CN104503453A - Mobile robot path planning method based on bacterial foraging potential field method - Google Patents

Abstract

The invention relates to a mobile robot path planning method based on a bacterial foraging potential field method and belongs to the technical field of artificial intelligence (AI) biomimetic control. The method comprises the following step of on the basis of imitating a bacterial foraging environment to establish a similar robot working potential field environmental model, through imitating chemotaxis behaviors of bacteria during a foraging process, establishing a perception and decision behavior control strategy of a mobile robot so as to drive the single mobile robot to complete a path planning target. The mobile robot path planning method based on the bacterial foraging potential field method provided by the invention conforms to the development trend of a robot path planning technology toward biomimetic intelligence, enriches mobile robot path planning methods, and is beneficial to promoting the application of integrated intelligence in the field of robots.

Description

The method for planning path for mobile robot of potential field method of looking for food based on bacterium

Technical field

The invention belongs to artificial intelligence (AI) biomimetic control technical field, relate to a kind of method for planning path for mobile robot of potential field method of looking for food based on bacterium.

Background technology

The world today, the application and development degree of Robotics has become one of standard of measurement national science and technology and industrialized level.Therefore, domestic and international Government and enterprise has aimed at robot industry one after another, and accelerates the research and development of Robotics and manufacture paces.Robot path planning's technique functions starts from nineteen seventies, and it is the important step of robot navigation.It is (as the shortest in walking path according to a certain performance index that path planning refers to robot, the shortest or the consumed energy of expending time in is minimum), in work space, search for one arrive the optimum of target location or the collisionless path of near-optimization from reference position.In recent years, along with rise and the development of artificial intelligence technology, robot path planning's technology just moves towards intelligent bionic future development by classic method.

Mobile robot, when the task such as perform assembling transport, rescue and relief work and helping the elderly is help the disabled, has applied to Path Planning Technique widely.Adopt good Path Planning Technique not only can reduce activity duration, the wearing and tearing of reduction robot of robot in a large number, also can save many man power and material's costs simultaneously.Therefore, the research work carrying out mobile robot path planning technology is very meaningful and valuable.1. on the one hand, along with the quickening of modern's rhythm of life and the aggravation of population in the world aging, the daily life of physical disabilities and old personage cannot thoughtfully be taken care of because of being busy with one's work of younger generation, and they start to create new demand to the accessible auxiliary of information society.These disabled persons or the elderly generally live in structurized room, and the furniture installation in room generally too large change can not occur in a short time.Therefore, for meeting their handicapped needs, swarm intelligence paths planning method often can be utilized to cook up one or more in this environment from diverse location, and the robot mobile route arriving different target point comes, thus is the action navigation of disabled person and the elderly.So, not only meet disabled person and old human needs, but also alleviate youthful worry.2. on the other hand, in integrated mill's workshop, often need to utilize the acting in conjunction between multirobot to transport goods assigned address, if the intelligent behavior characteristic in introduction swarm intelligence solves coordination and the cooperation problem of multirobot, also man power and material's cost be can save energetically, the mass production in factory floor and transport operation facilitated.

Therefore, to the research of mobile robot technology, particularly to the research of Path Planning Technique, no matter for individual machine people or multiple robot, utilize swarm intelligence method to solve these problems, have considerable application prospect and far-reaching meaning.The action process that bacterium is looked for food is the one in swarm intelligence method, to a certain extent, uses it for Research on Path Planning of Mobile Robot, and this will promote the application and development of swarm intelligence, promotes developing and the innovation of artificial intelligence subject.

Summary of the invention

In view of this, the object of the present invention is to provide a kind of method for planning path for mobile robot of potential field method of looking for food based on bacterium, the method is set up on the basis of similar robot working environment model copying bacterium scene of looking for food, by imitating the chemotactic behavior of bacterium in the process of looking for food, construct sensor model and the Motion Control Strategies of mobile robot, thus order about single mobile robot and complete path planning task.

For achieving the above object, the invention provides following technical scheme:

A kind of method for planning path for mobile robot of potential field method of looking for food based on bacterium, the method is set up on the basis of similar robot working environment model copying bacterium scene of looking for food, by imitating the chemotactic behavior of bacterium in the process of looking for food, construct sensor model and the Motion Control Strategies of mobile robot, passed judgment on the mode of motion of robot by fitness function value: advance or turn to, thus order about single mobile robot and complete path planning task.

Further, the method specifically comprises the following steps:

Step 1: initialization:

1) all kinds of parameters of initialization mobile human: starting point coordinate [X _o, Y _o] and coordinate of ground point [X _g, Y _g], robot perception radius R, robot moving step length λ, maximum chemotactic step number Step _max, the total number S of robot surrounding sensor _n, keep away barrier weights omega ₁with trend target weight ω ₂; 2) initialization context information: work space boundary [X _min, X _max] and [Y _min, Y _max], the center [x of each barrier _oi, y _oi], boundary shape C _oand they are at the reach δ of X-direction _xiwith the reach δ of Y-direction _yi; 3) the fitness value F=0 of initialization robot starting point, and Step=1 is set;

Step 2: fitness function value upgrades:

According to formula F=ω ₁f _g+ ω ₂f _o, calculating robot's current location (x, y) sentences the fitness function F (x at i-th sensor orientation place on sensing region that R is radius _i, y _i), i=1,2 ..., S _n;

Step 3: minimum point is explored:

According to formula $F(x^{*},y^{*})=\underset{n=1}{\overset{S_n}{\min }}\left(F\right),$ Find a sub-impact point and make $F(x_i^{*},y_i^{*})\≤F(x_i,y_i);$

Step 4: robot pose upgrades:

If total fitness value of the specific item punctuate position of robot is better than the fitness value of robot current location, so robot is according to formula: travel direction adjusts; Otherwise robot then continues to move a step-length to previous direction, and now, robot location is according to formula $\left[\begin{array}{c}x(t+1)\\ y(t+1)\end{array}\right]=\left[\begin{array}{c}x\left(t\right)\\ y\left(t\right)\end{array}\right]+\mathrm{\λ}\left[\begin{array}{c}\cos \mathrm{\θ}\left(t\right)\\ \sin \mathrm{\θ}\left(t\right)\end{array}\right]$ Upgrade;

Step5: judge termination condition:

If the current chemotactic step number of robot has reached maximum preset step number Step _max, then algorithm stops, and exports optimal location; Otherwise Step=Step+1, forwards Step 2 to; Repeat Step2 to Step5, until algorithm stops.

Beneficial effect of the present invention is: method of the present invention has complied with the development trend of robot path planning's technology towards bionic intelligence, has enriched method for planning path for mobile robot, facilitates the application of swarm intelligence robot field.

Accompanying drawing explanation

In order to make object of the present invention, technical scheme and beneficial effect clearly, the invention provides following accompanying drawing and being described:

Fig. 1 is environmental information model schematic;

Fig. 2 is robot motion model figure;

Fig. 3 is the possible direction of motion figure of robot;

Fig. 4 is robot perception illustraton of model;

Fig. 5 is the method for planning path for mobile robot process flow diagram of potential field method of looking for food based on bacterium;

Fig. 6 be under schematic model environment based on bacterium look for food potential field method robot path planning figure;

Fig. 7 be under four circular obstacle environment based on bacterium look for food potential field method robot path planning figure;

Fig. 8 be under 12 circular obstacle environment based on bacterium look for food potential field method robot path planning figure;

Fig. 9 be under eight circular obstacle environment based on bacterium look for food potential field method robot path planning figure;

Figure 10 is starting point and impact point for a change, the robot path planning figure under eight circular obstacle environment.

Embodiment

Below in conjunction with accompanying drawing, the preferred embodiments of the present invention are described in detail.

The method that the present invention realizes mobile robot path planning is: set up on the basis of similar robot working environment model copying bacterium scene of looking for food, by imitating the chemotactic behavior of bacterium in the process of looking for food, construct sensor model and the Motion Control Strategies of mobile robot, thus order about single mobile robot and complete path planning task.Specifically, first, imitative bacterium has been carried out to robot working environment and to have looked for food scene modeling, represented barrier with circular or oval Gauss's potential field skeleton pattern and their composite figure; Then a kind of robot tracking control model with annular perception environment potential field is devised; Finally imitate the chemotactic behavioral strategy of flora, thus realize route searching and the planning of mobile robot.

Logical specific embodiment is described in detail to technical solution of the present invention below.

(1) environmental modeling

Suppose that barrier is circular barrier, its radius expands according to robot radius size, and such mobile robot just can be considered as a particle.The Gaussian profile information of objective definition point is:

$U_{\mathrm{goal}}\left(X\right)=-\frac{k_g}{2}\exp (-\left(\frac{{(X-X_g)}^2}{r^2}\right))---\left(1\right)$

The Gaussian profile information of barrier is:

$U_{\mathrm{obstacle}}(x,y)=\frac{k_o}{2}\exp (-{(\frac{{(x-x_o)}^2}{{\mathrm{\δ}}_x^2}+\frac{{(y-y_o)}^2}{{\mathrm{\δ}}_y^2})}^{C_o})---\left(2\right)$

In above two formulas: k _g> 0, k _o> 0 represents the sucting strength regulatory factor of objective contour information and the repulsion intensity adjustment factor of barrier profile information respectively; R > 0 represents the reach of target, δ _x> 0, δ _y> 0, represents the radius of action of barrier in work space X and Y-direction; X=(x, y), represents the present co-ordinate position of robot; X _g=(x _g, y _g), represent the center position coordinates of target; X _o=(x _o, y _o), represent the center position coordinates of barrier, C _oa normal number, reflection be the shape of barrier potential field profile boundary.Environmental modeling as shown in Figure 1.

(2) robot motion model

Bacterium is when looking for food, if the nutriment of certain enriches, so bacterium can stay on and to look for food in this region, and it once moves about towards previous direction of looking for food; If the nutriment of bacterium at this place is deficient or found there is the objectionable impurities threatening its own existence, so can there is once inside out in original place in bacterium, and start away from this region.Formula (3) describes the position updating process of this behavior of bacterium:

$\left[\begin{array}{c}x(t+1)\\ y(t+1)\end{array}\right]=\left[\begin{array}{c}x\left(t\right)\\ y\left(t\right)\end{array}\right]+\mathrm{\λ}\left[\begin{array}{c}\cos \mathrm{\θ}\left(t\right)\\ \sin \mathrm{\θ}\left(t\right)\end{array}\right]$

Wherein: λ represents the once travelling step-length of bacterium, squint angle when θ represents that bacterium overturns.

Mobile robot simulate bacterium execution route planning tasks time, can by advance and two kinds of behaviors of turning distinguish corresponding bacterium moving about and upset behavior.For this reason, the present embodiment establishes moveable robot movement model as shown in Figure 2.In practical application, it can be a kind of wheeled robot be made up of pair of driving wheels and a universal wheel.Its pose can represent with (x, y, θ), and its equation of motion can be expressed as follows:

$\left[\begin{array}{c}\stackrel{\·}{x}\left(t\right)\\ \stackrel{\·}{y}\left(t\right)\\ \stackrel{\·}{\mathrm{\θ}}\left(t\right)\end{array}\right]=\left[\begin{array}{c}v\left(t\right)\cos \mathrm{\θ}\left(t\right)\\ v\left(t\right)\sin \mathrm{\θ}\left(t\right)\\ \mathrm{\ω}\left(t\right)\end{array}\right]---\left(4\right)$

In formula: [x, y] represents the Geometric center coordinates of Robot, θ represents the deflection of Robot in t, and v (t) and ω (t) are Robot at the linear velocity of t and angular velocity respectively.

(3) robot motion and perceptual strategy

Mobile robot, when simulating bacterium execution route planning tasks, can use " advance " and " turning to " two kinds of behaviors corresponding with " moving about " and " upset " behavior of bacterium.In the bacterium chemotactic behavior introduced above, the direction of bacterium upset is random direction, and the optimization like this not only bad for bacterium is looked for food, and this Motion is used for mobile robot path planning, can cause robot motion blindly toward contact, reduce path planning performance.Therefore, if the steering direction of robot to be subdivided into several possibilities direction (as shown in Figure 3) centered by robot, utilize robot self-sensor device to carry out perception and evaluate the environment potential field information in these directions, so more will there be directivity in robot when turning to, thus avoid the ineffective activity brought because Stochastic choice direction is not good.

In sum, the present embodiment constructs robot perception model as shown in Figure 4, and in figure, R represents the perception radius of Robot, and λ represents the moving step sizes of Robot, and θ represents the moving direction angle of robot t.Robot is regarded as a particle by Fig. 4, and robot utilizes the sensor S evenly spreading over self surrounding ₁, S ₂..., S _n, the conjunction potential field profile information of target and barrier in environment can be obtained, by the fitness value size of calculating robot in each sensor orientation current, thus select an optimum direction next step working direction as robot.Suppose that robot is when pivot stud, is not subjected to displacement change, so often to perform the location updating after a gravitaxis as follows in robot:

$\left[\begin{array}{c}x(t+1)\\ y(t+1)\end{array}\right]=\left[\begin{array}{c}x\left(t\right)\\ y\left(t\right)\end{array}\right]+\mathrm{\λ}\left[\begin{array}{c}\cos \mathrm{\θ}\left(t\right)\\ \sin \mathrm{\θ}\left(t\right)\end{array}\right]---\left(5\right)$

(4) fitness function builds and evaluates

In order to make mobile robot optimizing Experiential Search path according to the mankind in motion process, mobile robot, when execution route planning tasks, not only will tend to target location motion, i.e. taxis as far as possible; Also to ensure robot self safety in the process of moving, i.e. security.Therefore, taxis F is being ensured _gwith security F _ocondition under, in conjunction with Gauss's potential field environmental model above, according to the thought of weighted sum method, building the path fitness function of mobile robot is:

F＝ω ₁F _g+ω ₂F _o(6)

Wherein:

F _g＝(x _g-x) ²+(y _g-y) ²(7)

$F_o=\underset{i=1}{\overset{K}{\mathrm{\Σ}}}\left(\exp \right[-{(\frac{{(x-x_{\mathrm{oi}})}^2}{{{\mathrm{\δ}}_{\mathrm{ix}}}^2}+\frac{{(y-y_{\mathrm{oi}})}^2}{{{\mathrm{\δ}}_{\mathrm{iy}}}^2})}^{C_o}\left]\right)---\left(8\right)$

ω ₁, ω ₂represent the control weight tending to and keep away barrier respectively, ω ₂generally ω can be compared ₁much larger; F _grepresent robot current path point apart from impact point Euclidean distance square, it has ensured the gravitaxis of robot; F _orepresent the repulsion potential field sum of K barrier to robot current location, it has ensured the security of robot; [x, y] and [x _oi, y _oi] represent the centre coordinate of robot present co-ordinate position and i-th barrier respectively; δ _ix, δ _iyrepresent the radius of action of i-th barrier in X and Y-direction.

By setting up the path fitness function of formula (6), people's sensor model of machine just can be utilized to evaluate robot at S _nthe fitness value of individual sensor orientation.Here, what choose is the minimum direction of fitness value, and therefore robot is when determining next step working direction, needs to search out a sub-impact point and meet formula (9), thus order about robot and turn to.

$F(x^{*},y^{*})=\underset{n=1}{\overset{S_n}{\min }}\left(F\right)---\left(9\right)$

(5) method performing step

Based on bacterium look for food potential field method method for planning path for mobile robot flowchart as shown in Figure 5, concrete steps are as follows:

1) initialization.1. all kinds of parameters of initialization mobile human: starting point coordinate [X _o, Y _o] and coordinate of ground point [X _g, Y _g], robot perception radius R, robot moving step length λ, maximum chemotactic step number Step _max, the total number S of robot surrounding sensor _n, keep away barrier weights omega ₁with trend target weight ω ₂; 2. initialization context information: work space boundary [X _min, X _max] and [Y _min, Y _max], the center [x of each barrier _oi, y _oi], boundary shape C _oand they are at the reach δ of X-direction _xiwith the reach δ of Y-direction _yi; 3. the fitness value F=0 of initialization robot starting point, and Step=1 is set.

2) fitness function value upgrades.According to formula (6), calculating robot's current location (x, y) sentences the fitness function F (x at i-th sensor orientation place on sensing region that R is radius _i, y _i), i=1,2 ..., S _n.

3) minimum point is explored.According to formula (9), find a sub-impact point (x _i ^*, y _i ^*), and make F (x _i ^*, y _i ^*)≤F (x _i, y _i).

4) robot pose upgrades.If total fitness value of the specific item punctuate position of robot is better than the fitness value of robot current location, so robot adjusts according to formula (10) travel direction; Otherwise robot then continues to move a step-length to previous direction.Now, robot location upgrades according to formula (5):

$\mathrm{\θ}(t+1)=\mathrm{\θ}\left(t\right)+\frac{2\mathrm{\π}}{S_n}\×(S_{t+1}-S_t)---\left(10\right)$

5) termination condition is judged.If the current chemotactic step number of robot has reached maximum preset step number Step _max, then algorithm stops, and exports optimal location; Otherwise chemotactic step number Step=Step+1, forwards step 2 to).

Finally utilize the method, the experimental result under the environment of different scales size is as shown in Fig. 6 ~ Figure 10.

What finally illustrate is, above preferred embodiment is only in order to illustrate technical scheme of the present invention and unrestricted, although by above preferred embodiment to invention has been detailed description, but those skilled in the art are to be understood that, various change can be made to it in the form and details, and not depart from claims of the present invention limited range.

Claims (2)

1. the method for planning path for mobile robot of a potential field method of looking for food based on bacterium, it is characterized in that: the method is set up on the basis of similar robot working environment model copying bacterium scene of looking for food, by imitating the chemotactic behavior of bacterium in the process of looking for food, construct sensor model and the Motion Control Strategies of mobile robot, passed judgment on the mode of motion of robot by fitness function value: advance or turn to, thus order about single mobile robot and complete path planning task.

2. the method for planning path for mobile robot of a kind of potential field method of looking for food based on bacterium according to claim 1, is characterized in that: the method specifically comprises the following steps:

Step 1: initialization:

1) all kinds of parameters of initialization mobile human: starting point coordinate [X _o, Y _o] and coordinate of ground point [X _g, Y _g], robot perception radius R, robot moving step length λ, maximum chemotactic step number Step _max, the total number S of robot surrounding sensor _n, keep away barrier weights omega ₁with trend target weight ω ₂; 2) initialization context information: work space boundary [X _min, X _max] and [Y _min, Y _max], the center [x of each barrier _oi, y _oi], boundary shape C _oand they are at the reach δ of X-direction _xiwith the reach δ of Y-direction _yi; 3) the fitness value F=0 of initialization robot starting point, and Step=1 is set;

Step 2: fitness function value upgrades:

According to formula F=ω ₁f _g+ ω ₂f _o, calculating robot's current location (x, y) sentences the fitness function F (x at i-th sensor orientation place on sensing region that R is radius _i, y _i), i=1,2 ..., S _n;

Step 3: minimum point is explored:

According to formula $F(x^{*},y^{*})=\underset{n=1}{\overset{S_n}{\min }}\left(F\right),$ Find a sub-impact point and make $F(x_i^{*},y_i^{*})\≤F(x_i,y_i);$

Step 4: robot pose upgrades:

If total fitness value of the specific item punctuate position of robot is better than the fitness value of robot current location, so robot is according to formula: travel direction adjusts; Otherwise robot then continues to move a step-length to previous direction, and now, robot location is according to formula $\left[\begin{array}{c}x(t+1)\\ y(t+1)\end{array}\right]=\left[\begin{array}{c}x\left(t\right)\\ y\left(t\right)\end{array}\right]+\mathrm{\λ}\left[\begin{array}{c}\cos \mathrm{\θ}\left(t\right)\\ \sin \mathrm{\θ}\left(t\right)\end{array}\right]$ Upgrade;

Step5: judge termination condition:

If the current chemotactic step number of robot has reached maximum preset step number Step _max, then algorithm stops, and exports optimal location; Otherwise Step=Step+1, forwards Step 2 to; Repeat Step2 to Step5, until algorithm stops.