CN110243385A - A kind of ant group algorithm applied to robot path planning - Google Patents
A kind of ant group algorithm applied to robot path planning Download PDFInfo
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- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
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Abstract
The present invention relates to a kind of ant group algorithms applied to robot path planning, the problem that the ant group algorithm is deficient for previous information element and causes convergence rate slow, the weight parameter α (information heuristic factor) and β (expectation heuristic factor) of pheromones and heuristic information are improved, dynamic adjusts two kinds of parameters；In addition, the guiding mechanism in local optimum direction is added, to construct new Path selection probability, the convergence rate of ant group algorithm of the invention is faster.
Description
Technical field
The present invention relates to a kind of ant group algorithms applied to robot path planning, belong to field of computer technology.
Background technique
Recently as the extensive application of mobile robot technology, Path Planning Technique as its important branch also by
The extensive concern of people.So-called path planning is to find one from origin-to-destination in the planning space full of barrier
Optimal, shortest path, and can successfully get around barrier all in environment without collision.
Currently, the algorithm applied in path planning field all Shortcomings to some extent, for example, gradient method is also easy to produce
The problems such as Local Minimum, enumerative technique and too low Monte Carlo analysis computational efficiency.In recent years, many scholars are calculated with improved heredity
The methods of method, neural network, random tree plan robot path, however these methods there are search spaces big, algorithm
The problems such as complicated, inefficient.Compared to algorithm above, it has stronger robustness, excellent distributed computing to ant group algorithm
Mechanism is easy to the advantages that combining with other algorithms.But for traditional ant group algorithm since its complexity generally requires very
Long search time, and the disadvantages of the blindness at algorithm search initial stage also be easy to cause algorithm the convergence speed slow.For its disadvantage
Propose a kind of improved ant colony optimization algorithm.
Summary of the invention
The present invention in order to solve the problems in the existing technology, provide it is a kind of can be improved convergence rate be applied to machine
The ant group algorithm of people's path planning.
In order to achieve the above object, a kind of technical solution proposed by the present invention are as follows: ant applied to robot path planning
Group's algorithm, which comprises the steps of:
Step 1: establishing the simulated environment that robot is run using Grid Method；
Step 2: the Pheromone Matrix that input is initial, select initial point and terminal and following parameter is arranged: iteration is always secondary
Number N, per generation ant sum M, pheromones strength factor Q, pheromones volatility coefficient ρ, local direction factor strength factorPart
D-factor attenuation coefficient ω, constant const；Pheromones heuristic factor α, it is expected that heuristic factor β；
Step 3: the node that selection can reach in next step from initial point, utilizes following formula according to the pheromones of each node
The probability for going to each node is found out, and chooses next step initial point；
Wherein τij(t) pheromone concentration on i to the j of path, n are indicatedij(t)=1/dijIndicate the inspiration on i to the j of path
Formula information；
θ indicates the line vector of starting point i and terminal and the angle of i to j line vector；allowedkIt is to be visited for ant k
The set of node, the then set of the non-accessed node of behalf；T indicates the time；N indicates that current iteration number, N indicate that iteration is always secondary
Number；A, b, c and d are adjustable parameter；
Step 4: according to the next step initial point more new route and path length of step 3 selection；
Step 5: repeat Step 3: four, until ant reach home or because entering trap it is dead；
Step 6: repeating step 3 to five, until all ants of this generation all traverse；
Step 7: updating pheromone concentration；
Step 8: repeat step 3 to seven, to the last generation ant iteration terminates and jumps to step 2 to reset ginseng
NumberWith the value of ω；
Step 9: repeating step 3 to seven, all terminate until reaching the entire iteration of total degree N=100.
Be further designed to above-mentioned technical proposal: a parameter value is respectively as follows: iteration total degree N in the step 2
=100, per generation ant sum M=50, pheromones strength factor Q=1, pheromones volatility coefficient ρ=0.9, local direction factor
Strength factor3 are taken, local direction factor attenuation coefficient ω takes 4, constant const=50.
Pheromones heuristic factor α in the step 2, it is expected that heuristic factor β, which is utilized respectively following formula, carries out value；
Pheromones heuristic factor α in the step 2, the value interval for it is expected heuristic factor β be respectively [2~4] and [7~
9]。
The invention has the benefit that
Ant group algorithm of the invention on the one hand, by weight parameter α to pheromones and heuristic information (information inspire because
Son) and β (it is expected heuristic factor) improve；Improve convergence speed of the algorithm.On the other hand, ant group algorithm addition office is improved
Portion direction guiding mechanism, and new Path selection new probability formula is constructed whereby, calculation is also avoided while improving convergence rate
Method falls into locally optimal solution.
Detailed description of the invention
Fig. 1 is the map that Grid Method is established in the present invention；
Fig. 2 is traditional ant group algorithm motion profile；
Fig. 3 is traditional ant group algorithm convergence curve variation tendency；
Fig. 4 is ant group algorithm motion profile of the present invention；
Fig. 5 is ant group algorithm convergence curve variation tendency of the present invention.
Specific embodiment
With reference to the accompanying drawing and specific embodiment the present invention is described in detail.
Embodiment
Ant group algorithm applied to robot path planning of the invention is deficient for ant group algorithm previous information element and leads
The problem for causing convergence rate slow, weight parameter α (information heuristic factor) and β (the expectation inspiration to pheromones and heuristic information
The factor) it improves, dynamic adjusts two kinds of parameters；In addition, the guiding mechanism in local optimum direction is added, to construct new path
Select probability.The specific steps of which are as follows:
1) simulated environment that robot is run is established using Grid Method, as shown in Figure 1, wherein black patch represents barrier,
White square represents the ground that robot can walk；
2) initial Pheromone Matrix is inputted, initial point and terminal are selected and various parameters are set.The related ginseng of algorithm
Number setting are as follows: iteration total degree N=100, per generation ant sum M=50, pheromones strength factor Q=1, pheromones volatility coefficient
ρ takes 0.9, local direction factor strength factor3, ω local direction factor attenuation coefficient is taken to take 4, const=50.
In traditional ant group algorithm, indicate ant from i-node to the probability of j node with following formula (1):
Wherein τij(t) pheromone concentration on i to the j of path, n are indicatedij(t) heuristic information on i to the j of path is indicated；
α is pheromones heuristic factor, reflects τij(t) importance in ant colony search；β is desired heuristic factor, is reflected next
Importance of the position of node in ant colony search, when β is bigger, state transition probability is closer to greedy algorithm.
nij(t+1) concentration of time information element is relative to τij(t) update of time information element according to formula (2), formula (3) at
Reason:
τij(t+1)=(1- ρ) * τij(t)+Δτij(t) (2)
ρ indicates pheromones volatility coefficient, and value range is ρ ∈ [0,1]；Wherein Δ τij(t) this circulating path is indicated
Pheromones increment on (i, j), initial time Δ τij(t)=0,Path, which is retained in, in table circulation shows that kth ant exists
Information content on this (i, j) is acquired by formula (4):
Wherein LkThe path length passed by this circulation by kth ant.
Found out by formula (1) when α is excessive, pheromones influence weight on path can be made excessive, so that ant easily enters office
Portion's optimal solution.And when α is too small, then the randomness of ant walking is again too strong, and convergence rate is too slow.The value of β influences similar to α.α
Local optimum is fallen into the too large or too small search for being all easy to cause ant group algorithm of β or is fallen into random and can not be found optimal
Solution.On the problem of seeking shortest path, the optimum valuing range of α and β are respectively [2~4] and [7~9].
The present embodiment uses sinusoidal letter to the value of α and β respectively in order to the change of smoother completion weight coefficient
Several and cosine function mode, as shown in formula (2), formula (3):
In above formula, n indicates that current iteration number, N indicate iteration total degree；A, b, c, d in formula (5), formula (6) are
Artificial adjustable parameter can adjust according to demand.
3) node that can be reached in next step from initial point is selected, is found out according to the pheromones of each node using formula (7)
The probability of each node is gone to, and chooses next step initial point.
In traditional ant group algorithm, the major influence factors of the state transition probability of ant are pheromones and inspiration letter
Number, but it is not both to have known starting point and terminal in advance before path planning that path planning problem is maximum.Therefore, it is in and works as
The ant of front nodal point i is in Search-transform node, then it is believed that the line direction of present node i and terminal is optimal searcher
To.The superiority and inferiority of transfering node j, which is distributed with present node i with the angle theta of transfering node j and terminal line, to be measured.Wherein θ is got over
Small, then transfering node is closer to the optimal direction of search.In summary two improvement, finally determine improved Path selection probability
As shown in formula (7)
In above formula, wherein τij(t) pheromone concentration on i to the j of path, n are indicatedij(t)=1/dijIt indicates on i to the j of path
Heuristic information；θ indicates the line vector of starting point i and terminal and the angle of i to j line vector；allowedkIt is waited for for ant k
The set of accessed node, the then set of the non-accessed node of behalf；T indicates the time；N indicates that current iteration number, N indicate iteration
Total degree；A, b, c and d are adjustable parameter；Indicate local direction factor strength factor；ω indicates the decaying of local direction factor
Coefficient, value are greater than 1；θ indicates the line vector of i and terminal d and the angle of i to j line vector.From formula (7) it is found that ω value
Size affect the attenuation degree of local direction factor.Due to the primary stage in ant group algorithm, pheromones between each node
Difference is smaller, and transition probability randomness is larger, and algorithm the convergence speed is lower, at this moment can pass through the shadow of increasing local direction factor
It rings, assembles ant more to optimal route, the later period of algorithm should then reduce the influence of local direction factor, can be with
It is adjusted by n, wherein const is constant.So as to allow algorithmic statement faster.
4) more new route and path length.
5) repeat step 3), 4), until ant reach home or because entering trap it is dead.
6) step 3), 4), 5) is repeated, until M ant of this generation all traverses.
7) pheromone concentration is updated according to formula (2), formula (3).
8) step 3)~7 are repeated), until the n-th=50 generation ant iteration terminates and jumps to step 2 ReparametrizationWith
The value of ω
9) step 3)~7 are repeated), all terminate until reaching the entire iteration of total degree N=100.
It can be seen that the machine for having used improved ant group algorithm of the invention by the track of comparison chart 2 and Fig. 4 robot motion
Device people, which avoids, has fallen into locally optimal solution, has found the path more shorter than traditional ant group algorithm；It can by comparison chart 3 and Fig. 5
To find out traditional ant group algorithm iteration 100 times also non-Complete Convergences, and improved ant group algorithm when 50 times or so
Through Complete Convergence, it is seen that use the convergence rate of the improved ant group algorithm of the present invention faster.
Of the invention is not limited to the various embodiments described above, and all technical solutions obtained using equivalent replacement mode all fall within this
In the claimed range of invention.
Claims (4)
1. a kind of ant group algorithm applied to robot path planning, which comprises the steps of:
Step 1: establishing the simulated environment that robot is run using Grid Method；
Step 2: the Pheromone Matrix that input is initial, selects initial point and terminal and following parameter is arranged: iteration total degree N,
Per generation ant sum M, pheromones strength factor Q, pheromones volatility coefficient ρ, local direction factor strength factorLocal direction
Factor attenuation coefficient ω, constant const；Pheromones heuristic factor α, it is expected that heuristic factor β；
Step 3: the node that selection can reach in next step from initial point, is found out according to the pheromones of each node using following formula
The probability of each node is gone to, and chooses next step initial point；
Wherein τij(t) pheromone concentration on i to the j of path, n are indicatedij(t)=1/dijIndicate the heuristic letter on i to the j of path
Breath；θ indicates the line vector of starting point i and terminal and the angle of i to j line vector；allowedkFor ant k node to be visited
Set, then s indicates the set of non-accessed node；T indicates the time；N indicates that current iteration number, N indicate iteration total degree；a,b,
C and d is adjustable parameter；
Step 4: according to the next step initial point more new route and path length of step 3 selection；
Step 5: repeat Step 3: four, until ant reach home or because entering trap it is dead；
Step 6: repeating step 3 to five, until all ants of this generation all traverse；
Step 7: updating pheromone concentration；
τij(t+1)=(1- ρ)*τij(t)+Δτij(t)
ρ indicates pheromones volatility coefficient, and value range is ρ ∈ [0,1]；Wherein Δ τij(t) this circulating path (i, j) is indicated
On pheromones increment, initial time Δ τij(t)=0,Being retained in path in table circulation shows kth ant at this
Information content on (i, j)
Wherein LkThe path length passed by this circulation by kth ant；
Step 8: repeating step 3 to seven, to the last generation ant iteration terminates and jumps to step 2 ReparametrizationWith
The value of ω；
Step 9: repeating step 3 to seven, all terminate until reaching the entire iteration of total degree N=100.
2. being applied to the ant group algorithm of robot path planning according to claim 1, which is characterized in that in the step 2
A parameter value is respectively as follows: iteration total degree N=100, per generation ant sum M=50, pheromones strength factor Q=1, pheromones
Volatility coefficient ρ=0.9, local direction factor strength factor3 are taken, local direction factor attenuation coefficient ω takes 4, constant const=
50。
3. being applied to the ant group algorithm of robot path planning according to claim 1, which is characterized in that in the step 2
Pheromones heuristic factor α, it is expected that heuristic factor β, which is utilized respectively following formula, carries out value；
4. being applied to the ant group algorithm of robot path planning according to claim 3, which is characterized in that in the step 2
Pheromones heuristic factor α, the value interval for it is expected heuristic factor β are respectively [2~4] and [7~9].
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| CN113081257A (en) * | 2019-12-23 | 2021-07-09 | 四川医枢科技股份有限公司 | Automatic planning method for operation path |
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| CN113159391A (en) * | 2021-03-27 | 2021-07-23 | 桂林理工大学 | Multi-target archive ant colony optimization method for solving planning problem with traffic selection path |
| CN113625767A (en) * | 2021-09-02 | 2021-11-09 | 大连海事大学 | Fixed-wing unmanned aerial vehicle cluster collaborative path planning method based on preferred pheromone gray wolf 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 |
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