Method for herding and diffusion of a robotic vacuum cleaner based on a shepherd dog model

Abstract

The application discloses a sweeping robot driving and dispersing method based on a shepherd dog model, and specifically comprises the following steps: S1, initializing the sweeping robot and the driving robot; S2, the driving robot adopts a spiral motion mode to drive and disperse the sweeping robot, and a first dispersion result is obtained; S3, a sweeping area grid is constructed, and the number of sweeping robots of each grid is calculated according to the first dispersion result, and a density matrix is obtained; S4, the density maximum area and the density minimum area are obtained according to the density matrix, and then the driving robot drives the sweeping robots in the density maximum area to the density minimum area.

Description

Technical Field

[0001] This invention relates to the field of environmental protection technology, and in particular to a method for driving away and spreading debris using a sweeping robot based on a sheepdog model. Background Technology

[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, and large-area cleaning. Effective robot coverage is crucial for accomplishing these tasks. Traditional multi-robot systems require the robots to be quickly and evenly distributed to the target area to ensure that each robot efficiently performs its tasks within its assigned sub-area. How to achieve rapid robot dispersion and coverage is the core issue for improving the efficiency of multi-robot systems.

[0003] The first existing approach is a diffusion scheme based on global path planning: In most multi-robot systems, global path planning is the traditional method to achieve uniform robot coverage. Common global path planning techniques include: SLAM-based map building and navigation: SLAM technology allows robots to simultaneously build maps and perform self-localization in unknown environments; by building maps, robots can achieve global path planning and allocate different sub-regions based on each robot's starting and target positions. However, SLAM relies on strong computing power and synchronous transmission from communication systems, placing high demands on robot hardware, and its real-time performance and flexibility are limited 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 main characteristics of the particle diffusion model are as follows: Local rule control: Each robot only needs to perceive the positions of nearby robots, maintain a certain repulsive distance to avoid collisions, 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: Random diffusion is added to the repulsive force model to ensure that robots are uniformly dispersed in all directions; through random diffusion, robots can quickly and uniformly spread in uncovered areas. However, the particle diffusion model is prone to path overlap and uncovered areas in environments with dense obstacles, and cannot guarantee complete coverage. Some extended particle diffusion models also employ a shortest path guidance strategy, where robots choose the shortest path direction in the uncovered area for diffusion; these methods are suitable for coverage tasks, but due to the high randomness of path planning, they cannot guarantee rapid coverage within a limited time.

[0005] The third existing approach is based on a biomimetic dispersion strategy: biomimetic methods mimic group behaviors in nature, such as ant foraging and bee foraging, to achieve decentralized control of multi-robot systems. The main biomimetic methods include: Ant-based dispersion algorithms: Ant foraging behavior typically involves path selection, pheromone updates, and local optimum search. By mimicking ant foraging behavior, robots autonomously distribute themselves within a region. However, ant foraging algorithms rely on the transmission of environmental pheromones, requiring robots to mark themselves in the environment, which is difficult to achieve effectively in dynamic environments and large-area scenes. Bee-based adaptive distribution: Bee foraging behavior is based on swarm communication, where individual robots exchange location and environmental information to achieve uniform dispersion; this method requires a stable communication network and complex path planning algorithms.

[0006] In summary, existing multi-robot diffusion technologies mainly rely on global path planning and particle diffusion models to achieve uniform robot distribution over large areas. However, global path planning methods have high requirements for map building and communication synchronization, place high demands on robot hardware, and are limited in real-time performance and flexibility in complex environments; particle diffusion models are limited by randomness and are prone to creating uncovered areas. These methods struggle to achieve efficient autonomous diffusion, thus requiring a simpler and communication-independent diffusion strategy to improve system efficiency. Summary of the Invention

[0007] To address the problem that existing robotic vacuum cleaners cannot quickly disperse in large areas, this invention proposes a robotic vacuum cleaner dispersal method based on a sheepdog model. This method intelligently guides robotic vacuum cleaners (sheep flocks) to quickly distribute and effectively cover large areas using a herding robot (sheepdog).

[0008] To achieve the above objectives, the present invention provides the following technical solution:

[0009] The method for driving away and spreading debris from a robot vacuum cleaner based on a sheepdog model includes the following steps:

[0010] S1: Initialize the robot vacuum and the robot deterrent;

[0011] S2: The driving robot uses a spiral motion mode to drive away and disperse the sweeping robot, resulting in the first dispersion result;

[0012] S3: Construct a cleaning area grid and calculate the number of robotic vacuum cleaners in each grid based on the first dispersion result to obtain the density matrix;

[0013] S4: Obtain the areas with the highest and lowest density based on the density matrix, and then drive the robot from the area with the highest density to the area with the lowest density.

[0014] Preferably, it also includes S5: determining whether the robot vacuums are evenly distributed; if so, terminating the driving operation; otherwise, repeating S4.

[0015] Preferably, S1 includes:

[0016] S1-1: Initialize the robot vacuum cleaner:

[0017] If N robotic vacuum cleaners are placed in the center of the cleaning area, each robotic vacuum cleaner 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 coordinates and Y-axis coordinates of the robot vacuum cleaner, respectively; center_x and center_y represent the X-axis coordinates and Y-axis coordinates of the center of the cleaning area, respectively; and random_offset represents the random offset.

[0020] S1-2: Initialize the herding robot: Place the herding robot at the edge of the cleaning area, and set its initial position coordinates as Herd(herd_x,herd_y).

[0021] Preferably, in step S2, the spiral motion mode is as follows: the driving robot moves along a spiral path with an increasing radius, gradually dispersing the sweeping robot to the cleaning sub-area; wherein, the update formulas for the radius (radius) and angle (θ′) of the driving robot's spiral path are:

[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 current center coordinates of the sweeping robot and the line connecting the driving robot and the positive x-axis; β represents the increase in each time step; θ′ represents the updated angle.

[0024] Preferably, in each time step, the robot is driven towards the first target position p. d1 Forward, first target position p d1 Formula for calculating (pd_x, pd_y):

[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 coordinates and Y-axis coordinates of the first target position of the driving robot, respectively; sheep_center_x and sheep_center_y represent the X-axis coordinates and Y-axis coordinates of the center of the driving robot, respectively; 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.

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

[0028]

[0029] In formula (4), r d1 p represents the first displacement vector of the robot being driven away. d1 Indicates the first target location for driving away the robot; ||p d1 -Herd|| indicates the first target position p d1 The distance between the initial position of the driving robot and Herd; speed h This indicates the speed at which the robots are driven away.

[0030] Preferably, after each time step of driving away, if the radius r of the sweeping robot is greater than or equal to the radius R of the cleaning area, it indicates that the initial diffusion of the sweeping robot distribution has been completed, and the driving operation is terminated; otherwise, the driving robot continues to perform spiral motion and cycle through the driving operation.

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

[0032] A fixed-size grid 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 in the first dispersion result are traversed, and the number of sweeping robots in each grid is calculated to form the density matrix grids.

[0033] Preferably, S4 includes:

[0034] S4-1: Take point pd2 on the line connecting the first center of the region with the highest density and the second center of the region with the lowest density in the density matrix grids as the second target position, and drive the robot to the second target position, driving the robot in the region with the highest density to the center of the region with the lowest density:

[0035]

[0036] In formula (5), p d2 Indicates the location of the second target; high_density_center represents the coordinates of the center of the region with the highest density, low_density_center represents the coordinates of the center of the region with the lowest density, δd This represents the weight of the replacement term used to drive the robot to the second target position; ||high_density_center-low_density_center|| represents the distance between the center coordinates of the region with the lowest density and the center coordinates of the region with the highest density.

[0037] Then, the second displacement vector driving the robot at each time step is:

[0038]

[0039] In formula (6), r d2 p represents the second displacement vector used to drive the robot. d2 Represents the coordinates of the second target's location; ||P d2 -Herd|| indicates the second target position p d2 The distance between the initial position of the driving robot and Herd; speed h Indicates the speed at which the robots are driven away;

[0040] S4-2: After 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 The coordinates of the third target's location are indicated; low_density_center indicates the coordinates of the center of the region with the lowest density.

[0043] The third displacement vector of the driving robot at each time step:

[0044]

[0045] In formula (8), rd3 represents the third displacement vector of the driving robot; p d3 The coordinates representing the location of the third target; 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 step S5, the condition for the even 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 of the difference between the maximum and minimum densities.

[0049] In summary, by adopting the above technical solution, the present invention has at least the following beneficial effects compared with the prior art:

[0050] This invention first uses a spiral motion to quickly disperse the sweeping robot to the entire area, initially reducing high-density accumulation; then, the driving robot gradually drives away the remaining high-density areas, guiding the sweeping robot to low-density areas, thus achieving rapid and uniform coverage of the cleaning area by the sweeping robot. Attached Figure Description

[0051] Figure 1 This is a schematic diagram of a sweeping robot based on a sheepdog model according to an exemplary embodiment of the present invention to drive away the spread of germs.

[0052] Figure 2 This is a schematic diagram illustrating the initial distribution of a sweeping robot and a chasing robot according to an exemplary embodiment of the present invention. Detailed Implementation

[0053] The present invention will be further described in detail below with reference to embodiments and specific implementation methods. However, this should not be construed as limiting the scope of the above-described subject matter of the present invention to the following embodiments; all technologies implemented based on the content of the present invention fall within the scope of the present invention.

[0054] In the description of this invention, it should be understood that the terms "longitudinal", "lateral", "up", "down", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0055] like Figure 1 As shown, this invention provides a method for driving away and spreading debris from a sweeping robot based on a sheepdog model, specifically including the following steps:

[0056] S1: First, initialize the robot vacuum cleaner and the robot deterrent.

[0057] In this embodiment, the robot includes a sweeping robot and a driving robot; the sweeping robot is used to clean the sweeping area; the driving robot is used to drive the sweeping robot to its corresponding sweeping sub-area, that is, each sweeping robot is responsible for the cleaning work of its corresponding sweeping sub-area.

[0058] S1-1: Initialize the robot vacuum cleaner.

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

[0060] If N (e.g., 100) robotic vacuum cleaners are placed near the center of the cleaning area, each robotic vacuum cleaner will randomly generate corresponding position coordinates (with a certain random offset) to simulate the initial dense distribution of robotic vacuum cleaners.

[0061] The formula for generating position coordinates 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 coordinates and Y-axis coordinates of the robot vacuum cleaner, respectively; center_x and center_y represent the X-axis coordinates and Y-axis coordinates of the center of the cleaning area, respectively; and random_offset represents the random offset.

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

[0065] The robot to be driven away is placed outside the radius of the area where the robot vacuums gather, with an initial position coordinate of Herd(center_x, center_y-random_offset-50). This means that the initial position coordinates Herd are outside the radius of the robot vacuum group and are (0, -offset-50) away from the center of the group, so that the driving away operation can begin.

[0066] like Figure 2 As shown, the cleaning area is a rectangular area. The coordinate axes are established with the lower left corner as the origin 0, the horizontal direction as the X-axis (positive to the right), and the vertical direction as the Y-axis (positive upward). The robot vacuum cleaner is initially located in the center of the cleaning area with a gathering radius of r, while the driving robot is located outside the gathering radius of the robot vacuum cleaner's gathering area.

[0067] S2: The driving robot uses a spiral motion mode to drive away and disperse the sweeping robot, resulting in the first dispersion result.

[0068] In this embodiment, the circle_driving function is used to make the driving robot spiral around the sweeping robot, so that the sweeping robot can quickly disperse and cover the cleaning area, preventing excessive accumulation at the initial position of the sweeping robot.

[0069] Spiral Motion Mode: Starting from the center of a high-density area, the robot moves along a spiral path with increasing radius, gradually dispersing its position to cover different cleaning sub-areas. The update formulas for the radius (radius) and angle (θ') of the spiral path are as follows:

[0070]

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

[0072] In this embodiment, at each time step, the robot is driven towards the first target position p. d1 Moving forward drives the robot to its first target position p. d1 Formula for calculating (pd_x, pd_y):

[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 coordinates and Y-axis coordinates of the first target position of the driving robot, respectively; sheep_center_x and sheep_center_y represent the X-axis coordinates and Y-axis coordinates of the center of the driving robot, respectively; 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.

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

[0076]

[0077] In formula (4), r d1 p represents the first displacement vector of the robot being driven away. d1 Indicates the coordinates of the first target position to drive away the robot; ||p d1 -Herd|| indicates the first target position p d1 The distance between the initial position coordinates of Herd and the robot being driven away; This represents the transition from Herd to p. d1 The unit vector of direction; speed h This indicates the speed at which the robots are driven away.

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

[0079] Conditions for the initial dissemination of robotic vacuum cleaners:

[0080] r≥R

[0081] Where r is the radius of the robot vacuum cleaner, 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 robot is distributed, causing the sweeping robot to spread outward evenly and form an initial dispersion effect.

[0083] S3: Construct a cleaning area grid and calculate the number of robot vacuums in each grid based on the first dispersion result to obtain the density matrix.

[0084] In this embodiment, a grid of 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 areas with the highest and lowest density based on the density matrix, and then drive the robot from the area with the highest density to the area with the lowest 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 connecting the first center of the region with the maximum density and the second center of the region with the minimum density is used as the second target position. The robot is driven to the second target position, and a part of the sweeping robot in the region with the maximum density is driven to the center of the region with the minimum density.

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

[0089]

[0090] In formula (5), p d2The coordinates of the second target location are represented; high_density_center represents the center coordinates of the region with the highest density, low_density_center represents the center coordinates of the region with the lowest density, and δ d This represents the weight of the replacement term used to drive the robot to the second target position, for example, 20. ||high_density_center-low_density_center|| represents the distance between the center coordinates of the region with the lowest density and the center coordinates of the region with the highest density.

[0091] The second displacement vector of the robot at each time step:

[0092]

[0093] In formula (6), r d2 p represents the second displacement vector used to drive the robot. d2 Represents the coordinates of the second target's location; ||p d2 -Herd|| indicates the second target position p d2 The distance between the initial position of the driving robot and Herd; speed h This indicates the speed at which the robot is driven, for example, 2.

[0094] S4-2: After 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 The coordinates of the third target's location are indicated; low_density_center indicates the coordinates of the center of the region with the lowest density.

[0097] The third displacement vector of the driving robot at each time step:

[0098]

[0099] In formula (8), r d3 p represents the third displacement vector of the robot being driven away. d3 The coordinates representing the location of the third target; Indicates the position of the third target p d3 The distance between the initial position of the driving robot and Herd; speed h This indicates the speed at which the robots are driven away.

[0100] That is, at each time step, the robot is driven to move a certain distance toward the target location in order to drive the robot vacuum cleaner to move.

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

[0102] Conditions for even distribution of robot vacuum cleaners:

[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 of the difference between the maximum and minimum densities.

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

Claims

1. A method for driving away and spreading debris from a sweeping robot based on a sheepdog model, characterized in that, Specifically, the following steps are included: S1: Initialize the robot vacuum and the robot deterrent; S2: The driving robot uses a spiral motion mode to drive away and disperse the sweeping robot, resulting in the first dispersion result; S3: Construct a cleaning area grid and calculate the number of robotic vacuum cleaners in each grid based on the first dispersion result to obtain the density matrix; S4: Obtain the areas with the highest and lowest density based on the density matrix, and then drive the robot from the area with the highest density to the area with the lowest density.

2. The method for driving away and spreading debris from a sweeping robot based on a sheepdog model as described in claim 1, characterized in that, It also includes S5: Determine whether the robot vacuums are evenly distributed. If so, terminate the driving operation; otherwise, repeat S4.

3. The method for driving away and spreading debris from a sweeping robot based on a sheepdog model as described in claim 1, characterized in that, S1 includes: S1-1: Initialize the robot vacuum cleaner: If N robotic vacuum cleaners are placed in the center of the cleaning area, each robotic vacuum cleaner 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 coordinates and Y-axis coordinates of the robot vacuum cleaner, respectively; center_x and center_y represent the X-axis coordinates and Y-axis coordinates of the center of the cleaning area, respectively; random_offset represents the random offset. S1-2: Initialize the herding robot: Place the herding robot at the edge of the cleaning area, and set its initial position coordinates as Herd(herd_x,herd_y).

4. The method for driving away and spreading debris from a sweeping robot based on a sheepdog model as described in claim 1, characterized in that, In S2, the spiral motion mode is as follows: the driving robot moves along a spiral path with an increasing radius, gradually dispersing the sweeping robot to the cleaning sub-area; wherein, the update formulas for the radius (radius) and angle (θ') of the driving robot's spiral path are: 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 current center coordinates of the sweeping robot and the line connecting the driving robot and the positive x-axis; β represents the increase in each time step; θ' represents the updated angle.

5. The method for driving away and spreading debris from a sweeping robot based on a sheepdog model as described in claim 4, characterized in that, At each time step, the robot is driven towards the first target position p. d1 Forward, first target position p d1 Formula for calculating (pd_x, pd_y): 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 coordinates and Y-axis coordinates of the first target position of the driving robot, respectively; sheep_center_x and sheep_center_y represent the X-axis coordinates and Y-axis coordinates of the center of the driving robot, respectively; 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.

6. The method for driving away and spreading debris from a sweeping robot based on a sheepdog model as described in claim 5, characterized in that, At each time step, the first displacement vector of the driving robot is: In formula (4), r d1 p represents the first displacement vector of the robot being driven away. d1 Indicates the first target location for driving away the robot; ||p d1 -Herd|| indicates the first target position p d1 The distance between the initial position of the driving robot and Herd; speed h This indicates the speed at which the robots are driven away.

7. The method for driving away and spreading debris from a sweeping robot based on a sheepdog model as described in claim 5, characterized in that, After each time step of the drive, if the radius r of the sweeping robot is greater than or equal to the radius R of the cleaning area, it means that the initial diffusion of the sweeping robot distribution has been completed and the drive operation is terminated; otherwise, the drive robot continues to perform spiral motion and cycle through the drive operation.

8. The method for driving away and spreading debris from a sweeping robot based on a sheepdog model as described in claim 1, characterized in that, In S3, the density matrix is ​​obtained as follows: A fixed-size grid 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 in the first dispersion result are traversed, and the number of sweeping robots in each grid is calculated to form the density matrix grids.

9. The method for driving away and spreading debris from a sweeping robot based on a sheepdog model as described in claim 1, characterized in that, S4 includes: S4-1: Take point pd2 on the line connecting the first center of the region with the highest density and the second center of the region with the lowest density in the density matrix grids as the second target position, and drive the robot to the second target position, driving the robot in the region with the highest density to the center of the region with the lowest density: In formula (5), p d2 Indicates the location of the second target; high_density_center represents the coordinates of the center of the region with the highest density, low_density_center represents the coordinates of the center of the region with the lowest density, δ d This represents the weight of the replacement term used to drive the robot to the second target position; ||high_density_center-low_density_center|| represents the distance between the center coordinates of the region with the lowest density and the center coordinates of the region with the highest density. Then, the second displacement vector driving the robot at each time step is: In formula (6), r d2 p represents the second displacement vector used to drive the robot. d2 Represents the coordinates of the second target's location; ||p d2 -Herd|| indicates the second target position p d2 The distance between the initial position of the driving robot and Herd; speed h Indicates the speed at which the robots are driven away; S4-2: After 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 The coordinates of the third target's location are indicated; low_density_center indicates the coordinates of the center of the region with the lowest density. The third displacement vector of the driving robot at each time step: In formula (8), r d3 p represents the third displacement vector of the robot being driven away. d3 The coordinates representing the location of the third target; Indicates the position of the third target p d3 The speed is the distance between Herd, the initial position of the driving robot. h This indicates the speed at which the robots are driven away.

10. The method for driving away and spreading debris from a sweeping robot based on a sheepdog model as described in claim 2, characterized in that, In S5, the condition for the even distribution of the 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 of the difference between the maximum and minimum densities.

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