CN115454091A - Multi-vehicle path planning method and system equipment applied to airport luggage consignment - Google Patents
Multi-vehicle path planning method and system equipment applied to airport luggage consignment Download PDFInfo
- Publication number
- CN115454091A CN115454091A CN202211202538.1A CN202211202538A CN115454091A CN 115454091 A CN115454091 A CN 115454091A CN 202211202538 A CN202211202538 A CN 202211202538A CN 115454091 A CN115454091 A CN 115454091A
- Authority
- CN
- China
- Prior art keywords
- consignment
- baggage
- vehicle
- fleet
- airport
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 50
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 22
- 230000003993 interaction Effects 0.000 claims abstract description 13
- 238000004590 computer program Methods 0.000 claims description 15
- 230000008569 process Effects 0.000 claims description 12
- 238000013459 approach Methods 0.000 claims description 10
- 238000004891 communication Methods 0.000 claims description 8
- 238000012545 processing Methods 0.000 claims description 6
- 230000006399 behavior Effects 0.000 claims description 3
- 238000010586 diagram Methods 0.000 description 9
- 230000009471 action Effects 0.000 description 8
- 230000006870 function Effects 0.000 description 6
- 238000004364 calculation method Methods 0.000 description 3
- 238000004088 simulation Methods 0.000 description 3
- 230000004888 barrier function Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000000149 penetrating effect Effects 0.000 description 2
- 230000009467 reduction Effects 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 201000004569 Blindness Diseases 0.000 description 1
- 101100532584 Clostridium perfringens (strain 13 / Type A) sspC1 gene Proteins 0.000 description 1
- 101100410359 Drosophila melanogaster Patronin gene Proteins 0.000 description 1
- 101100256651 Homo sapiens SENP6 gene Proteins 0.000 description 1
- 101100095550 Homo sapiens SENP7 gene Proteins 0.000 description 1
- 101150038317 SSP1 gene Proteins 0.000 description 1
- 101150098865 SSP2 gene Proteins 0.000 description 1
- 101100125020 Schizosaccharomyces pombe (strain 972 / ATCC 24843) pss1 gene Proteins 0.000 description 1
- 101100018019 Schizosaccharomyces pombe (strain 972 / ATCC 24843) ssc1 gene Proteins 0.000 description 1
- 102100023645 Sentrin-specific protease 3 Human genes 0.000 description 1
- 102100023713 Sentrin-specific protease 6 Human genes 0.000 description 1
- 102100031406 Sentrin-specific protease 7 Human genes 0.000 description 1
- 108010092928 Stomoxys serine protease 3 Proteins 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000002068 genetic effect Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000000630 rising effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
- G05D1/0246—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using a video camera in combination with image processing means
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0214—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory in accordance with safety or protection criteria, e.g. avoiding hazardous areas
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0221—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving a learning process
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0287—Control of position or course in two dimensions specially adapted to land vehicles involving a plurality of land vehicles, e.g. fleet or convoy travelling
- G05D1/0291—Fleet control
- G05D1/0295—Fleet control by at least one leading vehicle of the fleet
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Aviation & Aerospace Engineering (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Multimedia (AREA)
- Electromagnetism (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention discloses a multi-vehicle path planning method and system equipment applied to airport luggage consignment, wherein the method comprises the following steps: dividing areas needing baggage consignment in an airport, setting area priority and generating a two-dimensional map; s2: marking a starting point and a target point on the two-dimensional map, enabling the baggage consignment fleet to continuously learn by self depending on the scene interaction between the baggage consignment fleet and surrounding entities, sensing and iterating towards the direction with the highest priority initially defined by the algorithm in the two-dimensional map, and searching for an obstacle-free path until the baggage consignment fleet reaches the target point. The problem that the path is searched by the moving body can be solved, and higher transportation efficiency is achieved.
Description
Technical Field
The invention belongs to the field of path planning, and relates to a multi-vehicle path planning method and system equipment applied to airport luggage consignment.
Background
With the rapid development of the current artificial intelligence driving market, various path planning algorithms related to the intelligent agents also make corresponding progress. The intelligent agent path planning algorithm is to use the existing technical conditions to sense, decide and control the intelligent agent under a complex scene, and find a collision-free path between a starting point and a target point. The current path planning algorithm is of various kinds, such as a _ star algorithm, Q-learning algorithm, ant colony algorithm, genetic algorithm, time sequence difference algorithm, actor-critic algorithm, RRT algorithm, etc.
The multi-vehicle consignment of luggage in an airport also belongs to the field of intelligent agent path planning, and the airport environment is different from expressways, urban roads, special operation sites and the like, and has the unique characteristics that: fewer obstacles, sparser obstacles, less likelihood of pedestrian presence on the ground, more fixed path of the movable obstacle, etc. Currently, airports are still in the stage of manually driving vehicles to arrive at a specified position from a specified position for baggage consignment. For the transportation efficiency, the whole vehicle is still of a traditional vehicle body structure, so that the whole vehicle is high in mass, long in vehicle body and not flexible in steering, and the efficiency is not high; in terms of cost, labor costs are rapidly rising, and the increase in transportation costs for consignors is also not beneficial for airport operations.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a multi-vehicle path planning method and system equipment applied to airport luggage consignment, which can solve the problem that a moving body searches for a path and realize higher transportation efficiency.
In order to achieve the purpose, the invention adopts the following technical scheme to realize the purpose:
a multi-vehicle path planning method applied to airport luggage consignment comprises the following processes:
s1: dividing areas needing baggage consignment in an airport, setting area priority and generating a two-dimensional map;
s2: marking a starting point and a target point on the two-dimensional map, enabling the baggage consignment fleet to continuously learn by self depending on the scene interaction between the baggage consignment fleet and surrounding entities, sensing and iterating towards the direction with the highest priority initially defined by the algorithm in the two-dimensional map, and searching for an obstacle-free path until the baggage consignment fleet reaches the target point.
Preferably, the method for dividing the region in S1 is as follows: the existing cameras of the airport are utilized to identify and analyze the positions of the global obstacles, and according to the principle that the number of the obstacles positively correlates with the probability of the infeasible paths, the probability of prejudging the infeasible paths is high in places with dense obstacles, the area grids are roughly divided, the probability of prejudging the infeasible paths is low in places with scattered obstacles, and the area grids are subdivided.
Preferably, the domain priority transactions in S1 are quantified as follows:
factor _0: a value from the target point;
factor _1: accumulating the numerical values of the distances;
factor _2: a value of a sum of distances from the target point and the starting point;
factor _3: the area is an obstacle or a boundary, and the numerical value is negative infinity;
on the basis, corresponding priority factors alpha, beta, gamma and eta are set, wherein, -0.6= < alpha < = -0.4, -2.0= < beta < = -1.2, -0.6= < gamma < = -0.4, and the value of eta is 1.
Preferably, in the step S2, the baggage consignment fleet learns the self-scene interaction with the surrounding entities continuously, the environment judges the behavior according to the preset priority level and performs forward feedback or backward feedback each time the baggage consignment fleet takes one step, and finally a scene interaction feedback table is formed, the path is searched for many times, the value of the priority factor in the priority level is changed continuously according to the feedback table, and the optimal path is optimized continuously.
Preferably, in S2, the vehicles in the baggage consignment fleet identify surrounding environments, mark the initialized map, and consider the current optimal selection in combination with the preset priority, and advance to the area with the highest priority;
establishing a connecting line between the current point and the target point, and solving the condition of the same area priority by using the number of the upper triangle and the lower triangle obstacles on the connecting line;
and judging whether the forward point is the target point or not, if not, continuing to search the next point, and if so, stopping searching.
Preferably, when the luggage consignment motorcade travels, the speed is reduced when the head part senses that a moving object approaches to a certain range, if the moving object stops moving, the motorcade continues to advance, and if the moving object continues to move, the motorcade stops to wait;
when the middle part senses that the moving object approaches to a certain range, the luggage consignment motorcade is separated from the consignment motorcade closest to the moving object, the front motorcade continues to advance, the rear motorcade repeats the vehicle head judgment process, the moving object drives away from the motorcade, and the rear motorcade serves as a new luggage consignment motorcade to search for an obstacle-free path again.
Further, when a certain vehicle in the luggage consignment motorcade senses that a moving object approaches to a certain range and decelerates or stops, the rear vehicle can perform deceleration or stop actions of different degrees through communication between the vehicles.
A multi-vehicle path planning system for airport baggage claim comprising:
a map generation module: the method comprises the steps of dividing regions needing baggage consignment in an airport, setting region priority and generating a two-dimensional map;
a travel module: the method is used for marking a starting point and a target point on the two-dimensional map, the baggage consignment fleet continuously learns by self depending on the scene interaction between the baggage consignment fleet and surrounding entities, and searches for a barrier-free path towards the direction sensing iteration with the highest priority initially defined by the algorithm in the two-dimensional map until the baggage consignment fleet reaches the target point.
A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor when executing the computer program implementing the steps of a method of multi-car path planning as applied to airport baggage consignment as defined in any one of the preceding claims.
A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of a method as set forth in any of the preceding claims applied to multi-vehicle path planning for baggage consignment at an airport.
Compared with the prior art, the invention has the following beneficial effects:
in the path planning process, sensing iteration is carried out according to the regional priority, a barrier-free path is searched, the problem that a moving body searches the path can be solved, higher transportation efficiency is achieved, and the starting point is movable, so that the local path planning effect is improved on the basis of macroscopic path planning, and macroscopic and local reasonable planning is achieved.
Furthermore, the relation between the next point of the starting point and the target point is digitized, so that the path updating is facilitated, the blindness in the learning process can be reduced, and the planning time is reduced.
Furthermore, the strategy of the baggage consignment motorcade in the process of traveling has higher local flexibility, and the planning and strain capacity of the motorcade on local obstacles can be enhanced. Therefore, the motorcade can take the flexibility and the smaller calculated amount into consideration in the path planning, and the luggage consignment motorcade is realized.
Drawings
FIG. 1 is a flow chart of a multi-car path planning method of the present invention applied to airport baggage consignment;
fig. 2 is a 10 x 10 map simulation diagram of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and 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.
It should be noted that the terms "front," "back," "left," "right," "upper" and "lower" used in the following description refer to directions in the drawings, and the terms "inner" and "outer" refer to directions toward and away from, respectively, the geometric center of a particular component.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
The invention relates to a multi-vehicle path planning method applied to airport luggage consignment, which comprises the following steps as shown in figure 1: the method comprises the steps of utilizing the existing high-precision map technology to position an area needing baggage consignment in an airport, inputting the area into a storage module of each vehicle of a baggage consignment fleet and storing the area after area division and area priority initialization, and installing an environment sensing module and a communication module between the vehicles under the prior art condition for each vehicle in the baggage consignment fleet.
Step two: marking a starting point and a target point on a stored map, and controlling the baggage consignment fleet to approach the target point.
The specific process of the second step is as follows:
SP1: and initializing the current point, and giving the position information of the starting point to the current point.
SP2: and judging whether the condition flag is 0 or not, and starting circulation.
SP3: exploring a plurality of areas around the current point, adding the areas around into the array according to corresponding rules, continuously learning by self through a reward table generated by continuous interaction of the vehicle and the environment, and updating the priority factor, wherein the rules are as follows:
if a certain area is perceived to be outside the map, the area is discarded; if a certain surrounding area already appears in the path array, the area is discarded; adding the perceived positions and priority information of the remaining non-obstacle areas into a temporary array, wherein the calculation method of the priority information of a certain area is as follows:
factor _0: the numerical value of the distance target point estimates the distance from the perceived residual points to the target point by utilizing a linear distance formula between two points, and priority information is sequentially given;
factor _1: accumulating the numerical value of the distance, calculating the accumulated distance by using the sum of the absolute values of the coordinate differences of each point in the existing path, and giving priority information to each point in sequence.
D 2 ＝|x 1 -x 2 |+|y 1 -y 2 |+|x 2 -x 3 |+|y 2 -y 3 |+...+|x n-1 -x n |+|y n-1 -y n |
factor _2: and estimating the numerical value of the distance sum between the target point and the starting point by using a linear distance formula between the two points.
factor _3: the area is an obstacle or boundary, the priority is minimal, D 4 Negative infinity.
And finally, weighting the priority:
SP4: judging whether a path is available, if the departure point or the target point is surrounded by the obstacle, indicating that no path is available, sending a prompt of 'no path available', waiting, executing the algorithm again, and if the path is not solved, performing manual intervention; if there is a path to go, continue to the next step.
SP5: finding out a point with the maximum priority in the surrounding environment sensed by the current point, iterating to be the next current point, putting the coordinates of the point into a path array, and controlling the movement of the luggage consignment motorcade to the point; if the same priority occurs, a min _ value function is executed to divide a straight line between the current point and the target point:
counting the number of obstacle points in a rectangular space with a current point and a target point as opposite angles, dividing obstacles above a straight line into up _ obsacles, dividing obstacles below the straight line into down _ obsacles, comparing the sizes of the obstacles and the down _ obsacles, and selecting an interval direction with few obstacles to advance.
SP6: judging whether the point is a target point, if not, continuing to circulate; if the target point is the target point, the value of flag is assigned to 0, and the path array is output.
SP7: the consignment of luggage arrives at the destination, and the route of the luggage is visualized.
During the travel of the baggage consignment fleet, the following three situations inevitably occur: the whole course barrier is in a static state, stops when a moving body approaches to a certain range, and continues to move when the moving body approaches to the certain range, and the invention adopts the following four measures:
SSP1: aiming at the condition that the whole-course barrier is in a static state, the whole luggage consignment motorcade only needs to run according to the running track of the lead vehicle.
SSP2: when the distance between a certain vehicle and the moving body reaches a certain range, the vehicle carries out deceleration operation, and the rear vehicle carries out deceleration actions of different degrees through communication between the vehicles. If the moving body continues to approach the luggage delivery fleet to another distance range, the vehicle closest to the moving body stops for waiting, and the rear vehicle performs deceleration or even stopping actions of different degrees through the communication between the vehicles. In this case, since the moving body is finally stopped, the host vehicle closest to the moving body serves as the head vehicle of the rear vehicle after the moving body is stopped, and the rear vehicle group returns to the original speed and related operation.
SSP3: when the distance between a certain vehicle and the moving body reaches a certain range, the vehicle performs speed reduction operation, and the rear vehicle performs speed reduction actions of different degrees through communication between the vehicles. If the moving body is continuously close to the motorcade to another distance range, the vehicle closest to the moving body stops and waits, and the rear vehicle performs deceleration or even parking actions of different degrees through the communication between the vehicles. In this case, the moving body penetrates the vehicle group, if the moving body stops within a certain distance after penetrating the vehicle group, the hosting vehicle closest to the moving body serves as the head vehicle of the rear vehicle, and the rear vehicle group resumes running after several seconds, the algorithm is continuously executed, and if the vehicle moves after penetrating the baggage hosting vehicle group, the hosting vehicle closest to the moving body serves as the head vehicle of the rear vehicle, and the rear vehicle group resumes commuting when the moving body travels away from the area, and the algorithm is continuously executed.
SSP4: the luggage consignment motorcade judges whether to utilize airport internal communication to send an alarm signal to a tower and open a vehicle-mounted double flash through broadcasting in the motorcade according to whether a vehicle and vehicle-mounted goods are damaged or not and whether the waiting time of the vehicle is more than a set value or not, the tower responds to a relevant alarm, and the behavior of the luggage consignment motorcade is taken over through an airport internal command system.
The motorcade strategy simulates the advancing of human teams and carries out multi-vehicle cooperation.
Firstly, the method comprises the following steps: compliance. Each vehicle in the fleet can sense the surrounding environment such as the front, the side and the like, but the influence of the self observation on the whole fleet has different weights. The front vehicle has a high weight value, and can determine whether the vehicle and the rear vehicle are moving or not when facing a movable or immovable obstacle, whereas the rear vehicle cannot determine the front vehicle.
II, secondly: high local flexibility. Although the determination of the rear vehicle cannot influence the front vehicle, when a movable and immovable obstacle exists in the middle of the vehicle team, the vehicle behind the vehicle can determine whether the vehicle and the rear vehicle are separated from the front vehicle or not, and the vehicle team moves to the target point after the dangerous condition is over.
By means of compliance, the calculation amount of the whole vehicle in path planning can be greatly reduced. The calculated amount of the front vehicle is high, and the calculated amount of the rear vehicle is small. By means of high local flexibility, the planning and strain capacity of the fleet for local obstacles can be enhanced. Therefore, the motorcade can take flexibility and smaller calculation amount into consideration in path planning.
The simulation result of the invention is shown in figure 2, and the simulation result shows that the multi-vehicle path planning algorithm applied to airport luggage consignment can solve the problems of low transportation efficiency and high labor cost of airport luggage consignment vehicles.
The following are embodiments of the apparatus of the present invention that may be used to perform embodiments of the method of the present invention. For details of non-careless mistakes in the embodiment of the apparatus, please refer to the embodiment of the method of the present invention.
In another embodiment of the present invention, a multi-vehicle path planning system for airport baggage check-out is provided, which may be used to implement the multi-vehicle path planning method for airport baggage check-out described above, and specifically, the multi-vehicle path planning system for airport baggage check-out includes a map generation module and a travel module.
The map generation module is used for dividing the areas needing baggage consignment in the airport, setting the priority of the areas and generating a two-dimensional map.
The traveling module is used for marking a starting point and a target point on the two-dimensional map, the baggage consignment fleet continuously learns by self depending on the situation interaction of the baggage consignment fleet and surrounding entities, and searches for a barrier-free path towards the direction sensing iteration with the highest priority initially defined by the algorithm in the two-dimensional map until the baggage consignment fleet reaches the target point.
In yet another embodiment of the present invention, a terminal device is provided that includes a processor and a memory for storing a computer program comprising program instructions, the processor being configured to execute the program instructions stored by the computer storage medium. The Processor may be a Central Processing Unit (CPU), or may be other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable gate array (FPGA) or other Programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, etc., which is a computing core and a control core of the terminal, and is adapted to implement one or more instructions, and is specifically adapted to load and execute one or more instructions to implement a corresponding method flow or a corresponding function; the processor of the embodiment of the invention can be used for the operation of a multi-vehicle path planning method applied to airport luggage consignment, and comprises the following steps: s1: dividing areas needing baggage consignment in an airport, setting area priority and generating a two-dimensional map; s2: marking a starting point and a target point on the two-dimensional map, continuously learning by the baggage consignment fleet by depending on the self-scene interaction with surrounding entities, sensing and iterating towards the direction with the highest priority initially defined by the algorithm in the two-dimensional map, and searching for a barrier-free path until the baggage consignment fleet reaches the target point.
In still another embodiment, the present invention also provides a computer-readable storage medium (Memory) which is a Memory device in a terminal device and stores programs and data. It is understood that the computer readable storage medium herein may include a built-in storage medium in the terminal device, and may also include an extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space storing an operating system of the terminal. Also, the memory space stores one or more instructions, which may be one or more computer programs (including program code), adapted to be loaded and executed by the processor. It should be noted that the computer-readable storage medium may be a high-speed RAM memory, or may be a non-volatile memory (non-volatile memory), such as at least one disk memory.
One or more instructions stored in a computer-readable storage medium may be loaded and executed by a processor to perform the corresponding steps of the above-described embodiments with respect to a multi-vehicle path planning method for airport baggage consignment; one or more instructions in the computer readable storage medium are loaded by the processor and perform the steps of: s1: dividing areas needing baggage consignment in an airport, setting area priority and generating a two-dimensional map; s2: marking a starting point and a target point on the two-dimensional map, enabling the baggage consignment fleet to continuously learn by self depending on the scene interaction between the baggage consignment fleet and surrounding entities, sensing and iterating towards the direction with the highest priority initially defined by the algorithm in the two-dimensional map, and searching for an obstacle-free path until the baggage consignment fleet reaches the target point.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and so forth) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
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.
It is to be understood that the above description is intended to be illustrative, and not restrictive. Many embodiments and many applications other than the examples provided would be apparent to those of skill in the art upon reading the above description. The scope of the present teachings should, therefore, be determined not with reference to the above description, but should instead be determined with reference to the pending claims along with the full scope of equivalents to which such claims are entitled. The disclosures of all articles and references, including patent applications and publications, are hereby incorporated by reference for all purposes. The omission in the foregoing claims of any aspect of subject matter that is disclosed herein is not intended to forego such subject matter, nor should the applicant consider that such subject matter is not considered part of the disclosed subject matter.
Claims (10)
1. A multi-vehicle path planning method applied to airport luggage consignment is characterized by comprising the following processes:
s1: dividing areas needing baggage consignment in an airport, setting area priority and generating a two-dimensional map;
s2: marking a starting point and a target point on the two-dimensional map, continuously learning by the baggage consignment fleet by depending on the self-scene interaction with surrounding entities, sensing and iterating towards the direction with the highest priority initially defined by the algorithm in the two-dimensional map, and searching for a barrier-free path until the baggage consignment fleet reaches the target point.
2. The method for planning the multi-vehicle path for baggage consignment at an airport according to claim 1, wherein the method for dividing the area in S1 comprises: the existing cameras of the airport are utilized to identify and analyze the positions of the global obstacles, and according to the principle that the number of the obstacles positively correlates with the probability of the infeasible paths, the probability of prejudging the infeasible paths is high in places with dense obstacles, the area grids are roughly divided, the probability of prejudging the infeasible paths is low in places with scattered obstacles, and the area grids are subdivided.
3. The method for multi-vehicle path planning for baggage claim 1 at an airport, wherein the area priority items in S1 are quantified as follows:
factor _0: a value from the target point;
factor _1: accumulating the numerical values of the distances;
factor _2: a value of a sum of distances from the target point and the starting point;
factor _3: the area is an obstacle or a boundary, and the numerical value is negative infinity;
on the basis, corresponding priority factors alpha, beta, gamma and eta are set, wherein, -0.6= < alpha < = -0.4, -2.0= < beta < = -1.2, -0.6= < gamma < = -0.4, and the value of eta is 1.
4. The method of claim 1, wherein in step S2, the baggage consignment fleet learns themselves continuously in a contextual interaction with surrounding entities, and when the baggage consignment fleet takes one step, the environment determines the behavior according to the preset priority, and performs forward feedback or backward feedback, thereby finally forming a contextual interaction feedback table, and after multiple routes are searched, the value of the priority factor in the priority is continuously changed according to the feedback table, and the optimal route is continuously optimized.
5. The method for planning the multi-vehicle path for baggage claim 1, wherein in S2, the vehicles in the baggage fleet identify the surrounding environment, mark the environment on the map after initialization processing, and go forward to the area with the highest priority by considering the current optimal selection in combination with the preset priority;
establishing a connecting line between the current point and the target point, and solving the condition of the same area priority by using the number of the upper triangle and the lower triangle obstacles on the connecting line;
and judging whether the forward point is the target point or not, if not, continuing to search the next point, and if so, stopping searching.
6. The method as claimed in claim 1, wherein the vehicle fleet for baggage consignment is decelerated when the head part of the vehicle senses that a moving object approaches to a certain range during the traveling process, the vehicle fleet continues to move if the moving object stops moving, and the vehicle fleet stops waiting if the moving object continues to move;
when the middle part senses that the moving object approaches to a certain range, the luggage consignment motorcade is separated from the consignment motorcade closest to the moving object, the front motorcade continues to advance, the rear motorcade repeats the vehicle head judgment process, the moving object drives away from the motorcade, and the rear motorcade is used as a new luggage consignment motorcade to search for an obstacle-free path again.
7. The method as claimed in claim 6, wherein when a vehicle in the baggage claim fleet senses that a moving object approaches to a certain range and decelerates or stops, the following vehicle is allowed to perform different degrees of deceleration or stop by the communication between vehicles.
8. A multi-vehicle path planning system for airport baggage consignment, comprising:
a map generation module: the method comprises the steps of dividing regions needing baggage consignment in an airport, setting region priority and generating a two-dimensional map;
a travel module: the method is used for marking a starting point and a target point on the two-dimensional map, the baggage consignment fleet continuously learns by self depending on the scene interaction between the baggage consignment fleet and surrounding entities, and searches for a barrier-free path towards the direction sensing iteration with the highest priority initially defined by the algorithm in the two-dimensional map until the baggage consignment fleet reaches the target point.
9. A computer arrangement comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor when executing the computer program carries out the steps of the method for multi-car path planning for baggage claim 1 to 7 for use in airport baggage.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of a method for multi-car path planning for the consignment of baggage at an airport according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211202538.1A CN115454091A (en) | 2022-09-29 | 2022-09-29 | Multi-vehicle path planning method and system equipment applied to airport luggage consignment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211202538.1A CN115454091A (en) | 2022-09-29 | 2022-09-29 | Multi-vehicle path planning method and system equipment applied to airport luggage consignment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115454091A true CN115454091A (en) | 2022-12-09 |
Family
ID=84309805
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211202538.1A Pending CN115454091A (en) | 2022-09-29 | 2022-09-29 | Multi-vehicle path planning method and system equipment applied to airport luggage consignment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115454091A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116576865A (en) * | 2023-07-07 | 2023-08-11 | 民航成都电子技术有限责任公司 | Flight area path planning method, device, equipment and medium |
| CN116976535A (en) * | 2023-06-27 | 2023-10-31 | 上海师范大学 | Path planning algorithm based on fusion of few obstacle sides and steering cost |
-
2022
- 2022-09-29 CN CN202211202538.1A patent/CN115454091A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116976535A (en) * | 2023-06-27 | 2023-10-31 | 上海师范大学 | Path planning algorithm based on fusion of few obstacle sides and steering cost |
| CN116976535B (en) * | 2023-06-27 | 2024-05-17 | 上海师范大学 | Path planning method based on fusion of few obstacle sides and steering cost |
| CN116576865A (en) * | 2023-07-07 | 2023-08-11 | 民航成都电子技术有限责任公司 | Flight area path planning method, device, equipment and medium |
| CN116576865B (en) * | 2023-07-07 | 2023-10-17 | 民航成都电子技术有限责任公司 | Flight area path planning method, device, equipment and medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111670468B (en) | Moving body behavior prediction device and moving body behavior prediction method | |
| CN109724612B (en) | AGV path planning method and device based on topological map | |
| CN110333714B (en) | Unmanned vehicle path planning method and device | |
| CN115454091A (en) | Multi-vehicle path planning method and system equipment applied to airport luggage consignment | |
| Pomerleau et al. | Long-term 3D map maintenance in dynamic environments | |
| WO2020243162A1 (en) | Methods and systems for trajectory forecasting with recurrent neural networks using inertial behavioral rollout | |
| CN115551758A (en) | Unstructured vehicle path planner | |
| CN111532641B (en) | Parallel path planning method for automatic guide vehicle in storage sorting | |
| WO2017144350A1 (en) | Method for motion planning for autonomous moving objects | |
| US11932282B2 (en) | Vehicle trajectory control using a tree search | |
| Yoganandhan et al. | Fundamentals and development of self-driving cars | |
| CN115116220B (en) | Unmanned multi-vehicle cooperative control method for mining area loading and unloading scene | |
| Li et al. | Adaptive sampling-based motion planning with a non-conservatively defensive strategy for autonomous driving | |
| CN114169628B (en) | Shipboard aircraft scheduling optimization method and system based on A-star algorithm and genetic algorithm | |
| Garg et al. | Deep learning for obstacle avoidance in autonomous driving | |
| WO2024049925A1 (en) | Trajectory prediction based on a decision tree | |
| CN117075613A (en) | Unmanned vehicle sensing and obstacle avoidance method, system, computer equipment and storage medium | |
| WO2023135271A1 (en) | Motion prediction and trajectory generation for mobile agents | |
| CN115107806A (en) | Vehicle track prediction method facing emergency scene in automatic driving system | |
| Fernández et al. | Anomalous trajectory detection for automated traffic video surveillance | |
| EP4146510A1 (en) | Trajectory classification | |
| Qiu | A Review of Motion Planning for Urban Autonomous Driving | |
| CN115246416A (en) | Trajectory prediction method, apparatus, device and computer readable storage medium | |
| Sama et al. | Learning how to drive in blind intersections from human data | |
| Keppler et al. | SAFESTOP: Disturbance handling in prioritized multi-robot trajectory planning |
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 |