CN105911992B - A kind of automatic path planning method and mobile robot of mobile robot - Google Patents
A kind of automatic path planning method and mobile robot of mobile robot Download PDFInfo
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Abstract
The invention discloses a kind of automatic path planning method of mobile robot and using the mobile robot of this method, method includes the following steps: acquisition environmental information；It is modeled by the region that collected environmental information is ready for path planning to mobile robot to construct two-dimensional plane coordinate figure, and determines the coordinate position of starting point, terminal and barrier；Based on two-dimensional plane coordinate figure, path optimizing is carried out by the glowworm swarm algorithm of initialization of population based on Sobol sequence and dynamic adjustment coefficient of disturbance Population Regeneration from the path of origin-to-destination to mobile robot, thus in two-dimensional plane coordinate figure planning plan to implement into path optimizing；According to the path optimizing that planning is completed, mobile robot is driven to be moved.The present invention overcomes the insufficient problems of existing glowworm swarm algorithm constringency performance, enable mobile robot quickly and accurately automatic path planning, improve the ability that mobile robot carries out path planning.
Description
Technical field
The present invention relates to electronic robot technical fields, are to be related to a kind of automatic rule of mobile robot more specifically
Draw Path Method and mobile robot.
Background technique
Mobile robot (Mobile robot) is a kind of by sensor, remote manipulator and the mobile vehicle of automatic control
The robot system of composition, is the product of the integrated application of an interdisciplinary study developed in recent years, it has concentrated mechanical, electricity
The multidisciplinary newest research results such as son, computer, automatic control and artificial intelligence, represent the highest of electromechanical integration at
Just.Mobile robot has locomotive function, and dangerous, operation and the environment work less than people under adverse circumstances are being engaged in instead of people
Aspect has bigger mobility, flexibility than general robot.Mobile robot gradually using with industrial production agricultural,
The different industries such as medical treatment.
In the research of mobile robot the relevant technologies, airmanship is its core, and path planning is navigation research
One important link and project.So-called path planning refers to mobile robot according to a certain performance indicator (such as distance, time, energy
Source consumption etc.) optimal or sub-optimal path of the search one from initial state to dbjective state.The problem of path planning relates generally to
Including: (1) establishes relatively reasonable model using the mobile robot environmental information of acquisition, then with certain algorithm find one from
Initial state is to optimal or near-optimization the collisionless path of dbjective state；(2) it is capable of handling uncertain in environmental model
The error occurred in factor and path trace is minimized influence of the external object to robot；(3) using known all
Information carrys out the movement of guided robot, to obtain more preferably behaviour decision making relatively.Currently, for mobile robot path planning
The research of technology has been achieved for a large amount of achievement, and many scientists are studied from different aspect.Wherein, from robot pair
The research of the angle of environment sensing, method for planning path for mobile robot is divided into three types: the planning side based on environmental model
The paths planning method of method, the planing method of vision based and Behavior-based control；The journey that environmental information is grasped from robot
Degree, and global path planning based on environment priori Complete Information can be divided into and based on the local paths planning of sensor information；
From the aspect of whether changing over time from planning environment, it can also be divided into static path planning and active path planning；From mobile machine
On the specific algorithm of people's path planning and strategy, planning technology can be divided into following four classes: stencil matching Path Planning Technique, artificial
Potential field Path Planning Technique, map structuring Path Planning Technique and artificial intelligence Path Planning Technique.Artificial intelligence path planning
Technology be by modern artificial intelligence technology be applied to mobile robot path planning in, as artificial neural network, evolutionary computation,
Fuzzy logic and swarm intelligence algorithm etc..Wherein, the Path Planning Technique based on artificial intelligence is research hotspot in recent years.
Glowworm swarm algorithm (Firefly Algorithm) is a kind of new intelligence proposed by Yang Xin-she in 2008
Can optimization algorithm, the biological characteristics of fire fly luminescence develop in the algorithm simulation nature, with particle swarm algorithm and
Ant group algorithm is the same and a kind of naturally heuristic Stochastic Optimization Algorithms based on group.The algorithm is once proposition, by the country
The concern of outer scholar becomes a new research hotspot of computational intelligence research field, and the algorithm has been applied in function at present
Optimization, image procossing, Combinatorial Optimization, feature selecting, clustering, Stock Price Forecasting, protein structure prediction and dynamic city
The research fields such as field price.The computational efficiency of existing firefly group algorithm is high, and memory overhead is small, and the parameter of adjusting is few, simply
It is easily achieved, but the coefficient of disturbance α in existing glowworm swarm algorithm is usually fixed constant, this is for the search of algorithm
Be it is defective, there is also Premature convergences as other random search algorithms.
Summary of the invention
It is an object of the invention to overcome drawbacks described above in the prior art, a kind of automatic planning of mobile robot is provided
Path Method and mobile robot adjust the strategy of coefficient of disturbance based on Sobol sequence initialization population and dynamic, by right
Coefficient of disturbance in glowworm swarm algorithm carries out adaptive adjustment to enhance convergence energy, to improve mobile robot
Carry out the ability of path planning.
To achieve the above object, first aspect present invention provides a kind of automatic path planning method of mobile robot,
The following steps are included:
Acquire environmental information；
It is modeled by the region that collected environmental information is ready for path planning to mobile robot to construct
Two-dimensional plane coordinate figure, and determine the coordinate position of starting point, terminal and barrier；
Based on two-dimensional plane coordinate figure, mobile robot is passed through from the path of origin-to-destination based on Sobol sequence
Initialization of population and the glowworm swarm algorithm of dynamic adjustment coefficient of disturbance Population Regeneration carry out path optimizing, to sit in two-dimensional surface
Mark on a map it is middle planning plan to implement into path optimizing；
According to the path optimizing that planning is completed, mobile robot is driven to be moved.
Preferably, in the above-mentioned methods, described to be based on two-dimensional plane coordinate figure, to mobile robot from
Point passes through the firefly of initialization of population and dynamic adjustment coefficient of disturbance Population Regeneration based on Sobol sequence to the path of terminal
Algorithm carry out path optimizing, thus in two-dimensional plane coordinate figure planning plan to implement into path optimizing the step of specifically include:
The basic parameter of glowworm swarm algorithm is imported, and initializes each basic parameter of glowworm swarm algorithm；
Using Sobol sequence initialization population, the position of popN firefly is generated, calculates the target letter of every firefly
Number selects brightness maximum as optimal location to obtain corresponding brightness；
The Attraction Degree for calculating every firefly is guided the movement of other fireflies by the firefly with maximum brightness,
The position of every firefly is updated, and recalculates the brightness of firefly；
It when reaching maximum search number, then exports optimum individual and stops algorithm, otherwise, recalculate every firefly
Attraction Degree.
Preferably, in the above-mentioned methods, the Sobol sequence is with 2 for base, by one group of direction number V1,
V2, V3..., Vi..., VnIt generates, wherein Vi∈ (0,1), in Sobol sequence, the value of i-th of element jth dimension can be obtained by formula
It arrives:
Preferably, in the above-mentioned methods, the calculation formula of the Attraction Degree of the firefly are as follows:
In formula, β0Attraction when be two firefly distances being zero, γ is the absorption coefficient of light, dijIt is firefly i and firefly
The distance between fireworm j.
Preferably, in the above-mentioned methods, the location update formula of the firefly are as follows:
Xj(t+1)=Xj(t)+βij(Xi(t)-Xj(t))+α(t)*εj；
In formula, Xi(t) and XjIt (t) is the spatial position of firefly i and firefly j in the t times iteration respectively, α is disturbance
Coefficient, εjIt is random number vector, T is the number of iterations.
Second aspect of the present invention provides a kind of mobile robot of automatic path planning characterized by comprising
Environment information acquisition module, for acquiring environmental information；
Environmental information modeling module, for being ready for path planning to mobile robot by collected environmental information
Region modeled to construct two-dimensional plane coordinate figure, and determine the coordinate position of starting point, terminal and barrier；
Path planning module, it is logical from the path of origin-to-destination to mobile robot for being based on two-dimensional plane coordinate figure
The glowworm swarm algorithm progress path for crossing the initialization of population based on Sobol sequence and dynamic adjustment coefficient of disturbance Population Regeneration is sought
It is excellent, thus in two-dimensional plane coordinate figure planning plan to implement into path optimizing；
Mobile drive module, the path optimizing for being completed according to planning, drives mobile robot to be moved.
Preferably, in the scheme of above-mentioned mobile robot, the path planning module is specifically included:
Basic parameter input unit for importing the basic parameter of glowworm swarm algorithm, and initializes each of glowworm swarm algorithm
A basic parameter；
Sobol sequence initialization kind group unit generates popN firefly for using Sobol sequence initialization population
Position；
Dynamic disturbances coefficient path optimizing unit obtains corresponding bright for calculating the objective function of every firefly
Degree, and select brightness maximum as optimal location；And the Attraction Degree of every firefly is calculated, by the firefly with maximum brightness
Fireworm guides the movements of other fireflies, updates the position of every firefly, and recalculate the brightness of firefly；When reaching
Maximum search number then exports optimum individual and stops algorithm, otherwise, recalculates the Attraction Degree of every firefly.
Preferably, in the scheme of above-mentioned mobile robot, the Sobol sequence is with 2 for base, by one
Group direction number V1, V2, V3..., Vi..., VnIt generates, wherein Vi∈ (0,1), in Sobol sequence, the value of i-th of element jth dimension
It can be obtained by formula:
Preferably, in the scheme of above-mentioned mobile robot, the calculating of the Attraction Degree of the firefly is public
Formula are as follows:
In formula, β0Attraction when be two firefly distances being zero, γ is the absorption coefficient of light, dijIt is firefly i and firefly
The distance between fireworm j.
Preferably, in the scheme of above-mentioned mobile robot, the location update formula of the firefly are as follows:
Xj(t+1)=Xj(t)+βij(Xi(t)-Xj(t))+α(t)*εj；
In formula, Xi(t) and XjIt (t) is the spatial position of firefly i and firefly j in the t times iteration respectively, α is disturbance
Coefficient, εjIt is random number vector, T is the number of iterations.
Compared with prior art, the beneficial effects of the present invention are:
1, the present invention can construct two-dimensional plane coordinate figure according to collected environmental information, and initialization component is called to adopt
With Sobol sequence initialization population, dynamic disturbances coefficient update population is then based on to plan in two-dimensional plane coordinate figure
Plan to implement into path, finally two-dimensional plane coordinate figure and the path planned is combined to be moved.The present invention is based on Sobol sequences
Initialization population and dynamic adjust the strategy of coefficient of disturbance, by carrying out to the key parameter in glowworm swarm algorithm-coefficient of disturbance
Adaptive adjustment overcomes the insufficient problem of existing glowworm swarm algorithm constringency performance, makes to move to enhance convergence energy
Mobile robot can quickly and accurately automatic path planning, improve the ability that mobile robot carries out path planning.
2, the present invention initializes firefly population using Sobol sequence, preferable sampling coverage rate can be obtained, to protect
Demonstrate,prove the uniformity of initial population distribution.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is the present invention
Some embodiments for those of ordinary skill in the art without creative efforts, can also basis
These attached drawings obtain other attached drawings.
Fig. 1 is a kind of flow chart of the automatic path planning method of mobile robot provided by the invention；
Fig. 2 is the schematic diagram of two-dimensional plane coordinate figure provided by the invention；
Fig. 3 is a kind of structural block diagram of the mobile robot of automatic path planning provided by the invention；
Fig. 4 is the structural block diagram of path planning module provided by the invention.
Specific embodiment
In order to make the object, technical scheme and advantages of the embodiment of the invention clearer, below in conjunction with the embodiment of the present invention
In attached drawing, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that described embodiment is
A part of the embodiment of the present invention, instead of all the embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art
Every other embodiment obtained without creative efforts, shall fall within the protection scope of the present invention.
Embodiment one
The embodiment of the present invention one provides a kind of automatic path planning method of mobile robot, right with reference to the accompanying drawing
The present embodiment is described in detail.Fig. 1 is the method flow diagram of the embodiment of the present invention one, referring to FIG. 1, the embodiment of the present invention
Method the following steps are included:
Step S1, environmental information is acquired；
Wherein, mobile robot can obtain external environmental information by infrared sensor or the scanning of other acquisition devices.
Step S2, it is modeled by the region that collected environmental information is ready for path planning to mobile robot
To construct two-dimensional plane coordinate figure, and determine the coordinate position of starting point, terminal and barrier；
Path planning refer in the working environment for having barrier find one from origin-to-destination, bypass without collision
The motion path (that is: finding out the collisionless shortest path from A point to B point) of all barriers.
As shown in Fig. 2, the environmental information modeling module of mobile robot can when two-dimensional plane coordinate figure constructs completion
Identify the position coordinates of barrier (such as: a, b, c) in the global area of quasi- exploration.It is more due to having from A point to the path of B point
Item, it is therefore desirable to therefrom identify shortest path using the improved glowworm swarm algorithm of the present invention.
Step S3, it is based on two-dimensional plane coordinate figure, to mobile robot from the path of origin-to-destination by being based on Sobol
The initialization of population of sequence and the glowworm swarm algorithm of dynamic adjustment coefficient of disturbance Population Regeneration carry out path optimizing, thus in two dimension
In plane coordinates figure planning plan to implement into path optimizing；
Furthermore, step S3 specifically includes the following steps:
Step S31, the basic parameter of glowworm swarm algorithm is imported, and initializes each basic parameter of glowworm swarm algorithm；
Wherein, the basic parameter may include population quantity popN, the number of iterations T, initial Attraction Degree β0, light absorption system
Number γ, coefficient of disturbance α etc..Their initial value can be as shown in following table one:
Step S32, using Sobol sequence initialization population, the position of popN firefly is generated, calculates every firefly
Objective function to obtain corresponding brightness, and select brightness maximum as optimal location；
Specifically, the Sobol sequence is with 2 for base, by one group of direction number V1, V2, V3..., Vi ..., VnIt generates,
In, Vi ∈ (0,1).Assuming that mono- group of sequence of Sobol is x1, x2, x3..., xi..., xn,Indicate i-th of element in Sobol sequence
The value of jth dimension, can be obtained by formula:
There is sample distribution and be really distributed inconsistent, Sobol stochastic ordering in pseudo-random number sequence common at present
Column are that ((low-discrepancy sequences) is a stable random sequence, and distribution is equal for a kind of low diversity sequence
Even property is good.The present invention initializes firefly population using Sobol sequence, preferable sampling coverage rate can be obtained, to guarantee
The uniformity of initial population distribution.
Glowworm swarm algorithm is a kind of heuritic approach, and the algorithm simulation weaker firefly of brightness is to the stronger light of firefly of brightness
The mobile random search of worm, usually represents target function value, i.e. f (x with the absolute brightness of firefly in glowworm swarm algorithm*)=
maxx∈sF (x), which solves this optimization problem by iteration using the firefly population that quantity is popN, at the beginning of algorithm
Stage beginning, all fireflies are probabilistically assigned in the s of search space.xiIndicate one of i-th of firefly in the t times iteration
A solution, f (xi) mean that the absolute brightness of its corresponding firefly.
Step S33, the Attraction Degree for calculating every firefly, other fireflies are guided by the firefly with maximum brightness
Movement, update the position of every firefly, and recalculate the brightness of firefly；
Every firefly has an attraction β to other fireflies, if the absolute brightness of firefly i is greater than firefly
The absolute brightness of j, then firefly j will be attracted mobile to i by firefly i.Attraction β of the firefly i to firefly jijPublic affairs
Formula is defined as:
Wherein, β0Attraction when be two firefly distances being zero, γ is the absorption coefficient of light (Light Absorption
Coefficient), dijIt is the distance between firefly i and firefly j.
If firefly j is mobile to firefly i, then the location update formula of firefly j in the t times iteration are as follows:
Xj(t+1)=Xj(t)+βij(Xi(t)-Xj(t))+α(t)*εj (2)
In formula, Xi(t) and XjIt (t) is the spatial position of firefly i and firefly j in the t times iteration respectively, α is disturbance
Coefficient, εjIt is random number vector.
The more new formula that the present invention uses α is as follows:
From the point of view of the operation of algorithm, a biggish α value is conducive to global search, and a lesser α value is conducive to office
Portion's search, therefore convergence energy is improved by carrying out dynamic adjustment to α.
Step S34, it when reaching maximum search number, then exports optimum individual and stops algorithm, otherwise, return step S33
Recalculate the Attraction Degree of every firefly.
Wherein, maximum search number refers to the optimizing number of glowworm swarm algorithm, i.e. the number of iterations T.
The pseudocode of the algorithm is as follows:
Step S4, the path optimizing completed according to planning, drives mobile robot to be moved.
Wherein, the path that optimum individual is passed by is exactly optimal path, when path planning module determines an optimal path
Afterwards, mobile robot will be moved along the paths.
Method of the invention adjusts the strategy of coefficient of disturbance based on Sobol sequence initialization population and dynamic, by firefly
Coefficient of disturbance in fireworm algorithm carries out adaptive adjustment to enhance convergence energy, overcomes existing glowworm swarm algorithm and receives
The insufficient problem for holding back performance, enables mobile robot quickly and accurately automatic path planning, improve mobile robot into
The ability of row path planning.
Embodiment two
The embodiment of the present invention two provides a kind of mobile robot of automatic path planning, referring to FIG. 3, the present invention is real
The mobile robot for applying example includes environmental perception module 1, path planning module 2 and mobile drive module 3, wherein environment sensing
Module 1 is equipped with environment information acquisition module 11 and environmental information modeling module 12, will carry out below to the function of above-mentioned module detailed
Thin explanation.
Environment information acquisition module 11, for acquiring environmental information.
Wherein, environment information acquisition module 11 can be set to infrared sensor or other acquisition devices, mobile robot
External environmental information can be obtained by infrared sensor or the scanning of other acquisition devices.
Environmental information modeling module 12, for being ready for path rule to mobile robot by collected environmental information
The region drawn is modeled to construct two-dimensional plane coordinate figure, and determines the coordinate position of starting point, terminal and barrier.
Path planning refer in the working environment for having barrier find one from origin-to-destination, bypass without collision
The motion path (that is: finding out the collisionless shortest path from A point to B point) of all barriers.
As shown in Fig. 2, when two-dimensional plane coordinate figure constructs completion, 12 energy of environmental information modeling module of mobile robot
Enough identify the position coordinates of barrier (such as: a, b, c) in the global area of quasi- exploration.It is more due to having from A point to the path of B point
Item, it is therefore desirable to therefrom identify shortest path using path planning module 2.
Path planning module 2, it is logical from the path of origin-to-destination to mobile robot for being based on two-dimensional plane coordinate figure
The glowworm swarm algorithm progress path for crossing the initialization of population based on Sobol sequence and dynamic adjustment coefficient of disturbance Population Regeneration is sought
It is excellent, thus in two-dimensional plane coordinate figure planning plan to implement into path optimizing.
As shown in figure 4, furthermore, in the present embodiment, the path planning module 2 specifically includes:
Basic parameter input unit 21 for importing the basic parameter of glowworm swarm algorithm, and initializes glowworm swarm algorithm
Each basic parameter；
Wherein, the basic parameter may include population quantity popN, the number of iterations T, initial Attraction Degree β0, light absorption system
Number γ, coefficient of disturbance α etc..Their initial value can be as shown in following table one:
Sobol sequence initialization kind group unit 22 generates the popN light of firefly for using Sobol sequence initialization population
The position of worm；
Specifically, the Sobol sequence is with 2 for base, by one group of direction number V1, V2, V3..., Vi ..., VnIt generates,
In, Vi ∈ (0,1).Assuming that mono- group of sequence of Sobol is x1, x2, x3..., xi..., xn,Indicate i-th of element in Sobol sequence
The value of jth dimension, can be obtained by formula:
There is sample distribution and be really distributed inconsistent, Sobol stochastic ordering in pseudo-random number sequence common at present
Column are that ((low-discrepancy sequences) is a stable random sequence, and distribution is equal for a kind of low diversity sequence
Even property is good.The present invention initializes firefly population using Sobol sequence, preferable sampling coverage rate can be obtained, to guarantee
The uniformity of initial population distribution.
Dynamic disturbances coefficient path optimizing unit 23 obtains corresponding for calculating the objective function of every firefly
Brightness, and select brightness maximum as optimal location；And the Attraction Degree of every firefly is calculated, by with maximum brightness
Firefly guides the movements of other fireflies, updates the position of every firefly, and recalculate the brightness of firefly；When reaching
It to maximum search number, then exports optimum individual and stops algorithm, otherwise, recalculate the Attraction Degree of every firefly.
Glowworm swarm algorithm is a kind of heuritic approach, and the algorithm simulation weaker firefly of brightness is to the stronger light of firefly of brightness
The mobile random search of worm, usually represents target function value, i.e. f (x with the absolute brightness of firefly in glowworm swarm algorithm*)=
maxx∈sF (x), which solves this optimization problem by iteration using the firefly population that quantity is popN, at the beginning of algorithm
Stage beginning, all fireflies are probabilistically assigned in the s of search space.xiIndicate one of i-th of firefly in the t times iteration
A solution, f (xi) mean that the absolute brightness of its corresponding firefly.
Every firefly has an attraction β to other fireflies, if the absolute brightness of firefly i is greater than firefly
The absolute brightness of j, then firefly j will be attracted mobile to i by firefly i.Attraction β of the firefly i to firefly jijPublic affairs
Formula is defined as:
Wherein, β0Attraction when be two firefly distances being zero, γ is the absorption coefficient of light (Light Absorption
Coefficient), dijIt is the distance between firefly i and firefly j.
If firefly j is mobile to firefly i, then the location update formula of firefly j in the t times iteration are as follows:
Xj(t+1)=Xj(t)+βij(Xi(t)-Xj(t))+α(t)*εj (2)
In formula, Xi(t) and XjIt (t) is the spatial position of firefly i and firefly j in the t times iteration respectively, α is disturbance
Coefficient, εjIt is random number vector.
The more new formula that the present invention uses α is as follows:
From the point of view of the operation of algorithm, a biggish α value is conducive to global search, and a lesser α value is conducive to office
Portion's search, therefore convergence energy is improved by carrying out dynamic adjustment to α.
Mobile drive module 3, the path optimizing for being completed according to planning, drives mobile robot to be moved.
Wherein, the path that optimum individual is passed by is exactly optimal path, when path planning module 2 determines an optimal path
Afterwards, mobile drive module 3 will drive mobile robot to move along the paths.
Shifter people of the invention adjusts the strategy of coefficient of disturbance based on Sobol sequence initialization population and dynamic, passes through
Adaptive adjustment is carried out to enhance convergence energy to the coefficient of disturbance in glowworm swarm algorithm, existing firefly is overcome and calculates
The insufficient problem of method constringency performance, can quickly and accurately automatic path planning, improve path planning ability.
It should be noted that a kind of mobile robot of automatic path planning provided by the above embodiment, only with above-mentioned each
The division progress of functional module can according to need and for example, in practical application by above-mentioned function distribution by different function
Energy module is completed, i.e., the internal structure of system is divided into different functional modules, to complete whole described above or portion
Divide function.
Those of ordinary skill in the art will appreciate that implement the method for the above embodiments be can be with
Relevant hardware is instructed to complete by program, the program can be stored in a computer-readable storage medium
In, the storage medium, such as ROM/RAM, disk, CD.
The above embodiment is a preferred embodiment of the present invention, but embodiments of the present invention are not by above-described embodiment
Limitation, other any changes, modifications, substitutions, combinations, simplifications made without departing from the spirit and principles of the present invention,
It should be equivalent substitute mode, be included within the scope of the present invention.
Claims (4)
1. a kind of automatic path planning method of mobile robot, which is characterized in that method includes the following steps:
Acquire environmental information；
It is modeled by the region that collected environmental information is ready for path planning to mobile robot to construct two dimension
Plane coordinates figure, and determine the coordinate position of starting point, terminal and barrier；
Based on two-dimensional plane coordinate figure, the population based on Sobol sequence is passed through from the path of origin-to-destination to mobile robot
The glowworm swarm algorithm of initialization and dynamic adjustment coefficient of disturbance Population Regeneration carries out path optimizing, thus in two-dimensional plane coordinate figure
It is middle planning plan to implement into path optimizing；
According to the path optimizing that planning is completed, mobile robot is driven to be moved；
Wherein, described to be based on two-dimensional plane coordinate figure, to mobile robot from the path of origin-to-destination by being based on Sobol sequence
The initialization of population of column and the glowworm swarm algorithm of dynamic adjustment coefficient of disturbance Population Regeneration carry out path optimizing, thus flat in two dimension
In areal coordinate figure planning plan to implement into path optimizing the step of specifically include:
The basic parameter of glowworm swarm algorithm is imported, and initializes each basic parameter of glowworm swarm algorithm；
Using Sobol sequence initialization population, generate the position of popN firefly, the objective function of every firefly of calculating with
Corresponding brightness is obtained, and selects brightness maximum as optimal location；
The Attraction Degree for calculating every firefly is guided the movement of other fireflies by the firefly with maximum brightness, is updated
The position of every firefly, and recalculate the brightness of firefly；
It when reaching maximum search number, then exports optimum individual and stops algorithm, otherwise, recalculate the attraction of every firefly
Degree；
The calculation formula of the Attraction Degree of the firefly are as follows:
In formula, β0Attraction when be two firefly distances being zero, γ is the absorption coefficient of light, dijIt is firefly i and firefly j
The distance between；
The location update formula of the firefly are as follows:
Xj(t+1)=Xj(t)+βij(Xi(t)-Xj(t))+α(t)*εj；
In formula, Xi(t) and XjIt (t) is the spatial position of firefly i and firefly j in the t times iteration respectively, α is disturbance system
Number, εjIt is random number vector, T is the number of iterations.
2. the automatic path planning method of mobile robot according to claim 1, which is characterized in that the Sobol sequence
Column are with 2 for base, by one group of direction number V1, V2, V3..., Vi..., VnIt generates, wherein Vi∈ (0,1), in Sobol sequence, the
The value of i element jth dimension can be obtained by formula:
3. a kind of mobile robot of automatic path planning characterized by comprising
Environment information acquisition module, for acquiring environmental information；
Environmental information modeling module, for being ready for the area of path planning to mobile robot by collected environmental information
Domain is modeled to construct two-dimensional plane coordinate figure, and determines the coordinate position of starting point, terminal and barrier；
Path planning module passes through base from the path of origin-to-destination to mobile robot for being based on two-dimensional plane coordinate figure
Path optimizing is carried out in the initialization of population of Sobol sequence and the glowworm swarm algorithm of dynamic adjustment coefficient of disturbance Population Regeneration, from
And in two-dimensional plane coordinate figure planning plan to implement into path optimizing；
Mobile drive module, the path optimizing for being completed according to planning, drives mobile robot to be moved；
Wherein, the path planning module specifically includes:
Basic parameter input unit for importing the basic parameter of glowworm swarm algorithm, and initializes each base of glowworm swarm algorithm
This parameter；
Sobol sequence initialization kind group unit generates the position of popN firefly for using Sobol sequence initialization population
It sets；
Dynamic disturbances coefficient path optimizing unit, for calculating the objective function of every firefly to obtain corresponding brightness,
And select brightness maximum as optimal location；And the Attraction Degree of every firefly is calculated, by the light of firefly with maximum brightness
Worm guides the movements of other fireflies, updates the position of every firefly, and recalculate the brightness of firefly；When reaching most
Big searching times then export optimum individual and stop algorithm, otherwise, recalculate the Attraction Degree of every firefly；
The calculation formula of the Attraction Degree of the firefly are as follows:
In formula, β0Attraction when be two firefly distances being zero, γ is the absorption coefficient of light, dijIt is firefly i and firefly j
The distance between；
The location update formula of the firefly are as follows:
Xj(t+1)=Xj(t)+βij(Xi(t)-Xj(t))+α(t)*εj；
In formula, Xi(t) and XjIt (t) is the spatial position of firefly i and firefly j in the t times iteration respectively, α is disturbance system
Number, εjIt is random number vector, T is the number of iterations.
4. the mobile robot of automatic path planning according to claim 3, which is characterized in that the Sobol sequence is
It is base with 2, by one group of direction number V1, V2, V3..., Vi..., VnIt generates, wherein Vi∈ (0,1), in Sobol sequence, i-th
The value of element jth dimension can be obtained by formula:
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