CN103336526A - Robot path planning method based on coevolution particle swarm rolling optimization - Google Patents
Robot path planning method based on coevolution particle swarm rolling optimization Download PDFInfo
- Publication number
- CN103336526A CN103336526A CN2013102468261A CN201310246826A CN103336526A CN 103336526 A CN103336526 A CN 103336526A CN 2013102468261 A CN2013102468261 A CN 2013102468261A CN 201310246826 A CN201310246826 A CN 201310246826A CN 103336526 A CN103336526 A CN 103336526A
- Authority
- CN
- China
- Prior art keywords
- particle
- current
- robot
- subgroup
- population
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Landscapes
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
- Manipulator (AREA)
Abstract
The invention discloses a robot path planning method adopting multi-subgroup coevolution particle swarm optimization to perform real-time rolling optimization obstacle avoidance processing, which can enable a robot to realize autonomous navigation in a working condition with stationary obstacles at unknown positions. The method has the following advantages: the method can realize autonomous navigation of the robot in a working condition with stationary obstacle at unknown position; the method introduces group mass center strategy to the multi-subgroup coevolution, so as to increase the search capacity of the population; compared with a conventional path planning method which adopts PSO (Particle Swarm Optimization) and CPSO (Coevolution Particle Swarm Optimization), the method provided by the invention adopts survival of the fittest strategy, thereby being more advantageous in respect to rate and accuracy of evolution convergence; the method provided by the invention can real-timely plan an effective obstacle avoidance path in a complex obstacle environment, and has the advantages of good robustness, high solving efficiency, and the like.
Description
Technical field
The invention belongs to the real-time planning technology of robot path field, be specifically related to a kind of robot path planning method based on coevolution population rolling optimization.
Background technology
Path planning refers to by performance requirements such as distance, consuming time or securities, the safe clear path of searching machine people between from the starting point to the impact point, and it is robot field's important topic.According to the understanding to environment knowledge, path planning can be divided into two kinds of the path planning of known environment and the path plannings under the circumstances not known, the former is also referred to as global path planning; The latter then requires robot to utilize the sensor of self to survey the interior information of limited range on every side, constantly carries out dynamic path planning, is also referred to as online path planning or local paths planning.Handle owing to need to realize the online barrier of keeping away, therefore online path planning than the global path planning complexity many.
Current, local paths planning method mainly contains: Artificial Potential Field method, rolling window law of planning and intelligent algorithm law of planning etc.Artificial Potential Field method algorithm structure is simple but exist local extremum, and robot can be absorbed in local extremum and fluctuate; Adopting in the rolling window planing method exists deadlock now to look like.In recent years, along with going deep into that bionic intelligence is studied, many intelligent optimization algorithms are used for the robot path planning gradually, as genetic algorithm, ant group algorithm and particle cluster algorithm.
Summary of the invention
In order to overcome the defective that exists in the prior art, provide a kind of robot that can make under the working environment with static-obstacle thing and the unknown of barrier position, to realize independent navigation, adopt many subgroups coevolution particle cluster algorithm to carry out real-time rolling optimization and keep away the robot path planning method that barrier is handled.The concrete technical scheme of the present invention is as follows:
Based on the robot path planning method of coevolution population rolling optimization, be used for the walking path of a robot is planned, make robot avoid some stationary obstruction in the working environment, comprise the steps:
Step S1 carries out modeling to working environment;
Step S2, the distance between the current point of detection machine people and the impact point if distance, is then thought the arrival impact point less than default distance error, is exported the terminal point collection of each time planning, the acquisition programme path; Otherwise change step S3 over to;
Step S3 obtains current optimal movement orientation angle according to the current point of robot and impact point;
Step S4 according to default robot field-of-view angle, divides the GOTO field of robot; Determine that according to current optimal movement orientation angle the motion of current robot is towards the GOTO field at place;
Step S5, if clear in the view field of current robot, be that limit is set up the local pole coordinate system with current point, the local endpoint that obtains this planning according to default radius of view and current optimal movement orientation angle, and deposit local endpoint in the terminal point collection, as the current point of planning next time, change local endpoint over to step S2; If in the view field of current robot barrier is arranged, changes step S6 over to;
Step S6 adopts the mode of interative computation to keep away barrier based on the coevolution particle cluster algorithm and handles; Step S6 further comprises:
Step S61 is divided into several segmentations with radius of view, determines several segment movement orientation angles simultaneously, and the segment movement orientation angle is less than field-of-view angle; Some particles are set, each particle is encoded to the sequence of being made up of some segment movement orientation angles, some particles are formed an initial population, and initial population is carried out initialization, obtain an initial population; If the initialization success is divided into several subgroups with initial population, change step S62 over to; If the initialization failure changes step S7 over to;
Step S62 calculates the adaptive value of each particle in each subgroup, and the particle in each subgroup is sorted by adaptive value, selects the less particle of adaptive value in each subgroup by default gene-ratio again, forms a gene pool;
Step S63 according to the adaptive value of each particle in each subgroup, obtains the optimum solution of each subgroup, and tries to achieve a population center of mass point according to optimum solution;
Step S64 upgrades particle's velocity and position in each subgroup; If upgrade successfully, then change step S65 over to, if upgrade failure, change step S7 over to;
Step S65 upgrades the adaptive value of particle in each subgroup, the individual optimum solution of particle and the globally optimal solution of subgroup;
Step S66 judges whether the evolution cycle that reaches default, does not then directly change step S67 over to if reach evolution cycle; Otherwise upgrade subgroup and gene pool, new population center of mass point more changes step S67 again over to then;
Step S67 judges whether the maximum iteration time that reaches default, if reached maximum iteration time, termination of iterations then obtains the local endpoint of this planning, and local endpoint is added terminal point concentrates, as the current point of planning next time, change local endpoint over to step S2; If do not reach maximum iteration time, then change step S64 over to and continue iteration;
Step S7 keeps away the barrier failure and handles, and changes robot over to next GOTO field, returns step S61 and keeps away barrier; If robot all keeps away the barrier failure at all GOTO fields, then can't realize keeping away barrier, failure is jumped out.
As prioritization scheme, the working environment modeling of step S1 specifically comprises the steps:
Step S11 ignores the elevation information in the working environment, sets up two-dimensional coordinate system;
Step S12, the view field of establishing robot for (r, θ), wherein r is radius of view, θ is field-of-view angle; Obtain the starting point S of robot and the coordinate of impact point G; With convex polygon barrier is described, coordinate the unknown of barrier;
Step S13 carries out expandedly to barrier according to the size of robot, and robot handled as a particle;
Step S14, it is 1 that current scrolling planning number of times t is set, and current some P of robot is set
tBe starting point S, and with current some P
tDeposit terminal point collection { P} in.
As prioritization scheme, step S3 is specially:
Current optimal movement orientation angle
Refer to current motion towards with the angle of two-dimensional coordinate system x axle, current motion is oriented current some P
tRectilinear direction to impact point G; Current optimal movement orientation angle
Computing formula as the formula (1):
Wherein, t is current scrolling planning number of times, if
Then order
G
xBe the coordinate figure of described impact point G at the x axle, G
yBe the coordinate figure of described impact point G at the y axle, P
x tBe described current some P
tAt the coordinate figure of x axle, P
y tBe described current some P
tCoordinate figure at the y axle.
As prioritization scheme, step S4 is specially:
According to field-of-view angle θ, the GOTO field of robot is divided into K=2 π/θ, according to current optimal movement orientation angle
Determine the current motion of robot towards the GOTO field k at place, current motion towards the computing formula of the GOTO field k at place as the formula (2):
Wherein, if k=0 then makes k=K, wherein ceil () is the function that rounds up, and mod () is for getting complementary function.
As prioritization scheme, step S5 is specially:
Local endpoint P
t' computing formula as the formula (3):
(3)
Wherein, P
t'
xBe described local endpoint P
t' at the coordinate figure of x axle, P
t'
yBe described local endpoint P
t' at the coordinate figure of y axle, P
x tBe described current some P
tAt the coordinate figure of x axle, P
y tBe described current some P
tCoordinate figure at the y axle; With described local endpoint P
t' deposit described terminal point collection in and { behind the P}, make t=t+1.
As prioritization scheme, step S61 further comprises the steps:
Step S611 is divided into the m section with radius of view r, obtains a segmentation radius λ, determines m segment movement orientation angle at random
Wherein,
I=1,2 ... m obtains some segmented paths dist
i, by some segmented paths dist
iConnect to form the path length overall dist that robot avoiding obstacles in radius of view r scope need pass through
t
Step S612 arranges the interative computation that some particles are used for carrying out particle cluster algorithm, and particle is encoded to by some segment movement orientation angles
The sequence of forming, the dimension of establishing some particles is m, according to the GOTO field k of current motion towards the place, the scope that defines some particles is [(k-1) θ, k θ], wherein, and k=1,2 ... K, K=2 π/θ;
Step S613 determines path length overall dist
t, path length overall dist
tComputing formula as the formula (4):
Wherein, i=2,3 ..., m; λ=r/m, namely radius of view r makes the section length behind the branches such as m; x
iI dimension for the back particle of encoding;
Obtain local endpoint P
t' to the air line distance of impact point G
Air line distance
Computing formula as the formula (5):
According to path length overall dist
tAnd air line distance
Set an adaptive value function, the adaptive value function as the formula (6):
ω wherein
1With ω
2Be weight coefficient;
Step S614 forms an initial population with some particles, and initial population is carried out initialization; Initialization requires current some P from this planning
tBeginning is with segmented paths dist
iFor there not being barrier in the view field of radius of view, otherwise the initialization failure;
Step S615 if the initialization success is divided into s subgroup with initial population, according to the GOTO field k of current motion towards the place, defines the scope [x of some particles
Min, x
Max] be [(k-1) θ, k θ]; The maximal rate that particle is set is 0.1 times of particle range.
As prioritization scheme, the initialization among the step S614 is specially:
The setting original dimensions is that m, initial scale are N, carry out initialization before, the population scale of initial population is set much larger than initial scale N; The maximum initialization times p of particle is set
MaxWith the maximum initialization times d of dimension
MaxIf certain one dimension of a certain particle is being made d in the initial population
MaxSuccess not yet then reinitializes current dimension after the inferior initialization; If this particle is through p
MaxInferior initialization is still failed, and then thinks current particle initialization failure;
Abandon the particle of initialization failure, the initialized particle of success is formed initial population; If the scale of initial population reaches initial scale N, then initialization success; Otherwise, the initialization failure.
As prioritization scheme, population center of mass point P among the step S63
cDefinition as the formula (7):
As prioritization scheme, step S64 is specially:
Upgrade particle's velocity and position in each subgroup according to formula (8) and formula (9):
(8)
Wherein iter is the population evolutionary generation, and j is the subgroup mark, and i is particle mark in the subgroup,
Be i particle's velocity in j the subgroup; P
i jIt is the historical optimum solution of i particle in j the subgroup;
Optimum solution for subgroup, current particle place; c
1And c
2Be the study factor; c
3For exchanging the factor; r
1, r
2, r
3Be the random number between [0,1]; ω is inertia weight, and inertia weight ω adopts the linear decrease strategy, as the formula (10):
ω(iter)=ω
max-iter×(ω
max-ω
min)/iter
max (10)
Particle's velocity and location boundary processing are comprised: if the d of a certain particle i dimension speed among the j of subgroup
Then order
If
Then order
If
Then order
If
Then
Each dimension after the particle position renewal is required to comprise: from current some P
tBeginning is with segmented paths dist
iFor there not being barrier in the view field of radius of view, if there is then current dimension renewal failure of barrier; If certain one dimension of a certain particle is made d
MaxInferior renewal is success not yet, and then the current dimension of this particle is upgraded failure, if a certain particle is made p
MaxInferior renewal is success not yet, and then this particle upgrades failure; If all subgroups all upgrade failure, termination of iterations and change step S7 over to then.
As prioritization scheme, step S66 is specially:
Judge whether the evolution cycle R that reaches default, if reach then at first according to default dead ratio, from gene pool, select particle to substitute the most bad particle in each subgroup at random; Again according to adaptive value, if the adaptive value of a certain particle is less than the adaptive value of a certain particle in the gene pool in each subgroup, then with the corresponding particle in this particle replacement gene pool in each subgroup; Final updating population barycenter P
c
As prioritization scheme, the method that among the step S7 robot is changed over to next GOTO field is: if described current optimal movement orientation angle
The local endpoint P of this Rolling Planning
t' be positioned at current some P
tThe y axle increase to, then each GOTO field is rotated counterclockwise test, otherwise each GOTO field is made the dextrorotation transfer to test.
Compared with prior art, the present invention has following beneficial effect:
(1) the present invention can realize the independent navigation of robot under the working environment with static-obstacle thing and the unknown of barrier position;
(2) the present invention introduces colony's barycenter strategy in the coevolution of many subgroups, has improved the search capability of population;
(3) with respect to the paths planning method of existing employing standard particle group algorithm (PSO) and coevolution particle cluster algorithm (CPSO), the present invention has adopted survival of the fittest strategy, has more superiority in evolution speed of convergence and precision;
(4) the present invention has to cook up in real time in the complex barrier substance environment and effectively keeps away the barrier path, have robustness good, find the solution the efficient advantages of higher.
Description of drawings
Fig. 1 is overview flow chart of the present invention;
Fig. 2 is the process flow diagram of step S6 of the present invention;
Fig. 3 is the synoptic diagram of working environment modeling among the step S1 of the present invention;
Fig. 4 keeps away the synoptic diagram of barrier for segmentation among the step S6 of the present invention;
Fig. 5 is the fall into a trap synoptic diagram of point counting section path length overall of step S63 of the present invention;
Fig. 6 is for turning to the synoptic diagram of domain test among the step S7 of the present invention;
Fig. 7 is the path planning result in the embodiment of the invention 1 typical obstacle environment;
Fig. 8 carries out the comparison diagram of path planning for adopting three kinds of algorithms of different;
Fig. 9 is the path planning result in the embodiment of the invention 1 slit environment;
Figure 10 is the path planning result in the embodiment of the invention 1 strip obstacle environment;
Figure 11 is the path planning result in the embodiment of the invention 1 spill obstacle environment.
Embodiment
Describe the present invention below in conjunction with accompanying drawing in detail in the mode of embodiment.
Embodiment 1:
As shown in Figure 1, based on the robot path planning method of coevolution population rolling optimization, be used for the walking path of a robot is planned, make robot avoid some stationary obstruction in the working environment, comprise the steps:
Step S1 carries out modeling to working environment, as shown in Figure 3, specifically comprises the steps:
Step S11 ignores the elevation information in the working environment, sets up two-dimensional coordinate system.This step has been ignored the elevation information of robot and barrier, the working environment of robot is described as the environment of a two-dimentional bounded.
Step S12, the view field of establishing robot for (r, θ), wherein r is radius of view, θ is field-of-view angle; Obtain the starting point S of robot and the coordinate of impact point G; In two-dimensional coordinate system, with convex polygon barrier is described, coordinate the unknown of barrier.
Step S13 carries out expandedly to barrier according to the size of robot, and robot handled as a particle.Shape and the structure of ignoring robot itself in path planning of the present invention are regarded it as particle and are handled.
Step S14, it is 1 that current scrolling planning number of times t is set, and current some P of robot is set
tBe starting point S, and with current some P
tDeposit terminal point collection { P} in.
Step S2, current some P of detection machine people
tAnd the distance between the impact point G, if distance, is then thought the arrival impact point less than default distance error ξ, export terminal point collection { P}, the acquisition programme path of each time planning; Otherwise change step S3 over to;
Step S3 is according to current some P of robot
tObtain current optimal movement orientation angle with impact point G
Current optimal movement orientation angle
Refer to current motion towards with the angle of two-dimensional coordinate system x axle, current motion is oriented current some P
tRectilinear direction to impact point G; Current optimal movement orientation angle
Computing formula as the formula (1):
Wherein, t is current scrolling planning number of times, if
Then order
G
xBe the coordinate figure of described impact point G at the x axle, G
yBe the coordinate figure of described impact point G at the y axle, P
x tBe described current some P
tAt the coordinate figure of x axle, P
y tBe described current some P
tCoordinate figure at the y axle.
Step S4 according to field-of-view angle θ, is divided into K=2 π/θ with the GOTO field of robot, according to current optimal movement orientation angle
Determine the current motion of robot towards the GOTO field k at place, current motion towards the computing formula of the GOTO field k at place as the formula (2):
Wherein, if k=0 then makes k=K, wherein ceil () is the function that rounds up, and mod () is for getting complementary function.
Step S5 is if clear in the view field of current robot is that limit is set up the local pole coordinate system with current point, according to default radius of view r and current optimal movement orientation angle
Obtain the local endpoint P of this planning
t', local endpoint P
t' computing formula as the formula (3):
Wherein, P
t'
xBe described local endpoint P
t' at the coordinate figure of x axle, P
t'
yBe described local endpoint P
t' at the coordinate figure of y axle, P
x tBe described current some P
tAt the coordinate figure of x axle, P
y tBe described current some P
tCoordinate figure at the y axle;
With local endpoint P
t' { P} makes t=t+1, and with local endpoint P to deposit the terminal point collection in
t' as the current some P that plans next time
t, change step S2 over to; If in the view field of current robot barrier is arranged, changes step S6 over to.
As shown in Figure 2, step S6 adopts the mode of interative computation to keep away barrier based on the coevolution particle cluster algorithm and handles; Specifically comprise the steps:
Step S61, the initialization of population step specifically comprises the steps:
Step S611 is divided into the m section with radius of view r, obtains a segmentation radius λ, determines m segment movement orientation angle simultaneously at random
, wherein,
I=1,2 ... m, segment movement orientation angle
Field-of-view angle θ less than robot; This step be attempt with less segmentation and less angle attempt " around " the mistake barrier, as shown in Figure 4, according to segmentation radius λ and segment movement orientation angle
Can in the view field of robot, obtain m waypoint; Distance between adjacent two waypoints is segmented paths dist
iThereby, obtain m segmented paths dist
i, by m segmented paths dist
iConnect to form the path length overall dist that robot avoiding obstacles in radius of view r scope need pass through
t
Step S612 arranges the interative computation that some particles are used for carrying out particle cluster algorithm, and particle is encoded to by some segment movement orientation angles
The sequence of forming, the dimension of establishing some particles is m, according to the GOTO field k of current motion towards the place, the scope that defines some particles is [(k-1) θ, k θ], (k=1,2 ... K, K=2 π/θ).
Step S613 as shown in Figure 5, can determine path length overall dist according to the cosine law
t, path length overall dist
tComputing formula as the formula (4):
Wherein, i=2,3 ..., m; λ=r/m, namely radius of view r makes the segmentation radius behind the branches such as m; First segmented paths dist
1=λ, x
iI dimension for the back particle of encoding;
Obtain local endpoint P
t' to the air line distance of impact point G
Air line distance
Computing formula as the formula (5):
According to path length overall dist
tAnd air line distance
Set an adaptive value function, the adaptive value function as the formula (6):
ω wherein
1With ω
2Be weight coefficient.
Step S614 forms an initial population with some particles, and initial population is carried out initialization; Initialization requires current some P from this planning
tBeginning is with segmented paths dist
iFor there not being barrier in the view field of radius of view, otherwise the initialization failure; Step S614 further comprises:
The setting original dimensions is that m, initial scale are N, carry out initialization before, the population scale of initial population is set much larger than initial scale N; The maximum initialization times p of particle is set
MaxWith the maximum initialization times d of dimension
MaxIf certain one dimension of a certain particle is being made d in the initial population
MaxSuccess not yet then reinitializes current dimension after the inferior initialization; If this particle is through p
MaxInferior initialization is still failed, and then thinks current particle initialization failure;
Abandon the particle of initialization failure, the initialized particle of success is formed initial population; If the scale of initial population reaches initial scale N, then initialization success changes next step over to; Otherwise the initialization failure changes step S7 over to.
Step S615 if the initialization success is divided into s subgroup with initial population, according to the GOTO field k of current motion towards the place, defines the scope [x of some particles
Min, x
Max] be [(k-1) θ, k θ]; The maximal rate that particle is set is 0.1 times of particle range.
Step S62 calculates the adaptive value of each particle in each subgroup, and the particle in each subgroup is sorted by adaptive value, selects the higher particle of adaptive value in each subgroup by default gene-ratio again, forms a gene pool.This step has adopted " survival of the fittest " strategy, selects " elite " particle optimum in each subgroup to create gene pool by certain gene-ratio, and the particle in this gene pool is less " elite " particle of adaptive value.
Step S63 according to the adaptive value of each particle in each subgroup, obtains the optimum solution of each subgroup
And try to achieve a population center of mass point P according to optimum solution
cPopulation center of mass point P
cDefinition as the formula (7):
Wherein, R is evolution cycle,
It is the globally optimal solution of i subgroup.Population center of mass point P
cBe the mean value of each subgroup optimum solution, represented the desired center of whole population, it upgrades with evolution cycle, no speed component.The population center of mass point is the average of the globally optimal solution of each subgroup, it is a kind of desirable barycenter, it is not the particle of a reality, the reason of introducing barycenter is, it is desirable optimum to follow individual historical optimum, subgroup global optimum and population when particle circles in the air in solution space, because a population is desirable optimum than having Duoed in the standard particle group evolutionary equation, it is when so particle flies in theory that directivity is clearer and more definite, can reduce the randomness of particle flight, certain " interchange " also arranged between each subgroup, therefore can significantly improve the search capability of population.
Step S64, from then on step begins the interative computation that rolls, and upgrades particle's velocity and position in each subgroup according to formula (8) and formula (9):
(8)
Wherein iter is the population evolutionary generation, and j is the subgroup mark, and i is particle mark in the subgroup,
Be i particle's velocity in j the subgroup; P
i jIt is the historical optimum solution of i particle in j the subgroup;
Optimum solution for subgroup, current particle place; c
1And c
2Be the study factor; c
3For exchanging the factor; r
1, r
2, r
3Be the random number between [0,1]; ω is inertia weight, and inertia weight ω adopts the linear decrease strategy, as the formula (10):
ω(iter)=ω
max-iter×(ω
max-ω
min)/iter
max (10)
Particle's velocity and location boundary processing are comprised: if the d of a certain particle i dimension speed among the j of subgroup
Then order
If
Then order
If
Then order
If
Then
Each dimension after the particle position renewal is required to comprise: from current some P
tBeginning is with segmented paths dist
iFor there not being barrier in the view field of radius of view, if there is then current dimension renewal failure of barrier; If certain one dimension of a certain particle is made d
MaxInferior renewal is success not yet, and then the current dimension of this particle is upgraded failure, if a certain particle is made p
MaxInferior renewal is success not yet, and then this particle upgrades failure; If all subgroups all upgrade failure, termination of iterations and change step S7 over to then; Successfully then change step S65 over to if upgrade.
Step S65 upgrades the adaptive value of particle in each subgroup, the individual optimum solution of particle and the globally optimal solution of subgroup.
Step S66 judges whether the evolution cycle R that reaches default, does not then directly change step S67 over to if reach evolution cycle R; If reach evolution cycle R then at first according to default dead ratio, from gene pool, select particle to substitute the most bad particle in each subgroup at random; If the adaptive value of a certain particle is less than the adaptive value of a certain particle in the gene pool in each subgroup, then with the corresponding particle in this particle replacement gene pool in each subgroup; New population barycenter P more then
cChange step S67 at last over to.This step has adopted " survival of the fittest " strategy, and the adaptive value of particle is more little, and then this particle is more outstanding.Eliminate the higher particle of a part of adaptive value in each subgroup according to dead ratio earlier, from gene pool, select particle to replace; Eliminate the higher particle of adaptive value in the gene pool again, from each subgroup, choose outstanding particle and replace; Thereby make the particle in gene pool and each subgroup all keep less adaptive value, make the speed of convergence of evolution faster, precision is higher.
Step S67 judges whether the maximum iteration time iter that reaches default
Max, if reached maximum iteration time iter
Max, termination of iterations then, the local endpoint P that obtains this planning according to globally optimal solution and the radius of view r of all subgroups
t', and with local endpoint P
t' add the terminal point collection and { among the P}, make t=t+1, with local endpoint P
t' as the current some P that plans next time
t, change step S2 over to; If do not reach maximum iteration time iter
Max, then change step S64 over to and continue iteration;
Step S7 keeps away the barrier failure and handles, and changes robot over to next GOTO field, and the method that changes next GOTO field over to is: if current optimal movement orientation angle
The local endpoint P of this Rolling Planning
t' be positioned at current some P
tThe y axle increase to, then each GOTO field is rotated counterclockwise test, namely k=mod (k+i, K), i=1,2 ... K-1; Otherwise each GOTO field is made the dextrorotation transfer to test.When turning clockwise, to k~1 GOTO field press k=mod (k-i, K) conversion GOTO field, i=0 wherein, 1,2 ... k-1; To K~k GOTO field press k=mod (K-i, K) conversion GOTO field, i=0 wherein, 1,2 ... K-k-1, as the formula (11):
After changing next GOTO field over to, return step S61 and keep away barrier, as shown in Figure 6; If robot all keeps away the barrier failure at all GOTO fields, then can't realize keeping away barrier, failure is jumped out.
In the present embodiment, select following parameter to further specify.
(1) population parameter: get initial scale N=20, subcluster number s=4, subgroup scale are 5, particle dimension m=10, learning and exchange factor c
1=c
2=c
3=2, inertia weight ω drops to 0.4 from 0.9 linearity, weight coefficient ω in the adaptive value function
1=ω
2=1, maximum iteration time iter
Max=100; Population evolution cycle R=4, the maximum initialization times p of particle
MaxWith the maximum initialization times d of dimension
MaxBe p
Max=d
Max=20, gene-ratio is 0.2, and dead ratio is 0.2.
(2) robot running parameter: radius of view r=5, field-of-view angle is θ=π/3, GOTO field is K=2 π/θ=6, distance error ξ=2.
The establishment size is 100 * 100 typical barrier working environment, wherein exists several static-obstacle things, and it is S (10,10) that starting point is set, and impact point is G (90,90), and the path of cooking up as shown in Figure 7.
Systematic parameter is identical, selection closes on the point (45 of barrier, 29), use standard particle group's algorithm (PSO), coevolution particle cluster algorithm (CPSO) to adopt many subgroups coevolution particle cluster algorithm (MCPSO) of " survival of the fittest " strategy to keep away the barrier test respectively with the present invention, the adaptive value change curve of three kinds of algorithms as shown in Figure 8, the MCPSO algorithm has more superiority in evolution speed of convergence and precision as seen from the figure.
Under special obstacle environment, as the slit environment among Fig. 9, huge long strip type obstacle environment among Figure 10 and the matrix obstacle environment among Figure 11 adopt this programme also can be implemented in thread path planning effectively, and Fig. 7, Fig. 8 and Fig. 9 are seen in the path of cooking up.
More than disclosed only be several specific embodiments of the application, but the not limited thereto any those skilled in the art of the application can think variation, all should drop in the application's the protection domain.
Claims (11)
1. based on the robot path planning method of coevolution population rolling optimization, be used for the walking path of a robot is planned, make robot avoid some stationary obstruction in the working environment, it is characterized in that, comprise the steps:
Step S1 carries out modeling to described working environment;
Step S2 detects the distance between the current point of described robot and the impact point, if described distance, is then thought the arrival impact point less than default distance error, exports the terminal point collection of each time planning, the acquisition programme path; Otherwise change step S3 over to;
Step S3 obtains current optimal movement orientation angle according to the current point of described robot and impact point;
Step S4 according to default robot field-of-view angle, divides the GOTO field of robot; Determine that according to described current optimal movement orientation angle the motion of current described robot is towards the GOTO field at place;
Step S5, if clear in the view field of current described robot, be that limit is set up the local pole coordinate system with described current point, the local endpoint that obtains this planning according to default radius of view and described current optimal movement orientation angle, and deposit described local endpoint in described terminal point collection, as the current point of planning next time, change described local endpoint over to step S2; If in the view field of current described robot barrier is arranged, changes step S6 over to;
Step S6 adopts the mode of interative computation to keep away barrier based on the coevolution particle cluster algorithm and handles; Described step S6 further comprises:
Step S61 is divided into several segmentations with described radius of view, determines several segment movement orientation angles simultaneously, and described segment movement orientation angle is less than described field-of-view angle; Some particles are set, each described particle is encoded to the sequence of being made up of some described segment movement orientation angles, some described particles are formed an initial population, and described initial population is carried out initialization, obtain an initial population; If the initialization success is divided into several subgroups with described initial population, change step S62 over to; If the initialization failure changes step S7 over to;
Step S62 calculates the adaptive value of each particle in each described subgroup, and the particle in each described subgroup is sorted by adaptive value, selects the less particle of adaptive value in each described subgroup by default gene-ratio again, forms a gene pool;
Step S63 according to the adaptive value of each particle in each described subgroup, obtains the optimum solution of each described subgroup, and tries to achieve a population center of mass point according to described optimum solution;
Step S64 upgrades particle's velocity and position in each described subgroup; If upgrade successfully, then change step S65 over to, if upgrade failure, change step S7 over to;
Step S65 upgrades the adaptive value of particle in each described subgroup, the individual optimum solution of particle and the globally optimal solution of subgroup;
Step S66 judges whether the evolution cycle that reaches default, does not then directly change step S67 over to if reach described evolution cycle; Otherwise upgrade described subgroup and described gene pool, upgrade described population center of mass point then, change step S67 again over to;
Step S67, judge whether the maximum iteration time that reaches default, if reached described maximum iteration time, termination of iterations then, obtain the local endpoint of this planning, and described local endpoint is added described terminal point concentrate, as the current point of planning next time, change described local endpoint over to step S2; If do not reach described maximum iteration time, then change step S64 over to and continue iteration;
Step S7 keeps away the barrier failure and handles, and changes described robot over to next GOTO field, returns step S61 and keeps away barrier; If described robot all keeps away the barrier failure at all GOTO fields, then can't realize keeping away barrier, failure is jumped out.
2. the robot path planning method based on coevolution population rolling optimization according to claim 1 is characterized in that, the described working environment modeling of described step S1 specifically comprises the steps:
Step S11 ignores the elevation information in the described working environment, sets up two-dimensional coordinate system;
Step S12, the view field of establishing described robot for (r, θ), wherein r is radius of view, θ is field-of-view angle; Obtain the starting point S of described robot and the coordinate of impact point G; With convex polygon described barrier is described, coordinate the unknown of described barrier;
Step S13 carries out expandedly to described barrier according to the size of described robot, and described robot handled as a particle;
Step S14, it is 1 that current scrolling planning number of times t is set, and current some P of described robot is set
tBe described starting point S, and with described current some P
tDeposit terminal point collection { P} in.
3. the robot path planning method based on coevolution population rolling optimization according to claim 2 is characterized in that, described step S3 is specially:
Described current optimal movement orientation angle
Refer to current motion towards with the angle of described two-dimensional coordinate system x axle, described current motion is oriented described current some P
tRectilinear direction to described impact point G; Described current optimal movement orientation angle
Computing formula as the formula (1):
Wherein, t is current scrolling planning number of times, if
Then order
G
xBe the coordinate figure of described impact point G at the x axle, G
yBe the coordinate figure of described impact point G at the y axle,
Be described current some P
tAt the coordinate figure of x axle,
Be described current some P
tCoordinate figure at the y axle.
4. the robot path planning method based on coevolution population rolling optimization according to claim 3 is characterized in that, described step S4 is specially:
According to described field-of-view angle θ, the GOTO field of described robot is divided into K=2 π/θ, according to described current optimal movement orientation angle
Determine the current motion of described robot towards the GOTO field k at place, described current motion towards the computing formula of the GOTO field k at place as the formula (2):
Wherein, if k=0 then makes k=K, wherein ceil () is the function that rounds up, and mod () is for getting complementary function.
5. the robot path planning method based on coevolution population rolling optimization according to claim 4 is characterized in that, described step S5 is specially:
Described local endpoint P
t' computing formula as the formula (3):
Wherein, P
t'
xBe described local endpoint P
t' at the coordinate figure of x axle, P
t'
yBe described local endpoint P
t' at the coordinate figure of y axle, P
x tBe described current some P
tAt the coordinate figure of x axle, P
y tBe described current some P
tCoordinate figure at the y axle; With described local endpoint P
t' deposit described terminal point collection in and { behind the P}, make t=t+1.
6. the robot path planning method based on coevolution population rolling optimization according to claim 5 is characterized in that, described step S61 further comprises the steps:
Step S611 is divided into the m section with described radius of view r, obtains a segmentation radius λ, determines m described segment movement orientation angle at random
Wherein,
I=1,2 ... m obtains some segmented paths dist
i, by described some segmented paths dist
iConnect to form described robot and in described radius of view r scope, avoid the path length overall dist that described barrier need pass through
t
Step S612 arranges the interative computation that some particles are used for carrying out particle cluster algorithm, and described particle is encoded to by some described segment movement orientation angles
The sequence of forming, the dimension of establishing described some particles is m, according to the GOTO field k of described current motion towards the place, the scope that defines described some particles is [(k-1) θ, k θ], wherein, and k=1,2 ... K, K=2 π/θ;
Step S613 determines described path length overall dist
t, described path length overall dist
tComputing formula as the formula (4):
Wherein, i=2,3 ..., m; λ=r/m, namely radius of view r makes the section length behind the branches such as m; x
iI dimension for the back particle of encoding;
Obtain described local endpoint P
t' to the air line distance of described impact point G
Described air line distance
Computing formula as the formula (5):
According to described path length overall dist
tWith described air line distance
Set an adaptive value function, described adaptive value function as the formula (6):
ω wherein
1With ω
2Be weight coefficient;
Step S614 forms an initial population with described some particles, and initial population is carried out initialization; Described initialization requires current some P from this planning
tBeginning is with described segmented paths dist
iFor there not being barrier in the view field of radius of view, otherwise the initialization failure;
Step S615 if the initialization success is divided into s subgroup with described initial population, according to the GOTO field k of described current motion towards the place, defines the scope [x of described some particles
Min, x
Max] be [(k-1) θ, k θ]; The maximal rate that particle is set is 0.1 times of particle range.
7. the robot path planning method based on coevolution population rolling optimization according to claim 6 is characterized in that, the initialization among the described step S614 is specially:
The setting original dimensions is that m, initial scale are N, carry out initialization before, the population scale of described initial population is set much larger than initial scale N; The maximum initialization times p of particle is set
MaxWith the maximum initialization times d of dimension
MaxIf certain one dimension of a certain particle is being made d in the described initial population
MaxSuccess not yet then reinitializes current dimension after the inferior initialization; If this particle is through p
MaxInferior initialization is still failed, and then thinks current particle initialization failure;
Abandon the particle of initialization failure, the initialized particle of success is formed described initial population; If the scale of described initial population reaches initial scale N, then initialization success; Otherwise, the initialization failure.
8. the robot path planning method based on coevolution population rolling optimization according to claim 7 is characterized in that, the P of population center of mass point described in the described step S63
cDefinition as the formula (7):
9. the robot path planning method based on coevolution population rolling optimization according to claim 8 is characterized in that, described step S64 is specially:
Upgrade particle's velocity and position in each described subgroup according to formula (8) and formula (9):
(8)
Wherein iter is the population evolutionary generation, and j is the subgroup mark, and i is particle mark in the subgroup,
Be i particle's velocity in j the subgroup; P
i jIt is the historical optimum solution of i particle in j the subgroup;
Optimum solution for subgroup, current particle place; c
1And c
2Be the study factor; c
3For exchanging the factor; r
1, r
2, r
3Be the random number between [0,1]; ω is inertia weight, and described inertia weight ω adopts the linear decrease strategy, as the formula (10):
ω(iter)=ω
max-iter×(ω
max-ω
min)/iter
max (10)
Particle's velocity and location boundary processing are comprised: if the d of a certain particle i dimension speed among the j of subgroup
Then order
If
Then order
If
Then order
If
Then
Each dimension after the particle position renewal is required to comprise: from described current some P
tBeginning is with described segmented paths dist
iFor there not being barrier in the view field of radius of view, if there is then current dimension renewal failure of barrier; If certain one dimension of a certain particle is made d
MaxInferior renewal is success not yet, and then the current dimension of this particle is upgraded failure, if a certain particle is made p
MaxInferior renewal is success not yet, and then this particle upgrades failure; If all subgroups all upgrade failure, termination of iterations and change step S7 over to then.
10. the robot path planning method based on coevolution population rolling optimization according to claim 9 is characterized in that, described step S66 is specially:
Judge whether the evolution cycle R that reaches default, if reach then at first according to default dead ratio, from described gene pool, select particle to substitute the most bad particle in each described subgroup at random; If the adaptive value of a certain particle is less than the adaptive value of a certain particle in the described gene pool in each described subgroup, then replace corresponding particle in the described gene pool with this particle in each described subgroup; Final updating population barycenter P
c
11. the robot path planning method based on coevolution population rolling optimization according to claim 10 is characterized in that, the method that among the described step S7 described robot is changed over to next GOTO field is: if described current optimal movement orientation angle
The local endpoint P of this Rolling Planning
t' be positioned at current some P
tThe y axle increase to, then each GOTO field is rotated counterclockwise test, otherwise each GOTO field is made the dextrorotation transfer to test.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201310246826.1A CN103336526B (en) | 2013-06-20 | 2013-06-20 | Based on the robot path planning method of coevolution population rolling optimization |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201310246826.1A CN103336526B (en) | 2013-06-20 | 2013-06-20 | Based on the robot path planning method of coevolution population rolling optimization |
Publications (2)
Publication Number | Publication Date |
---|---|
CN103336526A true CN103336526A (en) | 2013-10-02 |
CN103336526B CN103336526B (en) | 2015-08-05 |
Family
ID=49244717
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201310246826.1A Expired - Fee Related CN103336526B (en) | 2013-06-20 | 2013-06-20 | Based on the robot path planning method of coevolution population rolling optimization |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN103336526B (en) |
Cited By (23)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN104020665A (en) * | 2014-06-25 | 2014-09-03 | 北京邮电大学 | Minimum saltus trajectory optimization method of mechanical arm based on multi-objective particle swarm optimization algorithm |
CN105302136A (en) * | 2015-09-23 | 2016-02-03 | 上海物景智能科技有限公司 | Area segmentation method based on cleaning robot |
WO2016045615A1 (en) * | 2014-09-25 | 2016-03-31 | 科沃斯机器人有限公司 | Robot static path planning method |
CN105717929A (en) * | 2016-04-29 | 2016-06-29 | 中国人民解放军国防科学技术大学 | Planning method for mixed path of mobile robot under multi-resolution barrier environment |
CN106257240A (en) * | 2015-06-18 | 2016-12-28 | 金宝电子工业股份有限公司 | Positioning navigation method and electronic device thereof |
CN106289293A (en) * | 2016-08-11 | 2017-01-04 | 浪潮电子信息产业股份有限公司 | Urban content positioning navigation system based on intelligent learning algorithm |
CN107450563A (en) * | 2017-09-21 | 2017-12-08 | 景德镇陶瓷大学 | A kind of adaptive information feedback population robot path system of selection and system based on more subgroups |
CN108241375A (en) * | 2018-02-05 | 2018-07-03 | 景德镇陶瓷大学 | A kind of application process of self-adaptive genetic operator in mobile robot path planning |
WO2018176595A1 (en) * | 2017-03-31 | 2018-10-04 | 深圳市靖洲科技有限公司 | Unmanned bicycle path planning method based on ant colony algorithm and polar coordinate transformation |
CN108748160A (en) * | 2018-06-21 | 2018-11-06 | 河南大学 | Manipulator motion planning method based on particle cluster algorithm on multiple populations |
CN108983770A (en) * | 2018-07-02 | 2018-12-11 | 四川大学 | Data processing method, device, electronic equipment and storage medium |
CN109062214A (en) * | 2018-08-22 | 2018-12-21 | 北京云迹科技有限公司 | A kind of routing resource and distributed robot |
CN109318890A (en) * | 2018-06-29 | 2019-02-12 | 北京理工大学 | A kind of unmanned vehicle dynamic obstacle avoidance method based on dynamic window and barrier potential energy field |
CN109581987A (en) * | 2018-12-29 | 2019-04-05 | 广东飞库科技有限公司 | A kind of AGV scheduling paths planning method and system based on particle swarm algorithm |
CN109582027A (en) * | 2019-01-14 | 2019-04-05 | 哈尔滨工程大学 | A kind of USV cluster collision-avoidance planning method based on Modified particle swarm optimization algorithm |
CN109782807A (en) * | 2019-03-08 | 2019-05-21 | 哈尔滨工程大学 | A kind of AUV barrier-avoiding method under back-shaped obstacle environment |
CN109871031A (en) * | 2019-02-27 | 2019-06-11 | 中科院成都信息技术股份有限公司 | A kind of method for planning track of fixed-wing unmanned plane |
CN109871021A (en) * | 2019-03-18 | 2019-06-11 | 安徽大学 | A kind of robot navigation method based on particle swarm optimization algorithm |
CN109991976A (en) * | 2019-03-01 | 2019-07-09 | 江苏理工学院 | A method of the unmanned vehicle based on standard particle group's algorithm evades dynamic vehicle |
CN110653831A (en) * | 2019-09-19 | 2020-01-07 | 常熟理工学院 | Multi-odor-searching robot for underground comprehensive pipe gallery and positioning system and method for dangerous gas leakage source |
CN111505935A (en) * | 2020-04-30 | 2020-08-07 | 内蒙古工业大学 | Automatic guided vehicle control method and system |
CN112214930A (en) * | 2020-09-29 | 2021-01-12 | 中国航空工业集团公司沈阳飞机设计研究所 | Multi-machine collaborative route planning method and system based on collaborative particle swarm optimization algorithm |
CN113762565A (en) * | 2020-08-21 | 2021-12-07 | 北京沃东天骏信息技术有限公司 | Path planning method, device, computing equipment and medium |
Families Citing this family (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN107150341A (en) * | 2017-06-13 | 2017-09-12 | 南京理工大学 | A kind of welding robot path of welding planing method based on discrete particle cluster algorithm |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101604166A (en) * | 2009-07-10 | 2009-12-16 | 杭州电子科技大学 | A kind of method for planning path for mobile robot based on particle swarm optimization algorithm |
CN101837591A (en) * | 2010-03-12 | 2010-09-22 | 西安电子科技大学 | Robot path planning method based on two cooperative competition particle swarms and Ferguson spline |
KR20110026776A (en) * | 2009-09-08 | 2011-03-16 | 부산대학교 산학협력단 | Method for path-planning for actual robots |
CN102722749A (en) * | 2012-06-01 | 2012-10-10 | 哈尔滨工程大学 | Self-adaptive three-dimensional space path planning method based on particle swarm algorithm |
-
2013
- 2013-06-20 CN CN201310246826.1A patent/CN103336526B/en not_active Expired - Fee Related
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101604166A (en) * | 2009-07-10 | 2009-12-16 | 杭州电子科技大学 | A kind of method for planning path for mobile robot based on particle swarm optimization algorithm |
KR20110026776A (en) * | 2009-09-08 | 2011-03-16 | 부산대학교 산학협력단 | Method for path-planning for actual robots |
CN101837591A (en) * | 2010-03-12 | 2010-09-22 | 西安电子科技大学 | Robot path planning method based on two cooperative competition particle swarms and Ferguson spline |
CN102722749A (en) * | 2012-06-01 | 2012-10-10 | 哈尔滨工程大学 | Self-adaptive three-dimensional space path planning method based on particle swarm algorithm |
Non-Patent Citations (3)
Title |
---|
FRANS VAN DEN BERGH ET AL.: "A Cooperative Approach to Particle Swarm Optimization", 《IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION》, vol. 8, no. 3, 30 June 2004 (2004-06-30), pages 225 - 239 * |
彭虎 等: "多子群协同进化的多目标微粒群优化算法", 《计算机应用》, vol. 32, no. 2, 1 February 2012 (2012-02-01), pages 456 - 460 * |
赵先章等: "一种基于粒子群算法的移动机器人路径规划方法", 《计算机应用研究》, vol. 24, no. 03, 31 March 2007 (2007-03-31) * |
Cited By (34)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN104020665A (en) * | 2014-06-25 | 2014-09-03 | 北京邮电大学 | Minimum saltus trajectory optimization method of mechanical arm based on multi-objective particle swarm optimization algorithm |
WO2016045615A1 (en) * | 2014-09-25 | 2016-03-31 | 科沃斯机器人有限公司 | Robot static path planning method |
CN106257240A (en) * | 2015-06-18 | 2016-12-28 | 金宝电子工业股份有限公司 | Positioning navigation method and electronic device thereof |
CN106257240B (en) * | 2015-06-18 | 2019-05-21 | 金宝电子工业股份有限公司 | Positioning navigation method and electronic device thereof |
CN105302136A (en) * | 2015-09-23 | 2016-02-03 | 上海物景智能科技有限公司 | Area segmentation method based on cleaning robot |
CN105302136B (en) * | 2015-09-23 | 2018-02-23 | 上海物景智能科技有限公司 | A kind of region segmentation method based on clean robot |
CN105717929B (en) * | 2016-04-29 | 2018-06-15 | 中国人民解放军国防科学技术大学 | Mobile robot mixed path planing method under a kind of multiresolution obstacle environment |
CN105717929A (en) * | 2016-04-29 | 2016-06-29 | 中国人民解放军国防科学技术大学 | Planning method for mixed path of mobile robot under multi-resolution barrier environment |
CN106289293B (en) * | 2016-08-11 | 2019-02-15 | 浪潮电子信息产业股份有限公司 | Urban content positioning navigation system based on intelligent learning algorithm |
CN106289293A (en) * | 2016-08-11 | 2017-01-04 | 浪潮电子信息产业股份有限公司 | Urban content positioning navigation system based on intelligent learning algorithm |
WO2018176595A1 (en) * | 2017-03-31 | 2018-10-04 | 深圳市靖洲科技有限公司 | Unmanned bicycle path planning method based on ant colony algorithm and polar coordinate transformation |
CN107450563A (en) * | 2017-09-21 | 2017-12-08 | 景德镇陶瓷大学 | A kind of adaptive information feedback population robot path system of selection and system based on more subgroups |
CN107450563B (en) * | 2017-09-21 | 2020-08-25 | 景德镇陶瓷大学 | Self-adaptive information feedback particle swarm robot path selection method based on multiple subgroups |
CN108241375A (en) * | 2018-02-05 | 2018-07-03 | 景德镇陶瓷大学 | A kind of application process of self-adaptive genetic operator in mobile robot path planning |
CN108241375B (en) * | 2018-02-05 | 2020-10-30 | 景德镇陶瓷大学 | Application method of self-adaptive ant colony algorithm in mobile robot path planning |
CN108748160A (en) * | 2018-06-21 | 2018-11-06 | 河南大学 | Manipulator motion planning method based on particle cluster algorithm on multiple populations |
CN109318890A (en) * | 2018-06-29 | 2019-02-12 | 北京理工大学 | A kind of unmanned vehicle dynamic obstacle avoidance method based on dynamic window and barrier potential energy field |
CN108983770A (en) * | 2018-07-02 | 2018-12-11 | 四川大学 | Data processing method, device, electronic equipment and storage medium |
CN108983770B (en) * | 2018-07-02 | 2019-07-05 | 四川大学 | Data processing method, device, electronic equipment and storage medium |
CN109062214A (en) * | 2018-08-22 | 2018-12-21 | 北京云迹科技有限公司 | A kind of routing resource and distributed robot |
CN109581987A (en) * | 2018-12-29 | 2019-04-05 | 广东飞库科技有限公司 | A kind of AGV scheduling paths planning method and system based on particle swarm algorithm |
CN109582027A (en) * | 2019-01-14 | 2019-04-05 | 哈尔滨工程大学 | A kind of USV cluster collision-avoidance planning method based on Modified particle swarm optimization algorithm |
CN109582027B (en) * | 2019-01-14 | 2022-02-22 | 哈尔滨工程大学 | Improved particle swarm optimization algorithm-based USV cluster collision avoidance planning method |
CN109871031A (en) * | 2019-02-27 | 2019-06-11 | 中科院成都信息技术股份有限公司 | A kind of method for planning track of fixed-wing unmanned plane |
CN109991976A (en) * | 2019-03-01 | 2019-07-09 | 江苏理工学院 | A method of the unmanned vehicle based on standard particle group's algorithm evades dynamic vehicle |
CN109782807B (en) * | 2019-03-08 | 2021-10-01 | 哈尔滨工程大学 | AUV obstacle avoidance method under environment of square-shaped obstacle |
CN109782807A (en) * | 2019-03-08 | 2019-05-21 | 哈尔滨工程大学 | A kind of AUV barrier-avoiding method under back-shaped obstacle environment |
CN109871021A (en) * | 2019-03-18 | 2019-06-11 | 安徽大学 | A kind of robot navigation method based on particle swarm optimization algorithm |
CN109871021B (en) * | 2019-03-18 | 2022-04-15 | 安徽大学 | Robot navigation method based on particle swarm optimization algorithm |
CN110653831A (en) * | 2019-09-19 | 2020-01-07 | 常熟理工学院 | Multi-odor-searching robot for underground comprehensive pipe gallery and positioning system and method for dangerous gas leakage source |
CN111505935A (en) * | 2020-04-30 | 2020-08-07 | 内蒙古工业大学 | Automatic guided vehicle control method and system |
CN111505935B (en) * | 2020-04-30 | 2022-10-18 | 内蒙古工业大学 | Automatic guided vehicle control method and system |
CN113762565A (en) * | 2020-08-21 | 2021-12-07 | 北京沃东天骏信息技术有限公司 | Path planning method, device, computing equipment and medium |
CN112214930A (en) * | 2020-09-29 | 2021-01-12 | 中国航空工业集团公司沈阳飞机设计研究所 | Multi-machine collaborative route planning method and system based on collaborative particle swarm optimization algorithm |
Also Published As
Publication number | Publication date |
---|---|
CN103336526B (en) | 2015-08-05 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN103336526A (en) | Robot path planning method based on coevolution particle swarm rolling optimization | |
CN109947136B (en) | Collaborative active sensing method for unmanned aerial vehicle group rapid target search | |
CN107168305B (en) | Bezier and VFH-based unmanned vehicle track planning method under intersection scene | |
Xu et al. | Behavior‐Based Formation Control of Swarm Robots | |
CN106525047A (en) | Unmanned aerial vehicle path planning method based on floyd algorithm | |
CN109871032A (en) | A kind of multiple no-manned plane formation cooperative control method based on Model Predictive Control | |
CN103528585A (en) | Path planning method of passable area divided at unequal distance | |
CN106647771A (en) | Multi-mobile-robot minimum step formation method | |
CN104121903A (en) | Rolling route planning method based on boundary value problem | |
Bonny et al. | Highly optimized Q‐learning‐based bees approach for mobile robot path planning in static and dynamic environments | |
CN108445894A (en) | A kind of secondary paths planning method considering unmanned boat movenent performance | |
CN110412984B (en) | Cluster safety consistency controller and control method thereof | |
Sun et al. | Path planning of UAVs based on improved ant colony system | |
Tian | Research on robot optimal path planning method based on improved ant colony algorithm | |
Bin et al. | Recurrent neural network for robot path planning | |
Wang et al. | Trajectory planning for an unmanned ground vehicle group using augmented particle swarm optimization in a dynamic environment | |
Li et al. | An improved differential evolution based artificial fish swarm algorithm and its application to AGV path planning problems | |
CN113759935B (en) | Intelligent group formation mobile control method based on fuzzy logic | |
CN111474925A (en) | Path planning method for irregular-shape mobile robot | |
Chen et al. | A multirobot cooperative area coverage search algorithm based on bioinspired neural network in unknown environments | |
CN111427368A (en) | Improved multi-target collision-prevention driving method for unmanned intelligent vehicle | |
CN115454061B (en) | Robot path obstacle avoidance method and system based on 3D technology | |
Chen et al. | Cooperative Search Self-Organizing Strategy for Multiple Unmanned Aerial Vehicles Based on Probability Map and Uncertainty Map | |
Lin et al. | Research on multi-usv cooperative search method | |
Cheng et al. | Route planning of mixed ant colony algorithm based on Dubins path |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
C06 | Publication | ||
PB01 | Publication | ||
C10 | Entry into substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
C14 | Grant of patent or utility model | ||
GR01 | Patent grant | ||
CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20150805 Termination date: 20180620 |
|
CF01 | Termination of patent right due to non-payment of annual fee |