CN107976998A - A kind of grass-removing robot map building and path planning system and method - Google Patents
A kind of grass-removing robot map building and path planning system and method Download PDFInfo
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Abstract
The invention discloses a kind of grass-removing robot map building and path planning system and method, the system comprises be arranged on grass cutter human organism drive mechanism, tool mechanical structure, sensor-based system, remote control module, communication module, touch-control module；The drive mechanism is used to drive grass cutter human action, and the tool mechanical structure configures different shape blade, and the communication module is used for the wired or wireless communication of grass-removing robot；The sensor-based system configuration multiple sensors are used for avoidance and positioning；The remote control module is used for people's grass-removing robot in order to control.The present invention can free people from uninteresting work, has saved substantial amounts of human resources to modern society's production and life, has met the trend and needs of social development.
Description
Technical Field
The invention relates to a mowing robot map creating and path planning system and method, and belongs to the field of robots.
Background
With the development of socio-economy, the greening coverage process is gradually accelerated, and a large number of park lawns, football field lawns, golf courses, public green lands and general family green lands exist in both cities and villages. These lawns require regular trimming, maintenance, and the construction of a special holiday's grass art shape. The traditional manual maintenance has large workload, and needs to consume a large amount of manpower, capital and time; meanwhile, the lawn maintenance work is monotonous, the labor intensity is high, the lawn maintenance work is full of noise pollution, and the working efficiency of workers is low due to a plurality of factors. In order to solve the inconvenience and harm brought by the traditional manual mowing, more and more research institutions and science and technology companies are dedicated to developing intelligent mowing robots capable of working autonomously in recent years.
The full-coverage path planning is a hot research direction of the autonomous mowing robot, and plays a crucial role in improving the working efficiency of the robot.
The Chinese patent number CN201110056133.7 discloses a multi-robot path planning system based on collaborative co-evolution and multi-population genetic algorithm, which takes the shortest path, smoothness and safety distance as three targets of a path fitness function, and each robot adopts the fitness functions to obtain an optimal path through genetic algorithm optimization.
Chinese patent No. CN201110056133.7 discloses a path planning method for a mobile robot. The method adopts a fish swarm algorithm to optimize the controller of the robot neural network structure. The robot is trained in the template map, and then the aims of barrier and target point reaching are achieved by means of generalization performance of the neural network.
However, the working environment in which the autonomous lawn mowing robot is located is varied, such as various lawns that may work in different shapes, and various flowers and trees, rockery, sculpture, and other obstacles that may be contained in the lawn. Meanwhile, the mowing robot needs to face different customer requirements, such as the need of trimming a specific artistic pattern on a lawn. Therefore, how to reasonably construct a map and design a pattern and plan an optimal full-coverage path without collision and meeting the requirements of customers is an unsolved problem.
Disclosure of Invention
The invention aims to solve the problems and provides a mowing robot map creating and path planning system and a mowing robot map creating and path planning method, which are based on double-layer heuristic optimization.
The technical scheme of the invention is as follows:
a mowing robot map creating and path planning system comprises a driving mechanical structure, a cutter mechanical structure, a sensing system, a remote control module, a communication module and a touch module, wherein the driving mechanical structure, the cutter mechanical structure, the sensing system, the remote control module, the communication module and the touch module are arranged on a mowing robot body;
the driving mechanical structure is used for driving the mowing robot to move, the cutter mechanical structure is provided with blades in different shapes, and the communication module is used for wired or wireless communication of the mowing robot; the sensing system is provided with a plurality of sensors for obstacle avoidance and positioning; the remote control module is used for artificially controlling the mowing robot.
The system also comprises a safety protection module, wherein the safety protection module is used for timely cutting off a power supply under the condition of an accident in work so as to prevent the motor and the driving circuit from being burnt; or when the grass is too long or dense and the mechanical structure of the cutter cannot run, carrying out overload protection.
A mowing robot map creating and path planning method utilizes the system and comprises the following steps:
(1) Map construction
Aiming at a target grassland with obstacles, a mowing robot firstly models the target grassland and determines the position of a mowing robot body;
(2) Design of pattern
According to different requirements, lawn patterns with different shapes and matched light and dark colors are freely designed;
(3) Route planning
The mowing robot divides the modeled environment into a plurality of grid areas by adopting a layered partitioning strategy, and plans a path for a high-level grid and a full-coverage path for a low-level grid by adopting a double-layer heuristic optimization algorithm, so that the autonomous optimal path of the mowing robot is planned.
The step of modeling the target grassland with the obstacle in the step (1) is as follows:
(4a) Carrying out geometric processing on the obstacle of the grassland to approximate the obstacle to the shape of a geometric polygon;
(4b) Establishing a coordinate system, and marking the horizontal axis as [0, alpha ]]The vertical axis is [0, beta ]]Mapping each obstacle to the horizontal axis and the vertical axis respectively, and recording as [ alpha ] respectively i ,α j ]And [ beta ] i ,β j ]。
The step of dividing the modeled environment into a plurality of grid regions by using the hierarchical partitioning strategy in the step (3) is as follows:
the mowing robot divides the modeled environment into a plurality of sub-areas by adopting a layering and partitioning strategy, wherein the sub-areas comprise an upper-layer coarse grid partitioning treatment on the whole working area and a lower-layer fine grid treatment on each coarse grid;
for the construction of the upper coarse grid, carrying out coarse grid zoning on the whole working area according to the radius of a cutter selected by the mowing robot, the length and the width of the periphery of the actual working environment and the size of an obstacle of an infeasible area in the working environment;
(5a) Assuming that the radius of a circular cutter of the mowing robot is gamma, the length and the width of each subarea meet the condition S x ,S y ∈[θ 1 γ,θ 2 γ]，S x Indicating the length of the region, S y Denotes the width of the region, where θ 1 And theta 2 The weight value is selected according to the actual grassland condition;
(5b) The following partitioning rules are adopted: when the length and width of the obstacle are larger than the upper limit value theta 2 At γ, according to θ 2 The length of gamma divides the region; when the length and width of the obstacle are at the boundary value theta 1 Gamma and theta 2 When the distance is within gamma, dividing the region according to the length of the horizontal axis and the vertical axis of the projection value of the obstacle; when the length and width of the obstacle are smaller than the lower limit value theta 1 Gamma, this sub-region is not divided;
the construction of the lower fine mesh is carried out on the basis of the construction of the upper mesh, modeling is carried out by adopting a grid method, the working space of the robot is decomposed into a series of grid units with binary information, the size of the grid is determined as the self size gamma of the robot for each coarse mesh, the walking speed of the robot is assumed to be fixed, the built grid is divided into a free grid and an obstacle grid, the grid without obstacles is called a free grid, and the grid containing obstacles is called an obstacle grid.
The double-layer heuristic optimization algorithm adopted in the step (3) is based on an ant colony algorithm to carry out global path planning, and the steps are as follows:
(6a) Initial setting of parameters
Assuming that the total number of ants in an ant colony is m, the iteration number is t, the number of ants is k, the importance degree alpha of information, the importance degree beta of a heuristic function, an pheromone volatilization factor rho and an pheromone release amount Q;
(6b) The cycle number t = t +1;
(6c) The taboo list index number k =1 of the ant;
(6d) The number of ants k = k +1;
(6e) The ant individual selects elements according to the probability calculated by the state transition probability formula and advances, and the probability distribution of the starting point is calculated according to the following formula:
wherein t is ij Is the pheromone volatilization amount, eta, between node i and node j ij Is a heuristic function;
(6f) Modifying the taboo list pointer, namely moving the ants to a new node after selection, and moving the node to the taboo list of the ant individual;
(6g) If the nodes in the set are not traversed, namely k is less than m, jumping to the step (6 d), otherwise executing the step (6 h);
(6h) And updating the pheromone, wherein the pheromone updating formula is as follows:
where Δ t ij Represents the sum of pheromones released by all ants on the path;
(6i) If the maximum iteration times maxgen are reached, the algorithm is terminated, and the optimal path bestTour and the optimal path length are output; otherwise, go to step (6 b).
The double-layer heuristic optimization algorithm adopted in the step (3) is based on a tabu search algorithm to carry out local path planning, and the steps are as follows:
(7a) Giving algorithm parameters, randomly generating an initial solution x 0 Calculating its objective function value, initializing the current point x = x 0 Optimum point x best ＝x 0 ，f(x best )＝f(x 0 ) And the tabu list is empty;
(7b) Judging whether the algorithm termination condition is met; if yes, finishing the algorithm and outputting an optimization result; otherwise, continuing the following step (7 c);
(7c) Generating all or a plurality of neighborhood solutions by utilizing the neighborhood function of the current solution x, and determining a plurality of candidate solutions from the neighborhood solutions;
(7d) Judging whether scofflaw criteria are met or not for the candidate solutions; if yes, replacing x with the optimal state y satisfying the scofflaw criterion as a new current solution, namely x = y, and updating the optimal point x best ＝y，f(x best ) = f (y), and replaces the taboo object that entered the taboo table earliest with the taboo object corresponding to y, and replaces the "best so far" state with y, and then goes to step (7 f); otherwise, continuing the following step (7 e);
(7e) Judging the taboo attribute of each object corresponding to the candidate solution, selecting the optimal state y corresponding to the non-taboo object in the candidate solution set as a new current solution, and simultaneously replacing the taboo object element which enters the taboo list earliest by the taboo object corresponding to the taboo object;
(7f) And judging whether the algorithm termination condition is met, if so, ending the algorithm and outputting an optimization result, otherwise, turning to the step (7 c).
The invention achieves the following beneficial effects:
the mowing robot map creating and full-coverage path planning method based on the double-layer heuristic optimization relieves people from boring labor by virtue of the automatic working characteristic of the mowing robot map creating and full-coverage path planning method, saves a large amount of human resources for production and life of the modern society, and meets the trend and the requirement of social development. The advantages are that: firstly, the lawn mower is suitable for lawns of various shapes and can properly treat various landscape obstacles appearing in the lawns; secondly, lawns with various artistic shapes can be constructed, and the life of people is enriched; thirdly, an optimal full-coverage mowing path is planned through double-layer heuristic optimization. The invention has a plurality of advantages, and can be widely applied to park lawns, football grass, golf courses, public greenbelts, general family greenbelts and the like.
Drawings
FIG. 1 is a general block diagram;
FIG. 2 is a schematic view of a mowing robot;
FIG. 3 is a schematic diagram of modeling a mowing environment;
FIG. 4 is a schematic view of an artistic design;
FIG. 5 is a schematic illustration of a work environment partition;
FIG. 6 is a schematic diagram of a zoning environment gridding;
FIG. 7 is a flowchart of ant colony algorithm based global path planning;
fig. 8 is a flow chart of local path planning based on tabu search algorithm.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
As shown in fig. 2, a mowing robot map creating and path planning system includes a driving mechanism, a cutter mechanism, a sensing system, a remote control module, a communication module, and a touch module, which are disposed on a mowing robot body;
the driving mechanism is used for driving the mowing robot to move, for example, the mowing robot can be driven by a wheel mechanism comprising four wheels as shown in fig. 2, and the mowing robot has 2 driving wheels and 2 guide wheels.
The cutter mechanical structure is provided with blades with different shapes, and the blades can be freely switched and adjusted in height. For example, the mowing robot in fig. 2 is provided with a circular cutter, indicated by a circular broken line, with a radius γ, and three heights are adjustable.
The communication module is used for wired or wireless communication of the mowing robot; the communication module of robot mows can be wired communication, also can be wireless communication, such as gsm communication module, wifi communication, gprs wireless transmission module, bluetooth module etc.. The communication equipment is connected with external equipment through a CDMA/3D/4D base station, and the equipment comprises a mobile phone, a computer or a tablet and the like.
The sensing system is provided with a plurality of sensors for obstacle avoidance and positioning; these sensor sensing devices are distributed at different locations of the robot lawnmower, as shown in fig. 2, and are graphically represented by different shapes. These sensors may be GPS positioning systems, cameras, ultrasonic sensors, infrared sensors, temperature sensors, pyroelectric infrared sensors, contact sensors, collision sensors, etc.
The remote control module is convenient for manually controlling the mowing robot in certain accidents or special conditions.
As a preferred scheme, the system also comprises a safety protection module, wherein the safety protection module is used for timely cutting off a power supply under the condition of an accident in work so as to prevent the motor and the driving circuit from being burnt; or when the grass is too long or dense and the mechanical structure of the cutter cannot run, carrying out overload protection.
As shown in fig. 1, a mowing robot map creating and path planning method using the system includes the steps of:
(1) Map construction
Aiming at a target grassland with obstacles, a mowing robot firstly models the target grassland and determines the position of a mowing robot body; as shown in fig. 2, the boundary and the non-feasible area of the mowing environment are recorded during the traveling process through the mobile phone GPS positioning, and the whole working environment map is transferred to the body of the mowing robot through wireless or wired communication.
In the embodiment shown in fig. 3: the mowing robot travels under the remote control operation of a worker, and the boundary of a working area is recorded by a sensor for detecting the position and the angle, which is carried by the robot body, and is indicated by a black frame in the figure. The mobile robot is remotely controlled to other positions, such as various obstacles like flowers and trees, rockery, sculpture and the like, and the robot is guided to record the edge of the obstacle, which is indicated by a gray border in the figure.
(2) And the design of the patterns can freely design lawn patterns with different shapes and matching colors according to different requirements. In the embodiment of fig. 4, a cutter is used to match three different heights (height a, height B and height C) to form different shades of light gray, gray and dark gray, and a five-pointed star pattern is designed in the lawn.
(3) Route planning
The mowing robot divides the modeled environment into a plurality of grid areas by adopting a layered partitioning strategy, and plans a path for a high-level grid and a full-coverage path for a low-level grid by adopting a double-layer heuristic optimization algorithm, so that the autonomous optimal path of the mowing robot is planned.
The step of modeling the target grassland with the obstacle in the step (1) is as follows:
(4a) Carrying out geometric processing on the obstacle of the grassland to approximate the obstacle to the shape of a geometric polygon;
(4b) Establishing a coordinate system, and marking the horizontal axis as [0, alpha ]]And the vertical axis is [0, beta ]]Mapping each obstacle to the horizontal axis and the vertical axis respectively, and recording as [ alpha ] respectively i ,α j ]And [ beta ] i ,β j ]。
The step of dividing the modeled environment into a plurality of grid regions by using the hierarchical partitioning strategy in the step (3) is as follows:
the mowing robot divides the modeled environment into a plurality of sub-areas by adopting a layering and partitioning strategy, wherein the sub-areas comprise an upper-layer coarse grid partitioning treatment on the whole working area and a lower-layer fine grid treatment on each coarse grid;
for the construction of the upper coarse grid, carrying out coarse grid zoning on the whole working area according to the radius of a cutter selected by the mowing robot, the length and the width of the periphery of the actual working environment and the size of an obstacle of an infeasible area in the working environment; in the embodiment shown in FIG. 5, each sub-region is here S1-S16.
(5a) Assuming that the radius of a circular cutter of the mowing robot is gamma, the length and the width of each subarea satisfy the condition S x ,S y ∈[θ 1 γ,θ 2 γ]，S x Indicating the length of the region, S y Denotes the width of the region, where θ 1 And theta 2 The weight value is selected according to the actual grassland condition;
(5b) The following partitioning rules are adopted: when the length and width of the obstacle are larger than the upper limit value theta 2 At γ, according to θ 2 The length of gamma divides the region; when the length and width of the obstacle are at the boundary value theta 1 Gamma and theta 2 When the distance is within gamma, dividing the region according to the length of the horizontal axis and the vertical axis of the projection value of the obstacle; when the length and width of the obstacle are smaller than the lower limit value theta 1 Gamma, this sub-region is not divided;
the value of β is plotted on the ordinate of fig. 5. We choose α = β =500, γ =0.2, θ 1 ＝5，θ 2 =10, as shown in fig. 5, e.g. 0 and β 1 Less than 5 gamma, so that this sub-region does not need to be divided, as also beta 3 And beta 6 Is greater than 10 gamma, so this sub-region is divided by 10 gamma instead of beta 6 。
As shown in FIG. 6, the coarse mesh at the upper layer of S1 to S16 and the fine mesh at the lower layer of G1 to G11. The construction of the lower fine mesh is carried out on the basis of the construction of the upper mesh, modeling is carried out by adopting a grid method, the working space of the robot is decomposed into a series of grid units with binary information, the size of the grid is determined as the self size gamma of the robot for each coarse mesh, the walking speed of the robot is assumed to be fixed, the built grid is divided into a free grid and an obstacle grid, the grid without obstacles is called a free grid, and the grid containing obstacles is called an obstacle grid. Whether there is an obstacle or not is indicated by the grids, which makes the problem simple and shallow. The main problem of the grid method is to define the size of the grid, if the size is too large, the resolution is low, the obstacle is represented inaccurately, but the planning time can be reduced; if too small, the resolution becomes high, indicating that the obstacle is accurate, but increasing the time for path planning. Therefore, when the environment model is built by using the grid method, a proper grid size must be determined. In the embodiment of fig. 6, to achieve full-area coverage of grass, the size of the grid is slightly smaller than the radius length γ of the robot's circular tool in each upper coarse-grid area
As shown in fig. 7, the global path planning is performed based on the ant colony algorithm in the above step (3) by using the double-layer heuristic optimization algorithm, assuming that the total number of ants in the ant colony is m, pheromone between all nodes is represented by a matrix pheromone, the optimal path length is bestLength, and the optimal path is bestTour. Each ant has its own memory, and the memory stores the nodes that the ant has visited by a list, which indicates that the ant will not be able to access the nodes in the later search; another node table allowing access is used for storing the nodes which can be accessed by the node table; in addition, a matrix is used to store the pheromones released by the pheromones to the paths in a cycle (or iteration); there are also other data, such as control parameters to assist in calculating pheromone volatilization, next node hit probability, etc., total cost or distance (tourLength) for the ant to travel through the course, etc. Assume that the algorithm runs maxgen total times, with the current iteration number being t.
The method comprises the following steps:
(6a) Initial setting of parameters
The number of ants is k, the importance degree alpha of information, the importance degree beta of a heuristic function, the pheromone volatilization factor rho and the pheromone release amount Q;
(6b) The cycle number t = t +1;
(6c) The taboo list index number k =1 of the ant;
(6d) The number of ants k = k +1;
(6e) The ant individual selects elements according to the probability calculated by the state transition probability formula and advances, and the probability distribution of the starting point is calculated according to the following formula:
wherein t is ij Is the pheromone volatilization amount, eta, between node i and node j ij Is a heuristic function;
(6f) Modifying the pointer of the tabu table, namely moving the ants to a new node after selection, and moving the node to the tabu table of the ant individual;
(6g) If the nodes in the set are not traversed, namely k is less than m, jumping to the step (6 d), otherwise executing the step (6 h);
(6h) And updating the pheromone, wherein the pheromone updating formula is as follows:
where Δ t ij Represents the sum of pheromones released by all ants on the path;
(6i) If the maximum iteration times maxgen are reached, the algorithm is terminated, and the optimal path bestTour and the optimal path length are output; otherwise, go to step (6 b).
As shown in fig. 8, the above step (3) of adopting the double-layer heuristic optimization algorithm is based on the tabu search algorithm to perform local path planning, and includes the following steps:
(7a) Given the algorithm parameters, an initial solution x is randomly generated 0 Calculating its objective function value, initializing the current point x = x 0 Optimum point x best ＝x 0 ，f(x best )＝f(x 0 ) And the tabu list is empty;
(7b) Judging whether the algorithm termination condition is met; if yes, finishing the algorithm and outputting an optimization result; otherwise, continuing the following step (7 c);
(7c) Generating all or a plurality of neighborhood solutions by utilizing the neighborhood function of the current solution x, and determining a plurality of candidate solutions from the neighborhood solutions;
(7d) Judging whether scofflaw criteria are met or not for the candidate solutions; if so, replacing x with the best state y satisfying the scofflaw criteria becomes the new current solution, i.e., x = y, while updating the optimal point x best ＝y，f(x best ) = f (y), and replaces the taboo object that entered the taboo table earliest with the taboo object corresponding to y, and replaces the "best so far" state with y, and then goes to step (7 f); otherwise, continuing the following step (7 e);
(7e) Judging the taboo attribute of each object corresponding to the candidate solution, selecting the optimal state y corresponding to the non-taboo object in the candidate solution set as a new current solution, and simultaneously replacing the taboo object element which enters the taboo list earliest by the taboo object corresponding to the taboo object;
(7f) And judging whether the algorithm termination condition is met, if so, ending the algorithm and outputting an optimization result, otherwise, turning to the step (7 c).
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
Claims (7)
1. A mowing robot map creating and path planning system is characterized by comprising a driving mechanical structure, a cutter mechanical structure, a sensing system, a remote control module, a communication module and a touch module, wherein the driving mechanical structure, the cutter mechanical structure, the sensing system, the remote control module, the communication module and the touch module are arranged on a mowing robot body;
the driving mechanical structure is used for driving the mowing robot to move, the cutter mechanical structure is provided with blades in different shapes, and the communication module is used for wired or wireless communication of the mowing robot; the sensing system is provided with a plurality of sensors for obstacle avoidance and positioning; the remote control module is used for artificially controlling the mowing robot.
2. The lawn mowing robot map creation and path planning system of claim 1, wherein: the system also comprises a safety protection module, wherein the safety protection module is used for timely cutting off a power supply under the condition of an accident in work so as to prevent the motor and the driving circuit from being burnt; or when the grass is too long or dense and the mechanical structure of the cutter cannot run, carrying out overload protection.
3. A mowing robot map creation and path planning method, characterized by using the system of claim 1, comprising the steps of:
(1) Map construction
Aiming at a target grassland with obstacles, the mowing robot firstly models the target grassland and determines the position of a mowing robot body;
(2) Design of pattern
According to different requirements, lawn patterns with different shapes and matched light and dark colors are freely designed;
(3) Route planning
The mowing robot divides the modeled environment into a plurality of grid areas by adopting a layered partitioning strategy, and plans a path for a high-level grid and a full-coverage path for a low-level grid by adopting a double-layer heuristic optimization algorithm, so that the autonomous optimal path of the mowing robot is planned.
4. The mowing robot map creation and path planning method according to claim 3, wherein: the step (1) of modeling the target grassland with the obstacle comprises the following steps:
(4a) Carrying out geometric processing on the obstacle of the grassland to approximate the obstacle to the shape of a geometric polygon;
(4b) Establishing a coordinate system, and marking the horizontal axis as [0, alpha ]]The vertical axis is [0, beta ]]Mapping each obstacle to the horizontal axis and the vertical axis respectively, and recording as [ alpha ] respectively i ,α j ]And [ beta ] i ,β j ]。
5. The mowing robot map creation and path planning method according to claim 3, wherein: the step of dividing the modeled environment into a plurality of grid regions by adopting a hierarchical partitioning strategy in the step (3) is as follows:
the mowing robot divides the modeled environment into a plurality of sub-areas by adopting a layering and partitioning strategy, wherein the sub-areas comprise an upper-layer coarse grid partitioning treatment on the whole working area and a lower-layer fine grid treatment on each coarse grid;
for the construction of the upper coarse grid, carrying out coarse grid zoning on the whole working area according to the radius of a cutter selected by the mowing robot, the length and the width of the periphery of the actual working environment and the size of an obstacle of an infeasible area in the working environment;
(5a) Assuming that the radius of a circular cutter of the mowing robot is gamma, the length and the width of each subarea meet the condition S x ,S y ∈[θ 1 γ,θ 2 γ]，S x Indicating the length of the region, S y Denotes the width of the region, where θ 1 And theta 2 The weight value is selected according to the actual grassland condition;
(5b) The following partitioning rules are adopted: when the length and width of the obstacle are larger than the upper limit value theta 2 Gamma, according to theta 2 The length of gamma divides the region; when the length and width of the obstacle are at the boundary value theta 1 Gamma and theta 2 When the projection value of the obstacle is within gamma, dividing the area according to the length of the horizontal axis and the vertical axis of the projection value of the obstacle; when the length and width of the obstacle are smaller than the lower limit value theta 1 Gamma, this sub-region is not divided;
the construction of the lower fine mesh is carried out on the basis of the construction of the upper mesh, modeling is carried out by adopting a grid method, the working space of the robot is decomposed into a series of grid units with binary information, the size of the grid is determined as the self size gamma of the robot for each coarse mesh, the walking speed of the robot is assumed to be fixed, the built grid is divided into a free grid and an obstacle grid, the grid without obstacles is called a free grid, and the grid containing obstacles is called an obstacle grid.
6. The mowing robot map creation and path planning method according to claim 3, wherein: the double-layer heuristic optimization algorithm adopted in the step (3) is based on an ant colony algorithm to carry out global path planning, and the steps are as follows:
(6a) Initial setting of parameters
Assuming that the total number of ants in an ant colony is m, the iteration number is t, the number of ants is k, the importance degree alpha of information, the importance degree beta of a heuristic function, an pheromone volatilization factor rho and an pheromone release amount Q;
(6b) The cycle number t = t +1;
(6c) The taboo list index number k =1 of the ant;
(6d) The number of ants k = k +1;
(6e) The ant individual selects elements according to the probability calculated by the state transition probability formula and advances, and the probability distribution of the starting point is calculated according to the following formula:
wherein t is ij Is the pheromone volatilization amount, eta, between the node i and the node j ij Is a heuristic function;
(6f) Modifying the taboo list pointer, namely moving the ants to a new node after selection, and moving the node to the taboo list of the ant individual;
(6g) If the nodes in the set are not traversed, namely k is less than m, jumping to the step (6 d), otherwise executing the step (6 h);
(6h) And updating the pheromone, wherein the pheromone updating formula is as follows:
where Δ t ij Pheromone indicating release of all ants on pathThe sum of (a);
(6i) If the maximum iteration times maxgen are reached, the algorithm is terminated, and the optimal path bestTour and the optimal path length are output; otherwise, go to step (6 b).
7. The mowing robot map creation and path planning method according to claim 3, wherein: the double-layer heuristic optimization algorithm adopted in the step (3) is based on a tabu search algorithm to carry out local path planning, and comprises the following steps:
(7a) Given the algorithm parameters, an initial solution x is randomly generated 0 Calculating its objective function value, initializing the current point x = x 0 Optimum point x best ＝x 0 ，f(x best )＝f(x 0 ) And the tabu list is empty;
(7b) Judging whether the algorithm termination condition is met; if yes, finishing the algorithm and outputting an optimization result; otherwise, continuing the following step (7 c);
(7c) Generating all or a plurality of neighborhood solutions by utilizing the neighborhood function of the current solution x, and determining a plurality of candidate solutions from the neighborhood solutions;
(7d) Judging whether scofflaw criteria are met or not for the candidate solutions; if so, replacing x with the best state y satisfying the scofflaw criteria becomes the new current solution, i.e., x = y, while updating the optimal point x best ＝y，f(x best ) = f (y), and replaces the taboo object that entered the taboo table earliest with the taboo object corresponding to y, and replaces the "best so far" state with y, and then goes to step (7 f); otherwise, continuing the following step (7 e);
(7e) Judging the taboo attribute of each object corresponding to the candidate solution, selecting the optimal state y corresponding to the non-taboo object in the candidate solution set as a new current solution, and simultaneously replacing the taboo object element which enters the taboo list earliest by the taboo object corresponding to the taboo object;
(7f) And judging whether the algorithm termination condition is met, if so, ending the algorithm and outputting an optimization result, otherwise, turning to the step (7 c).
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