CN113110472A - Path planning method and device and terminal - Google Patents
Path planning method and device and terminal Download PDFInfo
- Publication number
- CN113110472A CN113110472A CN202110449364.8A CN202110449364A CN113110472A CN 113110472 A CN113110472 A CN 113110472A CN 202110449364 A CN202110449364 A CN 202110449364A CN 113110472 A CN113110472 A CN 113110472A
- Authority
- CN
- China
- Prior art keywords
- target
- path
- point
- iteration
- route
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 51
- 238000004364 calculation method Methods 0.000 claims abstract description 31
- 238000005457 optimization Methods 0.000 claims abstract description 10
- 239000003016 pheromone Substances 0.000 claims description 31
- 230000007704 transition Effects 0.000 claims description 28
- 241000257303 Hymenoptera Species 0.000 description 15
- 239000011159 matrix material Substances 0.000 description 9
- 230000008569 process Effects 0.000 description 8
- 230000006872 improvement Effects 0.000 description 7
- 238000010586 diagram Methods 0.000 description 6
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 238000012545 processing Methods 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 238000012804 iterative process Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 238000009825 accumulation Methods 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000002068 genetic effect Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 238000010845 search algorithm Methods 0.000 description 1
- 238000002922 simulated annealing Methods 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0223—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving speed control of the vehicle
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0221—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving a learning process
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0276—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle
- G05D1/0285—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle using signals transmitted via a public communication network, e.g. GSM network
Abstract
The invention discloses a path planning method, a device and a terminal, wherein the method comprises the following steps: s1, acquiring the positions of the point part and a plurality of target clients, defining the position of the point part as a starting point, and defining the positions of the plurality of target clients as target points; s2, based on the starting point and the target point, performing path optimization calculation through an ant colony algorithm; during calculation, the ant colony is respectively searched for a target point according to different preset search strategies in the early stage and the later stage of iterative calculation; s3, after each iteration is finished, updating the optimal path stored in the route tabu table; and S4, when iteration reaches a preset number, taking the optimal path in the route tabu table as a global optimal path. Through the method, the method can respectively adopt the appropriate search strategies in the early stage and the later stage of querying the optimal path through the ant colony algorithm so as to accelerate the efficiency of searching the optimal solution.
Description
Technical Field
The present application relates to the field of path planning technologies, and in particular, to a path planning method, an apparatus, and a terminal.
Background
The logistics business generally uses a logistics network of common services to pick and dispatch goods through logistics business branches in different areas. In fact, during the process of picking/dispatching goods, the logistics vehicles often need to be picked/dispatched by a plurality of customers, the planning of the routes generally depends on the experience of staff on the peripheral routes, scientific route selection basis is not provided, the selection of the picking/dispatching routes is relatively blind, even the most unreasonable picking/dispatching routes are sometimes selected, the logistics cost of the picking/dispatching links is greatly increased, and the satisfaction degree of the customers is also influenced. Therefore, whether the arrangement of the goods taking and dispatching path is reasonable or not greatly influences the speed, the cost and the benefit of the goods taking and dispatching, and the optimization of the goods taking and dispatching path by adopting a scientific and reasonable method is the key point for completing the goods taking and dispatching task with high efficiency and high quality in the logistics industry.
At present, the solution methods for the path planning problem can be divided into two categories, one is that the precise algorithm can be used for precisely solving, but the algorithm efficiency is low, and along with the problem scale enlargement, a large amount of time is consumed, so that the method is not suitable for solving the problem with a large scale, and is limited in practical application. Secondly, Heuristic algorithms are high in efficiency and can approach to optimal solutions, the prior Heuristic algorithms can be divided into a traditional Heuristic algorithm (Heuristic) and a megaset Heuristic algorithm (Meta-Heuristic), and the traditional Heuristic algorithm is proved to be trapped in local optimal solutions, such as a mileage-saving method, and the method is easy to realize, but the non-combination points are disordered, the edge points are difficult to combine, and the algorithm is trapped in the local optimal solutions; however, the macro heuristic algorithm tries to jump out of the local solution in different ways, and in an acceptable time, obtains an approximate optimal solution, or even obtains a global optimal solution. At present, the classical macro-set heuristic algorithm mainly comprises a genetic algorithm, a simulated annealing algorithm, a tabu search algorithm, an ant colony algorithm and the like, wherein the ant colony algorithm has greater advantages in path planning compared with other algorithms. However, the existing ant colony algorithm still has the problems of long search time and low search efficiency when solving the optimal path of the actual problem.
Disclosure of Invention
The application provides a path planning method, a path planning device and a path planning terminal, which aim to solve the problems of long search time and low efficiency of the existing ant colony algorithm.
In order to solve the technical problem, the application adopts a technical scheme that: a path planning method is provided, which comprises the following steps: s1, acquiring the positions of the point part and a plurality of target clients, defining the position of the point part as a starting point and defining the positions of the target clients as target points; s2, performing path optimization calculation through an ant colony algorithm based on the starting point and the target point; during calculation, the ant colony is respectively searched for a target point according to different preset search strategies in the early stage and the later stage of iterative calculation; s3, after each iteration is finished, updating the optimal path stored in the route tabu table; and S4, when the iteration reaches the preset times, taking the optimal path in the route tabu table as the global optimal path.
As a further improvement of the present application, in the step S2, in the early stage and the later stage of the iterative computation, the searching of the target point by the ant colony according to different preset search strategies includes: judging whether the current iteration times are smaller than a preset search control threshold value or not; if so, searching a target point by the ant colony according to a random search strategy; and if not, the ant colony searches the target point according to the determined search strategy.
As a further improvement of the present application, the enabling of the ant colony to search for a target point according to a random search strategy includes: and calculating the transition probability of each ant moving from the current position to all feasible next target points under the current iteration number, and randomly selecting the next moving target point from the current position through a roulette algorithm according to the transition probability.
As a further improvement of the present application, the enabling of the ant colony to search for a target point according to a certain search strategy includes: and calculating the transition probability of each ant moving from the current position to all feasible next target points under the current iteration number, and selecting the target point with the highest transition probability as the next moving target point.
As a further improvement of the present application, step S3 includes: judging whether the current optimal path generated by each iteration exists in a route tabu table or not; if not, and when the cost value of the sub-optimal path is smaller than the minimum cost value of all paths stored in the route tabu table, the sub-optimal path is inserted into the route tabu table.
As a further improvement of the present application, step S3 further includes: and updating the pheromone on the path newly inserted into the route taboo table at least twice.
As a further improvement of the present application, before the step S2, the method further includes: and setting the maximum iteration times according to the number of the target points.
As a further improvement of the present application, before the step S2, the method further includes: and calculating the predicted arrival time and the predicted arrival mileage between the starting point, the multiple target points and the multiple target points.
In order to solve the above technical problem, another technical solution adopted by the present application is: provided is a path planning device including: the acquisition module is used for acquiring the positions of the point part and the target clients, defining the positions of the point part as a starting point and defining the positions of the target clients as target points; the iteration module is used for carrying out path optimization calculation through an ant colony algorithm based on the starting point and the target point; during calculation, the ant colony is respectively searched for a target point according to different preset search strategies in the early stage and the later stage of iterative calculation; the updating module is used for updating the optimal path stored in the route tabu table after each iteration is finished; and the output module is used for taking the optimal path in the route tabu table as a global optimal path when iteration reaches a preset number.
In order to solve the above technical problem, the present application adopts another technical solution that: there is provided a terminal comprising a processor, a memory coupled to the processor, the memory having stored therein program instructions which, when executed by the processor, cause the processor to carry out the steps of the path planning method as claimed in any one of the preceding claims.
The beneficial effect of this application is: the path planning method achieves path planning through the ant colony algorithm, and during the process of searching for the target point, the target point is searched according to different search strategies in the early stage and the later stage of iterative computation respectively, so that the search speed of the ant colony algorithm in the whole iterative process is increased, and the efficiency of searching for the optimal solution is improved.
Drawings
Fig. 1 is a schematic flow chart of a path planning method according to an embodiment of the present invention;
FIG. 2 is a functional block diagram of a path planning apparatus according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a terminal according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a storage medium according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terms "first", "second" and "third" in the present invention are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first," "second," or "third" may explicitly or implicitly include at least one of the feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise. All directional indicators (such as up, down, left, right, front, and rear … …) in the embodiments of the present invention are only used to explain the relative positional relationship between the components, the movement, and the like in a specific posture (as shown in the drawings), and if the specific posture is changed, the directional indicator is changed accordingly. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the invention. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
Fig. 1 is a schematic flow chart of a path planning method according to an embodiment of the present invention. It should be noted that the method of the present invention is not limited to the flow sequence shown in fig. 1 if the results are substantially the same. As shown in fig. 1, the method comprises the steps of:
step S1, acquiring the positions of the point part and the target clients, and defining the position of the point part as a starting point and the positions of the target clients as target points.
Specifically, when the logistics pickup and delivery path planning is performed, the current positions of the point part and all the target customers are obtained, and the positions of the point part are used as the starting points of all ants in the ant colony algorithm, and the positions of the target customers are used as the target points.
Step S2, based on the starting point and the target point, performing path optimization calculation through an ant colony algorithm; during calculation, the ant colony is respectively searched for a target point according to different preset search strategies in the early stage and the later stage of iterative calculation.
In step S2, when the ant colony algorithm is used to perform path planning, the ant colony algorithm is first initialized with parameters, and the maximum iteration number N is setmaxAnd the number m of ants, wherein the maximum number of iterations NmaxPresetting maximum iteration number NmaxThe setting mode is two, one is a fixed value, and the other is set according to the number of the target points, in this embodiment, it is preferable to set according to the number of the target points, specifically, the maximum iteration number NmaxThe number of target points, where the coefficient a is preferably 1.5, is found by testing to be better at this preferred value. In addition, setting the maximum number of iterations by using the number of target points coefficient is beneficial to adapting to all path planning requests, if the maximum number of iterations is set to be fixedThe numerical value is obviously unreasonable and needs to be modified when the path planning request has a small or large number of target points, which wastes time, and the mode of adopting the target point number coefficient is set, so that the path planning request can be adapted to each time no matter how many target points are, thereby reducing invalid iteration times, and saving time without modification.
In addition, in the embodiment, different search strategies are respectively set for the early stage and the later stage of the ant colony algorithm iteration so that ants can search for target points, and the search efficiency of the whole ant colony algorithm is improved. Specifically, the embodiment determines, according to the current accumulated iteration count, whether the iteration is performed in the early stage or the later stage, which search strategy is to be adopted, so that the ant colony searches for the target point according to different preset search strategies, specifically including:
1. and judging whether the current iteration times are smaller than a preset search control threshold value or not.
Specifically, the preset search control threshold is set in advance. Preferably, in view of the maximum number of iterations NmaxMay be set according to the number of target points, and thus, the preset search control threshold is set to NmaxX S, wherein S is a search control coefficient with a value range between 0 and 1, so that the preset search control threshold value can be changed along with the maximum iteration number NmaxAnd (4) changing. In this embodiment, the current iteration number N is less than NmaxAnd when the iteration time is multiplied by S, namely when the current iteration time is less than a preset search control threshold value, the iteration is in the early stage.
2. And if so, searching the target point by the ant colony according to a random search strategy.
Specifically, when the current iteration number is smaller than a preset search control threshold, ants in the ant colony adopt a random search strategy to search for a target point when selecting a next target.
Preferably, the searching the ant colony for the target point according to the random search strategy specifically includes:
and calculating the transition probability of each ant moving from the current position to all feasible next target points under the current iteration number, and randomly selecting the next moving target point from the current position through a roulette algorithm according to the transition probability.
Specifically, the calculation formula of the transition probability is as follows:
wherein the content of the first and second substances,
is the transition probability, tau, of the current kth ant from the current position i to the next current selectable target point j at time tij(t) is the pheromone from the current position i to the next target point j at time t, alpha is the pheromone importance factor, etaij(t) is the visibility degree from the current position i to the next target point j at the moment t, beta is a factor of significance degree of heuristic information, allowedkRepresenting the set of target points allowed to be selected by the current kth ant, N being the current iteration number, NmaxFor the maximum iteration number, the value of alpha reflects the importance of ant historical track information, and beta can control the importance of heuristic information, namely the importance of the current ant walking path.
After the transition probabilities of the ants moving from the current position to all the feasible next target points are obtained, the next moving target point is randomly selected for the ants based on the transition probabilities and a roulette algorithm, namely the calculated transition probability of each next target point is regarded as a sector of the roulette wheel, and the next target point corresponding to the transition probability is selected to move by rotating the roulette wheel and stopping the pointer on which sector.
3. And if not, the ant colony searches the target point according to the determined search strategy.
Specifically, when the current iteration number is greater than or equal to a preset search control threshold, that is, at the later iteration stage, ants in the ant colony adopt a certain search strategy to search for a target point when selecting a next target. The searching of the target point by the ant colony according to the determined search strategy specifically comprises the following steps:
and calculating the transition probability of each ant moving from the current position to all feasible next target points under the current iteration number, and selecting the target point with the highest transition probability as the next moving target point.
The formula for calculating the transition probability refers to the formula (1), which is not described herein again, and after the transition probabilities of ants moving from the current position to all feasible next target points are calculated, a target point with the highest transition probability is selected as the next moving target point.
In this embodiment, the global search capability of the ant colony algorithm needs to be improved in the early stage of the iterative computation, so that the random search strategy is adopted, and when the search result tends to be stable in the later stage of the iterative computation, if the random search strategy is continuously used, the capability of the ant colony algorithm for searching the optimal solution is damaged, and therefore, the determined search strategy is adopted for searching.
Through the search strategy, ants finish walking each target point from the point part, so that one path is generated, when each ant in an ant colony finishes one time in the process of one iteration, a plurality of paths of the iteration can be obtained, then the cost value of each path is calculated, and the path with the lowest cost value is selected from the paths to serve as the local optimal path of the iteration. The cost value in this embodiment is the mileage of the path.
Further, in order to avoid frequently calculating the cost value of each path, before step S2, it is preferable to further include:
and calculating the predicted arrival time and the predicted arrival mileage between the starting point, the multiple target points and the multiple target points.
Specifically, the estimated arrival time and the estimated arrival mileage between the starting point, the multiple target points and the multiple target points are obtained by matrix calculation, and the matrix calculation result is expressed as follows:
wherein, aijThe estimated arrival time and the estimated arrival range from the point i to the point j are shown, and w represents the target point total.
Through the matrix representation, after a path is obtained, the corresponding predicted mileage can be directly found out from the matrix representation in a gathering way, and the cost value of the path is obtained through accumulation, so that the mileage does not need to be independently calculated every time to obtain the cost value.
And step S3, after each iteration is finished, updating the optimal path stored in the route tabu table.
In step S3, the route tabu table functions to: after the optimal path generated by each iteration is added into the tabu table, ants will not walk the path in the tabu table in subsequent iterations, and in addition, only ants do not walk the complete path in the route tabu table, and part of the road sections in the path still have the situation that ants walk. The route tabu table is preset, an empty table is initially set, after iteration is started, the local optimal path obtained by the first iteration is added into the route tabu table, and in the subsequent iteration process, after each iteration is completed to obtain a local optimal path, the optimal path stored in the route tabu table is updated according to the local optimal path.
Specifically, the step S3 includes:
1. and judging whether the current optimal path generated by each iteration exists in the route tabu table or not.
Specifically, after the iteration is completed to obtain a current sub-optimal path, the current sub-optimal path is compared with each optimal path in the route tabu table one by one to determine whether the current sub-optimal path exists in the route tabu table.
2. If not, and when the cost value of the sub-optimal path is smaller than the minimum cost value of all paths stored in the route tabu table, the sub-optimal path is inserted into the route tabu table.
Specifically, if the sub-optimal path reroute tabu table does not exist, the cost value of the sub-optimal path and the cost value of each optimal path in the reroute tabu table are obtained, the cost value of the sub-optimal path is compared with the minimum cost value of the optimal path stored in the reroute tabu table, and the sub-optimal path is inserted into the reroute tabu table when the cost value of the sub-optimal path is smaller than the minimum cost values of all paths stored in the reroute tabu table.
Further, in this embodiment, the length of the route tabu table is preset, that is, only a fixed number of optimal paths can be stored in the route tabu table. And when the number of the optimal paths stored in the route tabu table reaches a threshold value, removing the earliest stored optimal path according to the time sequence.
It should be noted that the optimal path removed from the route tabu table is reconsidered into the feasible solution to increase the performance of the optimal solution. Through the route tabu table, the situation that the ant colony is trapped into the local optimal solution to carry out multiple invalid iterations is avoided.
Further, in this embodiment, after updating the route tabu table, the pheromone is updated, specifically: the path taken by the ant is updated with pheromone, and the pheromone on the path newly inserted into the route tabu table is updated at least twice. In this embodiment, the number of updates is two.
Specifically, the pheromone matrix is updated first, and the update formula of the pheromone matrix is as follows:
τij←(1-ρ)×τij； (4)
wherein, CkRepresenting the total cost of the route taken by the kth ant, Q being a preset pheromone residual coefficient, component (i, j) being the edge from the point i to the point j, m being the total number of ants, and pheromone increment on the edge from the point i to the point j of the kth ant can be obtained through the formula (2)
Equation (3) represents the pheromone value τ on the edge from point i to point jijThe original pheromone on the edge from point i to point j is added with m in the current Nth iterationPheromone increment of only ants on the side from point i to point j; formula (4) is a pheromone volatilization formula, rho is a preset volatilization rate, the value range is between 0 and 1, and (1-rho) is a pheromone residual coefficient, and the pheromone matrix after the Nth iteration is obtained through formula (4).
Then, after updating the pheromone matrix, updating the pheromone of the optimal path generated by the current iteration, wherein the updating formula is as follows:
where b is the ant walking out the optimal path in the Nth iteration, Cb is the cost value of the path that the b ant walks, CijThe path length of the b-th ant on the edge from the point i to the point j is obtained through a formula (5), and pheromone increment of the b-th ant on the edge from the point i to the point j is obtained; the formula (6) is that the pheromone increment of the ant b walking out of the optimal path is multiplied by e, the e is a preset importance degree parameter, and the value range is between 0 and 1. So the pheromone value tau on the side from i to j in the updated pheromone matrixijEqual to the pheromone originally on the i to j edges plus the pheromone increment of the b-th ant on the i to j edges.
In this embodiment, the pheromone on the current optimal path corresponding to the ant walking out of the optimal path at present is updated for the second time to increase the concentration of the pheromone on the optimal path, so that the concentration of pheromones on the optimal path is higher than those on other paths, although the pheromone concentration of the optimal path is higher than that of the other paths after the first updating, the difference is not particularly obvious, the difference is opened after the second updating, so that when the ant searches the next target point in the subsequent iteration process and calculates the transition probability, because the pheromone is one of the factors in the transition probability calculation formula, the transition probability of a target point on the path can be improved, so that the ants can iterate to generate a road section with high pheromone concentration on the optimal path to walk before quickly selecting, and the efficiency of finding the optimal path is improved.
And step S4, when the iteration reaches the preset times, taking the optimal path in the route tabu table as the global optimal path.
In step S4, after the iteration number reaches the maximum iteration number, the cost value of each optimal path in the route tabu table is obtained, and then an optimal path with the smallest cost value is selected as the global optimal path.
The path planning method of the embodiment implements path planning through the ant colony algorithm, and when the target point is searched, the target point is searched according to different search strategies in the early stage and the later stage of iterative computation, so that the search speed of the ant colony algorithm in the whole iterative process is increased, and the efficiency of searching the optimal solution is improved.
Fig. 2 is a functional module diagram of a path planning apparatus according to an embodiment of the present invention. As shown in fig. 2, the path planning apparatus 20 includes an obtaining module 21, an iteration module 22, an updating module 23, and an output module 24.
An acquisition module 21, which acquires the positions of the point part and the target customers, and defines the position of the point part as a starting point and the positions of the target customers as target points;
the iteration module 22 is configured to perform path optimization calculation through an ant colony algorithm based on the starting point and the target point; during calculation, the ant colony is respectively searched for a target point according to different preset search strategies in the early stage and the later stage of iterative calculation;
the updating module 23 is configured to update the optimal path stored in the route tabu table after each iteration is completed;
and the output module 24 is configured to use the optimal path in the route tabu table as the global optimal path when iteration reaches a preset number of times.
Optionally, the iteration module 22 may execute the operation of enabling the ant colony to search for the target point according to different preset search strategies in the early stage and the later stage of the iterative computation, respectively, by: judging whether the current iteration times are smaller than a preset search control threshold value or not; if so, searching a target point by the ant colony according to a random search strategy; and if not, the ant colony searches the target point according to the determined search strategy.
Optionally, the operation performed by the iteration module 22 to make the ant colony perform the search for the target point according to the random search strategy may also be: and calculating the transition probability of each ant moving from the current position to all feasible next target points under the current iteration times, and randomly selecting the next moving target point from the current position through a roulette wheel algorithm according to the transition probability.
Optionally, the operation performed by the iteration module 22 to make the ant colony perform the search for the target point according to the determined search strategy may also be: and calculating the transition probability of each ant moving from the current position to all feasible next target points under the current iteration number, and selecting the target point with the highest transition probability as the next moving target point.
Optionally, after the update module 23 performs each iteration, the operation of updating the optimal path stored in the route tabu table may further be: judging whether the current optimal path generated by each iteration exists in a route tabu table or not; if not, and when the cost value of the sub-optimal path is smaller than the minimum cost value of all paths stored in the route tabu table, the sub-optimal path is inserted into the route tabu table.
Optionally, the updating module 23 is further configured to update the pheromone on the path newly inserted into the route taboo table at least twice.
Optionally, the iteration module 22 performs path optimization calculation through an ant colony algorithm based on the starting point and the target point; during calculation, before the ant colony performs the operation of searching the target points according to different preset search strategies in the early stage and the later stage of iterative calculation, the maximum iteration times are set according to the number of the target points.
Optionally, the iteration module 22 performs path optimization calculation through an ant colony algorithm based on the starting point and the target point; during calculation, before the ant colony performs the operation of searching the target points according to different preset search strategies in the early stage and the later stage of iterative calculation, the ant colony is further used for calculating the predicted arrival time and the predicted arrival mileage between the starting point, the multiple target points and the multiple target points.
For other details of the technical solutions implemented by the modules in the path planning apparatus in the foregoing embodiments, reference may be made to the description of the path planning method in the foregoing embodiments, and details are not repeated here.
It should be noted that, in the present specification, the embodiments are all described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments may be referred to each other. For the device-like embodiment, since it is basically similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
Referring to fig. 3, fig. 3 is a schematic structural diagram of a terminal according to an embodiment of the present invention. As shown in fig. 3, the terminal 30 includes a processor 31 and a memory 32 coupled to the processor 31, where the memory 32 stores program instructions, and the program instructions, when executed by the processor 31, cause the processor 31 to execute the steps of the path planning method according to any of the embodiments described above.
The processor 31 may also be referred to as a CPU (Central Processing Unit). The processor 31 may be an integrated circuit chip having signal processing capabilities. The processor 31 may also be a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
Referring to fig. 4, fig. 4 is a schematic structural diagram of a storage medium according to an embodiment of the invention. The storage medium of the embodiment of the present invention stores a program file 41 capable of implementing all the methods described above, where the program file 41 may be stored in the storage medium in the form of a software product, and includes several instructions to enable a computer device (which may be a personal computer, a server, or a network device) or a processor (processor) to execute all or part of the steps of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a mobile hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, or terminal devices, such as a computer, a server, a mobile phone, and a tablet.
In the embodiments provided in the present invention, it should be understood that the disclosed terminal, apparatus and method can be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, a division of a unit is merely a logical division, and an actual implementation may have another division, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit. The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A method of path planning, comprising:
s1, acquiring the positions of the point part and a plurality of target clients, defining the position of the point part as a starting point, and defining the positions of the plurality of target clients as target points;
s2, based on the starting point and the target point, performing path optimization calculation through an ant colony algorithm; during calculation, the ant colony is respectively searched for a target point according to different preset search strategies in the early stage and the later stage of iterative calculation;
s3, after each iteration is finished, updating the optimal path stored in the route tabu table;
and S4, when iteration reaches a preset number, taking the optimal path in the route tabu table as a global optimal path.
2. The path planning method according to claim 1, wherein the step S2 of searching the ant colony for the target point according to different preset search strategies in the early stage and the later stage of the iterative computation respectively comprises:
judging whether the current iteration times are smaller than a preset search control threshold value or not;
if so, searching a target point by the ant colony according to a random search strategy;
and if not, the ant colony searches the target point according to the determined search strategy.
3. The path planning method according to claim 2, wherein the allowing the ant colony to search for the target point according to a random search strategy includes:
and calculating the transition probability of each ant moving from the current position to all feasible next target points under the current iteration number, and randomly selecting the next moving target point from the current position through a roulette algorithm according to the transition probability.
4. The path planning method according to claim 2, wherein the allowing the ant colony to search for the target point according to the determined search strategy includes:
and calculating the transition probability of each ant moving from the current position to all feasible next target points under the current iteration number, and selecting the target point with the highest transition probability as the next moving target point.
5. The path planning method according to claim 1, wherein step S3 includes:
judging whether the current optimal path generated by each iteration exists in a route tabu table or not;
if not, and when the cost value of the sub-optimal path is smaller than the minimum cost value of all paths stored in the route tabu table, the sub-optimal path is inserted into the route tabu table.
6. The path planning method according to claim 5, wherein step S3 further includes: and updating the pheromone on the path newly inserted into the route taboo table at least twice.
7. The path planning method according to claim 1, wherein before the step of S2, the method further comprises: and setting the maximum iteration times according to the number of the target points.
8. The path planning method according to claim 1, wherein before the step S2, the method further comprises: and calculating the predicted arrival time and the predicted arrival mileage between the starting point, the multiple target points and the multiple target points.
9. A path planning apparatus, comprising:
the acquisition module is used for acquiring the positions of the point part and the target clients, defining the positions of the point part as a starting point and defining the positions of the target clients as target points;
the iteration module is used for carrying out path optimization calculation through an ant colony algorithm based on the starting point and the target point; during calculation, the ant colony is respectively searched for a target point according to different preset search strategies in the early stage and the later stage of iterative calculation;
the updating module is used for updating the optimal path stored in the route tabu table after each iteration is finished;
and the output module is used for taking the optimal path in the route tabu table as a global optimal path when iteration reaches a preset number.
10. A terminal, characterized in that the terminal comprises a processor, a memory coupled to the processor, in which memory program instructions are stored, which program instructions, when executed by the processor, cause the processor to carry out the steps of the path planning method according to any of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110449364.8A CN113110472A (en) | 2021-04-25 | 2021-04-25 | Path planning method and device and terminal |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110449364.8A CN113110472A (en) | 2021-04-25 | 2021-04-25 | Path planning method and device and terminal |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113110472A true CN113110472A (en) | 2021-07-13 |
Family
ID=76719997
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110449364.8A Pending CN113110472A (en) | 2021-04-25 | 2021-04-25 | Path planning method and device and terminal |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113110472A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113766131A (en) * | 2021-09-15 | 2021-12-07 | 广州市明美光电技术有限公司 | Multi-target-point focusing method and application thereof |
| CN114217607A (en) * | 2021-11-23 | 2022-03-22 | 桂林航天工业学院 | Takeout delivery path planning method, system and storage medium |
| CN116673968A (en) * | 2023-08-03 | 2023-09-01 | 南京云创大数据科技股份有限公司 | Mechanical arm track planning element selection method and system based on reinforcement learning |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106225788A (en) * | 2016-08-16 | 2016-12-14 | 上海理工大学 | The robot path planning method of ant group algorithm is expanded based on path |
| CN110220525A (en) * | 2019-05-14 | 2019-09-10 | 昆明理工大学 | A kind of paths planning method based on potential field ant group algorithm |
| CN110244733A (en) * | 2019-06-20 | 2019-09-17 | 西南交通大学 | A kind of method for planning path for mobile robot based on improvement ant group algorithm |
| WO2019196127A1 (en) * | 2018-04-11 | 2019-10-17 | 深圳大学 | Cloud computing task allocation method and apparatus, device, and storage medium |
| CN111857141A (en) * | 2020-07-13 | 2020-10-30 | 武汉理工大学 | Robot path planning method, device, equipment and storage medium |
-
2021
- 2021-04-25 CN CN202110449364.8A patent/CN113110472A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106225788A (en) * | 2016-08-16 | 2016-12-14 | 上海理工大学 | The robot path planning method of ant group algorithm is expanded based on path |
| WO2019196127A1 (en) * | 2018-04-11 | 2019-10-17 | 深圳大学 | Cloud computing task allocation method and apparatus, device, and storage medium |
| CN110220525A (en) * | 2019-05-14 | 2019-09-10 | 昆明理工大学 | A kind of paths planning method based on potential field ant group algorithm |
| CN110244733A (en) * | 2019-06-20 | 2019-09-17 | 西南交通大学 | A kind of method for planning path for mobile robot based on improvement ant group algorithm |
| CN111857141A (en) * | 2020-07-13 | 2020-10-30 | 武汉理工大学 | Robot path planning method, device, equipment and storage medium |
Non-Patent Citations (2)
| Title |
|---|
| 赖智铭 等: "基于自适应阈值蚁群算法的路径规划算法", 《计算机系统应用》, vol. 23, no. 2 * |
| 陈晶 等: "改进蚁群算法求解同型机任务调度问题", 《计算机工程应用》, vol. 47, no. 6, 31 December 2011 (2011-12-31), pages 44 - 48 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113766131A (en) * | 2021-09-15 | 2021-12-07 | 广州市明美光电技术有限公司 | Multi-target-point focusing method and application thereof |
| CN114217607A (en) * | 2021-11-23 | 2022-03-22 | 桂林航天工业学院 | Takeout delivery path planning method, system and storage medium |
| CN116673968A (en) * | 2023-08-03 | 2023-09-01 | 南京云创大数据科技股份有限公司 | Mechanical arm track planning element selection method and system based on reinforcement learning |
| CN116673968B (en) * | 2023-08-03 | 2023-10-10 | 南京云创大数据科技股份有限公司 | Mechanical arm track planning element selection method and system based on reinforcement learning |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113110472A (en) | Path planning method and device and terminal | |
| US10655975B2 (en) | System and method for routing optimization | |
| Xiang et al. | Demand coverage diversity based ant colony optimization for dynamic vehicle routing problems | |
| Yu et al. | A hybrid algorithm for vehicle routing problem with time windows | |
| Zhang et al. | Integrated ant colony and tabu search approach for time dependent vehicle routing problems with simultaneous pickup and delivery | |
| CN111144568A (en) | Multi-target urban logistics distribution path planning method | |
| CN105701216A (en) | Information pushing method and device | |
| Avci et al. | An adaptive large neighborhood search approach for multiple traveling repairman problem with profits | |
| CN110751441A (en) | Method and device for optimizing storage position in logistics storage system | |
| CN111709560A (en) | Method for solving vehicle path problem based on improved ant colony algorithm | |
| CN112884409A (en) | Distribution route recommendation method, device, equipment and storage medium | |
| Berghida et al. | EBBO: an enhanced biogeography-based optimization algorithm for a vehicle routing problem with heterogeneous fleet, mixed backhauls, and time windows | |
| CN114781966B (en) | Logistics distribution path planning method, device, equipment and storage medium | |
| CN109754203B (en) | Intelligent logistics picking path combination method | |
| CN114139392A (en) | Vehicle path problem method and system based on self-adaptive large-scale field search algorithm | |
| CN114253975A (en) | Load-aware road network shortest path distance calculation method and device | |
| CN112035483A (en) | Knowledge base knowledge storage and retrieval method and device | |
| Žunić et al. | Parameter setting problem in the case of practical vehicle routing problems with realistic constraints | |
| CN114154394B (en) | Parallel time window vehicle path planning method based on improved ant colony algorithm | |
| CN116227817A (en) | Dynamic vehicle path all-link problem analysis and model solving method | |
| CN110930092A (en) | Distribution route adjusting method and device, electronic equipment and storage medium | |
| CN113139669B (en) | Method, device, electronic equipment and storage medium for multi-objective route planning | |
| CN112862212B (en) | Multi-AGV (automatic guided vehicle) scheduling method, device and equipment based on improved sparrow search algorithm | |
| El Hassani et al. | A hybrid ant colony system approach for the capacitated vehicle routing problem and the capacitated vehicle routing problem with time windows | |
| CN114548880A (en) | Logistics planning method, equipment, device and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB02 | Change of applicant information |
Address after: 518000 floor 4, building a, Lufthansa aviation Park, hourui community, Hangcheng street, Bao'an District, Shenzhen City, Guangdong Province Applicant after: Shenzhen Leap New Technology Co.,Ltd. Address before: 518000 Room 201, building A, No. 1, Qian Wan Road, Qianhai Shenzhen Hong Kong cooperation zone, Shenzhen, Guangdong (Shenzhen Qianhai business secretary Co., Ltd.) Applicant before: Shenzhen Leap New Technology Co.,Ltd. |
|
| CB02 | Change of applicant information |