CN116720642A - Method and system for optimizing path of cooperative distribution of vehicle and unmanned aerial vehicle - Google Patents
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Abstract
The application provides a path optimization method and a system for collaborative distribution of vehicles and unmanned aerial vehicles, and relates to the technical field of path planning. According to the method, vehicle information, unmanned aerial vehicle information, demand node information and vehicle restricted area information are acquired; based on vehicle information, unmanned plane information, demand node information and vehicle restricted area information, constructing a vehicle-machine cooperative distribution model by taking the shortest running time of all vehicles returned to a warehouse as a target; and solving the vehicle-machine cooperative distribution model to obtain an optimal vehicle-unmanned aerial vehicle cooperative distribution path. The application provides a path optimization method for collaborative distribution of vehicles and unmanned aerial vehicles in consideration of a vehicle limiting area, which solves the technical problem that the optimized path obtained by the prior art cannot meet actual requirements, realizes specific vehicle-machine collaborative distribution path design aiming at different area divisions, meets different distribution requirements, and improves distribution accuracy and feasibility.
Description
Technical Field
The application relates to the technical field of path planning, in particular to a path optimization method and a system for collaborative distribution of vehicles and unmanned aerial vehicles.
Background
In recent years, unmanned aerial vehicle equipment has obvious application in the aspect of epidemic prevention material distribution at home and abroad, and Beijing dong conveys materials for sealing and controlling villages and towns through unmanned aerial vehicles, so that unmanned aerial vehicles developed in sequence also successfully convey epidemic prevention materials to hospitals, and the effective application of unmanned aerial vehicles in hierarchical distribution is embodied. The use of vehicle-mounted robots to effectively reduce delivery costs and reduce delivery time through path planning has attracted increasing attention. Considering the problem of classified distribution of epidemic prevention materials, according to epidemic prevention strategies divided by different areas implemented in an epidemic area, the cooperative transportation mode of the vehicle and the unmanned aerial vehicle can effectively reduce distribution cost, and can provide material distribution plans in areas with different control levels for related departments, so that the method has very strong practical significance.
However, in the prior art, a specific vehicle-machine cooperative distribution path design is not made aiming at implementing epidemic situation prevention and control strategies such as different region division, so that the optimized path cannot adapt to actual requirements.
Disclosure of Invention
(one) solving the technical problems
Aiming at the defects of the prior art, the application provides a method and a system for optimizing a path of collaborative distribution of a vehicle and an unmanned aerial vehicle, which solve the technical problem that an optimized path obtained by the prior art cannot meet actual demands.
(II) technical scheme
In order to achieve the above purpose, the application is realized by the following technical scheme:
in a first aspect, the present application provides a method for optimizing a path for collaborative delivery of a vehicle and an unmanned aerial vehicle, including:
s1, acquiring vehicle information, unmanned aerial vehicle information, demand node information and vehicle restriction area information;
s2, based on vehicle information, unmanned plane information, demand node information and vehicle limiting area information, constructing a vehicle-machine cooperative distribution model by taking the shortest running time of all vehicles returning to a warehouse as a target;
and S3, solving the vehicle-machine cooperative distribution model to obtain an optimal vehicle-unmanned aerial vehicle cooperative distribution path.
Preferably, the vehicle-machine collaborative distribution model comprises an objective function and constraint conditions; wherein, the liquid crystal display device comprises a liquid crystal display device,
the objective function is the shortest running time of all vehicles returned to the warehouse, and the expression is as follows:
the constraint conditions include:
constraint (1) ensures that each demand point must be accessed by a vehicle or unmanned aerial vehicle;
the constraint (2) guarantees the flow constraint of the vehicle at the warehouse node;
constraint (3) ensures flow balance of the vehicle at a demand point;
the constraint (4) and the constraint (5) respectively ensure the flow balance of the unmanned aerial vehicle at the take-off node and the landing node;
the constraint (6) and the constraint (7) ensure that the flow of the unmanned aerial vehicle is balanced at a demand point for providing service;
the constraint (8) is a vehicle capacity constraint, wherein the unmanned aerial vehicle has a one-to-one correspondence with the vehicles, and k is the same as the vehicle and the unmanned aerial vehicle with the same serial number k;
the constraint (9) is the secondary capacity constraint of the unmanned rack;
the constraint (10) is the secondary endurance constraint of the unmanned rack;
the constraint (11) and the constraint (12) adjust the time of the vehicle and the unmanned aerial vehicle at the take-off node and the landing node to be consistent;
constraints (13) and (14) represent run-time inequalities of the vehicle and the drone;
constraint (15) represents a point within the area where the vehicle cannot restrict;
constraints (16) and (17) indicate that the vehicle, unmanned path, at the customer node, is not allowed to appear as a sub-loop;
constraint (18) and constraint (19) represent the value constraints of all variables;
wherein V is 0 Representing a warehouse node collection, including a departure point 0(s) and a return point 0 (r); v (V) C Representing a client node set (namely, client nodes requiring epidemic prevention materials in the embodiment of the application); k (K) T Representing a collection of vehicles; k (K) D Representing a collection of unmanned aerial vehicles; v (V) T Representing a set of vehicle service nodes; v (V) D Representing a set of unmanned aerial vehicle service nodesCombining; v (V) L Representing a take-off node set of the unmanned aerial vehicle; v (V) R Representing a landing node set of the unmanned aerial vehicle; r is R k' Representing a k' rack set of the unmanned aerial vehicle; v (V) NT Representing a set of nodes within a restricted area; d (D) i Representing the demand of a service node i; q represents the maximum load of the vehicle; m represents the maximum number of vehicles that can be invoked; q represents the maximum load of the unmanned aerial vehicle; b represents the maximum endurance of the unmanned aerial vehicle;representing the run time of vehicle k through arc (i, j); />Representing the run time of the unmanned aerial vehicle k' through the arc (i, j); t (T) i k Representing the accumulated running time of the vehicle k reaching the node i; />Representing the accumulated running time of the unmanned aerial vehicle k' reaching the node i; />Indicating that if the vehicle k service point i is 1, otherwise, the vehicle k service point i is 0; />Indicating that if the service point i of the unmanned plane k 'is 1, otherwise, the service point i of the unmanned plane k' is 0; />Indicating that if vehicle k is traveling from point i to point j, it is 1, otherwise it is 0; />Indicating that 1 is present if the p-th leg of drone k 'is traveling from point i to point j, otherwise 0, where p e Rk'.
Preferably, the solving the vehicle-machine cooperative distribution model to obtain an optimal vehicle-unmanned aerial vehicle cooperative distribution path includes:
and solving the vehicle-machine collaborative distribution model through a two-stage heuristic algorithm, wherein the first stage generates an initial solution through a greedy algorithm, and the second stage acquires an optimal vehicle-unmanned aerial vehicle collaborative distribution path through a tabu search algorithm idea improved genetic algorithm.
Preferably, the first stage generates the initial solution by a greedy algorithm, including:
s301, preprocessing related parameters in a vehicle-machine collaborative distribution model;
s302, acquiring execution parameters of a greedy algorithm, wherein the execution parameters comprise a greedy maximum vehicle number M and a current vehicle number M;
s303, adding all client nodes to the non-access node set;
s304, finding an unaccessed node closest to the origin as a first departure point of the unmanned plane;
s305, judging whether the client point is traversed, if yes, executing S315, otherwise, executing the next step;
s306, searching a node closest to the unmanned aerial vehicle from a customer to be served, and adding the node to the unmanned aerial vehicle path;
s307, judging that the capacity of the unmanned aerial vehicle exceeds the constraint or the flight time of the unmanned aerial vehicle exceeds the constraint, if yes, executing the next step; otherwise, the client is added into the service path of the unmanned aerial vehicle, and marked as accessed, and the step S305 is returned;
s308, moving the last node of the unmanned aerial vehicle path back to the customer set to be serviced to obtain an initial unmanned aerial vehicle path;
s309, removing nodes except for the head and tail nodes in the initial unmanned aerial vehicle path from a to-be-accessed list of the vehicle;
s310, judging whether the client point is traversed, if yes, executing S315, otherwise, executing the next step;
s311, searching a node closest to the vehicle from the customer to be served, and adding the node to the vehicle path;
s312, judging whether the capacity of the vehicle exceeds the constraint, if so, executing the next step, otherwise, returning to S310;
s313, moving the final node of the vehicle path back to the customer set to be serviced to obtain an initial vehicle path;
s314, judging whether the number m=m+1 of vehicles is equal to or greater than M, if yes, executing S315, otherwise, returning to S304, and carrying out path planning again;
s315, ending the greedy algorithm, and outputting a set of the initial vehicle path and the initial unmanned plane path as an initial solution.
Preferably, the second stage obtains an optimal cooperative distribution path of the vehicle and the unmanned aerial vehicle through a tabu search algorithm idea improvement genetic algorithm, and the method comprises the following steps:
setting execution parameters of a genetic algorithm before executing the second stage, wherein the execution parameters comprise a maximum iteration number N and a current iteration number N;
s316, using the initial solution generated in the first stage as an initial population of a genetic algorithm, and initializing a tabu table according to the initial population, wherein the tabu table is used for recording accessed solutions;
s317, n=n+1, judging whether N is equal to or greater than N, if yes, executing S326, otherwise, executing the next step;
s318, selecting a parent population from the current solution according to the fitness value of the solution in a random mode through roulette selection operation; the fitness value is the reciprocal of an objective function in the vehicle-machine cooperative distribution model;
s319, performing cross operation on the parent population, and generating a new offspring population by exchanging partial genes of the parent;
s320, carrying out mutation operation on the offspring population, and increasing the diversity of the population by changing part of genes to obtain a mutated offspring population;
s321, carrying out fitness value comparison on the variant offspring population, and selecting an optimal individual as a candidate solution;
s322, judging whether the candidate solution meets the scofflaw, if yes, executing S323, otherwise, executing S324;
s323, replacing the candidate solution with the current optimal solution, updating the tabu table, returning to S317,
s324, checking whether the candidate solution exists in the tabu list, if so, returning to S317, otherwise, executing the next step;
s325, taking the candidate solution as a new current solution, updating a tabu table, and returning to S317;
s326, outputting the current solution as an optimal solution, and outputting the distribution of the optimal solution to consume total time.
In a second aspect, the present application provides a path optimization system for collaborative delivery of a vehicle and a drone, comprising:
the information acquisition module is used for acquiring vehicle information, unmanned aerial vehicle information, demand node information and vehicle restriction area information;
the model building module is used for building a vehicle-machine cooperative distribution model by taking the shortest running time of all vehicles returning to a warehouse as a target based on vehicle information, unmanned aerial vehicle information, demand node information and vehicle limiting area information;
and the solving module is used for solving the vehicle-machine collaborative distribution model to obtain an optimal vehicle-unmanned aerial vehicle collaborative distribution path.
Preferably, the vehicle-machine collaborative distribution model comprises an objective function and constraint conditions; wherein, the liquid crystal display device comprises a liquid crystal display device,
the objective function is the shortest running time of all vehicles returned to the warehouse, and the expression is as follows:
the constraint conditions include:
constraint (1) ensures that each demand point must be accessed by a vehicle or unmanned aerial vehicle;
the constraint (2) guarantees the flow constraint of the vehicle at the warehouse node;
constraint (3) ensures flow balance of the vehicle at a demand point;
the constraint (4) and the constraint (5) respectively ensure the flow balance of the unmanned aerial vehicle at the take-off node and the landing node;
the constraint (6) and the constraint (7) ensure that the flow of the unmanned aerial vehicle is balanced at a demand point for providing service;
the constraint (8) is a vehicle capacity constraint, wherein the unmanned aerial vehicle has a one-to-one correspondence with the vehicles, and k is the same as the vehicle and the unmanned aerial vehicle with the same serial number k;
the constraint (9) is the secondary capacity constraint of the unmanned rack;
the constraint (10) is the secondary endurance constraint of the unmanned rack;
the constraint (11) and the constraint (12) adjust the time of the vehicle and the unmanned aerial vehicle at the take-off node and the landing node to be consistent;
constraints (13) and (14) represent run-time inequalities of the vehicle and the drone;
constraint (15) represents a point within the area where the vehicle cannot restrict;
constraints (16) and (17) indicate that the vehicle, unmanned path, at the customer node, is not allowed to appear as a sub-loop;
constraint (18) and constraint (19) represent the value constraints of all variables;
wherein V is 0 Representing a warehouse node collection, including a departure point 0(s) and a return point 0 (r); v (V) C Representing a set of client nodes (implementation of the applicationIn the example, a client node requiring epidemic prevention materials); k (K) T Representing a collection of vehicles; k (K) D Representing a collection of unmanned aerial vehicles; v (V) T Representing a set of vehicle service nodes; v (V) D Representing a service node set of the unmanned aerial vehicle; v (V) L Representing a take-off node set of the unmanned aerial vehicle; v (V) R Representing a landing node set of the unmanned aerial vehicle; r is R k 'represents the unmanned aerial vehicle k' set of frames; v (V) NT Representing a set of nodes within a restricted area; d (D) i Representing the demand of a service node i; q represents the maximum load of the vehicle; m represents the maximum number of vehicles that can be invoked; q represents the maximum load of the unmanned aerial vehicle; b represents the maximum endurance of the unmanned aerial vehicle;representing the run time of vehicle k through arc (i, j); />Representing the run time of the unmanned aerial vehicle k' through the arc (i, j); t (T) i k Representing the accumulated running time of the vehicle k reaching the node i; />Representing the accumulated running time of the unmanned aerial vehicle k' reaching the node i; />Indicating that if the vehicle k service point i is 1, otherwise, the vehicle k service point i is 0; />Indicating that if the service point i of the unmanned plane k 'is 1, otherwise, the service point i of the unmanned plane k' is 0; />Indicating that if vehicle k is traveling from point i to point j, it is 1, otherwise it is 0; />Indicating that 1 is present if the p-th leg of drone k 'is traveling from point i to point j, otherwise 0, where p e Rk'.
Preferably, the solving the vehicle-machine cooperative distribution model to obtain an optimal vehicle-unmanned aerial vehicle cooperative distribution path includes:
and solving the vehicle-machine collaborative distribution model through a two-stage heuristic algorithm, wherein the first stage generates an initial solution through a greedy algorithm, and the second stage acquires an optimal vehicle-unmanned aerial vehicle collaborative distribution path through a tabu search algorithm idea improved genetic algorithm.
In a third aspect, the present application provides a computer-readable storage medium storing a computer program for path optimization of cooperative delivery of a vehicle and a drone, wherein the computer program causes a computer to execute the path optimization method of cooperative delivery of a vehicle and a drone as described above.
In a fourth aspect, the present application provides an electronic device comprising:
one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs comprising a path optimization method for performing the co-delivery of vehicles and drones as described above.
(III) beneficial effects
The application provides a path optimization method and device for collaborative distribution of vehicles and unmanned aerial vehicles. Compared with the prior art, the method has the following beneficial effects:
according to the method, vehicle information, unmanned aerial vehicle information, demand node information and vehicle restricted area information are acquired; based on vehicle information, unmanned plane information, demand node information and vehicle restricted area information, constructing a vehicle-machine cooperative distribution model by taking the shortest running time of all vehicles returned to a warehouse as a target; and solving the vehicle-machine cooperative distribution model to obtain an optimal vehicle-unmanned aerial vehicle cooperative distribution path. The application provides a path optimization method for collaborative distribution of vehicles and unmanned aerial vehicles in consideration of a vehicle limiting area, which solves the technical problem that the optimized path obtained by the prior art cannot meet actual requirements, realizes specific vehicle-machine collaborative distribution path design aiming at different area divisions, meets different distribution requirements, and improves distribution accuracy and feasibility.
Drawings
In order to more clearly illustrate the embodiments of the application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a block diagram of a method for optimizing a path for collaborative delivery of a vehicle and a drone in an embodiment of the present application;
FIG. 2 is a flow chart of a two-stage heuristic;
fig. 3 is an exemplary diagram of a vehicle and drone cooperative distribution path.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application are clearly and completely described, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
According to the method and the system for optimizing the vehicle and unmanned aerial vehicle collaborative distribution path, the technical problem that the optimized path obtained in the prior art cannot meet actual requirements is solved, specific vehicle-machine collaborative distribution path designs are made according to different area divisions, different distribution requirements are met, and distribution accuracy and feasibility are improved.
The technical scheme in the embodiment of the application aims to solve the technical problems, and the overall thought is as follows:
the embodiment of the application provides vehicle distribution limits in different areas, is in accordance with the process of cooperative distribution of epidemic prevention material vehicles, can meet different distribution requirements, and improves the accuracy and feasibility of distribution.
In order to better understand the above technical solutions, the following detailed description will refer to the accompanying drawings and specific embodiments.
The embodiment of the application provides a path optimization method for collaborative distribution of a vehicle and an unmanned aerial vehicle, which is shown in fig. 1 and comprises the following steps:
s1, acquiring vehicle information, unmanned aerial vehicle information, demand node information and vehicle restriction area information;
s2, based on vehicle information, unmanned plane information, demand node information and vehicle limiting area information, constructing a vehicle-machine cooperative distribution model by taking the shortest running time of all vehicles returning to a warehouse as a target;
and S3, solving the vehicle-machine cooperative distribution model to obtain an optimal vehicle-unmanned aerial vehicle cooperative distribution path.
The embodiment of the application provides a path optimization method for collaborative distribution of vehicles and unmanned aerial vehicles in consideration of a vehicle limiting area, which solves the technical problem that the optimized path obtained by the prior art cannot meet actual demands, realizes specific vehicle-machine collaborative distribution path design aiming at different area divisions, meets different distribution demands, and improves distribution accuracy and feasibility.
It should be noted that, the path optimization method for collaborative distribution of vehicles and unmanned aerial vehicles in the embodiment of the application is not only suitable for epidemic prevention materials, but also can be applied to other application scenarios, such as material distribution in areas where vehicles cannot pass conveniently (the areas correspond to the vehicle limiting areas in the embodiment of the application).
The following describes the steps in detail:
in step S1, vehicle information, unmanned plane information, demand node information, and vehicle restriction area information are acquired. The specific implementation process is as follows:
the vehicle information includes information such as the number of vehicles, the maximum load, and the like.
The unmanned aerial vehicle information comprises the number of unmanned aerial vehicles, unmanned aerial vehicle take-off nodes, unmanned aerial vehicle landing nodes, unmanned aerial vehicle maximum load, unmanned aerial vehicle maximum endurance and the like.
The demand node information includes the number of demand nodes, location information, and demand.
The vehicle restriction area information includes a set of nodes in the restriction area, and the nodes in the restriction area refer to nodes that can only be distributed by the unmanned aerial vehicle.
In step S2, a vehicle-machine cooperative distribution model is constructed based on the vehicle information, the unmanned aerial vehicle information, the demand node information, and the vehicle limit area information, with the minimum running time of all vehicles returning to the warehouse as a target. The specific implementation process is as follows:
the vehicle-machine collaborative distribution model comprises an objective function and constraint conditions. The method comprises the following steps:
the objective function is the shortest running time of all vehicles returned to the warehouse, and the expression is as follows:
the constraint conditions include:
constraint (1) ensures that each demand point must be accessed by a vehicle or unmanned aerial vehicle;
the constraint (2) guarantees the flow constraint of the vehicle at the warehouse node;
constraint (3) ensures flow balance of the vehicle at a demand point;
the constraint (4) and the constraint (5) respectively ensure the flow balance of the unmanned aerial vehicle at the take-off node and the landing node;
the constraint (6) and the constraint (7) ensure that the flow of the unmanned aerial vehicle is balanced at a demand point for providing service;
the constraint (8) is a vehicle capacity constraint, wherein the unmanned aerial vehicle has a one-to-one correspondence with the vehicles, and k is the same as the vehicle and the unmanned aerial vehicle with the same serial number k;
the constraint (9) is the secondary capacity constraint of the unmanned rack;
the constraint (10) is the secondary endurance constraint of the unmanned rack;
the constraint (11) and the constraint (12) adjust the time of the vehicle and the unmanned aerial vehicle at the take-off node and the landing node to be consistent;
constraints (13) and (14) represent run-time inequalities of the vehicle and the drone;
constraint (15) is an original constraint, representing a point in the area where the vehicle cannot be restricted;
constraints (16) and (17) indicate that the vehicle, unmanned path, at the customer node, is not allowed to appear as a sub-loop;
constraint (18) and constraint (19) represent the value constraints of all variables;
wherein V is 0 Representing a warehouse node collection, including a departure point 0(s) and a return point 0 (r); v (V) C Representing a client node set (namely, client nodes requiring epidemic prevention materials in the embodiment of the application); k (K) T Representing a collection of vehicles; k (K) D Representing a collection of unmanned aerial vehicles; v (V) T Representing a set of vehicle service nodes; v (V) D Representing a service node set of the unmanned aerial vehicle; v (V) L Representing a take-off node set of the unmanned aerial vehicle; v (V) R Representing a landing node set of the unmanned aerial vehicle; r is R k 'represents the unmanned aerial vehicle k' set of frames; v (V) NT Representation ofLimiting the node set in the region; d (D) i Representing the demand of a service node i; q represents the maximum load of the vehicle; m represents the maximum number of vehicles that can be invoked; q represents the maximum load of the unmanned aerial vehicle; b represents the maximum endurance of the unmanned aerial vehicle;representing the run time of vehicle k through arc (i, j); />Representing the run time of the unmanned aerial vehicle k' through the arc (i, j); t (T) i k Representing the accumulated running time of the vehicle k reaching the node i; />Representing the accumulated running time of the unmanned aerial vehicle k' reaching the node i; />Indicating that if the vehicle k service point i is 1, otherwise, the vehicle k service point i is 0; />Indicating that if the service point i of the unmanned plane k 'is 1, otherwise, the service point i of the unmanned plane k' is 0; />Indicating that if vehicle k is traveling from point i to point j, it is 1, otherwise it is 0; />Indicating that 1 is present if the p-th leg of drone k 'is traveling from point i to point j, otherwise 0, where p e Rk'.
In step S3, the cooperative vehicle-unmanned aerial vehicle distribution model is solved, and an optimal cooperative vehicle-unmanned aerial vehicle distribution path is obtained. The specific implementation process is as follows:
in the specific implementation process, the vehicle-machine cooperative distribution model can be solved by a plurality of methods, and the embodiment of the application solves the vehicle-machine cooperative distribution model by a two-stage heuristic algorithm, wherein the first stage generates an initial solution by a greedy algorithm, and the second stage generates a vehicle-unmanned plane cooperative distribution path by improving a genetic algorithm through a tabu search algorithm idea, so that an excellent vehicle-unmanned plane path scheme is finally obtained. As shown in fig. 2, specifically:
the specific process of generating the initial solution by the greedy algorithm in the first stage includes:
s301, preprocessing related parameters in a vehicle-machine collaborative distribution model, wherein the preprocessing comprises the steps of data conversion and the like;
s302, setting execution parameters of a genetic algorithm and acquiring execution parameters of a greedy algorithm. The method specifically comprises the following steps:
the execution parameters of the genetic algorithm include, for example, population size, maximum iteration number N, current iteration number N, tabu table length, etc.
The execution parameters of the greedy algorithm are maximum vehicle number M, current vehicle number M, various coordinate points and the like.
S303, adding all client nodes to the non-access node set. The method specifically comprises the following steps:
all clients are marked as not-accessed in order to track which clients have been serviced and which have not.
S304, finding an unaccessed node closest to the origin as a first departure point of the unmanned plane. The method comprises the following steps:
selecting the first customer to be serviced for the drone, selecting the nearest customer may reduce the time of flight of the drone.
S305, judging whether the client point is traversed, if yes, executing S315, otherwise, executing the next step;
s306, searching a node closest to the unmanned aerial vehicle from a customer to be served, and adding the node to the unmanned aerial vehicle path;
s307, judging that the capacity of the unmanned aerial vehicle exceeds the constraint or the flight time of the unmanned aerial vehicle exceeds the constraint, if yes, executing the next step; otherwise, the client is added into the service path of the unmanned aerial vehicle, and marked as accessed, and the step S305 is returned;
s308, moving the last node of the unmanned aerial vehicle path back to the customer set to be serviced to obtain an initial unmanned aerial vehicle path;
s309, removing nodes except for the head and tail nodes in the initial unmanned aerial vehicle path from a to-be-accessed list of the vehicle;
s310, judging whether the client point is traversed, if yes, executing S315, otherwise, executing the next step;
s311, searching a node closest to the vehicle from the customer to be served, and adding the node to the vehicle path;
s312, judging whether the capacity of the vehicle exceeds the constraint, if so, executing the next step, otherwise, returning to S310;
and S313, moving the final node of the vehicle path back to the customer set to be serviced to obtain an initial vehicle path.
S314, judging whether the number m=m+1 of vehicles is equal to or larger than M, if yes, executing S315, otherwise, returning to S304, and carrying out path planning again.
S315, ending the initial solution algorithm, and outputting a set of the initial vehicle path and the initial unmanned aerial vehicle path as an initial population.
And in the second stage, a genetic algorithm is improved through a tabu search algorithm idea to generate a cooperative distribution path of the vehicle and the unmanned aerial vehicle. It should be noted that, in the specific implementation process, the cooperative distribution path of the vehicle and the unmanned aerial vehicle may also be generated through other heuristic algorithms, and the embodiment of the present application is described in detail by taking a genetic algorithm as an example.
S316, initializing a tabu table according to the initial population, wherein the tabu table is used for recording the accessed solutions so as to prevent the algorithm from falling into local optimum or repeatedly accessing the same solution in the searching process, thereby improving the searching efficiency.
S317, n=n+1, judging whether N is equal to or greater than N, if yes, executing S326, otherwise, executing the next step;
s318, selecting a parent population from the current solution according to the fitness value of the solution in a random mode through roulette selection operation. It should be noted that, in the first iteration process, the current solution is the initial solution. In the embodiment of the application, the fitness value is the reciprocal of the objective function, and the larger the fitness value is, the larger the selection probability is. The roulette selection operation selects the father generation according to the fitness value of the solution in a random mode, so that the solution with high fitness value can be guaranteed to have higher selection probability, and meanwhile, the solution with low fitness value is guaranteed to have a certain survival chance, so that the diversity of the population is maintained.
S319, performing cross operation on the parent population, and generating a new offspring population by exchanging partial genes of the parent;
s320, carrying out mutation operation on the offspring population, and increasing the diversity of the population by changing part of genes to obtain the mutated offspring population. The mutation operation is used for introducing new genes, breaking the local optimization of the existing population, and thus increasing the diversity and searching range of the population.
S321, comparing fitness values of the variant offspring populations, and selecting the optimal individuals as candidate solutions. The candidate solutions are selected from all generated solutions based on some evaluation criteria (e.g., fitness value). This ensures that after each iteration it is possible to find a better solution.
S322, judging whether the candidate solution meets the scofflaw, if yes, executing S323, otherwise, executing S324;
s323, replacing the candidate solution with the current optimal solution, updating the tabu table, returning to S317,
s324, checking whether the candidate solution exists in the tabu list, if so, returning to S317, otherwise, executing the next step;
s325, taking the candidate solution as a new current solution, updating a tabu table, and returning to S317;
s326, outputting the current solution as an optimal solution, and outputting the distribution of the optimal solution to consume total time.
It should be noted that, in the embodiment of the present application, the optimal solution corresponds to a cooperative delivery path of the vehicle and the unmanned aerial vehicle, as shown in fig. 3.
The embodiment of the application also provides a path optimization system for the cooperative distribution of the vehicle and the unmanned aerial vehicle, which comprises the following steps:
the information acquisition module is used for acquiring vehicle information, unmanned aerial vehicle information, demand node information and vehicle restriction area information;
the model building module is used for building a vehicle-machine cooperative distribution model by taking the shortest running time of all vehicles returning to a warehouse as a target based on vehicle information, unmanned aerial vehicle information, demand node information and vehicle limiting area information;
and the solving module is used for solving the vehicle-machine collaborative distribution model to obtain an optimal vehicle-unmanned aerial vehicle collaborative distribution path.
It may be understood that the path optimization device for collaborative delivery of a vehicle and an unmanned aerial vehicle provided in the embodiment of the present application corresponds to the path optimization method for collaborative delivery of a vehicle and an unmanned aerial vehicle, and the explanation, the examples, the beneficial effects, and the like of the relevant contents may refer to the corresponding contents in the path optimization method for collaborative delivery of a vehicle and an unmanned aerial vehicle, which are not described herein.
The embodiment of the application also provides a computer readable storage medium storing a computer program for optimizing a path for the cooperative distribution of a vehicle and a unmanned aerial vehicle, wherein the computer program causes the computer to execute the path optimizing method for the cooperative distribution of the vehicle and the unmanned aerial vehicle.
The embodiment of the application also provides electronic equipment, which comprises:
one or more processors;
a memory; and
one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs comprising a path optimization method for performing the co-delivery of vehicles and drones as described above.
In summary, compared with the prior art, the method has the following beneficial effects:
1. the embodiment of the application provides a path optimization method for collaborative distribution of vehicles and unmanned aerial vehicles in consideration of a vehicle limiting area, which solves the technical problem that the optimized path obtained by the prior art cannot meet actual demands, realizes specific vehicle-machine collaborative distribution path design aiming at different area divisions, meets different distribution demands, and improves distribution accuracy and feasibility.
2. The heuristic path planning algorithm provided by the embodiment of the application can effectively plan the multiple take-off and landing paths of the unmanned aerial vehicle in the cooperative delivery process of the vehicle and the machine, and improves the instantaneity of conveying epidemic prevention materials.
3. According to the embodiment of the application, the genetic algorithm is improved through the tabu search algorithm idea to generate the collaborative distribution path of the vehicle and the unmanned aerial vehicle, so that the optimal solution can be efficiently searched under the condition of limited search space, and the efficiency and quality of path planning are improved.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above embodiments are only for illustrating the technical solution of the present application, and are not limiting; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application.
Claims (10)
1. The path optimization method for the cooperative distribution of the vehicle and the unmanned aerial vehicle is characterized by comprising the following steps of:
s1, acquiring vehicle information, unmanned aerial vehicle information, demand node information and vehicle restriction area information;
s2, based on vehicle information, unmanned plane information, demand node information and vehicle limiting area information, constructing a vehicle-machine cooperative distribution model by taking the shortest running time of all vehicles returning to a warehouse as a target;
and S3, solving the vehicle-machine cooperative distribution model to obtain an optimal vehicle-unmanned aerial vehicle cooperative distribution path.
2. The method for optimizing a path for collaborative delivery of a vehicle and an unmanned aerial vehicle according to claim 1, wherein the vehicle-to-machine collaborative delivery model includes an objective function and constraints; wherein, the liquid crystal display device comprises a liquid crystal display device,
the objective function is the shortest running time of all vehicles returned to the warehouse, and the expression is as follows:
the constraint conditions include:
constraint (1) ensures that each demand point must be accessed by a vehicle or unmanned aerial vehicle;
the constraint (2) guarantees the flow constraint of the vehicle at the warehouse node;
constraint (3) ensures flow balance of the vehicle at a demand point;
the constraint (4) and the constraint (5) respectively ensure the flow balance of the unmanned aerial vehicle at the take-off node and the landing node;
the constraint (6) and the constraint (7) ensure that the flow of the unmanned aerial vehicle is balanced at a demand point for providing service;
the constraint (8) is a vehicle capacity constraint, wherein the unmanned aerial vehicle has a one-to-one correspondence with the vehicles, and k is the same as the vehicle and the unmanned aerial vehicle with the same serial number k;
the constraint (9) is the secondary capacity constraint of the unmanned rack;
the constraint (10) is the secondary endurance constraint of the unmanned rack;
the constraint (11) and the constraint (12) adjust the time of the vehicle and the unmanned aerial vehicle at the take-off node and the landing node to be consistent;
constraints (13) and (14) represent run-time inequalities of the vehicle and the drone;
constraint (15) represents a point within the area where the vehicle cannot restrict;
constraints (16) and (17) indicate that the vehicle, unmanned path, at the customer node, is not allowed to appear as a sub-loop;
constraint (18) and constraint (19) represent the value constraints of all variables;
wherein V is 0 Representing a warehouse node collection, including a departure point 0(s) and a return point 0 (r); v (V) C Representing a client node set (namely, client nodes requiring epidemic prevention materials in the embodiment of the application); k (K) T Representing a collection of vehicles; k (K) D Representing a collection of unmanned aerial vehicles; v (V) T Representing a set of vehicle service nodes; v (V) D Representing a service node set of the unmanned aerial vehicle; v (V) L Representing a take-off node set of the unmanned aerial vehicle; v (V) R Representing a landing node set of the unmanned aerial vehicle; r is R k' Representing a k' rack set of the unmanned aerial vehicle; v (V) NT Representing a set of nodes within a restricted area; d (D) i Representing the demand of a service node i; q represents the maximum load of the vehicle; m represents the maximum number of vehicles that can be invoked; q represents the maximum load of the unmanned aerial vehicle; b represents the maximum endurance of the unmanned aerial vehicle;representing the run time of vehicle k through arc (i, j); />Representing the run time of the unmanned aerial vehicle k' through the arc (i, j); t (T) i k Representing the accumulated running time of the vehicle k reaching the node i; />Representing the accumulated running time of the unmanned aerial vehicle k' reaching the node i; />Indicating that if the vehicle k service point i is 1, otherwise, the vehicle k service point i is 0; />Indicating that if the service point i of the unmanned plane k 'is 1, otherwise, the service point i of the unmanned plane k' is 0; />Indicating if the vehicle k is fromThe point i runs to the point j, and is 1, otherwise, is 0; />Indicating that 1 is present if the p-th leg of drone k 'is traveling from point i to point j, otherwise 0, where p e Rk'.
3. The method for optimizing a path for collaborative delivery of a vehicle and an unmanned aerial vehicle according to claim 1, wherein solving the vehicle-to-machine collaborative delivery model to obtain an optimal path for collaborative delivery of a vehicle and an unmanned aerial vehicle comprises:
and solving the vehicle-machine collaborative distribution model through a two-stage heuristic algorithm, wherein the first stage generates an initial solution through a greedy algorithm, and the second stage acquires an optimal vehicle-unmanned aerial vehicle collaborative distribution path through a tabu search algorithm idea improved genetic algorithm.
4. The method for optimizing a path for collaborative delivery of a vehicle and a drone of claim 3, wherein the first stage generates an initial solution by a greedy algorithm comprising:
s301, preprocessing related parameters in a vehicle-machine collaborative distribution model;
s302, acquiring execution parameters of a greedy algorithm, wherein the execution parameters comprise a greedy maximum vehicle number M and a current vehicle number M;
s303, adding all client nodes to the non-access node set;
s304, finding an unaccessed node closest to the origin as a first departure point of the unmanned plane;
s305, judging whether the client point is traversed, if yes, executing S315, otherwise, executing the next step;
s306, searching a node closest to the unmanned aerial vehicle from a customer to be served, and adding the node to the unmanned aerial vehicle path;
s307, judging that the capacity of the unmanned aerial vehicle exceeds the constraint or the flight time of the unmanned aerial vehicle exceeds the constraint, if yes, executing the next step; otherwise, the client is added into the service path of the unmanned aerial vehicle, and marked as accessed, and the step S305 is returned;
s308, moving the last node of the unmanned aerial vehicle path back to the customer set to be serviced to obtain an initial unmanned aerial vehicle path;
s309, removing nodes except for the head and tail nodes in the initial unmanned aerial vehicle path from a to-be-accessed list of the vehicle;
s310, judging whether the client point is traversed, if yes, executing S315, otherwise, executing the next step;
s311, searching a node closest to the vehicle from the customer to be served, and adding the node to the vehicle path;
s312, judging whether the capacity of the vehicle exceeds the constraint, if so, executing the next step, otherwise, returning to S310;
s313, moving the final node of the vehicle path back to the customer set to be serviced to obtain an initial vehicle path;
s314, judging whether the number m=m+1 of vehicles is equal to or greater than M, if yes, executing S315, otherwise, returning to S304, and carrying out path planning again;
s315, ending the greedy algorithm, and outputting a set of the initial vehicle path and the initial unmanned plane path as an initial solution.
5. The path optimization method for cooperative delivery of a vehicle and a unmanned aerial vehicle according to claim 3 or 4, wherein the second stage obtains an optimal cooperative delivery path of a vehicle and a unmanned aerial vehicle by improving a genetic algorithm through a tabu search algorithm idea, and comprises:
setting execution parameters of a genetic algorithm before executing the second stage, wherein the execution parameters comprise a maximum iteration number N and a current iteration number N;
s316, using the initial solution generated in the first stage as an initial population of a genetic algorithm, and initializing a tabu table according to the initial population, wherein the tabu table is used for recording accessed solutions;
s317, n=n+1, judging whether N is equal to or greater than N, if yes, executing S326, otherwise, executing the next step;
s318, selecting a parent population from the current solution according to the fitness value of the solution in a random mode through roulette selection operation; the fitness value is the reciprocal of an objective function in the vehicle-machine cooperative distribution model;
s319, performing cross operation on the parent population, and generating a new offspring population by exchanging partial genes of the parent;
s320, carrying out mutation operation on the offspring population, and increasing the diversity of the population by changing part of genes to obtain a mutated offspring population;
s321, carrying out fitness value comparison on the variant offspring population, and selecting an optimal individual as a candidate solution;
s322, judging whether the candidate solution meets the scofflaw, if yes, executing S323, otherwise, executing S324;
s323, replacing the candidate solution with the current optimal solution, updating the tabu table, returning to S317,
s324, checking whether the candidate solution exists in the tabu list, if so, returning to S317, otherwise, executing the next step;
s325, taking the candidate solution as a new current solution, updating a tabu table, and returning to S317;
s326, outputting the current solution as an optimal solution, and outputting the distribution of the optimal solution to consume total time.
6. A path optimization system for collaborative delivery of a vehicle and a drone, comprising:
the information acquisition module is used for acquiring vehicle information, unmanned aerial vehicle information, demand node information and vehicle restriction area information;
the model building module is used for building a vehicle-machine cooperative distribution model by taking the shortest running time of all vehicles returning to a warehouse as a target based on vehicle information, unmanned aerial vehicle information, demand node information and vehicle limiting area information;
and the solving module is used for solving the vehicle-machine collaborative distribution model to obtain an optimal vehicle-unmanned aerial vehicle collaborative distribution path.
7. The path optimization system for collaborative delivery of a vehicle and a drone of claim 6, wherein the vehicle-to-machine collaborative delivery model includes an objective function and constraints; wherein, the liquid crystal display device comprises a liquid crystal display device,
the objective function is the shortest running time of all vehicles returned to the warehouse, and the expression is as follows:
the constraint conditions include:
constraint (1) ensures that each demand point must be accessed by a vehicle or unmanned aerial vehicle;
the constraint (2) guarantees the flow constraint of the vehicle at the warehouse node;
constraint (3) ensures flow balance of the vehicle at a demand point;
the constraint (4) and the constraint (5) respectively ensure the flow balance of the unmanned aerial vehicle at the take-off node and the landing node;
the constraint (6) and the constraint (7) ensure that the flow of the unmanned aerial vehicle is balanced at a demand point for providing service;
the constraint (8) is a vehicle capacity constraint, wherein the unmanned aerial vehicle has a one-to-one correspondence with the vehicles, and k is the same as the vehicle and the unmanned aerial vehicle with the same serial number k;
the constraint (9) is the secondary capacity constraint of the unmanned rack;
the constraint (10) is the secondary endurance constraint of the unmanned rack;
the constraint (11) and the constraint (12) adjust the time of the vehicle and the unmanned aerial vehicle at the take-off node and the landing node to be consistent;
constraints (13) and (14) represent run-time inequalities of the vehicle and the drone;
constraint (15) represents a point within the area where the vehicle cannot restrict;
constraints (16) and (17) indicate that the vehicle, unmanned path, at the customer node, is not allowed to appear as a sub-loop;
constraint (18) and constraint (19) represent the value constraints of all variables;
wherein V is 0 Representing a warehouse node collection, including a departure point 0(s) and a return point 0 (r); v (V) C Representing a client node set (namely, client nodes requiring epidemic prevention materials in the embodiment of the application); k (K) T Representing a collection of vehicles; k (K) D Representing a collection of unmanned aerial vehicles; v (V) T Representing a set of vehicle service nodes; v (V) D Representing a service node set of the unmanned aerial vehicle; v (V) L Representing unmanned aerial vehicle takeoff node collection；V R Representing a landing node set of the unmanned aerial vehicle; r is R k 'represents the unmanned aerial vehicle k' set of frames; v (V) NT Representing a set of nodes within a restricted area; d (D) i Representing the demand of a service node i; q represents the maximum load of the vehicle; m represents the maximum number of vehicles that can be invoked; q represents the maximum load of the unmanned aerial vehicle; b represents the maximum endurance of the unmanned aerial vehicle;representing the run time of vehicle k through arc (i, j); />Representing the run time of the unmanned aerial vehicle k' through the arc (i, j); t (T) i k Representing the accumulated running time of the vehicle k reaching the node i; />Representing the accumulated running time of the unmanned aerial vehicle k' reaching the node i; />Indicating that if the vehicle k service point i is 1, otherwise, the vehicle k service point i is 0; />Indicating that if the service point i of the unmanned plane k 'is 1, otherwise, the service point i of the unmanned plane k' is 0; />Indicating that if vehicle k is traveling from point i to point j, it is 1, otherwise it is 0; />Indicating that 1 is present if the p-th leg of drone k 'is traveling from point i to point j, otherwise 0, where p e Rk'.
8. The system for optimizing a path for collaborative delivery of a vehicle and a drone of claim 6, wherein solving the vehicle-to-drone collaborative delivery model to obtain an optimal vehicle-to-drone collaborative delivery path comprises:
and solving the vehicle-machine collaborative distribution model through a two-stage heuristic algorithm, wherein the first stage generates an initial solution through a greedy algorithm, and the second stage acquires an optimal vehicle-unmanned aerial vehicle collaborative distribution path through a tabu search algorithm idea improved genetic algorithm.
9. A computer-readable storage medium storing a computer program for path optimization of a vehicle co-delivery with a drone, wherein the computer program causes a computer to execute the path optimization method of a vehicle co-delivery with a drone according to any one of claims 1 to 5.
10. An electronic device, comprising:
one or more processors, memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs comprising a path optimization method for performing the co-delivery of a vehicle with a drone according to any one of claims 1-5.
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| CN117114375A (en) * | 2023-10-25 | 2023-11-24 | 深圳大学 | Multi-truck multi-unmanned aerial vehicle task allocation method based on mileage and marginal cost saving |
| CN117151422A (en) * | 2023-09-28 | 2023-12-01 | 汕头大学 | Truck-unmanned aerial vehicle multi-target collaborative distribution planning method and system |
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| CN117151422A (en) * | 2023-09-28 | 2023-12-01 | 汕头大学 | Truck-unmanned aerial vehicle multi-target collaborative distribution planning method and system |
| CN117151422B (en) * | 2023-09-28 | 2024-03-26 | 汕头大学 | Truck-unmanned aerial vehicle multi-target collaborative distribution planning method and system |
| CN117114375A (en) * | 2023-10-25 | 2023-11-24 | 深圳大学 | Multi-truck multi-unmanned aerial vehicle task allocation method based on mileage and marginal cost saving |
| CN117114375B (en) * | 2023-10-25 | 2024-03-15 | 深圳大学 | Multi-truck multi-unmanned aerial vehicle task allocation method based on mileage and marginal cost saving |
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