CN113902289A - AGV path planning method based on flexible space-time network model - Google Patents

AGV path planning method based on flexible space-time network model Download PDF

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CN113902289A
CN113902289A CN202111170026.7A CN202111170026A CN113902289A CN 113902289 A CN113902289 A CN 113902289A CN 202111170026 A CN202111170026 A CN 202111170026A CN 113902289 A CN113902289 A CN 113902289A
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辛健斌
魏刘倩
张方方
王东署
彭金柱
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Abstract

The method comprises the steps of determining sub-target tasks of each automatic guided vehicle by acquiring target tasks; determining a decision variable and space-time constraint thereof based on the target task to obtain a time target function and an energy consumption target function for model construction, and introducing a mixed element heuristic algorithm to obtain an AGV space-time network model; inputting the information of each automatic guided vehicle and the corresponding sub-target tasks thereof into an AGV space-time network model to obtain a path planning result and running time of each automatic guided vehicle, namely constructing a mixed heuristic algorithm based on a distribution estimation algorithm and a genetic algorithm to carry out model solution, obtaining the path planning result of the task vehicle by decoding an approximate optimal solution, and running time of the vehicle on each track to ensure that no collision exists among the AGV vehicles and reduce the total energy consumption of the vehicle under the condition of ensuring that the target tasks are completed in a short time as much as possible.

Description

AGV path planning method based on flexible space-time network model
Technical Field
The application relates to the field of computers, in particular to an Automatic Guided Vehicle (AGV) path planning method based on a flexible space-time network model.
Background
In the prior art, AGVs are computer controlled unmanned vehicles used to transport materials. Automated guided vehicles have been successfully used in many areas, such as warehouses, container terminals, transportation and manufacturing systems, since their introduction in 1955. In particular, with the rapid development of smart manufacturing industry in recent years, AGVs are increasingly popular with many manufacturing companies due to their outstanding features of simple operation, rapid response, and high efficiency. The main purpose of using AGV systems is to increase productivity and reduce costs in practice, for which reason the control systems employed must perform the path planning of the AGVs in an efficient manner, i.e. to deal with the coordination of the vehicles, avoiding collisions, collisions and deadlocks.
The path problem for an AGV can be divided into two successive stages: a scheduling phase and a path planning phase. Tasks are assigned to automated guided vehicles during the dispatch phase and then the tasks assigned to each automated guided vehicle are sequenced. And finding the optimal path in the path planning stage based on the sequence generated in the scheduling stage. The scholars at home and abroad have made many relevant studies on the same. Under the assumption that the vehicle speed is fixed And cannot be adjusted, a Conflict-Free pick-up And delivery Problem (DCFRP) with a time window is studied in more detail. To optimize the task allocation, objective functions such as completion time, total completion time, etc. are used. In the field of automated guided vehicles for manufacturing systems, energy consumption of mobile robots is also a major concern of automated guided vehicle systems, but there are few documents on energy consumption, and it is an important direction for those skilled in the art to reduce the total energy consumption of the system while trying to ensure a short task completion time.
Disclosure of Invention
An object of the present application is to provide an AGV path planning method based on a flexible spatio-temporal network model, so as to solve the problem in the prior art how to ensure reasonable optimization of the path in task completion time and reduce total energy consumption.
According to one aspect of the present application, there is provided an AGV path planning method based on a flexible spatiotemporal network model, including:
acquiring a target task, and determining sub-target tasks of each automatic guided vehicle based on the target task;
determining a decision variable and space-time constraint thereof based on the target task to obtain a time target function and an energy consumption target function for model construction, and introducing a mixed element heuristic algorithm to obtain an AGV space-time network model;
and inputting the information of each automatic guided vehicle and the corresponding sub-target tasks thereof into an AGV space-time network model to obtain a path planning result and driving time of each automatic guided vehicle.
Further, in the AGV path planning method based on the flexible space-time network model, determining a decision variable and a space-time constraint thereof based on the target task to obtain a time objective function and an energy consumption objective function for model construction, the method includes:
the target task comprises a task starting point, a task end point, cargo information, starting time and planning time, the planning time is discretized into a plurality of time gaps, mathematical description is carried out based on the target task, and relevant decision variables are obtained;
respectively carrying out time constraint, space constraint and space-time constraint on the decision variables;
determining the time objective function and the energy consumption objective function based on the decision variables and the time constraints, the spatial constraints, and the spatiotemporal constraints corresponding thereto.
Further, in the AGV path planning method based on the flexible space-time network model, determining the time objective function and the energy consumption objective function based on the decision variables and the time constraint, the space constraint, and the space-time constraint corresponding thereto includes:
obtaining a time objective function by utilizing the decision variables and the time constraint, the space constraint and the total travel time problem determined by the space-time constraint corresponding to the decision variables and based on mixed integer optimization;
determining a total energy consumption problem on the basis of the total travel time problem, wherein the total energy consumption problem comprises vehicle acceleration energy consumption and rolling friction energy consumption;
and adding an acceleration variable and a constraint thereof and a time gap number variable in the total energy consumption problem, and obtaining an energy consumption objective function based on mixed integer nonlinear programming.
Further, in the AGV path planning method based on the flexible spatio-temporal network model, the obtaining of the AGV spatio-temporal network model by introducing the hybrid heuristic algorithm includes:
and designing a two-dimensional coding frame of a mixed heuristic algorithm of the running time and the path planning result, constructing an AGV probability model by adopting a population increment learning algorithm, and forming a fitness function by utilizing a penalty function and the energy consumption target function to obtain an AGV space-time network model.
Further, in the AGV path planning method based on the flexible spatio-temporal network model, the first dimension in the hybrid heuristic algorithm two-dimensional coding frame corresponds to the path planning result, and the second dimension corresponds to the travel time.
Further, in the AGV path planning method based on the flexible space-time network model, the AGV probability model is constructed by adopting the population increment learning algorithm based on the time dimension, the AGV probability model comprises a time population, an energy consumption population and a learning population, and the time population, the energy consumption population and the learning population are set according to a preset proportion.
Further, in the AGV path planning method based on the flexible space-time network model, the step of inputting the information of each automatic guided vehicle and the corresponding sub-target tasks into the AGV space-time network model to obtain the path planning result and the travel time of each automatic guided vehicle includes:
inputting the information of each automatic guided vehicle and the corresponding sub-target tasks into an AGV space-time network model;
based on the information of each automatic guided vehicle and the corresponding sub-target tasks thereof, obtaining an initial population through the AGV probability model;
calculating by using the fitness function on the basis of the initial population to obtain an elite population, performing mutation operation to generate a path vector of a new generation population, obtaining a time vector of the new generation population through the AGV probability model according to the elite population, repeating the steps until fitness evaluation calculation for preset specified times is completed, obtaining the optimal path vector and time vector of the new generation population, and outputting the optimal path vector and time vector as a path planning result and driving time of each automatic guided vehicle.
Compared with the prior art, the method and the device have the advantages that the sub-target tasks of each automatic guided vehicle are determined based on the target tasks by acquiring the target tasks; determining a decision variable and space-time constraint thereof based on the target task to obtain a time target function and an energy consumption target function for model construction, and introducing a mixed element heuristic algorithm to obtain an AGV space-time network model; inputting the information of each automatic guided vehicle and the corresponding sub-target tasks thereof into an AGV space-time network model to obtain a path planning result and running time of each automatic guided vehicle, namely constructing a mixed heuristic algorithm based on a distribution estimation algorithm and a genetic algorithm to carry out model solution, obtaining the path planning result of the known target task vehicle through decoding of an approximate optimal solution, and obtaining the running time of the vehicle on each track to ensure that no collision exists among the AGVs, and reducing the total energy consumption of the vehicle under the condition of ensuring that the target task completion time is short as much as possible.
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Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:
FIG. 1 illustrates a flow diagram of an AGV path planning method based on a flexible spatiotemporal network model in accordance with an aspect of the present application;
FIG. 2 illustrates a schematic diagram of cumulative flow variables a (i, j, k, t) and d (i, j, k, t) in an embodiment of a method for AGV path planning based on a flexible spatiotemporal network model in accordance with an aspect of the subject application;
FIG. 3 illustrates a hierarchical architecture diagram of an AGV system according to an embodiment of a method for AGV path planning based on a flexible spatiotemporal network model in accordance with an aspect of the present application.
The same or similar reference numbers in the drawings identify the same or similar elements.
Detailed Description
The present application is described in further detail below with reference to the attached figures.
Fig. 1 is a block diagram illustrating an AGV path planning method based on a flexible spatio-temporal network model according to an aspect of the present application, the method is suitable for path planning of unmanned vehicles in industrial manufacturing processes such as warehouses and manufacturing workshops, and the method includes steps S1, S2 and S3, wherein the method specifically includes:
step S1, acquiring a target task, and determining sub-target tasks of each automatic guided vehicle based on the target task;
step S2, determining decision variables and space-time constraints thereof based on the target task, obtaining a time target function and an energy consumption target function for model construction, and introducing a mixed meta-heuristic algorithm to obtain an AGV space-time network model;
and step S3, inputting the information of each automatic guided vehicle and the corresponding sub-target tasks into an AGV space-time network model to obtain a path planning result and driving time of each automatic guided vehicle. The intelligent control automatic guided vehicle is used for transferring goods in the actual industrial production manufacturing process, so that the labor cost is reduced, the productivity is improved, a plurality of vehicles are coordinated to finish target tasks, conflicts, collisions and deadlocks are avoided, and the energy consumption is effectively controlled by reasonably planning paths and the driving time.
In the steps from S1 to S3, the present application determines the sub-target tasks of each automated guided vehicle based on the target task by acquiring the target task; determining a decision variable and space-time constraint thereof based on the target task to obtain a time target function and an energy consumption target function for model construction, and introducing a mixed element heuristic algorithm to obtain an AGV space-time network model; inputting the information of each automatic guided vehicle and the corresponding sub-target tasks thereof into an AGV space-time network model to obtain a path planning result and running time of each automatic guided vehicle, namely constructing a mixed heuristic algorithm based on a distribution estimation algorithm and a genetic algorithm to carry out model solution, obtaining the path planning result of the known target task vehicle through decoding of an approximate optimal solution, and obtaining the running time of the vehicle on each track to ensure that no collision exists among the AGVs, and reducing the total energy consumption of the vehicle under the condition of ensuring that the target task completion time is short as much as possible.
Next, in the above embodiment, in the AGV path planning method using a flexible space-time network model, a time objective function and an energy consumption objective function are obtained to perform model construction by determining a decision variable and a space-time constraint thereof based on the target task, and the method includes:
the target task comprises a task starting point, a task end point, cargo information, starting time and planning time, the planning time is discretized into a plurality of time gaps, mathematical description is carried out based on the target task, and relevant decision variables are obtained;
respectively carrying out time constraint, space constraint and space-time constraint on the decision variables;
determining the time objective function and the energy consumption objective function based on the decision variables and the time constraints, the spatial constraints, and the spatiotemporal constraints corresponding thereto. The method ensures the collision-free path planning between the automatic guided vehicles by determining the decision variables related to the running time and the energy consumption and carrying out constraint, thereby being convenient for further obtaining a time objective function and an energy consumption objective function.
For example, in a manufacturing or warehousing system, AGVs are often used to transport materials between job origins and destinations in a mesh routing environment. In a mesh routing environment, the map is represented as a directed graph G ═ V, E, where V is a set of nodes representing the load and unload locations and the locations where the AGV can switch directions; e { (i, j) | i ∈ V, j ≠ V, i ≠ j } is an arc set, which represents a connection path between two adjacent nodes. Each task of each vehicle is to transport material from its origin to its destination. With respect to this process, the following assumptions are made:
assuming that the size of the AGV is small enough relative to the AGV to act as a virtual point;
each AGV may transport a single load at any time;
one node can only be occupied by one AGV at most at one time;
one arc can be occupied by only one AGV at most at one moment;
the turning time is included in the transport time, the time for loading and unloading goods is negligible compared to the transport time;
given the number of vehicles, the assigned transport task for each AGV is deterministic, with no duplicate nodes in the assigned task.
An AGV space-time network model is established based on the assumptions, the planning time T multiplied by delta T is discretized into a series of time gaps { delta T, 2 delta T,.
First, the symbols and corresponding descriptions of the models are introduced, as in table 1.
TABLE 1 model notation and description
Figure BDA0003292590820000071
The decision variables of the system model are as follows:
x (i, j, k): a variable of 0-1, arc (i, j) is 1 when selected as part of the path of automated guided vehicle k, and 0 otherwise;
y (i, j, k, t): a variable of 0 to 1, wherein the automatic guided vehicle k is 1 when the t-th time gap occupies the arc (i, j), and is 0 if the t-th time gap occupies the arc (i, j);
a (i, j, k, t): a variable of 0-1, automated guided vehicle k being 1 when the t-th time gap has reached arc (i, j), otherwise 0;
d (i, j, k, t): a variable of 0-1, automated guided vehicle k being 1 when the t-th time gap has left the arc (i, j), otherwise 0;
TT (i, j, k): and (4) an integer variable representing the operation time of the automated guided vehicle k in the arc (i, j).
Fig. 2 is a schematic diagram of the cumulative flow variables a (i, j, k, t) and d (i, j, k, t) in one embodiment. As can be seen from fig. 2, when the automated guided vehicle k arrives at arc (i, j) at time 4 and departs from arc (i, j) at time 7, the number of time windows in which the automated guided vehicle k travels over arc (i, j) can be expressed as Σta(i，j，k，t)-∑td (i, j, k, t), i.e. 3 time windows.
The flexible spatiotemporal network model realizes collision-free guidance paths among the AGVs through constraint among variables. The constraints include temporal constraints, spatial constraints, and spatio-temporal constraints, as follows:
and (3) space constraint:
Figure BDA0003292590820000081
Figure BDA0003292590820000082
Figure BDA0003292590820000083
space-time constraint:
Figure BDA0003292590820000084
Figure BDA0003292590820000085
Figure BDA0003292590820000086
Figure BDA0003292590820000087
Figure BDA0003292590820000088
and (3) time constraint:
Figure BDA0003292590820000091
Figure BDA0003292590820000092
Figure BDA0003292590820000093
Figure BDA0003292590820000094
Figure BDA0003292590820000095
constraints (1) - (3) correspond to the spatial constraints of the starting point, intermediate point and end point of the path of the AGV, respectively. Constraint (4) is a constraint between the time vehicle k arrives at arc (i, j) and leaves arc (i, j). The constraint (5) ensures the continuity of the vehicle k at the point j in time. Constraints (6) tie temporal and spatial constraints together. Constraint (7) describes a variable y (i, j, k, t) indicating whether arc (i, j) is occupied by vehicle k at time t. Constraints (8) ensure that an arc can only be occupied by one AGV at most at the same time. Constraint (9) is a descriptive variable TT (i, j, k) representing the vehicle k's run time on arc (i, j). The constraint (10) is a minimum run time (travel time) constraint. The constraint (11) indicates that at most one AGV occupies each site at the same time. Constraints (12) and (13) guarantee temporal continuity.
Determining the time objective function and the energy consumption objective function based on the decision variables and the time constraints, the spatial constraints, and the spatiotemporal constraints corresponding thereto.
Next to the foregoing embodiment, in the AGV path planning method using a flexible space-time network model, the determining the time objective function and the energy consumption objective function based on the decision variables and the corresponding time constraint, space constraint, and space-time constraint includes:
obtaining a time objective function by utilizing the decision variables and the time constraint, the space constraint and the total travel time problem determined by the space-time constraint corresponding to the decision variables and based on mixed integer optimization;
determining a total energy consumption problem on the basis of the total travel time problem, wherein the total energy consumption problem comprises vehicle acceleration energy consumption and rolling friction energy consumption;
and adding an acceleration variable and a constraint thereof and a time gap number variable in the total energy consumption problem, and obtaining an energy consumption objective function based on mixed integer nonlinear programming. And establishing a model based on the obtained energy consumption objective function, and ensuring that the energy consumption of each automatic guided vehicle after reasonable path planning is lower, thereby achieving the purpose of saving consumption cost while completing the objective task.
For example, the AGV path planning problem includes a total travel time problem and a total energy consumption problem, and the primary objective is to optimize the total travel time of the task, which can be expressed as J1：
J1＝∑k∑tt×∑i(d(i，j，k，t)-d(i，j，k，t-1))
Target J1Is a mixed integer optimization, and the solution to the objective is defined as the problem P1。P1The solution can be performed by a commercial solver, such as Gurobi.
At problem P1Further considering the system energy consumption on the basis, and recording the system energy consumption as J2Mainly comprises two parts. Wherein, JaGenerated by vehicle acceleration, i.e. vehicleAccelerated energy consumption, JrRolling friction is the energy consumption generated by rolling friction.
J2＝Ja+Jr
Ja＝0.5M∑k∑t[v2(k，t)-v2(k，t-1)]，v(k，t)≥v(k，t-1)
Due to JaContaining conditional constraints, new variables z (k, t) need to be added: the variable 0-1, automated guided vehicle k is 1 when accelerating at t, otherwise it is 0. Due to-vmax≤v(k，t)-v(k，t-1)≤vmaxThe constraint on z (k, t) can be expressed as:
v(k，t-1)-v(k，t)≤vmax(1-z(k，t))
v(k，t-1)-v(k，t)≥ε-z(k，t)(vmax+ε)
where epsilon is a small tolerance, i.e., machine accuracy.
v (k, t) may be represented by S (i, j)/TT (i, j, k), but since TT (i, j, k) may be 0, further processing is required, where a discrete integer μ (μ ∈ {1, 2.., h }) is introduced, h being the maximum number of time slots the AGV can run on the arc. Introducing a variable L (i, j, k, μ) indicating that the number of time slots of vehicle k on arc (i, j) is μ, then v (k, t) can be expressed as:
Figure BDA0003292590820000101
substituting v (k, t) into JaIn (1), an objective function J can be obtained2And is non-linear. Will be at J1J1For the objective function J in the minimum case2Is recorded as a problem P2This is a mixed integer nonlinear programming problem that can be solved using commercial solvers, such as Baron. The layered architecture of the AGV system is shown in FIG. 3 below.
The controller for each AGV calculates the minimum time T to run on arc (i, j)min(i, j, k) to a supervisory controller. The rear supervisory controller determines the optimal transport time TT (i, j, k), transmits the optimal transport time TT (i, j, k) to the controllers of all the AGVs,thereby controlling the speed of the AGV.
Next, in the above embodiment, the method introduces a hybrid heuristic algorithm to obtain an AGV spatiotemporal network model, including:
and designing a two-dimensional coding frame of a mixed heuristic algorithm of the running time and the path planning result, establishing an AGV probability model by adopting a population increment learning algorithm, and forming a fitness function by utilizing a penalty function and the energy consumption target function to obtain an AGV space-time network model. Here, the problem is coded, which is highly related to the quality of the solution, and the construction of the two-dimensional coding framework is beneficial to improving the quality of the solution; the AGV probability model is related to the performance of the algorithm, and the AGV probability model is established by adopting a population increment learning algorithm, so that a good initial solution can be obtained, and the performance of the algorithm is improved; and a fitness function is introduced to ensure that a high-quality solution has better fitness and avoid searching in an infeasible space by an algorithm. For example, the fitness function f (X) of solution X consists of two parts: the objective function J (X) is J2And a penalty function p (x). The penalty function p (x) avoids the algorithm to search in the infeasible solution space, which is as follows:
p(X)＝p1(X)+p2(X)
Figure BDA0003292590820000111
Figure BDA0003292590820000112
wherein the content of the first and second substances,
Figure BDA0003292590820000113
is the shortest completion time, R, solved by the traditional spatio-temporal network model1And R2Are relatively large constants. p is a radical of1Ensuring lower fitness value of solutions with shorter completion time, p2And ensuring that the adaptability of the infeasible solution is low.
Following the above embodiments of the present application, the hybrid heuristic algorithm in the method is two-dimensionally compiledThe first dimension in the code frame corresponds to a path planning result, and the second dimension corresponds to driving time. Here, the path planning result of the first-dimension correspondence solution is solved by GA, and the travel time of the second-dimension correspondence solution is solved by EDA. In the framework designed, each individual contains two pieces of information: path vector
Figure BDA0003292590820000121
And time vector
Figure BDA0003292590820000122
Wherein the content of the first and second substances,
Figure BDA0003292590820000123
is a variable representing the station selected at the ith position of the path vector for vehicle k,
Figure BDA0003292590820000124
is a variable representing the selected time slot for the ith position of the time vector for vehicle k. And is
Figure BDA0003292590820000125
Variables corresponding to spatio-temporal network models
Figure BDA0003292590820000126
Thus can be obtained if
Figure BDA0003292590820000127
For l e { N1，...,N}
Figure BDA0003292590820000128
According to the embodiment of the application, the AGV probability model is constructed by adopting the population increment learning algorithm based on the time dimension in the method, the AGV probability model comprises a time population, an energy consumption population and a learning population, and the time population, the energy consumption population and the learning population are arranged according to a preset proportion.
For example, for the time dimension, there are N-1 variables to determine for each AGV, whereA probabilistic model is constructed using a group incremental learning algorithm. Description solution
Figure BDA0003292590820000129
The probability distribution model of (a) can be described as:
Figure BDA00032925908200001210
time probability matrix C of vehicle k at iter's iterationk(iter) the design is as follows:
Figure BDA00032925908200001211
wherein the content of the first and second substances,
Figure BDA0003292590820000131
representing probabilities
Figure BDA0003292590820000132
For the research problem P2The double-target function of (2) divides the population of the algorithm into three parts: time group PtEnergy consumption group PeAnd learning population PlThe proportion of the three types of populations is set as 1: 1: 2. the probability matrix is initialized as follows:
for time population PtThe AGV travels at maximum speed to minimize total completion time, a time probability matrix
Figure BDA0003292590820000133
The initial settings are as shown in the above equation:
Figure BDA0003292590820000134
to the consumption population PeAGV traveling at relatively high speeds to reduce energy consumption, an energy consumption probability matrix
Figure BDA0003292590820000135
The initial settings are shown in the following formula:
Figure BDA0003292590820000136
study population PlThe probability matrix of (1) is initialized as shown in the following formula, and the probability that each time slot is selected is consistent and is 1/h.
Figure BDA0003292590820000137
In another embodiment of the present application, the method for inputting the information of each automated guided vehicle and the corresponding sub-target tasks into an AGV spatio-temporal network model to obtain a path planning result and a travel time of each automated guided vehicle includes:
inputting the information of each automatic guided vehicle and the corresponding sub-target tasks into an AGV space-time network model;
based on the information of each automatic guided vehicle and the corresponding sub-target tasks thereof, obtaining an initial population through the AGV probability model;
calculating by using the fitness function on the basis of the initial population to obtain an elite population, performing mutation operation to generate a path vector of a new generation population, obtaining a time vector of the new generation population through the AGV probability model according to the elite population, repeating the steps until fitness evaluation calculation for preset specified times is completed, obtaining the optimal path vector and time vector of the new generation population, and outputting the optimal path vector and time vector as a path planning result and driving time of each automatic guided vehicle.
For example, in step one, an initial population is first determined according to a generation method of an initial solution, that is, the initial population is obtained through the AGV probability model, and the initial population is divided into three classes.
And secondly, calculating population fitness by using the fitness function on the basis of the initial group, and selecting an elite population according to the fitness.
And thirdly, performing mutation operation by adopting a genetic algorithm to generate a path vector of the new generation of population, wherein the selection probabilities of the three mutation operations of turning, exchanging and sliding are consistent. Wherein Dijkstra's algorithm is used to guarantee the continuity of the path.
Step four, updating the probability matrix C according to the elite populationk(iter), a probability updating method using a group-Based Incremental Learning (PBIL) algorithm.
And step five, acquiring the time vector of the new generation of population according to the probability matrix sampling.
And step six, judging whether fitness evaluation calculation is carried out for preset specified times. If yes, outputting the result, otherwise, returning to the step two.
The specific mixed heuristic algorithm pseudo-code is as follows:
inputting: task allocation for AGV
And (3) outputting: optimal individuals Xbest
Figure BDA0003292590820000141
Figure BDA0003292590820000151
In summary, the sub-target tasks of each automatic guided vehicle are determined based on the target tasks by acquiring the target tasks; determining a decision variable and space-time constraint thereof based on the target task to obtain a time target function and an energy consumption target function for model construction, and introducing a mixed element heuristic algorithm to obtain an AGV space-time network model; inputting the information of each automatic guided vehicle and the corresponding sub-target tasks thereof into an AGV space-time network model to obtain a path planning result and running time of each automatic guided vehicle, namely constructing a mixed heuristic algorithm based on a distribution estimation algorithm and a genetic algorithm to carry out model solution, obtaining the path planning result of the known target task vehicle through decoding of an approximate optimal solution, and obtaining the running time of the vehicle on each track to ensure that no collision exists among the AGVs, and reducing the total energy consumption of the vehicle under the condition of ensuring that the target task completion time is short as much as possible.
It should be noted that the present application may be implemented in software and/or a combination of software and hardware, for example, implemented using Application Specific Integrated Circuits (ASICs), general purpose computers or any other similar hardware devices. In one embodiment, the software programs of the present application may be executed by a processor to implement the steps or functions described above. Likewise, the software programs (including associated data structures) of the present application may be stored in a computer readable recording medium, such as RAM memory, magnetic or optical drive or diskette and the like. Additionally, some of the steps or functions of the present application may be implemented in hardware, for example, as circuitry that cooperates with the processor to perform various steps or functions.
In addition, some of the present application may be implemented as a computer program product, such as computer program instructions, which when executed by a computer, may invoke or provide methods and/or techniques in accordance with the present application through the operation of the computer. Program instructions which invoke the methods of the present application may be stored on a fixed or removable recording medium and/or transmitted via a data stream on a broadcast or other signal-bearing medium and/or stored within a working memory of a computer device operating in accordance with the program instructions. An embodiment according to the present application comprises an apparatus comprising a memory for storing computer program instructions and a processor for executing the program instructions, wherein the computer program instructions, when executed by the processor, trigger the apparatus to perform a method and/or a solution according to the aforementioned embodiments of the present application.
It will be evident to those skilled in the art that the present application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned. Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the apparatus claims may also be implemented by one unit or means in software or hardware. The terms first, second, etc. are used to denote names, but not any particular order.

Claims (7)

1. An AGV path planning method based on a flexible space-time network model is characterized by comprising the following steps:
acquiring a target task, and determining sub-target tasks of each automatic guided vehicle based on the target task;
determining a decision variable and space-time constraint thereof based on the target task to obtain a time target function and an energy consumption target function for model construction, and introducing a mixed element heuristic algorithm to obtain an AGV space-time network model;
and inputting the information of each automatic guided vehicle and the corresponding sub-target tasks thereof into the AGV space-time network model to obtain a path planning result and driving time of each automatic guided vehicle.
2. The method of claim 1, wherein the determining the decision variables and the space-time constraints thereof based on the objective task to obtain a time objective function and an energy consumption objective function for model construction comprises:
the target task comprises a task starting point, a task end point, cargo information, starting time and planning time, the planning time is discretized into a plurality of time gaps, mathematical description is carried out based on the target task, and relevant decision variables are obtained;
respectively carrying out time constraint, space constraint and space-time constraint on the decision variables;
determining the time objective function and the energy consumption objective function based on the decision variables and the time constraints, the spatial constraints, and the spatiotemporal constraints corresponding thereto.
3. The method of claim 2, wherein determining the temporal objective function and the energy consumption objective function based on the decision variables and their corresponding temporal, spatial, and spatiotemporal constraints comprises:
obtaining a time objective function by utilizing the decision variables and the time constraint, the space constraint and the total travel time problem determined by the space-time constraint corresponding to the decision variables and based on mixed integer optimization;
determining a total energy consumption problem on the basis of the total travel time problem, wherein the total energy consumption problem comprises vehicle acceleration energy consumption and rolling friction energy consumption;
and adding an acceleration variable and a constraint thereof and a time gap number variable in the total energy consumption problem, and obtaining an energy consumption objective function based on mixed integer nonlinear programming.
4. The method of claim 3, wherein the introducing of the hybrid heuristic algorithm to obtain the AGV spatiotemporal network model comprises:
and designing a two-dimensional coding frame of a mixed heuristic algorithm of the running time and the path planning result, establishing an AGV probability model by adopting a population increment learning algorithm, and forming a fitness function by utilizing a penalty function and the energy consumption target function to obtain an AGV space-time network model.
5. The method of claim 4, wherein a first dimension of the hybrid heuristic two-dimensional coding framework corresponds to the path planning result and a second dimension corresponds to the travel time.
6. The method of claim 4, wherein the AGV probability model is constructed based on a time dimension using the population increment learning algorithm, the AGV probability model comprises a time population, an energy consumption population and a learning population, and the time population, the energy consumption population and the learning population are arranged according to a preset ratio.
7. The method according to any one of claims 1-6, wherein said inputting the information of each automated guided vehicle and the corresponding sub-target tasks into an AGV space-time network model to obtain a path planning result and a travel time of each automated guided vehicle comprises:
inputting the information of each automatic guided vehicle and the corresponding sub-target tasks thereof into the AGV space-time network model;
based on the information of each automatic guided vehicle and the corresponding sub-target tasks thereof, obtaining an initial population through the AGV probability model;
calculating by using the fitness function on the basis of the initial population to obtain an elite population, performing mutation operation to generate a path vector of a new generation population, obtaining a time vector of the new generation population through the AGV probability model according to the elite population, repeating the steps until fitness evaluation calculation for preset specified times is completed, obtaining the optimal path vector and time vector of the new generation population, and outputting the optimal path vector and time vector as a path planning result and driving time of each automatic guided vehicle.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115081674A (en) * 2022-05-09 2022-09-20 香港理工大学深圳研究院 Local container transportation typesetting optimization method under novel truck queuing driving mode
CN115456489A (en) * 2022-11-11 2022-12-09 北京大学 Inventory path planning method and device for hybrid energy storage system and electronic equipment

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115081674A (en) * 2022-05-09 2022-09-20 香港理工大学深圳研究院 Local container transportation typesetting optimization method under novel truck queuing driving mode
CN115456489A (en) * 2022-11-11 2022-12-09 北京大学 Inventory path planning method and device for hybrid energy storage system and electronic equipment

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