CN113515124A - Improved ant colony algorithm suitable for mobile robot path planning technology and integrating fuzzy control - Google Patents
Improved ant colony algorithm suitable for mobile robot path planning technology and integrating fuzzy control Download PDFInfo
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Abstract
The invention provides an improved ant colony algorithm for fusion fuzzy control suitable for path planning of a mobile robot, which comprises an ant colony algorithm pheromone updating strategy for fusion fuzzy control, a self-adaptive weight factor adjusting strategy and an optimized path by utilizing a Floyd thought, wherein the ant colony algorithm pheromone updating strategy for fusion fuzzy control is based on the level of a common ant colony algorithm, and a fusion fuzzy controller is used for increasing the updating quality of pheromones on effective path nodes; the self-adaptive weight factor adjusting strategy dynamically adjusts the weight factor of the node searching formula by using the optimal path obtained by each iteration, and accelerates the convergence of the algorithm; and setting the safe distance of the obstacle to carry out path smoothing and optimization by utilizing a path optimization algorithm based on the Floyd algorithm idea. The method overcomes the defects of low convergence speed and poor path optimization of the common ant colony algorithm in a complex environment, improves the convergence speed of the algorithm, and optimizes the final path.
Description
Technical Field
The invention relates to the technical field of mobile robot path planning, in particular to an improved ant colony algorithm which is suitable for a mobile robot path planning technology and integrates fuzzy control, and is used for accelerating the speed and the precision of the mobile robot path planning.
Background
Path planning is an indispensable technology in the development process of mobile robots. At present, intelligent algorithms for path planning of mobile robots are numerous, for example, an artificial potential field method, a particle swarm algorithm, an ant colony algorithm and the like. Some of the algorithms are developed by inspiring natural characteristics of living beings, such as ant colony algorithm.
The ant colony algorithm is a meta-heuristic algorithm, which was proposed in the last 90 th century and is a biological heuristic algorithm with positive feedback and heuristic search characteristics. The ant colony algorithm is used as a global optimization path planning algorithm, can effectively solve the path planning problem of the mobile robot, and is widely applied by researchers. Ant colony algorithms still have certain deficiencies. For example: in a complex environment, the path planning performance of the ant colony algorithm has the problems of low efficiency, poor path optimization capability and the like.
The ant colony algorithm uses an iteration idea to carry out path optimization and has the characteristic of global property. The ant colony algorithm improvement based on the path planning of the mobile robot is mainly embodied in two aspects: on one hand, the improvement of the pheromone concentration updating mode or the initial value has strict requirements on updating parameters, the ant colony convergence is easy to enter a local optimal value, and the path optimization effect is poor; on the other hand, the ant colony path node selection strategy is improved by optimizing ant colonies by adopting the 'elite ant colony' idea and the like, and the selection of the path node has a high heuristic effect, but the global search capability is poor, and the situation is easy to fall into local optimization.
In order to solve the above problems, an ideal algorithm improvement scheme is always sought.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and solve the technical problems that: aiming at the problems of slow convergence speed, multiple redundant nodes and the like of the ant colony algorithm in a complex environment, the fuzzy controller is used for adjusting the pheromone updating strategy and adaptively adjusting the weight factors to improve and optimize the ant colony algorithm, the convergence speed of the ant colony algorithm is accelerated, the iteration times are reduced, the Floyd idea is used for optimizing the ant colony algorithm iteration path, the redundant nodes are deleted, the shortest planning path length is reduced, and the ant colony algorithm result is optimized.
The technical solution of the invention is as follows: an improved ant colony algorithm suitable for fusion fuzzy control of a mobile robot path planning technology. The method comprises the following steps:
step one, setting an environment grid map, setting information of a starting point, a destination point, a barrier and the like, initializing an ant colony algorithm, and starting iteration.
And step two, designing a two-dimensional fuzzy controller, adding the output quantity of the two-dimensional fuzzy controller to a pheromone updating strategy formula, and performing subsequent pheromone updating.
And step three, adaptively updating the weight factors in the node probability formula of the ant colony algorithm according to the result of searching the path by the ants, and updating the node searching probability formula according to the actual parameter setting.
And step four, judging whether the iteration times of the improved ant colony algorithm reach the preset iteration times, if so, performing the fifth step, otherwise, returning to the second step.
And step five, providing an optimized path algorithm based on the Floyd algorithm, extracting the optimal path node obtained by the improved algorithm, setting the safety radius of the barrier, removing redundant nodes, integrating path information and updating the optimal path.
Those skilled in the art will appreciate that the invention may be practiced without these specific details.
The principle of the invention is as follows: the ant colony algorithm is used for searching an optimal path and mainly selects the path according to different pheromone concentrations of different nodes, and in order to ensure the global search capability of the algorithm, the ant colony algorithm uses a rotary betting method as a node selection mode, but in a complex environment, redundant nodes are easy to appear. The node pheromone concentration and the traditional pheromone increment are logically reasoned through the fuzzy controller, pheromones with proper numerical values are output and added into a pheromone updating strategy formula, the enlightening effect of the pheromones is increased, and the search convergence capability of the ant colony on the optimal path is promoted while the global search capability is ensured. Meanwhile, the selection capability of the ant colony algorithm for the optimal node is improved by adaptively adjusting the pheromone weight factor value, the algorithm convergence capability is accelerated, finally, an optimized path algorithm is provided based on the Floyd algorithm to judge and delete the redundant node, the final path is further optimized, and the performance of the improved algorithm is improved.
Compared with the prior art, the invention has the advantages that:
(1) the fuzzy control technology and the ant colony algorithm are fused, the fuzzy calculation is carried out on the ant colony algorithm pheromone variable, the actual pheromone value is approached according to the specific iterative operation condition of the algorithm, the heuristic action of the ant colony algorithm pheromone concentration is improved, the problem that the diversity of searching is easily reduced in the prior art is solved, and the global optimization capability and the optimal solution searching speed of the algorithm are improved.
(2) Meanwhile, the weight factor is adjusted in a self-adaptive manner, so that the convergence effect of the algorithm is further promoted, and the problem of slow iteration speed in the prior art is solved.
(3) An optimized path algorithm is provided based on the Floyd algorithm, the final path obtained by improving the ant colony algorithm is optimized, the optimal path length is further reduced, the problem of redundant nodes in the prior art is solved, and the algorithm performance is improved.
The improved ant colony algorithm which is suitable for the mobile robot path planning technology and integrates fuzzy control carries out effect verification through MATLAB simulation.
Drawings
Fig. 1 is a flowchart of an improved ant colony algorithm with fuzzy control fusion suitable for a mobile robot path planning technology according to the present invention.
Fig. 2 is a comparison graph of the path planning effect of the algorithm (right) and the common ant colony algorithm (left) provided by the invention. The shortest path of the algorithm path planning of the invention is 37.9100mm (wherein the shortest path of iterative convergence is 39.5563mm, and the shortest path of Floyd algorithm idea is 37.9100mm after optimization), and the shortest path of the common algorithm is 42.9706 mm.
Fig. 3 is a comparison graph of the convergence effect of the algorithm proposed by the present invention and a common ant colony algorithm.
Detailed Description
The technical solution of the present invention is further described in detail by the following embodiments.
The invention provides an improved ant colony algorithm for fusing fuzzy control and suitable for a path planning technology of a mobile robot, which comprises the following steps:
the method comprises the steps of firstly, taking a grid map as a main use scene, setting information such as a starting point, a destination point and an obstacle of path planning, initializing an ant colony algorithm, and starting iteration.
And step two, when one ant search path is completed, updating the node pheromone concentration by using the fuzzy controller to obtain a fuzzy controller updating value for improving the pheromone updating strategy of the ant colony algorithm.
And step three, adaptively updating pheromone weight factors in a node probability formula of the ant colony algorithm according to the result of searching the path by the ants.
And step four, judging whether the iteration times of the improved ant colony algorithm reach the preset iteration times, if so, performing the fifth step, otherwise, returning to the second step.
And step five, providing an optimized path algorithm based on the Floyd algorithm, extracting the optimal path node obtained by the improved algorithm, setting the safety radius of the barrier, removing redundant nodes, integrating path information and updating the optimal path.
2. The improved ant colony algorithm fused with Fuzzy control of claim 1, wherein the Fuzzy controller is designed to be a two-dimensional Fuzzy controller, the input variables are node pheromone concentration Tau and pheromone concentration variation Δ Tau _ tra, respectively, the output variable is a single variable, namely pheromone increment Δ Tau _ Fuzzy, and the pheromone increment of the output variable is added to the pheromone updating formula of the ant colony algorithm to increase the enlightening effect of pheromone. The specific pheromone updating strategy formula is as follows:
wherein, tauj(t) is the pheromone concentration at node j at time t,
is the pheromone increment value of z th ant in the common ant colony algorithm on the node j, m is the number of ants, rho is the pheromone evaporation speed, LZThe path length found for ant z, Q is constant.
3. The improved ant colony algorithm with fusion fuzzy control as claimed in claim 2, wherein the membership function of the fuzzy controller input variables is a generalized bell-shaped membership function, and the two input variables are divided into three fuzzy sets respectively: negative large (NB), Zero (ZO), positive large (PB). The output quantity membership function is a triangular membership function and is divided into seven fuzzy sets: negative large (NB), Negative Medium (NM), Negative Small (NS), Zero (ZO), Positive Small (PS), Positive Medium (PM), positive large (PB).
4. The improved ant colony algorithm with fuzzy control fusion as claimed in claim 3, wherein the fuzzy rule of the fuzzy controller is:
(1)IF Tau IS NB AND ΔTau_tra IS NB THEN ΔTau_Fuzzy IS ZO
(2)IF Tau IS NB AND ΔTau_tra IS ZO THEN ΔTau_Fuzzy IS PM
(3)IF Tau IS NB AND ΔTau_tra IS PB THEN ΔTau_Fuzzy IS PB
(4)IF Tau IS ZO AND ΔTau_tra IS NB THEN ΔTau_Fuzzy IS NB
(5)IF Tau IS ZO AND ΔTau_tra IS ZO THEN ΔTau_Fuzzy IS PM
(6)IF Tau IS ZO AND ΔTau_tra IS PB THEN ΔTau_Fuzzy IS PB
(7)IF Tau IS PB AND ΔTau_tra IS NB THEN ΔTau_Fuzzy IS PS
(8)IF Tau IS PB AND ΔTau_tra IS ZO THEN ΔTau_Fuzzy IS PM
(9)IF Tau IS PB AND ΔTau_tra IS PB THEN ΔTau_Fuzzy IS PB
5. the improved ant colony algorithm fusing the fuzzy control as claimed in claim 1, wherein the adaptive adjustment strategy for the ant colony algorithm node search formula is to update pheromone weight factors and heuristic information weight factors in the node probability formula in real time by using optimal path length information obtained by each iteration of the ant colony, to adaptively adjust corresponding weights, and to accelerate the convergence speed of the algorithm. The node probability formula is:
Wherein, tauj(t) is the value of the pheromone of the j node at time t, ηij(t) is the value of the heuristic function between inode and j at time t. dijThe manhattan distance between the node i and the node j is represented by the following coordinates: (x)i，yi)，(xj，yj). Alpha and beta represent the relative importance weights of the pheromone and the elicitor, respectively, Jz(i) Representing the set of nodes ant z is allowed to select next.
The method adaptively adjusts the alpha weight value to accelerate algorithm convergence, and the adjustment formula is as follows:
wherein, ro _ min (k) represents the shortest path numerical value of the k-th improved algorithm iteration, W is a fixed constant, the value of W is set according to the specific grid map environment, and the numerical value of W is generally set to be one half of the shortest path length of the traditional ant colony algorithm path planning. V is a preset value for the ant colony algorithm initialization alpha. α' represents the pheromone weight factor value at the current iteration.
6. The improved ant colony algorithm fusing fuzzy control according to claim 1, wherein an optimized path algorithm is provided based on the Floyd algorithm to delete redundant nodes and optimize paths. The method comprises the following steps:
step one, extracting the improved ant colony algorithm iteration to obtain a final path, judging whether each node in the path is an initial point, an inflection point and a destination point, if so, retaining, and otherwise, deleting.
And step two, carrying out iterative judgment on the reserved nodes from the initial point by using the principle that three points are taken as a group, judging whether each group of intermediate nodes is a redundant node, if so, deleting, and otherwise, reserving.
And step three, performing path updating on the reserved nodes according to the sequential connection and calculating the path length.
7. The optimized path algorithm proposed based on the Floyd algorithm according to claim 6, wherein: and for the path nodes obtained by the improved algorithm, starting from the starting point, every three nodes form a group, judging whether the included angle between the vector formed by the first node and the second node and the vector formed by the first node and the third node is 0 degree or 180 degrees, if so, taking the middle node as an inflection point, reserving, and otherwise, deleting. And circulating the above processes until all the nodes finish circulating once.
8. The optimized path algorithm proposed based on the Floyd algorithm according to claim 6, wherein: and establishing a linear equation of the first node and the third node by taking the three nodes as a group, and judging whether the distances between the centers of the obstacles in a rectangular range with the first node and the third node as opposite angles are less than a safe distance D, wherein the length of the linear equation of the first node and the third node is generally the radius of a circumscribed circle of the grid, if the distances are less than the safe distance D, the straight line passes through the obstacles, the intermediate nodes cannot be deleted, otherwise, the intermediate nodes are redundant nodes and can be deleted.
Claims (8)
1. An improved ant colony algorithm suitable for fusion fuzzy control of a mobile robot path planning technology is characterized by comprising the following steps:
firstly, an improved algorithm takes a grid map as a main use scene, sets information such as a starting point, a destination point and an obstacle of path planning, initializes an ant colony algorithm and starts iteration;
step two, when one ant search path is completed, the node pheromone concentration is updated by using the fuzzy controller to obtain a fuzzy controller update value which is used for improving the pheromone update strategy of the ant colony algorithm;
step three, adaptively updating pheromone weight factors in a node probability formula of the ant colony algorithm according to the result of searching the path by the ants;
step four, judging whether the iteration times of the improved ant colony algorithm reach the preset iteration times, if so, performing the fifth step, otherwise, returning to the second step;
and step five, providing an optimized path algorithm based on the Floyd algorithm, extracting the optimal path node obtained by the improved algorithm, setting the safety radius of the barrier, removing redundant nodes, integrating path information and updating the optimal path.
2. The improved ant colony algorithm fused with Fuzzy control as claimed in claim 1, wherein the Fuzzy controller is designed as a two-dimensional Fuzzy controller, the input variables are node pheromone concentration Tau and pheromone concentration variation Δ Tau _ tra, respectively, the output variable is a single variable, namely pheromone increment Δ Tau _ Fuzzy, the pheromone increment of the output variable is added to the pheromone updating formula of the ant colony algorithm, and the enlightening effect of pheromone is increased; the specific pheromone updating strategy formula is as follows:
wherein, tauj(t) is the pheromone concentration at node j at time t,
is the pheromone increment value of z th ant in the common ant colony algorithm on the node j, m is the number of ants, rho is the pheromone evaporation speed, LZThe path length found for ant z, Q is constant.
3. The improved ant colony algorithm with fusion fuzzy control as claimed in claim 2, wherein the membership function of the fuzzy controller input variables is a generalized bell-shaped membership function, and the two input variables are divided into three fuzzy sets respectively: negative large (NB), Zero (ZO), positive large (PB); the output quantity membership function is a triangular membership function and is divided into seven fuzzy sets: negative large (NB), Negative Medium (NM), Negative Small (NS), Zero (ZO), Positive Small (PS), Positive Medium (PM), positive large (PB).
4. The improved ant colony algorithm with fuzzy control fusion as claimed in claim 3, wherein the fuzzy rule of the fuzzy controller is:
(1)IF Tau IS NB AND △Tau_tra IS NB THEN △Tau_Fuzzy IS ZO
(2)IF Tau IS NB AND △Tau_tra IS ZO THEN △Tau_Fuzzy IS PM
(3)IF Tau IS NB AND △Tau_tra IS PB THEN △Tau_Fuzzy IS PB
(4)IF Tau IS ZO AND △Tau_tra IS NB THEN △Tau_Fuzzy IS NB
(5)IF Tau IS ZO AND △Tau_tra IS ZO THEN △Tau_Fuzzy IS PM
(6)IF Tau IS ZO AND △Tau_tra IS PB THEN △Tau_Fuzzy IS PB
(7)IF Tau IS PB AND △Tau_tra IS NB THEN △Tau_Fuzzy IS PS
(8)IF Tau IS PB AND △Tau_tra IS ZO THEN △Tau_Fuzzy IS PM
(9)IF Tau IS PB AND △Tau_tra IS PB THEN △Tau_Fuzzy IS PB。
5. the improved ant colony algorithm fusing the fuzzy control as claimed in claim 1, wherein the adaptive adjustment strategy for the ant colony algorithm node search formula is to update pheromone weight factors and heuristic information weight factors in the node probability formula in real time by using optimal path length information obtained by each iteration of the ant colony, adaptively adjust corresponding weights, and accelerate the convergence speed of the algorithm; the node probability formula is:
Wherein, tauj(t) is the value of the pheromone of the j node at time t, ηij(t) is the value of the heuristic function between the inode and the j node at time t; dijThe manhattan distance between the node i and the node j is represented by the following coordinates: (x)i，yi)，(xj，yj) (ii) a Alpha and beta represent the relative importance weights of the pheromone and the elicitor, respectively, Jz(i) Representing the set of nodes that ant z allows to select next;
the method adaptively adjusts the alpha weight value to accelerate algorithm convergence, and the adjustment formula is as follows:
wherein, ro _ min (k) represents the shortest path numerical value of the k-th improved algorithm iteration, W is a fixed constant, the value of W is set according to the specific grid map environment, and the numerical value of W is generally set to be one half of the shortest path length of the path planning of the traditional ant colony algorithm; v is a preset value of the ant colony algorithm initialization alpha; α' represents the pheromone weight factor value at the current iteration.
6. The improved ant colony algorithm fusing fuzzy control according to claim 1, characterized in that an optimized path algorithm is proposed based on the Floyd algorithm to delete redundant nodes and optimize paths; the method comprises the following steps:
extracting the improved ant colony algorithm iteration to obtain a final path, judging whether each node in the path is an initial point, an inflection point and a destination point, if so, retaining, and otherwise, deleting;
step two, carrying out iterative judgment on the reserved nodes from the initial point by using the principle that three points are taken as a group, judging whether each group of intermediate nodes is a redundant node, if so, deleting, and otherwise, reserving;
and step three, performing path updating on the reserved nodes according to the sequential connection and calculating the path length.
7. The optimized path algorithm proposed based on the Floyd algorithm according to claim 6, wherein: for the path nodes obtained by the improved algorithm, starting from a starting point, every three nodes form a group, judging whether an included angle between a vector formed by a first node and a second node and a vector formed by the first node and a third node is 0 degree or 180 degrees, if so, taking an intermediate node as an inflection point, reserving, and otherwise, deleting; and circulating the above processes until all the nodes finish circulating once.
8. The optimized path algorithm proposed based on the Floyd algorithm according to claim 6, wherein: and establishing a linear equation of the first node and the third node by taking the three nodes as a group, and judging whether the distances between the centers of the obstacles in a rectangular range with the first node and the third node as opposite angles are less than a safe distance D, wherein the length of the linear equation of the first node and the third node is generally the radius of a circumscribed circle of the grid, if the distances are less than the safe distance D, the straight line passes through the obstacles, the intermediate nodes cannot be deleted, otherwise, the intermediate nodes are redundant nodes and can be deleted.
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