CN114964261A - Mobile robot path planning method based on improved ant colony algorithm - Google Patents
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Abstract
The invention discloses a mobile robot path planning method based on an improved ant colony algorithm, and belongs to the field of mobile robot control. According to the method, firstly, the map obstacles are subjected to cluster analysis through a K-medoids algorithm, the initial pheromone concentration is differentiated, and the early convergence speed is improved; then, an ant colony algorithm is improved, and by introducing a self-adaptive heuristic function, heuristic factors are properly amplified according to different positions of nodes to be accessed, so that the speed of searching to a target point is increased; a bidirectional searching mechanism is set, the problem of searching a target point is converted into the problem that two ants meet each other, the algorithm efficiency is improved, and therefore the path planning speed is improved. Compared with the existing path planning scheme, the simulation data show that the shortest path length of the robot is shortened by 5%, the turn times in the process are reduced by 30%, and the convergence algebra is reduced by 46.2%, so that the shortest path of the robot can be found more quickly by the method, and the path quality is better.
Description
Technical Field
The invention relates to a mobile robot path planning method based on an improved ant colony algorithm, and belongs to the field of mobile robot control.
Background
In recent years, with the rapid increase of online shopping transaction amount, the pressure which can be borne by the field of express logistics is gradually saturated. The intelligent logistics concept aims at improving the accuracy and efficiency of links such as logistics sorting, transportation and distribution through technologies such as the Internet of things, robots and 5G communication. In this context, a mobile robot facing smart warehousing is a key element of industry moving towards automation.
Path planning is one of the core technologies for realizing autonomous navigation of a mobile robot, and is an important research topic in the robot field. The method is characterized in that the mobile robot searches a shortest feasible path from a starting point to a terminal point according to a set constraint condition in a working scene, and the process is safe and has no collision. At present, algorithms for solving the global path planning problem are mainly divided into two categories, including traditional algorithms such as depth-first search, A-star algorithm and Dijkstra algorithm, and colony intelligent optimization algorithms such as ant colony algorithm, particle swarm algorithm and simulated annealing algorithm.
The ant colony algorithm is a heuristic algorithm for seeking the optimal solution of the problem through random search, and is suitable for solving the path planning problem of the mobile robot by virtue of the characteristics of a positive feedback mechanism, parallel search, strong robustness, easiness in expansibility and the like. However, the traditional ant colony algorithm has the defects of low convergence speed in the early stage, easy falling into local extreme values, more inflection points, poor precision and the like, and the efficiency and the stability of the algorithm can not meet the requirements when the path planning problem of the mobile robot is solved.
Therefore, many scholars have made relevant improvements to the traditional ant colony algorithm in combination with the above-mentioned practical problems. Majowa et al (majowa, courtey, research on robot path planning of improved ant colony algorithm [ J ]. computer engineering and application, 2021,57(5):6.) propose an improved ant colony algorithm for robot path planning, establish an initial pheromone matrix on the basis of a pre-planned path, and avoid blind search of the algorithm in the early stage; the improved ant colony algorithm and the A-star algorithm are organically fused, and the search directionality and the convergence speed of the ant colony algorithm are further improved. The improved algorithm has the following limitations:
firstly, in order to reduce the blindness of the early-stage search, the starting point and the target point are connected, and the initial pheromone concentration near the straight line is increased. The method does not consider the distribution situation of obstacles in the map, and a large number of ants may be trapped in deadlock situations;
secondly, referring to an A-star algorithm, the estimation cost from the current node to the target point is introduced into the heuristic function of the traditional ant colony algorithm, so that the convergence speed is improved to a certain extent, but the algorithm efficiency is still to be improved;
and thirdly, the oblique angle direction is reserved during turning, and the precision is influenced when the robot actually runs.
In summary, although the improved ant colony algorithm can solve the path planning problem of the robot and has certain improvements in convergence speed and search efficiency, the improved ant colony algorithm also has the problems of easy deadlock, poor path feasibility and the like, and the algorithm efficiency still needs to be further improved.
Disclosure of Invention
In order to solve the problems of poor path feasibility, easy deadlock trapping, low search efficiency and the like of the conventional path planning method, the invention provides a mobile robot path planning method based on an improved ant colony algorithm, which comprises the following steps:
step 1: regarding the working environment of the mobile robot as a two-dimensional plane coordinate system, and constructing the working environment into a static grid map by using a grid method;
step 2: preprocessing the static grid map by using a clustering algorithm, dividing the map into a plurality of clustering partitions, defining a dredging value and a congestion value to quantify the complexity of the regional environment, and differentiating the concentration of pheromones;
and step 3: initializing various parameters, including: the population quantity M, the pheromone weight alpha, the heuristic function weight beta and the pheromone volatilization coefficient rho, initializing a Tabu table and setting the maximum iteration number K;
and 4, step 4: respectively placing M ants at a starting point S and a terminal point E of the working environment of the mobile robot, starting from the M-1 pair of ants, checking whether the walking paths of the pair of ants have intersection, if not, transferring to the next node according to a node transfer probability formula, and adding the previous node into respective taboo table; if yes, jumping to step 5;
and 5: obtaining a path solution according to the walking path of the ants, and calculating the path length; judging whether path searching of the ants by the M pairs is completely finished, and if M is equal to M, finding out the optimal solution and the worst solution of the current iteration; otherwise, turning to the step 4, and restarting the next pair of ants to find the path;
and 6: when each iteration is finished, updating the concentration of the node pheromone, wherein the iteration number k is k + 1; and when K is equal to K, finishing all iterations, and outputting a global optimal path solution and a corresponding shortest path length as the optimal path of the mobile robot.
Optionally, the node transition probability formula in step 4 is:
wherein,
representing a node transition probability; tau is ab (t) is the pheromone concentration between nodes a and b; eta ab (t) is a heuristic function of nodes a to b; alpha and beta represent the pheromone concentration and the weight of the heuristic function respectively; tau is as (t) is the pheromone concentration between nodes a and s, η as (t) is a heuristic function between nodes b and s, and allow represents the set of nodes that ants are allowed to access next.
Optionally, the heuristic function η ab (t) is:
wherein H ab Is the Manhattan distance, H, from node a to node b bE Is node bManhattan distance to end point E, a x 、a y Is the abscissa and ordinate of the node a, b x And b y The abscissa and ordinate of the node b.
Optionally, the node transfer policy in step 4 further includes a policy of marking fallback, where the policy of marking fallback is:
when the ants walk to the node B, the node set allowed to be accessed next step is empty, the nodes are trapped into deadlock, the node B is marked as a barrier and retreats to the node A, the situation that the subsequent ants enter the trap again is avoided, and path searching resources are saved.
Optionally, the method for updating the node pheromone concentration includes: introducing a reward and punishment strategy and introducing an inflection point evaluation function;
the reward and punishment introducing strategy is as follows: when each iteration is finished, only the path solution of the target point is reserved, the path node pheromone exceeding the pseudo-global optimal solution is improved, the pheromone concentration of the worst solution in all the current path solutions is reduced, and the node pheromone concentration formula is as follows:
q is an initial pheromone value, iter _ best is an optimal solution in the iteration, iter _ worst is a worst solution in the iteration, and best is a pseudo-global optimal solution; t represents the t-th moment, namely the t-th generation of ants, and rho is the volatilization coefficient of the pheromone;
the inflection point evaluation function defines the corresponding extension path length according to the difference of each turning angle, and the formula is as follows:
where θ is the turning angle, and f (θ) represents the extended path length.
Optionally, in the step 2, a K-medoids algorithm is used for clustering analysis of the map obstacles, a map partition is defined according to each clustering center point, and the complexity of the regional environment is represented by a dredging value and a congestion value index;
the sum of dissimilarity for determining the cluster center point is formulated as follows:
wherein, TC ih Represents the center point O i Is not a central point O h Total cost after substitution, C jih Represents the center point O i Is not a central point O h After-replacement off-center point O j The cost of (d);
defining a Dredging value P i0 Describing the number of grids which can be straightly passed through in the subarea, and defining the congestion value P i Describing the concentration of obstacles in the partition, the formula is as follows:
P i0 ＝N i0 /S i
P i ＝N i /S i
in the formula, N i0 Represents a partition Y i Number of internally divided rectilinear grids, N i Representing a partition Y i Number of cataract obstructing cells, S i Representing the total number of grids in the partition;
dredging value P partitioned by clusters i0 And congestion value P i Defining an environment complexity function P (u), wherein the formula is as follows:
P(u)＝exp(P i0 -P i )。
optionally, the process of obtaining a path solution according to the walking paths of a pair of ants in the step 4 and the step 5 includes:
each iteration is respectively provided with a group of ants at the starting point and the end point of the working environment of the mobile robot, and each ant at the starting point and the end point is respectively marked as S k (k-1, 2, … m) and E k (k is 1,2, … m), taking one ant from the starting point and the end point respectively to perform path search, and recording each pair of ants as (S) k ,E k ) Wherein the starting pointAnts start forward search from the starting point, and end-point ants start reverse search from the end point;
let Path (S) k ) Is ant S k Set of traversed Path nodes, Path (E) k ) Is an ant E k Set of traversed path nodes, J (S) k ,E k ) The intersection of the pair of ants passing through the node;
when J (S) k ,E k ) When the set is not empty, the two ants meet each other on the way, namely a feasible path is found, and then the search is stopped; solving Path into Ant S k Node of and ant E k The node (2) is connected in series in a reverse order, and the formula is as follows:
J(S k ,E k )＝Path(S k )∩Path(E k )
Path＝Path(S k )+fliplr(Path(E k ))
wherein, the fliplr (×) represents a left-right flip matrix.
Optionally, the constructing the working environment into the static grid map by using the grid method in step 1 includes:
numbering all grids from left to right and from top to bottom in sequence, wherein the expression of the grid numbers and the actual coordinates is as follows:
wherein i is a grid number, i x Is the grid abscissa, i y Is the grid ordinate, mapLen is the number of rows and columns of the grid map, mod is the remainder operator, ceil is the ceiling operator.
The invention also provides a mobile robot path planning device, comprising: a memory and a processor coupled to the memory, the processor configured to: and executing the mobile robot path planning method based on the improved ant colony algorithm based on the instructions stored in the memory.
The invention further provides a computer-readable storage medium, which stores a program, and when the program is executed by a processor, the program implements the method for planning the path of the mobile robot based on the improved ant colony algorithm.
The invention has the beneficial effects that:
according to the mobile robot path planning method, firstly, the map obstacles are subjected to cluster analysis through a K-medoids algorithm, the initial pheromone concentration is differentiated, and the problem of low convergence speed in the early stage of path planning is effectively solved; then, an ant colony algorithm is improved, and by introducing a self-adaptive heuristic function, heuristic factors are properly amplified according to different positions of nodes to be accessed, so that the speed of searching to a target point is increased; a bidirectional searching mechanism is set, the problem of searching a target point is converted into the problem that two ants meet each other, and the algorithm efficiency is improved; the ants can only transversely and longitudinally move, so that the precision is ensured, and the path feasibility is better.
The invention utilizes the improved ant colony algorithm to plan the path of the mobile robot, and simulation experiments prove that compared with the existing path planning scheme utilizing the ant colony algorithm, the path planning method of the invention shortens the shortest path length of the robot by 5 percent, reduces the turn times in the process by 30 percent, and reduces the convergence algebra by 46.2 percent, so the invention can find the shortest path of the robot more quickly and has better path quality.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic diagram of a path planning of a conventional ant colony algorithm.
Fig. 2 is a schematic diagram of a path planning of a conventional improved ant colony algorithm.
FIG. 3 is a clustering partition diagram of the grid map preprocessed by the K-medoids clustering algorithm in the second embodiment of the present invention.
Fig. 4 is a schematic diagram of the improved bidirectional ant colony algorithm path planning according to the second embodiment of the present invention.
Fig. 5 is a comparison graph of the iterative convergence curves of the three algorithms.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in detail with reference to the accompanying drawings.
The first embodiment is as follows:
the embodiment provides a mobile robot path planning method based on an improved ant colony algorithm, which comprises the following steps:
step 1: regarding the working environment of the mobile robot as a two-dimensional plane coordinate system, and constructing the working environment into a static grid map by using a grid method;
step 2: preprocessing the static grid map by using a clustering algorithm, dividing the map into a plurality of clustering partitions, defining a dredging value and a congestion value to quantify the complexity of the regional environment, and differentiating the concentration of pheromones;
and step 3: initializing various parameters, including: the population quantity M, the pheromone weight alpha, the heuristic function weight beta and the pheromone volatilization coefficient rho, initializing a Tabu table and setting the maximum iteration number K;
and 4, step 4: respectively placing M ants at a start point S and an end point E of the working environment of the mobile robot, starting from the mth to 1 pair of ants, checking whether the walking paths of the pair of ants have intersection or not, if not, transferring to the next node according to a node transfer probability formula, and adding the previous node into respective taboo table; if yes, jumping to step 5;
and 5: obtaining a path solution according to the walking path of the ants, and calculating the path length; judging whether path searching of the ants by the M pairs is completely finished, and if M is equal to M, finding out the optimal solution and the worst solution of the current iteration; otherwise, turning to the step 4, and restarting the next pair of ants to find the path;
step 6: when each iteration is finished, updating the concentration of the node pheromone, wherein the iteration number k is k + 1; and when K is equal to K, all iterations are completed, and a global optimal path solution and the corresponding shortest path length are output as the optimal path of the mobile robot.
Example two:
the embodiment provides a mobile robot path planning method based on an improved ant colony algorithm, which is mainly divided into the following parts.
1. Environmental modeling and preprocessing
(1) Environmental modeling
The warehouse logistics center where the mobile robot is located can be regarded as a two-dimensional plane coordinate system, and a working environment is constructed into a static grid map by utilizing a grid method. The original obstacles are irregular in shape, so that the obstacles are puffed and unified into squares. Numbering all grids from left to right and from top to bottom in sequence, wherein the expression of the grid numbers and the actual coordinates is as follows:
wherein i is a grid number, i x Is the grid abscissa, i y Is the grid ordinate, mapLen is the number of rows and columns of the grid map, mod is the remainder operator, ceil is the rounding-up operator.
(2) Map preprocessing
And clustering and analyzing the map obstacles by using a K-medoids algorithm, dividing map partitions according to each clustering central point, and expressing the complexity of the regional environment by using dredging values and congestion value indexes. The sum of dissimilarity for determining the cluster center point is formulated as follows:
in the formula, TC ih Represents the center point O i Is not a central point O h Total cost after substitution, C jih Represents the center point O i Is not a central point O h Non-center point O after replacement j The cost of (a).
Defining a Dredging value P i0 Describing the number of grids which can be straightly passed through in the subarea, and defining the congestion value P i Description partitioningThe concentration of internal obstacles is given by the formula:
P i0 ＝N i0 /S i
P i ＝N i /S i
in the formula, N i0 Represents a partition Y i Number of internally divided rectilinear grids, N i Representing a partition Y i Number of cataract obstructing grids, S i Indicating the total number of grids in the partition.
And defining an environment complexity function P (u) according to the dredging value and the congestion value of each clustering partition, wherein the formula is as follows. The larger the value of P (u), the fewer obstacles in the area where the representative node u is located, and the less the mobile robot spends passing through the area, so that the initial pheromone concentration can be appropriately increased.
P(u)＝exp(P i0 -P i )
2. Bidirectional search mechanism
Each iteration is provided with a group of ants at the starting point and the end point respectively, and each ant is marked as S k (k-1, 2, … m) and E k (k is 1,2, … m), one ant is taken from S, E groups for path search, and each pair of ants is marked as (S) k ,E k ). Wherein, S groups of ants start forward search from the starting point, and E groups of ants start reverse search from the target point.
Let Path (S) k ) Is ant S k Set of traversed Path nodes, Path (E) k ) Is an ant E k Set of traversed path nodes, J (S) k ,E k ) The intersection of the pair of ants passing through the node. When J (S) k ,E k ) When the set is not empty, the two ants meet each other on the way, namely a feasible path is found, and the search is stopped. Solving Path into Ant S k Node of and ant E k The nodes of (1) are connected in series in a reverse order, and the formula is as follows.
J(S k ,E k )＝Path(S k )∩Path(E k )
Path＝Path(S k )+fliplr(Path(E k ))
3. Improved node transition probability formula
The node transition probability is used as a core formula of the ant colony algorithm, and each ant selects the position of the next node according to the node transition probability. In the formula:
representing a node transition probability; tau is ab (t) is the pheromone concentration between nodes a and b; eta ab (t) is a heuristic function from node a to node b; alpha, beta respectively represent the pheromone concentration and the weight of the heuristic function, tau as (t) pheromone concentration between nodes a and s, η as (t) is a heuristic function between nodes b and s, and allow represents the set of nodes that ants are allowed to access next.
(1) Introducing adaptive heuristic functions
In order to increase the transition probability difference between the nodes to be selected, a self-adaptive heuristic function is introduced, and the heuristic factor is properly amplified according to the position difference of the nodes to be selected so as to accelerate the searching speed to the target point, wherein the formula is as follows:
in the formula, H ab Is the Manhattan distance, H, of node a to node b bE Manhattan distance of node b to end point E, a x 、a y Is the abscissa and ordinate of the node a, b x And b y The abscissa and ordinate of the node b.
(2) Deadlock marker rollback processing
If a U-shaped barrier exists in the map environment, part of ants are deadlocked in the route searching process, so that a marking and returning strategy is designed. When the ant walks to the node B, the set of nodes to be selected is empty, and the ant is trapped into deadlock. At the moment, the node B is marked as an obstacle and retreats to the node A, so that the situation that subsequent ants enter a trap again can be avoided, and path-finding resources are saved.
4. Improved pheromone update formula
(1) Pheromone reward and punishment strategy
The pheromone concentration has an important influence on the performance of the ant colony algorithm, during the process of searching the paths by the ants, the pheromone is released to mark a better path solution, and meanwhile, along with the volatilization of the pheromone, after a plurality of iterations, the offspring ants gradually tend to the optimal path by virtue of the pheromone.
In order to avoid the algorithm from falling into local optimum and improve convergence speed, a reward and punishment strategy is introduced into the pheromone concentration, and the formula is as follows. When each iteration is finished, only the path solution of the target point is reserved, the path node pheromone exceeding the pseudo-global optimal solution is improved, and the pheromone concentration of the worst solution in all the path solutions of the current generation is reduced.
In the formula, Q is an initial pheromone value, iter _ best is an optimal solution in the iteration, iter _ worst is a worst solution in the iteration, and best is a pseudo-global optimal solution.
(2) Introducing an inflection point evaluation function
Because the turning times and angles of the mobile robot in an actual working scene can influence the consumption of the driving process, the invention introduces an inflection point evaluation function, and defines the corresponding extension path length according to the difference of each turning angle, and the formula is as follows. The 45 degree oblique angle steering can not ensure the running precision, so the method is not considered.
Where θ is the turning angle, and f (θ) represents the extended path length.
Based on the above improvement on the ant colony algorithm, the mobile robot path planning method of the embodiment specifically includes the following steps:
step 1: regarding the working environment of the mobile robot as a two-dimensional plane coordinate system, and constructing the working environment into a static grid map by using a grid method;
step 2: preprocessing the static grid map by using a K-medoids algorithm, dividing the map into a plurality of clustering partitions, defining a dredging value and a congestion value to quantify the complexity of the regional environment, and differentiating the concentration of pheromones;
and step 3: initializing various parameters, including: the population quantity M, the pheromone weight alpha, the heuristic function weight beta and the pheromone volatilization coefficient rho, initializing a Tabu table and setting the maximum iteration number K;
and 4, step 4: respectively placing M ants at a starting point S and a terminal point E of the working environment of the mobile robot, starting from the M-1 pair of ants, checking whether the walking paths of the pair of ants have intersection, if not, transferring to the next node according to a node transfer probability formula, and adding the previous node into respective taboo table; if yes, jumping to step 5;
and 5: obtaining a path solution according to the walking path of the ants, and calculating the path length; judging whether path searching of the ants by the M pairs is completely finished, and if M is equal to M, finding out the optimal solution and the worst solution of the current iteration; otherwise, turning to the step 4, and restarting the next pair of ants to find the path;
step 6: when each iteration is finished, updating the concentration of the node pheromone, wherein the iteration number k is k + 1; and when K is equal to K, finishing all iterations, and outputting a global optimal path solution and a corresponding shortest path length as the optimal path of the mobile robot.
In order to verify the effect of the invention, the grid map with specification of 20 × 20 is used to test the path planning method of the mobile robot based on the improved bidirectional ant colony algorithm, and the experimental result is compared with the path planning method using the traditional ant colony algorithm and the existing improved ant colony algorithm, and the comparison result is shown in table 1 (for comparison, only transverse and longitudinal movement is reserved for the traditional ant colony algorithm and the existing improved ant colony algorithm).
TABLE 1 comparison of the present invention with existing ant colony algorithms
| Algorithm | Shortest path length | Number of turns | Convergent algebra |
| Traditional ant colony algorithm | 44 | 18 | 23 |
| Existing improved |
40 | 15 | 13 |
| Improved bidirectional ant colony algorithm | 38 | 12 | 7 |
As can be seen from table 1, in the path planning method of the present invention, the shortest path length of the mobile robot is 38, the number of turns is 12, and the algorithm convergence algebra is 7, and compared with the two schemes in the prior art, not only the path is optimized, but also the planning speed is significantly improved, so that the path planning method of the mobile robot of the present invention can find the shortest path faster, and the path solution quality is better and more feasible.
Some steps in the embodiments of the present invention may be implemented by software, and the corresponding software program may be stored in a readable storage medium, such as an optical disc or a hard disk.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (10)
1. A mobile robot path planning method based on an improved ant colony algorithm is characterized by comprising the following steps:
step 1: regarding the working environment of the mobile robot as a two-dimensional plane coordinate system, and constructing the working environment into a static grid map by using a grid method;
step 2: preprocessing the static grid map by using a clustering algorithm, dividing the map into a plurality of clustering partitions, defining a dredging value and a congestion value to quantify the complexity of the regional environment, and differentiating the concentration of pheromones;
and step 3: initializing various parameters, including: the population quantity M, the pheromone weight alpha, the heuristic function weight beta and the pheromone volatilization coefficient rho, initializing a Tabu table and setting the maximum iteration number K;
and 4, step 4: respectively placing M ants at a starting point S and a terminal point E of the working environment of the mobile robot, starting from the M-1 pair of ants, checking whether the walking paths of the pair of ants have intersection, if not, transferring to the next node according to a node transfer probability formula, and adding the previous node into respective taboo table; if yes, jumping to step 5;
and 5: obtaining a path solution according to the walking path of the ants, and calculating the path length; judging whether path searching of the ants by the M pairs is completely finished, and if M is equal to M, finding out the optimal solution and the worst solution of the current iteration; otherwise, turning to the step 4, and restarting the next pair of ants to find the path;
step 6: when each iteration is finished, updating the concentration of the node pheromone, wherein the iteration number k is k + 1; and when K is equal to K, finishing all iterations, and outputting a global optimal path solution and a corresponding shortest path length as the optimal path of the mobile robot.
2. The method of claim 1, wherein the node transition probability formula in step 4 is:
wherein,
representing a node transition probability; tau is ab (t) is the pheromone concentration between nodes a and b; eta ab (t) is a heuristic function from node a to node b; alpha and beta represent the pheromone concentration and the weight of the heuristic function respectively; tau is as (t) is the pheromone concentration between nodes a and s, η as (t) is a heuristic function between nodes b and s, and allow represents the set of nodes that ants are allowed to access next.
3. The method according to claim 2, wherein said heuristic function η ab (t) is:
wherein H ab Is the Manhattan distance, H, of node a to node b bE Manhattan distance of node b to end point E, a x 、a y Is the abscissa and ordinate of the node a, b x And b y The abscissa and ordinate of the node b.
4. The method of claim 3, wherein the node transfer policy in step 4 further comprises a policy of marking rollback, and the policy of marking rollback is:
when the ants walk to the node B, the node set allowed to be accessed next step is empty, the nodes are trapped into deadlock, the node B is marked as a barrier and retreats to the node A, the situation that the subsequent ants enter the trap again is avoided, and path searching resources are saved.
5. The method of claim 4, wherein the method of updating node pheromone concentration comprises: introducing a reward and punishment strategy and introducing an inflection point evaluation function;
the reward and punishment introducing strategy is as follows: when each iteration is finished, only the path solution of the target point is reserved, the path node pheromone exceeding the pseudo-global optimal solution is improved, the pheromone concentration of the worst solution in all the current path solutions is reduced, and the node pheromone concentration formula is as follows:
wherein Q is an initial value of the pheromone, iter _ best is an optimal solution in the iteration, iter _ worst is a worst solution in the iteration, and best is a pseudo global optimal solution; t represents the t-th moment, namely the t-th generation of ants, and rho is the volatilization coefficient of the pheromone;
the inflection point evaluation function defines the corresponding extension path length according to the difference of each turning angle, and the formula is as follows:
where θ is the turning angle, and f (θ) represents the extended path length.
6. The method according to claim 1, wherein the step 2 utilizes a K-medoids algorithm to cluster and analyze the map obstacles, demarcates map partitions according to each cluster center point, and expresses the complexity of the regional environment by indexes of dredging values and congestion values;
the sum of dissimilarity for determining the cluster center point is formulated as follows:
wherein, TC ih Represents the center point O i Is not a central point O h Total cost after substitution, C jih Represents the center point O i Is not a central point O h After-replacement off-center point O j The cost of (d);
defining a Dredging value P i0 Describing the number of the grids which can be straightly passed in the subarea, and defining the congestion value P i Describing the concentration of obstacles in the partition, the formula is as follows:
P i0 ＝N i0 /S i
P i ＝N i /S i
in the formula, N i0 Represents a partition Y i Number of internally divided rectilinear grids, N i Represents a partition Y i Number of cataract obstructing cells, S i Representing the total number of grids in the partition;
dredging value P partitioned by clusters i0 And congestion value P i Defining an environment complexity function P (u), wherein the formula is as follows:
P(u)＝exp(P i0 -P i )。
7. the method as claimed in claim 1, wherein the step 4 and step 5 of obtaining a path solution according to the traveling paths of a pair of ants comprises:
each iteration is respectively provided with a group of ants at the starting point and the end point of the working environment of the mobile robot, and each ant at the starting point and the end point is respectively marked as S k (k ═ 1,2, m) and E k And (k is 1,2, m), taking one ant from the starting point and the end point respectively to perform path search, and recording each pair of ants as (S) k ,E k ) Wherein, the ants at the starting point start to search in the positive direction from the starting point, and the ants at the end point start to search in the reverse direction from the end point;
let Path (S) k ) Is ant S k Set of traversed Path nodes, Path (E) k ) Is an ant E k Set of traversed path nodes, J (S) k ,E k ) The intersection of the pair of ants passing through the node;
when J (S) k ,E k ) When the set is not empty, the two ants meet each other on the way, namely a feasible path is found, and then the search is stopped; solving Path into Ant S k Node of and ant E k The node (2) is connected in series in a reverse order, and the formula is as follows:
J(S k ,E k )＝Path(S k )∩Path(E k )
Path＝Path(S k )+fliplr(Path(E k ))
wherein, the fliplr (×) represents a left-right flip matrix.
8. The method of claim 1, wherein constructing the work environment as a static grid map by using a grid method in step 1 comprises:
numbering all grids from left to right and from top to bottom in sequence, wherein the expression of the grid numbers and the actual coordinates is as follows:
wherein i is a grid number, i x Is the grid abscissa, i y Is the grid ordinate, mapLen is the number of rows and columns of the grid map, mod is the remainder operator, ceil is the rounding-up operator.
9. A mobile robot path planning apparatus, comprising: a memory and a processor coupled to the memory, the processor configured to: executing a method for mobile robot path planning based on an improved ant colony algorithm according to any one of claims 1-8 based on instructions stored in the memory.
10. A computer-readable storage medium, on which a program is stored, which, when executed by a processor, implements a method for mobile robot path planning based on an improved ant colony algorithm according to any one of claims 1 to 8.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115454070A (en) * | 2022-09-15 | 2022-12-09 | 安徽工程大学 | K-Means ant colony algorithm multi-robot path planning method |
| CN115597621A (en) * | 2022-11-08 | 2023-01-13 | 广东嘉腾机器人自动化有限公司（Cn） | AGV path optimization and efficiency improvement path generation method |
| CN115791887A (en) * | 2023-02-08 | 2023-03-14 | 北京汉飞航空科技有限公司 | Self-adaptive measurement algorithm of turbine blade based on six-point measuring tool |
| CN116147636A (en) * | 2023-04-20 | 2023-05-23 | 甘肃省公安厅 | Automatic path finding calculation method for optimal distance based on geographic space |
| CN117387628A (en) * | 2023-12-11 | 2024-01-12 | 深圳大学 | Underwater robot path planning method and device based on directed particle ant colony |
| CN117437382A (en) * | 2023-12-19 | 2024-01-23 | 成都电科星拓科技有限公司 | Updating method and system for data center component |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111857141A (en) * | 2020-07-13 | 2020-10-30 | 武汉理工大学 | Robot path planning method, device, equipment and storage medium |
| CN112650229A (en) * | 2020-12-15 | 2021-04-13 | 扬州大学 | Mobile robot 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 |
| CN113985888A (en) * | 2021-11-08 | 2022-01-28 | 合肥工业大学 | Forklift path planning method and system based on improved ant colony algorithm |
| CN114355913A (en) * | 2021-12-27 | 2022-04-15 | 浙江工业大学 | Mobile robot path planning method based on space-time self-adaptive bidirectional ant colony algorithm |
-
2022
- 2022-05-20 CN CN202210563301.XA patent/CN114964261A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111857141A (en) * | 2020-07-13 | 2020-10-30 | 武汉理工大学 | Robot path planning method, device, equipment and storage medium |
| CN112650229A (en) * | 2020-12-15 | 2021-04-13 | 扬州大学 | Mobile robot 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 |
| CN113985888A (en) * | 2021-11-08 | 2022-01-28 | 合肥工业大学 | Forklift path planning method and system based on improved ant colony algorithm |
| CN114355913A (en) * | 2021-12-27 | 2022-04-15 | 浙江工业大学 | Mobile robot path planning method based on space-time self-adaptive bidirectional ant colony algorithm |
Non-Patent Citations (1)
| Title |
|---|
| 马向华等: "改进蚁群算法在机器人路径规划上的研究", 计算机工程与应用, 28 October 2020 (2020-10-28), pages 210 - 215 * |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115454070A (en) * | 2022-09-15 | 2022-12-09 | 安徽工程大学 | K-Means ant colony algorithm multi-robot path planning method |
| CN115454070B (en) * | 2022-09-15 | 2024-04-05 | 安徽工程大学 | K-Means ant colony algorithm multi-robot path planning method |
| CN115597621A (en) * | 2022-11-08 | 2023-01-13 | 广东嘉腾机器人自动化有限公司（Cn） | AGV path optimization and efficiency improvement path generation method |
| CN115791887A (en) * | 2023-02-08 | 2023-03-14 | 北京汉飞航空科技有限公司 | Self-adaptive measurement algorithm of turbine blade based on six-point measuring tool |
| CN116147636A (en) * | 2023-04-20 | 2023-05-23 | 甘肃省公安厅 | Automatic path finding calculation method for optimal distance based on geographic space |
| CN117387628A (en) * | 2023-12-11 | 2024-01-12 | 深圳大学 | Underwater robot path planning method and device based on directed particle ant colony |
| CN117387628B (en) * | 2023-12-11 | 2024-02-23 | 深圳大学 | Underwater robot path planning method and device based on directed particle ant colony |
| CN117437382A (en) * | 2023-12-19 | 2024-01-23 | 成都电科星拓科技有限公司 | Updating method and system for data center component |
| CN117437382B (en) * | 2023-12-19 | 2024-03-19 | 成都电科星拓科技有限公司 | Updating method and system for data center component |
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