CN115562273A - Mobile robot path planning method and system based on hybrid improved ant colony algorithm - Google Patents
Mobile robot path planning method and system based on hybrid improved ant colony algorithm Download PDFInfo
- Publication number
- CN115562273A CN115562273A CN202211239943.0A CN202211239943A CN115562273A CN 115562273 A CN115562273 A CN 115562273A CN 202211239943 A CN202211239943 A CN 202211239943A CN 115562273 A CN115562273 A CN 115562273A
- Authority
- CN
- China
- Prior art keywords
- path
- ant colony
- algorithm
- path planning
- colony algorithm
- 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
- 238000004422 calculation algorithm Methods 0.000 title claims abstract description 130
- 238000000034 method Methods 0.000 title claims abstract description 47
- 239000003016 pheromone Substances 0.000 claims abstract description 40
- 230000002068 genetic effect Effects 0.000 claims abstract description 28
- 238000013507 mapping Methods 0.000 claims abstract description 9
- 230000007704 transition Effects 0.000 claims description 21
- 238000005457 optimization Methods 0.000 claims description 15
- 230000006870 function Effects 0.000 claims description 8
- 238000012216 screening Methods 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 claims description 5
- 230000008569 process Effects 0.000 claims description 4
- 238000003860 storage Methods 0.000 claims description 4
- 238000010276 construction Methods 0.000 claims description 2
- 238000010586 diagram Methods 0.000 description 5
- 239000011159 matrix material Substances 0.000 description 5
- 230000008901 benefit Effects 0.000 description 4
- 230000007547 defect Effects 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 230000006872 improvement Effects 0.000 description 3
- 238000004088 simulation Methods 0.000 description 3
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000035772 mutation Effects 0.000 description 2
- 239000011664 nicotinic acid Substances 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 241000251468 Actinopterygii Species 0.000 description 1
- 201000004569 Blindness Diseases 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000019637 foraging behavior Effects 0.000 description 1
- 238000009776 industrial production Methods 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 238000012795 verification 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, 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
Landscapes
- Engineering & Computer Science (AREA)
- Aviation & Aerospace Engineering (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
Abstract
The invention provides a mobile robot path planning method and a system based on a hybrid improved ant colony algorithm, which relate to the technical field of intelligent algorithm path planning and comprise the following steps: constructing an environment map by using a grid method, and mapping coordinates of grids in the map; based on an environment map, adopting a genetic algorithm to carry out path planning, and converting the obtained optimized solution into an initial pheromone value of an ant colony algorithm; the ant colony algorithm adopts an initial pheromone value, a dynamic heuristic information value and a pseudorandom state transfer rule to perform path planning again, and finally an optimal path is obtained; the method improves the ant colony algorithm, generates the initial pheromone of the ant colony algorithm by using the genetic algorithm, utilizes the good global search characteristic of the genetic algorithm, finds the optimal path in time in a short time, and obviously accelerates the convergence rate of the path length.
Description
Technical Field
The invention belongs to the technical field of intelligent algorithm path planning, and particularly relates to a mobile robot path planning method and system based on a hybrid improved ant colony algorithm.
Background
The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
With the continuous development of society and science and technology, mobile robots are widely applied to various industries, including industrial production, transportation, medical treatment, aerospace and other fields; the path planning is used as the basis of the field of mobile robots, is researched by numerous scholars at home and abroad, and becomes a research hotspot in the field of mobile robots; the method mainly aims to realize that the mobile robot finds a safe and feasible path in the obstructed environment, and simultaneously ensures that the path is the optimal path according to the performance indexes such as the shortest driving path length, the shortest driving time, the smallest turning angle and the like; when solving the path planning problem, the used optimization algorithm is mainly divided into a traditional path planning algorithm and an intelligent path planning algorithm.
The traditional path planning algorithm comprises an A-star algorithm, an artificial potential field method, a sampling method and the like, has good effect in the application of simple path planning, is widely applied to the path planning problem of simple mobile robots such as a single unmanned aerial vehicle, a single unmanned vehicle and the like, but is difficult to obtain ideal effect in the face of complex environment, so that the application of a heuristic intelligent algorithm with learning capability to the path planning is a current trend; the intelligent optimization algorithm is a group intelligent algorithm which is mostly put forward according to certain rules summarized according to group behaviors in nature; the algorithm has better solving capability on the optimization problem with high dimension, complexity and multiple constraints; common intelligent optimization algorithms include ant colony algorithm, particle swarm algorithm, genetic algorithm, fish swarm algorithm and the like.
The ant colony algorithm is an intelligent bionic algorithm which is provided by simulating foraging behavior of natural ant colony, and the core idea of the intelligent bionic algorithm is to continuously update the concentration of pheromone on a path in a positive feedback mode until the concentration of the pheromone on the optimal path is the highest, so as to find out the optimal solution. The ant colony algorithm has the characteristics of positive feedback, parallel calculation, good robustness and the like, and has a good effect in the application of path planning of the mobile robot. However, the classical ant colony algorithm still has many defects, such as blind initial search, slow convergence speed, easy trapping in local optimization and the like; therefore, the improvement of the classical ant colony algorithm and the research of the mixture with other algorithms are especially important for ensuring the efficient completion of the path planning of the mobile robot.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a mobile robot path planning method and a mobile robot path planning system based on a hybrid improved ant colony algorithm, which improve the ant colony algorithm, simultaneously use a genetic algorithm to generate the initial pheromone of the ant colony algorithm, utilize the good global search characteristic of the genetic algorithm, find out the optimal path in time in a short time and obviously accelerate the convergence rate of the path length.
To achieve the above object, one or more embodiments of the present invention provide the following technical solutions:
the invention provides a mobile robot path planning method based on a hybrid improved ant colony algorithm;
the mobile robot path planning method based on the hybrid improved ant colony algorithm comprises the following steps:
constructing an environment map by using a grid method, and mapping coordinates of grids in the map;
based on an environment map, adopting a genetic algorithm to carry out path planning, and converting the obtained optimized solution into an initial pheromone value of an ant colony algorithm;
the ant colony algorithm adopts the initial pheromone value, the dynamic heuristic information value and the pseudorandom state transition rule to perform path planning again, and finally obtains the optimal path.
Further, the genetic algorithm adopts a population fitness function based on the path length and the path smoothness.
Further, the path length is the euclidean distance between the starting point and the end point;
the path smoothness is the smoothness of points calculated according to the distance between every two points in the path.
Further, the specific steps of planning the path by adopting the genetic algorithm are as follows:
initializing a population;
calculating the initial fitness of the population based on a population fitness function;
screening individuals through copy, cross and variation operations in genetics, recalculating fitness, reserving individuals with high fitness and generating a new population;
performing the operations of copying, crossing, mutation, screening, calculation and generation in an iterative manner to continuously improve the individual fitness of the population until a set condition is met;
after the execution is stopped, each group generated in the execution process corresponds to one optimization solution to form an optimization solution set.
Further, the obtained optimization solution is converted into an initial pheromone value of the ant colony algorithm, a certain proportion of solutions are randomly selected from the optimization solution set, and the pheromone value is calculated in an iterative mode and serves as the initial pheromone value of the ant colony algorithm.
Further, the dynamic heuristic information value has a specific formula as follows:
wherein d is ij Is the distance, d, from the current path point i to the next path point j je Distance of path point j to end point, N max And N is the current iteration number.
Further, the pseudo-random state transition rule determines a dynamic value according to the iteration times of the algorithm and the length of the shortest path, generates a random number, and selects a path by adopting a classical roulette method if the random number is greater than the dynamic value; if the shortest path is smaller than the dynamic value, the shortest path is selected directly according to a state transition formula.
The invention provides a mobile robot path planning system based on a hybrid improved ant colony algorithm.
The mobile robot path planning system based on the hybrid improved ant colony algorithm comprises a map construction module, a genetic algorithm module and a path planning module;
a map building module configured to: constructing an environment map by using a grid method, and mapping coordinates of grids in the map;
a genetic algorithm module configured to: based on an environment map, adopting a genetic algorithm to carry out path planning, and converting the obtained optimized solution into an initial pheromone value of the ant colony algorithm;
a module configured to: the ant colony algorithm adopts the initial pheromone value, the dynamic heuristic information value and the pseudorandom state transition rule to carry out path planning again, and finally the optimal path is obtained.
A third aspect of the present invention provides a computer-readable storage medium, on which a program is stored, which, when being executed by a processor, implements the steps in the mobile robot path planning method based on the hybrid improved ant colony algorithm according to the first aspect of the present invention.
A fourth aspect of the present invention provides an electronic device, comprising a memory, a processor and a program stored on the memory and executable on the processor, wherein the processor implements the steps of the hybrid improved ant colony algorithm-based mobile robot path planning method according to the first aspect of the present invention when executing the program.
The above one or more technical solutions have the following beneficial effects:
the invention utilizes the good global search characteristic of the genetic algorithm, firstly uses the genetic algorithm to plan the path, converts a plurality of initial solutions obtained by the genetic algorithm into the initial pheromone value of the ant colony algorithm, and overcomes the inevitable defects existing in a single ant colony algorithm: the blindness is too large in the initial searching stage, so that the advantage complementation of the ant colony and the genetic algorithm is realized, the searching range of the path searching is reduced, and the searching efficiency of the optimal path is improved;
the invention improves the heuristic information formula, designs the dynamic heuristic information value: the iteration times of the algorithm are reduced along with the increase of the iteration times of the algorithm, so that the convergence speed is increased, and the influence of heuristic information on path selection at the later stage of searching is weakened;
the invention improves the state transition rule, adopts the pseudo-random state transition rule, determines a dynamic value according to the iteration times of the algorithm and the length of the shortest path, generates a random number, and adopts a classical roulette method to select the path if the value is lower than the value, thereby increasing the search globality; if the value is higher than the value, the shortest path is directly selected according to a state transition formula, the convergence speed is accelerated, and the searching efficiency of the algorithm is improved.
Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
FIG. 1 is a flow chart of the method of the first embodiment.
Fig. 2 is a schematic view of an environment map in the first embodiment.
FIG. 3 is an optimized solution path diagram in the first embodiment.
Fig. 4 is a diagram of an ant colony algorithm pheromone matrix in the first embodiment.
Fig. 5 is a diagram of the pseudo code converted in the first embodiment.
Fig. 6 is a graph showing the trend of the convergence curve in the first embodiment.
Fig. 7, fig. 8 and fig. 9 are diagrams of robot motion trajectories under various algorithms in the first embodiment.
Fig. 10 is a system configuration diagram of the second embodiment.
Detailed Description
The invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
Example one
The embodiment discloses a mobile robot path planning method based on a hybrid improved ant colony algorithm;
as shown in fig. 1, the method for planning the path of the mobile robot based on the hybrid improved ant colony algorithm includes:
s1, constructing an environment map by using a grid method, and mapping coordinates of grids in the map;
the environment map is constructed using a grid method, as shown in fig. 2, black squares represent obstacles and white squares represent feasible areas.
Since the environment map is converted into coordinate system information that can be recognized by the mobile robot, a serial number method is generally used to perform coordinate mapping, each grid in the grid map is numbered from left to right and from bottom to top, starting from a serial number 1, and the mapping relationship between the serial number and the coordinate system is as follows:
wherein i is the number of each grid, a is the side length of each grid, x and y are the x row and y column of the size of the grid map respectively, mod (i, y) is the remainder of dividing i by y, cei (n) is the minimum integer of a number n or more, and if x is the number n i = 0.5, then x i = y-0.5; in the present embodiment, a =1 is set.
S2, based on the environment map, path planning is carried out by adopting a genetic algorithm, and the obtained optimized solution is converted into an initial pheromone value of the ant colony algorithm, and the method specifically comprises the following steps:
(1) Initializing a population;
(2) Calculating the initial fitness of the population based on a population fitness function;
calculating a population fitness function by adopting the path length and the path smoothness, wherein the population fitness function is as follows:
fix_value＝ a*path_value -1 +b*path_smooth -1 (2)
wherein, path _ value is path length, path _ smooth is path smoothness, a and b are proportionality coefficients, the ratio of the path length and the path smoothness to the fitness function is determined, and a + b =1.
The path length is the Euclidean distance from the starting point to the end point, and the specific formula is as follows:
wherein, path _ value (i) is the Euclidean distance from the path point i to the path point i +1, and the path point n is a target point; if two waypoints are horizontally or vertically adjacent, path _ value (i) =1, and if two waypoints are at diagonal positions, path _ value (i) = √ 2.
The path smoothness is the path smoothness of the current iteration calculated according to the distance between each point in the path and every other point, and the specific formula is as follows:
wherein, N is the current iteration number, the path point N is the target point, and let s = (x) i -x i+2 ) 2 +(y i -y i+2 ) 2 The calculation formula of the current path smoothness path _ smooth (i, N) from the path point i to the path point i +2 is as follows:
wherein, the path _ smooth (i, N-1) is the smoothness of the path from the path point i to the path point i +2 in the (N-1) th iteration, and the initial value of the path _ smooth in the 1 st iteration is set to 0, namely the path smooth(i，1) ＝0。
When s is larger than 4 and smaller than or equal to 8, the path point is separated from every other path point by one row or one column, and the columns or the rows are adjacent; when s is more than or equal to 1 and less than or equal to 4, the path point and every other path point are shown as diagonal grids, and a lattice can be collapsed in the same row or column; when s is equal to 1, it indicates that the waypoint is adjacent to the next waypoint up and down or left and right, and obviously, waypoint i +1 or waypoint i +2 is redundant. In addition to the above, the set value is 0, and this waypoint and every other waypoint are in a diagonal relationship of a square at this time and are the smoothest route.
(3) Screening individuals through copy, cross and variation operations in genetics, recalculating fitness, reserving individuals with high fitness and generating a new population;
(4) Step (3) is executed in an iterative manner, so that the individual fitness of the population is continuously improved until the set condition is met;
performing the operations of copying, crossing, mutation, screening, calculation and generation in an iterative manner to continuously improve the individual fitness of the population until a set stopping condition is met;
after iteration is stopped, each group generated in the execution process corresponds to an optimization solution to form an optimization solution set.
(5) The obtained optimization solution is converted into the initial pheromone value of the ant colony algorithm, a certain proportion of solutions are randomly selected from the optimization solution set, and the pheromone value is calculated in an iterative mode and serves as the initial pheromone value of the ant colony algorithm.
Taking an optimal solution path (starting point is 1, end point is 400) in the optimal solution set as an example, the path points are shown in fig. 3, and all that is needed is to convert the path points of fig. 3 into initial pheromones of the ant colony algorithm.
The ant colony algorithm pheromone is a matrix of 400 × 400, that is, an adjacent matrix of a 20 × 20 grid map (the row label is a starting point, and the column label is an arrival point), and the matrix is too large, and for convenience of description, the first 11 × 11 area is taken as an example, as shown in fig. 4.
Taking the waypoint (1, 2, 22 \8230;) shown in fig. 3 as an example, the first row and the second column in the adjacency matrix are waypoints 1 to 2, the value of the waypoint is changed to 6 (all values are initialized to 3), the row 2 and the 22 nd column are also changed to 6, and so on until the end point 400 is reached, one path of the genetic algorithm is converted, and so on, 10% of the taken paths are converted, and the specific converted pseudo codes are shown in fig. 5.
S3, the ant colony algorithm adopts the initial pheromone value, the dynamic heuristic information value and the pseudorandom state transition rule to perform path planning again to finally obtain an optimal path, and the method comprises the following specific steps:
(1) Initializing each parameter of the ant colony algorithm, wherein the initial value of the pheromone is the pheromone value obtained in the step S2;
(2) Calculating heuristic information and a state transition formula and selecting a next path point according to the state transition formula by circularly executing the steps until an end point is reached or no selectable path point exists, generating a current iteration path and updating an pheromone value to complete the iteration of a complete path;
calculating heuristic information by adopting a dynamic heuristic information value formula:
wherein d is ij Is the distance, d, from the current path point i to the next path point j je Distance of path point j to end point, N max And N is the current iteration number.
A state transition formula, which adopts a pseudo-random state transition rule, determines a dynamic value according to the iteration times of the algorithm and the length of the shortest path, generates a random number, and adopts a classical roulette method to select a path if the random number is greater than the dynamic value; if the shortest path is smaller than the dynamic value, the shortest path is selected directly according to a state transition formula.
The pseudo-random state transition rule is shown as follows:
where t is a dynamic value between 0 and 1, t 0 Generated according to equation (9), d se Is the distance from the starting point to the end point,/ best The shortest path length for this iteration; in the formula (8), p' is the state transition rule of the classical ant colony algorithm, and when t is>t 0 In time, a classical roulette method is adopted to select a path, and the search globality is increased; when t is less than or equal to t 0 And in time, the shortest path is directly selected according to a state transition formula, so that the convergence speed is accelerated.
(3) Generating a path of the iteration, adding 1 to the iteration number, and updating the ant colony pheromone by using the following formula:
wherein rho is the volatilization coefficient of the pheromone,
for the increase in pheromone concentration in this iteration, the formula for calculating this increase is as follows:
judging whether a termination condition is met: and (3) if the iteration does not reach the maximum iteration number, returning to the step (2), otherwise, entering the next step.
(4) And comparing the lengths of the paths searched by each iteration and outputting the shortest path.
Simulation experiment
Using MATLAB software, simulation verification is performed in the grid map shown in fig. 2, and according to experience and experimental tests, various parameters of the algorithm are set as follows: maximum number of iterations N max =25, pheromone constant Q =1, number of ant colonies m =50, pheromone volatilization factor ρ =0.3, pheromone influence factor α =1, heuristic information influence factor β =7.
As a result of the simulation, as shown in fig. 6, 7, 8, and 9, fig. 6 is a graph of a change trend of a convergence curve, fig. 7 and 8 are respectively optimal paths searched by a classical ant colony algorithm and a mixed ant colony algorithm, and optimal paths searched by an improved ant colony algorithm and a mixed improved ant colony algorithm are the same, as shown in fig. 9.
It can be seen from the figure that: at the initial stage of algorithm iteration, compared with other algorithms such as a classical ant colony algorithm and the like, the hybrid improved ant colony algorithm provided by the invention has the advantages that the length of a searched path is obviously reduced, and meanwhile, the convergence rate is also obviously improved in the whole searching process; compared with the classical ant colony algorithm and the hybrid ant colony algorithm, the hybrid improved ant colony algorithm has obvious improvement on the path length and the number of corners, and compared with the improved ant colony algorithm, although the searched optimal paths are the same, the convergence speed is obviously improved, so that the hybrid improved ant colony algorithm realizes the advantage complementation of the ant colony and the genetic algorithm, reduces the search range of path search and improves the search efficiency of the optimal path.
Example two
The embodiment discloses a mobile robot path planning system based on a hybrid improved ant colony algorithm;
as shown in fig. 10, the mobile robot path planning system based on the hybrid improved ant colony algorithm includes a map building module, a genetic algorithm module, and a path planning module;
a map building module configured to: constructing an environment map by using a grid method, and mapping coordinates of grids in the map;
a genetic algorithm module configured to: based on an environment map, adopting a genetic algorithm to carry out path planning, and converting the obtained optimized solution into an initial pheromone value of the ant colony algorithm;
a path planning module configured to: the ant colony algorithm adopts the initial pheromone value, the dynamic heuristic information value and the pseudorandom state transition rule to perform path planning again, and finally obtains the optimal path.
EXAMPLE III
An object of the present embodiments is to provide a computer-readable storage medium.
A computer-readable storage medium, on which a computer program is stored, which when executed by a processor, implements the steps in the mobile robot path planning method based on the hybrid improved ant colony algorithm according to embodiment 1 of the present disclosure.
Example four
An object of the present embodiment is to provide an electronic device.
An electronic device comprising a memory, a processor and a program stored on the memory and executable on the processor, wherein the processor implements the steps of the hybrid improved ant colony algorithm-based mobile robot path planning method according to embodiment 1 of the present disclosure when executing the program.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. The mobile robot path planning method based on the hybrid improved ant colony algorithm is characterized by comprising the following steps:
constructing an environment map by using a grid method, and carrying out coordinate mapping on grids in the map;
based on an environment map, adopting a genetic algorithm to carry out path planning, and converting the obtained optimized solution into an initial pheromone value of an ant colony algorithm;
the ant colony algorithm adopts the initial pheromone value, the dynamic heuristic information value and the pseudorandom state transition rule to perform path planning again, and finally obtains the optimal path.
2. The hybrid-improved-ant-colony-algorithm-based mobile robot path planning method according to claim 1, wherein the genetic algorithm employs a population fitness function based on path length and path smoothness.
3. The mobile robot path planning method based on the hybrid improved ant colony algorithm as claimed in claim 2, wherein the path length is a euclidean distance between a starting point and an end point;
the path smoothness is the smoothness of points calculated according to the distance between every two points in the path.
4. The mobile robot path planning method based on the hybrid improved ant colony algorithm according to claim 2, wherein the path planning by the genetic algorithm comprises the following specific steps:
initializing a population;
calculating the initial fitness of the population based on a population fitness function;
screening individuals through copy, cross and variation operations in genetics, recalculating fitness, reserving individuals with high fitness and generating a new population;
iteratively executing the operations of copying, crossing, variation, screening, calculation and generation to continuously improve the individual fitness of the population until a set condition is met;
after iteration is stopped, each group generated in the execution process corresponds to an optimization solution to form an optimization solution set.
5. The hybrid-improved ant colony algorithm-based mobile robot path planning method according to claim 1, wherein the step of converting the obtained optimized solution into the initial pheromone value of the ant colony algorithm is to randomly take a certain proportion of solutions from the optimized solution set and iteratively calculate the pheromone value as the initial pheromone value of the ant colony algorithm.
6. The mobile robot path planning method based on the hybrid improved ant colony algorithm according to claim 1, wherein the dynamic heuristic information value is represented by a specific formula:
wherein d is ij Is the distance, d, from the current path point i to the next path point j jg Distance of path point j to end point, N max And N is the current iteration number.
7. The hybrid-improved ant colony algorithm-based mobile robot path planning method of claim 1, wherein the pseudo-random state transition rule determines a dynamic value according to the iteration number of the algorithm and the length of the shortest path, generates a random number, and selects a path by a classical roulette method if the random number is greater than the dynamic value; if the value is less than the dynamic value, the shortest path is selected directly according to a state transition formula.
8. The mobile robot path planning system based on the hybrid improved ant colony algorithm is characterized by comprising a map construction module, a genetic algorithm module and a path planning module;
a map building module configured to: constructing an environment map by using a grid method, and mapping coordinates of grids in the map;
a genetic algorithm module configured to: based on an environment map, adopting a genetic algorithm to carry out path planning, and converting the obtained optimized solution into an initial pheromone value of an ant colony algorithm;
a path planning module configured to: the ant colony algorithm adopts the initial pheromone value, the dynamic heuristic information value and the pseudorandom state transition rule to perform path planning again, and finally obtains the optimal path.
9. Computer readable storage medium, on which a program is stored which, when being executed by a processor, carries out the steps of the method for mobile robot path planning based on the hybrid improved ant colony algorithm according to any one of claims 1 to 7.
10. Electronic device comprising a memory, a processor and a program stored on the memory and executable on the processor, characterized in that the processor when executing the program carries out the steps of the method for mobile robot path planning based on hybrid improved ant colony algorithm according to any of the claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211239943.0A CN115562273A (en) | 2022-10-11 | 2022-10-11 | Mobile robot path planning method and system based on hybrid improved ant colony algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211239943.0A CN115562273A (en) | 2022-10-11 | 2022-10-11 | Mobile robot path planning method and system based on hybrid improved ant colony algorithm |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115562273A true CN115562273A (en) | 2023-01-03 |
Family
ID=84744540
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211239943.0A Pending CN115562273A (en) | 2022-10-11 | 2022-10-11 | Mobile robot path planning method and system based on hybrid improved ant colony algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115562273A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116107328A (en) * | 2023-02-09 | 2023-05-12 | 陕西科技大学 | Optimal automatic obstacle avoidance method for ornithopter based on improved genetic algorithm |
| CN116592890A (en) * | 2023-05-17 | 2023-08-15 | 广东海洋大学 | Picking robot path planning method, picking robot path planning system, electronic equipment and medium |
| CN117232512A (en) * | 2023-08-04 | 2023-12-15 | 广东工业大学 | Unmanned aerial vehicle path acquisition method for efficient search and nested cooperation optimization strategy |
| CN117232512B (en) * | 2023-08-04 | 2024-05-24 | 广东工业大学 | Unmanned aerial vehicle path acquisition method for efficient search and nested cooperation optimization strategy |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105527965A (en) * | 2016-01-04 | 2016-04-27 | 江苏理工学院 | Route planning method and system based on genetic ant colony algorithm |
| CN109919391A (en) * | 2019-03-19 | 2019-06-21 | 北京航空航天大学 | A kind of autonomous optimizing path planning method of solar powered aircraft and system |
| CN109940623A (en) * | 2018-10-26 | 2019-06-28 | 广东工业大学 | A kind of robot path planning method applied to weld seam |
| CN110705742A (en) * | 2019-08-21 | 2020-01-17 | 浙江工业大学 | Logistics distribution method based on improved ant colony algorithm |
| CN110928295A (en) * | 2019-10-16 | 2020-03-27 | 重庆邮电大学 | Robot path planning method integrating artificial potential field and logarithmic ant colony algorithm |
| CN111780759A (en) * | 2020-05-28 | 2020-10-16 | 南京邮电大学 | Mobile robot path planning method based on improved genetic algorithm |
| CN111982125A (en) * | 2020-08-31 | 2020-11-24 | 长春工业大学 | Path planning method based on improved ant colony algorithm |
| CN113848919A (en) * | 2021-09-29 | 2021-12-28 | 河北大学 | Ant colony algorithm-based indoor AGV path planning method |
-
2022
- 2022-10-11 CN CN202211239943.0A patent/CN115562273A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105527965A (en) * | 2016-01-04 | 2016-04-27 | 江苏理工学院 | Route planning method and system based on genetic ant colony algorithm |
| CN109940623A (en) * | 2018-10-26 | 2019-06-28 | 广东工业大学 | A kind of robot path planning method applied to weld seam |
| CN109919391A (en) * | 2019-03-19 | 2019-06-21 | 北京航空航天大学 | A kind of autonomous optimizing path planning method of solar powered aircraft and system |
| CN110705742A (en) * | 2019-08-21 | 2020-01-17 | 浙江工业大学 | Logistics distribution method based on improved ant colony algorithm |
| CN110928295A (en) * | 2019-10-16 | 2020-03-27 | 重庆邮电大学 | Robot path planning method integrating artificial potential field and logarithmic ant colony algorithm |
| CN111780759A (en) * | 2020-05-28 | 2020-10-16 | 南京邮电大学 | Mobile robot path planning method based on improved genetic algorithm |
| CN111982125A (en) * | 2020-08-31 | 2020-11-24 | 长春工业大学 | Path planning method based on improved ant colony algorithm |
| CN113848919A (en) * | 2021-09-29 | 2021-12-28 | 河北大学 | Ant colony algorithm-based indoor AGV path planning method |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116107328A (en) * | 2023-02-09 | 2023-05-12 | 陕西科技大学 | Optimal automatic obstacle avoidance method for ornithopter based on improved genetic algorithm |
| CN116592890A (en) * | 2023-05-17 | 2023-08-15 | 广东海洋大学 | Picking robot path planning method, picking robot path planning system, electronic equipment and medium |
| CN116592890B (en) * | 2023-05-17 | 2024-03-26 | 广东海洋大学 | Picking robot path planning method, picking robot path planning system, electronic equipment and medium |
| CN117232512A (en) * | 2023-08-04 | 2023-12-15 | 广东工业大学 | Unmanned aerial vehicle path acquisition method for efficient search and nested cooperation optimization strategy |
| CN117232512B (en) * | 2023-08-04 | 2024-05-24 | 广东工业大学 | Unmanned aerial vehicle path acquisition method for efficient search and nested cooperation optimization strategy |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN115562273A (en) | Mobile robot path planning method and system based on hybrid improved ant colony algorithm | |
| CN112650229B (en) | Mobile robot path planning method based on improved ant colony algorithm | |
| CN109945881B (en) | Mobile robot path planning method based on ant colony algorithm | |
| CN108036790B (en) | Robot path planning method and system based on ant-bee algorithm in obstacle environment | |
| Gong et al. | Multi-objective Particle Swarm Optimization for Robot Path Planning in Environment with Danger Sources. | |
| CN111664852B (en) | Unmanned aerial vehicle path planning method and device | |
| CN107229287A (en) | A kind of unmanned plane global path planning method based on Genetic Ant algorithm | |
| CN116242383B (en) | Unmanned vehicle path planning method based on reinforced Harris eagle algorithm | |
| CN108413963A (en) | Bar-type machine people's paths planning method based on self study ant group algorithm | |
| Wang et al. | Optimal path planning based on hybrid genetic-cuckoo search algorithm | |
| Zhang et al. | Path planning of mobile robots based on genetic algorithm | |
| Zhang et al. | Robot path planning based on genetic algorithm with hybrid initialization method | |
| Wang et al. | Research on dynamic path planning of wheeled robot based on deep reinforcement learning on the slope ground | |
| Zhou et al. | Enhanced ant colony optimization algorithm for global path planning of mobile robots | |
| Ma et al. | A Fast path re-planning method for UAV based on improved A* algorithm | |
| Zeng et al. | Robotic global path-planning based modified genetic algorithm and A* algorithm | |
| Dongdong et al. | Research on path planning of mobile robot based on improved genetic algorithm | |
| Tusi et al. | Using ABC and RRT algorithms to improve mobile robot path planning with danger degree | |
| CN112504274A (en) | Mobile robot path planning method based on Dsl _ GA algorithm | |
| CN112148030B (en) | Underwater glider path planning method based on heuristic algorithm | |
| CN115129064A (en) | Path planning method based on fusion of improved firefly algorithm and dynamic window method | |
| Tang et al. | On the use of ant colony algorithm with weighted penalty strategy to optimize path searching | |
| CN114415668A (en) | Mobile robot path planning method based on extended-field self-adaptive ant colony algorithm | |
| He et al. | ERCP: speedup path planning through clustering and presearching | |
| Jiao et al. | Path planning of escort robot based on improved quantum particle swarm optimization |
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 |