CN110726408A - Mobile robot path planning method based on improved ant colony algorithm - Google Patents
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Abstract
The invention relates to a mobile robot path planning method based on an improved ant colony algorithm, which comprises the following steps: (1) adopting a new heuristic function in the ant colony algorithm to obtain an angle theta between a straight line from the current node to the next node and a straight line from the current node to the end point1And an angle theta formed by a straight line from the current node to the next node and a straight line from the next node to the end point2Adding the orientation into an enlightening function, and overcoming the problem of easy falling into local optimum; (2) a new pheromone volatilization factor is adopted in the ant colony algorithm, the pheromone volatilization factor is dynamically adjusted, and the pheromone volatilization factor is adaptively adjusted along with the increase of the ant colony iteration times, so that the volatilization factor is smaller in the early stage and larger in the later stage, and the global search capability and the later stage convergence speed are accelerated. Through the improvement, the invention overcomes the problem of local optimization while selecting the optimal path, and can improve the algorithm precision and convergenceSpeed.
Description
Technical Field
The invention relates to the field of artificial intelligence and robot navigation, in particular to a mobile robot path planning method research based on an improved ant colony algorithm.
Background
Path planning is a core technology essential to navigation of mobile robots and is one of the popular fields of robot research of researchers today. The path planning refers to a shortest path for the robot to move from a specified starting point to a specified end point without colliding with an obstacle. Path planning has very important applications in many fields, such as unmanned aerial vehicle driving, serving robot autonomous motion, GPS navigation, and so on.
Route planning originates from the 20 60's world, route planning algorithms appearing in the early days are mainly based on static environment models, such as a grid method, a visual graph method, a mixed route method and the like, but with the rapid development of the society, environment models are more and more complex, the traditional route planning methods based on the static models cannot meet the requirements of people, and then route planning algorithms based on dynamic environment models, such as a genetic algorithm, a simulated annealing method, an artificial potential field method, an ant colony algorithm and other intelligent bionic algorithms, appear. The ant colony algorithm is the most widely applied, is a probability algorithm for searching an optimized path, and is a process for simulating ants to search food, wherein the ants can release a substance which can be called as pheromone in the process of searching food, the ants with shorter paths can release more pheromones, the ants can move towards paths with more pheromones, the shorter paths can accumulate more and more pheromones along with the advance of time, then more and more ants select the paths, and finally the optimal path is searched under the positive feedback mechanism. The traditional ant colony algorithm has good global optimization capability, but also has the problems of low algorithm precision, low later convergence speed and easy falling into local optimization.
Disclosure of Invention
Aiming at the defects that the ant colony algorithm is low in convergence speed, poor in global search capability, prone to falling into local optimization and the like, the invention provides a mobile robot path planning method research based on the improved ant colony algorithm. The invention adopts a new path transfer probability heuristic function, and the straight line from the current node to the next node and the straight line from the current node to the target point are combinedAngle theta of1An angle theta formed by a straight line from the current node to the next node and a straight line from the next node to the target point2Adding the algorithm into the heuristic function, so that the algorithm has better orientation, the precision and the speed are improved, and the algorithm is not easy to fall into local optimum, as shown in the attached figure 2; and the pheromone volatilization factor is improved, and the pheromone volatilization factor is dynamically adjusted, so that the early-stage searching capability and the later-stage convergence speed of the improved ant colony algorithm can be increased.
The specific invention content is as follows: a mobile robot path planning method based on an improved ant colony algorithm comprises the following steps:
step one, modeling an environment map by adopting a grid method, and establishing an obstacle matrix;
initializing various parameters of the algorithm, including a starting point, an end point, iteration times M, the number M of ants in each generation, pheromone evaporation factors rho, heuristic factors alpha and beta, and initial pheromone concentration tauij(t), etc.;
step three, the first generation of first ants starts to search, all nodes which can be reached next time are selected by utilizing the improved heuristic function path transfer probability, the next node is selected through the transfer probability until the ants reach the end point, grids which are passed by the ants are recorded and added into a taboo table, and the path length which is passed by the ants is calculated;
step four, removing ants which do not reach the target point after all ants in the current generation reach the target point, calculating the path length of all ants, and selecting an optimal path;
step five, sorting the path quality of all ants passing through the path of the current generation, and dynamically adjusting pheromone volatilization factors and updating the pheromone concentration on each path by adopting an improved method on the optimal path;
step six, repeating the step two to the step five until the iteration times of the ants reach the maximum, calculating the optimal path of each generation of ants, and synthesizing all the iteration times to obtain a global optimal path;
the heuristic function improved in the third step is as follows:
the heuristic function of the traditional ant colony algorithm is etaij(t)＝1/dij，dijThe Euclidean distance from the current node to the next node is smaller, and the smaller the Euclidean distance is, the better the orientation from the next node to the target node is, but the local optimum is easy to fall into; the method adds an angle theta formed by a straight line from a current node to a next node and a straight line from the current node to a target node1An angle theta formed by a straight line from the current node to the next node and a straight line from the next node to the end point2The three components form a new heuristic function. This can be obtained by two proofs: as shown in FIG. 2, A is the current node, E is the destination, B, C, D is the next reachable node when θ1When ∠ CAE are not equal, theta1The smaller the path, the shorter the theta1And ∠ CAE are equal, then θ2The larger the path, the shorter the path, the better the orientation, and the smaller the estimated cost value.
Prove one, compare paths AC + CE and AB + BE, ∠ CAE and theta first1Size of (2)
∠ ABC and ∠ EBC are all greater than 90 degrees no matter how many degrees ∠ CAE are
∴AB＜AC,BE＜CE
∴AC+CE＜AB+BE
At this time theta1＜∠CAE，∠ACE＜θ2Satisfies theta1The smaller, theta2The larger, dij+θ1+1/θ2The smaller, etaijThe larger (t) is, the better the orientation is, and the smaller the estimated belt value is, the less the local optimum is not likely to be fallen into.
And (2) proving that: then compare AD + DE and AB + DE, at which time theta1＝∠CAE
∵DB+BE＞DE
∴AD+DE＜AD+DB+BE
∴AD+DE＜AB+BE
So when theta is1When equal to ∠ CAE, theta2The larger, dij+θ1+1/θ2The smaller is, thenijThe larger (t) is, the shorter the path is, the better the orientation is, and the more the estimated cost value isXiao, after the syndrome is completed.
The calculation formula of the pheromone volatilization factor dynamically adjusted in the step five is as follows:
in the formula, M is the total iteration number, N is the current iteration number, delta is a volatilization factor influence factor, dynamic adjustment can be realized, rho is an initial pheromone volatilization factor, and rho (N) is always required to be less than 1; is defined in
The iteration times are the first half period before and the second half period after;
the first half period p (N) is relatively small,
and isAlways less than 1, then
The smaller the positive power of the ant is, the lower the pheromone concentration difference of each path is, the guide effect of the ant colony is reduced, the early search range of the ant is enlarged, and the algorithm precision is improved; the latter half period ρ (N) is relatively large, and
become smaller and smaller, then
And the opening number is larger and larger, then rho (N) is larger and larger, the volatilization of the path with less pheromone is faster, the later stage is close to 0, and the path with more pheromone is also fast in volatilization, but the pheromone on the optimal path is still more through the accumulation of the earlier stage, so that the later stage convergence speed of the algorithm is accelerated.
In conclusion, the invention has the positive effects that: the invention provides a research of a mobile robot path planning method based on an improved ant colony algorithm, which is improved aiming at the defects of large estimated cost value, poor initial global search capability, low later convergence speed, easy falling into local optimum and the like of the traditional ant colony algorithm.
Drawings
FIG. 1 is a flowchart of a mobile robot path planning method based on an improved ant colony algorithm according to the present invention
FIG. 2 is a graph of the improved heuristic function provided by the present invention in step three of demonstrating the present invention
Detailed Description
The advantages and objects of the present invention will be further illustrated by the following examples, which are provided for the purpose of illustration only and are not intended to limit the invention.
The invention provides a research of a mobile robot path planning method based on an improved ant colony algorithm, the designed mobile robot path planning of the improved ant colony algorithm is improved around a heuristic function and an pheromone volatilization factor in the path transfer probability, and the heuristic function is added into an angle theta formed by a straight line from a current node to a next node and a straight line from the current node to a target node1An angle theta formed by a straight line from the current node to the next node and a straight line from the next node to the end point2As shown in fig. 2; the improved pheromone volatilization factor changes the fixed value of the traditional ant colony algorithm into self-adaptive adjustment along with the iteration times. The method comprises the following specific steps:
step one, modeling an environment map by adopting a grid method, and establishing an obstacle matrix, wherein the obstacle matrix is represented by 0 and 1, 0 represents a movable space, and 1 represents an obstacle.
Step two, initializing various parameters of the algorithm, including a starting point, an end point, iteration times M, the number M of ants, an pheromone evaporation factor rho and a heuristic factor alphaAnd β, initial pheromone concentration τij(t), etc.
And step three, the first generation of first ants starts to search, all nodes which can be reached next time are selected by utilizing the improved heuristic function path transfer probability, the next node is selected by the maximum value of the transfer probability until the ants reach the terminal point, grids which are passed by the ants are recorded and added into a taboo table, and the path length which is passed by the ants is calculated.
The path transition probability formula of all next nodes which can be reached by the current node of the traditional ant colony algorithm is as follows:
in the formula (1), the first and second groups,
represents the transition probability, tau, of the kth ant from the current node i to the next all nodes jij(t) represents the pheromone concentration on the path from node i to node j, ηij(t) is a heuristic function from node i to node j, expressed as ηij(t)＝1/dij，dijIs the Euclidean distance of node i to node j, i.e.
Point (x)i,yi) Is the coordinate of point i, point (x)j,yj) Is the coordinate of point j; all (k) is all nodes that the kth ant can reach next time.
Heuristic function eta of traditional ant colony algorithmij(t) is only related to the Euclidean distance between the current node i and the next node j, but the Euclidean distance is locally optimal, and the estimated cost value is higher, so that the method combines the included angle theta formed by the straight line from the current node to the next node and the straight line from the current node to the terminal point1An included angle theta formed by a straight line from the current node to the next node and a straight line from the next node to the terminal point2And d isijForming a new heuristic function, wherein the formula is as follows:
this formula has been demonstrated in the summary of the invention: when d isijSmaller, theta1Smaller, theta2Greater, ηijThe larger (t) is, the better the orientation is, and the smaller the estimation cost value is, the less the local optimum is not likely to be trapped.
The ant will choose the next node according to the maximum value of the path transition probability, then check whether the end point is reached, if not, continue to choose the next node in the previous step until the end point is reached, calculate the path length and save, then the next ant will find the path from the starting point by the same method.
And step four, removing ants which do not reach the target point after all ants in the current generation reach the target point, and calculating the path length of all ants.
And step five, sorting the path quality of all the ants in the current generation according to the path length, and dynamically adjusting pheromone volatilization factors and updating the pheromone concentration on each path by adopting an improved method on the optimal path.
The pheromone concentration updating formula is specifically as follows:
τij(t+1)＝(1-ρ)τij(t)+Δτij(t) (3)
wherein tau isij(t +1) is the pheromone concentration on the path from node i to node j after updating, tauij(t) is the pheromone concentration on the path from the node i to the node j before updating, rho is the pheromone volatilization factor, the fixed value is (0,1), and delta tau is takenij(t) is the sum of the concentrations of all ants pheromones passing through the path in the searching process, and the specific formula is as follows:
Δτij(t)kthe concentration of pheromones left for each ant to pass through the path is specifically given by:
wherein Q is pheromone concentration factor carried by kth ant, LdThe path length of the path traveled by the kth ant after completing the path search.
The pheromone volatilization factor rho of the traditional ant colony algorithm is a fixed value set during initialization, but when rho is too large, the global search capability of the algorithm is reduced in the early period, and the algorithm falls into local optimum; when rho is too small, the convergence rate of the algorithm is reduced in the later period, and the efficiency is influenced. Therefore, the invention provides the self-adaptive adjustment of the pheromone volatilization factors, so that the early pheromone volatilization factors are relatively small, the pheromone concentration difference of each path is reduced, the global search capability is increased, the later pheromone volatilization factors are relatively large, and the later algorithm convergence speed is accelerated. The formula is as follows:
in formula (6), M is the total number of iterations,
the current iteration times are delta, the dynamic adjustment can be realized, rho is an initial pheromone volatilization factor, and rho (N) is required to be always less than 1; is defined in
The iteration times are the first half period before and the second half period after;
the first half period ρ (N) is relatively small, and
always less than 1, then
The smaller the positive power of the ant is, the lower the pheromone concentration difference of each path is, the guide effect of the ant colony is reduced, the early search range of the ant is enlarged, and the algorithm precision is improved; the second half period ρ (N) is relatively large, and
become smaller and smaller, thenAnd the opening number is larger and larger, then rho (N) is larger and larger, the volatilization of the path with less pheromone is faster, the later stage is close to 0, and the path with more pheromone is also fast in volatilization, but the pheromone on the optimal path is still more through the accumulation of the earlier stage, so that the later stage convergence speed of the algorithm is accelerated.
And step six, repeating the step two to the step five until the iteration times of the ants reach the maximum, calculating the optimal path of each generation of ants, and synthesizing all the iteration times to obtain the global optimal path.
By the improved method, the defects that the traditional ant colony algorithm is large in estimated cost value, poor in initial global search capability, low in later-stage convergence speed, easy to fall into local optimum and the like are improved, the path transfer probability of the improved heuristic function and the algorithm for adaptively adjusting pheromone volatilization factors are introduced, the estimated cost value of path transfer is reduced, the orientation is improved, the early-stage global search capability of the algorithm is enhanced, the algorithm precision is improved, the later-stage convergence speed of the algorithm is accelerated, and the operation efficiency of the algorithm is improved.
Claims (3)
1. A mobile robot path planning method based on an improved ant colony algorithm is characterized by comprising the following steps:
step one, modeling an environment map by adopting a grid method, and establishing an obstacle matrix;
initializing various parameters of the algorithm, including a starting point, an end point, iteration times M, the number M of ants, an initial pheromone evaporation factor rho, heuristic factors alpha and beta, and an initial pheromone concentration tauij(t), etc.;
step three, searching a first generation of first ants from a starting point, selecting all nodes which can be reached next time by using the improved heuristic function path transfer probability, selecting the next node through the transfer probability until the ants reach an end point, recording grids passed by the ants, adding the grids into a taboo table, and calculating the path length passed by the ants;
step four, removing ants which do not reach the target point after all ants in the current generation reach the target point, calculating the path length of all ants and storing;
step five, sorting the path quality of all ants passing through the path, dynamically adjusting pheromone volatilization factors on the optimal path to update the pheromone concentration on each path, and storing the optimal path;
and step six, repeating the step two to the step five until the iteration times of the ants reach the maximum, calculating the optimal path of each generation of ants, and synthesizing all the iteration times to obtain the global optimal path.
2. The method for planning the path of the mobile robot based on the improved ant colony algorithm according to claim 1, wherein the method comprises the following steps:
the heuristic function of the new invention in the third step is as follows:
the heuristic function of the traditional ant colony algorithm is etaij(t)＝1/dij，dijThe Euclidean distance from the current node to the next node, the angle theta formed by the straight line from the current node to the next node and the straight line from the current node to the target node is added into the method1An angle theta formed by a straight line from the current node to the next node and a straight line from the next node to the end point2The three components form a new heuristic function, and the new heuristic function has the advantages of better orientation, small estimated cost value and difficult falling into local optimization.
3. The method for planning the path of the mobile robot based on the improved ant colony algorithm according to claim 1, wherein the method comprises the following steps:
and the dynamic adjustment pheromone volatilization factor in the step five is as follows:
the dynamic adjustment can be realized by taking the formula M as the total iteration number, taking N as the current iteration number and taking delta as the volatilization factor influence factor, wherein rho is the initial pheromone volatilization factor, and rho (N) is always required to be less than 1; the first half period is defined before the number of N-M/2 iterations, and the second half period is defined after the number of N-M/2 iterations;
the first half period p (N) is relatively small,
and is
Always less than 1, thenThe smaller the positive power of the ant is, the lower the pheromone concentration difference of each path is, the guide effect of the ant colony is reduced, the early search range of the ant is enlarged, and the algorithm precision is improved;
the latter half period ρ (N) is relatively large, and
become smaller and smaller, then
And the root number is larger and larger, so that rho (N) is larger and larger, the later convergence speed of the algorithm is accelerated, and the algorithm efficiency is improved.
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| CN111523698A (en) * | 2020-03-20 | 2020-08-11 | 全球能源互联网集团有限公司 | Ant colony site selection method and device for macroscopically site selection of clean energy base |
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| CN113253756A (en) * | 2021-05-18 | 2021-08-13 | 河北科技大学 | Unmanned aerial vehicle conflict resolution method based on improved ant colony algorithm |
| CN113703450A (en) * | 2021-08-23 | 2021-11-26 | 皖西学院 | Mobile robot path planning method for improving ant colony algorithm based on smooth factors |
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| CN116558527A (en) * | 2023-07-10 | 2023-08-08 | 无锡军工智能电气股份有限公司 | Route planning method for underground substation inspection cleaning robot |
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Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111523698A (en) * | 2020-03-20 | 2020-08-11 | 全球能源互联网集团有限公司 | Ant colony site selection method and device for macroscopically site selection of clean energy base |
| CN111523698B (en) * | 2020-03-20 | 2023-08-08 | 全球能源互联网集团有限公司 | Ant colony site selection method and device for macroscopic site selection of clean energy base |
| CN111857141A (en) * | 2020-07-13 | 2020-10-30 | 武汉理工大学 | Robot path planning method, device, equipment and storage medium |
| CN111896001A (en) * | 2020-07-17 | 2020-11-06 | 上海电机学院 | Three-dimensional ant colony track optimization method |
| CN113110465A (en) * | 2021-04-22 | 2021-07-13 | 哈尔滨理工大学 | Module path planning method based on improved ant colony algorithm |
| CN113253756A (en) * | 2021-05-18 | 2021-08-13 | 河北科技大学 | Unmanned aerial vehicle conflict resolution method based on improved ant colony algorithm |
| CN113703450A (en) * | 2021-08-23 | 2021-11-26 | 皖西学院 | Mobile robot path planning method for improving ant colony algorithm based on smooth factors |
| CN113703450B (en) * | 2021-08-23 | 2024-03-29 | 皖西学院 | Mobile robot path planning method based on smoothing factor improved ant colony algorithm |
| CN114610045A (en) * | 2022-05-12 | 2022-06-10 | 南京铉盈网络科技有限公司 | Robot path planning method and system based on improved ant colony algorithm |
| CN116558527A (en) * | 2023-07-10 | 2023-08-08 | 无锡军工智能电气股份有限公司 | Route planning method for underground substation inspection cleaning robot |
| CN116558527B (en) * | 2023-07-10 | 2023-09-26 | 无锡军工智能电气股份有限公司 | Route planning method for underground substation inspection cleaning robot |
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Application publication date: 20200124 |