CN110275526A - A kind of method for planning path for mobile robot based on improved adaptive GA-IAGA - Google Patents
A kind of method for planning path for mobile robot based on improved adaptive GA-IAGA Download PDFInfo
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- CN110275526A CN110275526A CN201910407620.XA CN201910407620A CN110275526A CN 110275526 A CN110275526 A CN 110275526A CN 201910407620 A CN201910407620 A CN 201910407620A CN 110275526 A CN110275526 A CN 110275526A
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0217—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory in accordance with energy consumption, time reduction or distance reduction criteria
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/12—Computing arrangements based on biological models using genetic models
- G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
Abstract
The invention discloses a kind of method for planning path for mobile robot based on improved adaptive GA-IAGA, it includes the following steps: (1) environmental modeling: being pre-processed using Grid Method to map, environmental map is divided into rule and uniform grid；Step 2, the beginning and end for setting robot, beginning and end position is in free grid；Step 3 uses improved adaptive GA-IAGA planning robot's optimal path；Step 4, smooth paths determine that path point as control point, is smoothed optimal path using B-spline Curve, generates final path from the optimal path that algorithm generates；It solves the prior art and needs a large amount of iteration to solve optimal path for algorithm existing for mobile robot path planning, therefore low efficiency, convergence rate are slow, and easily fall into the technical problems such as local optimum.
Description
Technical field
The invention belongs to mobile robot path planning technical field more particularly to a kind of shiftings based on improved adaptive GA-IAGA
Mobile robot paths planning method.
Background technique
With the continuous development of science and technology, airmanship has become the research hotspot of robot field, and path
Planning algorithm is the important leverage for realizing Mobile Robotics Navigation.Path planning refers to robot according to certain criterion, in ring
In border search for one from starting point to target point, can be with the optimal path of avoiding obstacles.Traditional path planning algorithm someone
Work potential field method, simulated annealing, fuzzy control, neural network algorithm etc., but the computation complexity of these algorithms is larger,
Existing defects are applied under real scene.The intelligent bionics algorithm such as genetic algorithm, ant group algorithm, particle swarm algorithm needs a large amount of
Iteration solve optimal path, therefore low efficiency, convergence rate are slow, and easily fall into local optimum.
Summary of the invention
The technical problem to be solved by the present invention is providing a kind of mobile robot path planning based on improved adaptive GA-IAGA
Method needs a large amount of iteration to solve optimal road to solve the prior art for algorithm existing for mobile robot path planning
Diameter, therefore low efficiency, convergence rate are slow, and easily fall into the technical problems such as local optimum.
The technical scheme is that
A kind of method for planning path for mobile robot based on improved adaptive GA-IAGA, it includes:
Step 1, environmental modeling: being pre-processed using Grid Method to map, environmental map is divided into regular and uniform
Grid；
Step 2, the beginning and end for setting robot, beginning and end position is in free grid；
Step 3 uses improved adaptive GA-IAGA planning robot's optimal path；
Step 4, smooth paths determine path point as control point, using B sample three times from the optimal path that algorithm generates
Curve is smoothed optimal path, generates final path.
Grid described in step 1, each grid only occupies or free two states, and the grid for occupying state indicates obstacle
The grid of object, free state indicates robot walkable region.
Include: using the method for improved adaptive GA-IAGA planning robot's optimal path described in step 3
Step 3.1, setting population scale size M, initial temperature parameter T=T0, genetic algebra counter initialization gen=
0, maximum genetic algebra Maxgen=50, temperature terminal parameter ε=0.1；
Step 3.2 generates initial population P (gen), and M new individual is generated between robot motion's starting point S to terminal G；
Step 3.3, the fitness value for calculating all individuals, individual fitness evaluation function are as follows:
In formula, n is the grid number summation that the individual passes through, and D is the corresponding path length of the individual；
Step 3.4 executes selection, intersection, variation, insertion and delete operation.
A series of generation method of new individual described in step 3.2 are as follows: with random selections, freedom, continuous or discontinuous grid
Lattice serial number connection source S to terminal G, these grid serial numbers just constitute a paths, and a paths are an individual, judge road
Whether the line of diameter coding intersects with barrier, gives up if intersecting and regenerates a new individual.
Selection, intersection, variation, insertion and the method for delete operation are executed described in step 3.4 includes:
Step 3.4.1, selection operation is executed to initial population P (gen) and obtains Ps(gen), selection operation is selected using ratio
Operator is selected, makes individual according to the probability directly proportional to fitness to next-generation population regularities；
Step 3.4.2, using single point crossing method to Ps(gen) it carries out crossover operation and obtains Pc(gen), when satisfaction is intersected
Two individuals are randomly selected when probability in population, selects the identical point of grid serial number to carry out crossover operation, works as coincidence point
When more than one, random selection one is intersected；When without coincidence point, without crossover operation；If Ps(gen) certain individual P insi
(gen) new individual after making a variation is Pci(gen), acceptance probability P is calculated according to Metroplis acceptance criteriai:
In formula (2), T (t) is Current Temperatures, and F (si) is individual Psi(gen) fitness value, F (ci)
For by Psi(gen) make a variation obtained new individual Pci(gen) fitness value；
Step 3.4.3, to the P of step 3.4.2c(gen) it carries out mutation operation and obtains Pm(gen)；
Step 3.4.4, population Pm(gen) individual in executes insertion operation when there is interruption path, and interruption path is used
Free grid makes up, and becomes continuous path；
Step 3.4.5, identical together with two by the redundancy serial number in the population at individual of step 3.4.4 between two same sequence numbers
Any one in serial number is cast out together, to simplify path；
Step 3.4.6, Population Regeneration, gen=gen+1, return step 3.3 continues to evolve if gen < Maxgen, no
Then go to step 4；
Step 3.4.7, temperature termination condition judges: if Current Temperatures T (t) > ε, updates temperature by temperature renewal function
It spends parameter T (t), t ← t+1, turns to step 3.3；Otherwise algorithm terminates outgoing route, temperature renewal function are as follows:
T (t)=T0exp(-Ct1/M) (5)
T (t) indicates Current Temperatures, T in formula (5)0Indicate initial temperature, C is given constant, and t is the number of iterations, M be to
The number of inverted parameters calculates to simplify, and formula (5) are rewritten are as follows:
The value range of α is 0.7 < α < 1 in formula (6), and 1/M takes 0.5 in calculating.
Continuous path, judgment method described in step 3.4.4 are as follows: judge in individual that two is adjacent using following discriminates
Serial number Nk、Nk+1It is whether continuous:
Δ=max { abs (xk+1-xk),abs(yk+1-yk)} (3)
In formula, xk、yk、xk+1、yk+1Respectively Nk、Nk+1Corresponding rectangular co-ordinate,
If Δ=1, NkWith Nk+1Continuously, otherwise discontinuously；
When discontinuous, candidate insertion point is calculated by median method:
If the N ' being calculatedkFor free grid, then it is inserted directly into；If N 'kIt is obstacle grid, then selects one and N 'kAway from
From nearest free grid as new candidate insertion point, if can not find such candidate insertion point, i.e. declaration insertion operation is lost
It loses, casts out the individual；Otherwise N just is inserted into new candidate pointkWith Nk+1Between, above-mentioned insertion process repeats, until a
Body becomes continuously can be until pass.
Path point is determined described in step 4 from the optimal path that algorithm generates as control point, using B-spline Curve
The method for generating final path is smoothed to optimal path are as follows:
Relationship between B-spline Curve and control point are as follows:
T ∈ [0,1], P in formula (7)i、Pi+1、Pi+2、Pi+3For the control point of B-spline Curve, finally it is sequentially connected complete
3 rank B-spline curves section of portion generates final path.
The beneficial effects of the present invention are:
A kind of method for planning path for mobile robot based on improved adaptive GA-IAGA is provided, by fast simulated annealing algorithm with
Genetic algorithm combines, which not only has stronger global and local search capability, but also it is existing to overcome genetic algorithm precocity
As, local optimal searching ability is poor the deficiencies of.And path is generated to algorithm using B-spline Curve and is smoothed, can have
Improve path planning quality in effect ground.
Advantages of the present invention:
(1) present invention optimizes genetic algorithm using the stronger fast simulated annealing algorithm of local search ability, adopts
Receive and give up new explanation with Metropolis criterion, so as to avoid genetic algorithm low efficiency, convergence is relatively slow, easily sunken people is local
The disadvantages of extreme point, so that genetic algorithm and fast simulated annealing algorithm achieve the purpose that mutual supplement with each other's advantages in path planning.
(2) not only global optimizing ability is strong for improved adaptive GA-IAGA of the invention, and search speed is fast, and to complex environment
It is adaptable, it can efficiently solve the problems, such as mobile robot path planning.
(3) present invention is smoothed using the path of B-spline Curve generated to innovatory algorithm, thus by road
Diameter becomes the smooth curve that robot can actually walk, and significantly improves path quality, so that the invention is easy to apply to machine
In the practical navigation of device people.
It solves the prior art and needs a large amount of iteration to solve most for algorithm existing for mobile robot path planning
Shortest path, therefore low efficiency, convergence rate are slow, and easily fall into the technical problems such as local optimum.
Specific embodiment
Step 1, environmental modeling: being pre-processed using Grid Method to map, environmental map is divided into regular and uniform
Grid.Each grid only occupies or free two states, and the grid for occupying state indicates barrier, the grid of free state
Indicate robot walkable region.
Step 2, the beginning and end for setting robot, beginning and end position must be in free grid；
Step 3, using improved adaptive GA-IAGA planning robot's optimal path, the innovatory algorithm includes:
Step 3.1, setting population scale size M, initial temperature parameter T=T0, genetic algebra counter initialization gen=
0, maximum genetic algebra Maxgen=50, temperature terminal parameter ε=0.1.
Step 3.2 generates initial population P (gen).Between robot motion's starting point S to terminal G, with a series of random
Selection, freely, not necessarily continuous grid serial number connects S to G, these grid serial numbers just constitute a paths, a paths
For an individual.Judge whether the line of path code intersects with barrier, give up if intersecting and regenerates new individual.When
Number of individuals performs the next step when being M.
Step 3.3, the fitness value for calculating all individuals, individual fitness evaluation function are as follows:
In formula (1), n is the grid number summation that the individual passes through, and D is the corresponding path length of the individual.It can by formula (1)
Know, ideal adaptation angle value is directly proportional to path length D, and it is most short to be excellent with path to evaluate path quality, therefore is generating novel species
The low individual of fitness value can be retained when group.
Step 3.4 executes selection, intersection, variation, insertion, delete operation.
Step 3.4.1, selection operation is executed to initial population P (gen) and obtains Ps(gen).Selection operation is selected using ratio
Operator is selected, makes individual according to the probability directly proportional to fitness to next-generation population regularities.
Step 3.4.2, using single point crossing method to Ps(gen) it carries out crossover operation and obtains Pc(gen).Intersect when meeting
Two individuals are randomly selected when probability in population, the identical point of grid serial number is selected to carry out crossover operation.Work as coincidence point
When more than one, random selection one is intersected；When without coincidence point, without crossover operation.If Ps(gen) certain individual in
Psi(gen) new individual after making a variation is Pci(gen), acceptance probability P is calculated according to Metroplis acceptance criteriai:
In formula (2), T (t) is Current Temperatures, and F (si) is individual Psi(gen) fitness value, F (ci) are by Psi
(gen) make a variation obtained new individual Pci(gen) fitness value.According to acceptance probability PiDetermine whether to receive new individual Pci
(gen)。
Step 3.4.3, to the P of step 3.4.2c(gen) it carries out mutation operation and obtains Pm(gen).Work as Pc(gen) individual in
It when meeting mutation probability, is randomly selected from individual a bit, another serial number being randomly generated replaces it.
Step 3.4.4, the population P of step 3.4.3m(gen) individual in executes insertion operation when there is interruption path,
Interruption path is made up with free grid, makes continuous path.Two adjacent sequence in individual is judged using following discriminates
Number Nk、Nk+1It is whether continuous:
Δ=max { abs (xk+1-xk),abs(yk+1-yk)} (3)
In formula, xk、yk、xk+1、yk+1Respectively Nk、Nk+1Corresponding rectangular co-ordinate.If Δ=1, NkWith Nk+1Continuously,
Otherwise discontinuous.
When discontinuous, candidate insertion point is calculated by median method:
If the N ' being calculatedkFor free grid, then can be inserted directly into.If N 'kIt is obstacle grid, then selects one
With N 'kThe nearest free grid of distance is as new candidate insertion point, if can not find such candidate insertion point, i.e. declaration insertion
Operation failure casts out the individual, is otherwise just inserted into N with new candidate pointkWith Nk+1Between.Above-mentioned insertion process repeats,
Until individual become continuously can pass until.
Step 3.4.5, identical together with two by the redundancy serial number in the population at individual of step 3.4.4 between two same sequence numbers
One in serial number is cast out together, to simplify path.
Step 3.4.6, Population Regeneration, gen=gen+1.Return step 3.3 continues to evolve if gen < Maxgen, otherwise
Go to step 4.
Step 3.4.7, temperature termination condition judges.If Current Temperatures T (t) > ε, is updated by temperature renewal function
Temperature parameter T (t), t ← t+1 turn to step 3.3；Otherwise algorithm terminates outgoing route.Temperature renewal function are as follows:
T (t)=T0exp(-Ct1/M) (5)
T (t) indicates Current Temperatures, T in formula (5)0Indicate initial temperature.C is given constant, and t is the number of iterations, M be to
The number of inverted parameters.It is calculated to simplify, formula (5) is rewritten are as follows:
The value range of α is 0.7 < α < 1 in formula (6), and 1/M takes 0.5 in actually calculating.
Step 4, smooth paths.Path point is determined from the optimal path that algorithm generates as control point, using B sample three times
Curve is smoothed optimal path, to make path become the smooth curve that robot can actually walk, greatly
Improve path quality.Relationship between B spline curve and control point three times are as follows:
T ∈ [0,1], P in formula (7)i、Pi+1、Pi+2、Pi+3For the control point of B-spline Curve.It is finally sequentially connected complete
3 rank B-spline curves section of portion produces final path.
Claims (7)
1. a kind of method for planning path for mobile robot based on improved adaptive GA-IAGA, it includes:
Step 1, environmental modeling: being pre-processed using Grid Method to map, and environmental map is divided into rule and uniform grid
Lattice；
Step 2, the beginning and end for setting robot, beginning and end position is in free grid；
Step 3 uses improved adaptive GA-IAGA planning robot's optimal path；
Step 4, smooth paths determine path point as control point, using cubic B-spline song from the optimal path that algorithm generates
Line is smoothed optimal path, generates final path.
2. a kind of method for planning path for mobile robot based on improved adaptive GA-IAGA according to claim 1, feature
Be: grid described in step 1, each grid only occupies or free two states, and the grid for occupying state indicates barrier,
The grid of free state indicates robot walkable region.
3. a kind of method for planning path for mobile robot based on improved adaptive GA-IAGA according to claim 1, feature
It is: includes: using the method for improved adaptive GA-IAGA planning robot's optimal path described in step 3
Step 3.1, setting population scale size M, initial temperature parameter T=T0, genetic algebra counter initialization gen=0, most
Big genetic algebra Maxgen=50, temperature terminal parameter ε=0.1；
Step 3.2 generates initial population P (gen), and M new individual is generated between robot motion's starting point S to terminal G；
Step 3.3, the fitness value for calculating all individuals, individual fitness evaluation function are as follows:
In formula, n is the grid number summation that the individual passes through, and D is the corresponding path length of the individual；
Step 3.4 executes selection, intersection, variation, insertion and delete operation.
4. a kind of method for planning path for mobile robot based on improved adaptive GA-IAGA according to claim 3, feature
It is: the generation method of new individual described in step 3.2 are as follows: with a series of random selections, freedom, continuous or discontinuous grid sequence
Number connection source S to terminal G, these grid serial numbers just constitute a paths, and a paths are an individual, judge that path is compiled
Whether the line of code intersects with barrier, gives up if intersecting and regenerates a new individual.
5. a kind of method for planning path for mobile robot based on improved adaptive GA-IAGA according to claim 3, feature
Be: the method that selection, intersection, variation, insertion and delete operation are executed described in step 3.4 includes:
Step 3.4.1, selection operation is executed to initial population P (gen) and obtains Ps(gen), selection operation is calculated using ratio selection
Son makes individual according to the probability directly proportional to fitness to next-generation population regularities；
Step 3.4.2, using single point crossing method to Ps(gen) it carries out crossover operation and obtains Pc(gen), when meeting crossover probability
When randomly select two individuals in population, select the identical point of grid serial number to carry out crossover operation, when coincidence point is more than
At one, random selection one is intersected；When without coincidence point, without crossover operation；If Ps(gen) certain individual P insi
(gen) new individual after making a variation is Pci(gen), acceptance probability P is calculated according to Metroplis acceptance criteriai:
In formula (2), T (t) is Current Temperatures, and F (si) is individual Psi(gen) fitness value, F (ci) are by Psi(gen) it makes a variation
Obtained new individual Pci(gen) fitness value；
Step 3.4.3, to the P of step 3.4.2c(gen) it carries out mutation operation and obtains Pm(gen)；
Step 3.4.4, population Pm(gen) individual in executes insertion operation when there is interruption path, interruption path free grid
Lattice make up, and become continuous path；
Step 3.4.5, by the redundancy serial number in the population at individual of step 3.4.4 between two same sequence numbers, together with two same sequence numbers
In any one cast out together, to simplify path；
Step 3.4.6, Population Regeneration, gen=gen+1, return step 3.3 continues to evolve if gen < Maxgen, otherwise goes to
Step 4；
Step 3.4.7, temperature termination condition judges: if Current Temperatures T (t) > ε, temperature ginseng is updated by temperature renewal function
Number T (t), t ← t+1 turn to step 3.3；Otherwise algorithm terminates outgoing route, temperature renewal function are as follows:
T (t)=T0exp(-Ct1/M) (5)
T (t) indicates Current Temperatures, T in formula (5)0Indicate initial temperature, C is given constant, and t is the number of iterations, and M is to join to inverting
Several numbers calculates to simplify, and formula (5) are rewritten are as follows:
The value range of α is 0.7 < α < 1 in formula (6), and 1/M takes 0.5 in calculating.
6. a kind of method for planning path for mobile robot based on improved adaptive GA-IAGA according to claim 5, feature
It is: continuous path, judgment method described in step 3.4.4 are as follows: two adjacent sequence in individual is judged using following discriminates
Number Nk、Nk+1It is whether continuous:
Δ=max { abs (xk+1-xk),abs(yk+1-yk)} (3)
In formula, xk、yk、xk+1、yk+1Respectively Nk、Nk+1Corresponding rectangular co-ordinate, if Δ=1, NkWith Nk+1Continuously, otherwise not
Continuously；
When discontinuous, candidate insertion point is calculated by median method:
If the N ' being calculatedkFor free grid, then it is inserted directly into；If N 'kIt is obstacle grid, then selects one and N 'kDistance is most
Close free grid is as new candidate insertion point, if can not find such candidate insertion point, i.e. declaration insertion operation failure, and house
Remove the individual；Otherwise N just is inserted into new candidate pointkWith Nk+1Between, above-mentioned insertion process repeats, until individual becomes
At continuously can be until pass.
7. a kind of method for planning path for mobile robot based on improved adaptive GA-IAGA according to claim 1, feature
It is: determines path point described in step 4 from the optimal path that algorithm generates as control point, using B-spline Curve pair
Optimal path is smoothed the method for generating final path are as follows:
Relationship between B-spline Curve and control point are as follows:
T ∈ [0,1], P in formula (7)i、Pi+1、Pi+2、Pi+3For the control point of B-spline Curve, it is finally sequentially connected all 3 rank B
Spline curve section generates final path.
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