CN105045260A - Mobile robot path planning method in unknown dynamic environment - Google Patents

Abstract

The invention discloses a mobile robot path planning method in an unknown dynamic environment. The method enables each position to be converted into a nerve cell, employs the activity values of nerve cells to represent environment changes, and ingeniously employs a biological motivation nerve network model to simulate a dynamic environment. The biological motivation nerve network model guarantees that the activity values of positive nerve cells can be transmitted outside to affect the whole state space, and the activity values of negative nerve cells just act on a local part. A decision at the next position point comprises a maximum nerve cell activity value and minimum robot corner factor, and an obtained path is the shortest one or about the shortest one. Moreover, the path is smooth, and the number of corners is smaller. The method does not have a problem of local minimum points, is small in calculation amount, is simple in implementation, and is good in adaptability in the unknown dynamic environment.

Description

Method for planning path for mobile robot under a kind of Unknown Dynamic Environment

Technical field

The present invention relates to the paths planning method of a kind of mobile robot, particularly relate to a kind of method for planning path for mobile robot under dynamic unknown complex environment.

Background technology

Along with the development of technology, the range of application of mobile robot is more and more extensive.Mobile robot starts to be extended to more complicated, the irregular outdoor non-structure environment of feature from the indoor environment of rule gradually.Path planning is the important step of robot navigation's technology, it be according to a certain performance index search for an optimum from original state to dbjective state or near-optimization without touching path.

Common paths planning method mainly contains Artificial Potential Field Method, A* algorithm, neural network etc.But always there is certain defect for different practical problemss in these methods.There is local minimum points problem in Artificial Potential Field Method, robot can be caused to arrive impact point.A* algorithm needs the priori to environment, and when environmental information changes, need again to plan, calculated amount is large.Some neural network models only can process static environment, and BP neural network model can produce keeps away barrier track, but needs learning process, and speed is slower.Glasius proposes a kind of dynamic realtime Obstacle avoidance model based on Hopfield network, can solve Local Minimum problem, but is difficult to adapt to dynamic environment at a high speed.The biologically inspired neural network method that SimonX.Yang proposes in NeuralNetworkApproachtoDynamicCollision-FreeTrajectoryGe neration mono-literary composition is that road strength planning proposes a kind of effective solution, but there will be path misjudgement problem in any case, and path is level and smooth not.

Summary of the invention

For the problems referred to above, the present invention proposes the paths planning method under a kind of new Dynamic Unknown Environment, the method does not need any priori of real-time dynamic environment, and calculated amount is little, is applicable to the path planning under Unknown Dynamic Environment.

A method for planning path for mobile robot under Unknown Dynamic Environment, comprises following step:

Step 1: build grating map;

With the geometric center point of mobile robot for moving coordinate system initial point, the positive dirction being oriented to X-axis of mobile robot, sets up moving coordinate system;

Start mobile front position for global coordinate system initial point with mobile robot, mobile robot starts the positive dirction being oriented to X-axis before moving, and sets up global coordinate system;

Global coordinate system is converted into grating map according to the planning precision p of setting, in described grating map each grid length and be widely respectively Δ x=p and Δ y=p;

P span is 0.1m to 0.3m;

By mobile robot known static-obstacle thing, impact point and the coordinate transformation of mobile robot's current location in global coordinate system be in the work environment that grating map coordinate represents:

Wherein, (x*, y*) represents at the coordinate of object in global coordinate system;

Step 2: using each coordinate points in grating map as a neuron, and each neuronic activity value of initialization is 0;

Step 3: judge that whether the current position of mobile robot is identical with aiming spot, if identical, then current path planning terminates; Otherwise, enter step 4;

Step 4: read sonar ranging data, and upgrade neural dynamics;

Interval time, T read the ranging data of barrier in the working environment that the sonar sensor on a mobile robot gathers, and obtain the global coordinate system coordinate of barrier according to ranging data, and by neuronic activity value x corresponding for coordinate each in global coordinate system _iupgrade according to following formula:

$\frac{d{x}_i}{\mathrm{dt}}=-A{x}_i+(B-x_i)S_i^e-(D+x_i){\left[I_i\right]}^{-}$

Wherein, x _ii-th neuronic activity value; A represents attenuation rate, and span is [8,15]; B and D is respectively the upper bound and the lower bound of neural dynamics, and value is respectively 1 and-1;

Interval time the span of T be (0,1s];

I _irepresent i-th neuronic outside input:

If the coordinate position that i-th neuron is corresponding is aiming spot, then I _i=E;

If the coordinate position that i-th neuron is corresponding is Obstacle Position, then I _i=-E;

In other situations, I _i=0;

E is outside input constant, gets the integer being more than or equal to 80;

S _irepresent i-th neuronic excitation input, whether its value is between barrier according to i-th neuron is determined:

When the coordinate position that i-th neuron is corresponding is between barrier:

$S_i={\left[I_i\right]}^{+}+\frac{{\mathrm{\Σ}}_{j=1}^{n^i}{\mathrm{\ω}}_{\mathrm{ij}}{\left[x_j^i\right]}^{+}}{n_{\mathrm{pos}}^i}{n}^i$

When the coordinate position that i-th neuron is corresponding is between barrier:

$S_i={\left[I_i\right]}^{+}+\frac{{\mathrm{\Σ}}_{j=1}^{n^i}{\mathrm{\ω}}_{\mathrm{ij}}{\left[x_j^i\right]}^{+}}{n_{\mathrm{pos}}^i}(n^i-n_{\mathrm{neg}}^i)$

Wherein, [a] ^-=max{-a, 0}, [a] ⁺=max{a, 0}; d _ijrepresent two neuron q _iand q _jbetween Euclidean distance, μ ₀represent distance constant, span be (0.2,1.2]; represent the neural dynamics that a jth consecutive point of the coordinate position that i-th neuron is corresponding are corresponding; n ⁱrepresent the consecutive point number of the coordinate position that i-th neuron is corresponding, all possible next location point of mobile robot is called consecutive point; be the number that neural dynamics corresponding in the consecutive point of the coordinate position that i-th neuron is corresponding is greater than the point of 0, it is the number that neural dynamics corresponding in the consecutive point of the coordinate position that i-th neuron is corresponding is less than the point of 0;

Step 5: according to the neural dynamics upgraded and minimum corner, decision-making is carried out to the next shift position of mobile robot, obtains next shift position;

Step 6: advancing in the position that mobile robot obtains according to step 5, returns step 3.

In described step 5, the decision-making of next shift position is determined according to following formula:

$q_n\⇐x_{\mathrm{qn}}=\max \{x_j^i/x_i+c{y}_j^i,j=\mathrm{1,2,3}...n^i\}$

Wherein, q _nrepresent the next position that robot may move to, one namely in consecutive point, represent consecutive point q _nactivity value;

[neural dynamics that in grating map, each coordinate position is corresponding is known, according to the neuron of known activity value, chooses arbitrarily corresponding coordinate position as next shift position; ]

C represents weight factor, and span is [0.3,3], represent corner factor of influence,

$\mathrm{\Δ}{\mathrm{\θ}}_j^i=|{\mathrm{\θ}}_j^i-{\mathrm{\θ}}_c^i|=|a\tan 2(y_{\mathrm{qj}}^i-y_{\mathrm{qc}}^i,x_{\mathrm{qj}}^i-x_{\mathrm{qc}}^i)-a\tan 2(y_{\mathrm{qc}}^i-y_{\mathrm{qp}}^i,x_{\mathrm{qc}}^i-x_{\mathrm{qp}}^i)|$

Wherein, with be respectively a jth consecutive point q of coordinate position corresponding to i-th neuron _jthe horizontal ordinate in grating map and ordinate, with be respectively the coordinate position place current location point q that i-th neuron is corresponding _chorizontal ordinate in grating map and ordinate, with be respectively a location point q on coordinate position place corresponding to i-th neuron _phorizontal ordinate in grating map and ordinate.

When the setting angle of the upper sonar sensor installed of described mobile robot is α, detects position that front distance is d when having a barrier, make the orientation angle of mobile robot in global coordinate system be θ _rworld coordinates is (x _r, y _r), then the coordinate of barrier in grating map is: $x_i=\frac{d\sin ({\mathrm{\θ}}_r+\mathrm{\α})}{\mathrm{\Δx}}+x_r,y_t=\frac{d\cos ({\mathrm{\θ}}_r+\mathrm{\α})}{\mathrm{\Δy}}+y_r.$

The sensing of robot and the angle of global coordinate system X-axis positive dirction, scope is that+180 degree are to-179 degree;

Described outside input constant A value is 10.The distribution of size to neural dynamics of this constant has conclusive effect, and when A value too small (being less than 3) can cause activity value saturated rapidly, path planning effect is bad; When A value excessive (being greater than 20), the effect of robotic tracking's target trajectory can be caused bad.

Described weight factor c value is 1.When value is 1, path planning effect is better, and when c value is greater than 1, the proportion of corner factor is bigger than normal, causes the mistake decision-making in path; When value is less than 1, corner factor proportion is less than normal, and path point smoothness is lower.

Distance constant μ ₀value is 1.This constant is distributed with vital role to neural dynamics, when this value obtains too small (being less than 0.2), the propagation of positive activity value can be affected, when this value obtains excessive (being greater than 1.5), activity value can be caused saturated rapidly, likely make robot arrive at impact point.

Beneficial effect

The invention provides the method for planning path for mobile robot under a kind of Unknown Dynamic Environment, each position is converted into a neuron by the method, utilize neuronic activity value to carry out representing ambient change, utilize biologically inspired neural network model to simulate dynamic environment cleverly; Biologically inspired neural network model ensure that positive neural dynamics outwards can be propagated and affect whole state space, and negative neural dynamics only acts on local.The neural dynamics of impact point and barrier is in crest and trough place respectively, and therefore, impact point attracts robot by the propagation of neural dynamics in whole state space, and barrier is merely able to repel robot in local, there is not minimal point problem.Point near border and between barrier carries out special processing, adds imaginary non-barrier point of proximity, effectively can solve path misjudgement problem.Consider that activity value is maximum simultaneously and effectively can ensure that the length in path is the shortest and reduction turns to number of times with corner minimum factor, substantially increase path quality.

When the method can be applied in the unknown or complete the unknown of environmental information part and there is Static and dynamic barrier, contain the maximum and robot corner minimum factor of neural dynamics in the decision-making of next location point simultaneously, the path obtained is not only the shortest or close the shortest, and path smooth, turn less.This method does not have local minimum points problem, and calculated amount is little, realizes simple, under Dynamic Unknown Environment, has good adaptability.

Accompanying drawing explanation

Fig. 1 is the process flow diagram of the method;

Fig. 2 robot interior coordinate system

Fig. 3 is sonar setting angle schematic diagram;

Fig. 4 is the point of proximity schematic diagram between barrier, does not have the point of proximity outside border;

Fig. 5 is the point of proximity schematic diagram between barrier, has the point of proximity outside border;

Fig. 6 is the point of proximity schematic diagram between non-barrier, does not have the point of proximity outside border;

Fig. 7 is the point of proximity schematic diagram between non-barrier, has the point of proximity outside border;

Fig. 8 is the generation pass of the method in U-shaped barrier;

Fig. 9 is the generation pass of the method under Unknown Dynamic Environment.

Embodiment

Below in conjunction with drawings and Examples, the present invention is further illustrated.

As shown in Figure 1, be the process flow diagram of the method for the invention, the method for planning path for mobile robot under a kind of Unknown Dynamic Environment, comprises following step:

Step 1: build grating map;

With the geometric center point of mobile robot for moving coordinate system initial point, the positive dirction being oriented to X-axis of mobile robot, sets up moving coordinate system, as shown in Figure 2;

Start mobile front position for global coordinate system initial point with mobile robot, mobile robot starts the positive dirction being oriented to X-axis before moving, and sets up global coordinate system;

Global coordinate system is converted into grating map according to the planning precision p of setting, in described grating map each grid length and be widely respectively Δ x=p and Δ y=p;

P value is 0.3m;

By mobile robot known static-obstacle thing, impact point and the coordinate transformation of mobile robot's current location in global coordinate system be in the work environment that grating map coordinate represents:

Wherein, (x*, y*) represents at the coordinate of object in global coordinate system;

Step 2: using each coordinate points in grating map as a neuron, and each neuronic activity value of initialization is 0;

Step 3: judge that whether the current position of mobile robot is identical with aiming spot, if identical, then current path planning terminates; Otherwise, enter step 4;

Step 4: read sonar ranging data, and upgrade neural dynamics;

As shown in Figure 3, when the setting angle of the upper sonar sensor installed of described mobile robot is α, detects position that front distance is d when having a barrier, make the orientation angle of mobile robot in global coordinate system be θ _rworld coordinates is (x _r, y _r) then the coordinate of barrier in grating map be: $x_i=\frac{d\sin ({\mathrm{\θ}}_r+\mathrm{\α})}{\mathrm{\Δx}}+x_r,y_t=\frac{d\cos ({\mathrm{\θ}}_r+\mathrm{\α})}{\mathrm{\Δy}}+y_r.$

The sensing of robot and the angle of global coordinate system X-axis positive dirction, scope is that+180 degree are to-179 degree;

Interval time, T read the ranging data of barrier in the working environment that the sonar sensor on a mobile robot gathers, and obtain the global coordinate system coordinate of barrier according to ranging data, and by neuronic activity value x corresponding for coordinate each in global coordinate system _iupgrade according to following formula:

$\frac{d{x}_i}{\mathrm{dt}}=-A{x}_i+(B-x_i)S_i-(D+x_i){\left[I_i\right]}^{-}$

Wherein, x _ii-th neuronic activity value; A represents attenuation rate, and span is [8,15], and in this example, value is 10; B and D is respectively the upper bound and the lower bound of neural dynamics, and value is respectively 1 and-1;

Interval time T value 100ms;

I _irepresent i-th neuronic outside input:

If the coordinate position that i-th neuron is corresponding is aiming spot, then I _i=E;

If the coordinate position that i-th neuron is corresponding is Obstacle Position, then I _i=-E;

In other situations, I _i=0;

E is outside input constant, gets the integer being more than or equal to 80;

S _irepresent i-th neuronic excitation input, whether its value is between barrier according to i-th neuron is determined:

When the coordinate position that i-th neuron is corresponding is between barrier:

$S_i={\left[I_i\right]}^{+}+\frac{{\mathrm{\Σ}}_{j=1}^{n^i}{\mathrm{\ω}}_{\mathrm{ij}}{\left[x_j^i\right]}^{+}}{n_{\mathrm{pos}}^i}{n}^i$

When the coordinate position that i-th neuron is corresponding is between barrier:

$S_i={\left[I_i\right]}^{+}+\frac{{\mathrm{\Σ}}_{j=1}^{n^i}{\mathrm{\ω}}_{\mathrm{ij}}{\left[x_j^i\right]}^{+}}{n_{\mathrm{pos}}^i}(n^i-n_{\mathrm{neg}}^i)$

Wherein, [a] ^-=max{-a, 0}, [a] ⁺=max{a, 0}; d _ijrepresent two neuron q _iand q _jbetween Euclidean distance, μ ₀represent distance constant, span be (0.2,1.2]; n ⁱrepresent the consecutive point number of the coordinate position that i-th neuron is corresponding, all possible next location point of mobile robot is called consecutive point; As shown in FIG. 4,5,6, 7, label is that 8 grids of the grid surrounding of 5 are its consecutive point;

Fig. 4,5,6,7 is neuronic nine neighborhood schematic diagram, and thick solid line represents border, and thin solid box represents the point in border, dotted line frame representative edge point out-of-bounds.The grid being numbered 5 is current location point, and shaded box represents obstacle object point, and shadeless grid represents non-obstacle object point.When grid 5 left and right, up and down or two of diagonal angle grids, when namely grid 1 and 9,3 and 7,4 and 6,2 and 8 is obstacle object point simultaneously, can think that this neuron is between barrier.

Shown in Fig. 4,5 is the situation that neuron is between barrier;

be the number that neural dynamics corresponding in the consecutive point of the coordinate position that i-th neuron is corresponding is greater than the point of 0, it is the number that neural dynamics corresponding in the consecutive point of the coordinate position that i-th neuron is corresponding is less than the point of 0;

Step 5: according to the neural dynamics upgraded and minimum corner, decision-making is carried out to the next shift position of mobile robot, obtains next shift position;

Step 6: advancing in the position that mobile robot obtains according to step 5, returns step 3.

In described step 5, the decision-making of next shift position is determined according to following formula:

$q_n\⇐x_{\mathrm{qn}}=\max \{x_j^i/x_i+c{y}_j^i,j=\mathrm{1,2,3}...n^i\}$

Wherein, represent the neural dynamics that a jth consecutive point of the coordinate position that i-th neuron is corresponding are corresponding; q _nrepresent the next position that robot may move to, one namely in consecutive point, represent consecutive point q _nactivity value;

[neural dynamics that in grating map, each coordinate position is corresponding is known, according to the neuron of known activity value, chooses arbitrarily corresponding coordinate position as next shift position; ]

C represents weight factor, and span is [0.3,3], and in this example, value is 2; represent corner factor of influence, $y_j^i=1-\frac{\mathrm{\Δ}{\mathrm{\θ}}_j^i}{\mathrm{\π}},\mathrm{\Δ}{\mathrm{\θ}}_j^i\∈[0,\mathrm{\π}];$

$\mathrm{\Δ}{\mathrm{\θ}}_j^i=|{\mathrm{\θ}}_j^i-{\mathrm{\θ}}_c^i|=|a\tan 2(y_{\mathrm{qj}}^i-y_{\mathrm{qc}}^i,x_{\mathrm{qj}}^i-x_{\mathrm{qc}}^i)-a\tan 2(y_{\mathrm{qc}}^i-y_{\mathrm{qp}}^i,x_{\mathrm{qc}}^i-x_{\mathrm{qp}}^i)|$

Wherein, with be respectively a jth consecutive point q of coordinate position corresponding to i-th neuron _jthe horizontal ordinate in grating map and ordinate, with be respectively the coordinate position place current location point q that i-th neuron is corresponding _chorizontal ordinate in grating map and ordinate, with be respectively a location point q on coordinate position place corresponding to i-th neuron _phorizontal ordinate in grating map and ordinate.

that robot moves to the absolute corner of next position from current location, such as, when robot straight ahead, $\mathrm{\Δ}{\mathrm{\θ}}_j^i=0,$ When robot reverses end for end, $\mathrm{\Δ}{\mathrm{\θ}}_j^i=\mathrm{\π}.$

Fig. 8 is the path that this algorithm generates under U-shaped barrier problem, as can be seen from the figure, does not have local minimum points problem, path smooth, turns few.

Fig. 9 is the generation pass of the method under dynamic environment.Figure hollow core point represents physical presence but not by the point that sensor detects, solid dot represents the point detected by sensor, and in environment, the right and left has the fixed obstacle of hook-type, and there is the fixed obstacle of a straight line top.Centre has two at a distance of the rectilinear movement barrier of 5 units, the ordinate of barrier 1 initial position is 20, the ordinate of barrier 2 initial position is 14, when robot starts mobile, two barriers start to move up and down with constant speed simultaneously, and translational speed is 1/3rd of robot translational speed.The starting point coordinate of robot is (15,1), and coordinate of ground point is (15,30).Pentagram number in figure represents robot and moves to path, and plus sige represents the increment type map that robot creates in moving process.

Claims (6)

1. the method for planning path for mobile robot under Unknown Dynamic Environment, is characterized in that, comprises following step:

Step 1: build grating map;

With the geometric center point of mobile robot for moving coordinate system initial point, the positive dirction being oriented to X-axis of mobile robot, sets up moving coordinate system;

Start mobile front position for global coordinate system initial point with mobile robot, mobile robot starts the positive dirction being oriented to X-axis before moving, and sets up global coordinate system;

Global coordinate system is converted into grating map according to the planning precision p of setting, in described grating map each grid length and be widely respectively Δ x=p and Δ y=p;

P span is 0.1m to 0.3m;

By mobile robot known static-obstacle thing, impact point and the coordinate transformation of mobile robot's current location in global coordinate system be in the work environment that grating map coordinate represents:

Wherein, (x*, y*) represents at the coordinate of object in global coordinate system;

Step 2: using each coordinate points in grating map as a neuron, and each neuronic activity value of initialization is 0;

Step 3: judge that whether the current position of mobile robot is identical with aiming spot, if identical, then current path planning terminates; Otherwise, enter step 4;

Step 4: read sonar ranging data, and upgrade neural dynamics;

Interval time, T read the ranging data of barrier in the working environment that the sonar sensor on a mobile robot gathers, and obtain the global coordinate system coordinate of barrier according to ranging data, and by neuronic activity value x corresponding for coordinate each in global coordinate system _iupgrade according to following formula:

$\frac{d{x}_i}{\mathrm{dt}}=-A{x}_i+(B-x_i)S_i-(D+x_i){\left[I_i\right]}^{-}$

Wherein, x _ii-th neuronic activity value; A represents attenuation rate, and span is [3,20]; B and D is respectively the upper bound and the lower bound of neural dynamics, and value is respectively 1 and-1;

Interval time the span of T be (0,1s];

I _irepresent i-th neuronic outside input:

If the coordinate position that i-th neuron is corresponding is aiming spot, then I _i=E;

If the coordinate position that i-th neuron is corresponding is Obstacle Position, then I _i=-E;

In other situations, I _i=0;

E is outside input constant, gets the integer being more than or equal to 80;

S _irepresent i-th neuronic excitation input, whether its value is between barrier according to i-th neuron is determined:

When the coordinate position that i-th neuron is corresponding is between barrier:

$S_i={\left[I_i\right]}^{+}+\frac{{\mathrm{\Σ}}_{j=1}^{n^i}{\mathrm{\ω}}_{\mathrm{ij}}{\left[x_j^i\right]}^{+}}{n_{\mathrm{pos}}^i}{n}^i$

When the coordinate position that i-th neuron is corresponding is between barrier:

$S_i={\left[I_i\right]}^{+}+\frac{{\mathrm{\Σ}}_{j=1}^{n^i}{\mathrm{\ω}}_{\mathrm{ij}}{\left[x_j^i\right]}^{+}}{n_{\mathrm{pos}}^i}(n^i-n_{\mathrm{neg}}^i)$

Wherein, [a] ^-=max{-a, 0}, [a] ⁺=max{a, 0}; d _ijrepresent two neuron q _iand q _jbetween Euclidean distance, μ ₀represent distance constant, span be (0.2,1.2]; n ⁱrepresent the consecutive point number of the coordinate position that i-th neuron is corresponding, all possible next location point of mobile robot is called consecutive point; represent the neural dynamics that a jth consecutive point of the coordinate position that i-th neuron is corresponding are corresponding; be the number that neural dynamics corresponding in the consecutive point of the coordinate position that i-th neuron is corresponding is greater than the point of 0, it is the number that neural dynamics corresponding in the consecutive point of the coordinate position that i-th neuron is corresponding is less than the point of 0;

Step 5: according to the neural dynamics upgraded and minimum corner, decision-making is carried out to the next shift position of mobile robot, obtains next shift position;

Step 6: advancing in the position that mobile robot obtains according to step 5, returns step 3.

2. the method for planning path for mobile robot under a kind of Unknown Dynamic Environment according to claim 1, is characterized in that, in described step 5, the decision-making of next shift position is determined according to following formula:

$q_n\⇐x_{\mathrm{qn}}=\max \{x_j^i/x_i+{\mathrm{cy}}_j^i,j=\mathrm{1,2,3}...n^i\}$

Wherein, q _nrepresent the next position that robot may move to, one namely in consecutive point, represent consecutive point q _nactivity value; C represents weight factor, and span is [0.3,3], represent corner factor of influence,

$\mathrm{\Δ}{\mathrm{\θ}}_j^i=|{{\mathrm{\θ}}_j}^i-{\mathrm{\θ}}_c^i|=|a\tan 2(y_{\mathrm{qj}}^i-y_{\mathrm{qc}}^i,x_{\mathrm{qj}}^i-x_{\mathrm{qc}}^i)-a\tan 2(y_{\mathrm{qc}}^i-y_{\mathrm{qp}}^i,x_{\mathrm{qc}}^i-x_{\mathrm{qp}}^i)|$

Wherein, with be respectively a jth consecutive point q of coordinate position corresponding to i-th neuron _jthe horizontal ordinate in grating map and ordinate, with be respectively the coordinate position place current location point q that i-th neuron is corresponding _chorizontal ordinate in grating map and ordinate, with be respectively a location point q on coordinate position place corresponding to i-th neuron _phorizontal ordinate in grating map and ordinate.

3. the method for planning path for mobile robot under a kind of Unknown Dynamic Environment according to claim 2, it is characterized in that, when the setting angle of the upper sonar sensor installed of described mobile robot is α, detect front distance for the position of d have a barrier time, make the orientation angle of mobile robot in global coordinate system be θ _rworld coordinates is (x _r, y _r), then the coordinate of barrier in grating map is: $x_t=\frac{d\sin ({\mathrm{\θ}}_r+\mathrm{\α})}{\mathrm{\Δx}}+x_r,y_t=\frac{d\cos ({\mathrm{\θ}}_r+\mathrm{\α})}{\mathrm{\Δy}}+y_r.$

4. the method for planning path for mobile robot under a kind of Unknown Dynamic Environment according to any one of claim 1-3, is characterized in that, described attenuation rate A value is 10.

5. the method for planning path for mobile robot under a kind of Unknown Dynamic Environment according to any one of claim 1-3, is characterized in that, described weight factor c value is 1.

6. the method for planning path for mobile robot under a kind of Unknown Dynamic Environment according to any one of claim 1-3, is characterized in that, described distance constant μ ₀value is 1.