according to the transportation task completion degree and the energy conservation.
In the specific implementation, a genetic algorithm is adopted to optimize a preset multi-vehicle automatic driving vehicle route, and the total iteration number G can be set firstly, so that the current iteration number G =1;
and evaluating the fitness value fitness of each individual chromosome, wherein the evaluation criteria comprise: transport task completion fit 1 Energy saving fit 2 Defining the individual chromosome fitness function as:
fitness(k)＝αfit 1 (k)+βfit 2 (k)
wherein, alpha and beta are weight factors of the fitness function, and k represents the kth individual.
Transport task completion fit 1 : all automatic driving vehicles can transport the goods in the set C from the starting point to the specified target point according to the route represented by the chromosome, if the quantity is more, the transport task completion degree is larger, and fit is defined 1 Comprises the following steps:
fit 1 (k)＝T(k)/c
wherein T represents T numbers C in the set C p Goods can be taken from d p Road section transport e p A road segment.
Energy-saving fit 2 : all automatic driving vehicles drive according to the route represented by the chromosome, all goods are transported to corresponding target points, the total mileage driven by the vehicles at the moment is calculated, and if the total mileage is smaller, the energy saving performance is higher.
fit 2 (k)＝1/[w 1 (k)s 1 +w 2 (k)s 2 +…+w i (k)s i +…+w I (k)s I ]
Wherein, w i Indicates that all the automatic driving vehicles pass through a during running i The number of road segments.
And S232, sequencing the fitness values, replacing the individual chromosomes with the lowest fitness by the individual chromosomes with the highest fitness, performing intersection and variation operation on the individual chromosomes in the current population, and iteratively recalculating the fitness values of the individual chromosomes until a preset iteration number is reached.
It can be understood that if G < G, G +1 → G is assigned, then the fitness of the individuals in the current population is ranked according to the principle of selecting copy by the genetic algorithm, the individuals with the highest fitness are used for replacing the individuals with the lowest fitness, further, the chromosome segments of the individuals in the current population can be subjected to cross operation and variation operation according to the principle of cross variation of the genetic algorithm, and the fitness value of each individual chromosome is iteratively recalculated until the preset number of iterations is reached.
And step S233, obtaining the optimal individual chromosome with the highest fitness in each current population after iteration, and converting and generating the optimal driving route for multi-vehicle driving according to each optimal individual chromosome.
It should be understood that the optimal individual chromosome with the highest fitness in each current population after iteration is obtained, so that the optimal individual chromosome can be converted into an optimal driving route, that is, the optimal driving route for driving multiple vehicles is generated according to the conversion of each optimal individual chromosome.
In a specific implementation, the optimal driving route can also be checked. Specifically, whether the optimal route can convey all goods to a specified target point or not is judged, if yes, the running route of each automatic driving vehicle is determined, and if not, an error mark is marked, so that manual intervention is reminded.
According to the scheme, the transport task completion degree and the energy conservation of each individual chromosome in each initial generation population are obtained, and the fitness value of each individual chromosome is determined according to the transport task completion degree and the energy conservation; sequencing the fitness values, replacing the individual chromosome with the lowest fitness by the individual chromosome with the highest fitness, performing intersection and variation operation on the individual chromosomes in the current population, and iteratively recalculating the fitness values of the individual chromosomes until a preset iteration number is reached; the method comprises the steps of obtaining the optimal individual chromosome with the highest fitness in each current population after iteration, converting and generating the optimal running route for multi-vehicle running according to each optimal individual chromosome, obtaining the optimal running route formed by multiple vehicles, providing reasonable planning and scheduling for garden logistics automatic driving vehicles, achieving efficient running of freight transportation, avoiding congestion of intersections, meeting scene requirements of multi-vehicle freight transportation, and improving speed and efficiency of garden logistics automatic driving multi-vehicle scheduling.
Further, fig. 7 is a schematic flow diagram of a sixth embodiment of the campus logistics automatic driving multi-vehicle scheduling method according to the present invention, and as shown in fig. 7, the sixth embodiment of the campus logistics automatic driving multi-vehicle scheduling method according to the present invention is proposed based on the first embodiment, in this embodiment, the step S30 specifically includes the following steps:
and S31, controlling the automatic driving vehicle to drive in the current park according to the optimal driving route.
It should be noted that the autonomous vehicle may be controlled to travel along the travel route in the current park by the optimal travel route.
And S32, acquiring the vehicle position information, the road section information and the intersection information of the road section of each automatic driving vehicle in real time, and determining the amount of the coming traffic in each intersection buffer area according to the vehicle position information, the road section information and the intersection information of the road section.
It should be understood that the vehicle position information, the road section information and the intersection information of the road section are obtained in real time, and then the amount of the incoming vehicles in the buffer area of each intersection is determined.
In the specific implementation, the position, the road section and the intersection of the road section of each automatic driving vehicle are obtained in real time, and the position information comes from a system receiving module and receives the reported information of the automatic driving vehicles; the information of the located road section refers to the road section where the automatic driving vehicle runs currently, and is obtained through system query, or can be obtained through a receiving module by receiving the reported information of the automatic driving vehicle; the intersection information of the road section refers to that the automatic driving vehicle runs along the current road section and is acquired through system query or through a receiving module after receiving the reported information of the automatic driving vehicle.
And S33, when the quantity of the coming vehicles is larger than or equal to a preset quantity threshold value, controlling and determining a passing sequence according to the time marks when the respective automatic driving vehicles enter the corresponding intersection buffer areas, controlling the automatic driving vehicles to run according to the passing sequence, and deleting the time marks of the target automatic driving vehicles after the target automatic driving vehicles leave the corresponding intersection buffer areas.
It can be understood that the incoming vehicles in the buffer areas of each intersection are judged, and the entering time is marked for each vehicle entering the buffer areas, so that the incoming vehicles at each intersection can be determined according to the positions of the vehicles, the road sections where the vehicles are located and the intersections where the vehicles are located, and whether the incoming vehicles are in the buffer areas or not is judged according to the positions; and then marking the entering time when the vehicle just enters the buffer areas, monitoring the amount of the coming vehicles in each intersection buffer area in the actual operation, setting a preset quantity threshold value as 2 for any intersection, controlling each automatic driving vehicle to run according to the passing sequence if the amount of the coming vehicles is more than or equal to 2, and deleting the time mark of the target automatic driving vehicle after the target automatic driving vehicle leaves the corresponding intersection buffer area.
And step S34, when the vehicle amount is smaller than a preset number threshold value, controlling each automatic driving vehicle to normally run.
It should be understood that when the vehicle amount is less than the preset number threshold, the respective automatic driving vehicle may be controlled to normally run, in actual operation, taking the preset number threshold as 2 as an example, if the vehicle amount is less than 2, the normal traffic information is output.
In the specific implementation, according to the time of a vehicle mark in an intersection buffer area, an automatic driving vehicle which is preferentially passed is determined, other automatic driving vehicles stop at a stop line, a first-in first-out principle is adopted, namely, the vehicle which enters the buffer area first (the time mark is the earliest), the automatic driving vehicle has the right of passing at the intersection, a system sends normal passing information to the automatic driving vehicle, stop line parking waiting information is sent to other vehicles, and after the vehicle which is to be preferentially passed leaves a turning area, the time mark of the vehicle is deleted.
According to the scheme, the automatic driving vehicle is controlled to drive in the current park according to the optimal driving route; acquiring vehicle position information, road section information and intersection information of a road section of each automatic driving vehicle in real time, and determining the incoming traffic of each intersection buffer area according to the vehicle position information, the road section information and the intersection information of the road section; when the quantity of the coming vehicles is larger than or equal to a preset quantity threshold value, determining a passing sequence according to time mark control of each automatic driving vehicle entering a corresponding intersection buffer area, controlling each automatic driving vehicle to run according to the passing sequence, and deleting the time marks of the target automatic driving vehicles after the target automatic driving vehicles leave the corresponding intersection buffer areas; when the quantity of coming traffic is less than the predetermined quantity threshold value, control each self-propelled vehicle and normally travel, can provide reasonable planning and dispatching for garden commodity circulation automatic driving vehicle, realize freight's high-efficient operation, avoided blocking up of intersection, can satisfy the scene demand of many cars freight, can carry out the reasonable route planning of a plurality of self-propelled vehicles under garden commodity circulation scene, guarantee freight's fast dispatch, promoted speed and the efficiency of garden commodity circulation automatic driving many cars dispatch.
Correspondingly, the invention further provides a dispatching device for automatically driving multiple vehicles in the logistics of the park.
Referring to fig. 8, fig. 8 is a functional block diagram of a first embodiment of the automatic driving multi-vehicle dispatching device for logistics in a park according to the present invention.
In a first embodiment of the automatic driving multi-vehicle scheduling device for logistics in a park, the automatic driving multi-vehicle scheduling device for logistics in the park comprises:
the route generation module 10 is configured to establish a park road model and a task model of a current park, and determine an initial driving route of the autonomous vehicle according to the road model and the task model.
And the route optimization module 20 is configured to optimize the initial driving route according to a preset genetic algorithm to obtain an optimal driving route for driving multiple vehicles.
And the scheduling management module 30 is configured to control the automatic driving vehicles to travel according to the optimal travel route, monitor vehicle position information of each automatic driving vehicle in real time, and perform intersection scheduling management according to the vehicle position information.
The route generating module 10 is further configured to analyze a park road network of the current park to obtain a road section set and an intersection set, and generate a park road model according to the road section set and the intersection set; acquiring a cargo transportation demand, determining a cargo set, a cargo starting point set and a cargo target point set according to the cargo transportation demand, and generating a task model according to the cargo set, the cargo starting point set and the cargo target point set; and determining the initial driving route of each automatic driving vehicle for executing the corresponding task according to the road model and the task model.
The route generation module 10 is further configured to analyze a campus road network of a current campus to obtain an analysis result, assign unique numbers to each road segment and each intersection in the campus road network according to the analysis result, and count the total number of each road segment and each intersection to obtain a road segment set and an intersection set; and integrating the data in the road section set and the intersection set to generate a park road model.
The route generation module 10 is further configured to obtain a cargo transportation demand, determine the total quantity of the cargo according to the cargo transportation demand, assign a unique cargo number to each cargo, and construct a cargo set according to the total quantity of the cargo and the cargo number; acquiring a road section where a starting point of each cargo in the cargo set is located, and constructing a cargo starting point set according to the road section where the starting point is located; acquiring a road section where a target point of each cargo in the cargo collection is located, and constructing a cargo target point collection according to the road section where the target point is located; and generating a task model according to the cargo set, the cargo starting point set and the cargo target point set.
The route optimization module 20 is further configured to randomly encode the driving routes of the automatic vehicles in the initial driving route to generate a plurality of individual chromosomes; forming corresponding initial generation populations of individual chromosomes of the same driving route in each individual chromosome; and acquiring fitness values of chromosomes of each initial generation population, and optimizing the target chromosomes in each initial generation population according to the fitness values to obtain an optimal driving route for multi-vehicle driving.
The route optimization module 20 is further configured to obtain the transportation task completion degree and the energy saving property of each individual chromosome in each initial generation population, and determine the fitness value of each individual chromosome according to the transportation task completion degree and the energy saving property; sequencing the fitness values, replacing the individual chromosomes with the lowest fitness with the individual chromosomes with the highest fitness, performing intersection and variation operation on the individual chromosomes in the current population, and iteratively recalculating the fitness values of the individual chromosomes until a preset iteration number is reached; and obtaining the optimal individual chromosome with the highest fitness in each current population after iteration, and converting according to each optimal individual chromosome to generate an optimal driving route for driving multiple vehicles.
The scheduling management module 30 is further configured to control the autonomous vehicle to travel in the current park according to the optimal travel route; acquiring vehicle position information, road section information and intersection information of a road section of each automatic driving vehicle in real time, and determining the incoming traffic of each intersection buffer area according to the vehicle position information, the road section information and the intersection information of the road section; when the number of the coming vehicles is larger than or equal to a preset number threshold value, controlling and determining a passing sequence according to the time marks when the respective automatic driving vehicles enter the corresponding intersection buffer areas, controlling the automatic driving vehicles to run according to the passing sequence, and deleting the time marks of the target automatic driving vehicles after the target automatic driving vehicles leave the corresponding intersection buffer areas; and when the amount of the coming vehicles is less than a preset number threshold value, controlling the respective automatic driving vehicles to normally run.
The steps realized by each functional module of the campus logistics automatic driving multi-vehicle scheduling device can refer to each embodiment of the campus logistics automatic driving multi-vehicle scheduling method, and are not described again here.
In addition, an embodiment of the present invention further provides a storage medium, where a campus logistics automatic driving multi-vehicle scheduling program is stored in the storage medium, and when executed by a processor, the campus logistics automatic driving multi-vehicle scheduling program implements the following operations:
establishing a park road model and a task model of a current park, and determining an initial driving route of an automatic driving vehicle according to the road model and the task model;
optimizing the initial driving route according to a preset genetic algorithm to obtain an optimal driving route for driving multiple vehicles;
and controlling the automatic driving vehicles to run according to the optimal running route, monitoring the vehicle position information of each automatic driving vehicle in real time, and carrying out intersection scheduling management according to the vehicle position information.
Further, when executed by the processor, the campus logistics automatic driving multi-vehicle scheduling program further realizes the following operations:
analyzing a park road network of a current park to obtain a road section set and an intersection set, and generating a park road model according to the road section set and the intersection set;
acquiring a cargo transportation demand, determining a cargo set, a cargo starting point set and a cargo target point set according to the cargo transportation demand, and generating a task model according to the cargo set, the cargo starting point set and the cargo target point set;
and determining the initial driving route of each automatic driving vehicle for executing the corresponding task according to the road model and the task model.
Further, when executed by the processor, the campus logistics automatic driving multi-vehicle scheduling program further realizes the following operations:
analyzing a park road network of a current park to obtain an analysis result, allocating unique numbers to each road section and each intersection in the park road network according to the analysis result, and counting the total number of each road section and each intersection to obtain a road section set and an intersection set;
and integrating the data in the road section set and the intersection set to generate a park road model.
Further, when executed by the processor, the campus logistics automatic driving multi-vehicle scheduling program further realizes the following operations:
acquiring a cargo transportation demand, determining the total quantity of cargos according to the cargo transportation demand, allocating a unique cargo number to each cargo, and constructing a cargo set according to the total quantity of the cargos and the cargo number;
acquiring a road section where a starting point of each cargo in the cargo set is located, and constructing a cargo starting point set according to the road section where the starting point is located;
acquiring a road section where a target point of each cargo in the cargo set is located, and constructing a cargo target point set according to the road section where the target point is located;
and generating a task model according to the cargo set, the cargo starting point set and the cargo target point set.
Further, when executed by the processor, the campus logistics automatic driving multi-vehicle scheduling program further realizes the following operations:
randomly coding the driving route of each automatic vehicle in the initial driving route to generate a plurality of individual chromosomes;
forming corresponding initial generation populations of individual chromosomes of the same driving route in each individual chromosome;
and acquiring fitness values of chromosomes of each initial generation population, and optimizing the target chromosomes in each initial generation population according to the fitness values to obtain an optimal driving route for multi-vehicle driving.
Further, when executed by the processor, the campus logistics automatic driving multi-vehicle scheduling program further realizes the following operations:
acquiring the transport task completion degree and the energy conservation of each individual chromosome in each initial generation population, and determining the fitness value of each individual chromosome according to the transport task completion degree and the energy conservation;
sequencing the fitness values, replacing the individual chromosomes with the lowest fitness with the individual chromosomes with the highest fitness, performing intersection and variation operation on the individual chromosomes in the current population, and iteratively recalculating the fitness values of the individual chromosomes until a preset iteration number is reached;
and obtaining the optimal individual chromosome with the highest fitness in each current population after iteration, and converting according to each optimal individual chromosome to generate an optimal driving route for driving multiple vehicles.
Further, when being executed by the processor, the park logistics automatic driving multi-vehicle scheduling program further realizes the following operations:
controlling the automatic driving vehicle to drive in the current park according to the optimal driving route;
acquiring vehicle position information, road section information and intersection information of a road section of each automatic driving vehicle in real time, and determining the incoming traffic of each intersection buffer area according to the vehicle position information, the road section information and the intersection information of the road section;
when the quantity of the coming vehicles is larger than or equal to a preset quantity threshold value, determining a passing sequence according to time mark control of each automatic driving vehicle entering a corresponding intersection buffer area, controlling each automatic driving vehicle to run according to the passing sequence, and deleting the time marks of the target automatic driving vehicles after the target automatic driving vehicles leave the corresponding intersection buffer areas;
and when the amount of the coming vehicles is less than a preset number threshold value, controlling the respective automatic driving vehicles to normally run.
According to the scheme, the initial driving route of the automatic driving vehicle is determined according to the road model and the task model by establishing the park road model and the task model of the current park; optimizing the initial driving route according to a preset genetic algorithm to obtain an optimal driving route for driving multiple vehicles; according to the optimal running route control the automatic driving vehicles run, the vehicle position information of each automatic driving vehicle is monitored in real time, intersection scheduling management is carried out according to the vehicle position information, reasonable planning and scheduling can be provided for garden logistics automatic driving vehicles, efficient operation of freight transportation is achieved, congestion at intersections is avoided, the scene requirements of multi-vehicle freight transportation can be met, reasonable route planning of multiple automatic driving vehicles can be carried out under garden logistics scenes, fast scheduling of freight transportation is guaranteed, and speed and efficiency of garden logistics automatic driving multi-vehicle scheduling are improved.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising a … …" does not exclude the presence of another identical element in a process, method, article, or apparatus that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.

Claims (10)

1. The automatic driving multi-vehicle dispatching method for the park logistics is characterized by comprising the following steps of:
establishing a park road model and a task model of a current park, and determining an initial driving route of an automatic driving vehicle according to the road model and the task model;
optimizing the initial driving route according to a preset genetic algorithm to obtain an optimal driving route for driving multiple vehicles;
and controlling the automatic driving vehicles to run according to the optimal running route, monitoring the vehicle position information of each automatic driving vehicle in real time, and carrying out intersection scheduling management according to the vehicle position information.
2. The campus logistics autonomous driving multi-vehicle scheduling method of claim 1, wherein said establishing a campus road model and a mission model of a current campus, and determining an initial driving route of an autonomous vehicle according to said road model and said mission model comprises:
analyzing a park road network of a current park to obtain a road section set and an intersection set, and generating a park road model according to the road section set and the intersection set;
acquiring a cargo transportation demand, determining a cargo set, a cargo starting point set and a cargo target point set according to the cargo transportation demand, and generating a task model according to the cargo set, the cargo starting point set and the cargo target point set;
and determining the initial driving route of each automatic driving vehicle for executing the corresponding task according to the road model and the task model.
3. The automatic driving multi-vehicle scheduling method for logistics in a park of claim 2, wherein the analyzing a park road network of a current park to obtain a road section set and an intersection set, and generating a park road model according to the road section set and the intersection set comprises:
analyzing a park road network of a current park to obtain an analysis result, allocating unique numbers to each road section and each intersection in the park road network according to the analysis result, and counting the total number of each road section and each intersection to obtain a road section set and an intersection set;
and integrating the data in the road section set and the intersection set to generate a park road model.
4. The campus logistics automatic driving multi-vehicle dispatching method of claim 2, wherein the obtaining of the cargo transportation demand, determining a cargo set, a cargo starting point set and a cargo target point set according to the cargo transportation demand, and generating a task model according to the cargo set, the cargo starting point set and the cargo target point set comprises:
acquiring a cargo transportation demand, determining the total quantity of cargos according to the cargo transportation demand, allocating a unique cargo number to each cargo, and constructing a cargo set according to the total quantity of the cargos and the cargo number;
acquiring a road section where a starting point of each cargo in the cargo set is located, and constructing a cargo starting point set according to the road section where the starting point is located;
acquiring a road section where a target point of each cargo in the cargo set is located, and constructing a cargo target point set according to the road section where the target point is located;
and generating a task model according to the cargo set, the cargo starting point set and the cargo target point set.
5. The campus logistics automatic driving multi-vehicle scheduling method of claim 1, wherein the optimizing the initial driving route according to a preset genetic algorithm to obtain an optimal driving route for multi-vehicle driving comprises:
randomly coding the driving route of each automatic vehicle in the initial driving route to generate a plurality of individual chromosomes;
forming corresponding initial generation populations of individual chromosomes of the same driving route in each individual chromosome;
and acquiring fitness values of chromosomes of each initial generation population, and optimizing the target chromosomes in each initial generation population according to the fitness values to obtain an optimal driving route for driving multiple vehicles.
6. The method according to claim 5, wherein the obtaining of fitness values of chromosomes of individuals in each population of the initial generation, and the optimizing of target chromosomes in each population of the initial generation according to the fitness values to obtain an optimal driving route for driving a plurality of vehicles comprises:
acquiring the transport task completion degree and the energy conservation of each individual chromosome in each initial generation population, and determining the fitness value of each individual chromosome according to the transport task completion degree and the energy conservation;
sequencing the fitness values, replacing the individual chromosomes with the lowest fitness with the individual chromosomes with the highest fitness, performing intersection and variation operation on the individual chromosomes in the current population, and iteratively recalculating the fitness values of the individual chromosomes until a preset iteration number is reached;
and obtaining the optimal individual chromosome with the highest fitness in each current population after iteration, and converting according to each optimal individual chromosome to generate an optimal driving route for driving multiple vehicles.
7. The campus logistics automatic driving multi-vehicle scheduling method of claim 1, wherein the controlling the automatic driving vehicles to travel according to the optimal travel route, monitoring vehicle position information of each automatic driving vehicle in real time, and performing intersection scheduling management according to the vehicle position information comprises:
controlling the automatic driving vehicle to drive in the current park according to the optimal driving route;
acquiring vehicle position information, road section information and intersection information of a road section of each automatic driving vehicle in real time, and determining the incoming traffic of each intersection buffer area according to the vehicle position information, the road section information and the intersection information of the road section;
when the number of the coming vehicles is larger than or equal to a preset number threshold value, controlling and determining a passing sequence according to the time marks when the respective automatic driving vehicles enter the corresponding intersection buffer areas, controlling the automatic driving vehicles to run according to the passing sequence, and deleting the time marks of the target automatic driving vehicles after the target automatic driving vehicles leave the corresponding intersection buffer areas;
and when the amount of the coming vehicles is less than a preset number threshold value, controlling the respective automatic driving vehicles to normally run.
8. The utility model provides a many cars scheduling device of garden commodity circulation autopilot which characterized in that, many cars scheduling device of garden commodity circulation autopilot includes:
the route generation module is used for establishing a park road model and a task model of the current park and determining an initial driving route of the automatic driving vehicle according to the road model and the task model;
the route optimization module is used for optimizing the initial driving route according to a preset genetic algorithm to obtain an optimal driving route for driving multiple vehicles;
and the dispatching management module is used for controlling the automatic driving vehicles to run according to the optimal running route, monitoring the vehicle position information of each automatic driving vehicle in real time and carrying out intersection dispatching management according to the vehicle position information.
9. The utility model provides a many cars of garden commodity circulation autopilot scheduling equipment which characterized in that, many cars of garden commodity circulation autopilot scheduling equipment includes: a memory, a processor, and a campus logistics automatic driving multi-car scheduler stored on the memory and operable on the processor, the campus logistics automatic driving multi-car scheduler configured to implement the steps of the method of campus logistics automatic driving multi-car scheduling of any one of claims 1 to 7.
10. A storage medium having stored thereon a campus logistics automatic driving multi-car scheduling program, the campus logistics automatic driving multi-car scheduling program when executed by a processor implementing the steps of the method of dispatching a campus logistics automatic driving multi-car according to any one of claims 1 to 7.
CN202211160104.XA 2022-09-22 2022-09-22 Garden logistics automatic driving multi-vehicle scheduling method, device, equipment and storage medium Pending CN115375184A (en)

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115587742A (en) * 2022-11-25 2023-01-10 万联易达物流科技有限公司 Vehicle entrance and exit scheduling method and device for logistics station
CN117689185A (en) * 2024-02-02 2024-03-12 深圳市拓远能源科技有限公司 Equipment data scheduling optimization method based on Internet of things
CN117877303A (en) * 2024-01-03 2024-04-12 亿海蓝（北京）数据技术股份公司 Vehicle route planning and indicating method, system, storage medium and electronic device

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115587742A (en) * 2022-11-25 2023-01-10 万联易达物流科技有限公司 Vehicle entrance and exit scheduling method and device for logistics station
CN115587742B (en) * 2022-11-25 2023-09-01 万联易达物流科技有限公司 Logistics station vehicle exit/entrance scheduling method and device
CN117877303A (en) * 2024-01-03 2024-04-12 亿海蓝（北京）数据技术股份公司 Vehicle route planning and indicating method, system, storage medium and electronic device
CN117689185A (en) * 2024-02-02 2024-03-12 深圳市拓远能源科技有限公司 Equipment data scheduling optimization method based on Internet of things
CN117689185B (en) * 2024-02-02 2024-05-07 深圳市拓远能源科技有限公司 Equipment data scheduling optimization method based on Internet of things

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