Obstacle Avoidance Method for Embodied Robot, Embodied Robot, Computer Device and Medium

Abstract

The present application relates to an obstacle avoidance method for an embodied robot, an embodied robot, a computer device, and a medium. The method includes: when there is a need for path planning in the embodied robot, for any candidate position point in the target area, determining the obstacle association data corresponding to the candidate position point according to the number of times the target obstacle is encountered at the candidate position point during the historical working process of the embodied robot; the target area is the non-first working area of the embodied robot; the obstacle association data is used to characterize the probability of the existence of the target obstacle at the corresponding candidate position point; there is no obstacle under the candidate position point in the pre-constructed map corresponding to the target area; determining the target obstacle identifier corresponding to the candidate position point according to the obstacle association data corresponding to the candidate position point; and controlling the embodied robot to avoid the target obstacle according to the target obstacle identifiers corresponding to the candidate position points in the target area. Using this method can improve the obstacle avoidance effect of the embodied robot.

Description

Technical Field

[0001] This application relates to the field of embodied intelligence technology, and in particular to an obstacle avoidance method for an embodied robot, an embodied robot, a computer device, and a medium. Background Art

[0002] With the rapid development of technology, people's living standards have been continuously improved, and embodied robots (such as cleaning robots) have been widely used in people's daily lives.

[0003] Although embodied robots have been widely used in daily life, their sensor technology still has limitations and it is difficult to comprehensively detect all obstacles in advance. Usually, when an embodied robot faces blind spots, viewing angle limitations, or dynamic environments (such as opening and closing doors and curtains), it cannot show good obstacle avoidance effects.

[0004] Based on this, there is an urgent need to provide an obstacle avoidance method to improve the obstacle avoidance effect and operation flexibility of embodied robots. Summary of the Invention

[0005] Based on this, in view of the above technical problems, it is necessary to provide an obstacle avoidance method for an embodied robot, an embodied robot, a computer device, and a medium that can improve the obstacle avoidance effect of the embodied robot.

[0006] In a first aspect, this application provides an obstacle avoidance method for an embodied robot, including:

[0007] In the case where the embodied robot has a path planning requirement, for any candidate position point in the target area, according to the number of times the target obstacle is encountered at the candidate position point during the historical work process of the embodied robot, determine the obstacle association data corresponding to the candidate position point; the target area is the non-first working area of the embodied robot; the obstacle association data is used to characterize the probability that the corresponding candidate position point has a target obstacle; in the pre-constructed map corresponding to the target area, there is no obstacle under the candidate position point;

[0008] According to the obstacle association data corresponding to the candidate position point, determine the target obstacle identifier corresponding to the candidate position point;

[0009] According to the target obstacle identifiers corresponding to each candidate position point in the target area, control the embodied robot to avoid the target obstacle;

[0010] Among them, the obstacle association data corresponding to any candidate position point is determined based on the following method: taking the number of times the embodied robot encounters the target obstacle at the candidate position point during the historical work process as the first reference data; taking the sum of the number of times the embodied robot encounters the target obstacle at the candidate position point during the historical work process and 1 as the second reference data; taking the ratio of the first reference data to the second reference data as the obstacle association data corresponding to the candidate position point.

[0011] In one embodiment, the number of times the embodied robot encounters the target obstacle at the candidate position point during the historical work process is determined based on the following method:

[0012] Obtain the latest triggering situation of the environmental perception sensor of the embodied robot at the candidate position point;

[0013] When the latest triggering situation indicates that the environmental perception sensor is triggered at the candidate position point, increment the count by 1;

[0014] When the latest triggering situation indicates that the environmental perception sensor is not triggered at the candidate position point, subtract a preset value; the preset value is not less than 1; the minimum value of the count is 0, and the initial value of the count is 0.

[0015] In one embodiment, determining the target obstacle identifier corresponding to the candidate position point according to the obstacle association data corresponding to the candidate position point includes:

[0016] Determine the target obstacle identifier corresponding to the candidate position point according to the size relationship between the obstacle association data corresponding to the candidate position point and the sample data corresponding to the candidate position point; the value of the sample data is within a preset interval.

[0017] In one embodiment, the preset interval is [0, 1]; the sample data is randomly sampled from a uniform distribution within the preset interval.

[0018] In one embodiment, determining the target obstacle identifier corresponding to the candidate position point according to the size relationship between the obstacle association data corresponding to the candidate position point and the sample data corresponding to the candidate position point includes:

[0019] When the obstacle association data corresponding to the candidate position point is not greater than the sample data corresponding to the candidate position point, determine that the target obstacle identifier corresponding to the candidate position point is the first identifier;

[0020] When the obstacle association data corresponding to the candidate position point is greater than the sample data corresponding to the candidate position point, determine that the target obstacle identifier corresponding to the candidate position point is the second identifier;

[0021] The first identifier indicates that there is no target obstacle at the candidate position point; the second identifier indicates that there is a target obstacle at the candidate position point.

[0022] In one embodiment, controlling the embodied robot to avoid the target obstacle according to the target obstacle identifiers corresponding to the candidate position points in the target area includes:

[0023] Adjusting the initial obstacle identifiers corresponding to the corresponding candidate position points in the pre-constructed map of the target area according to the target obstacle identifiers corresponding to the candidate position points in the target area to generate a target map; wherein, the target map is used to guide the embodied robot in the current working process for the target area;

[0024] Generating a target planning path based on the target map; the target planning path is used to instruct the embodied robot to avoid the target obstacle.

[0025] In one embodiment, adjusting the initial obstacle identifiers corresponding to the corresponding candidate position points in the pre-constructed map of the target area according to the target obstacle identifiers corresponding to the candidate position points in the target area includes:

[0026] For any candidate position point, when the target obstacle identifier corresponding to the candidate position point is different from the corresponding initial obstacle identifier, replacing the initial obstacle identifier corresponding to the corresponding candidate position point in the pre-constructed map of the target area with the target obstacle identifier.

[0027] In one embodiment, generating a target planning path based on the target map includes:

[0028] Generating a target planning path based on the target map by using the artificial potential field method.

[0029] In a second aspect, the present application further provides an embodied robot, including:

[0030] A first determination module, configured to, when there is a path planning requirement for the embodied robot, for any candidate position point in the target area, determine the obstacle association data corresponding to the candidate position point according to the number of times the embodied robot encounters the target obstacle at the candidate position point during the historical working process; the target area is a non-first working area of the embodied robot; the obstacle association data is used to characterize the probability that there is a target obstacle at the corresponding candidate position point; in the pre-constructed map corresponding to the target area, there is no obstacle at the candidate position point;

[0031] A second determination module, configured to determine the target obstacle identifier corresponding to the candidate position point according to the obstacle association data corresponding to the candidate position point;

[0032] An obstacle avoidance module, configured to control the embodied robot to avoid the target obstacle according to the target obstacle identifiers corresponding to the candidate position points in the target area;

[0033] Among them, the obstacle association data corresponding to any candidate position point is determined based on the following method: taking the number of times the embodied robot encounters the target obstacle at the candidate position point during its historical work process as the first reference data; taking the sum value of the number of times the embodied robot encounters the target obstacle at the candidate position point during its historical work process and 1 as the second reference data; taking the ratio of the first reference data to the second reference data as the obstacle association data corresponding to the candidate position point.

[0034] In a third aspect, the present application also provides a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0035] In the case where the embodied robot has a path planning requirement, for any candidate position point in the target area, determine the obstacle association data corresponding to the candidate position point according to the number of times the embodied robot encounters the target obstacle at the candidate position point during its historical work process; the target area is the non-first working area of the embodied robot; the obstacle association data is used to characterize the probability that the corresponding candidate position point has a target obstacle; in the pre-constructed map corresponding to the target area, there is no obstacle under the candidate position point;

[0036] Determine the target obstacle identifier corresponding to the candidate position point according to the obstacle association data corresponding to the candidate position point;

[0037] Control the embodied robot to avoid the target obstacle according to the target obstacle identifiers corresponding to the candidate position points in the target area;

[0038] Among them, the obstacle association data corresponding to any candidate position point is determined based on the following method: taking the number of times the embodied robot encounters the target obstacle at the candidate position point during its historical work process as the first reference data; taking the sum value of the number of times the embodied robot encounters the target obstacle at the candidate position point during its historical work process and 1 as the second reference data; taking the ratio of the first reference data to the second reference data as the obstacle association data corresponding to the candidate position point.

[0039] In a fourth aspect, the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0040] In the case where the embodied robot has a path planning requirement, for any candidate position point in the target area, determine the obstacle association data corresponding to the candidate position point according to the number of times the embodied robot encounters the target obstacle at the candidate position point during its historical work process; the target area is the non-first working area of the embodied robot; the obstacle association data is used to characterize the probability that the corresponding candidate position point has a target obstacle; in the pre-constructed map corresponding to the target area, there is no obstacle under the candidate position point;

[0041] Determine the target obstacle identifier corresponding to the candidate position point according to the obstacle association data corresponding to the candidate position point;

[0042] Control the embodied robot to avoid the target obstacle according to the target obstacle identifiers corresponding to the candidate position points in the target area;

[0043] Wherein, the obstacle association data corresponding to any candidate position point is determined based on the following method: taking the number of times the embodied robot encounters the target obstacle at the candidate position point during the historical working process as the first reference data; taking the sum value of the number of times the embodied robot encounters the target obstacle at the candidate position point during the historical working process and 1 as the second reference data; taking the ratio of the first reference data to the second reference data as the obstacle association data corresponding to the candidate position point.

[0044] In a fifth aspect, the present application further provides a computer program product, including a computer program, which when executed by a processor implements the following steps:

[0045] In the case where the embodied robot has a path planning requirement, for any candidate position point in the target area, determine the obstacle association data corresponding to the candidate position point according to the number of times the embodied robot encounters the target obstacle at the candidate position point during the historical working process; the target area is a non-first working area of the embodied robot; the obstacle association data is used to characterize the probability that the corresponding candidate position point has a target obstacle; in the pre-constructed map corresponding to the target area, there is no obstacle at the candidate position point;

[0046] Determine the target obstacle identifier corresponding to the candidate position point according to the obstacle association data corresponding to the candidate position point;

[0047] Control the embodied robot to avoid the target obstacle according to the target obstacle identifiers corresponding to the candidate position points in the target area;

[0048] Wherein, the obstacle association data corresponding to any candidate position point is determined based on the following method: taking the number of times the embodied robot encounters the target obstacle at the candidate position point during the historical working process as the first reference data; taking the sum value of the number of times the embodied robot encounters the target obstacle at the candidate position point during the historical working process and 1 as the second reference data; taking the ratio of the first reference data to the second reference data as the obstacle association data corresponding to the candidate position point.

[0049] The obstacle avoidance method for the embodied robot, the embodied robot, the computer device and the medium described above, before the embodied robot performs path planning on the target area, for any candidate position point in the target area, determine the obstacle association data corresponding to the candidate position point according to the number of times the embodied robot encounters the target obstacle at the candidate position point during the historical working process; and determine the target obstacle identifier corresponding to the candidate position point according to the obstacle association data corresponding to the candidate position point; control the embodied robot to avoid the target obstacle according to the target obstacle identifiers corresponding to the candidate position points in the target area. Since the obstacle association data is the ratio between the number of times the embodied robot encounters the target obstacle at the candidate position point during the historical working process and the sum of the number and 1, it can to a certain extent characterize the probability that the target obstacle exists at the candidate position point. This probability will not be greatly affected by the embodied robot misjudging encountering the target obstacle once or twice at the candidate position point as the number of times of encountering the target obstacle increases, thereby making the target obstacle identifiers corresponding to each candidate position point more reasonable and accurate. Further, during the process of controlling the embodied robot to avoid the target obstacle according to the target obstacle identifiers corresponding to the candidate position points in the target area, a better obstacle avoidance effect can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related art, the following will briefly introduce the drawings required for use in the description of the embodiments of the present application or the related art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0051] Figure 1 It is an application environment diagram of the obstacle avoidance method for the embodied robot in an embodiment;

[0052] Figure 2 It is a flowchart of the obstacle avoidance method for the embodied robot in an embodiment;

[0053] Figure 3 It is a flowchart of the step of determining the number of times of encountering the target obstacle in an embodiment;

[0054] Figure 4 It is a flowchart of the step of determining the target obstacle identifier in an embodiment;

[0055] Figure 5 It is a flowchart of the step of controlling the embodied robot to avoid the target obstacle in an embodiment;

[0056] Figure 6Schematic flowchart of the obstacle avoidance method for an embodied robot in another embodiment;

[0057] Figure 7 Block diagram of an embodied robot in one embodiment;

[0058] Figure 8 Internal structure diagram of a computer device in one embodiment. Detailed implementation manners

[0059] In order to make the objectives, technical solutions and advantages of the present application clearer and more understandable, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0060] The obstacle avoidance method for an embodied robot provided by an embodiment of the present application can be applied to an application environment as Figure 1 shown. Among them, the embodied robot 102 communicates with the server 104 through a network. The embodied robot 102 includes, but is not limited to, humanoid robots, robotic arms, cleaning robots (such as floor sweeping robots, mopping robots or sweeping and mopping integrated robots), etc. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or can be placed in the cloud or other network servers. The server 104 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0061] In an exemplary embodiment, as Figure 2 shown, an obstacle avoidance method for an embodied robot is provided. Taking the embodied robot in Figure 1 as an example, the method includes the following steps:

[0062] S210. When there is a path planning requirement for the embodied robot, for any candidate position point in the target area, determine the obstacle association data corresponding to the candidate position point according to the number of times the target obstacle is encountered at the candidate position point during the historical work process of the embodied robot.

[0063] Among them, the target area can be the place where the embodied robot is about to perform various operations and tasks, that is, the work area to be executed by the embodied robot. Generally, the target area can be the non-first work area of the embodied robot. That is to say, the embodied robot has completed work in the target area at some moments before the current moment. Taking a cleaning robot as an example, the target area can be the area that the cleaning robot is about to clean, and the cleaning robot has cleaned this area before the current moment.

[0064] It can be understood that, under normal circumstances, before the embodied robot performs a work task on the target area, environmental data of the target area will be collected through detection sensors (such as lidar, vision sensors, etc.) on the embodied robot to construct a map corresponding to the target area based on the environmental data, that is, a pre-constructed map. In the pre-constructed map, position points with fixed obstacles in the target area will be marked according to the environmental data. Among them, the positions of the fixed obstacles in the target area are relatively fixed and will not move randomly, such as walls, cabinets, etc. In the pre-constructed map corresponding to the target area, there are no obstacles under the candidate position points (that is, when constructing the pre-constructed map corresponding to the target area according to the environmental data, the detection sensor does not detect obstacles under the candidate position points).

[0065] Among them, for any candidate position point, the obstacle association data is used to represent the probability that there is a target obstacle at this candidate position point. The target obstacle is a non-fixed obstacle, which can also be called a dynamic obstacle (such as a door that can be opened and closed, a curtain, etc.) or an undetectable obstacle. It can be understood that the target obstacle does not specifically refer to obstacles on the ground, but can also include obstacles in space. For example, obstacles that the body or arm of the embodied robot may encounter; the undetectable obstacles include transparent objects (such as glass walls) that the detection sensor cannot detect technically, or obstacles that cannot be detected due to the position limitation of the detection sensor installed on the embodied robot.

[0066] It can be understood that the path planning requirement can be generated by the embodied robot in response to a work instruction. Correspondingly, in an optional implementation manner, the embodied robot can, in response to the work instruction, obtain the number of times the embodied robot encounters a target obstacle at each candidate position point in the target area during the historical work process, and then determine the obstacle association data corresponding to the corresponding candidate position point according to each number. Exemplarily, for any candidate position point, the ratio of the number of times the embodied robot encounters a target obstacle at this candidate position point during the historical work process to the total number of times the embodied robot passes through this candidate position point during the historical work process can be used as the obstacle association data corresponding to this candidate position point.

[0067] In another alternative implementation, for any candidate position point, the number of times the embodied robot encounters a target obstacle at this candidate position point during its historical work process can be input into a pre-trained associated data determination model to obtain the obstacle associated data corresponding to this candidate position point. Among them, the associated data determination model can be constructed based on a common neural network, which will not be elaborated here. Further, during the process of training the associated data determination model, the number of sample times can be input into the associated data determination model to obtain the predicted associated data corresponding to the sample data. Then, based on the predicted associated data and the associated data label corresponding to the corresponding sample data, the model parameters of the associated data determination model can be adjusted to improve the model accuracy of the associated data determination model.

[0068] In yet another alternative implementation, the corresponding relationship between the reference number of times and the reference associated data can be determined in advance. Further, for any candidate position point, the reference associated data corresponding to the number of times the embodied robot encounters a target obstacle at this candidate position point during its historical work process in this corresponding relationship can be used as the obstacle associated data corresponding to this candidate position point.

[0069] To improve the referenceability and accuracy of the obstacle associated data, in still another alternative implementation, the obstacle associated data corresponding to any candidate position point can be determined based on the following method: the number of times the embodied robot encounters a target obstacle at this candidate position point during its historical work process is used as the first reference data; the sum value of the number of times the embodied robot encounters a target obstacle at this candidate position point during its historical work process and 1 is used as the second reference data; the ratio of the first reference data to the second reference data is used as the obstacle associated data corresponding to this candidate position point.

[0070] Exemplarily, the obstacle associated data corresponding to the candidate position point can be determined based on the following formula:

[0071] ;

[0072] In the formula, is the obstacle associated data corresponding to the position point (i, j) in the pre-constructed map; n is the number of times the embodied robot encounters a target obstacle at the position point (i, j) during its historical work process; (i, j) is the coordinate position in the pre-constructed map.

[0073] It should be noted that when n is 0, P ij is 0, indicating that there is no target obstacle at this place, which is consistent with the annotation on the pre-constructed map. When n approaches infinity, P ij approaches 1, indicating that there must be a target obstacle at this place. When 0 < n < infinity, 0 < P ij<1 means that although the target obstacle is encountered here, it is not considered that there is a target obstacle here with 100% certainty, but only that the probability of the existence of a target obstacle here is relatively high.

[0074] In addition, for the same candidate position point, the probability difference of the embodied robot continuously encountering the target obstacle at this candidate position point can be expressed as:

[0075] ;

[0076] In the formula, is the probability difference of the embodied robot continuously encountering the target obstacle at this candidate position point; is the probability that the embodied robot encounters the target obstacle n times at this candidate position point during the historical working process; P n-1 is the probability that the embodied robot encounters the target obstacle n - 1 times at this candidate position point during the historical working process; n is the number of times the embodied robot encounters the target obstacle at the position point (i, j) during the historical working process.

[0077] It can be seen that as n increases, will gradually decrease. That is to say, the influence of the embodied robot on the obstacle correlation data after each encounter with the target obstacle is gradually decreasing, rather than being consistent. Therefore, it will not have too much impact on P ij due to the embodied robot misjudging as encountering the target obstacle one or two times at this candidate position point. The referenceability of the obstacle correlation data determined based on the above formula is relatively large and the accuracy is relatively high.

[0078] In addition, it is worth noting that during the limited historical working process of the embodied robot, will only approach 1, but will not really be equal to 1. Therefore, there will always be a certain probability that the obstacle correlation data will regard the candidate position point as a passable area for trial passage, which can avoid considering that there must be a target obstacle at this candidate position point, so that during the subsequent task execution process of the embodied robot, the problem of always avoiding the target obstacle can be avoided, improving the working efficiency of the embodied robot. However, as the number of encounters with obstacles increases, the probability of trial passage will become lower and lower.

[0079] S220. Determine the target obstacle identifier corresponding to this candidate position point according to the obstacle correlation data corresponding to this candidate position point.

[0080] Among them, the target obstacle identifier is used to characterize the existence situation of the target obstacle at this candidate position point.

[0081] In an alternative embodiment, a quantity threshold for determining the target obstacle identifier may be preset, and the target obstacle identifier corresponding to the candidate location point is determined according to the magnitude relationship between the obstacle association data and the quantity threshold. The quantity threshold may be determined based on manual experience, and the present application does not make any limitation thereto. Exemplarily, the quantity threshold may be 0.5. Correspondingly, when the obstacle association data is greater than 0.5, it is determined that there is an obstacle for the target obstacle identifier corresponding to the candidate location point; when the obstacle association data is not greater than 0.5, it is determined that there is no obstacle for the target obstacle identifier corresponding to the candidate location point.

[0082] In another alternative embodiment, the obstacle association data may be input into a pre-trained identifier determination model to obtain the target obstacle identifier corresponding to the candidate location point. The identifier determination model may be constructed based on a common neural network, which will not be elaborated herein. Further, during the process of training the identifier determination model, the sample association data may be input into the identifier determination model to obtain the predicted identifier corresponding to the sample association data, and then the model parameters of the identifier determination model are adjusted according to the predicted identifier and the identifier label corresponding to the corresponding sample association data to improve the model accuracy of the identifier determination model.

[0083] S230, according to the target obstacle identifiers corresponding to the candidate location points in the target area, control the embodied robot to avoid the target obstacles.

[0084] Optionally, during the process of controlling the embodied robot to move in the target area, the embodied robot may be controlled to avoid the candidate location points where the target obstacle identifier indicates the existence of an obstacle, so as to control the embodied robot to avoid the target obstacles.

[0085] Generally, according to the target obstacle identifiers corresponding to the candidate location points in the target area, path planning for the current work of the embodied robot in the target area may be performed, and the embodied robot is controlled to work based on the planned path, so as to control the embodied robot to avoid the target obstacles.

[0086] In the obstacle avoidance method of the above-mentioned embodied robot, before the embodied robot performs path planning for the target area, for any candidate position point in the target area, according to the number of times the embodied robot encounters the target obstacle at this candidate position point during its historical work process, the obstacle association data corresponding to this candidate position point is determined; and according to the obstacle association data corresponding to this candidate position point, the target obstacle identifier corresponding to this candidate position point is determined; according to the target obstacle identifiers corresponding to each candidate position point in the target area, the embodied robot is controlled to avoid the target obstacle. Since the obstacle association data is the ratio between the number of times the embodied robot encounters the target obstacle at the candidate position point during its historical work process and the sum of this number and 1, it can to a certain extent characterize the probability that the target obstacle exists at this candidate position point. This probability will not be greatly affected by the embodied robot misjudging as encountering the target obstacle once or twice at this candidate position point as the number of times of encountering the target obstacle increases, thereby making the target obstacle identifiers corresponding to each candidate position point more accurate. Further, during the process of controlling the embodied robot to avoid the target obstacle according to the target obstacle identifiers corresponding to each candidate position point in the target area, a better obstacle avoidance effect can be achieved.

[0087] Based on the technical solutions of the above embodiments, the present application also provides an optional embodiment. In this optional embodiment, the determination process of the number of times the embodied robot encounters the target obstacle at the candidate position point during its historical work process is introduced.

[0088] For any candidate position point, refer to Figure 3 the steps for determining the number of times the embodied robot encounters the target obstacle at this candidate position point during its historical work process shown in

[0089] S310, obtain the latest trigger situation of the environmental perception sensor of the embodied robot at the candidate position point.

[0090] Among them, the environmental perception sensor can help the embodied robot perceive obstacles, so that it can intelligently plan paths and avoid obstacles to adapt to different environments. The environmental perception sensor can include a collision sensor, specifically, it can include at least one of a contact sensor and a vibration sensor (such as a collision switch, an Inertial Measurement Unit (IMU), a stress sensor, etc.) and a detection sensor.

[0091] Among them, the latest trigger situation characterizes the situation where the embodied robot touches the target obstacle at this candidate position point during its most recent work process, and can include two situations: triggered and not triggered.

[0092] It can be understood that during the movement of an embodied robot, if a collision is detected through an environmental perception sensor, but the pre-constructed map indicates that there is no obstacle at this position point, it can be considered that the target obstacle has been encountered, that is, an undetectable obstacle or a dynamic obstacle.

[0093] Specifically, in this embodiment, the latest triggering situation of the environmental perception sensor at this candidate position point during the historical work process of the embodied robot can be obtained.

[0094] S320, when the latest triggering situation indicates that the environmental perception sensor is triggered at the candidate position point, the count is incremented by 1.

[0095] Wherein, being triggered indicates that the embodied robot touched the target obstacle at this candidate position point during the most recent work process, that is, the environmental perception sensor detected a collision at this candidate position point.

[0096] S330, when the latest triggering situation indicates that the environmental perception sensor is not triggered at the candidate position point, the count is decreased by a preset value.

[0097] Wherein, not being triggered indicates that the embodied robot did not touch the target obstacle at this candidate position point during the most recent work process, that is, the environmental perception sensor did not detect a collision at this candidate position point.

[0098] Wherein, the preset value is not less than 1; the minimum value of the count is 0, and the initial value of the count is 0.

[0099] Specifically, in this embodiment, for any candidate position point, the situation of the embodied robot touching the target obstacle at this candidate position point during the historical work process can be counted. Each time an obstacle is touched at this position, it is incremented by 1, and when no obstacle is touched, it is decreased by 1, or a preset value of an integer greater than 1, such as 3 or 5, until it is reduced to 0, or directly subtract the current counted number (i.e., clear to zero).

[0100] In the above embodiments, a specific method for determining the number of times a target obstacle is encountered at each candidate position point during the historical working process of the embodied robot is given, making the determination of the number more clear. Further, for any candidate position point, when the latest trigger situation indicates that the environmental perception sensor is triggered at this candidate position point, the number is incremented by 1; when the latest trigger situation indicates that the environmental perception sensor is not triggered at this candidate position point, the number is decreased by a preset value; the preset value is not less than 1, that is, when the latest trigger situation of the embodied robot indicates that the environmental perception sensor is not triggered at this candidate position point, it means that the embodied robot does not detect a target obstacle at this candidate position point this time, indicating that there is probably no obstacle at this candidate position point. If the number of times of encountering a target obstacle is not equal to 0 before this time, the rate of decrease of the number of times of encountering a target obstacle n is increased, which can make the obstacle association data determined based on this number more accurate.

[0101] Based on the technical solutions of the above embodiments, the present application also provides an alternative embodiment. In this alternative embodiment, the process of determining the target obstacle identifier corresponding to the candidate position point according to the obstacle association data corresponding to the candidate position point is refined.

[0102] See Figure 4 The target obstacle identifier determination steps shown in

[0103] S410, determine the target obstacle identifier corresponding to the candidate position point according to the magnitude relationship between the obstacle association data corresponding to the candidate position point and the sample data corresponding to the candidate position point.

[0104] Among them, the value of the sample data is within a preset interval, and the preset interval is [0, 1]; the sample data is randomly sampled from a uniform distribution within the preset interval. That is to say, the sample data is any random number between [0, 1], and the sample data corresponding to different obstacle association data may be different.

[0105] Exemplarily, when the obstacle association data corresponding to the candidate position point is not greater than the sample data corresponding to the candidate position point, determine that the target obstacle identifier corresponding to the candidate position point is the first identifier; when the obstacle association data corresponding to the candidate position point is greater than the sample data corresponding to the candidate position point, determine that the target obstacle identifier corresponding to the candidate position point is the second identifier.

[0106] Among them, the first identifier indicates that there is no target obstacle at the candidate position point; the second identifier indicates that there is a target obstacle at the candidate position point.

[0107] Exemplarily, the target obstacle identifier corresponding to the candidate position point can be determined based on the following formula:

[0108] ;

[0109] In the formula, is the target obstacle identifier corresponding to the candidate position point. When equals 1, the corresponding target obstacle identifier is the second identifier; When it equals 0, the corresponding target obstacle identifier is the first identifier; is the obstacle association data corresponding to the position point (i, j) in the pre-built map; x is the sample data. , that is, x is the sampling result of a uniform distribution between 0 and 1. When x < , it is considered that there is a target obstacle at this candidate position point and avoidance should be carried out. At this time, the target obstacle identifier corresponding to this candidate position point is the second identifier; when x ≥ , it is considered that there is no target obstacle at this candidate position point and normal passage is possible. At this time, the target obstacle identifier corresponding to this candidate position point is the first identifier.

[0110] For example, if the obstacle association data corresponding to a certain candidate position point is 0.5 and the corresponding sample data is 0.3, then it is determined that the target obstacle identifier corresponding to this candidate position point is the second identifier, that is, it is determined that there is a target obstacle at this candidate position point. If the obstacle association data corresponding to a certain candidate position point is 0.5 and the corresponding sample data is 0.7, then it is determined that the target obstacle identifier corresponding to this candidate position point is the first identifier, that is, it is determined that there is no target obstacle at this candidate position point.

[0111] It should be noted that x is a random number sampled from a uniform distribution between 0 and 1, which can be 0.1, 0.2, 0.3, etc. The probability of any sampled random number is the same. Therefore, when is larger, the probability of x < is larger, and the probability that there is a target obstacle at this candidate position point is larger; when is smaller, the probability of x < is smaller, and the probability that there is a target obstacle at this candidate position point is smaller; taking as 0.8 as an example, the probability of x < is 80%, that is, the probability that there is a target obstacle at this candidate position point is 80%.

[0112] In the above embodiments, a specific method for determining the target obstacle identifier corresponding to the candidate position point is given. Specifically, based on the size relationship between the obstacle association data corresponding to the candidate position point and the corresponding sample data, the target obstacle corresponding to the corresponding candidate position point is determined, making the determination method of the target obstacle identifier simpler. In addition, since the sample data is a random number between [0, 1], the probability of any random number sampled is the same. During the limited number of task executions of the robot, it will only approach 1 and will not actually be equal to 1. Therefore, through this sample data, there will always be a certain probability of treating the candidate position point as a passable area for trial passage. However, as the number of encounters with obstacles increases, the probability of trial passage will become lower and lower, making the target obstacle identifier more referenceable.

[0113] Based on the technical solutions of the above embodiments, the present application also provides an alternative embodiment. In this alternative embodiment, the step of controlling the embodied robot to avoid the target obstacle according to the target obstacle identifiers corresponding to the candidate position points in the target area is refined.

[0114] See Figure 5 the steps of controlling the embodied robot to avoid the target obstacle shown in the figure, including:

[0115] S510, according to the target obstacle identifiers corresponding to the candidate position points in the target area, adjust the initial obstacle identifiers corresponding to the corresponding candidate position points in the pre-constructed map of the target area to generate a target map.

[0116] Among them, for any candidate position point, the corresponding initial obstacle identifier is used to represent the obstacle identifier of this position point in the pre-constructed map. Usually, the initial obstacle identifier corresponding to the candidate position point is the first identifier, that is, there is no fixed obstacle at the candidate position point.

[0117] Among them, the target map is used to guide the embodied robot for the current working process of the target area. That is to say, when the embodied robot works on the target area next time, its corresponding target map is still re-determined based on the method provided in the embodiments of the present application.

[0118] Exemplarily, for any candidate position point, when the target obstacle identifier corresponding to this candidate position point is different from the corresponding initial obstacle identifier, replace the initial obstacle identifier corresponding to the corresponding candidate position point in the pre-constructed map of the target area with the target obstacle identifier.

[0119] Exemplarily, the target obstacle identifiers corresponding to the candidate position points can be fused with the pre-constructed map of the target area to form a fused map, and the fused map is used as the target map. For example:

[0120] ;

[0121] In the formula, represents the target map; represents the updated obstacle identifier corresponding to the candidate position point (i, j) in the target map; is the target obstacle identifier corresponding to this candidate position point, when it is equal to 1, the corresponding target obstacle identifier is the second identifier; when it is equal to 0, the corresponding target obstacle identifier is the first identifier; represents the initial obstacle identifier corresponding to this candidate position point; is the obstacle association data corresponding to the position point (i, j) in the pre-constructed map.

[0122] Based on this, the target obstacle identifiers corresponding to each candidate position point in the target map are updated.

[0123] S520. Generate a target planned path based on the target map.

[0124] Among them, the target planned path is used to instruct the embodied robot to avoid obstacles of the target obstacle.

[0125] Exemplarily, based on the target map, the artificial potential field method can be used to generate the target planned path to achieve path planning by simulating the movement of the embodied robot in the attraction and repulsion force fields, and obstacles can be effectively avoided; or based on the target map, the grid method can be used to generate the target planned path; or based on the target map, the random covering method can be used to generate the target planned path.

[0126] In the above embodiments, specific ways of controlling the embodied robot to avoid obstacles of the target obstacle according to the target obstacle identifiers corresponding to each candidate position point in the target area are given.

[0127] It should be noted that during the process of the embodied robot working on the target area this time, the latest triggering situations of the environmental perception sensors of the embodied robot at each candidate position point will be recorded for future use.

[0128] Based on the technical solutions of the above embodiments, the present application also provides an alternative embodiment. In this alternative embodiment, the obstacle avoidance method of the embodied robot provided by the present application is introduced in detail.

[0129] See Figure 6 The obstacle avoidance method of the embodied robot shown, including:

[0130] S610. Determine the number of times the embodied robot encounters a target obstacle at each candidate position point in the target area during its historical work process;

[0131] S620. For any candidate position point, use the number of times the embodied robot encounters a target obstacle at this candidate position point during its historical work process as the first reference data;

[0132] S630. Use the sum of the number of times the embodied robot encounters a target obstacle at this candidate position point during its historical work process and 1 as the second reference data;

[0133] S640. Use the ratio of the first reference data to the second reference data as the obstacle association data corresponding to this candidate position point;

[0134] S650. When the embodied robot has a path planning requirement, for any candidate position point in the target area, obtain the obstacle association data corresponding to this candidate position point;

[0135] S660A. When the obstacle association data corresponding to this candidate position point is not greater than the sample data corresponding to the candidate position point, determine that the target obstacle identifier corresponding to this candidate position point is the first identifier;

[0136] S660B. When the obstacle association data corresponding to this candidate position point is greater than the sample data corresponding to the candidate position point, determine that the target obstacle identifier corresponding to this candidate position point is the second identifier;

[0137] Wherein, the first identifier indicates that there is no target obstacle at the candidate position point; the second identifier indicates that there is a target obstacle at the candidate position point.

[0138] S670. For any candidate position point, when the target obstacle identifier corresponding to the candidate position point is different from the corresponding initial obstacle identifier, replace the initial obstacle identifier corresponding to the corresponding candidate position point in the pre-constructed map of the target area with the target obstacle identifier;

[0139] Wherein, the target map is used to guide the embodied robot during the current work process for the target area;

[0140] S680. Based on the target map, use the artificial potential field method to generate a target planning path;

[0141] Wherein, the target planning path is used to indicate the embodied robot to avoid target obstacles.

[0142] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are sequentially shown according to the indications of the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless specifically stated herein, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or steps or stages in other steps.

[0143] Based on the same inventive concept, an embodiment of the present application further provides an embodied robot for implementing the obstacle avoidance method of the embodied robot involved above. The solution provided by this device to solve the problem is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the embodied robot provided below can refer to the limitations on the obstacle avoidance method of the embodied robot in the above text, and will not be repeated here.

[0144] In an exemplary embodiment, as Figure 7 shown, an embodied robot is provided, including: a first determination module 710, a second determination module 720, and an obstacle avoidance module 730, where:

[0145] The first determination module 710 is configured to, when there is a need for path planning for the embodied robot, for any candidate position point in the target area, determine the obstacle association data corresponding to the candidate position point according to the number of times the target obstacle is encountered at the candidate position point during the historical working process of the embodied robot; the target area is the non-first working area of the embodied robot; the obstacle association data is used to characterize the probability that the target obstacle exists at the corresponding candidate position point; in the pre-constructed map corresponding to the target area, there is no obstacle at the candidate position point;

[0146] The second determination module 720 is configured to determine the target obstacle identifier corresponding to the candidate position point according to the obstacle association data corresponding to the candidate position point;

[0147] The obstacle avoidance module 730 is configured to control the embodied robot to avoid the target obstacle according to the target obstacle identifiers corresponding to the candidate position points in the target area;

[0148] Among them, the obstacle association data corresponding to any candidate position point is determined based on the following method: the number of times the embodied robot encounters the target obstacle at the candidate position point during the historical working process is used as the first reference data; the sum value of the number of times the embodied robot encounters the target obstacle at the candidate position point during the historical working process and 1 is used as the second reference data; the ratio of the first reference data to the second reference data is used as the obstacle association data corresponding to the candidate position point.

[0149] In one embodiment, the embodied robot further includes a third determination module, including an acquisition unit configured to acquire the latest triggering situation of the environmental perception sensor of the embodied robot at the candidate position point; a first update unit configured to increment the number of times when the latest triggering situation indicates that the environmental perception sensor is triggered at the candidate position point; a second update unit configured to decrement the number of times by a preset value when the latest triggering situation indicates that the environmental perception sensor is not triggered at the candidate position point; the preset value is not less than 1; the minimum value of the number of times is 0, and the initial value of the number of times is 0.

[0150] In one embodiment, the second determination module 720 is specifically configured to determine the target obstacle identifier corresponding to the candidate position point according to the magnitude relationship between the obstacle association data corresponding to the candidate position point and the sample data corresponding to the candidate position point; the value of the sample data is within a preset interval. The preset interval is [0, 1]; the sample data is randomly sampled from a uniform distribution within the preset interval.

[0151] In one embodiment, the second determination module 720 includes a first determination unit configured to determine that the target obstacle identifier corresponding to the candidate position point is the first identifier when the obstacle association data corresponding to the candidate position point is not greater than the sample data corresponding to the candidate position point; a second determination unit configured to determine that the target obstacle identifier corresponding to the candidate position point is the second identifier when the obstacle association data corresponding to the candidate position point is greater than the sample data corresponding to the candidate position point; the first identifier indicates that there is no target obstacle at the candidate position point; the second identifier indicates that there is a target obstacle at the candidate position point.

[0152] In one embodiment, the obstacle avoidance module 730 includes a first generation unit configured to adjust the initial obstacle identifier corresponding to the corresponding candidate position point in the pre-constructed map of the target area according to the target obstacle identifier corresponding to each candidate position point in the target area to generate a target map; wherein, the target map is used to guide the embodied robot for the current working process of the target area; a second generation unit configured to generate a target planning path based on the target map; the target planning path is used to instruct the embodied robot to avoid the target obstacle.

[0153] In one embodiment, the first generating unit is specifically configured to, for any candidate position point, when the target obstacle identifier corresponding to the candidate position point is different from the corresponding initial obstacle identifier, replace the initial obstacle identifier corresponding to the corresponding candidate position point in the pre-constructed map of the target area with the target obstacle identifier.

[0154] In one embodiment, the second generating unit is specifically configured to generate a target planned path based on the target map by using the artificial potential field method.

[0155] Each module in the above-mentioned embodied robot can be implemented in whole or in part by software, hardware, and their combination. The above-mentioned modules can be embedded in the processor in the computer device in the form of hardware or be independent of it, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above-mentioned modules.

[0156] In an exemplary embodiment, a computer device is provided. The computer device can be a terminal, and its internal structure diagram can be as Figure 8 shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface, the display unit, and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner. The wireless manner can be implemented through WIFI, a mobile cellular network, near field communication (NFC), or other technologies. The computer program, when executed by the processor, implements an obstacle avoidance method for an embodied robot. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covered on the display screen, or a button, a trackball, or a touchpad set on the shell of the computer device, or an external keyboard, a touchpad, or a mouse, etc.

[0157] Those skilled in the art can understand, Figure 8The structure shown is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0158] In an exemplary embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are implemented.

[0159] In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0160] In an embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0161] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in this application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in this application can be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, data processing logics based on quantum computing, artificial intelligence (AI) processors, etc., without limitation.

[0162] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as within the scope recorded in this application.

[0163] The above embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation to the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. An obstacle avoidance method for an embodied robot, characterized in that: The method comprises: In the case where the embodied robot has a need for path planning, for any candidate position point in the target area, the obstacle association data corresponding to the candidate position point is determined according to the number of times the embodied robot encounters the target obstacle at the candidate position point during its historical working process; the target area is a non-first working area of ​​the embodied robot; the obstacle association data is used to characterize the probability that the target obstacle exists at the corresponding candidate position point; in the pre-constructed map corresponding to the target area, there is no obstacle under the candidate position point; When the obstacle-associated data corresponding to the candidate position point is not greater than the sample data corresponding to the candidate position point, the target obstacle identifier corresponding to the candidate position point is determined to be a first identifier; the first identifier indicates that there is no target obstacle at the candidate position point; the value of the sample data is within a preset interval; the preset interval is [0, 1]; the sample data is randomly sampled from a uniform distribution in the preset interval; When the obstacle-related data corresponding to the candidate position point is greater than the sample data corresponding to the candidate position point, determining that the target obstacle identifier corresponding to the candidate position point is a second identifier; the second identifier indicates that a target obstacle exists at the candidate position point; Controlling the embodied robot to avoid the target obstacle according to the target obstacle identifier corresponding to each candidate position point in the target area; Among them, the obstacle association data corresponding to any candidate position point is determined based on the following method: the number of times the embodied robot encounters the target obstacle at the candidate position point during its historical working process is used as the first reference data; the sum of the number of times the embodied robot encounters the target obstacle at the candidate position point during its historical working process and 1 is used as the second reference data; the ratio of the first reference data to the second reference data is used as the obstacle association data corresponding to the candidate position point.

2. The method according to claim 1, characterized in that The number of times the embodied robot encounters a target obstacle at the candidate position during its historical operation is determined based on the following method: Obtain the latest triggering status of the environment perception sensor of the embodied robot at the candidate position point; When the latest triggering condition indicates that the environment perception sensor is triggered at the candidate position point, the number of times is increased by 1; When the latest triggering condition indicates that the environment perception sensor is not triggered at the candidate location point, the number of times is reduced by a preset value; the preset value is not less than 1; The minimum value of the number is 0, and the initial value of the number is 0.

3. The method according to claim 1, characterized in that The target obstacle identifier is used to characterize the existence of the target obstacle at the candidate location point.

4. The method according to claim 2, characterized in that: The environmental perception sensor includes a collision sensor.

5. The method according to claim 4, characterized in that The collision sensor includes at least one of a contact sensor, a vibration sensor and a detection sensor.

6. The method according to any one of claims 1 to 5, characterized in that The step of controlling the embodied robot to avoid the target obstacle according to the target obstacle identifier corresponding to each candidate position point in the target area includes: According to the target obstacle identifiers corresponding to the candidate positions in the target area, the initial obstacle identifiers corresponding to the corresponding candidate positions in the pre-constructed map of the target area are adjusted to generate a target map; wherein the target map is used to guide the embodied robot in the current working process of the target area; Based on the target map, a target planning path is generated; the target planning path is used to instruct the embodied robot to avoid target obstacles.

7. The method according to claim 6, characterized in that The adjusting, according to the target obstacle identifiers corresponding to the candidate position points in the target area, the initial obstacle identifiers corresponding to the corresponding candidate position points in the pre-built map of the target area comprises: For any candidate position point, when the target obstacle identifier corresponding to the candidate position point is different from the corresponding initial obstacle identifier, the initial obstacle identifier corresponding to the corresponding candidate position point in the pre-constructed map of the target area is replaced with the target obstacle identifier.

8. The method according to claim 6, characterized in that The generating a target planning path based on the target map includes: Based on the target map, an artificial potential field method is used to generate a target planning path.

9. An embodied robot, characterized in that: The embodied robot comprises: A first determination module is used to determine, for any candidate position point in a target area, obstacle association data corresponding to the candidate position point according to the number of times the embodied robot encounters a target obstacle at the candidate position point during its historical working process, when there is a need for path planning for the embodied robot; the target area is a non-first working area of ​​the embodied robot; the obstacle association data is used to characterize the probability that a target obstacle exists at the corresponding candidate position point; and in a pre-constructed map corresponding to the target area, no obstacle exists under the candidate position point; A second determination module is used to determine that the target obstacle identifier corresponding to the candidate position point is a first identifier when the obstacle-associated data corresponding to the candidate position point is not greater than the sample data corresponding to the candidate position point; the first identifier indicates that there is no target obstacle at the candidate position point; the value of the sample data is within a preset interval; the preset interval is [0, 1]; the sample data is randomly sampled from a uniform distribution in the preset interval; when the obstacle-associated data corresponding to the candidate position point is greater than the sample data corresponding to the candidate position point, determine that the target obstacle identifier corresponding to the candidate position point is a second identifier; the second identifier indicates that there is a target obstacle at the candidate position point; An obstacle avoidance module, used to control the embodied robot to avoid target obstacles according to target obstacle identifiers corresponding to each candidate position point in the target area; Among them, the obstacle association data corresponding to any candidate position point is determined based on the following method: the number of times the embodied robot encounters the target obstacle at the candidate position point during its historical working process is used as the first reference data; the sum of the number of times the embodied robot encounters the target obstacle at the candidate position point during its historical working process and 1 is used as the second reference data; the ratio of the first reference data to the second reference data is used as the obstacle association data corresponding to the candidate position point.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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