CN115826587A - Mobile robot path planning method and device based on road network ant colony algorithm - Google Patents
Mobile robot path planning method and device based on road network ant colony algorithm Download PDFInfo
- Publication number
- CN115826587A CN115826587A CN202310107530.5A CN202310107530A CN115826587A CN 115826587 A CN115826587 A CN 115826587A CN 202310107530 A CN202310107530 A CN 202310107530A CN 115826587 A CN115826587 A CN 115826587A
- Authority
- CN
- China
- Prior art keywords
- point
- grid
- points
- road network
- 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.)
- Granted
Links
Images
Abstract
The invention provides a mobile robot path planning method, a device, electronic equipment and a storage medium based on a road network ant colony algorithm, wherein the path planning method sets a motion starting point and a motion target point for a mobile robot by establishing a grid map; connecting the starting point with the target point, making N parallel line segments with equal distance perpendicular to a connecting line between the starting point and the target point, and dividing the map space between the starting point and the target point into N-1 parts; selecting M points with the maximum fitness on each line segment through a wolf colony algorithm to form a road network comprising M (N-2) +2 points; wherein the distance between the two points is greater than L/2m; l is the length of the current layer line segment; and finding out the optimal path based on the formed road network through an ant colony algorithm. According to the method, a certain grid is screened out from the grid map through the wolf colony algorithm to form the road network comprising the target point and the departure point, and then the ant colony algorithm is used for planning the path, so that the calculation amount at the initial stage of the ant colony search is reduced, and the calculation speed is improved.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a mobile robot path planning method and device based on a road network ant colony algorithm, electronic equipment and a storage medium.
Background
With the rapid development of artificial intelligence technology, robots are widely used in the fields of warehouse logistics, manufacturing factories, intelligent medical treatment and the like. And path planning is an important branch of mobile robot research. The path planning means that the mobile robot searches a collision-free path avoiding all obstacles from a starting point so as to smoothly reach a target point. The traditional path planning method comprises an artificial potential field method, a Dijkstra algorithm, a visual graph method and the like. With the increase of obstacles, the complexity of the scale of the problem is continuously increased, and the traditional algorithm has certain limitation, so that some bionic intelligent optimization algorithms, such as an ant colony algorithm, are produced at the same time.
When the ant colony algorithm is applied to robot path planning, the algorithm is easy to fall into local optimum and has low convergence speed. In the algorithm, each ant needs to select the next node according to a probability function, and the probability depends on the distance of the place and the concentration of the pheromone. Therefore, the algorithm needs more calculation amount in the early stage and consumes more time.
Disclosure of Invention
The invention provides a mobile robot path planning method, a mobile robot path planning device, electronic equipment and a storage medium based on a road network ant colony algorithm, which are used for reducing the calculation amount at the initial stage of ant colony search and improving the calculation speed.
In a first aspect, an embodiment of the present invention provides a mobile robot path planning method based on a road network ant colony algorithm, where the path planning method includes:
establishing a grid map, and setting a motion starting point and a motion target point for the mobile robot; wherein each barrier unit and non-barrier unit in the grid map comprises a number of bounded grid units;
connecting the starting point with the target point, making N parallel line segments with equal distance perpendicular to a connecting line between the starting point and the target point, and dividing the map space between the starting point and the target point into N-1 parts;
selecting M points with the maximum fitness on each line segment through a wolf colony algorithm to form a road network comprising M (N-2) +2 points; wherein the distance between the two points is greater than L/2m; l is the length of the current layer line segment;
and finding out an optimal path through an ant colony algorithm based on the formed road network.
As an embodiment of possible implementation, the selecting, by a wolf colony algorithm, M points with the largest fitness on each line segment to form a road network including M (N-2) +2 points in total includes:
and selecting M points with the maximum fitness on each line segment on the N line segments according to a wolf pack algorithm, wherein the points of adjacent parallel lines are connected with each other to form a road network.
As one implementation of possible implementation, the fitness function of the wolf pack algorithm is:
in the formula
All coefficients are used for balancing the weight of each part of the fitness function;
the visualized length of the current grid and the line segment of the previous layer,
the total length of the current grid point and the segment of the previous layer,
for the visualization length of the current grid and the next layer line segment,
the total length of the current grid point and the next layer line segment,
is the included angle between the connecting line of the current grid point and the departure point and the ST line, S is the departure point, T is the target point, ST line is the connecting line of the departure point and the target point,
for Euclidean distance between current grid and selected point of previous layerAnd (5) separating.
As one possible implementation manner, when the y-th layer road network point location is selected, firstly, visually judging all grid points on the y-th line from points selected from the y-1 th layer, screening grid point locations or point location intervals which can be connected to the last layer from the y-th layer, and then calculating according to a fitness function to select a target grid.
As one implementation of possible implementation, the wolf pack algorithm is defined as follows
Wherein, t: representing the current iteration number;
: represents [0,1 ]]A random vector of (1); a. C: representing a vector of co-ordinates;
: a position vector representing a prey; x i (t): position vector representing current gray wolf
A, determining a search direction;
c, deciding whether to favor global search or local search;
the iteration position of each wolf is determined by three wolfs.
As an example of one possible implementation,
the ant colony algorithm searches according to the following rules:
wherein, P k ij (t) represents the probability of an ant going from inode to j-node,
representing the set of neighboring grids that ants can reach next,
representing the pheromone concentration of ants from the current node i to the next node j at the moment t,
indicates ants at time tHeuristic information from the current node i to the next node j,
and
expressing an pheromone factor and a heuristic factor, wherein the heuristic function expression is as follows:
In a second aspect, an embodiment of the present invention provides a mobile robot path planning device based on a road network ant colony algorithm, where the path planning device includes:
the grid map creating module is used for creating a grid map and setting a motion starting point and a motion target point for the mobile robot; wherein each barrier unit and non-barrier unit in the grid map comprises a number of bounded grid units;
the map dividing module is used for connecting the starting point and the target point, making N parallel line segments which are perpendicular to a connecting line between the starting point and the target point and are equidistant, and dividing the map space between the starting point and the target point into N-1 parts;
the first calculation module selects M points with the maximum fitness on each line segment through a wolf colony algorithm to form a road network comprising M (N-2) +2 points; wherein the distance between the two points is greater than L/2m; l is the length of the current layer line segment;
and the second calculation module finds out the optimal path through an ant colony algorithm based on the formed road network.
Wherein, the fitness function of the wolf colony algorithm is as follows:
in the formula
All coefficients are used for balancing the weight of each part of the fitness function;
the visualized length of the current grid and the line segment of the previous layer,
the total length of the current grid point and the segment of the previous layer,
for the visualization length from the current grid to the next level line segment,
the total length of the current grid point and the next layer line segment,
is the included angle between the connecting line of the current grid point and the departure point and the ST line, S is the departure point, T is the target point, ST line is the connecting line of the departure point and the target point,
is the euclidean distance of the current grid from the last layer of selected points.
In a third aspect, an embodiment of the present invention provides an electronic device, including a memory and a processor, where the memory stores a computer program thereon, and the processor implements the method according to any one of the first aspect when executing the program.
In a fourth aspect, an embodiment of the invention provides a computer-readable storage medium on which is stored a computer program which, when executed by a processor, implements the method of any one of the first aspects.
Advantageous effects
The invention provides a mobile robot path planning method, a device, electronic equipment and a storage medium based on a road network ant colony algorithm, wherein the path planning method sets a motion starting point and a motion target point for a mobile robot by establishing a grid map; wherein each barrier unit and non-barrier unit in the grid map comprises a number of bounded grid units; connecting the starting point with the target point, making N parallel line segments with equal distance perpendicular to a connecting line between the starting point and the target point, and dividing the map space between the starting point and the target point into N-1 parts; selecting M points with the maximum fitness on each line segment through a wolf colony algorithm to form a road network comprising M (N-2) +2 points; wherein the distance between the two points is greater than L/2m; l is the length of the current layer line segment; and finding out the optimal path based on the formed road network through an ant colony algorithm. According to the method, a certain grid is screened out from the grid map through the wolf colony algorithm to form the road network comprising the target point and the departure point, and then the ant colony algorithm is used for planning the path, so that the calculation amount at the initial stage of the ant colony search is reduced, and the calculation speed is improved.
It should be understood that the statements made in this summary are not intended to limit the key or critical features of the embodiments of the present invention, or to limit the scope of the invention. Other features of the present invention will become apparent from the following description.
Drawings
The above and other features, advantages and aspects of various embodiments of the present invention will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. In the drawings, the same or similar reference numerals denote the same or similar elements.
Fig. 1 is a flowchart illustrating a mobile robot path planning method based on a road network ant colony algorithm according to an embodiment of the present invention;
FIG. 2 shows a schematic diagram of a road network planned according to the wolf pack algorithm according to an embodiment of the invention;
fig. 3 is a schematic structural diagram of a mobile robot path planning apparatus based on a road network ant colony algorithm according to an embodiment of the present invention;
fig. 4 shows a block diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make those skilled in the art better understand the technical solutions in one or more embodiments of the present disclosure, the technical solutions in one or more embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in one or more embodiments of the present disclosure, and it is obvious that the described embodiments are only a part of the embodiments of the present disclosure, and not all embodiments. All other embodiments that can be derived by a person skilled in the art from one or more of the embodiments described herein without making any inventive step shall fall within the scope of protection of this document.
It should be noted that, the description of the embodiment of the present invention is only for clearly illustrating the technical solutions of the embodiment of the present invention, and does not limit the technical solutions provided by the embodiment of the present invention.
In the related art, a wolf pack algorithm adopts a bottom-up design method based on an artificial wolf subject and a collaborative search path structure based on responsibility division. The whole process of hunting of the wolf pack is finally realized through the exploration of the wolf pack individuals on the smell of the prey and the environmental information, the mutual information sharing and interaction of the artificial wolfs and the decision of the individual behaviors of the artificial wolfs based on the self duties.
The ant colony Algorithm (ACO) is a colony algorithm extracted by simulating the collective path-finding behavior of ants in nature, and is characterized in that pheromones are left on paths traveled by the ants, and the pheromones are gradually volatilized along with time, and simultaneously attract other ants to gradually approach the paths, thereby forming a positive feedback mechanism. From some experimental analysis in the field, the ant colony algorithm obtains better experimental results in the aspects of solving the scheduling of the travel operation and task allocation. Compared with genetic algorithm, particle swarm algorithm and the like, the ant colony algorithm has more stable searching performance and higher accuracy. Based on the above advantages, the ant colony algorithm attracts many scholars' attention and proposes many optimizations. However, the ant colony algorithm has many disadvantages such as slow initial convergence speed.
Fig. 1 is a flowchart illustrating a method for planning a path of a mobile robot based on a road network ant colony algorithm according to an embodiment of the present invention; as shown in fig. 1, the path planning method includes:
s20, creating a grid map, and setting a motion starting point and a motion target point for the mobile robot; wherein each barrier unit and non-barrier unit in the grid map comprises a number of bounded grid units; the black part in the map represents an obstacle;
s40, connecting the starting point and the target point, making N parallel line segments which are perpendicular to a connecting line between the starting point and the target point and are equidistant, and dividing the map space between the starting point and the target point into N-1 parts; specifically, as shown in fig. 2, the robot sets a starting point and a target point of the movement, called ST line, and makes N line segments parallel to and equidistant from the ST line vertically, so as to divide the map space between the starting point and the target point into N-1 parts.
S60, selecting M points with the maximum fitness on each line segment through a wolf colony algorithm to form a road network comprising M (N-2) +2 points; wherein the distance between the two points is greater than L/2m; l is the length of the current layer line segment;
the road network drawn by the wolf colony algorithm is shown in fig. 2:
specifically, selecting M points with the maximum fitness on each line segment through a wolf colony algorithm to form a road network containing M (N-2) +2 points in total comprises the following steps:
and selecting M points with the maximum fitness on each line segment on the N line segments according to a wolf pack algorithm, wherein the points of adjacent parallel lines are connected with each other to form a road network.
Wherein, the fitness function of the wolf colony algorithm is as follows:
in the formula
All coefficients are used for balancing the weight of each part of the fitness function;
the visualized length of the current grid and the line segment of the previous layer,
the total length of the current grid point and the segment of the previous layer,
for the visualization length of the current grid and the next layer line segment,
the total length of the current grid point and the next layer line segment,
is the included angle between the connecting line of the current grid point and the departure point and the ST line, S is the departure point, T is the target point, ST line is the connecting line of the departure point and the target point,
is the euclidean distance of the current grid from the last layer of selected points.
When the y-layer road network point location is selected, firstly, visually judging all grid points on the y-layer line from points selected from the y-1 layer, screening grid point locations or point location intervals which can be connected to the upper layer of the y-layer, calculating according to a fitness function, and selecting a target grid.
Specifically, the wolf pack algorithm is defined as follows:
wherein, t: representing the current iteration number;
: represents [0,1 ]]A random vector of (1); a. C: representing a vector of co-ordinates;
: a position vector representing a prey; x i (t): position vector representing current gray wolf
A, determining a searching direction;
c, deciding whether to favor global search or local search;
the iteration position of each wolf is determined by three wolfs.
And S80, finding out the optimal path based on the formed road network through an ant colony algorithm.
Specifically, the ant colony algorithm searches according to the following rules:
wherein, P k ij (t) represents the probability of an ant going from inode to j-node,
representing the set of neighboring grids that ants can reach next,
representing the pheromone concentration of ants from the current node i to the next node j at the moment t,
indicating heuristic information for ants from the current node i to the next node j at time t,
and
expressing an pheromone factor and a heuristic factor, wherein the heuristic function expression is as follows:
The embodiment provides a mobile robot path planning method based on a road network ant colony algorithm, wherein a starting point and a target point of motion are set for a mobile robot by creating a grid map; wherein each barrier unit and non-barrier unit in the grid map comprises a number of bounded grid units; connecting the starting point with the target point, making N parallel line segments with equal distance perpendicular to a connecting line between the starting point and the target point, and dividing the map space between the starting point and the target point into N-1 parts; selecting M points with the maximum fitness on each line segment through a wolf colony algorithm to form a road network comprising M (N-2) +2 points; wherein the distance between the two points is greater than L/2m; l is the length of the current layer line segment; and finding out the optimal path based on the formed road network through an ant colony algorithm. According to the method, a certain grid is screened out from the grid map through the wolf colony algorithm to form the road network comprising the target point and the departure point, and then the ant colony algorithm is used for planning the path, so that the calculation amount at the initial stage of the ant colony search is reduced, and the calculation speed is improved.
Based on the same inventive concept, the embodiment of the present invention further provides a mobile robot path planning device based on a road network ant colony algorithm, which can be used to implement the mobile robot path planning method based on the road network ant colony algorithm described in the above embodiments, as described in the following embodiments: because the principle of solving the problems of the mobile robot path planning device based on the road network ant colony algorithm is similar to that of the mobile robot path planning method based on the road network ant colony algorithm, the implementation of the mobile robot path planning device based on the road network ant colony algorithm can be referred to the implementation of the mobile robot path planning method based on the road network ant algorithm, and repeated parts are not repeated. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.
Fig. 3 is a schematic structural diagram of a mobile robot path planning device based on a road network ant colony algorithm according to an embodiment of the invention; as shown in fig. 3, the path planning apparatus includes:
the grid map creating module 20 is used for creating a grid map and setting a motion starting point and a motion target point for the mobile robot; wherein each barrier unit and non-barrier unit in the grid map comprises a number of bounded grid units;
the map dividing module 40 is used for connecting the starting point and the target point, making N parallel line segments which are perpendicular to a connecting line between the starting point and the target point and are equidistant, and dividing the map space between the starting point and the target point into N-1 parts;
the first calculation module 60 selects M points with the maximum fitness on each line segment through a wolf colony algorithm to form a road network comprising M (N-2) +2 points; wherein the distance between the two points is greater than L/2m; l is the length of the current layer line segment;
the second calculation module 80 finds an optimal path through an ant colony algorithm based on the formed road network.
Wherein, the fitness function of the wolf colony algorithm is as follows:
in the formula
All coefficients are used for balancing the weight of each part of the fitness function;
the visualized length of the current grid and the line segment of the previous layer,
the total length of the current grid point and the segment of the previous layer,
for the visualization length from the current grid to the next level line segment,
the total length of the current grid point and the next layer line segment,
is the included angle between the connecting line of the current grid point and the departure point and the ST line, S is the departure point, T is the target point, ST line is the connecting line of the departure point and the target point,
is the euclidean distance of the current grid from the last layer of selected points.
The road network drawn by the wolf colony algorithm is shown in fig. 2:
specifically, selecting M points with the maximum fitness on each line segment through a wolf colony algorithm to form a road network containing M (N-2) +2 points in total comprises the following steps:
and selecting M points with the maximum fitness on each line segment on the N line segments according to a wolf pack algorithm, wherein the points of adjacent parallel lines are connected with each other to form a road network.
Wherein, the fitness function of the wolf colony algorithm is as follows:
in the formula
All coefficients are used for balancing the weight of each part of the fitness function;
the visualized length of the current grid and the line segment of the previous layer,
the total length of the current grid point and the segment of the previous layer,
for the visualization length of the current grid and the next layer line segment,
the total length of the current grid point and the next layer line segment,
is the included angle between the connecting line of the current grid point and the departure point and the ST line, S is the departure point, T is the target point, ST line is the connecting line of the departure point and the target point,
is the euclidean distance of the current grid from the last layer of selected points.
When the y-layer road network point location is selected, firstly, visually judging all grid points on the y-layer line from points selected from the y-1 layer, screening grid point locations or point location intervals which can be connected to the upper layer of the y-layer, calculating according to a fitness function, and selecting a target grid.
Specifically, the wolf pack algorithm is defined as follows:
wherein, t: representing the current iteration number;
: represents [0,1 ]]A random vector of (1); a. C: representing a vector of co-ordinates;
: a position vector representing a prey; x i (t): a position vector representing the current gray wolf;
a, determining a search direction;
c, deciding whether to favor global search or local search;
the iteration position of each wolf is determined by three wolfs.
The embodiment provides a mobile robot path planning device based on a road network ant colony algorithm, which creates a grid map through a grid map creation module 20, and sets a motion starting point and a motion target point for a mobile robot; wherein each barrier unit and non-barrier unit in the grid map comprises a number of bounded grid units; connecting the starting point and the target point through a map dividing module 40, making N parallel line segments which are perpendicular to a connecting line between the starting point and the target point and are equidistant, and dividing the map space between the starting point and the target point into N-1 parts; the first calculation module 60 selects M points with the maximum fitness on each line segment through a wolf colony algorithm to form a road network comprising M (N-2) +2 points; wherein the distance between the two points is greater than L/2m; l is the length of the current layer line segment; the second calculation module 80 finds the optimal path by the ant colony algorithm based on the formed road network.
Fig. 4 is a schematic structural diagram of an electronic device to which an embodiment of the present invention can be applied, and as shown in fig. 4, the electronic device includes a Central Processing Unit (CPU) 401 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 402 or a program loaded from a storage section 408 into a Random Access Memory (RAM) 403. In the RAM 403, various programs and data necessary for system operation are also stored. The CPU 401, ROM 402, and RAM 403 are connected to each other via a bus 404. An input/output (I/O) interface 405 is also connected to bus 404.
The following components are connected to the I/O interface 405: an input section 406 including a keyboard, a mouse, and the like; an output section 407 including a display device such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 408 including a hard disk and the like; and a communication section 409 including a network interface card such as a LAN card, a modem, or the like. The communication section 409 performs communication processing via a network such as the internet. A driver 410 is also connected to the I/O interface 405 as needed. A removable medium 411 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 410 as necessary, so that a computer program read out therefrom is mounted into the storage section 408 as necessary.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules or modules described in the embodiments of the present invention may be implemented by software or hardware. The modules or modules described may also be provided in a processor, and may be described as: a processor includes a first calculation module 60 and a second calculation module 80, wherein the names of these modules do not in some cases constitute a limitation on the modules themselves, e.g., the second calculation module 80, and finding the optimal path through an ant colony algorithm based on a formed road network can also be described as "the second calculation module 80 finding the optimal path through an ant colony algorithm based on a formed road network".
As another aspect, the present invention further provides a computer-readable storage medium, which may be the computer-readable storage medium included in the road network ant colony algorithm based mobile robot path planning apparatus in the foregoing embodiments; or it may be a computer-readable storage medium that exists separately and is not built into the electronic device. The computer readable storage medium stores one or more programs for one or more processors to execute the road network ant colony algorithm-based mobile robot path planning method described in the present invention.
The foregoing description is only exemplary of the preferred embodiments of the invention and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention herein disclosed is not limited to the particular combination of features described above, but also encompasses other arrangements formed by any combination of the above features or their equivalents without departing from the spirit of the invention. For example, the above features and (but not limited to) features having similar functions disclosed in the present invention are mutually replaced to form the technical solution.
Claims (10)
1. The mobile robot path planning method based on the road network ant colony algorithm is characterized by comprising the following steps:
establishing a grid map, and setting a motion starting point and a motion target point for the mobile robot; wherein each barrier unit and non-barrier unit in the grid map comprises a number of bounded grid units;
connecting the starting point with the target point, making N parallel line segments with equal distance perpendicular to a connecting line between the starting point and the target point, and dividing the map space between the starting point and the target point into N-1 parts;
selecting M points with the maximum fitness on each line segment through a wolf colony algorithm to form a road network comprising M (N-2) +2 points; wherein the distance between the two points is greater than L/2m; l is the length of the current layer line segment;
and finding out the optimal path based on the formed road network through an ant colony algorithm.
2. The path planning method according to claim 1, wherein the selecting M points with the maximum fitness on each line segment by the wolf pack algorithm to form a road network including M (N-2) +2 points comprises:
and selecting M points with the maximum fitness on each line segment on the N line segments according to a wolf pack algorithm, wherein the points of adjacent parallel lines are connected with each other to form a road network.
3. The path planning method according to claim 2, wherein the fitness function of the wolf pack algorithm is:
in the formula
All coefficients are used for balancing the weight of each part of the fitness function;
the visualized length of the current grid and the line segment of the previous layer,
the total length of the current grid point and the segment of the previous layer,
is associated with the current grid and the next gridThe visualized length of the segment of the layer,
the total length of the current grid point and the next layer line segment,
is the included angle between the connecting line of the current grid point and the departure point and the ST line, S is the departure point, T is the target point, ST line is the connecting line of the departure point and the target point,
is the euclidean distance of the current grid from the last layer of selected points.
4. The path planning method according to claim 3, wherein when the y-th layer of road network point locations is selected, the points selected from the y-1-th layer are visually judged on all grid points on the y-th line, grid point locations or point location intervals which can be connected to the last layer on the y-th layer are screened out, and then calculation is performed according to the fitness function, so that the target grid is selected.
5. The path planning method according to claim 4, wherein the wolf pack algorithm is defined as follows
Wherein, t: representing the current iteration number;
: represents [0,1 ]]A random vector of (1); a. C: representing a vector of co-ordinates;
: a position vector representing a prey; x i (t): position vector representing current gray wolf
A, determining a search direction;
c, deciding whether to favor global search or local search;
the iteration position of each wolf is determined by three wolfs.
6. The path planning method according to claim 5,
the ant colony algorithm searches according to the following rules:
wherein, P k ij (t) represents the probability of an ant going from inode to j-node,
representing the set of neighboring grids that ants can reach next,
representing the pheromone concentration of ants from the current node i to the next node j at the moment t,
indicating heuristic information between ants from the current node i to the next node j at time t,
and
expressing an pheromone factor and a heuristic factor, wherein the heuristic function expression is as follows:
7. Mobile robot path planning device based on road network ant colony algorithm, characterized in that, the path planning device includes:
the grid map creating module is used for creating a grid map and setting a motion starting point and a motion target point for the mobile robot; wherein each barrier unit and non-barrier unit in the grid map comprises a number of bounded grid units;
the map dividing module is used for connecting the starting point and the target point, making N parallel line segments which are perpendicular to a connecting line between the starting point and the target point and are equidistant, and dividing the map space between the starting point and the target point into N-1 parts;
the first calculation module selects M points with the maximum fitness on each line segment through a wolf colony algorithm to form a road network comprising M (N-2) +2 points; wherein the distance between the two points is greater than L/2m; l is the length of the current layer line segment;
and the second calculation module finds out the optimal path through an ant colony algorithm based on the formed road network.
8. The path planner as claimed in claim 7 wherein the fitness function of the wolf pack algorithm is:
in the formula
All coefficients are used for balancing the weight of each part of the fitness function;
the visualized length of the current grid and the line segment of the previous layer,
is the total length of the current grid point and the segment of the previous layer，
For the visualization length of the current grid and the next layer line segment,
the total length of the current grid point and the next layer line segment,
is the included angle between the connecting line of the current grid point and the departure point and the ST line, S is the departure point, T is the target point, ST line is the connecting line of the departure point and the target point,
is the euclidean distance of the current grid from the last layer of selected points.
9. An electronic device comprising a memory and a processor, the memory having stored thereon a computer program, wherein the processor, when executing the computer program, implements the method of any of claims 1 to 6.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310107530.5A CN115826587B (en) | 2023-02-14 | 2023-02-14 | Mobile robot path planning method and device based on road network ant colony algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310107530.5A CN115826587B (en) | 2023-02-14 | 2023-02-14 | Mobile robot path planning method and device based on road network ant colony algorithm |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115826587A true CN115826587A (en) | 2023-03-21 |
| CN115826587B CN115826587B (en) | 2023-06-06 |
Family
ID=85521145
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310107530.5A Active CN115826587B (en) | 2023-02-14 | 2023-02-14 | Mobile robot path planning method and device based on road network ant colony algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115826587B (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111457927A (en) * | 2020-04-26 | 2020-07-28 | 北京工商大学 | Unmanned cruise ship multi-target path planning method under dynamic barrier |
| CN113725861A (en) * | 2021-08-31 | 2021-11-30 | 湖北工业大学 | Electric vehicle charging station distribution planning scheme based on wolf algorithm |
| CN114047770A (en) * | 2022-01-13 | 2022-02-15 | 中国人民解放军陆军装甲兵学院 | Mobile robot path planning method for multi-inner-center search and improvement of wolf algorithm |
| CN115169690A (en) * | 2022-07-05 | 2022-10-11 | 水利部交通运输部国家能源局南京水利科学研究院 | Dam-break flood-avoiding transfer dynamic path optimization method based on improved ant colony algorithm |
-
2023
- 2023-02-14 CN CN202310107530.5A patent/CN115826587B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111457927A (en) * | 2020-04-26 | 2020-07-28 | 北京工商大学 | Unmanned cruise ship multi-target path planning method under dynamic barrier |
| CN113725861A (en) * | 2021-08-31 | 2021-11-30 | 湖北工业大学 | Electric vehicle charging station distribution planning scheme based on wolf algorithm |
| CN114047770A (en) * | 2022-01-13 | 2022-02-15 | 中国人民解放军陆军装甲兵学院 | Mobile robot path planning method for multi-inner-center search and improvement of wolf algorithm |
| CN115169690A (en) * | 2022-07-05 | 2022-10-11 | 水利部交通运输部国家能源局南京水利科学研究院 | Dam-break flood-avoiding transfer dynamic path optimization method based on improved ant colony algorithm |
Non-Patent Citations (2)
| Title |
|---|
| 王奔 等: "改进的蚁群算法在定制公交路径规划中的应用", vol. 50, no. 5, pages 995 - 1000 * |
| 薛锋 等: "基于动态罚函数的铁路车流分配与径路优化模型", vol. 57, no. 5, pages 941 - 948 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115826587B (en) | 2023-06-06 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Dhiman et al. | EMoSOA: a new evolutionary multi-objective seagull optimization algorithm for global optimization | |
| US8620510B1 (en) | Adaptive multi-vehicle area coverage optimization system and method | |
| Kumar et al. | A novel binary seagull optimizer and its application to feature selection problem | |
| CN112905801A (en) | Event map-based travel prediction method, system, device and storage medium | |
| Hu et al. | An improved Harris’s Hawks optimization for SAR target recognition and stock market index prediction | |
| CN116301048A (en) | Unmanned plane path planning method, unmanned plane path planning system, electronic equipment and storage medium | |
| Li et al. | Improved elephant herding optimization using opposition-based learning and K-means clustering to solve numerical optimization problems | |
| WO2020181934A1 (en) | Method and device for determining position of target object on the basis of particle swarm algorithm | |
| Zhai et al. | A Novel Teaching-Learning-Based Optimization with Error Correction and Cauchy Distribution for Path Planning of Unmanned Air Vehicle. | |
| Jin et al. | Conflict-based search with D* lite algorithm for robot path planning in unknown dynamic environments | |
| Pu et al. | 3D path planning for a robot based on improved ant colony algorithm | |
| Kundu et al. | A hybrid salp swarm algorithm based on TLBO for reliability redundancy allocation problems | |
| Anantathanavit et al. | Using K-means radius particle swarm optimization for the travelling salesman problem | |
| Li et al. | Research on global path planning of unmanned vehicles based on improved ant colony algorithm in the complex road environment | |
| CN113158030B (en) | Recommendation method and device for remote interest points, electronic equipment and storage medium | |
| Huang et al. | A hybrid Aquila optimizer and its K-means clustering optimization | |
| Alhenawi et al. | Parallel ant colony optimization algorithm for finding the shortest path for mountain climbing | |
| CN112508177A (en) | Network structure searching method and device, electronic equipment and storage medium | |
| CN115826587A (en) | Mobile robot path planning method and device based on road network ant colony algorithm | |
| US11947503B2 (en) | Autoregressive graph generation machine learning models | |
| Abdulraheem et al. | Hyper-parameter tuning for support vector machine using an improved cat swarm optimization algorithm | |
| Li | GeoAI and deep learning | |
| Hao et al. | A search and rescue robot search method based on flower pollination algorithm and Q-learning fusion algorithm | |
| CN114897290A (en) | Evolution identification method and device of business process, terminal equipment and storage medium | |
| CN114611990A (en) | Method and device for evaluating contribution rate of element system of network information system |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |