A distributed collaborative scale decision-making method and device for multi-UAV formation obstacle avoidance

Abstract

The present invention discloses a distributed collaborative scale decision method and device for obstacle avoidance of a multi-UAV formation, including: establishing a mapping relationship from obstacle information to the expected local scale of each UAV in the multi-UAV formation; constructing a smoothing filter based on the expected local scale, the smoothing filter is used to generate a local scale obstacle avoidance strategy; based on the local scale obstacle avoidance strategy, generating a distributed collaborative scale decision of the UAV formation, the distributed collaborative decision is used to indicate a consistent scale parameter to each UAV. The method only performs a scale transformation of the formation, while the relative directions of the UAVs do not change, and has stronger intelligence and adaptability.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle (UAV) technology, and in particular to a distributed collaborative scale decision-making method and device for multi-UAV formation obstacle avoidance. Background Art

[0002] With the development of robotics technology, drones have gradually entered people's lives. They are used in aerial photography, express delivery, power grid inspection, etc. For a system composed of multiple drones, formation behavior is a very important collaborative behavior. Multi-drone formation technology has been applied to regional monitoring, collaborative transportation, target tracking, etc. Obstacles are commonly encountered in actual mission execution. Therefore, intelligent obstacle avoidance methods are very important for systems composed of multiple drones.

[0003] In recent years, formation methods have been widely studied by scholars, including formation methods based on consistency, angular rigidity, and affine transformation, etc. These methods can partially solve the problem of tracking and maintaining the desired formation of multiple UAVs. However, there is still a lack of research on how to design the desired formation. Mainly, the current desired formation is still published in a centralized manner, especially for obstacle avoidance, the desired formation after obstacle avoidance, and the distributed desired formation generation method that is more suitable for multiple UAVs. There is almost no relevant research, especially to achieve directional formation transformation, that is, only the scale of the formation is transformed, and the relative direction between UAVs does not change. This is especially important for vision-based positioning formations, but there is no relevant research in the existing technology.

[0004] Therefore, in order to solve such a problem, the present invention proposes a distributed collaborative scale decision-making method and device for multi-UAV formation obstacle avoidance. Summary of the invention

[0005] In view of this, the present invention provides a distributed collaborative scale decision method and device for multi-UAV formation obstacle avoidance, which can solve the technical problems of multi-UAV formation obstacle avoidance, the expected formation after obstacle avoidance, and the generation of distributed expected formations suitable for multiple UAVs.

[0006] In order to solve the above technical problems, the present invention is implemented as follows.

[0007] A distributed collaborative scale decision-making method for multi-UAV formation obstacle avoidance, comprising:

[0008] Step S1: Obtain obstacle information, and in a multi-UAV formation formed by multiple UAVs, establish a mapping relationship between the obstacle information and the expected local scale of each UAV in the multi-UAV formation, wherein the expected local scale is an unprocessed scale obtained according to the obstacle information, and is used to characterize the scaling scale of the space occupied by the corresponding UAV in the formation;

[0009] Step S2: constructing a smoothing filter based on the desired local scale, wherein the smoothing filter is used to generate a local scale obstacle avoidance strategy;

[0010] Step S3: Based on the local scale obstacle avoidance strategy, a distributed collaborative scale decision of the UAV formation is generated, and the distributed collaborative decision is used to indicate a consistent scale parameter to each UAV.

[0011] Preferably, the step S1, establishing a mapping relationship between the obstacle information and the expected local scale of each drone in the multi-drone formation, includes:

[0012] For each drone in the multi-drone formation:

[0013] The generated vector field f of the drone numbered i i =[f ix f iy f iz ] T , where i is the number of the drone in the multi-drone formation, 1≤i≤num, num is the size of the multi-drone formation, and f ix , f iy , f iz are the components of the vector field in the x, y, and z directions respectively, and T is the transpose;

[0014] According to the vector field, a mapping relationship is constructed, where the mapping relationship is a correspondence between the obstacle information and the desired local scale:

[0015]

[0016] is the expected local scale of the UAV numbered i according to the obstacle information, where κ nom and κ min Represent the nominal scale and the minimum scale respectively, both of which are pre-given fixed values, satisfying κ nom >κ min >0. δ>0 is a parameter that can adjust the mapping relationship. Proj(f i ) represents the influence of the vector field and is defined as follows:

[0017]

[0018] Among them, f i is the vector field generated by the sensor system of the UAV numbered i, r i Indicates the current formation;

[0019] r i =p i -pref

[0020] p i Indicates the current spatial position of the drone numbered i.

[0021] Preferably, the constructing of a smoothing filter, wherein the smoothing filter is used to generate a local scale obstacle avoidance strategy, comprises:

[0022] A smoothing filter is constructed, and the smoothing filter is used to implement the following smoothing filter calculation for each drone, that is, for each drone:

[0023]

[0024]

[0025] in and and Respectively represent the local scale obstacle avoidance strategy after filtering The upper bound and The upper bound of and are all pre-given parameters that can characterize the strength of filter smoothing; k1>0, k2>0 are control gains, v i is the auxiliary variable of the drone numbered i, is the derivative of the local obstacle avoidance scale.

[0026] Preferably, generating the distributed collaborative scale decision of the UAV formation based on the local scale obstacle avoidance strategy includes:

[0027] For each drone in the multi-drone formation, perform the following operations:

[0028] Construct a consistency filter to avoid the local scale obstacle The consistency filter uses the following consistency filtering algorithm

[0029]

[0030] Among them, α>1, β>0, α, β are gains, κ i , and They represent the scale parameter, scale parameter derivative and scale parameter second-order derivative of the UAV numbered i, respectively. and They represent the local-scale obstacle avoidance strategy, the local-scale obstacle avoidance strategy derivative, and the second-order derivative of the local-scale obstacle avoidance strategy of the UAV numbered i, respectively. represents the set of all neighboring drones of the drone numbered i, κ j is the scale parameter of the UAV numbered j, is the scale parameter derivative of the UAV numbered j, ψ and To give the control parameters, the following conditions need to be met:

[0031]

[0032] Among them, μ represents the difference in the local scale obstacle avoidance strategies corresponding to any two UAVs, and its specific value is:

[0033]

[0034] Among them, i and j are any two drone numbers, for The upper bound of and Respectively The upper bound and The upper bound of χ i , χ j are all algebraic symbols, for The upper bound of the derivative is, for The second derivative of the upper bound of ;

[0035] Based on the consistency filter, all drones can obtain the consistency scale parameter κ i , that is, the collaborative scale decision of the formation, and then tracking the desired position of each UAV based on the formation control.

[0036] A distributed collaborative scale decision-making device for multi-UAV formation obstacle avoidance, comprising:

[0037] A mapping module: configured to obtain obstacle information, and establish a mapping relationship between the obstacle information and the expected local scale of each drone in the multi-drone formation formed by the plurality of drones, wherein the expected local scale is an unprocessed scale obtained according to the obstacle information, and is used to characterize the scaling scale of the space occupied by the corresponding drone in the formation;

[0038] A local obstacle avoidance module: configured to construct a smoothing filter based on the desired local scale, wherein the smoothing filter is used to generate a local scale obstacle avoidance strategy;

[0039] The collaboration module is configured to generate a distributed collaborative scale decision of the UAV formation based on the local scale obstacle avoidance strategy, wherein the distributed collaborative decision is used to indicate a consistent scale parameter to each UAV.

[0040] A system, characterized in that the system comprises:

[0041] A processor, which is used to execute multiple instructions;

[0042] A memory for storing a plurality of instructions;

[0043] The plurality of instructions are used to be stored by the memory and loaded and executed by the processor to implement the method as described above.

[0044] A computer-readable storage medium stores a plurality of instructions; the plurality of instructions are used for a processor to load and execute the method as described above.

[0045] Beneficial effects:

[0046] (1) The present invention proposes a distributed collaborative scale decision-making method for multi-UAV formation obstacle avoidance, which can solve the formation obstacle avoidance problem.

[0047] (2) The present invention adopts a distributed method, which is more intelligent and adaptable than the traditional centralized formation obstacle avoidance method.

[0048] (3) The present invention can realize directional formation transformation, that is, only the scale of the formation is transformed, and the relative direction of the UAVs does not change. This is especially important for vision-based positioning formations, but it is difficult to achieve in traditional distributed formation obstacle avoidance methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 A schematic diagram of the process flow of the distributed collaborative scale decision-making method for multi-UAV formation obstacle avoidance provided by the present invention;

[0050] Figure 2 A schematic diagram of the trajectory of the formation obstacle avoidance simulation result provided by the present invention;

[0051] Figure 3 A schematic diagram of a local scale obstacle avoidance strategy after smoothing and filtering of formation obstacle avoidance simulation results provided by the present invention;

[0052] Figure 4 A schematic diagram of collaborative scale decision after final consistent filtering of formation obstacle avoidance simulation results provided by the present invention;

[0053] Figure 5 A schematic diagram of the structure of a distributed collaborative scale decision-making device for multi-UAV formation obstacle avoidance provided by the present invention. DETAILED DESCRIPTION

[0054] The present invention is described in detail below in conjunction with the accompanying drawings and embodiments.

[0055] like Figure 1 As shown, the present invention proposes a distributed collaborative scale decision-making method for multi-UAV formation obstacle avoidance, comprising the following steps:

[0056] Step S1: Obtain obstacle information, and in a multi-UAV formation formed by multiple UAVs, establish a mapping relationship between the obstacle information and the expected local scale of each UAV in the multi-UAV formation, wherein the expected local scale is an unprocessed scale obtained according to the obstacle information, and is used to characterize the scaling scale of the space occupied by the corresponding UAV in the formation;

[0057] Step S2: constructing a smoothing filter based on the desired local scale, wherein the smoothing filter is used to generate a local scale obstacle avoidance strategy;

[0058] Step S3: Based on the local scale obstacle avoidance strategy, a distributed collaborative scale decision of the UAV formation is generated, and the distributed collaborative decision is used to indicate a consistent scale parameter to each UAV.

[0059] The step S1 is to obtain obstacle information, and establish a mapping relationship between the obstacle information and the expected local scale of each drone in the multi-drone formation formed by multiple drones, wherein:

[0060] In a UAV formation, since the obstacle avoidance problem is related to the three-dimensional unknowns of each UAV, each UAV is abstracted as a three-degree-of-freedom particle model.

[0061] n drones form a formation, mission trajectory Describes the spatial trajectory of the formation. The mission trajectory is a spatial trajectory that is determined by the formation mission and is a trajectory for the formation as a whole. The mission trajectory is known in advance by all the drones in the multi-drone formation; fixed nominal formation It describes the spatial position of each drone in the multi-drone formation relative to the target trajectory. The expected trajectory (position) of each drone of the target needs to be calculated based on the mission trajectory, parameters and nominal formation. The nominal formation is known in advance by all drones in the multi-drone formation. The drone swarm, that is, the multi-drone formation can be scaled. The purpose of the scale transformation is to control the size of the multi-drone swarm formation and reduce the overall formation size. In particular, for narrow areas, the overall formation size can be reduced by reducing the formation scale, thereby achieving the obstacle avoidance function. This transformation is controlled by the parameter κ i Therefore, the expected position of each UAV in the multi-UAV formation is The desired local scale is calculated independently by each drone.

[0062] The multi-UAV formation senses obstacles in the environment during flight. Based on the sensors carried by each UAV, the UAV sensor system generates a vector field f for each UAV: i =[fix f iy f iz ] T , where i is the number of the drone in the multi-drone formation, 1≤i≤num, num is the size of the multi-drone formation, and f ix , f iy , f iz are the components of the vector field in the x, y, and z directions respectively, and T is the transpose.

[0063] According to the vector field, a mapping relationship is constructed, where the mapping relationship is a correspondence between the obstacle information and the desired local scale:

[0064]

[0065] is the expected local scale of the UAV numbered i according to the obstacle information, where κ nom and κ min Represent the nominal scale and the minimum scale respectively, both of which are pre-given fixed values, satisfying κ nom >κ min >0. δ>0 is a parameter that can adjust the mapping relationship. Proj(f i ) represents the influence of the vector field and is defined as follows:

[0066]

[0067] Among them, f i is the vector field generated by the sensor system of the UAV numbered i, r i Indicates the current formation;

[0068] r i =p i -p ref

[0069] p i Indicates the current spatial position of the drone numbered i.

[0070] The step S2, wherein:

[0071] The local scale obstacle avoidance strategy satisfies the following dynamics:

[0072]

[0073] in represents the virtual input used to drive formula (2), is the second-order derivative of the local-scale obstacle avoidance strategy of the UAV numbered i, represents the local scale obstacle avoidance strategy of the UAV numbered i. The local scale obstacle avoidance strategy includes a scale parameter, a scale parameter derivative, and a second-order derivative of the scale parameter. The information of the local scale obstacle avoidance strategy can be directly calculated or integrated by formula (2).

[0074] A smoothing filter is constructed, and the smoothing filter is used to implement the following smoothing filter calculation for each drone, that is, for each drone:

[0075]

[0076]

[0077] in and and Respectively represent the local scale obstacle avoidance strategy after filtering The upper bound and The upper bound of and are all pre-given parameters that can characterize the strength of filter smoothing; k1>0, k2>0 are control gains, v i is the auxiliary variable of the drone numbered i, is the derivative of the local obstacle avoidance scale.

[0078] Based on formulas (2)-(4), solve the local scale obstacle avoidance strategy of UAV numbered i: and According to formula (3) and formula (4), we can know that the local scale obstacle avoidance strategy after filtering is The derivative is bounded, that is

[0079] The step S3: based on the local scale obstacle avoidance strategy, generating a distributed collaborative scale decision of the UAV formation, wherein the distributed collaborative decision is used to indicate a consistent scale parameter to each UAV, wherein:

[0080] For each drone in the multi-drone formation, perform the following operations:

[0081] Construct a consistency filter to avoid the local scale obstacle The consistency filter uses the following consistency filtering algorithm

[0082]

[0083] Among them, α>1, β>0, α, β are gains, κ i , and They represent the scale parameter, scale parameter derivative and scale parameter second-order derivative of the UAV numbered i, respectively. and They respectively represent the local-scale obstacle avoidance strategy, the local-scale obstacle avoidance strategy derivative, and the local-scale obstacle avoidance strategy second-order derivative (calculated in step S2) of the UAV numbered i, represents the set of all neighboring drones of the drone numbered i, κ j is the scale parameter of the UAV numbered j, is the scale parameter derivative of the UAV numbered j, ψ and To give the control parameters, the following conditions need to be met:

[0084]

[0085] Among them, μ represents the difference in the local scale obstacle avoidance strategies corresponding to any two UAVs, and its specific value is:

[0086]

[0087] Among them, i and j are any two drone numbers, (2) It can be seen for The upper bound of and Respectively The upper bound and The upper bound of χ i , χ j are all algebraic symbols, for The upper bound of the derivative, for The upper bound of the second-order derivative is .

[0088] Using this consistency filter, all drones can obtain a consistent scale parameter κ i , that is, the collaborative scale decision of the formation, and then track the expected position of each UAV based on the formation control:

[0089] For the UAV numbered i, its expected formation position is

[0090] Get the expected position of all drones, since the size of the drone formation is determined by the parameter κ i Changes have occurred, therefore, enabling formation obstacle avoidance.

[0091] The present invention further provides an embodiment to illustrate the distributed collaborative scale decision-making method for multi-UAV formation obstacle avoidance of the present invention.

[0092] According to the proposed method, simulation experiments were carried out and the results are shown in Figure 2-Figure 4 As shown in Figure 2, a team of drones can complete the formation obstacle avoidance task. The smoothed local obstacle avoidance strategy generated by each drone based on the obstacle environment is shown in Figure 2. Figure 3 As shown, Figure 4 The collaborative scale decision results after the final consistent filtering are displayed, showing that a consistent final formation scale decision is consistently maintained in the task.

[0093] The present invention also provides a distributed collaborative scale decision-making device for multi-UAV formation obstacle avoidance, such as Figure 5 As shown, the device comprises:

[0094] A mapping module: configured to obtain obstacle information, and establish a mapping relationship between the obstacle information and the expected local scale of each drone in the multi-drone formation formed by the plurality of drones, wherein the expected local scale is an unprocessed scale obtained according to the obstacle information, and is used to characterize the scaling scale of the space occupied by the corresponding drone in the formation;

[0095] A local obstacle avoidance module: configured to construct a smoothing filter based on the desired local scale, wherein the smoothing filter is used to generate a local scale obstacle avoidance strategy;

[0096] The collaboration module is configured to generate a distributed collaborative scale decision of the UAV formation based on the local scale obstacle avoidance strategy, wherein the distributed collaborative decision is used to indicate a consistent scale parameter to each UAV.

[0097] The above specific embodiments only describe the design principle of the present invention. The shapes and names of the components in the description may be different and are not limited. Therefore, those skilled in the art in the field of the present invention may modify or replace the technical solutions recorded in the above embodiments; and these modifications and replacements do not deviate from the creative purpose and technical solutions of the present invention and should all fall within the protection scope of the present invention.

Claims

1. A distributed collaborative scale decision-making method for multi-UAV formation obstacle avoidance, characterized in that: include: Step S1: Obtain obstacle information, and in a multi-UAV formation formed by multiple UAVs, establish a mapping relationship between the obstacle information and the expected local scale of each UAV in the multi-UAV formation, wherein the expected local scale is an unprocessed scale obtained according to the obstacle information, and is used to characterize the scaling scale of the space occupied by the corresponding UAV in the formation; Step S2: constructing a smoothing filter based on the desired local scale, wherein the smoothing filter is used to generate a local scale obstacle avoidance strategy; Step S3: Based on the local scale obstacle avoidance strategy, a distributed collaborative scale decision of the UAV formation is generated, and the distributed collaborative scale decision is used to indicate a consistent scale parameter to each UAV; The step S1, establishing a mapping relationship between the obstacle information and the expected local scale of each drone in the multi-drone formation, includes: For each drone in the multi-drone formation: The vector field f of the drone numbered i i =[f ix f iy f iz ] T , where i is the number of the drone in the multi-drone formation, 1≤i≤num, num is the size of the multi-drone formation, and f ix , f iy , f iz are the components of the vector field in the x, y, and z directions respectively, and T is the transpose; According to the vector field, a mapping relationship is constructed, where the mapping relationship is a correspondence between the obstacle information and the desired local scale: is the expected local scale of the UAV numbered i according to the obstacle information, where κ nom and κ min Represent the nominal scale and the minimum scale respectively, both of which are pre-given fixed values, satisfying κ nom >κ min >0; δ>0 is a parameter that can adjust the mapping relationship. Proj(f i ) represents the influence of the vector field and is defined as follows: Among them, f i is the vector field generated by the sensor system of the UAV numbered i, r i Indicates the current formation; r i =p i -p ref p i Indicates the current spatial position of the drone numbered i; p ref Represents the task trajectory; The constructing a smoothing filter, wherein the smoothing filter is used to generate a local scale obstacle avoidance strategy, comprises: A smoothing filter is constructed, and the smoothing filter is used to implement the following smoothing filter calculation for each drone, that is, for each drone: in and and Respectively represent the local scale obstacle avoidance strategy derivatives after filtering The upper bound of and the second-order derivative of the local-scale obstacle avoidance strategy The upper bound of and are all pre-given parameters that can characterize the strength of filter smoothing; k1>0, k2>0 are control gains, v i is the auxiliary variable of the drone numbered i, is the derivative of the local scale obstacle avoidance strategy, Represents a virtual input; The generating of the distributed collaborative scale decision of the UAV formation based on the local scale obstacle avoidance strategy includes: For each drone in the multi-drone formation, perform the following operations: Construct a consistency filter to avoid the local scale obstacle The consistency filter uses the following consistency filtering algorithm Among them, α>1, β>0, α, β are gains, κ i , and They represent the scale parameter, scale parameter derivative and scale parameter second-order derivative of the UAV numbered i, respectively. and They represent the local-scale obstacle avoidance strategy, the local-scale obstacle avoidance strategy derivative, and the second-order derivative of the local-scale obstacle avoidance strategy of the UAV numbered i, respectively. represents the set of all neighboring drones of the drone numbered i, κ j is the scale parameter of the UAV numbered j, is the scale parameter derivative of the UAV numbered j, ψ and To give the control parameters, the following conditions need to be met: Among them, μ represents the difference in the local scale obstacle avoidance strategies corresponding to any two UAVs, and its specific value is: Among them, i and j are any two drone numbers, for The upper bound of i , χ j are all algebraic symbols, for The upper bound of the derivative is, for The second derivative of the upper bound of ; Based on the consistency filter, all drones can obtain the consistency scale parameter κ i , that is, the collaborative scale decision of the formation, and then tracking the desired position of each UAV based on the formation control.

2. A distributed collaborative scale decision-making device for multi-UAV formation obstacle avoidance, characterized in that: include: A mapping module: configured to obtain obstacle information, and establish a mapping relationship between the obstacle information and the expected local scale of each drone in the multi-drone formation formed by the plurality of drones, wherein the expected local scale is an unprocessed scale obtained according to the obstacle information, and is used to characterize the scaling scale of the space occupied by the corresponding drone in the formation; A local obstacle avoidance module: configured to construct a smoothing filter based on the desired local scale, wherein the smoothing filter is used to generate a local scale obstacle avoidance strategy; A collaboration module: configured to generate a distributed collaborative scale decision of the UAV formation based on the local scale obstacle avoidance strategy, wherein the distributed collaborative decision is used to indicate a consistent scale parameter to each UAV; The establishing of a mapping relationship between the obstacle information and the expected local scale of each drone in the multi-drone formation includes: For each drone in the multi-drone formation: The vector field f of the drone numbered i i =[f ix f iy f iz ] T , where i is the number of the drone in the multi-drone formation, 1≤i≤num, num is the size of the multi-drone formation, and f ix , f iy , f iz are the components of the vector field in the x, y, and z directions respectively, and T is the transpose; According to the vector field, a mapping relationship is constructed, where the mapping relationship is a correspondence between the obstacle information and the desired local scale: is the expected local scale of the UAV numbered i according to the obstacle information, where κ nom and κ min Represent the nominal scale and the minimum scale respectively, both of which are pre-given fixed values, satisfying κ nom >κ min >0; δ>0 is a parameter that can adjust the mapping relationship. Proj(f i ) represents the influence of the vector field and is defined as follows: Among them, f i is the vector field generated by the sensor system of the UAV numbered i, r i Indicates the current formation; r i =p i -p ref p i Indicates the current spatial position of the drone numbered i; p ref Represents the task trajectory; The constructing a smoothing filter, wherein the smoothing filter is used to generate a local scale obstacle avoidance strategy, comprises: A smoothing filter is constructed, and the smoothing filter is used to implement the following smoothing filter calculation for each drone, that is, for each drone: in and and Respectively represent the local scale obstacle avoidance strategy derivatives after filtering The upper bound of and the second-order derivative of the local-scale obstacle avoidance strategy The upper bound of and are all pre-given parameters that can characterize the strength of filter smoothing; k1>0, k2>0 are control gains, v i is the auxiliary variable of the drone numbered i, is the derivative of the local scale obstacle avoidance strategy, Represents a virtual input; The generating of the distributed collaborative scale decision of the UAV formation based on the local scale obstacle avoidance strategy includes: For each drone in the multi-drone formation, perform the following operations: Construct a consistency filter to avoid the local scale obstacle The consistency filter uses the following consistency filtering algorithm Among them, α>1, β>0, α, β are gains, κ i , and They represent the scale parameter, scale parameter derivative and scale parameter second-order derivative of the UAV numbered i, respectively. and They represent the local-scale obstacle avoidance strategy, the local-scale obstacle avoidance strategy derivative, and the second-order derivative of the local-scale obstacle avoidance strategy of the UAV numbered i, respectively. represents the set of all neighboring drones of the drone numbered i, κ j is the scale parameter of the UAV numbered j, is the scale parameter derivative of the UAV numbered j, ψ and To give the control parameters, the following conditions need to be met: Among them, μ represents the difference in the local scale obstacle avoidance strategies corresponding to any two UAVs, and its specific value is: Among them, i and j are any two drone numbers, for The upper bound of i , χ j are all algebraic symbols, for The upper bound of the derivative is, for The second derivative of the upper bound of ; Based on the consistency filter, all drones can obtain the consistency scale parameter κ i , that is, the collaborative scale decision of the formation, and then tracking the desired position of each UAV based on the formation control.

3. A distributed collaborative scale decision-making system for multi-UAV formation obstacle avoidance, characterized in that: The system comprises: A processor, which is used to execute multiple instructions; A memory for storing a plurality of instructions; Wherein, the plurality of instructions are used to be stored by the memory, and loaded and executed by the processor according to the method as claimed in claim 1.

4. A computer-readable storage medium, wherein a plurality of instructions are stored in the storage medium; the plurality of instructions are used to be loaded by a processor and execute the method as claimed in claim 1.

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