Sweeping robot driving and diffusing method based on shepherd dog model

Abstract

The invention discloses a sweeping robot driving and diffusing method based on a shepherd dog model. The sweeping robot driving and diffusing method specifically comprises the following steps that S1, a sweeping robot and a driving robot are initialized; s2, the driving robot drives and disperses the sweeping robot in a spiral motion mode, and a first dispersion result is obtained; s3, constructing sweeping area grids, and calculating the number of the sweeping robots in each grid according to the first dispersion result to obtain a density matrix; and S4, obtaining a maximum density area and a minimum density area according to the density matrix, and then driving the sweeping robot in the maximum density area to the minimum density area by the driving robot.

Description

Technical Field

[0001] The invention relates to the technical field of environmental protection, and in particular to a sweeping robot driving and diffusing method based on a sheepdog model. Background Art

[0002] In the field of automation, multi-robot systems are widely used to cover and explore large areas, including environmental monitoring, emergency rescue, agricultural inspection, warehouse management, large-area cleaning and other scenarios. In order to complete these tasks, effective coverage of robots is the key. Traditional multi-robot systems need to quickly and evenly distribute robots to the target area to ensure that each robot can efficiently perform tasks in its respective sub-area. How to achieve rapid dispersion and coverage of robots is the core issue in improving the working efficiency of multi-robot systems.

[0003] The first existing method is a diffusion scheme based on global path planning: In most multi-robot systems, global path planning is the traditional method to achieve uniform coverage of robots. The following are common global path planning technologies: Map construction and navigation based on SLAM: SLAM technology allows robots to simultaneously build maps and self-localize in unknown environments; by building maps, robots can implement global path planning and allocate different sub-areas according to the starting and target positions of each robot. However, SLAM relies on strong computing power and synchronous transmission of communication systems, has high hardware requirements for robots, and is limited in real-time performance and flexibility in complex environments.

[0004] The second existing method is distributed diffusion based on the particle diffusion model: The particle diffusion model is a distributed adaptive diffusion method that treats robots as particles and achieves uniform diffusion through a repulsive force model. The following are the main features of the particle diffusion model: Local rule control: Each robot only needs to sense the position of nearby robots, maintain a certain repulsive distance to avoid mutual collision, and gradually diffuse in the environment. This local rule control method does not require global information and is suitable for robot distribution in large areas and unknown environments. Random diffusion: Add random diffusion to the repulsive force model to ensure that robots are evenly dispersed in all directions; through random diffusion, robots can quickly and evenly diffuse in uncovered areas. However, the particle diffusion model is prone to path overlap and uncovered areas in environments with dense obstacles, and cannot ensure complete coverage. Some extended particle diffusion models also adopt the shortest path guidance strategy, that is, the robot chooses the shortest path direction in the uncovered area for diffusion; this type of method is suitable for coverage tasks, but due to the high randomness of path planning, it cannot ensure rapid coverage within a limited time.

[0005] The third existing method is a decentralized strategy based on bionics: the bionic method achieves decentralized control of multi-robot systems by imitating group behaviors in nature, such as ant foraging, bee foraging, etc. The main bionic methods include the following: Decentralized algorithm based on ant foraging: Ant foraging behavior usually includes path selection, pheromone update and local optimal search. By imitating the foraging behavior of ants, robots are autonomously distributed in the area. However, the ant foraging algorithm relies on the transmission of environmental pheromones and requires robots to mark in the environment, which makes it difficult to achieve effective diffusion in dynamic environments and large-area scenes. Adaptive distribution based on bee foraging: Bee foraging behavior is based on swarm communication, and individual robots rely on communication to exchange location information and environmental information to achieve uniform diffusion; this method requires a stable communication network and a complex path planning algorithm.

[0006] In summary, existing multi-robot diffusion technologies mainly rely on global path planning, particle diffusion models, etc. to achieve uniform distribution of robots in large areas. However, the global path planning method has high requirements for map construction and communication synchronization, high requirements for robot hardware, and limited real-time performance and flexibility in complex environments; the particle diffusion model is limited by randomness and is prone to producing uncovered areas. These methods are difficult to achieve autonomous diffusion efficiently, so a simpler and communication-independent diffusion strategy is needed to improve system efficiency. Summary of the invention

[0007] In view of the problem in the prior art that sweeping robots cannot be quickly dispersed in a large area, the present invention proposes a sweeping robot driving and diffusion method based on a sheepdog model, which intelligently guides the sweeping robots (sheepdog) to quickly distribute and effectively cover a large area through the driving robot (sheepdog).

[0008] In order to achieve the above object, the present invention provides the following technical solutions:

[0009] The sweeping robot driving and diffusion method based on the shepherd dog model specifically includes the following steps:

[0010] S1: Initialize the sweeping robot and the driving robot;

[0011] S2: The driving robot drives and disperses the sweeping robot using a spiral motion mode to obtain a first dispersion result;

[0012] S3: construct a cleaning area grid, and calculate the number of cleaning robots in each grid according to the first dispersion result to obtain a density matrix;

[0013] S4: Obtain the area with the largest density and the area with the smallest density according to the density matrix, and then drive the sweeping robot in the area with the largest density to the area with the smallest density.

[0014] Preferably, the process further includes S5: determining whether the sweeping robots are evenly distributed, and if so, terminating the driving operation, otherwise, repeating S4.

[0015] Preferably, the S1 includes:

[0016] S1-1: Initialize the sweeping robot:

[0017] Place N sweeping robots in the center of the cleaning area, and each sweeping robot will randomly generate corresponding position coordinates:

[0018] sheep_x=center_x+random_offset, sheep_y=center_y+random_offset; (1)

[0019] In formula (1), sheep_x and sheep_y represent the X-axis coordinate and Y-axis coordinate of the sweeping robot respectively; center_x and center_y represent the X-axis coordinate and Y-axis coordinate of the center of the cleaning area respectively; andom_offset represents the random offset;

[0020] S1-2: Initialize the driving robot: place the driving robot at the edge of the cleaning area, and set the initial position coordinates to Herd (herd_x, herd_y).

[0021] Preferably, in S2, the spiral motion mode is: the robot is driven to go around in a spiral path with increasing radius, and the cleaning robot is driven to gradually disperse to the cleaning sub-areas; wherein, the radius radius and angle θ′ of the spiral path of the robot are updated by the formula:

[0022]

[0023] In formula (2), radius represents the radius of the spiral path of the driving robot; r is the radius of the sweeping robot, R is the radius of the cleaning area; θ represents the angle between the center coordinates of the current sweeping robot and the line connecting the driving robot and the positive x-axis; β represents the growth amount of each time step; and θ′ represents the update angle.

[0024] Preferably, in each time step, the robot is driven towards the first target position p d1 Go forward, first target position p d1 (pd_x, pd_y) calculation formula:

[0025] pd_x=sheep_center_x+Cosθ, pd_y=sheep_center_y+sinθ (3)

[0026] In formula (3), pd_x and pd_y represent the X-axis coordinate and Y-axis coordinate of the first target position of the driving robot, respectively; sheep_center_x and sheep_center_y represent the X-axis coordinate and Y-axis coordinate of the center of the driving robot, respectively; θ represents the angle between the center coordinate of the current sweeping robot and the connecting line of the driving robot and the positive x-axis.

[0027] Preferably, in each time step, the first displacement vector driving the robot is:

[0028]

[0029] In formula (4), r d1 represents the first displacement vector that drives the robot; p d1 Indicates the first target position of the driving robot; ||p d1 -Herd|| represents the first target position p d1 The distance between the initial position of the driving robot Herd; speed h Indicates the speed at which the robot is driven.

[0030] Preferably, after driving at each time step, if the sweeping robot radius r is greater than or equal to the cleaning area radius R, it means that the sweeping robot distribution has completed the initial diffusion and the driving operation is terminated, otherwise the robot is driven to continue to perform spiral motion and the driving operation is cyclic.

[0031] Preferably, in S3, the density matrix is ​​obtained by:

[0032] Generate a grid of fixed size on the cleaning area, and initialize the grid matrix grids to record the density of the sweeping robots in each grid; then traverse the positions of the sweeping robots in the first scattered result, calculate the number of sweeping robots in each grid, and thus form the density matrix grids.

[0033] Preferably, the S4 includes:

[0034] S4-1: Take a point pd2 on the line connecting the first center of the area with the largest density and the second center of the area with the smallest density in the density matrix grids as the second target position, drive the robot to the second target position, and drive the sweeping robot in the area with the largest density to the center of the area with the smallest density:

[0035]

[0036] In formula (5), p d2 Indicates the second target position; high_density_center indicates the center coordinates of the area with the largest density, low_density_center indicates the center coordinates of the area with the smallest density, δd Represents the weight of the replacement item that drives the robot to the second target position; ||high_density_center-low_density_center|| represents the distance between the center coordinates of the minimum density area and the center coordinates of the maximum density area;

[0037] Then the second displacement vector that drives the robot at each time step is:

[0038]

[0039] In formula (6), r d2 represents the second displacement vector that drives the robot; p d2 represents the coordinates of the second target position; ||P d2 -Herd|| represents the second target position p d2 The distance between the initial position of the driving robot Herd; speed h Indicates the speed of driving the robot;

[0040] S4-2: When the driving robot reaches the second target position pd2, it moves towards the third target position p d3 go ahead:

[0041] p d3 = low_density_center (7)

[0042] In formula (7), P d3 Indicates the coordinates of the third target position; low_density_center indicates the coordinates of the center of the minimum density area;

[0043] The third displacement vector that drives the robot at each time step:

[0044]

[0045] In formula (8), rd3 represents the third displacement vector of the driving robot; p d3 represents the coordinates of the third target position; The distance between the third target position pd3 and the initial position Herd of the driving robot is represented by speedh, and the speed of the driving robot is represented by speedh.

[0046] Preferably, in S5, the condition for balanced distribution of the sweeping robots is:

[0047] max_density-min_density≤density_threshold (9)

[0048] In formula (9), max_density represents the maximum density, min_density represents the minimum density, and density_threshold represents the predetermined threshold value of the maximum and minimum density difference.

[0049] In summary, due to the adoption of the above technical solution, compared with the prior art, the present invention has at least the following beneficial effects:

[0050] The present invention firstly drives the sweeping robot to quickly disperse the sweeping robot to the entire area through spiral motion, thereby preliminarily reducing high-density accumulation; then, the driving robot gradually drives away the remaining high-density areas and guides the sweeping robot to the low-density areas, thereby achieving rapid and uniform coverage of the sweeping robot in the cleaning area. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 Schematic diagram of a method for driving away and spreading a sweeping robot based on a sheepdog model according to an exemplary embodiment of the present invention.

[0052] Figure 2 A schematic diagram of initialization distribution of a cleaning robot and a driving robot according to an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0053] The present invention is further described in detail below in conjunction with the examples and specific implementation methods. However, this should not be understood as the scope of the above subject matter of the present invention being limited to the following examples, and all technologies realized based on the content of the present invention belong to the scope of the present invention.

[0054] In the description of the present invention, it is necessary to understand that the terms "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0055] like Figure 1 As shown, the present invention provides a sweeping robot driving diffusion method based on a shepherd dog model, which specifically includes the following steps:

[0056] S1: First, initialize the sweeping robot and the driving robot.

[0057] In this embodiment, the robots include a sweeping robot and a driving robot; the sweeping robot is used to clean the cleaning area; the driving robot is used to drive the sweeping robots to their corresponding cleaning sub-areas, that is, each sweeping robot is responsible for cleaning the corresponding cleaning sub-area.

[0058] S1-1: Initialize the sweeping robot.

[0059] In this embodiment, the initialization operation is performed in the initialization function of the sweeping robot program.

[0060] Place N (for example, 100) cleaning robots near the center of the cleaning area, and each cleaning robot will randomly generate corresponding position coordinates (with a certain random offset) to simulate the initial dense distribution of the cleaning robots.

[0061] The position coordinate generation formula is:

[0062] sheep_x=center_x+random_offset, sheep_y=center_y+random_offset; (1)

[0063] In formula (1), sheep_x and sheep_y represent the X-axis coordinate and Y-axis coordinate of the sweeping robot, respectively; center_x and center_y represent the X-axis coordinate and Y-axis coordinate of the center of the cleaning area, respectively; and andom_offset represents the random offset.

[0064] S1-2: Initialize the driving robot.

[0065] The driving robot is placed outside the gathering radius of the sweeping robot gathering area, and the initial position coordinate is Herd (center_x, center_y-random_offset-50), which means that the initial position coordinate Herd is outside the radius of the sweeping robot group and is (0, -offset-50) away from the group center, so as to start the driving operation.

[0066] like Figure 2 As shown, the cleaning area is a rectangular area, and the coordinate axis is established with the lower left corner as the origin 0, the horizontal direction as the X-axis (rightward is positive), and the vertical direction as the Y-axis (upward is positive); the sweeping robot is initially located at the center of the cleaning area with a gathering radius of r, and the driving robot is located outside the gathering radius of the sweeping robot gathering area.

[0067] S2: The driving robot adopts a spiral motion mode to drive and disperse the sweeping robot to obtain a first dispersion result.

[0068] In this embodiment, the circle_driving function is used to allow the driving robot to perform spiral driving around the sweeping robot, so that the sweeping robot can quickly disperse and cover the cleaning area, thereby preventing the sweeping robot from over-accumulating at its initial position.

[0069] Spiral motion mode: The driving robot starts from the center of the high-density area and moves around in a spiral path with increasing radius, driving the sweeping robot to gradually disperse to the cleaning sub-area; the radius radius and angle θ' of the spiral path of the driving robot are updated by the formula:

[0070]

[0071] In formula (2), radius represents the radius of the spiral path of the driving robot; r is the radius of the sweeping robot, R is the radius of the cleaning area, θ' represents the update angle of the driving robot; θ represents the angle between the current center coordinates of the sweeping robot and the line connecting the driving robot and the positive x-axis; β represents the growth amount of each time step.

[0072] In this embodiment, in each time step, the robot is driven toward the first target position p d1 Moving forward, the robot's first target position p is driven d1 (pd_x,pd_y) calculation formula:

[0073] pd_x=sheep_center_x+cosθ, pd_y=sheep_center_y+sinθ (3)

[0074] In formula (3), pd_x and pd_y represent the X-axis coordinate and Y-axis coordinate of the first target position of the driving robot, respectively; sheep_center_x and sheep_center_y represent the X-axis coordinate and Y-axis coordinate of the center of the driving robot, respectively; θ represents the angle between the center coordinate of the current sweeping robot and the connecting line of the driving robot and the positive x-axis.

[0075] In this embodiment, in each time step, the first displacement vector driving the robot is:

[0076]

[0077] In formula (4), r d1 represents the first displacement vector that drives the robot; p d1 Indicates the first target position coordinates of the driving robot; ||p d1 -Herd|| represents the first target position p d1 The distance between the initial position coordinates of the driving robot Herd; It means from Herd to p d1 Unit vector of direction; speed h Indicates the speed at which the robot is driven.

[0078] At each time step, the driving robot moves a certain distance toward the target position to drive the sweeping robot to move. After each driving operation, the sweeping robot radius is recalculated. If the sweeping robot radius r is greater than or equal to R, it means that the sweeping robot distribution has completed the initial diffusion and the driving operation is terminated. Otherwise, the driving robot continues to make spiral motion and the driving operation is cyclic.

[0079] The initial diffusion of sweeping robots is completed under the following conditions:

[0080] r≥R

[0081] Where r is the radius of the sweeping robot, and R is the radius of the cleaning area.

[0082] Repelling effect: As the radius of the spiral path increases, the repelling robot gradually covers all areas where the sweeping robots are distributed, prompting the sweeping robots to spread outward evenly, forming a preliminary dispersion effect.

[0083] S3: Construct a cleaning area grid, and calculate the number of cleaning robots in each grid according to the first dispersion result to obtain a density matrix.

[0084] In this embodiment, a grid of a fixed size is generated on the cleaning area, and a grid matrix grids is initialized to record the density of the sweeping robots in each grid; then the positions of the sweeping robots are traversed, and the number of sweeping robots in each grid is calculated, thereby forming a density matrix grids.

[0085] For example, the cleaning area size is 600×600 and the grid size is 100×100.

[0086] S4: Obtain the area with the largest density and the area with the smallest density according to the density matrix, and then drive the sweeping robot in the area with the largest density to the area with the smallest density.

[0087] S4-1: In this embodiment, the density matrix grids includes the maximum density and the minimum density; a point pd2 on the extension line of the line connecting the first center of the maximum density area and the second center of the minimum density area is used as the second target position, and the robot is driven to the second target position, and a part of the sweeping robot in the maximum density area is driven to the center of the minimum density area.

[0088] In this process, the second target position pd2 (pd_x, pd_y) is:

[0089]

[0090] In formula (5), p d2Indicates the coordinates of the second target position; high_density_center indicates the center coordinates of the area with the highest density, low_density_center indicates the center coordinates of the area with the lowest density, δ d Indicates the weight of the replacement item for driving the robot to the second target position, for example, 20. ||high_density_center-low_density_center|| indicates the distance between the center coordinates of the minimum density area and the center coordinates of the maximum density area.

[0091] The second displacement vector that drives the robot at each time step is:

[0092]

[0093] In formula (6), r d2 represents the second displacement vector that drives the robot; p d2 represents the coordinates of the second target position; ||p d2 -Herd|| represents the second target position p d2 The distance between the initial position of the driving robot Herd; speed h Indicates the speed of driving the robot, for example, 2.

[0094] S4-2: When the driving robot reaches the second target position pd2, it moves towards the third target position p d3 go ahead:

[0095] p d3 = low_density_center (7)

[0096] In formula (7), p d3 Indicates the coordinates of the third target position; low_density_center indicates the coordinates of the center of the minimum density area

[0097] The third displacement vector that drives the robot at each time step:

[0098]

[0099] In formula (8), r d3 represents the third displacement vector driving the robot; p d3 represents the coordinates of the third target position; represents the third target position p d3 The distance between the initial position of the driving robot Herd; speed h Indicates the speed at which the robot is driven.

[0100] That is, at each time step, the robot is driven to move a certain distance toward the target position to drive the sweeping robot to move.

[0101] S5: After each driving operation, recalculate the density of each grid and find the difference between the maximum density and the minimum density. If the difference is less than or equal to a predetermined threshold, it indicates that the distribution of the sweeping robots has reached equilibrium, and the driving operation is terminated. Otherwise, repeat S4.

[0102] Conditions for balanced distribution of sweeping robots:

[0103] max_density-min_density≤density_threshold (9)

[0104] In formula (9), max_density represents the maximum density, min_density represents the minimum density, and density_threshold represents the predetermined threshold value of the maximum and minimum density difference.

[0105] Those skilled in the art will appreciate that the above-mentioned embodiments are specific examples for implementing the present invention, and in actual applications, various changes may be made thereto in form and detail without departing from the spirit and scope of the present invention.

Claims

1. A sweeping robot driving and diffusion method based on a shepherd dog model, characterized in that: The specific steps include: S1: Initialize the sweeping robot and the driving robot; S2: The driving robot drives and disperses the sweeping robot using a spiral motion mode to obtain a first dispersion result; S3: construct a cleaning area grid, and calculate the number of cleaning robots in each grid according to the first dispersion result to obtain a density matrix; S4: Obtain the area with the largest density and the area with the smallest density according to the density matrix, and then drive the sweeping robot in the area with the largest density to the area with the smallest density.

2. The method for driving away and spreading dust using a sweeping robot based on a shepherd dog model as claimed in claim 1, characterized in that: The process also includes S5: determining whether the sweeping robots are evenly distributed, and if so, terminating the driving operation, and otherwise repeating S4.

3. The method for driving away and spreading dust using a sweeping robot based on a shepherd dog model as claimed in claim 1, characterized in that: The S1 includes: S1-1: Initialize the sweeping robot: Place N sweeping robots in the center of the cleaning area, and each sweeping robot will randomly generate corresponding position coordinates: sheep_x=center_x+random_offset, sheep_y=center_y+random_offset; (1) In formula (1), sheep_x and sheep_y represent the X-axis coordinate and Y-axis coordinate of the sweeping robot respectively; center_x and center_y represent the X-axis coordinate and Y-axis coordinate of the center of the cleaning area respectively; andom_offset represents the random offset; S1-2: Initialize the driving robot: place the driving robot at the edge of the cleaning area, and set the initial position coordinates to Herd (herd_x, herd_y).

4. The method for driving away and spreading dust using a sweeping robot based on a shepherd dog model as claimed in claim 1, characterized in that: In S2, the spiral motion mode is: the robot is driven to go around in a spiral path with increasing radius, and the sweeping robot is driven to gradually disperse to the cleaning sub-areas; wherein, the radius radius and angle θ' of the spiral path of the robot are updated by the formula: In formula (2), radius represents the radius of the spiral path of the driving robot; r is the radius of the sweeping robot, R is the radius of the cleaning area; θ represents the angle between the center coordinates of the current sweeping robot and the line connecting the driving robot and the positive x-axis; β represents the growth amount of each time step; θ' represents the update angle.

5. The method for driving away and spreading dust using a sweeping robot based on a shepherd dog model as claimed in claim 4, characterized in that: In each time step, the robot is driven towards the first target position p d1 Go forward, first target position p d1 (pd_x,pd_y) calculation formula: pd_x=sheep_center_x+cosθ, pd_y=sheep_center_y+sinθ (3) In formula (3), pd_x and pd_y represent the X-axis coordinate and Y-axis coordinate of the first target position of the driving robot, respectively; sheep_center_x and sheep_center_y represent the X-axis coordinate and Y-axis coordinate of the center of the driving robot, respectively; θ represents the angle between the center coordinate of the current sweeping robot and the connecting line of the driving robot and the positive x-axis.

6. The method for driving away and spreading dust using a sweeping robot based on a shepherd dog model as claimed in claim 5, characterized in that: In each time step, the first displacement vector that drives the robot is: In formula (4), r d1 represents the first displacement vector that drives the robot; p d1 Indicates the first target position of the driving robot; ||p d1 -Herd|| represents the first target position p d1 The distance between the initial position of the driving robot Herd; speed h Indicates the speed at which the robot is driven.

7. The method for driving away and spreading dust using a sweeping robot based on a shepherd dog model as claimed in claim 5, characterized in that: After each time step of driving, if the sweeping robot radius r is greater than or equal to the cleaning area radius R, it means that the sweeping robot distribution has completed the initial diffusion and the driving operation is terminated. Otherwise, the robot continues to make spiral motion and the driving operation is cyclic.

8. The method for driving away and spreading dust using a sweeping robot based on a shepherd dog model as claimed in claim 1, characterized in that: In S3, the method for obtaining the density matrix is: Generate a grid of fixed size on the cleaning area, and initialize the grid matrix grids to record the density of the sweeping robots in each grid; then traverse the positions of the sweeping robots in the first scattered result, calculate the number of sweeping robots in each grid, and thus form the density matrix grids.

9. The method for driving away and spreading dust using a sweeping robot based on a shepherd dog model as claimed in claim 1, characterized in that: The S4 includes: S4-1: Take a point pd2 on the line connecting the first center of the area with the largest density and the second center of the area with the smallest density in the density matrix grids as the second target position, drive the robot to the second target position, and drive the sweeping robot in the area with the largest density to the center of the area with the smallest density: In formula (5), p d2 Indicates the second target position; high_density_center indicates the center coordinates of the area with the largest density, low_density_center indicates the center coordinates of the area with the smallest density, δ d Represents the weight of the replacement item that drives the robot to the second target position; ||high_density_center-low_density_center|| represents the distance between the center coordinates of the minimum density area and the center coordinates of the maximum density area; Then the second displacement vector that drives the robot at each time step is: In formula (6), r d2 represents the second displacement vector that drives the robot; p d2 represents the coordinates of the second target position; ||p d2 -Herd|| represents the second target position p d2 The distance between the initial position of the driving robot Herd; speed h Indicates the speed of driving the robot; S4-2: When the driving robot reaches the second target position pd2, it moves towards the third target position p d3 go ahead: p d3 =low_density_center (7) In formula (7), p d3 Indicates the coordinates of the third target position; low_density_center indicates the coordinates of the center of the minimum density area; The third displacement vector that drives the robot at each time step: In formula (8), r d3 represents the third displacement vector driving the robot; p d3 represents the coordinates of the third target position; represents the third target position p d3 The distance speed between the initial position Herd of the driving robot h Indicates the speed at which the robot is driven.

10. The method for driving away and spreading dust using a sweeping robot based on a shepherd dog model as claimed in claim 2, characterized in that: In S5, the condition for balanced distribution of sweeping robots is: max_density-min_density≤density_threshold (9) In formula (9), max_density represents the maximum density, min_density represents the minimum density, and density_threshold represents the predetermined threshold value of the maximum and minimum density difference.

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