CN107702723A - A kind of robot path planning method, storage medium and equipment - Google Patents
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Abstract
The invention discloses a kind of robot path planning method, storage medium and equipment, methods described includes：The environmental map of robot ambulation is built using honeycomb Grid Method, establishes environmental map model；Optimum route search is carried out to the walking path of robot, cooks up an optimal path；The dog leg path of robot is smoothed.The present invention is divided using honeycomb raster based method to environmental map, avoids excessive corner in conventional grid method, validity and safety issue；And by honeycomb Grid Method and using plant as research object tree growth simulation algorithm combine, play each several part the advantages of, robot path planning's problem is probed into terms of new.
Description
Technical Field
The present invention relates to the field of robot path planning, and in particular, to a robot path planning method, a storage medium, and a device.
Background
With the rapid development of the robot industry and the increasing degree of intelligence, robots have been widely used in various fields, and a path planning problem is an important part in robot control and navigation, and a good path planning strategy can ensure that the robots safely and effectively complete designated tasks, so in recent years, the path planning problem of the robots has been widely explored and researched.
The traditional robot path planning mostly adopts a grid method to construct an environment map, an algorithm model is a traditional research method which mainly takes a simulation physical and chemical law or life ways of animals, insects, bacteria and other organisms as background technology, and the model is established to describe phenomena in the nature, so that the model can often obtain a good approximate solution or even an optimal solution, and the application field of the algorithm is rapidly widened.
From the survival perspective, the creatures need to complete some behaviors in a short time, and although the bionic algorithm can quickly converge to a satisfactory solution when solving an optimization problem, the algorithm convergence speed is high, so that the algorithm is easy to fall into a local extreme point. The traditional grid method also has the problems of overlarge rotation angle, low effectiveness and safety and the like.
The growth mode of the plant is different from that of other organisms, the plant has the advantages of slow growth speed, wide living area, long growth time and higher adaptability of the plant than other organism groups to a certain extent. Therefore, the tree growing algorithm with the plant growing mode as the background can provide a new idea for the robot path planning problem.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a robot path planning method, a storage medium and equipment, wherein a honeycomb grid method is adopted to divide an environment map, so that the problems of overlarge turning angle, effectiveness and safety in the traditional grid method are solved; and combines the honeycomb grid method with a tree growth simulation algorithm taking plants as research objects, exerts the advantages of each part and explores the path planning problem of the robot from a new aspect.
In a first aspect, the present invention provides a robot path planning method, including:
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.
Further, the building of the environment map of robot walking by using the honeycomb grid method to establish the environment map model specifically includes:
let the side length of the honeycomb grid be 1, xmax、ymaxMaximum 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
NXis the maximum sequence number on the X axis, NYThe maximum sequence number on the Y axis;
the grid coordinates are then related to the sequence number as:
wherein N isx1Is the maximum sequence number, N, of the first odd-numbered row gridx2The maximum sequence number of the first even row grid, the corresponding relationship between the sequence number and the grid is shown in FIG. 3.
Further, the searching for the optimal path of the walking path of the robot to plan an optimal path specifically includes:
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.
Further, the specific process of the tree growth simulation algorithm includes:
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.
Further, the expression of the illumination intensity of the branch at any position is as follows:
wherein I (i) represents a coordinate (x)i,yi) Intensity of light on branches, klRepresents the illumination intensity coefficient (x)T,yT) Is the coordinate of the target position, (x)B,yB) Is the coordinates of the starting position.
Further, the coordinate positions of the random branches are:
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.
Further, the smoothing 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.
Further, establishing a corresponding selection mechanism to select whether to avoid the obstacle or not according to whether to avoid the obstacle or not in the smoothed curve, specifically including:
setting the minimum distance from the planned path point to the obstacle to be LminSafe distance D from the path point to the obstaclesafe，
In the formula: x is the number ofi(t) and yi(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, time in the t Bezier curve formula;
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.
In a second aspect, the invention also provides a computer readable storage medium, which when executed by a processor, 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.
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 executable on the processor, the processor implementing the following steps when executing the program:
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.
As can be seen from the above technical solutions, the present invention provides a robot path planning method, a storage medium, and a device, and compared with the prior art, the robot path planning method has the following advantages:
(1) the environment map is divided by adopting a honeycomb grid (regular hexagon grid) method, a corresponding formula of the central coordinate position of each honeycomb grid and the grid sequence number is clearly given, the robot path planning point in the algorithm is corresponding to the grid position, and the problems of overlarge rotation angle, effectiveness and safety in the traditional grid method are solved.
(2) The method comprises the steps of systematically establishing a model by utilizing an optimization principle of tree-to-light branching growth, carrying out overall traversal type path planning on the mobile robot by using a tree growth simulation algorithm, and finding out an optimal path from a starting point to a terminal point, so that the algorithm can obtain a certain effect on solving the problem of overall path planning existing in the traditional bionic algorithm.
(3) In the path planning problem, a honeycomb grid method and a tree growth simulation algorithm taking plants as research objects are combined, and the advantages of each part are exerted, so that the robot path planning problem is explored from a new aspect.
Drawings
Fig. 1 is a schematic flow chart of a robot path planning method provided by the present invention.
Fig. 2 is a schematic view 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 the robot path planning method provided by the present invention.
FIG. 4 is a schematic flow chart of a tree growth simulation algorithm.
FIG. 5 is a graph showing the relationship between the growth rate of branches and leaves and the concentration of auxin.
Fig. 6 is a schematic diagram illustrating path smoothing of a part of polygonal line points in the travel path of the robot.
Fig. 7 is a schematic diagram of path selection when the path using the bezier curve is still between the obstacle and the broken-line path.
Fig. 8 is a schematic diagram of the path selection when the path using the bezier curve just passes through the 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 present invention will be described in detail below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and therefore are only examples, and the protection scope of the present invention is not limited thereby.
Example one
As shown in fig. 1, an embodiment of the present invention provides a robot path planning method based on a tree growth simulation algorithm, including:
and step S1, constructing an environment map of the robot walking by adopting a honeycomb grid method, and establishing an environment map model.
As shown in fig. 2, in the environment map composed of honeycomb grids, the environment is equally divided into hexagonal grids with the same shape, the environment map model of the embodiment is constructed in a two-dimensional space plane composed of 40 × 50 honeycomb grids, only static obstacles exist in the space, the static obstacles are represented by expanding the static obstacles into a circle, and the circle center and the radius of the static obstacles are determined to determine the corresponding positions of the static obstacles in the space.
The embodiment assumes that the starting position of the mobile robot in the environment map is the first honeycomb grid B (x)B,yB) Target position T (x)T,yT) The position and size of the static obstacle in the area are known, and a new honeycomb grid coordinate system is constructed according to the starting position, the target position and the obstacle position in the environment map, taking the horizontal axis as the X axis and the vertical axis as the Y axis, as shown in fig. 3.
In practical application, the mobile robot is reduced into a particle according to the size of the mobile robot, the movement of the robot in the grid map is regarded as the movement of the particle, and the boundary of an obstacle is correspondingly expanded and blurred in the environment.
In fig. 2, the area shown by the black shading is the processed obstacle, the blank grid represents the place where the robot can freely pass through, 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 fig. 3, the gray circle in the lower left corner is the start position of the robot path, and the gray circle in the upper right corner is the target position of the robot path. Therefore, the robot path planning problem in the space is converted into the shortest path searching problem in the grid graph, and the complexity of problem solving is simplified.
And step S2, carrying out optimal path search on the walking path of the robot, and planning an optimal path.
Specifically, a tree growth simulation algorithm is adopted to perform global traversal path optimization on the walking path of the robot, and an optimal path from an initial position to a target position is planned.
As shown in fig. 4, the tree growth simulation algorithm specifically comprises the following steps:
step S21, calculating the illumination intensity and the corresponding photosynthetic rate of branches at any position in the environment map, and establishing an expression of the illumination intensity and the photosynthetic rate;
firstly, using the principle that the illumination intensity between each branch and each leaf changes along with the length of the distance between the branches and the leaves, searching the illumination intensity and the corresponding photosynthetic rate of each position in the environment map model, namely searching an objective function; establishing an arbitrary location point (x) in a coordinate systemi,yi) And (3) an expression of the light intensity of the branch, wherein the expression can be expressed as:
wherein k islTo representCoefficient of illumination intensity, (x)T,yT) Is the coordinate of the target position, (x)B,yB) Is the coordinates of the starting tree bud (starting position).
Arbitrary position point (x)i,yi) The photosynthetic rate of the branch can be expressed as:
wherein α is the slope of the light response curve when the light intensity is zero in photosynthesis, i.e. the initial slope (initial quantum efficiency) of the light response curve, β is the correction coefficient, PRmaxIs the maximum net photosynthetic rate, and γ is the ratio of the initial quantum efficiency to the plant maximum photosynthetic rate, i.e.RdIs the dark respiration rate α, PRmaxAnd RdThese three parameters are all used to control the photosynthetic rate magnitude.
Step S22, calculating the coordinate position of the random branch at the position with the maximum illumination intensity;
biological experiments prove that auxin information determining the division and growth of the branch bud cells is not originally endowed for each cell, but the cell growth system receives position information of division and growth from the environment, and according to the information, the plant growth shows obvious phototropic growth characteristics.
Because the position with high illumination intensity has high photosynthetic rate and high growth rate when the tree grows, the auxin concentration is often near the optimal auxin point, the relationship between the auxin concentration of the bud and the growth rate is in a changing process, and the auxin concentration is too high or too low to greatly influence the growth rate of the bud, so that branches are most easily generated near the optimal bud auxin concentration position, namely, the position with the maximum illumination intensity is specified to correspond to the position with the maximum photosynthetic rate, and is also the position with the optimal auxin concentration, as shown in fig. 5.
According to the above principle, the branch expression can be expressed as:
wherein,representing the coordinate position of any position point i in the growth cycle of the t +1 generation branches and leaves;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, namely the coordinate position of the branch point generated by the light;represents a random number in the optical direction at the point, t represents the growth cycle of the branch and leaf before branching, and t +1 represents the growth cycle of the branch and leaf after branching.
Once the new branch sprouts, the new branch and the old branch are combined into a whole and are the same branch in the same plane.
And step S23, optimizing growth in the simulation environment and determining the optimal path.
During the growth process, plants are often affected by many influences, such as the influence of their top dominance on lateral branches, natural disasters (fire, lightning, etc.) and artificial effects (artificial pruning, etc.), which are uniformly divided into two typical cases for simplicity: the case without obstacles, the case with some obstacles.
The tree does not encounter obstacles in the growing process and keeps normal light growth; if an obstacle appears in the growth direction, the other direction becomes the growth direction; if there is no direction available during the growth process, the tree stops branching and no further calculations will be made. The obstacle may be the first occurring branch; the first occurring branch will grow first; other branches that are blocked by a branch will decide whether to continue growing or stop depending on where the obstacle is. The specific rule is as follows:
the terminal bud (optimal position) of the branch grows under the action of apical dominance and is a model of the path planning without encountering obstacles, and the model can be expressed as follows:
wherein,represents any position point i ' (P ') of t +1 generation branch and leaf growth cycle 'The corresponding coordinate position of i);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, namely the coordinate position of the branch point generated by the light;so as to makeThe coordinate position of any position point i in the t generation growth period in the neighborhood range as the center; growth is the weight, r is (0, 1)]A random number in between.
Due to insufficient photosynthesis caused by the occlusion of the branches and leaves, under the action of natural factors, the growth direction of branches is randomly and selectively changed, and the model is a model encountering obstacles in path planning and can be expressed as follows:
wherein,representing the coordinate position of any position point i' of the t +1 generation branch and leaf growth period; p (x)min,ymin)、P(xmax,ymax) Respectively is toAn optimal value point and a worst value point on a neighborhood range boundary as a center; wherein r is (0, 1)]A random number in between.
The lateral branches are not enough in photosynthesis during the growth process of the sunlight, the auxin concentration is not enough to provide energy required by the growth of branches and leaves, the growth of the branches is stopped, and the model is a model for trapping obstacles in path planning and can be expressed as follows:
wherein,representing the coordinate position of any position point i' of the t +1 generation branch and leaf growth period;and the strongest position of the illumination intensity sensed by the branches and leaves of the t generation is represented, namely the coordinate position of the optimal auxin point, namely the coordinate position of the branch point generated to the light.
Aiming at three different models of optimizing growth in a simulation environment, the optimal path planning design objective function under the algorithm is as follows:
wherein, mu1、μ2、μ3All the weight coefficients are used for adjusting and finding out an optimal path.
In step S3, the polygonal path of the robot is smoothed.
Step S3 specifically includes: and a third-order Bezier curve is adopted for smoothing the path, and a selection mechanism is established for selecting the two conditions according to the existence of obstacles avoided by the smoothed curve, so that the walking path of the robot is smoothed to the maximum extent.
As shown in fig. 6, the specific process of path smoothing is as follows:
in the path optimizing process, the adopted honeycomb grid method relieves the safety problem of the robot caused by turning to a certain extent, but considering that the smooth curve path of the robot is better than the broken line, a third-order Bezier curve (Bezier curve) is adopted for smoothing the path, and the broken line path is changed into the smooth curve path.
The parameter equation of each point of the n-degree Bezier curve is expressed as
In the formula,also called bernstein base polynomial of order n, defines 0! T represents time, 1. Point PiReferred to as control points of the bezier curve.
From P0、P1、P2、P3The four points define a third-order Bezier curve, and the parameter form of the third-order Bezier curve is as follows:
B(t)＝P0(1-t)3+3P1t(1-t)2+3P2t2(1-t)+P3t3,t∈[0,1]。
the complete optimal path can be regarded as formed by splicing a plurality of sections of third-order Bezier curves, and in order to ensure that the segmented parameter curve is in smooth transition from one section to another section, a parameter continuity derivative can be matched at a common part of the curve sections to ensure the parameter continuity.
The continuity of the 0 th order parameter, denoted as C0 continuity, may simply represent the connection of two curves. First order parameter continuity, denoted as C1 continuity, indicates that the equations for two adjacent curve segments have the same first derivative (tangent) at the intersection. Second order parameter continuity, denoted as C2 continuity, means that the two curve segments have the same first and second derivatives at the junction.
Provided with two segments of Bezier curves Q1(t) and Q2(t) characteristic polygon vertices are P0、P1、P2、P3And R0、R1、R2、R3。
Requirement P3＝R0And requires two curve segments at the junction point P3(R0) To achieve C1 continuity, then Q'1(1)＝3(P3-P2)，Q'2(0)＝3(R1-R0)，Q'2(0)＝αQ′1(1) I.e. (R)1-R0)＝α(P3-P2) Where α is a scale factor, the condition for achieving C1 continuity is P2，P3(R0)，R1On a straight line, and P2，R1At P3(R0) On both sides of the base.
Two situations can be encountered in the process of path smoothing, the first situation is that the path using the bezier curve is still between the obstacle and the broken line path, as shown in fig. 7, and then the path using the bezier curve is selected; another situation is that the path after using the bezier curve just passes through the obstacle, as shown in fig. 8, which makes no sense, so that the path planned by the bezier curve is abandoned when this situation is encountered.
For the two cases, a selection mechanism is established to select the two cases.
Assuming that the minimum distance from the planned path point to the obstacle is LminSetting the safe distance between the path point and the obstacle as Dsafe，
In the formula: x is the number ofi(t) and yi(t) is the coordinates of the path points after Bezier curve planning; x is the number of0(i) And y0(i) The coordinates of the center of each obstacle in the grid map (in this embodiment, the obstacle is a circle); r (i) is the size of the radius of each obstacle, and t is the time in the Bezier curve.
When the minimum distance between the path point and the barrier is greater than or equal to the safe distance, mainly considering the curve path section after Bezier curve planning; and when the minimum distance between the path point and the obstacle is smaller than the safety distance, abandoning the path planned by the Bezier curve in order to avoid the obstacle, and selecting a path planning section under the original algorithm.
An optimal path planned by the path planning method according to the first embodiment is shown in fig. 9.
Example two
Correspondingly to the second embodiment of the present invention, a computer-readable storage medium is provided, on which a computer program is stored, and the computer program, when executed by a processor, implements the following steps:
step S1, constructing an environment map of robot walking by adopting a honeycomb grid method, and establishing an environment map model;
step S2, carrying out optimal path search on the walking path of the robot, and planning an optimal path;
in step S3, the polygonal path of the robot is smoothed.
The storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
For the above specific limitations of the computer-readable storage medium, reference may be made to embodiment one, and details are not repeated here.
EXAMPLE III
Correspondingly to the embodiment of the present invention, a third embodiment of the present invention provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor, and when the processor executes the computer program, the following steps are implemented:
step S1, constructing an environment map of robot walking by adopting a honeycomb grid method, and establishing an environment map model;
step S2, carrying out optimal path search on the walking path of the robot, and planning an optimal path;
in step S3, the polygonal path of the robot is smoothed.
For the above specific limitations on the computer device, reference may be made to embodiment one, and details are not described here.
Compared with the prior art, the embodiment of the invention has the advantages that:
(1) by using the advantages of the traditional grid method for dividing the environment and eliminating the defects of the traditional grid method for dividing the environment, the existing honeycomb grid model is used for planning again, the corresponding formula of the central coordinate position of each honeycomb grid and the grid sequence number is clearly given, and the robot path planning point in the algorithm is corresponding to the grid position. The environment map is divided by a honeycomb grid (regular hexagon grid) method, so that the problems of overlarge corner, effectiveness and safety in the traditional grid method are solved.
(2) The method comprises the steps of establishing a model by utilizing an optimization principle of tree-to-light branching growth and a system, carrying out overall traversal type path planning on the mobile robot by using a tree growth simulation algorithm, and finding out an optimal path from a starting point to a terminal point, so that the algorithm can obtain a certain effect on overall path planning existing in the traditional bionic algorithm.
(3) In the traditional robot path planning, a grid method is mostly adopted to construct an environment map, and an algorithm model is a traditional research method mainly aiming at simulating natural laws or growth life ways of bacteria, insects and animals. In the embodiment of the invention, on the aspect of path planning, a honeycomb grid method and a tree growth simulation algorithm taking plants as research objects are combined, and the advantages of all parts are exerted, so that the problem of robot path planning is explored from a new aspect.
Various embodiments or examples and features of various embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
It will be understood that each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations, in the description of the invention, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present invention, and they should be construed as being included in the following claims and description.
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, xmax、ymaxMaximum 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>
NXis the maximum sequence number on the X axis, NYThe 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 isx1Is the maximum sequence number, N, of the first odd-numbered row gridx2The 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,yi) Intensity of light on branches, klRepresents the illumination intensity coefficient (x)T,yT) Is the coordinate of the target position, (x)B,yB) 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 LminSafe distance D from the path point to the obstaclesafe，
<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>&GreaterEqual;</mo> <msub> <mi>D</mi> <mi>safe</mi> </msub> </mtd> </mtr> <mtr> <mtd> <mn>0</mn> </mtd> <mtd> <mn>0</mn> <mo>&le;</mo> <msub> <mi>L</mi> <mi>min</mi> </msub> <mo><</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>&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 ofi(t) andyi(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.
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