CN103926930A - Multi-robot cooperation map building method based on Hilbert curve detection - Google Patents
Multi-robot cooperation map building method based on Hilbert curve detection Download PDFInfo
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Abstract
The invention discloses a multi-robot cooperation map building method based on Hilbert curve detection. According to the method, an MAPSO global optimization method and a Hilbert curve detection method are combined, so that the environment detection precision of multiple robots can be effectively improved, and the times of repeated detection are reduced. The characteristics of a Hilbert curve and the detection radii of the robots are comprehensively utilized, a MAPSO algorithm is used for conducting global optimization on distribution of subregions to be detected of the robots, so that repeated detection is avoided, and the detection efficiency is improved. In the Hilbert curve detection process, the robots only conduct detection on the respective subregions, and the condition of collision of the robots is avoided.
Description
Technical field
The present invention relates to a kind of multiple mobile robot's map constructing method, relate in particular to a kind of multiple mobile robot who surveys based on multi-Agent particle group optimizing (MAPSO) algorithm and Hilbert curve map constructing method that cooperates.
Background technology
Map structuring is a multi-robot coordination problem with typicalness and versatility, is the basis of multi-robot coordination problem.Map structuring technology has been widely used in the fields such as national defence, industrial or agricultural, flexible manufacturing industry and unmanned exploration.Conventional cartographic representation is divided three classes at present: Grid Method, topological approach and geological information representation.Method based on grid only needs to determine that each grid is that empty (and the value of grid is 0) still exists barrier (value that is grid is 1), other features of environment are lost interest in, and grating map is easily set up, safeguarded; Topological approach is applicable to structured environment, is not suitable for destructuring environment; The method representing based on geological information is comparatively compact, is convenient to target identification and location estimation.
Multi-Agent particle swarm optimization algorithm (MAPSO) needs the parameter of adjustment less, is easy to realize, and can carries out parallel computation and without gradient calculation, have good ability of searching optimum.In MAPSO system, adopt competition, motion search model, each alternative solution is called as " particle ", and this particle is solution space candidate solution, and the good and bad degree of solution is determined by fitness function.Wherein, fitness function defines according to optimization aim.Multiple particles are also deposited, and by obtaining optimal value with the competition of neighbours' particle, the cooperation chosen, speed determines the displacement of particle at search volume unit's iterations.
Hilbert curve detection method coordinates the detectable radius of robot, can avoid repeating to survey identical region.Between multirobot, the collaborative detection to circumstances not known, has improved detection accuracy significantly, saves detection time.
Summary of the invention
The present invention proposes a kind of Hilbert curve based on MAPSO and surveys the method that builds map, subregion being carried out in the realization of Hilbert curve detection, introduce MAPSO algorithm robot subregion assignment problem to be measured is carried out to global optimization, realize multi-robot Cooperation and build figure, reach minimizing multirobot and build the repetition detection times in figure in cooperation, improve the object of detection efficiency.
Overall solution of the present invention is: complete Hilbert curve with multirobot and survey, and use MAPSO algorithm to carry out global optimization, make robot find near-optimization target area to survey.Adopt Grid Method to carry out Map building, set square area as region to be detected according to the entirety size of circumstances not known.Be some subregions according to the radius of investigation of robot by Region Segmentation to be detected, wherein the length of side of every sub regions is the integral multiple of robot probe's radius.And every sub regions is cut apart and is traditional grid, the sizableness of grid is in the detectable range of robot, and to adopt MAPSO algorithm be that multiple robots distribute preferred subregion, and surveys at the inner Hilbert of employing of subregion curve.
Method of the present invention specifically comprises the steps:
Step 1: the environment of required detection is divided into some subregions.
Step 2: make to find near-optimization target area in four robots of current location by MAPSO algorithm.If each robot finds effective search coverage, forward step 3 to; If each robot does not find effective search coverage, forward step 5 to, task finishes; If only have part robot not find effective search coverage, carry out next round MAPSO algorithmic rule according to current position, turn back to step 2, until each robot finds effective search coverage.
Step 3: four robots adopt distributed frame to carry out a step Hilbert curve to target area and survey.
Step 4: judge whether that four robots all complete Hilbert curve detection process, if so, robot will carry out information fusion, draws the result figure surveying, and forwards step 2 to; Otherwise, return to step 3, carry out next step Hilbert curve and survey.
Step 5: task finishes.
The present invention surveys and improves mainly for the Hilbert curve of standard, and introduce MAPSO algorithm and carry out global optimization,
The multirobot environment detection method based on MAPSO algorithm that the present invention proposes adopts MAPSO global optimization method to combine with Hilbert curve detection method, make multiple robots avoid repeating surveying as far as possible, keep mutually away from, nearest apart from original position, realize multi-robot Cooperation and build figure.This method can effectively improve multirobot environment detection precision, reduces and repeats detection times.The radius of investigation of integrated use Hilbert curve characteristic and robot, can avoid repeating surveying, and improves detection efficiency.In Hilbert curve detection process, each robot only can survey at the subregion of oneself, the situation of having avoided robot to bump.
Brief description of the drawings
The multi-robot Cooperation of Fig. 1 based on multi-Agent particle group optimizing and the detection of Hilbert curve built the process flow diagram of drawing method.
Embodiment
Below in conjunction with accompanying drawing, the present invention is further detailed.
As shown in Figure 1, the method that the present invention uses MAPSO algorithm to survey with Hilbert curve combines, and specific implementation step is as follows:
Step 1: according to the radius of investigation of robot, the environment of required detection is divided into some subregions, for MAPSO global optimization is prepared.
Step 2: make to find near-optimization target subregion in four robots of current location by MAPSO algorithm.In the time that algorithm finishes, if each robot finds effective search coverage, carry out next step Hilbert curve and survey, forward step 3 to; If each robot does not find effective search coverage, forward step 5 to, task finishes; If only have part robot not find effective search coverage, carry out next round MAPSO algorithmic rule according to current position, turn back to step 2, until each robot finds effective search coverage.
Particularly, multi-Agent particle swarm optimization algorithm has the feature of evolutionary computation and colony intelligence concurrently, utilizes this algorithm to make multiple robots keep mutually nearest away from the original position of, distance as far as possible, and ensures that search coverage does not repeat as far as possible.
Mainly comprise following components in multi-Agent particle swarm optimization algorithm process:
(1) initialization particle colony: MAPSO initialization a group first randomly particle, wherein i particle is X in the position of d dimension solution space
i=(X
i1, X
i2, X
i3..., X
id), the individual optimal value (p of two extreme values of initialization particle
best) and global optimum (g
best).
(2) competition: each particle is chosen from own 8 nearest particles as neighbours' particle from environment, and according to calculating fitness value and adjust the positional information of oneself.
(3) upgrade: particle is by dynamically following the tracks of p
bestand g
bestupgrade its speed and position, its formula is as follows:
v
id(t+1)＝wv
id(t)+c
1r
1[p
best(t)-x
id(t)]+c
2r
2[g
best(t)-x
id(t)] (1)
x
id(t+1)＝x
id(t)+v
id(t+1 )(2)
In formula, v
id(t+1) represent the speed of t+1 moment i particle in d dimension solution space, x
id(t+1) represent the position of t+1 moment i particle in d dimension solution space, c
1=c2=2, is speedup factor, and w=0.7298 is inertial factor, r
1and r
2be the random number between [0,1], t represents moment t.
(4) calculate fitness value: calculate the fitness value of each particle according to fitness function, its formula is as follows:
f
fitness＝w
1D
1-w
2D
2+w
3S
1 (3)
In formula, D
1represent the next detection of a target of robot and the distance summation of current location, in experiment, wish that this value is less; D
2represent in next target area the distance summation of each robot; When robot is in the time that next target location is surveyed, S
1be proportional to the ratio of search coverage area and target area.W
1, w
2, w
3for the weighting factor w of first three conditional function
1+ w
2+ w
3=1.
According to the more global optimum of new particle of fitness function.In the time calculating the optimal value arrival designing requirement of gained or when algorithm arrives maximum iteration time, exit MAPSO optimizing algorithm; Otherwise turn back to (2).
Step 3: four robots adopt distributed frame to carry out a step Hilbert curve to target area and survey.
The present invention does hypothesis to acquisition environment:
(1) environment adopts Grid Method to carry out modeling and description, and each grid cell (i, j) is worth cell[i] [j] ∈ 0,1}, and 0 represents not occupied by barrier, and 1 represents to be occupied by barrier.
(2) known its initial position of robot.Robot can calculate according to the position coordinates at its current place next step position, disregards the positioning error causing because of robot and any physical factor in emulation.Robot can detecting obstacles thing scope be the appreciable scope of sensor.
(3) environment is unknown to robot, after it is surveyed environment, and could constructing environment map.
(4) each robot can preserve local map, and before whole map structuring completes, each robot preserves the own map of surveying.While finishing emulation, multi-robot system passed to by the local map of its preservation by robot, at this moment, complete the structure of whole map, and the environmental map that each robot preserves put sky again.
The principle that Hilbert curve is surveyed is that robot surveys environment along four end to end Hilbert curves, and each step Hilbert curve is surveyed that two kinds of possible state: flag=0 survey, flag=1 keeps away barrier.
Survey: robot is at initial time or exit and keep away when barrier, robot enters acquisition mode automatically.
Keep away barrier: in the time that robot runs into barrier, just enter the barrier state of keeping away, the left side of Robot barrier is surveyed, in the time that the point of finding obstacle is got back to by robot, robot automatically exits and keeps away barrier state, enters acquisition mode.If when two sub regions has been crossed at barrier place, each robot follows the only subregion detection principles at oneself, returns along former direction at the boundary of subregion.The border that arrives again subregion when robot is, is just interpreted as the detection mission that completes own region, and robot exits the barrier state of keeping away.Robot moves accordingly according to the value of flag.
Step 4: judge whether that four robots all complete Hilbert curve detection process, if so, robot will carry out information fusion, draws the result figure surveying, and forwards step 2 to; Otherwise, return to step 3, carry out next step Hilbert curve and survey.
Step 5: task finishes.
Below by reference to the accompanying drawings the specific embodiment of the present invention is described; but these explanations can not be understood to limit scope of the present invention; protection scope of the present invention is limited by the claims of enclosing, and any change on the claims in the present invention basis is all protection scope of the present invention.
Claims (5)
1. the multi-robot Cooperation map constructing method based on multi-Agent particle group optimizing and the detection of Hilbert curve, is characterized in that, said method comprising the steps of:
Step 1: according to the radius of investigation of robot, the environment of required detection is divided into some subregions, prepares for MAPSO carries out global optimization;
Step 2: adopt multi-Agent particle cluster algorithm to carry out global optimization, make to find near-optimization target area in four robots of current location; If each robot finds effective search coverage, continue execution step 3; If each robot does not find effective search coverage, forward step 5 to; If only have part robot not find effective search coverage, carry out next round MAPSO algorithmic rule according to current position, return to step 2, until each robot finds effective search coverage;
Step 3: four robots adopt distributed frame to carry out a step Hilbert curve to target area and survey;
Step 4: judge whether that four robots all complete Hilbert curve detection process, if so, robot will carry out information fusion, draws the result figure surveying, and forwards step 2 to; Otherwise, return to step 3, carry out next step Hilbert curve and survey.
Step 5: task finishes.
2. multi-robot Cooperation map constructing method according to claim 1, it is characterized in that, described step 1 specifically adopts Grid Method to carry out Map building, set square area as region to be detected according to the entirety size of circumstances not known, be some subregions Region Segmentation to be detected, wherein the length of side of every sub regions is the integral multiple of robot probe's radius, and every sub regions cuts apart and be traditional grid, and the sizableness of grid is in the detectable range of robot.
3. multi-robot Cooperation map constructing method according to claim 1, is characterized in that, the process that the employing multi-Agent particle cluster algorithm in described step 2 carries out global optimization comprises following components:
(1) initialization particle colony: MAPSO initialization a group first randomly particle, wherein i particle is X in the position of d dimension solution space
i=(X
i1, X
i2, X
i3..., X
id), the individual optimal value (p of two extreme values of initialization particle
best) and global optimum (g
best);
(2) competition: each particle is according to choosing from environment from own 8 nearest particles as choosing neighbours' particle, and according to calculating fitness value and adjust the positional information of oneself;
(3) upgrade: particle is by dynamically following the tracks of p
bestand g
bestupgrade its speed and position, its formula is as follows:
v
id(t+1)＝wv
id(t)+c
1r
1[p
best(t)-x
id(t)]+c
2r
2[g
best(t)-x
id(t)] (1)
x
id(t+1)＝x
id(t)+v
id(t+1) (2)
In formula, v
id(t+1) represent the speed of t+1 moment i particle in d dimension solution space, x
id(t+1) represent the position of t+1 moment i particle in d dimension solution space, c
1=c2=2, is speedup factor, and w=0.7298 is inertial factor, r
1and r
2be the random number between [0,1], t represents moment t;
(4) calculate fitness value: calculate the fitness value of each particle according to fitness function, its formula is as follows:
f
fitness＝w
1D
1-w
2D
2+w
3S
1 (3)
In formula, D
1represent the next detection of a target of robot and the distance summation of current location, in experiment, wish that this value is less; D
2represent in next target area the distance summation of each robot; When robot is in the time that next target location is surveyed, S
1be proportional to the ratio of search coverage area and target area.W
1, w
2, w
3for the weighting factor w of first three conditional function
1+ w
2+ w
3=1;
According to the more global optimum of new particle of fitness function, in the time calculating the optimal value arrival designing requirement of gained or when algorithm arrives maximum iteration time, exit MAPSO optimizing algorithm; Otherwise turn back to (2).
4. multi-robot Cooperation map constructing method according to claim 1, it is characterized in that, the Hilbert curve detection process of described step 3 is as follows: robot surveys environment along four end to end Hilbert curves, robot moves accordingly according to the value of flag, and each step Hilbert curve is surveyed that two kinds of possible state: flag=0 survey, flag=1 keeps away barrier;
Survey: robot is at initial time or exit and keep away when barrier, robot enters acquisition mode automatically;
Keep away barrier: in the time that robot runs into barrier, just enter the barrier state of keeping away, the left side of Robot barrier is surveyed, in the time that the point of finding obstacle is got back to by robot, robot automatically exits and keeps away barrier state, enters acquisition mode; If when two sub regions has been crossed at barrier place, each robot follows the only subregion detection principles at oneself, returns along former direction at the boundary of subregion; The border that arrives again subregion when robot is, is just interpreted as the detection mission that completes own region, and robot exits the barrier state of keeping away.
5. multi-robot Cooperation map constructing method according to claim 4, it is characterized in that, in step 3, need to build environment: the position of known barrier, size, obtain global information by robot probe's device, be stored in robot interior information bank, for all robots are shared, in the time entering robot and need to take to keep away the scope of barrier behavior, extract corresponding barrier or other robot information.
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Application publication date: 20140716 |