CN113485364B - Distribution robot path planning system - Google Patents
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- CN113485364B CN113485364B CN202110885323.3A CN202110885323A CN113485364B CN 113485364 B CN113485364 B CN 113485364B CN 202110885323 A CN202110885323 A CN 202110885323A CN 113485364 B CN113485364 B CN 113485364B
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0221—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving a learning process
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Abstract
The application discloses a distribution robot path planning system, which comprises: the target place acquisition module is used for acquiring a plurality of target places; the planning module performs path planning based on the acquired multiple target sites to acquire multiple path planning results; preference and deletion module: the method comprises the steps of selecting a preferred result and a part of preferred paths in path planning results, and deleting the part of path planning results; and a cross recombination module: the method comprises the steps of crossing paths in a deleted path planning result, and carrying out random transformation on two target sites in a part of path planning result to obtain a reorganization result; the evaluation module is used for evaluating the path planning result, and finishing planning after reaching the termination condition to obtain an optimal path; the application improves the accuracy of the distribution road section planning of the distribution robot, reduces the duration of the planning process, saves the cost and improves the distribution experience.
Description
Technical Field
The invention relates to the field of path planning, in particular to a path planning system of a distribution robot.
Background
For larger institutions, such as large hospitals, there are many articles (medicines, medical materials, living goods, household garbage, etc.) which need to be distributed inside, and for instance, food distribution in restaurants, raw materials, seasonings, prepared foods, etc. are also many. The distribution mode in the prior art is basically finished by manpower, the manual service can be influenced by environment, time and places, and also can be influenced by wages and self moods, the problems of time and labor waste and high error rate exist, and the manual work can not work for 24 hours continuously, so that the defects of low service quality and short time exist. If the express logistics distribution in the district is carried out, a plurality of owners get off the business very late, and the delivery station for express delivery is already off the business after the owners get off the business and return to the district, the trouble of inconvenient express delivery is brought to the owners, and the defect that the manual work time is short and the work cannot be stopped for 24 hours is reflected.
At present, the distribution robot is increasingly applied to various places to replace manual work, and serves various fields, and with continuous progress of technology, the working capacity and the quality of service of the distribution robot are gradually improved and perfected. Although the distribution robot solves the defects that cannot be overcome by a large number of manpower, a great number of problems still exist, wherein the biggest problem is path planning, and the problems of inaccurate planning of a distribution road section, long time for the planning process and the like exist in the path planning method in the prior art, so a method for solving the problems in the prior art is needed in the society.
Disclosure of Invention
The invention aims to provide a path planning system for a delivery robot, which solves the problems in the prior art.
In order to achieve the above object, the present invention provides the following solutions:
The invention provides a distribution robot path planning system, which comprises:
the target place acquisition module is used for acquiring a plurality of target places;
the planning module performs path planning based on the acquired plurality of target sites to acquire a plurality of path planning results;
Preference and deletion module: the method comprises the steps of selecting a preferred result and a part of preferred paths in the path planning result, and deleting a part of the path planning result;
And a cross recombination module: the method comprises the steps of crossing paths in a deleted path planning result, and carrying out random transformation on two target sites in part of the path planning result to obtain a reorganization result;
the evaluation module is used for evaluating the path planning result, and finishing planning after reaching a termination condition to obtain an optimal path;
The target place acquisition module, the planning module, the optimizing and deleting module, the cross reorganization module and the evaluation module are sequentially connected, wherein the evaluation module is also connected with the target place acquisition module.
Further, the planning module comprises a path planning module and a cross path rejecting module;
The path planning module is used for planning paths for a plurality of target sites to obtain a plurality of first path planning results, wherein the first path planning results are that the paths passing through the target sites are completed once and the paths passing through the target sites are not repeated;
the cross path eliminating module is used for eliminating the cross paths in the first path planning result to obtain a plurality of second path planning results without cross paths as path planning results.
Further, in the process of planning paths for a plurality of target sites by the path planning module, a minimum quadrilateral algorithm is adopted to plan the paths.
Further, in the process of removing the cross paths in the first path planning result by the cross path removing module, a Complete 2-Opt algorithm is adopted to remove the cross paths.
Further, in the process that the optimizing and deleting module deletes part of the path planning result, a deleting operator is adopted for deleting, and the judgment basis for making the deleting decision is as follows: the specific method for judging the path length and the path times comprises the following steps: judging whether the total length of the path and the number of paths of the path planning result exceed preset values, if so, deleting the path planning result, otherwise, reserving.
Further, in the process of selecting the partial preferred path by the preferred and deleting module, the preferred path is collected based on a best partial collector BPC algorithm, and the best partial collector BPC algorithm is also used for judging and collecting the preferred result.
Further, the optimizing and deleting module recombines the partial optimizing path to replace the partial path in the optimizing result based on the optimal partial collector BPC algorithm to obtain a brand new and better path planning result.
Further, the specific method for the crossover recombination module to crossover the deleted paths in the path planning result is as follows: and crossing part of paths in each two deleted path planning results to form a new path planning result, wherein an edge exchange crossing operator EXX is adopted in the crossing process, the original paths are crossed through the edge exchange crossing operator EXX, and then the crossed edges are removed through the crossing path removing module to obtain a better path planning result.
Further, the reorganization result needs to be input into the planning module to conduct path re-planning.
Further, the termination condition of the evaluation module is: the evaluation module achieves the preset iteration times.
The invention discloses the following technical effects:
The application adopts a method of solving the path planning of the delivery robot by combining a Complete 2-Opt algorithm and a minimum quadrilateral algorithm, wherein the minimum quadrilateral algorithm can select a short path planning mode, the Complete 2-Opt algorithm is based on 2-Opt heuristic search, and if the repeatability is large enough, all crossed edges in traversal can be removed. Genetic operators are added for optimization, the genetic operators comprise a crossover operator and a mutation operator, the crossover operator changes the sequence of two target sites, the occurrence probability of a local optimal solution is reduced, the local random searching capability is improved through the mutation operator, the efficiency of obtaining the optimal solution is improved, the results with poor effects are deleted through a deletion operator and an optimal part collector, the results with good effects are selected, the results with good effects are added into a local optimal path, and the efficiency of obtaining the optimal solution is improved. By combining the algorithms, the method improves the accuracy of planning the delivery road section of the delivery robot, reduces the duration of the planning process, saves the cost and improves the delivery experience.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic diagram of a system structure according to an embodiment of the present invention.
Detailed Description
Various exemplary embodiments of the invention will now be described in detail, which should not be considered as limiting the invention, but rather as more detailed descriptions of certain aspects, features and embodiments of the invention.
It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. In addition, for numerical ranges in this disclosure, it is understood that each intermediate value between the upper and lower limits of the ranges is also specifically disclosed. Every smaller range between any stated value or stated range, and any other stated value or intermediate value within the stated range, is also encompassed within the invention. The upper and lower limits of these smaller ranges may independently be included or excluded in the range.
It will be apparent to those skilled in the art that various modifications and variations can be made in the specific embodiments of the application described herein without departing from the scope or spirit of the application. Other embodiments will be apparent to those skilled in the art from consideration of the specification of the present application. The specification and examples of the present application are exemplary only.
As used herein, the terms "comprising," "including," "having," "containing," and the like are intended to be inclusive and mean an inclusion, but not limited to.
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
The invention provides an evolutionary multi-heuristic algorithm based on Genetic Local Search (GLS) to solve a path planning optimization system of a distribution robot. The system combines two main operations of complete 2-Opt (C2 Opt) and minimum quadrangle (SS), and is applied to a distribution robot path planning optimization system. C2Opt is based on a 2-Opt heuristic search that can remove all intersecting edges in the traversal if the repeatability is large enough. SS selects edges shorter than C2 Opt. The problem with SS is that the original city order changes when applied. Therefore, the intersecting edge cannot be completely removed. However, combining C2Opt and SS with genetic operators gives a good result for the path planning optimization algorithm of the dispensing robot. The system also employs both delete and best partial collector operations. Deletion is an effective method for removing duplicates from a population, BPC is an effective method for collecting the best part of the culture preferred path from the path.
The invention discloses a distribution robot path planning system, which comprises:
the target place acquisition module is used for acquiring a plurality of target places;
the planning module performs path planning based on the acquired plurality of target sites to acquire a plurality of path planning results;
Preference and deletion module: the method comprises the steps of selecting a preferred result and a part of preferred paths in the path planning results, and deleting a part of the path planning results, wherein the preferred result is the path planning result: the path planning result, the path direction of which is consistent with the shortest path direction and the total length of which is smaller than the preset value of the preferable result, is used as the preferable result; the partial preferred path is a partial path with the direction consistent with the shortest path direction and the partial path length smaller than the preset value of the partial preferred path length in the planning result of each path, and is used as the partial preferred path; the result of the deleted partial path planning is that the path direction is inconsistent with the shortest path direction, and the total length of the path exceeds a deletion threshold; the preset value of the preferable result is specifically a preset value for judging whether the preferable result is the preferable result, and the value is the value of the total length of the path; the preset value of the partial preferred path length is specifically a preset value for judging whether the partial preferred path is a value of the path length; the deletion threshold is specifically a preset value for judging whether to delete the path planning result, and the value is the value of the total path length.
And a cross recombination module: the method comprises the steps of crossing paths in a deleted path planning result, and carrying out random transformation on two target sites in part of the path planning result to obtain a reorganization result;
the evaluation module is used for evaluating the path planning result, and finishing planning after reaching a termination condition to obtain an optimal path;
The target place acquisition module, the planning module, the optimizing and deleting module, the cross reorganization module and the evaluation module are sequentially connected, wherein the evaluation module is also connected with the target place acquisition module.
Further, the planning module comprises a path planning module and a cross path rejecting module;
The path planning module is used for planning paths for a plurality of target sites to obtain a plurality of first path planning results, wherein the first path planning results are that the paths passing through the target sites are completed once and the paths passing through the target sites are not repeated;
the cross path eliminating module is used for eliminating the cross paths in the first path planning result to obtain a plurality of second path planning results without cross paths as path planning results.
Further, in the process of planning paths for a plurality of target sites by the path planning module, a minimum quadrilateral algorithm is adopted to plan the paths.
Further, in the process of removing the cross paths in the first path planning result by the cross path removing module, a Complete 2-Opt algorithm is adopted to remove the cross paths.
Further, in the process that the optimizing and deleting module deletes part of the path planning result, a deleting operator is adopted for deleting, and the judgment basis for making the deleting decision is as follows: the specific method for judging the path length and the path times comprises the following steps: judging whether the total length of the path and the number of paths of the path planning result exceed preset values, if so, deleting the path planning result, otherwise, reserving.
Further, in the process of selecting the partial preferred path by the preferred and deleting module, the preferred path is collected based on a best partial collector BPC algorithm, and the best partial collector BPC algorithm is also used for judging and collecting the preferred result.
Further, the optimizing and deleting module recombines the partial optimizing path to replace the partial path in the optimizing result based on the optimal partial collector BPC algorithm to obtain a brand new and better path planning result.
Further, the specific method for the crossover recombination module to crossover the deleted paths in the path planning result is as follows: and crossing part of paths in each two deleted path planning results to form a new path planning result, wherein an edge exchange crossing operator EXX is adopted in the crossing process, the original paths are crossed through the edge exchange crossing operator EXX, and then the crossed edges are removed through the crossing path removing module to obtain a better path planning result.
Further, the reorganization result needs to be input into the planning module to conduct path re-planning.
Further, the termination condition of the evaluation module is: the evaluation module achieves the preset iteration times.
The genetic algorithm is an optimization algorithm simulating the natural evolution process. Conventional genetic algorithms typically consist of crossover, selection and mutation operators that are modeled from biological and genetic processes. However, conventional genetic algorithms are inefficient in solving large optimization problems.
The genetic algorithm is applied to the distribution robot path planning optimization algorithm, which is a well-known and important combined optimization problem, namely the TSP problem of the tourist. In the dispensing robot path planning, each distance between two cities is given for a set of n cities. Our goal is to find a shortest trip, visit only once per city, and then return to the departure city.
Genetic operators in the present application include: the crossover operator and the mutation operator explore the diversity of the search space, improve the convergence speed and avoid premature convergence to some suboptimal solutions. And the C2Opt and SS operation is combined, so that the ergodic property of the search space is improved, and the C2Opt is based on a 2-Opt heuristic search method. The path created by C2Opt is typically a locally optimal path in which the order of some cities may be the same as in the optimal path. This is an ideal attribute of the local search operation. The crossover operator is an edge-swap crossover operator (EXX). Mutation operator is a two-point transformation method for randomly changing the sequence of two cities. EXX and mutations insert new diversity in populations. The path through crossover and mutation recombination may contain new crossover edges, becoming new candidates for C2Opt and SS operations.
The other two operators used in the system are the delete operator and the Best Partial Collector (BPC). The deletion reserves an effective space, and the same path sequence is used as the replacement position of other path planning results by deleting the path planning results for a local searching method. The removed path is then regenerated by a preset mutation operator. The BPC is used to collect the best parts from the different path planning results and to culture the preferred paths. All parts of the collection contain the same city, with the same starting and ending cities. If one path is found to be shorter than the paths in the preferred paths, the selected path in the preferred paths will be recombined with the shorter path. By this process, BPC can significantly improve path quality.
In the description of the present invention, it should be understood that the terms "longitudinal," "transverse," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like indicate or are based on the orientation or positional relationship shown in the drawings, merely to facilitate description of the present invention, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus should not be construed as limiting the present invention.
The above embodiments are only illustrative of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention, and various modifications and improvements made by those skilled in the art to the technical solutions of the present invention should fall within the protection scope defined by the claims of the present invention without departing from the design spirit of the present invention.
Claims (1)
1. A delivery robot path planning system, characterized by: comprising the following steps:
the target place acquisition module is used for acquiring a plurality of target places;
the planning module performs path planning based on the acquired plurality of target sites to acquire a plurality of path planning results;
Preference and deletion module: the method comprises the steps of selecting a preferred result and a part of preferred paths in the path planning result, and deleting a part of the path planning result;
And a cross recombination module: the method comprises the steps of crossing paths in a deleted path planning result, and carrying out random transformation on two target sites in part of the path planning result to obtain a reorganization result;
the evaluation module is used for evaluating the path planning result, and finishing planning after reaching a termination condition to obtain an optimal path;
The target place acquisition module, the planning module, the optimizing and deleting module, the cross reorganization module and the evaluation module are sequentially connected, wherein the evaluation module is also connected with the target place acquisition module;
The planning module comprises a path planning module and a cross path eliminating module;
The path planning module is used for planning paths for a plurality of target sites to obtain a plurality of first path planning results, wherein the first path planning results are that the paths passing through the target sites are completed once and the paths passing through the target sites are not repeated;
The cross path removing module is used for removing the cross paths in the first path planning result to obtain a plurality of second path planning results without cross paths, and the second path planning results are used as path planning results;
The path planning module plans paths by adopting a minimum quadrilateral algorithm in the process of planning paths of a plurality of target places;
In the process of removing the cross paths in the first path planning result by the cross path removing module, removing the cross paths by adopting a Complete 2-Opt algorithm;
in the process of deleting part of the path planning result by the optimizing and deleting module, deleting is performed by adopting a deleting operator, and judgment basis for deleting decision is as follows: the specific method for judging the path length and the path times comprises the following steps: judging whether the total length of the path and the number of paths of the path planning result exceed preset values, if so, deleting the path planning result, otherwise, reserving;
The optimal part collector BPC algorithm is also used for judging and collecting the optimal result in the process of selecting the part optimal path by the optimal part and deleting module;
The optimizing and deleting module recombines partial preferred paths to replace partial paths in the preferred results based on the optimal partial collector BPC algorithm to obtain brand new and better path planning results;
The specific method for the crossover recombination module to crossover the deleted paths in the path planning result is as follows: crossing part of paths in each two deleted path planning results to form a new path planning result, adopting an edge exchange crossing operator EXX in the crossing process, crossing the original paths through the edge exchange crossing operator EXX, and then removing the crossed edges through the crossing path removing module to obtain a better path planning result;
the reorganization result is required to be input into the planning module to carry out path re-planning;
The termination conditions of the evaluation module are: the evaluation module achieves the preset iteration times.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102081752A (en) * | 2011-01-27 | 2011-06-01 | 西北工业大学 | Dynamic flight path planning method based on adaptive mutation genetic algorithm |
| CN110879596A (en) * | 2019-12-05 | 2020-03-13 | 中国北方车辆研究所 | Autonomous operation system and autonomous operation method of low-cost automatic mower |
| CN111428931A (en) * | 2020-03-24 | 2020-07-17 | 上海东普信息科技有限公司 | Logistics distribution line planning method, device, equipment and storage medium |
| CN112149921A (en) * | 2020-10-20 | 2020-12-29 | 国网重庆市电力公司营销服务中心 | Large-scale electric logistics vehicle path planning method and system and charging planning method |
| CN112650248A (en) * | 2020-12-23 | 2021-04-13 | 齐鲁工业大学 | Routing inspection robot path planning method and system based on improved genetic algorithm |
| CN113033895A (en) * | 2021-03-25 | 2021-06-25 | 浙江中烟工业有限责任公司 | Multi-source multi-point path planning method, equipment and storage medium |
| CN113031578A (en) * | 2019-12-24 | 2021-06-25 | 牟茹月 | Sweeping robot sweeping walking path planning method based on genetic algorithm |
-
2021
- 2021-08-03 CN CN202110885323.3A patent/CN113485364B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102081752A (en) * | 2011-01-27 | 2011-06-01 | 西北工业大学 | Dynamic flight path planning method based on adaptive mutation genetic algorithm |
| CN110879596A (en) * | 2019-12-05 | 2020-03-13 | 中国北方车辆研究所 | Autonomous operation system and autonomous operation method of low-cost automatic mower |
| CN113031578A (en) * | 2019-12-24 | 2021-06-25 | 牟茹月 | Sweeping robot sweeping walking path planning method based on genetic algorithm |
| CN111428931A (en) * | 2020-03-24 | 2020-07-17 | 上海东普信息科技有限公司 | Logistics distribution line planning method, device, equipment and storage medium |
| CN112149921A (en) * | 2020-10-20 | 2020-12-29 | 国网重庆市电力公司营销服务中心 | Large-scale electric logistics vehicle path planning method and system and charging planning method |
| CN112650248A (en) * | 2020-12-23 | 2021-04-13 | 齐鲁工业大学 | Routing inspection robot path planning method and system based on improved genetic algorithm |
| CN113033895A (en) * | 2021-03-25 | 2021-06-25 | 浙江中烟工业有限责任公司 | Multi-source multi-point path planning method, equipment and storage medium |
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