Robot indoor inspection positioning optimization method and system

Abstract

The invention relates to a robot indoor inspection positioning optimization method and system. The method comprises the following steps: acquiring real-time positioning data of a robot, interference information data of an indoor environment and real-time interference information data of an environment interference factor easy-to-change area; preprocessing the interference information data of the indoor environment and the real-time positioning data of the robot to obtain an interference information reference data set; and constructing an interference regression positioning model according to the interference information reference data set, correcting a UWB measurement distance through an interference compensation algorithm in combination with the real-time interference information data of the environment interference factor easy-to-change area, and converting the UWB measurement distance into a revised coordinate. Compared with the prior art, the method improves the positioning precision and robustness of the robot in a complex environment and application scene, and enables the robot to carry out tour inspection according to a set track.

Description

Technical Field

[0001] The present invention relates to the technical field of indoor robot positioning, and in particular to a robot indoor inspection positioning optimization method and system. Background Art

[0002] With the rapid development of science and technology, indoor positioning technology is increasingly used in security protection, traffic management, industrial automation and other fields. Traditional positioning systems mainly rely on a single sensor for video acquisition. Although they can provide real-time information, they are easily affected by environmental interference in complex environments, resulting in inaccurate positioning. For example, it is difficult for cameras to provide sufficient depth information and three-dimensional spatial information, and UWB signals are easily interfered by other environmental signals. This limits its positioning accuracy and reliability in complex environments. During inspections in complex indoor environments, there may be problems such as deviations from the established trajectory and inability to conduct inspections according to the set trajectory.

[0003] In order to solve this problem, the use of UWB combined with digital twin technology to enhance indoor positioning capabilities is currently being explored. Ultra-wideband (UWB) technology, as a wireless communication technology, is used in the field of positioning due to its high-precision time positioning capability. UWB technology determines the location of the device by measuring the time difference of the signal and has centimeter-level positioning accuracy. Although UWB technology performs relatively well in the field of indoor positioning, its anti-interference ability in complex environments is limited, and the signal coverage range is limited, which poses risks such as breaking into security restricted areas.

[0004] In view of the limitations of the above technologies, a method for indoor positioning that can be applied to complex environments has been sought. Digital Twin is a simulation process that fully utilizes data such as physical models, sensors, and operation history, and integrates multidisciplinary and multi-scale processes. It serves as a mirror image of physical products in virtual space, reflecting the entire life cycle of the corresponding physical products. The ideal system should be able to combine the precise time positioning capability of UWB to achieve efficient and accurate positioning in various environments. However, existing technologies have not yet been able to effectively use digital twins to improve UWB positioning accuracy to achieve optimal performance improvement. Specifically, how to reduce system costs while ensuring positioning accuracy, and how to improve the robustness of the system in complex environments, are still technical problems that need to be solved urgently. Summary of the invention

[0005] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and to provide a robot indoor patrol positioning optimization method and system, which realizes the positioning accuracy and robustness of the robot in complex environments and application scenarios, and enables the robot to patrol according to the set trajectory.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] A robot indoor inspection positioning optimization method comprises the following steps:

[0008] Obtain real-time positioning data of the robot, interference information data of the indoor environment, and real-time interference information data of areas where environmental interference factors are prone to change;

[0009] Preprocessing the interference information data of the indoor environment and the real-time positioning data of the robot to obtain an interference information benchmark data set;

[0010] An interference regression positioning model is constructed according to the interference information benchmark data set, and the UWB measurement distance is corrected through an interference compensation algorithm in combination with the real-time interference information data of the area where the environmental interference factors are prone to change, and the UWB measurement distance is converted into revised coordinates.

[0011] Furthermore, production equipment and industrial equipment that are prone to generate electromagnetic interference and dust are installed in the area where the environmental interference factors are prone to change.

[0012] Furthermore, the robot is provided with a UWB positioning beacon, which is used to send a positioning signal to a UWB base station deployed in an indoor environment. The UWB base station is used to receive the positioning signal from the UWB positioning beacon and obtain the positioning coordinates of the robot in real time by calculating the propagation time of the positioning signal.

[0013] Furthermore, preprocessing the interference information data of the indoor environment and the real-time positioning data of the robot includes filtering using a standard fractional statistical method to remove missing, abnormal and outlier data.

[0014] Furthermore, a checkpoint is set in the indoor environment, and the interference information reference data set is used to record the interference information data received by the robot when it is located at the checkpoint and the distance between the robot and the UWB base station measured by UWB.

[0015] Furthermore, the interference information benchmark data set is:

[0016]

[0017] Where D is the interference information benchmark dataset, BS j is the UWB base station ID, d true,j is the actual distance between the robot and the jth UWB base station, O ij is the interference information data under the i-th interference factor, d j is the distance between the robot and the UWB base station measured by UWB, Δd j is the positioning error between the UWB beacon and the jth UWB base station.

[0018] Furthermore, the interference regression positioning model is:

[0019] y=X·k

[0020] Wherein, y is the positioning error vector, X is the design matrix, the design matrix X includes different interference terms and their square and cross terms, and k is the regression coefficient vector.

[0021] Furthermore, the specific steps of the interference compensation algorithm include:

[0022] The positioning error of the nth UWB base station is obtained by the interference regression positioning model according to the design matrix of the nth UWB base station, and the calculation formula is:

[0023] Δd n =X (n) ·k

[0024] In the formula, Δd n is the positioning error of the nth UWB base station, X (n) is the design matrix of the nth UWB base station, the design matrix X (n) The interference information data in the is composed of the interference information benchmark data set and the real-time interference information data of the area where the environmental interference factors are prone to change, and k is the regression coefficient vector;

[0025] According to the positioning error of the nth UWB base station and the distance between the robot and the nth UWB base station obtained by UWB measurement, the corrected UWB measurement distance of the nth UWB base station is obtained, and the calculation formula is:

[0026] d C,n =d n +Δd n

[0027] Where, d C,n is the corrected UWB measurement distance of the nth UWB base station, d n is the distance between the robot and the nth UWB base station measured by UWB, Δd n is the positioning error of the nth UWB base station.

[0028] Furthermore, the inspection path of the robot in the digital twin scene is corrected according to the revised coordinates, and the calculation formula of the revised coordinates is:

[0029]

[0030] Where, d C,n is the corrected UWB measurement distance of the nth UWB base station, (x n ,y n ) is the coordinate of the nth UWB base station, (x C ,yC ) are revised coordinates.

[0031] According to another aspect of the present invention, a robot indoor inspection positioning optimization system is provided, comprising an interference information acquisition module, a UWB positioning module and a digital twin module.

[0032] The interference information acquisition module is arranged in an area where environmental interference factors are prone to change, and is used to obtain interference information data of the indoor environment and real-time interference information data of the area where environmental interference factors are prone to change;

[0033] The UWB positioning module includes a UWB positioning beacon and a UWB base station, which are used to obtain real-time positioning data of the robot;

[0034] The digital twin module includes a data acquisition unit and a data processing unit. The data acquisition unit is used to collect interference information data of the indoor environment and real-time positioning data of the robot, and pre-process the interference information data of the indoor environment and the real-time positioning data of the robot to obtain an interference information benchmark data set. The data processing unit is used to construct an interference regression positioning model according to the interference information benchmark data set, optimize the positioning coordinates of the robot through an interference compensation algorithm, and obtain revised coordinates.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] 1. The present invention utilizes UWB technology and digital twin technology, collects interference information data of the indoor environment and the real-time positioning data of the robot, pre-processes the interference information data of the indoor environment and the real-time positioning data of the robot, and obtains an interference information benchmark data set. A data processing unit constructs an interference regression positioning model according to the interference information benchmark data set, and uses an interference compensation algorithm to optimize the positioning coordinates of the robot, thereby obtaining revised coordinates, thereby achieving the purpose of correcting the inspection trajectory, reducing the trial and error cost, and generally improving the positioning accuracy of the robot positioning and inspection technology.

[0037] 2. The interference regression positioning model constructed by the present invention includes a variety of interference factors and their nonlinear combinations. The interference regression positioning model is scalable. More interference factors can be added by simply expanding the design matrix X, making the system more stable in a complex environment where multiple interference sources coexist, thereby increasing the stability of the robot positioning inspection. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 A schematic diagram of a process flow of a robot indoor inspection positioning optimization method proposed by the present invention;

[0039] Figure 2 This is a schematic diagram of the structure of a robot indoor inspection positioning optimization system proposed by the present invention;

[0040] Figure 3 Schematic diagram of robot positioning optimization in indoor environment, where (3a) is the schematic diagram of unoptimized positioning coordinates, and (3b) is the schematic diagram of optimized revised coordinates;

[0041] Legend: 1. Interference information collection module; 2. UWB positioning module; 3. Digital twin module; 301. Data collection unit; 302. Data processing unit. DETAILED DESCRIPTION

[0042] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0043] Abbreviations involved:

[0044] Ultra Wide Band: Ultra Wide Band, UWB

[0045] Example 1

[0046] This embodiment provides a robot indoor inspection positioning optimization method, such as Figure 1 As shown, the following steps are included:

[0047] S1. Obtain real-time positioning data of the robot, interference information data of the indoor environment, and real-time interference information data of areas where environmental interference factors are prone to change.

[0048] Deploy UWB base stations indoors and deploy UWB positioning beacons on patrol robots. Set the interference information acquisition module 1 in areas where environmental interference factors are prone to change. Areas where environmental interference factors are prone to change are equipped with production equipment and industrial equipment that are prone to electromagnetic interference and dust. Areas where environmental interference factors are prone to change include: near industrial equipment, near server equipment, and crowded areas. UWB base stations follow the principle of optimal layout: reduce the probability of signal blocking as much as possible; the distance between the base station receiving the synchronization signal and the beacon signal is no more than 35 meters; the base station installation position is kept 30 centimeters away from the wall. Minimize the impact of base station deployment on positioning accuracy.

[0049] S2. Preprocess the interference information data of the indoor environment and the real-time positioning data of the robot to obtain an interference information benchmark data set.

[0050] Checkpoints are set up in the indoor environment, and the interference information benchmark data set is used to record the interference information data and UWB measurement distance received by the robot when it is located at the checkpoint. Preprocessing the interference information data of the indoor environment and the real-time positioning data of the robot includes filtering using standard fractional statistics methods to remove missing, abnormal and outlier data to obtain the interference information benchmark data set. Calculate the mean and standard deviation of the positioning error:

[0051]

[0052] Where μ is the mean, σ is the standard deviation, N is the number of UWB base stations, and Δd j is the positioning error between the UWB beacon and the jth UWB base station.

[0053] Calculate the standard score for each localization error:

[0054]

[0055] In the formula, Z j is the standard score, and the absolute value of the standard score is greater than the threshold Z. threshold to obtain a valid data set.

[0056] The interference information benchmark dataset is:

[0057]

[0058] Where D is the interference information benchmark dataset, BS j is the UWB base station ID, d true,j is the actual distance between the robot and the jth UWB base station, O ij is the interference information data under the i-th interference factor, d j is the distance between the robot and the UWB base station measured by UWB, Δd j is the positioning error between the UWB beacon and the jth UWB base station.

[0059] S3. An interference regression positioning model is constructed based on the interference information benchmark data set, and the UWB measurement distance is corrected through an interference compensation algorithm in combination with the real-time interference information data in areas where environmental interference factors are prone to change, and the UWB measurement distance is converted into revised coordinates.

[0060] Map the target to the plane, the coordinates of n base stations are BS 1 (x 1 ,y 1 ), BS 2 (x 2 ,y 2 ), BS 3 (x 3 ,y 3,),……,BS n (x n ,y n ), the distances between the robot and the base station measured by UWB are d 1 d 2 d 3 ,……,d n , the robot's positioning coordinates are T(x,y), such as Figure 3 as shown in (3a).

[0061] The interference regression positioning model in the digital twin scene is constructed using the collected benchmark data set to obtain the relationship between the interference factor data and the error distance. The interference regression positioning model is:

[0062] y=X·k

[0063] Where y is the positioning error vector, X is the design matrix, which contains different interference terms and their square and cross terms, and k is the regression coefficient vector.

[0064] The regression coefficient vector is solved using the ridge regression least squares method, going through all the checkpoints. When a region is dominated by a specific disturbance, the region has spatial specificity, and the regression coefficients vary significantly in different regions. The global model may not be able to capture local features. Regional modeling is adopted, combining spatial coordinates and disturbance features, dividing the sub-regions and training the model separately for each sub-region.

[0065] Assuming there are two interference factors, use the interference regression positioning model:

[0066]

[0067]

[0068] In the formula, Δd jn is the positioning error, O ijn is the jth interference factor under the i-th interference factor n The interference information data of UWB base stations, n is greater than the number of k, k 1 ,k 2 ,k 3 ,k 4 ,k 5 is the regression coefficient. Interference factors include electromagnetic, dust, indoor shielding, air pressure and air humidity.

[0069] When the checkpoint is affected by any interference information, the positioning error Δd of the robot measured by the nth base station is obtained based on the design matrix X and the regression coefficient vector k. n The calculation formula of the interference compensation algorithm is:

[0070] d C,n =dn +Δd n

[0071] Δd n =X (n) ·k

[0072] Where, d C,n is the corrected UWB measurement distance, d n is the distance between the robot and the UWB base station measured by UWB, Δd n is the positioning error, X (n) is the design matrix, the design matrix X (n) The interference information data in the environment is composed of an interference information benchmark data set and real-time interference information data in areas where environmental interference factors are prone to change. When not affected by areas where environmental interference factors are prone to change, the interference information data in the benchmark data set is directly used. When affected by areas where environmental interference factors are prone to change, the interference information data collected in real time is used. k is the regression coefficient vector.

[0073] The revised coordinates of the robot are obtained through coordinate transformation according to the corrected UWB measurement distance. The calculation formula of the revised coordinates is:

[0074]

[0075] Where, d C,n is the corrected UWB measurement distance, (x n ,y n ) is the coordinate of the nth UWB base station, (x C ,y C ) are revised coordinates.

[0076] The optimized revised coordinates are as follows Figure 3 As shown in (3b), the revised coordinates of the robot are T C (x C ,y C ).

[0077] In another preferred embodiment, the steps are further included:

[0078] S4. Correct the inspection path according to the revised coordinates, and transmit the data back to the robot, so that the robot can perform more accurate positioning and inspection according to the corrected data.

[0079] According to the revised coordinates, the robot's inspection path in the digital twin scene is corrected. In the digital twin scene, the robot is simulated to perform inspections along the revised coordinates. The positioning error on the virtual inspection path and the deviation of the inspection path are recorded, and the deviation between the virtual inspection path and the actual inspection path is compared. The deviation between the virtual inspection path and the actual inspection path is analyzed to evaluate the effectiveness of the correction algorithm.

[0080] Example 2

[0081] This embodiment provides a robot indoor inspection positioning optimization system. Figure 2 As shown, it includes: interference information acquisition module 1, UWB positioning module 2 and digital twin module 3,

[0082] The interference information acquisition module 1 is set in the area where the environmental interference factors are prone to change, and is used to obtain the interference information data of the indoor environment and the real-time interference information data of the area where the environmental interference factors are prone to change;

[0083] The UWB positioning module 2 includes a UWB positioning beacon and a UWB base station, which are used to obtain real-time positioning data of the robot;

[0084] The digital twin module 3 includes a data acquisition unit 301 and a data processing unit 302. The data acquisition unit 301 is used to collect interference information data of the indoor environment and the real-time positioning data of the robot, and pre-process the interference information data of the indoor environment and the real-time positioning data of the robot to obtain an interference information benchmark data set. The data processing unit 302 is used to construct an interference regression positioning model based on the interference information benchmark data set, optimize the positioning coordinates of the robot through an interference compensation algorithm, and obtain revised coordinates.

[0085] The rest is the same as in Example 1.

[0086] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A robot indoor inspection positioning optimization method, characterized in that: The following steps are involved: Obtain real-time positioning data of the robot, interference information data of the indoor environment, and real-time interference information data of areas where environmental interference factors are prone to change; Preprocessing the interference information data of the indoor environment and the real-time positioning data of the robot to obtain an interference information benchmark data set; An interference regression positioning model is constructed according to the interference information benchmark data set, and the UWB measurement distance is corrected through an interference compensation algorithm in combination with the real-time interference information data of the area where the environmental interference factors are prone to change, and the UWB measurement distance is converted into revised coordinates.

2. The robot indoor inspection positioning optimization method according to claim 1 is characterized in that: The area where the environmental interference factors are prone to change is equipped with production equipment and industrial equipment that are prone to generate electromagnetic interference and dust.

3. The robot indoor inspection positioning optimization method according to claim 1 is characterized in that: The robot is provided with a UWB positioning beacon, which is used to send a positioning signal to a UWB base station deployed in an indoor environment. The UWB base station is used to receive the positioning signal from the UWB positioning beacon and obtain the positioning coordinates of the robot in real time by calculating the propagation time of the positioning signal.

4. The robot indoor inspection positioning optimization method according to claim 1 is characterized in that: Preprocessing the interference information data of the indoor environment and the real-time positioning data of the robot includes filtering using a standard fractional statistical method to remove missing, abnormal and outlier data.

5. The robot indoor inspection positioning optimization method according to claim 1 is characterized in that: A checkpoint is set in the indoor environment, and the interference information reference data set is used to record the interference information data received by the robot when it is located at the checkpoint and the distance between the robot and the UWB base station obtained by UWB measurement.

6. The robot indoor inspection positioning optimization method according to claim 1, characterized in that: The interference information benchmark data set is: Where D is the interference information benchmark dataset, BS j is the UWB base station ID, d true,j is the actual distance between the robot and the jth UWB base station, O ij is the interference information data under the i-th interference factor, d j is the distance between the robot and the UWB base station measured by UWB, Δd j is the positioning error between the UWB beacon and the jth UWB base station.

7. The robot indoor inspection positioning optimization method according to claim 1, characterized in that: The interference regression positioning model is: y=X·k Wherein, y is the positioning error vector, X is the design matrix, the design matrix X includes different interference terms and their square and cross terms, and k is the regression coefficient vector.

8. The robot indoor inspection positioning optimization method according to claim 1, characterized in that: The specific steps of the interference compensation algorithm include: The positioning error of the nth UWB base station is obtained by the interference regression positioning model according to the design matrix of the nth UWB base station, and the calculation formula is: Δd n =X (n) ·k In the formula, Δd n is the positioning error of the nth UWB base station, X (n) is the design matrix of the nth UWB base station, the design matrix X (n) The interference information data in the is composed of the interference information benchmark data set and the real-time interference information data of the area where the environmental interference factors are prone to change, and k is the regression coefficient vector; According to the positioning error of the nth UWB base station and the distance between the robot and the nth UWB base station obtained by UWB measurement, the corrected UWB measurement distance of the nth UWB base station is obtained, and the calculation formula is: d C,n =d n +Δd n Where, d C,n is the corrected UWB measurement distance of the nth UWB base station, d n is the distance between the robot and the nth UWB base station measured by UWB, Δd n is the positioning error of the nth UWB base station.

9. The robot indoor inspection positioning optimization method according to claim 1, characterized in that: The inspection path of the robot in the digital twin scene is corrected according to the revised coordinates. The calculation formula of the revised coordinates is: Where, d C,n is the corrected UWB measurement distance of the nth UWB base station, (xn,yn) is the coordinate of the nth UWB base station, (x C ,y C ) are revised coordinates.

10. A robot indoor inspection positioning optimization system, characterized in that: It includes an interference information acquisition module (1), a UWB positioning module (2) and a digital twin module (3), The interference information acquisition module (1) is arranged in an area where environmental interference factors are prone to change, and is used to obtain interference information data of the indoor environment and real-time interference information data of the area where environmental interference factors are prone to change; The UWB positioning module (2) comprises a UWB positioning beacon and a UWB base station, and is used to obtain real-time positioning data of the robot; The digital twin module (3) comprises a data acquisition unit (301) and a data processing unit (302), wherein the data acquisition unit (301) is used to collect interference information data of the indoor environment and real-time positioning data of the robot, pre-process the interference information data of the indoor environment and the real-time positioning data of the robot to obtain an interference information benchmark data set, and the data processing unit (302) is used to construct an interference regression positioning model based on the interference information benchmark data set, optimize the positioning coordinates of the robot through an interference compensation algorithm, and obtain revised coordinates.

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