CN107702723A - A kind of robot path planning method, storage medium and equipment - Google Patents

Abstract

The invention discloses a robot path planning method, storage medium and equipment. The method includes: constructing an environment map for robot walking by adopting a honeycomb grid method, and establishing an environment map model; searching for an optimal path for the robot's walking path , plan an optimal path; smooth the polyline path of the robot. The invention uses the honeycomb grid method to divide the environmental map, avoiding the problems of excessive corners, effectiveness and safety in the traditional grid method; and combining the honeycomb grid method with the tree growth simulation algorithm with plants as the research object, Give full play to the advantages of each part, and explore the problem of robot path planning from a new aspect.

Description

A robot path planning method, storage medium and device

technical field

The invention relates to the field of robot path planning, in particular to a robot path planning method, storage medium and equipment.

Background technique

With the rapid development of the robot industry and the increasing level of intelligence, robots have been widely used in various fields, and the path planning problem is an important part of robot control and navigation. A good path planning strategy can ensure the safety of robots To efficiently complete specified tasks, the problem of path planning for robots has been extensively explored and studied in recent years.

Traditional robot path planning mostly uses the grid method to construct environmental maps. The algorithm model is mainly based on the traditional research method of simulating physical and chemical laws or the lifestyles of animals, insects, bacteria and other organisms. It describes the phenomena in nature by establishing models. , this kind of model can often get a good approximate solution or even an optimal solution, so that the application field of the algorithm is rapidly expanded.

From the perspective of survival, the above-mentioned organisms must complete some behaviors in a relatively short period of time. Although this type of bionic algorithm can quickly converge to a satisfactory solution when solving an optimization problem, the algorithm has a fast convergence speed, and it is easy to cause the algorithm to fall into a local extreme. value points. The traditional grid method also has problems such as too large corners, low effectiveness and safety.

The growth mode of plants is different from other organisms. Its growth rate is slow, its living area is wider, and its growth time is longer. The adaptability of plants exceeds other biological groups to a certain extent. It can be seen that the tree growth algorithm based on the growth mode of plants can provide a new way of thinking for the robot path planning problem.

Contents of the invention

Aiming at the defects in the prior art, the present invention provides a robot path planning method, storage medium and equipment, adopts the honeycomb grid method to divide the environment map, and avoids the excessive corner angle, effectiveness and safety in the traditional grid method problems; combine the honeycomb grid method with the tree growth simulation algorithm with plants as the research object, give full play to the advantages of each part, and explore the robot path planning problem from a new aspect.

In a first aspect, the present invention provides a robot path planning method, the method comprising:

The honeycomb grid method is used to construct the environment map of the robot walking, and the environment map model is established;

Search for the optimal path for the walking path of the robot, and plan an optimal path;

Smoothes the robot's polyline path.

Further, the honeycomb grid method is used to construct the environment map for robot walking, and the environment map model is established, which specifically includes:

Assuming that the side length of the honeycomb grid is 1, x _max and y _max represent the maximum values in the X-axis direction and the Y-axis direction respectively; e ₁ and e ₂ represent a unit vector on the X-axis and Y-axis respectively, and

N _X is the maximum serial number on the X axis, N _Y is the maximum serial number on the Y axis;

Then the relationship between the grid coordinates and the serial number is expressed as:

Among them, N _x1 is the maximum serial number of the grid in the first odd row, N _x2 is the maximum serial number of the grid in the first even row, and the corresponding relationship between the serial number and the grid is shown in Figure 3.

Further, the searching for the optimal path of the walking path of the robot, and planning an optimal path specifically includes:

Using the optimization principle of tree-to-light branch growth, the tree growth simulation algorithm is used to plan the mobile robot's global traversal path to find an optimal path from the start point to the end point.

Further, the specific process of the tree growth simulation algorithm includes:

Calculate the light intensity and corresponding photosynthetic rate of branches at any position in the environment map, and establish the expressions of light intensity and photosynthetic rate;

Calculate the coordinate position of the random branch at the position where the light intensity is the largest;

Optimizing growth in a simulated environment to determine the optimal path.

Further, the expression of the light intensity of the branch at any position is:

Among them, I(i) represents the light intensity of the branches at the coordinates ( _xi , y _i ), k _l represents the coefficient of light intensity, (x _T , y _T ) is the coordinates of the target position, (x _B , y _B ) is the coordinates of the starting position.

Further, the coordinate position of the random branch is:

in, Indicates the strongest position of the light intensity sensed by the t-generation branches and leaves, that is, the coordinate position of the best auxin point, and also the coordinate position of the branch point that produces light; Indicates the random number in the light direction at the strongest position, t indicates the growth period of the branch before branching, and t+1 indicates the growth period of the branch after branching.

Further, the smoothing of the polyline path of the robot specifically includes:

A third-order Bezier curve is used for path smoothing. According to whether the smoothed curve avoids obstacles, a corresponding selection mechanism is established to choose between avoiding obstacles and not avoiding obstacles, so that the walking path of the robot Maximum smoothness.

Further, according to whether the smoothed curve avoids obstacles, a corresponding selection mechanism is established to select the two situations of avoiding obstacles and not avoiding obstacles, specifically including:

Set the minimum distance from the planned path point to the obstacle as L _min , and the safe distance from the path point to the obstacle as D _safe ,

In the formula: x _i (t) and y _i (t) are the path point coordinates after Bezier curve planning; x ₀ (i) and y ₀ (i) are the centers of obstacles in the grid map; R( i) is the radius size of each obstacle, the time in the t Bezier curve formula;

When the minimum distance between the path point and the obstacle is greater than or equal to the safety distance, select the curved path segment planned by the Bezier curve; when the minimum distance between the path point and the obstacle is less than the safety distance, abandon the path planned by the Bezier curve , select the path planning segment under the original algorithm.

In a second aspect, the present invention also provides a computer-readable storage medium, and the program implements the following steps when executed by a processor:

The honeycomb grid method is used to construct the environment map of the robot walking, and the environment map model is established;

Search for the optimal path for the walking path of the robot, and plan an optimal path;

Smoothes the robot's polyline path.

In a third aspect, the present invention also provides a computer device, including a memory, a processor, and a computer program stored on the memory and operable on the processor, and the processor implements the following steps when executing the program:

The honeycomb grid method is used to construct the environment map of the robot walking, and the environment map model is established;

Search for the optimal path for the walking path of the robot, and plan an optimal path;

Smoothes the robot's polyline path.

It can be seen from the above technical solutions that the present invention provides a robot path planning method, storage medium and equipment. Compared with the prior art, the advantages are as follows:

(1) The environment map is divided by honeycomb grid (regular hexagonal grid) method, and the corresponding formula between the coordinate position of each honeycomb grid center and the grid serial number is clearly given, and the robot path planning point in the algorithm is Corresponding to the grid position, it avoids the problems of excessive corners, effectiveness and safety in the traditional grid method.

(2) Utilize the optimization principle of tree-to-light branch growth, build a model systematically, use the tree growth simulation algorithm to carry out global traversal path planning for mobile robots, and find an optimal path from the starting point to the end point, so that the algorithm It can achieve certain effects in solving the problem of global path planning existing in traditional bionic algorithms.

(3) Combining the honeycomb grid method and the tree growth simulation algorithm with plants as the research object in the path planning problem, and giving full play to the advantages of each part, so as to explore the robot path planning problem from a new aspect.

Description of drawings

FIG. 1 is a schematic flowchart of a robot path planning method provided by the present invention.

FIG. 2 is a schematic diagram of an environment map of a robot path planning method provided by the present invention.

Fig. 3 is a schematic diagram of a honeycomb grid coordinate system established by a robot path planning method provided by the present invention.

Fig. 4 is a schematic flow chart of the tree growth simulation algorithm.

Figure 5 is a schematic diagram of the relationship between the growth rate of branches and leaves and the concentration of auxin.

FIG. 6 is a schematic diagram of path smoothing for some broken line points in the walking path of the robot.

FIG. 7 is a schematic diagram of path selection when the path after using the Bezier curve is still between obstacles and the polyline path.

FIG. 8 is a schematic diagram of path selection when the path using the Bezier curve just passes through an obstacle.

FIG. 9 is a schematic diagram of an optimal path planned by the robot path planning method provided by the present invention.

detailed description

Embodiments of the technical solutions of the present invention will be described in detail below in conjunction with the accompanying drawings. The following examples are only used to illustrate the technical solution of the present invention more clearly, so they are only examples, and should not be used to limit the protection scope of the present invention.

Embodiment one

As shown in Figure 1, Embodiment 1 of the present invention provides a robot path planning method based on a tree growth simulation algorithm, including:

In step S1, the honeycomb grid method is used to construct the environment map for the robot to walk, and an environment map model is established.

The environment map of robot walking is shown in Figure 2. In the environment map composed of honeycomb grids, the environment is equally divided into hexagonal grids with the same shape. The construction of the environment map model in this embodiment is based on 40× In the two-dimensional space plane composed of 50 honeycomb grids, and there are only static obstacles in the space, at the same time, the static obstacles are expanded into a circle to represent, and the center and radius of the circle are determined to determine the position of the static obstacles in the space. Corresponding position.

In this embodiment, it is assumed that the starting position of the mobile robot in the environment map is the first honeycomb grid B(x _B ,y _B ), the target position T(x _T ,y _T ), and the position and size of static obstacles in the area It is known that according to the starting position, target position and obstacle position in the environment map, the horizontal axis is the X axis and the vertical axis is the Y axis to construct a new honeycomb grid coordinate system, as shown in Figure 3.

In practical application, according to the size of the mobile robot, the mobile robot is reduced to a mass point, the movement of the robot in the grid map is regarded as the movement of the mass point, and the boundary of the obstacle is expanded and blurred in the environment.

In Figure 2, the area shown by the black shadow is the obstacle after processing, the blank grid indicates the place where the robot can pass freely, the gray dot in the lower left corner represents the starting position of the robot path, and the gray dot in the upper right corner represents The target position of the robot path; in Figure 3, the gray circle in the lower left corner is the starting position of the robot path, and the gray circle in the upper right corner is the target position of the robot path. In this way, the robot path planning problem in space is transformed into the shortest path search problem in the grid graph, which simplifies the complexity of problem solving.

Step S2, searching for an optimal path for the walking path of the robot, and planning an optimal path.

Specifically, the tree growth simulation algorithm is used to perform global traversal path optimization on the walking path of the robot, and an optimal path from the starting position to the target position is planned.

Among them, as shown in Figure 4, the specific process of the tree growth simulation algorithm is:

Step S21, calculate the light intensity and the corresponding photosynthetic rate of branches at any position in the environment map, and establish the expressions of light intensity and photosynthetic rate;

First, using the principle that the light intensity between the light and each branch and leaf changes with the distance between the two, search the light intensity and the corresponding photosynthetic rate at each position in the environment map model, that is, find the objective function; In the system, an expression of the light intensity of branches at any point ( _xi , y ₎ is established, which can be expressed as:

Among them, k _l represents the light intensity coefficient, (x _T , y _T ) is the coordinate of the target position, and (x _B , y _B ) is the coordinate of the starting tree bud (starting position).

The photosynthetic rate of branches at any point ( _xi ,y ₎ can be expressed as:

Among them, α is the slope of the photoresponse curve in photosynthesis when the light intensity is zero, that is, the initial slope of the photoresponse curve (initial quantum efficiency), β is the correction coefficient, PR _max is the maximum net photosynthetic rate, and γ is the initial quantum efficiency The ratio of the maximum photosynthetic rate of the plant, that is _Rd is the dark respiration rate. The three parameters α, PR _max and R _d are used to control the photosynthetic rate.

Step S22, calculating the coordinate position of the random branch at the position where the light intensity is maximum;

Biological experiments have proved that the auxin information that determines the division and growth of branch bud cells is not inherently endowed to each cell, but because the cell growth system receives the location information of division and growth from its environment, according to this Information, plant growth will show obvious phototropic growth characteristics.

Due to the location of high light intensity, the photosynthetic rate is fast when the tree grows, and the growth rate is fast. Here, the auxin concentration is often near the optimal auxin point, and the relationship between the auxin concentration and the growth rate of the buds is in a changing process. Too high or too low auxin concentration will have a great impact on the growth rate of buds, so it is most likely to produce branches near the optimum bud auxin concentration position, that is, the position with the maximum light intensity corresponds to the maximum photosynthetic rate, which is also the most optimal position. Optimum auxin concentration, as shown in Figure 5.

According to the above principles, the branch expression can be expressed as:

in, Indicates the coordinate position of any position point i point in the t+1 generation branch and leaf growth cycle; Indicates the strongest position of the light intensity sensed by the t-generation branches and leaves, that is, the coordinate position of the best auxin point, that is, the coordinate position of the branch point that generates light; Indicates the random number in the light direction at this point, t indicates the growth period of the branch before branching, and t+1 indicates the growth period of the branch after branching.

Once the new branch sprouts, the new branch and the old branch merge into one, the same branch in the same plane.

Step S23, optimize the growth in the simulated environment, and determine the optimal path.

During the growth process of plants, they are often affected by many influences, such as the influence of their own top advantages on side branches, natural disasters (fire, lightning strikes, etc.) and artificial effects (artificial pruning, etc.). Impacts are classified into two typical cases: the case without obstacles and the case with some obstacles.

The tree does not encounter obstacles during the growth process and maintains normal light-oriented growth; if an obstacle appears in the growth direction, another direction becomes the growth direction; if there is no usable direction during the growth process, the tree stops branching grow, no further calculations will be performed. The obstacle may be the first branch to appear; the first branch will grow first; other branches blocked by the branch will decide whether to continue growing or stop, depending on where the obstacle is. The specific rules are as follows:

The terminal bud (optimal position) of the branch grows under the action of the top advantage, which is a model without encountering obstacles in path planning. The model can be expressed as:

in, Indicates any point i'(P' The corresponding coordinate position of i); Indicates the strongest position of the light intensity sensed by the t-generation branches and leaves, that is, the coordinate position of the best auxin point, that is, the coordinate position of the branch point that generates light; so is the coordinate position of any position point i in the t-generation growth cycle within the neighborhood of the center; growth is the weight, and r is a random number between (0, 1].

Due to the lack of photosynthesis caused by the occlusion of the branches and leaves above, under the action of natural factors, the branches randomly and selectively change the growth direction, which is a model of obstacles encountered in path planning, which can be expressed as:

in, Indicates the coordinate position of any point i” in the growth cycle of the t+1 generation of branches and leaves; P(x _min , y _min ) and P(x _max , y _max ) are respectively The optimal value point and the worst value point on the boundary of the neighborhood range centered at the center; where, r is a random number between (0, 1].

Due to insufficient photosynthesis and insufficient auxin concentration to provide the energy required for the growth of branches and leaves during the process of lateral branch growth toward the light, the branches stop growing. This is an obstacle model in path planning, which can be expressed as:

in, Represents the coordinate position of any point i"' in the growth cycle of the t+1 generation of branches and leaves; Indicates the strongest position of the light intensity sensed by the t-generation branches and leaves, that is, the coordinate position of the best auxin point, that is, the coordinate position of the branch point that produces light.

For three different models of optimal growth in the simulated environment, the optimal path planning design objective function under the algorithm is:

Among them, μ ₁ , μ ₂ , and μ ₃ are weight coefficients, which are used to adjust and find an optimal path.

Step S3, smoothing the polyline path of the robot.

Step S3 specifically includes: using a third-order Bezier curve to smooth the path, and establishing a selection mechanism to select the two cases according to whether the smoothed curve avoids obstacles, so that the walking path of the robot is as smooth as possible.

As shown in Figure 6, the specific process of path smoothing is as follows:

In the process of path optimization, the honeycomb grid method used can alleviate the safety problems caused by the robot’s turning to a certain extent, but considering that the robot’s smooth curved path is better than that of a broken line, the third-order Bezier curve is used. (Bezier curve) for path smoothing, turning polyline paths into smooth curved paths.

The parameter equation of each point of the n-degree Bezier curve is expressed as

In the formula, Also known as the n-order Bernstein basis polynomial, define 0! =1, t represents time. The point P _i is called the control point of the Bezier curve.

The third-order Bezier curve is defined by four points P ₀ , P ₁ , P ₂ , and P ₃ , and the parameter form of the third-order Bezier curve is:

B(t)=P ₀ (1-t) ³ +3P ₁ t(1-t) ² +3P ₂ t ² (1-t)+P ₃ t ³ , t∈[0,1].

The complete optimal path can be regarded as a splicing of multiple third-order Bezier curves. In order to ensure the smooth transition of the segmented parameter curve from one segment to another, a parameter continuity derivative can be matched in the common part of the curve segment to ensure that the parameter continuity.

The 0th-order parameter continuity, denoted as C0 continuity, can simply indicate that two curves are connected. First-order parameter continuity, denoted as C1 continuity, means that the equations of two adjacent curve segments have the same first-order derivative (tangent vector) at the intersection. The second-order parameter continuity, denoted as C2 continuity, means that two curve segments have the same first-order and second-order derivatives at the junction.

There are two sections of Bezier curves Q ₁ (t) and Q ₂ (t), whose characteristic polygon vertices are P ₀ , P ₁ , P ₂ , P ₃ and R ₀ , R ₁ , R ₂ , R ₃ respectively.

Require P ₃ =R ₀ , and require two curve segments to achieve C1 continuity at the connection point P ₃ (R ₀ ), then Q′ ₁ (1)=3(P ₃ -P ₂ ), Q’ ₂ (0) =3(R ₁ -R ₀ ), Q' ₂ (0)=αQ' ₁ (1), ie (R ₁ -R ₀ )=α(P ₃ -P ₂ ), where α is a scaling factor. It shows that the condition for realizing C1 continuity is that P ₂ , P ₃ (R ₀ ), R ₁ are on a straight line, and P ₂ , R ₁ are on both sides of P ₃ (R ₀ ).

There are two situations in the path smoothing process. The first one is that the path after using the Bezier curve is still between the obstacle and the polyline path, as shown in Figure 7. At this time, we choose to use the Bezier curve The path; another case is that the path after using the Bezier curve just passes through the obstacle, as shown in Figure 8, which is meaningless, so in this case, the path planned by the Bezier curve should be abandoned.

In view of these two situations, a selection mechanism is established to select these two situations.

Assuming that the planned minimum distance from the path point to the obstacle is L _min , set the safe distance from the path point to the obstacle as D _safe ,

In the formula: x _i (t) and y _i (t) are the path point coordinates after Bezier curve planning; x ₀ (i) and y ₀ (i) are the center coordinates of each obstacle in the grid map (the present embodiment The obstacles in are circles); R(i) is the radius of each obstacle, and t is the time in the Bezier curve.

When the minimum distance between the path point and the obstacle is greater than or equal to the safety distance, the curved path segment planned by the Bezier curve is mainly considered; when the minimum distance between the path point and the obstacle is less than the safety distance, the Bezier curve is abandoned in order to avoid the obstacle For the planned path, select the path planning segment under the original algorithm.

The optimal path planned by the path planning method described in Embodiment 1 is shown in FIG. 9 .

Embodiment two

Corresponding to Embodiment 1 of the present invention, Embodiment 2 of the present invention provides a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, the following steps are implemented:

Step S1, using the honeycomb grid method to construct the environment map for the robot to walk, and establish the environment map model;

Step S2, searching for an optimal path for the walking path of the robot, and planning an optimal path;

Step S3, smoothing the polyline path of the robot.

The above-mentioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk, and other media capable of storing program codes.

For the above specific limitations on the computer-readable storage medium, reference may be made to Embodiment 1, and details are not repeated here.

Embodiment three

Corresponding to Embodiment 1 of the present invention, Embodiment 3 of the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and operable on the processor. When the processor executes the program, the computer program is implemented. The following steps:

Step S1, using the honeycomb grid method to construct the environment map for the robot to walk, and establish the environment map model;

Step S2, searching for an optimal path for the walking path of the robot, and planning an optimal path;

Step S3, smoothing the polyline path of the robot.

For the above specific limitations on the computer device, reference may be made to Embodiment 1, which will not be repeated here.

Compared with the prior art, the embodiments of the present invention have the following advantages:

(1) Learn from the advantages of the traditional grid method to divide the environment, and at the same time eliminate the defects of the traditional grid method to divide the environment, borrow the existing honeycomb grid model to re-plan, and clearly give the center coordinates of each honeycomb grid The corresponding formula of position and grid serial number corresponds to the robot path planning point in the algorithm and the grid position. The method of honeycomb grid (regular hexagonal grid) is used to divide the environmental map, which avoids the problems of too large corners, effectiveness and safety in the traditional grid method.

(2) Utilize the optimization principle of tree-to-light branch growth, establish a model of the system, use the tree growth simulation algorithm to carry out global traversal path planning for mobile robots, and find an optimal path from the starting point to the end point, so that the algorithm It can achieve a certain effect on the global path planning existing in the traditional bionic algorithm.

(3) Traditional robot path planning mostly uses the grid method to construct environmental maps, and the algorithm model is mainly based on traditional research methods that simulate natural laws or the growth and lifestyle of bacteria, insects, and animals. In the path planning problem, the embodiment of the present invention combines the honeycomb grid method and the tree growth simulation algorithm with plants as the research object, and makes use of the advantages of each part, so as to explore the robot path planning problem from a new aspect.

Those skilled in the art may combine and combine different embodiments or examples and features of different embodiments or examples described in this specification without conflicting with each other.

It should be noted that, each block in the block diagrams and/or flowcharts in the drawings of the specification of the present invention, and the combination of blocks in the block diagrams and/or flowcharts, can be used to perform specified functions or actions. It may be implemented as a hardware-based system, or it may be implemented as a combination of special-purpose hardware and machine instructions.

Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention, rather than limiting them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: It is still possible to modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the technical solutions of the various embodiments of the present invention. All of them should be covered by the scope of the claims and description of the present invention.

Claims (10)

1. A method of robot path planning, the method comprising:

constructing an environment map of robot walking by adopting a honeycomb grid method, and establishing an environment map model;

carrying out optimal path search on the walking path of the robot, and planning an optimal path;

and smoothing the broken line path of the robot.

2. The robot path planning method according to claim 1, wherein the building of the environment map of the robot walking by using a honeycomb grid method to establish the environment map model specifically comprises:

let the side length of the honeycomb grid be 1, x_max、y_maxMaximum values in the X-axis direction and the Y-axis direction are respectively represented; e.g. of the type₁、e₂Respectively represent vectors of one unit on the X-axis and the Y-axis, and

<mfenced open='{' close=''> <mtable> <mtr> <mtd> <msub> <mi>N</mi> <mi>x</mi> </msub> <mo>=</mo> <mfrac> <msub> <mi>x</mi> <mi>max</mi> </msub> <mrow> <mo>|</mo> <msub> <mi>e</mi> <mn>1</mn> </msub> <mo>|</mo> </mrow> </mfrac> </mtd> </mtr> <mtr> <mtd> <msub> <mi>N</mi> <mi>y</mi> </msub> <mo>=</mo> <mfrac> <msub> <mi>y</mi> <mi>max</mi> </msub> <mrow> <mo>|</mo> <msub> <mi>e</mi> <mn>2</mn> </msub> <mo>|</mo> </mrow> </mfrac> </mtd> </mtr> </mtable> </mfenced>

N_Xis the maximum sequence number on the X axis, N_YThe maximum sequence number on the Y axis;

the grid coordinates are then related to the sequence number as:

<mfenced open='{' close=''> <mtable> <mtr> <mtd> <msub> <mi>x</mi> <mi>i</mi> </msub> <mo>=</mo> <mfenced open='{' close=''> <mtable> <mtr> <mtd> <mn>2</mn> <mo>[</mo> <mrow> <mo>(</mo> <mi>i</mi> <mo>-</mo> <mn>1</mn> <mo>)</mo> </mrow> <mi>mod</mi> <msub> <mi>N</mi> <mrow> <mi>x</mi> <mn>1</mn> </mrow> </msub> <mo>]</mo> <mo>+</mo> <mn>1</mn> <msub> <mi>ify</mi> <mi>i</mi> </msub> <mi>mod</mi> <mn>2</mn> <mo>=</mo> <mn>1</mn> </mtd> </mtr> <mtr> <mtd> <mn>2</mn> <mo>[</mo> <mrow> <mo>(</mo> <mi>i</mi> <mo>-</mo> <mn>1</mn> <mo>)</mo> </mrow> <mi>mod</mi> <msub> <mi>N</mi> <mrow> <mi>x</mi> <mn>2</mn> </mrow> </msub> <mo>-</mo> <mn>19</mn> <mo>]</mo> <mi>else</mi> <msub> <mi>y</mi> <mi>i</mi> </msub> <mi>mod</mi> <mn>2</mn> <mo>=</mo> <mn>0</mn> </mtd> </mtr> </mtable> </mfenced> </mtd> </mtr> <mtr> <mtd> <msub> <mi>y</mi> <mi>i</mi> </msub> <mo>=</mo> <mi>int</mi> <mo>[</mo> <mrow> <mo>(</mo> <mi>i</mi> <mo>-</mo> <mn>1</mn> <mo>)</mo> </mrow> <mo>/</mo> <msub> <mi>N</mi> <mrow> <mi>x</mi> <mn>1</mn> </mrow> </msub> <mo>]</mo> <mo>+</mo> <mn>1</mn> </mtd> </mtr> </mtable> </mfenced>

wherein N is_x1Is the maximum sequence number, N, of the first odd-numbered row grid_x2The first even row grid maximum sequence number.

3. The robot path planning method according to claim 1, wherein the optimal path search is performed on the walking path of the robot to plan an optimal path, and specifically comprises:

and (3) carrying out global traversal type path planning on the mobile robot by using an optimization principle of tree-to-light branching growth and adopting a tree growth simulation algorithm, and finding out an optimal path from a starting point to a terminal point.

4. The method for planning a robot path according to claim 3, wherein the specific process of the tree growth simulation algorithm comprises:

calculating the illumination intensity and the corresponding photosynthetic rate of branches at any position in an environment map, and establishing an expression of the illumination intensity and the photosynthetic rate;

calculating the coordinate position of the random branch at the position with the maximum illumination intensity;

and optimizing growth in a simulation environment and determining an optimal path.

5. The robot path planning method according to claim 4, wherein the expression of the illumination intensity of the branch at any position is as follows:

<mrow> <mi>I</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mrow> <msub> <mi>k</mi> <mi>l</mi> </msub> <mo>[</mo> <msup> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mi>T</mi> </msub> <mo>-</mo> <msub> <mi>x</mi> <mi>B</mi> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>+</mo> <msup> <mrow> <mo>(</mo> <msub> <mi>y</mi> <mi>T</mi> </msub> <mo>-</mo> <msub> <mi>y</mi> <mi>B</mi> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>]</mo> </mrow> <mrow> <msup> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mi>i</mi> </msub> <mo>-</mo> <msub> <mi>x</mi> <mi>T</mi> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>+</mo> <msup> <mrow> <mo>(</mo> <msub> <mi>y</mi> <mi>i</mi> </msub> <mo>-</mo> <msub> <mi>y</mi> <mi>T</mi> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </mfrac> </mrow>

wherein I (i) represents a coordinate (x)_i,y_i) Intensity of light on branches, k_lRepresents the illumination intensity coefficient (x)_T,y_T) Is the coordinate of the target position, (x)_B,y_B) Is the coordinates of the starting position.

6. The robot path planning method according to claim 4, wherein the coordinate positions of the random branches are:

<mrow> <mi>P</mi> <mrow> <mo>(</mo> <msubsup> <mi>x</mi> <mi>i</mi> <mrow> <mi>t</mi> <mo>+</mo> <mn>1</mn> </mrow> </msubsup> <mo>,</mo> <msubsup> <mi>y</mi> <mi>i</mi> <mrow> <mi>t</mi> <mo>+</mo> <mn>1</mn> </mrow> </msubsup> <mo>)</mo> </mrow> <mo>=</mo> <mi>P</mi> <mrow> <mo>(</mo> <msubsup> <mi>x</mi> <mi>best</mi> <mi>t</mi> </msubsup> <mo>,</mo> <msubsup> <mi>y</mi> <mi>best</mi> <mi>t</mi> </msubsup> <mo>)</mo> </mrow> <mo>+</mo> <mi>rand</mi> <mrow> <mo>(</mo> <msubsup> <mi>x</mi> <mi>i</mi> <mi>t</mi> </msubsup> <mo>,</mo> <msubsup> <mi>y</mi> <mi>i</mi> <mi>t</mi> </msubsup> <mo>)</mo> </mrow> </mrow>

wherein,the strongest position of the illumination intensity sensed by the t generation branches and leaves is represented, namely the coordinate position of the optimal auxin point, and the coordinate position of the branch point generated by light;represents a random number in the direction of light at the strongest position, t represents the growth cycle of the branch before branching, and t +1 represents the growth cycle of the branch after branching.

7. The robot path planning method according to claim 1, wherein the smoothing of the polygonal line path of the robot specifically includes:

and a third-order Bezier curve is adopted to smooth the path, and a corresponding selection mechanism is established to select the obstacle avoidance condition and the obstacle avoidance condition according to the existence of the obstacle avoidance condition of the smoothed curve, so that the walking path of the robot is smoothed to the maximum extent.

8. The method for planning a robot path according to claim 7, wherein the step of establishing a corresponding selection mechanism to select whether the smoothed curve avoids the obstacle or not includes:

setting the minimum distance from the planned path point to the obstacle to be L_minSafe distance D from the path point to the obstacle_safe，

<mrow> <msub> <mi>L</mi> <mi>min</mi> </msub> <mo>=</mo> <mfenced open='{' close=''> <mtable> <mtr> <mtd> <msub> <mi>L</mi> <mi>min</mi> </msub> </mtd> <mtd> <msub> <mi>L</mi> <mi>min</mi> </msub> <mo>&amp;GreaterEqual;</mo> <msub> <mi>D</mi> <mi>safe</mi> </msub> </mtd> </mtr> <mtr> <mtd> <mn>0</mn> </mtd> <mtd> <mn>0</mn> <mo>&amp;le;</mo> <msub> <mi>L</mi> <mi>min</mi> </msub> <mo>&lt;</mo> <msub> <mi>D</mi> <mrow> <mi>safe</mi> <mo>,</mo> </mrow> </msub> </mtd> </mtr> </mtable> <mrow> <msub> <mi>L</mi> <mi>min</mi> </msub> <mo>=</mo> <munder> <mi>min</mi> <mrow> <mi>t</mi> <mo>&amp;Element;</mo> <mo>[</mo> <mn>0,1</mn> <mo>]</mo> </mrow> </munder> <mrow> <mo>(</mo> <msqrt> <msup> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>-</mo> <msub> <mi>x</mi> <mi>o</mi> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>+</mo> <msup> <mrow> <mo>(</mo> <msub> <mi>y</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>-</mo> <msub> <mi>y</mi> <mi>o</mi> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> <mo>)</mo> </mrow> <mn>2</mn> </msup> </msqrt> <mo>-</mo> <mi>R</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> <mo>)</mo> </mrow> <mo>,</mo> </mrow> </mfenced> </mrow>

In the formula: x is the number of_i(t) andy_i(t) coordinates of path points after Bezier curve planning; x is the number of₀(i) And y₀(i) The center of a circle of each obstacle in the grid map; r (i) is the radius of each obstacle;

when the minimum distance between the path point and the barrier is greater than or equal to the safe distance, selecting a curve path section after Bezier curve planning; and when the minimum distance between the path point and the barrier is smaller than the safety distance, abandoning the path planned by the Bezier curve, and selecting a path planning section under the original algorithm.

9. A computer-readable storage medium, on which a computer program is stored, which program, when executed by a processor, carries out the steps of:

constructing an environment map of robot walking by adopting a honeycomb grid method, and establishing an environment map model;

carrying out optimal path search on the walking path of the robot, and planning an optimal path;

and smoothing the broken line path of the robot.

10. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program performs the steps of:

constructing an environment map of robot walking by adopting a honeycomb grid method, and establishing an environment map model;

carrying out optimal path search on the walking path of the robot, and planning an optimal path;

and smoothing the broken line path of the robot.