A path planning method for a disinfection robot

Abstract

The application discloses a disinfecting robot path planning method. The steps of the method comprise the following steps: (1) establishing a disinfecting liquid spraying diffusion model, including a static diffusion model and a dynamic diffusion model; (2) indoor space gridding, discretizing and regularizing the indoor space; (3) disinfecting robot moving position selection, comprehensively judging the next moving position of the disinfecting robot according to the moving energy consumption of the disinfecting robot and the disinfecting liquid concentration; and (4) disinfecting robot dead zone escape, avoiding the disinfecting robot from being trapped in a dead zone and quickly completing the disinfecting task. The disinfecting efficiency of the disinfecting robot can be remarkably improved.

Description

Technical Field

[0001] This invention relates to a path planning algorithm for disinfection robots in indoor automatic disinfection applications, belonging to the field of robot intelligent control. Background Technology

[0002] Indoor disinfection is a common method for hygiene and epidemic prevention, effectively killing viruses and bacteria in the air and preventing the spread of infection. Traditional indoor disinfection often relies on manual methods, which are time-consuming, labor-intensive, and prone to causing human infection. Robotic automated disinfection can significantly reduce labor costs, simplify management, avoid cross-infection, and achieve on-demand or personalized disinfection through intelligent control, resulting in rapid disinfection and wasteful use of disinfectant. Disinfection robots typically spray disinfectant into the space; when the concentration of disinfectant in the air reaches a certain level, it can kill viruses in the environment. To ensure disinfection effectiveness, the disinfectant must completely cover the indoor space, and the concentration must exceed a certain threshold.

[0003] Existing robot path planning algorithms are mainly divided into two categories: obstacle space path planning and free space path planning. Free space path planning algorithms are primarily used for path planning in open indoor or outdoor environments, such as drone monitoring. Free space path planning typically establishes an environmental model based on information such as the shape and area of ​​the region, with path coverage and robot runtime as optimization objectives, ensuring that the robot or drone can complete the task more accurately, efficiently, and energy-savingly. Commonly used free space path planning algorithms include ant colony optimization, A* algorithm, fuzzy logic control algorithm, and rolling window method. Obstacle space path planning refers to the robot or drone selecting an optimal or suboptimal obstacle avoidance path from the starting point to the ending point within the task area, based on a certain optimization objective, such as minimizing workload, selecting the shortest path, and minimizing computation time. Common methods for obstacle space path planning include genetic algorithms and fast random search tree algorithms, which can quickly find obstacle avoidance paths for robots or intelligent vehicles in ground obstacle spaces, achieving full coverage of the ground space. However, existing free-space path planning algorithms and obstacle-space path planning algorithms focus on achieving coverage on a plane of movement space, such as robot coverage of ground space or drone coverage of a projected plane, meaning the path trajectory and the coverage area are on the same plane. In contrast, the path planning problem for disinfection robots involves a path trajectory in the ground obstacle space, while the area to be covered by the disinfectant is in the air; that is, the path trajectory and the coverage area are not on the same plane, and their spatial characteristics are also different.

[0004] The goal of the path planning problem for disinfection robots is to quickly cover an indoor space with disinfectant solution, while the constraint is the robot's movement trajectory within an obstacle-filled indoor space. This invention models the diffusion of disinfectant solution by the disinfection robot and proposes a full-coverage path planning algorithm based on this model. This algorithm satisfies both the robot's path planning constraints within obstacle spaces and enables precise control of the disinfection effect. This method can be applied to indoor spaces of varying sizes and structures, as well as different types of disinfection robots. Summary of the Invention

[0005] This invention provides a path planning method for a disinfection robot. Based on a combination of path planning in ground obstacle space and a free space diffusion model of disinfectant, and with the goal of rapid coverage of disinfectant, the obstacle space is divided into mobile units for the disinfection robot using a grid method. The optimal movement direction and position of the disinfection robot are determined by comprehensively considering turning energy consumption and disinfectant coverage. A dead zone escape strategy is used to prevent the disinfection robot from falling into dead zones, ultimately ensuring that the disinfection robot can achieve full coverage of disinfectant in indoor spaces in the shortest possible time.

[0006] The technical solution adopted in this invention is as follows:

[0007] A path planning method for a disinfection robot specifically includes the following steps:

[0008] (1) Construction of disinfectant spray diffusion model: Taking into account both static and dynamic diffusion models of disinfectant, the ceiling plane of the indoor space is used as the evaluation standard for the disinfection effect of the disinfectant concentration.

[0009] (2) Indoor space gridding: The indoor space where the disinfection robot works is divided into grids of the same size, and each grid serves as the basic unit for path planning;

[0010] (3) Selection of the movement position of the disinfection robot: Taking into account the energy consumption of the disinfection robot and the concentration of disinfectant in the indoor space, the next movement position of the disinfection robot is determined.

[0011] (4) Escape from the dead zone of the disinfection robot: When the disinfection robot is surrounded by obstacles or the concentration of the disinfectant solution reaches the required level, it will quickly escape to the nearest area to be disinfected and continue working.

[0012] The beneficial effects of this invention are as follows:

[0013] (1) The path of the disinfection robot is planned in accordance with the concentration of the disinfection solution and energy consumption, and the movement path of the disinfection robot is reduced, so that the indoor disinfection task can be completed in a short time.

[0014] (2) Taking into account both the static and dynamic diffusion models of the disinfectant solution, and considering the changes in the concentration of the disinfectant solution in the path planning of the disinfection robot, the amount of disinfectant solution used can be saved while ensuring the disinfection effect.

[0015] (3) Spatial gridding and dead zone escape strategies can cope with complex indoor environments and improve the environmental adaptability of disinfection robots. Attached Figure Description

[0016] Appendix Figure 1 This is the specific process of the path planning method for the disinfection robot of the present invention.

[0017] Appendix Figure 2 This is a schematic diagram of the directional change angle of the disinfection robot of the present invention.

[0018] Appendix Figure 3 This is a schematic diagram of the disinfection robot escaping from the dead zone in this invention.

[0019] Appendix Figure 4 This is the path planning process in an embodiment of the present invention.

[0020] Appendix Figure 5 These are the performance test results of an embodiment of the present invention. Detailed Implementation

[0021] The technical solution of the present invention will be further described below with reference to the embodiments.

[0022] The path planning method for disinfection robots proposed in this invention is mainly used for indoor intelligent disinfection robots. Currently, path planning for disinfection robots primarily relies on ground coverage. While various path planning methods can quickly find the optimal path on the ground, they lack consideration for disinfection effectiveness, i.e., the concentration of the disinfectant solution in the space. The flowchart of the proposed path planning method for disinfection robots is attached. Figure 1 As shown. This invention combines indoor floor path planning with indoor disinfectant coverage, and takes into account the energy consumption of the disinfection robot, to quickly and efficiently complete the disinfection task in indoor spaces. The specific path planning method includes the following four steps:

[0023] 1. Construction of a spray diffusion model for disinfectant solutions

[0024] To achieve effective disinfection of indoor spaces, the concentration of disinfectant within the space must be maintained at the standard value. Since the density of vaporized disinfectant solution is greater than that of air, the highest point in the indoor space will be the area with the lowest disinfectant concentration. Therefore, the concentration of disinfectant solution on the ceiling is used as the basis for judging whether the indoor disinfection is fully covered.

[0025] (1) Static Disinfectant Diffusion Model: The disinfection process of the disinfection robot is abstracted as the diffusion of disinfectant solution from a continuous point source, assuming that its concentration distribution follows a normal distribution. The disinfectant solution is transported downwind at an average wind speed, turbulent diffusion in the downwind direction is ignored, and vertical and horizontal diffusion are described by diffusion parameters. The static disinfectant diffusion model is as follows:

[0026]

[0027] Where c(x,y,z,t) represents the concentration of the drug solution at time t at the three-dimensional spatial coordinates x,y,z, and p0 is the concentration of the gas source, H0 is the height of the spray source, and R... x R y R z These represent diffusion parameters in three dimensions. Under windless conditions in an indoor space, the diffusion parameters are functions of atmospheric stability and diffusion time: R x =R y =ω1T,R z =ω2T, where T is the diffusion time, and ω1 and ω2 are constants under different atmospheric stability levels. When the atmospheric stability is A, B, C, D, E, or F, the values ​​of (ω1,ω2) are (0.93,1.57), (0.76,0.47), (0.55,0.21), (0.47,0.12), (0.44,0.07), and (0.44,0.05), respectively.

[0028] (2) Dynamic pesticide diffusion model: The continuous change in pesticide concentration is transformed into a discrete model based on time slices. Assume the length of each time slice in the movement of the pesticide robot is T, and the movement time of the pesticide robot within a given area A is t. A , i.e. t A The concentration of disinfectant solution generated by the disinfection robot in this area is determined by the evaporation of the solution over these tc / T time slices. When the disinfection robot is moving, the concentration of disinfectant solution in area A is:

[0029]

[0030] Where x and y are the two-dimensional coordinates of the liquid medicine evaporating from the indoor space onto the ceiling plane, and t is the time parameter. A =D / V,C k c represents the total concentration of the drug solution within region A. i Let be the concentration of the disinfectant solution generated in a single time slice in region A, D be the length of the given region A, and V be the average moving speed of the disinfection robot.

[0031] (3) Calculation of pesticide concentration in indoor space: During the movement of the disinfection robot, the pesticide concentration is updated once after each area is moved. The pesticide concentration in all areas can be calculated based on the diffusion model. The specific method is as follows:

[0032] ① Set a time variable T1 for each non-obstacle zone on the indoor floor;

[0033] ② When the disinfection robot passes through area A, if the time it takes to pass through the area is t A Then the time variable of all indoor floor areas increases by t. A And update the current concentration of the medicine solution in the area;

[0034] ③ When the disinfection robot repeatedly passes through the same area A, there will be differences in the diffusion of the disinfectant solution within the area over time. The concentration calculation in area A needs to take into account the time when the robot enters the area later. The concentration of the disinfectant solution in the repeatedly entered area A is:

[0035] C k (x,y,t)=C k (x,y,t)+C k (x,y,tt A n)

[0036] Where n represents the number of grids that the disinfection robot passes through repeatedly in area A.

[0037] 2. Indoor space gridding

[0038] The indoor floor space where the disinfection robot moves is divided into grids of equal size, and represented using a two-dimensional matrix. If the working area of ​​the disinfection robot is m×n, and each grid is a small square area with side length a, then the indoor floor space can be represented as a two-dimensional grid matrix X. rxs Where r and s represent the number of rows and columns of the matrix, respectively. Raster Matrix X rxs The element in row i and column j, x ij = {-1, 0.5, 1}, representing the i-th row and j-th column of the indoor floor grid as an obstacle, a disinfection-compliant area, and a non-disinfection-compliant area, respectively, where 1≤i≤r, 1≤j≤s. For ease of processing, irregular indoor spaces are filled with obstacle grids to form a regular rectangular grid matrix.

[0039] The grid size is set by the user based on the specific conditions of the indoor space. Different grid sizes have varying impacts on the performance of the path planning algorithm and the specific description of the map. A grid that is too large results in fewer grids dividing the indoor space, making path planning for the disinfection robot simpler but leading to poorer description of the indoor space's features and ultimately poorer disinfection results. Conversely, a grid that is too small results in more grids dividing the indoor space, leading to poorer path planning algorithm performance and longer paths, but more accurate description of the indoor space's features and ultimately better disinfection results.

[0040] 3. Selection of the movement location of the disinfection robot

[0041] (1) Determine the current position: The current position of the disinfection robot is its position in the grid, i.e., the grid matrix element x. ij , where 1≤i≤r, 1≤j≤s;

[0042] (2) Calculate the angle of change of direction: (See attached diagram) Figure 2 The diagram shows the change angle of the disinfection robot's movement direction. The change angle Δφ can be calculated from this diagram.

[0043]

[0044] in, These represent the current grid where the disinfection robot is located, the previous grid where it was located, and the uncovered grid, respectively.

[0045] (3) Calculate the turning confidence value: In order to control the direction of the disinfection robot and guide it to move towards the uncovered area, the turning confidence value of the disinfection robot is calculated. in Let Δφ be the angle of change of direction, and π be the mathematical constant pi. When Δφ = 0°, C = 1 is the maximum, the disinfection robot moves along a straight line without turning, consumes the least energy, and has the highest reliability; when Δφ = 180°, the disinfection robot moves in the opposite direction, with the largest turning angle and the most energy consumption, which should be avoided as much as possible in the actual operation of the disinfection robot.

[0046] (4) Calculate the movement cost: Based on the current position and turning confidence value of the disinfection robot, calculate the cost Y of the disinfection robot moving to each adjacent uncovered grid cell. k =X k +δC k Y k The node with the largest value is selected as the next moving grid for the disinfection robot, ensuring the highest efficiency. Here, δ∈(0,1] is the weighting coefficient, with a default setting of δ=0.5, meaning the robot always moves towards uncovered nodes during planning, 1≤k≤i, where i is the number of uncovered grids connected to the current grid of the disinfection robot.

[0047] 4. Disinfection robot escapes dead zone

[0048] If the disinfection robot gets stuck in a dead zone during its movement, meaning that all adjacent grids are obstacle grids, already disinfected grids, or boundary areas, it needs to find the shortest path to reach the grids that have not yet been disinfected to escape the dead zone. (See attached...) Figure 3 As shown, attached Figure 3 (a) indicates that the disinfection robot has entered a dead zone. Figure 3 (b) indicates that the disinfection robot has found the nearest feasible escape location. Figure 3 (c) indicates that the disinfection robot has escaped to the optimal grid. The specific steps for escaping from the dead zone are as follows:

[0049] (1) Find the grid Q that is at a distance L (initially L is 1) from the current position of the disinfection robot and has not yet been disinfected. i , where 1≤i≤n, and n represents the number of grid cells that satisfy the condition;

[0050] (2) Calculate the distance G from the current position of the disinfection robot to each grid found in step (1). i (1≤i≤n);

[0051] (3) Determine distances L and G i Are (1≤i≤n) equal? ​​If L=G i (1≤i≤n), then the corresponding grid is selected as the optimal grid, and the pest control robot moves to this optimal grid using the shortest path algorithm. If there are multiple optimal grids, then any one of them is selected as the target grid for the pest control robot; if L and any G i If (1≤i≤n) are all not equal, then proceed to step (4);

[0052] (4) Increment the search distance L by 1 and proceed to step (1) to perform a new round of search.

[0053] Example

[0054] For the path planning in this embodiment, please refer to the appendix. Figure 4 The indoor space is rasterized into a 3x3 grid matrix, with the number in each grid representing the concentration of the disinfectant solution. Obstacle grids are represented in gray, as shown in the attached diagram. Figure 4 As shown in (a). The specific path planning process is as follows:

[0055] ① The disinfection robot starts from the top left corner grid (1,1) of the map, with its initial orientation downwards, and performs the disinfectant diffusion for that time period, as shown in the attached diagram. Figure 4 As shown in (b);

[0056] ② Currently, the concentration around the disinfection robot is not up to standard and it has not entered a dead zone. Therefore, the movement values ​​in four directions are calculated. The current movement values ​​in the up, down, left, and right directions are -3, 1, -3, and 1, respectively. The direction with the maximum movement value is down. Therefore, the disinfection robot moves downwards while simultaneously spreading the disinfectant solution for that time period. (See attached diagram.) Figure 4 As shown in (c);

[0057] ③ If the concentration around the disinfection robot is not up to standard and it has not entered a dead zone, calculate the movement values ​​in four directions. The current movement values ​​in the up, down, left, and right directions are 0.47, 1, -3.25, and 0.51, respectively. The direction with the maximum movement value is down, so the disinfection robot moves downwards while simultaneously spreading the disinfectant solution for that period of time, as shown in the attached diagram. Figure 4 As shown in (d);

[0058] ④ If the concentration around the disinfection robot is not yet up to standard and it has not entered a dead zone, calculate the movement values ​​in four directions. The current movement values ​​in the up, down, left, and right directions are 0.23, -3, -3.25, and -2.57, respectively. The direction with the highest current movement value is upward. Therefore, the disinfection robot moves upward and simultaneously diffuses the disinfectant solution for this period of time, as shown in the attached diagram. Figure 4 As shown in (e);

[0059] ⑤ If the concentration around the disinfection robot is not yet up to standard and it has not entered a dead zone, calculate the movement values ​​in four directions. The current movement values ​​in the up, down, left, and right directions are: 0.23, 0.15, -3.25, and 0.02, respectively. The direction with the highest current movement value is upward. Therefore, the disinfection robot moves upward and simultaneously diffuses the disinfectant solution for this period of time, as shown in the attached diagram. Figure 4 As shown in (f);

[0060] ⑥ If the concentration around the disinfection robot is not up to standard and it has not entered a dead zone, calculate the movement values ​​in four directions. The current movement values ​​in the up, down, left, and right directions are -3, -0.54, -3.25, and -0.05, respectively. The direction with the maximum movement value is right. Therefore, the disinfection robot moves to the right while simultaneously spreading the disinfectant solution for that period of time, as shown in the attached diagram. Figure 4 (g) is shown;

[0061] ⑦ If the disinfection robot is stuck in a dead zone, find the nearest unmet target area: (2,3). Use the A* algorithm to calculate the shortest path: (2,2)->(2,3). The disinfection robot moves to (2,3) and performs the disinfectant diffusion for that time period, as shown in the attached diagram. Figure 4 (h) and appendix Figure 4 As shown in (i);

[0062] ⑧ When trapped in a dead zone and unable to find any accessible unqualified areas, the disinfection robot ceases operation and waits for the disinfectant spray to fully spread. The algorithm then terminates. (See attached diagram) Figure 4 As shown in (j).

[0063] In an indoor space with a 50*50 grid matrix and 1 / 10 of the grid cells being random obstacles, the path planning method of this invention was compared with an arc-shaped path algorithm with a liquid spray diffusion function. The results are shown in the appendix. Figure 5 As shown in the figure. Experimental results show that the path planning method of the present invention has significant advantages in terms of average concentration spillover ratio, path planning time and number of planning steps, and can achieve indoor space disinfection tasks faster and save disinfectant.

[0064] The embodiments described herein are merely exemplary implementations. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. These embodiments are provided to fully and completely disclose the invention and to fully convey the scope of the invention to those skilled in the art. The terminology used in the exemplary embodiments illustrated in the accompanying drawings is not intended to limit the invention. In the accompanying drawings, the same units / elements are referred to by the same reference numerals.

[0065] Unless otherwise stated, the terms used herein (including technical terms) have the common understanding meaning to those skilled in the art; in addition, it is understood that terms defined by commonly used dictionaries should be understood to have a meaning consistent with the context of their relevant field, and should not be understood to have an idealized or overly formal meaning.

Claims

1. A path planning method for a disinfection robot, characterized in that, The method specifically includes the following steps: (1) Construction of disinfectant spray diffusion model: Taking into account both static and dynamic diffusion models of disinfectant, the ceiling plane of the indoor space is used as the evaluation standard for the disinfection effect of the disinfectant concentration; (2) Indoor space gridding: The indoor space where the disinfection robot works is divided into grids of the same size, and each grid serves as the basic unit for path planning; (3) Selection of the movement position of the disinfection robot: Taking into account the energy consumption of the disinfection robot and the concentration of disinfectant in the indoor space, the next movement position of the disinfection robot is determined; (4) Escape from the dead zone of the disinfection robot: When the disinfection robot is surrounded by obstacles or the concentration of the disinfectant solution reaches the required level, it will quickly escape to the nearest area to be disinfected and continue working. The specific steps of step (1) are as follows: ① Static diffusion model of drug solution: ,in c ( x , y , z , t ) represents three-dimensional coordinates in space x , y , z Location in time t The concentration of the drug solution at that time For gas source concentration, The height of the spray source. R x , R y , R z These are diffusion parameters in three dimensions; under windless conditions in an indoor space, the diffusion parameters are functions of atmospheric stability and diffusion time: , ,in For diffusion time, and It is constant under different atmospheric stability conditions. When the atmospheric stability is classified as A, B, C, D, E, or F, ( , The values ​​are (0.93, 1.57), (0.76, 0.47), (0.55, 0.21), (0.47, 0.12), (0.44, 0.07), and (0.44, 0.05). ② Dynamic drug diffusion model: ,in, This represents the total concentration of the drug solution in the current area. x , y The coordinates represent the two-dimensional coordinates of the liquid medicine evaporating from the indoor space onto the ceiling plane. t For time parameters, t A = D / V , The concentration of the drug solution generated in a single time slice in the current region. Given the length of the current region, V This represents the average moving speed of the disinfection robot.

2. The path planning method for a disinfection robot according to claim 1, characterized in that, The specific steps of step (3) are as follows: ① Determine the current position: The current position of the disinfection robot is its position within the grid, i.e., the grid matrix element. x ij , where 1≤ i ≤ r ,1≤ j ≤ s ; ② Calculate the change in orientation angle: Based on the current position of the disinfection robot, the change in orientation angle can be calculated. ,in, , , These represent the current node, the parent node of the current node, and the uncovered node, respectively. ③ Calculate the turning reliability value: In order to control the direction of the disinfection robot and guide it to move towards uncovered areas, calculate the turning reliability value of the disinfection robot. ,in The angle of change of direction, π Pi; ④ Calculate movement cost: Based on the current position and turning confidence value of the disinfection robot, calculate the cost for the disinfection robot to move to each adjacent uncovered grid cell. , Y k The node with the largest value will be used as the next grid for the disinfection robot to move to. These are weighting coefficients, 1 ≤ k ≤ i, i is The number of uncovered grid cells connected to the current grid cell of the disinfection robot.

3. The path planning method for a disinfection robot according to claim 1, characterized in that, The specific steps of step (4) are as follows: ① Locate the distance between the current position and the disinfection robot. L , L A grid that starts at 1 and has not yet been completely disinfected. Q i , where 1≤ i ≤ n , n Indicates the number of grid cells that meet the condition; ② Calculate the distance the disinfection robot moves from its current position to each grid cell identified in step ①. G i ,1≤ i ≤ n ; ③ Determine distance L and distance G i ,1≤ i ≤ n Are they equal? ​​If L = G i If a grid cell is selected as the optimal grid cell, the disinfection robot will move to that optimal grid cell using the shortest path algorithm. If multiple optimal grid cells exist, any one of them will be selected as the disinfection robot's target. L With any G i If none of them are equal, proceed to step ④; ④ Search distance L Add 1, then proceed to step ① for a new round of searching.

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