CN107917711A - A kind of Robot Path Planning Algorithm based on optimization hybrid ant colony - Google Patents
A kind of Robot Path Planning Algorithm based on optimization hybrid ant colony Download PDFInfo
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- G01—MEASURING; TESTING
- 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
- G01C21/20—Instruments for performing navigational calculations
Abstract
A kind of Robot Path Planning Algorithm based on optimization hybrid ant colony is claimed in the present invention.The algorithm includes：S1, the synthesis heuristic information that the initial heuristic factor construction robot of target gravitation and ant group algorithm produced with reference to Artificial Potential Field moves；S2, the pheromones in ant group algorithm are updated using wolf pack distribution principle；S3 optimizes path planning using path optimization's algorithm.The present invention can be rapidly and efficiently cook up optimal path.
Description
Technical field
The invention belongs to Mobile Robotics Navigation field, particularly a kind of robot road based on optimization hybrid ant colony
Footpath planning algorithm.
Background technology
Path planning is to realize one of key technology of mobile robot control.The purpose is in certain environmental condition and
Under performance indicator requirement, optimal or suboptimum a safe collisionless path from initial position to target location is found.For
Robot path planning, domestic and foreign scholars propose many planing methods, wherein mainly having the Artificial Potential Field Method, neutral net adaptive
Answer law of planning, genetic algorithm, ant group algorithm, particle cluster algorithm etc..In recent years, more and more scholars are to path planning problem
More focus on multi-intelligence algorithm during research to be combined, to improve algorithm performance.Such as ImenChaari* is by genetic algorithm and ant colony
Algorithm is combined, and the last stage produces initial information element distribution with genetic algorithm, and the rear stage asks optimal solution, Neng Gouyou with ant group algorithm
The advantages of effect combines two algorithms, improves the search efficiency of ant colony, but may be absorbed in local optimum；X Wang et al. propose a kind of base
In particle group optimizing (Particle Swarm Optimization, PSO) and ant group optimization (Ant colony
Optimization, ACO) algorithm new paths planning method, the algorithm utilize population environmental modeling method, generate from
Starting point is then based on the path profile pheromones generated before, finally, uses improved optimization ant colony to the path of target point
To find optimal path, this method can shorten search time, but, bad adaptability higher to environmental requirement；T Zhu, G Dong
The algorithm that ant group algorithm is used in combination with Artificial Potential Field Method Deng proposition, the algorithm initialize overall path, optimization with potential field method
Paths ordering per generation ant, and according to the ranking replacement pheromones in ant path, meanwhile, in the pheromones of elitist ants
Under help, using mould because of the intersection and mutation operation of algorithm on each generation path, which improves convergence rate and steady
It is qualitative, but potential field method is easily trapped into local deadlock in itself, so the algorithm is easily trapped into local optimum when initially finding path.
The content of the invention
Present invention seek to address that above problem of the prior art.Propose one kind and effectively increase search efficiency, and introduce
Wolf pack distribution rule, avoids it from being absorbed in local optimum, makes the stability-enhanced robot road based on optimization hybrid ant colony
Footpath planning algorithm.Technical scheme is as follows：
A kind of Robot Path Planning Algorithm based on optimization hybrid ant colony, it comprises the following steps：
S1, establishment robot need to find the map of optimal path, and the matrix that the map is made of 0 and 1 represents, establishes
Object function：
If side length of element is m, then
L is the length that nothing from starting point to target point touches path, and T is passed through grid sum.
S2, each parameter of initialization, including establish the unit length of grid, the gravitational coefficients of Artificial potential functions, ant colony and search
Parameter including the ant quantity of rope, iterations, pheromones volatility coefficient；
S3, by m ant be placed on starting grid on, ant under heuristic information, by probability function select one permission
Path reaches next grid, and the grid positions are stored in the routing table of ant k foundation, when the arrival of all ants is next
Behind a position, the side passed by by Pheromone update function pair carries out local information element renewal, if routing table current ant k is most
The latter grid positions are not target locations, then continue search for next position；
S4, all ants reach target location after, calculate the path length that each Ant Search arrives, find out current search and arrive
Most short feasible path；
S5, judge algorithm whether iteration setting times N time, stop if satisfied, then calculating, and preserve it is most short can walking along the street
Footpath；Otherwise, S4 is gone to, continues to iterate to calculate；
S6, will find the processing of shortest path progress path optimization, obtain optimal path, export optimal path, algorithm terminates.
Further, the building method of heuristic information function is in the step S3：According to Artificial Potential Field gravitation principle, draw
Enter target attracting factor, ant initial time is tended to target direction movement, be implemented as follows：At the P of position, gravitation
The following form of potential field generally use represents
In formula, ζ ＞ 0 are gravitation potential field proportionality coefficient；D (P, G) represents the distance of robot present position and target,
Target attraction suffered by robot is the negative gradient of gravitational field
In formula, nPGFor the targeted unit vector of robot；
In route searching, the heuristic information that ant finds the next position is made of two parts, and a part is that ant is subject to
Target gravitation in environment, being formed makes ant be intended to the heuristic information walked along target direction, defines the part heuristic information
ForIn formula, 0 ＜ a ＜ 1 are constant, and under heuristic information work, ant is intended to select gravity direction walking under
One free space；
Another part heuristic information is provided by the intrinsic heuristic factor of ant, which is defined as followsThe heuristic information for constructing whole algorithm is as follows:
Further, step S3 Pheromone updates function uses for reference wolf pack allocation rule and pheromones is updated；
Pheromone updating rule is as follows：
In formula, ρ is pheromones volatility coefficient, and Q=1 is constant, Lb, LwBe respectively in current iteration local optimum path with
The length of worst path；B, w are local optimum path and the quantity of ant on worst path respectively.
Further, in step S6 in the Optimization Algorithm of path, path is generated using ant group algorithm, by generation path top
Point and flex point are numbered, according to the most short principle of straight line between 2 points, from the off, it is connected with next node on path
Connect and then do detection of obstacles processing, remove some unnecessary points, until leave do not collide with barrier it is minimum
Point, obtains path optimizing.
Advantages of the present invention and have the beneficial effect that：
The present invention proposes a kind of optimization hybrid ant colony for the robot global path planning being used under known environment.
This method is according to Artificial Potential Field gravitation principle, introduces target attracting factor, target is had sucking action to ant colony, so that initially
Moment can effectively increase search efficiency towards target search path, and introduce wolf pack distribution rule, avoid it from being absorbed in part
It is optimal, improve stability.
Brief description of the drawings
Fig. 1 is that the present invention provides preferred embodiment offer based on optimization hybrid ant colony operational flow diagram；
Fig. 2 is path optimization's exemplary plot provided by the invention.
Embodiment
Below in conjunction with the attached drawing in the embodiment of the present invention, the technical solution in the embodiment of the present invention is carried out clear, detailed
Carefully describe.Described embodiment is only the part of the embodiment of the present invention.
The present invention solve above-mentioned technical problem technical solution be：
As shown in Figure 1, the present invention provides a kind of mobile robot synchronous superposition based on Gaussian Profile,
It comprises the following steps：
The matrix that S1 inputs are made of 0 and 1 represents that robot needs to find the map of optimal path, establishes object function；
S2 initializes each parameter.Unit length including establishing grid；The basic parameter of Artificial potential functions；Such as gravitation system
Number；Ant quantity, iterations, the parameter such as pheromones volatility coefficient of ant colony search；
M ant is placed on starting grid by S3, and ant is in formula
Under the heuristic information of definition, by formula
The path of one permission of selection reaches next grid, and the grid positions are stored in the routing table of ant foundation
In.After all ants reach next position, by formula
Local information element renewal is carried out to the side passed by, if routing table current ant k last grid positions are not
It is target location, then continues search for next position；
S4 calculates the path length that each Ant Search arrives, finds out current search and arrive after all ants reach target location
Most short feasible path；
S5 judge algorithm whether iteration n times, stop if satisfied, then calculating, and preserve most short feasible path；Otherwise, go to
S4, continues to iterate to calculate；
S6 will find shortest path and carry out path optimization's processing, obtain optimal path, export optimal path, and algorithm terminates.
Path Optimization Algorithm in step S6:
A kind of new path optimization's algorithm is proposed, with the path that hybrid ant colony generates on grating map as schemed
(a) shown in, but in practice, such path be not necessarily suitable for robot perform and and it is non-optimal, such as Fig. 2 institutes
Show,
Assuming that each grid length of side is 1, { s is numbered in generation path vertices and flex point1,s2,s3,s4,s5,s6, ant
Path P ath obtained by group's algorithma={ s1,s2,s3,s4,s5,s6, path length 8.828.And indeed according to straight between 2 points
The most short principle of line, from the off, is connected with next node on path and then does detection of obstacles processing, remove some not
It is necessary, until leaving the minimum point not collided with barrier, obtain the path P ath of figure (c)a={ s1,s4,s5,
s6, path length 8.537, while path has the deflection angle of smaller, is more suitable for robot execution.
Comprise the following steps that：
S1:It is assigned to first by first point in certain paths and thirdly S and N；
S2:Judge whether the line between S and N connects, S4 is otherwise gone to if then going to S3；
S3:By the upper node valuation where N nodes to S, go to S2 and continue to judge；
S4:Next node is assigned to M, carries out detection of obstacles.Judge whether it is last point, be to go to S5,
It is not to go to S2；
S5:Terminate algorithm.
The present invention proposes the above embodiment and is interpreted as being merely to illustrate the present invention rather than the limitation present invention
Protection domain.After the content of record of the present invention has been read, technical staff can make the present invention various changes or repair
Change, these equivalence changes and modification equally fall into the scope of the claims in the present invention.
Claims (4)
1. a kind of Robot Path Planning Algorithm based on optimization hybrid ant colony, it is characterised in that comprise the following steps：
S1, establishment robot need to find the map of optimal path, and the matrix that the map is made of 0 and 1 represents, establishes target
Function：
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L is the length that nothing from starting point to target point touches path, and T is passed through grid sum；
S2, each parameter of initialization, including establish the unit length of grid, the gravitational coefficients of Artificial potential functions, ant colony and search for
Parameter including ant quantity, iterations, pheromones volatility coefficient；
S3, by m ant be placed on starting grid on, ant under heuristic information, by probability function select one permission path
Next grid is reached, and the grid positions are stored in the routing table of ant k foundation, when all ants reach next position
Postpone, the side passed by by Pheromone update function pair carries out local information element renewal, use for reference wolf pack allocation rule to pheromones into
Row renewal；If last grid positions of routing table current ant k are not target locations, next position is continued search for
Put；
S4, all ants reach target location after, calculate the path length that each Ant Search arrives, find out current search and arrive most
Short feasible path；
S5, judge algorithm whether iteration setting times N time, stop if satisfied, then calculating, and preserve most short feasible path；It is no
Then, S4 is gone to, continues to iterate to calculate；
S6, will find the processing of shortest path progress path optimization, obtain optimal path, export optimal path, algorithm terminates.
2. the Robot Path Planning Algorithm according to claim 1 based on optimization hybrid ant colony, it is characterised in that
The building method of heuristic information function is in the step S3：According to Artificial Potential Field gravitation principle, target attracting factor is introduced, is made
Ant initial time can tend to target direction movement, be implemented as follows：At the P of position, gravitation potential field generally use is as follows
Form represents
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In formula, ζ ＞ 0 are gravitation potential field proportionality coefficient；D (P, G) represents the distance of robot present position and target, machine
Target attraction suffered by people is the negative gradient of gravitational field
Fatt(P)=- ▽ Uatt(P)=ξ d (P, G) nPG
In formula, nPGFor the targeted unit vector of robot；
In route searching, the heuristic information that ant finds the next position is made of two parts, and a part is that ant is subject to environment
In target gravitation, formed make ant be intended to along target direction walk heuristic information, defining the part heuristic information isIn formula, 0 ＜ a ＜ 1 are constant, and under heuristic information work, ant is intended to select gravity direction to walk to next
Free space；
Another part heuristic information is provided by the intrinsic heuristic factor of ant, which is defined as followsStructure
The heuristic information for making whole algorithm is as follows:
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3. the Robot Path Planning Algorithm according to claim 1 or 2 based on optimization hybrid ant colony, its feature exist
In step S3 Pheromone updates function uses for reference wolf pack allocation rule and pheromones are updated；
Pheromone updating rule is as follows：
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In formula, ρ is pheromones volatility coefficient, and Q=1 is constant, Lb, LwBe respectively in current iteration local optimum path with it is worst
The length in path；B, w are local optimum path and the quantity of ant on worst path respectively.
4. the Robot Path Planning Algorithm according to claim 3 based on optimization hybrid ant colony, it is characterised in that
In step S6 in the Optimization Algorithm of path, path is generated using ant group algorithm, generation path vertices and flex point are compiled
Number, according to the most short principle of straight line between 2 points, from the off, it is connected with next node on path and then does barrier
Detection process, removes some unnecessary points, until leaving the minimum point not collided with barrier, obtains optimization road
Footpath.
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