An improved artificial potential field source searching path planning method and device and storage medium

Abstract

The application provides a source searching path planning method and device based on improved artificial potential field, and a storage medium, the method comprising: constructing an initial multi-source radiation field, reconstructing the initial multi-source radiation field, establishing an artificial potential field model, moving the robot according to the direction of the planned source searching path and measuring the signals of the radioactive sources along the path, if a maximum value point of the radiation intensity is detected in the multiple radioactive sources, adding an additional potential field at the position of the robot to obtain a new artificial potential field model, and obtaining the direction of a new source searching path based on the new artificial potential field model to search for the next radioactive source; the application changes the influence of the "proven hot spot" on the artificial potential field from gravity to repulsion, adds an additional potential field at the position of the robot to obtain a new artificial potential field model, so that the new artificial potential field model can guide the robot to smoothly leave the current radioactive source and start searching for the next radioactive source, and solves the problem that the traditional artificial potential field method will fall into a potential field trap in a multi-source environment.

Description

Technical Field

[0001] This invention relates to the field of radioactive source detection technology, specifically to an improved method, apparatus, and storage medium for source-finding path planning in artificial potential fields. Background Technology

[0002] Radioactive sources have made significant contributions to my country's economic development and social progress. However, during the use, storage, and transportation of radioactive sources, there have been instances of loss, theft, or illegal transfer leading to radioactive accidents. If these accidents are not handled promptly and effectively, they pose a significant risk to public health. In the event of lost or stolen radioactive sources, the ability to quickly deduce the location and radiation field distribution of the source based on data from multiple detectors is crucial for the rapid search, location, and safe removal of scattered radioactive sources. This is an important means of ensuring the safe and efficient use of nuclear technology and has significant practical implications for nuclear safety emergency response strategies.

[0003] Currently, source finding in multi-source radiation environments mostly focuses on open and simple scenarios, rarely considering the impact of obstacles on source finding. However, in real radioactive accidents, multiple radioactive sources may exist simultaneously due to factors such as radioactive material leaks, and the environment in which these sources are located is very complex, containing obstacles such as buildings, office facilities, and debris. Existing multi-source radiation field reconstruction methods based on particle filtering do not consider the impact of obstacles on the source distribution probability density function. When new obstacles appear, the original source distribution probability density function differs significantly from the actual distribution, failing to represent the true radiation field. Furthermore, traditional path planning methods based on artificial potential fields are ineffective when multiple radioactive sources exist; after finding a "hotspot," subsequent paths cannot be planned to find other sources, resulting in less than ideal performance. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide an improved method, apparatus and storage medium for source-finding path planning of artificial potential fields, which addresses the shortcomings of the prior art.

[0005] The technical solution of this invention to solve the above-mentioned technical problems is as follows: An improved source-finding path planning method for artificial potential fields, comprising the following steps:

[0006] Multiple particles are arranged within the radiation area, serving as multiple radiation sources. A pre-programmed robot detects the signals of these radiation sources within the radiation area to obtain parameter information of the multiple radiation sources. An initial multi-source radiation field is then constructed using this parameter information.

[0007] The initial multi-source radiation field is reconstructed, and an artificial potential field model is established in the reconstructed multi-source radiation field. The direction of the current radioactive source's homing path is obtained through the artificial potential field model.

[0008] The robot moves according to the planned source-finding path and measures the signals of radioactive sources along the way. If a point with maximum radiation intensity is detected among multiple radioactive sources, an additional potential field is added at the location of the robot to obtain a new artificial potential field model. The direction of the new source-finding path is obtained through the new artificial potential field model to search for the next radioactive source.

[0009] Another technical solution of the present invention to solve the above-mentioned technical problems is as follows: an improved source-finding path planning device for artificial potential fields, characterized in that it comprises:

[0010] The multi-source radiation field construction module is used to arrange multiple particles in the radiation area, using the multiple particles as multiple radiation sources. A preset robot performs signal detection of the radiation sources in the radiation area to obtain the parameter information of the multiple radiation sources, and constructs the initial multi-source radiation field based on the parameter information of the multiple radiation sources.

[0011] The radiation field reconstruction and artificial potential field model establishment module is used to reconstruct the initial multi-source radiation field and establish an artificial potential field model in the reconstructed multi-source radiation field. The direction of the current source-finding path of the radiation source is obtained through the artificial potential field model.

[0012] The artificial potential field model update module is used for the robot to move according to the planned source-finding path and measure the signals of radioactive sources along the way. If a point with maximum radiation intensity is detected among multiple radioactive sources, an additional potential field is added at the location of the robot to obtain a new artificial potential field model. The direction of the new source-finding path is obtained through the new artificial potential field model to perform the next radioactive source search.

[0013] Another technical solution of the present invention to solve the above-mentioned technical problems is as follows: an improved source path planning device for artificial potential fields, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, when the processor executes the computer program, it implements the improved source path planning method for artificial potential fields as described above.

[0014] Another technical solution of the present invention to solve the above-mentioned technical problems is as follows: a computer-readable storage medium storing a computer program, characterized in that, when the computer program is executed by a processor, it implements the improved artificial potential field source path planning method as described above.

[0015] The beneficial effects of this invention are: This invention realizes the reconstruction of radiation field in complex multi-source environments and establishes an artificial potential field model. It changes the influence of "identifying hotspots" on the artificial potential field from attraction to repulsion. An additional potential field is added at the robot's location to obtain a new artificial potential field model. This allows the new artificial potential field model to guide the robot to leave the current radiation source smoothly and begin the search for the next radiation source, solving the problem that traditional artificial potential field methods will fall into potential field traps in multi-source environments. Attached Figure Description

[0016] Figure 1 A flowchart illustrating the improved artificial potential field source-finding path planning method provided in this embodiment of the invention;

[0017] Figure 2 A block diagram of an improved artificial potential field source-finding path planning device provided in an embodiment of the present invention. Detailed Implementation

[0018] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0019] Example 1:

[0020] like Figure 1 As shown, an improved source-finding path planning method for artificial potential fields includes the following steps:

[0021] Multiple particles are arranged within the radiation area, serving as multiple radiation sources. A pre-programmed robot detects the signals of these radiation sources within the radiation area to obtain parameter information of the multiple radiation sources. An initial multi-source radiation field is then constructed using this parameter information.

[0022] The initial multi-source radiation field is reconstructed, and an artificial potential field model is established in the reconstructed multi-source radiation field. The direction of the current radioactive source's homing path is obtained through the artificial potential field model.

[0023] The robot moves according to the planned source-finding path and measures the signals of radioactive sources along the way. If a point with maximum radiation intensity is detected among multiple radioactive sources, an additional potential field is added at the location of the robot to obtain a new artificial potential field model. The direction of the new source-finding path is obtained through the new artificial potential field model to search for the next radioactive source.

[0024] In the above embodiments, radiation field reconstruction is realized in a complex multi-source environment, and an artificial potential field model is established. The influence of "identifying hotspots" on the artificial potential field is changed from attraction to repulsion. An additional potential field is added at the robot's location to obtain a new artificial potential field model. This allows the new artificial potential field model to guide the robot to leave the current radiation source smoothly and begin the search for the next radiation source, solving the problem that traditional artificial potential field methods will fall into potential field traps in multi-source environments.

[0025] Specifically, the construction of the initial multi-source radiation field using parameter information from multiple radiation sources involves:

[0026] Multiple particles are used as multiple radiation sources. A pre-programmed robot detects the signals of these sources within the radiation area. Each particle has its own weight, which represents the proportion of radiation sources present within the radiation area. The parameter information of the multiple radiation sources includes { X i,0,k , Y i,0,k , I i,0,k , w i,0,k} represents the set of all particles, where X i,0,k and Y i,0,k Indicates the location of the radioactive source. I i,0,k Indicates the source strength of the radioactive source. w i,0,k The weight of a particle is represented by , and the sum of the weights of all particles after the k-th detection is 1. The initial multi-source radiation field is a multi-source radiation field with obstacles. The point kernel calculation formula of the multilayer medium is used. After convergence, the initial multi-source radiation field is obtained as follows:

[0027] ,

[0028] in, Represents the Dirac function, This represents the radiation count rate value calculated based on the current radiation source term. In the formula, B represents the accumulation factor of the multilayer medium in the radiation field. This represents the attenuation coefficient when passing through medium j. Let F(E) represent the distance traveled through medium j, and F(E) be the selected flux-dose conversion factor.

[0029] Specifically, the process of reconstructing the radiation field from the initial multi-source radiation field includes:

[0030] S1.1: Perform importance sampling of particles:

[0031] Through the importance probability density function Perform initial sampling.

[0032] ,

[0033] in, By using a sequential importance sampling method, the importance weight of the next observation point is associated only with the importance weight of the previous observation. In step S1.1, a sequential importance sampling method is introduced, which ensures that the importance weight of the next observation point is only related to the importance weight of the previous one, thus solving the problems of wasted computing resources and high computational difficulty.

[0034] S1.2: Resampling based on real-time detection information:

[0035] When the robot obtains new detection information, it repeats step S1.1 to resample the importance of particles, and copies particles with high weight values ​​and discards particles with low weight values, and the number of copied particles is equal to the number of discarded particles.

[0036] S1.3: Estimation of radioactive source parameters:

[0037] Based on the location and intensity of the radiation field source term obtained by the robot, the radiation dose field is calculated using particle transport calculation tools to obtain the radiation dose value detected at the current detection point.

[0038] The radiation dose values ​​were linearly fitted to obtain the linear fitting equation. ,in,

[0039] ,

[0040] ,

[0041] Calculate the standard deviation of the linear fit With uncertainty :

[0042] ,

[0043] in, This represents the error caused by estimating the source strength at the Mth detection point.

[0044] Calculate goodness of fit , Where RSS represents the total sum of squares and TSS represents the residual sum of squares.

[0045] ,

[0046] Calculate the quality factor :

[0047] ,

[0048] The iteration stops if the quality factor meets the preset accuracy requirement; otherwise, the weighting function is calculated to weight the original system of equations, resulting in... The weighted initial source strength is calculated by repeatedly solving the least squares method, and the weighting function is as follows:

[0049] ,

[0050] By substituting the weighted initial source strength into the system of equations and continuing the iteration, the reconstructed multi-source radiation field is finally obtained.

[0051] For source-finding path planning in multi-source scenarios, once a radiation source is detected, its contribution to the gravitational function in the artificial potential field is removed, and the location is defined as a negative source, thus adding a repulsive term to the repulsive function. This allows the artificial potential field to guide the robot smoothly away from the radiation source and begin the search for the next one. When a source gets trapped in a "pseudo-hotspot," an additional potential field is added at that location to help the robot escape the "pseudo-hotspot" trap. The following section details how to add an additional potential field.

[0052] The step of establishing an artificial potential field model in the reconstructed multi-source radiation field and obtaining the source-finding path of the current radiation source through the artificial potential field model is as follows:

[0053] S2.1: Establish the gravitational potential field function of the radioactive source to be investigated, wherein the gravitational potential field function of the radioactive source to be investigated is:

[0054] (1)

[0055] in, Represents the gravitational scale factor. Indicates the robot's current position ( ) and the location of the radioactive source to be identified ( The distance,

[0056] The gravitational force acting on the robot is calculated using the negative gradient of the gravitational potential field, and the gravitational force is expressed as:

[0057] (2)

[0058] When an object is far from the target point, the gravitational force becomes particularly strong, while the relatively small repulsive force can even be ignored. In such cases, the object may encounter obstacles on its path.

[0059] Furthermore, a modified gravity function is introduced into the above formula to avoid excessive gravity due to the distance from the target point.

[0060] (3)

[0061] It should be understood that, compared with equation (1), equation (3) adds a range limitation, that is, a threshold is given to limit the effective distance of the gravitational field between the target and the object. d att .

[0062] The gravitational force acting on the robot is corrected as follows:

[0063] (4)

[0064] S2.2: Establish the repulsive potential field function of the obstacle, wherein the repulsive potential field function of the obstacle is:

[0065] (5)

[0066] in, Represents the repulsive force scale factor. Represents the current position of the object ( ) and obstacle location ( The distance, This represents the radius of influence of each obstacle. When the robot moves a certain distance away from an obstacle, the obstacle exerts no repulsive force on the robot.

[0067] The gradient of the repulsive field corresponding to the repulsive force is:

[0068] (6)

[0069] If an obstacle is located near the target point, the repulsive force may be greater than the attractive force, making the target unreachable. Therefore, this invention proposes to introduce a new repulsive force function so that when the robot approaches the target point, the gravitational potential decreases while the repulsive potential also decreases until the target point is reached, at which point both the gravitational and repulsive potentials decrease to 0, thereby solving the problem of the target being unreachable due to the obstacle being too close to the target point.

[0070] in, (7) n is an integer, usually n=2.

[0071] The original repulsive field is supplemented with the influence of the distance between the target and the object (n is an integer, usually n=2). When the object gets closer to the target, although the repulsive field increases, the distance decreases, so it can have a dragging effect on the repulsive field to a certain extent.

[0072] The corresponding repulsive force becomes:

[0073] (8)

[0074] in, , .

[0075] Specifically, the step of adding an additional potential field at the robot's location to obtain a new artificial potential field model, and then using this new artificial potential field model to obtain the direction of a new source-finding path for the next radiation source search, is as follows:

[0076] S3.1: Establish a repulsive potential field function for the detected radioactive source at the location of the robot, wherein the repulsive potential field function for the detected radioactive source is:

[0077] (9)

[0078] in, Indicates the current position of the object ( ) and identified radioactive sources ( The distance; This represents a given threshold, which limits the effective distance of the gravitational field between the detected radioactive source and the robot; when the robot moves away from the detected radioactive source by a certain distance, the detected radioactive source has no repulsive effect on the object.

[0079] The gradient of the repulsive field corresponding to the repulsive force is:

[0080] ;

[0081] S3.2: In a complex multi-source environment, a resultant force model is constructed based on the total potential field at the robot's location being the superposition of all repulsive fields, all gravitational fields, and the repulsive fields of the identified radioactive sources. This resultant force model is then used as a new artificial potential field model. The resultant force model is as follows:

[0082] .

[0083] Specifically, the step of obtaining the direction of the new source-finding path through the new artificial potential field model for the next radiation source search is as follows:

[0084] The resultant force is obtained based on the aforementioned resultant force model. The direction of the new source-finding path is then derived from this resultant force for the next radiation source search. The resultant force is:

[0085] .

[0086] The robot will locate the radiation source based on the direction of the resultant force.

[0087] Example 2:

[0088] like Figure 2 As shown, an improved source-finding path planning device for artificial potential fields includes:

[0089] The multi-source radiation field construction module is used to arrange multiple particles in the radiation area, using the multiple particles as multiple radiation sources. A preset robot performs signal detection of the radiation sources in the radiation area to obtain the parameter information of the multiple radiation sources, and constructs the initial multi-source radiation field using the parameter information of the multiple radiation sources.

[0090] The radiation field reconstruction and artificial potential field model establishment module is used to reconstruct the initial multi-source radiation field and establish an artificial potential field model in the reconstructed multi-source radiation field. The direction of the current source-finding path of the radiation source is obtained through the artificial potential field model.

[0091] The artificial potential field model update module is used for the robot to move according to the planned source-finding path and measure the signals of radioactive sources along the way. If a point with maximum radiation intensity is detected among multiple radioactive sources, an additional potential field is added at the location of the robot to obtain a new artificial potential field model. The direction of the new source-finding path is obtained through the new artificial potential field model to perform the next radioactive source search.

[0092] Specifically, in the multi-source radiation field construction module, an initial multi-source radiation field is constructed using parameter information from multiple radiation sources, as follows:

[0093] Multiple particles are used as multiple radiation sources. A pre-programmed robot detects the signals of these radiation sources within the radiation area. Each particle has its own weight, which represents the proportion of radiation sources present within the radiation area. The parameter information of the multiple radiation sources includes { X i,0,k , Y i,0,k , I i,0,k , w i,0,k} represents the set of all particles, where X i,0,k and Y i,0,k Indicates the location of the radioactive source. I i,0,k Indicates the source strength of the radioactive source. w i,0,k The weight of a particle is given, and the sum of the weights of all particles after the k-th detection is 1. The initial multi-source radiation field is a multi-source radiation field with obstacles. Using the point kernel calculation formula of a multilayer medium, the initial multi-source radiation field is obtained as follows:

[0094] ,

[0095] in, Represents the Dirac function, This represents the radiation count rate value calculated based on the current radiation source term. In the formula, B represents the accumulation factor of the multilayer medium in the radiation field. This represents the attenuation coefficient when passing through medium j. Let F(E) represent the distance traveled through medium j, and F(E) be the selected flux-dose conversion factor.

[0096] Example 3:

[0097] An improved source path planning device for an artificial potential field includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the improved source path planning method for an artificial potential field as described above.

[0098] Example 4:

[0099] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the improved artificial potential field source path planning method as described above.

[0100] The advantage of this invention is that it proposes an improved artificial potential field method that uses "identified hotspots" as "virtual repulsive forces," thus solving the potential field trap problem associated with "identified hotspots." Traditional path planning methods based on artificial potential fields, when multiple radiation sources exist, cannot plan subsequent paths to find other sources after identifying a "identified hotspot." This project introduces a new repulsive force function into the artificial potential field, transforming the influence of "identified hotspots" on the artificial potential field from attraction to repulsion, thus solving the problem of traditional artificial potential field methods falling into potential field traps in multi-source environments.

[0101] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0102] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the above-described apparatus and unit can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0103] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed.

[0104] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments of the present invention, depending on actual needs.

[0105] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0106] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0107] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An improved source-finding path planning method for artificial potential fields, characterized in that, Includes the following steps: Multiple particles are arranged within the radiation area, serving as multiple radiation sources. A pre-programmed robot detects the signals of these radiation sources within the radiation area to obtain parameter information of the multiple radiation sources. An initial multi-source radiation field is then constructed using this parameter information. The initial multi-source radiation field is reconstructed, and an artificial potential field model is established in the reconstructed multi-source radiation field. The direction of the current radioactive source's homing path is obtained through the artificial potential field model. The robot moves according to the planned source-finding path and measures the signals of the radioactive sources along the way. If a point with maximum radiation intensity is detected among multiple radioactive sources, an additional potential field is added at the location of the robot to obtain a new artificial potential field model. The direction of the new source-finding path is obtained through the new artificial potential field model to search for the next radioactive source. The initial multi-source radiation field is constructed using parameter information from multiple radiation sources, specifically as follows: Multiple particles are used as multiple radiation sources. A pre-programmed robot detects the signals of these radiation sources within the radiation area. Each particle has its own weight, which represents the proportion of radiation sources present within the radiation area. The parameter information of the multiple radiation sources includes { X i,0,k , Y i,0,k , I i,0,k , w i,0,k } represents the set of all particles, where X i,0,k and Y i,0,k Indicates the location of the radioactive source. I i,0,k Indicates the source strength of the radioactive source. w i,0,k The weight of a particle is given, and the sum of the weights of all particles after the k-th detection is 1. The initial multi-source radiation field is a multi-source radiation field with obstacles. Using the point kernel calculation formula of a multilayer medium, the initial multi-source radiation field is obtained as follows: ， in, Represents the Dirac function, This represents the radiation count rate value calculated based on the current radiation source term. In the formula, B represents the accumulation factor of the multilayer medium in the radiation field. This represents the attenuation coefficient when passing through medium j. Let F(E) represent the distance traveled through medium j, and F(E) be the selected flux-dose conversion factor.

2. The source-finding path planning method according to claim 1, characterized in that, The process of reconstructing the initial multi-source radiation field specifically involves: S1.1: Perform importance sampling of particles: Through the importance probability density function Perform initial sampling. ， in, By using a sequential importance sampling method, the importance weight of the next observation point is associated only with the importance weight of the previous observation. ; S1.2: Resampling based on real-time detection information: When the robot obtains new detection information, it repeats step S1.1 to resample the importance of particles, and copies particles with high weight values ​​and discards particles with low weight values, and the number of copied particles is equal to the number of discarded particles. S1.3: Estimation of radioactive source parameters: Based on the location and intensity of the radiation field source term obtained by the robot, the radiation dose field is calculated using particle transport calculation tools to obtain the radiation dose value detected at the current detection point. The radiation dose values ​​were linearly fitted to obtain the linear fitting equation. ,in, ， ， Calculate the standard deviation of the linear fit With uncertainty : ， in, This represents the error caused by estimating the source strength at the Mth detection point. Calculate goodness of fit , Where RSS represents the total sum of squares and TSS represents the residual sum of squares. ， Calculate the quality factor : ， The iteration stops if the quality factor meets the preset accuracy requirement; otherwise, the weighting function is calculated to weight the original system of equations, resulting in... The weighted initial source strength is calculated by repeatedly solving the least squares method, and the weighting function is as follows: ， By substituting the weighted initial source strength into the system of equations and continuing the iteration, the reconstructed multi-source radiation field is finally obtained.

3. The source-finding path planning method according to claim 2, characterized in that, The step of establishing an artificial potential field model in the reconstructed multi-source radiation field and obtaining the source-finding path of the current radiation source through the artificial potential field model is as follows: S2.1: Establish the gravitational potential field function of the radioactive source to be investigated, wherein the gravitational potential field function of the radioactive source to be investigated is: ， in, Represents the gravitational scale factor. Indicates the robot's current position Location of the radioactive source to be identified distance, The gravitational force acting on the robot is calculated using the negative gradient of the gravitational potential field, and the gravitational force is expressed as: ， And a modified gravity function is introduced into the above equation. ， The gravitational force acting on the robot is corrected as follows: ； S2.2: Establish the repulsive potential field function of the obstacle, wherein the repulsive potential field function of the obstacle is: ， in, Represents the repulsive force scale factor. Represents the current position of the object and obstacle location distance, This represents the radius of influence of each obstacle; The gradient of the repulsive field corresponding to the repulsive force is: ， in, n is an integer. The corresponding repulsive force becomes: ， in, , .

4. The source-finding path planning method according to claim 3, characterized in that, The process involves adding an additional potential field at the robot's location to obtain a new artificial potential field model. This new model is then used to determine the direction of a new source-finding path for the next radiation source search. Specifically: S3.1: Establish a repulsive potential field function for the detected radioactive source at the location of the robot, wherein the repulsive potential field function for the detected radioactive source is: ， in, Indicates the current position of the object and identified radioactive sources The distance; This represents a given threshold, which limits the effective distance of the gravitational field between the detected radioactive source and the robot; The gradient of the repulsive field corresponding to the repulsive force is: ； S3.2: Construct a resultant force model based on the total potential field at the robot's location, which is the superposition of all repulsive fields, all gravitational fields, and the repulsive fields of the identified radioactive sources. Use this resultant force model as a new artificial potential field model. The resultant force model is as follows: 。 5. The source-finding path planning method according to claim 4, characterized in that, The process of obtaining the direction of the new source-finding path through the new artificial potential field model for the next radiation source search is as follows: The resultant force is obtained based on the aforementioned resultant force model. The direction of the new source-finding path is then derived from this resultant force for the next radiation source search. The resultant force is: 。 6. An improved source-finding path planning device for artificial potential fields, characterized in that, include: The multi-source radiation field construction module is used to arrange multiple particles in the radiation area, using the multiple particles as multiple radiation sources. A preset robot performs signal detection of the radiation sources in the radiation area to obtain the parameter information of the multiple radiation sources, and constructs the initial multi-source radiation field based on the parameter information of the multiple radiation sources. The radiation field reconstruction and artificial potential field model establishment module is used to reconstruct the initial multi-source radiation field and establish an artificial potential field model in the reconstructed multi-source radiation field. The direction of the current source-finding path of the radiation source is obtained through the artificial potential field model. The artificial potential field model update module is used for the robot to move according to the planned source-finding path and measure the signals of the radioactive sources along the way. If a point with maximum radiation intensity is detected among multiple radioactive sources, an additional potential field is added at the location of the robot to obtain a new artificial potential field model. The new direction of the source-finding path is obtained through the new artificial potential field model to perform the next radioactive source search. In the multi-source radiation field construction module, an initial multi-source radiation field is constructed using parameter information from multiple radiation sources, specifically as follows: Multiple particles are used as multiple radiation sources. A pre-programmed robot detects the signals of these sources within the radiation area. Each particle has its own weight, which represents the proportion of radiation sources present within the radiation area. The parameter information of the multiple radiation sources includes { X i,0,k , Y i,0,k , I i,0,k , w i,0,k } represents the set of all particles, where X i,0,k and Y i,0,k Indicates the location of the radioactive source. I i,0,k Indicates the source strength of the radioactive source. w i,0,k The weight of a particle is represented by , and the sum of the weights of all particles after the k-th detection is 1. The initial multi-source radiation field is a multi-source radiation field with obstacles. Using the point kernel calculation formula for multilayer media, the initial multi-source radiation field is obtained as follows: ， in, Represents the Dirac function, This represents the radiation count rate value calculated based on the current radiation source term. In the formula, B represents the accumulation factor of the multilayer medium in the radiation field. This represents the attenuation coefficient when passing through medium j. Let F(E) represent the distance traveled through medium j, and F(E) be the selected flux-dose conversion factor.

7. An improved source-finding path planning device for artificial potential fields, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the improved artificial potential field source path planning method as described in any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the improved artificial potential field source path planning method as described in any one of claims 1 to 5.

Images