Indoor Mobile Robot Positioning Method and System with Online Attack Detection Function

Abstract

This invention belongs to the field of intelligent control and provides an indoor mobile robot localization method and system with online attack detection function. The method includes: establishing a discrete state-space expression for the mobile robot based on its kinematic model to obtain a recursive formula for its relative pose; acquiring the mobile robot's position signal; estimating the optimal pose of the mobile robot within the discrete state-space expression using a Kalman filter algorithm to achieve relative localization; comparing the estimated optimal pose with the actual pose value, calculating the residual between the two, and using chi-square detection to check for anomalies in the residual, thus achieving online attack detection for mobile robot localization. This improves the robustness of the mobile robot to external interference during localization, reduces localization errors, promptly detects attacks on the mobile robot system, and improves detection accuracy.

Description

Technical Field

[0001] This invention belongs to the field of intelligent control technology, specifically relating to an indoor mobile robot positioning method and system with online attack detection function. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] Mobile robot technology involves multiple disciplines such as computer science, sensors, human-computer interaction, and bionics. Among these, environmental perception, autonomous localization, and motion control are the three key issues in mobile robot technology. Autonomous localization of mobile robots is a prerequisite for completing other advanced tasks. Autonomous localization of mobile robots can be broadly divided into relative localization and absolute localization. Relative localization utilizes internal sensors to measure changes in the robot's pose over a unit of time, gradually calculating the robot's pose relative to its initial state, thus completing the localization process. Absolute localization, also known as global localization, requires a pre-defined environmental model or direct external position information from sensors to calculate the robot's position in the global coordinate system. Absolute localization is costly, and GPS cannot achieve accurate positioning in indoor environments, while relative localization meets the needs of most practical applications and is less expensive. Therefore, this invention adopts the relative localization method.

[0004] Since encoder data alone is insufficient for accurate positioning, a Kalman filter is used to fuse encoder and gyroscope data to achieve mobile robot localization. However, in practical applications, sensors are inevitably vulnerable to cyberattacks. Once attacked, the integrity, availability, and reliability of measurement data are compromised, leading to positioning errors in the mobile robot and potentially causing incalculable consequences. If attacks can be detected promptly and corresponding countermeasures designed, the positioning error rate of the mobile robot can be reduced, ensuring stable system operation and improving system robustness. Therefore, research on indoor mobile robot localization under attack has significant practical and theoretical implications. However, in existing technologies, traditional chi-square detection cannot accurately detect carefully designed spoofing attacks. Summary of the Invention

[0005] To address the aforementioned issues, this invention proposes an indoor mobile robot positioning method and system with online attack detection capabilities. This invention can promptly detect whether an indoor mobile robot is under attack during the positioning process, effectively suppressing the influence of external interference and ensuring the accuracy of mobile robot positioning.

[0006] According to some embodiments, the first aspect of the present invention provides an indoor mobile robot positioning method with online attack detection function, which adopts the following technical solution:

[0007] Indoor mobile robot localization methods with online attack detection capabilities include:

[0008] Based on the kinematic model of the mobile robot, a discrete state-space expression for the mobile robot is established, and a recursive formula for the relative pose of the mobile robot is obtained.

[0009] The position signal of the mobile robot is acquired, and the optimal pose of the mobile robot is estimated using the Kalman filter algorithm in the discrete state space expression of the mobile robot, so as to realize the relative positioning of the mobile robot.

[0010] By comparing the optimal pose estimate of the mobile robot with the actual pose value, the residual between the two is calculated, and the chi-square test is used to detect whether the residual is abnormal, thus realizing online attack detection for mobile robot localization.

[0011] According to some embodiments, the second aspect of the present invention provides an indoor mobile robot positioning system with online attack detection function, which adopts the following technical solution:

[0012] Indoor mobile robot positioning systems with online attack detection capabilities include:

[0013] The mobile robot state space representation determination module is configured to establish a discrete state space expression for the mobile robot based on the kinematic model of the mobile robot, and obtain the recursive formula for the relative pose of the mobile robot.

[0014] The mobile robot localization module is configured to acquire the position signal of the mobile robot, and use the Kalman filter algorithm to estimate the optimal pose of the mobile robot in the discrete state space expression of the mobile robot, thereby realizing the relative localization of the mobile robot.

[0015] The mobile robot localization online attack detection module is configured to compare the optimal pose estimate of the mobile robot with the actual pose value, calculate the residual between the two, and use chi-square test to detect whether the residual is abnormal, thereby realizing online attack detection for mobile robot localization.

[0016] According to some embodiments, a third aspect of the present invention provides a computer-readable storage medium.

[0017] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the indoor mobile robot localization method with online attack detection function as described in the first aspect above.

[0018] According to some embodiments, a fourth aspect of the present invention provides a computer device.

[0019] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the indoor mobile robot localization method with online attack detection function as described in the first aspect above.

[0020] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0021] This invention discloses an indoor mobile robot localization method with online attack detection function. In the indoor mobile robot localization process, a Kalman filter is used to fuse encoder and gyroscope data, which solves the problem of inaccurate localization relying solely on encoder data and ensures the localization of the indoor mobile robot.

[0022] This invention introduces an estimator capable of estimating state variables in the absence of attacks, enabling better tracking of the mobile robot's motion state. Furthermore, when the mobile robot is attacked, the designed online chi-square detection can promptly detect the presence or absence of an attack, allowing the system to adjust the robot's movement path in a timely manner and reduce the adverse effects of network attacks on the system.

[0023] This invention improves the robustness and security of the system, ensuring stable operation of the mobile robot system in interference environments, enabling online real-time detection of attacked abnormal data, and significantly improving the accuracy of indoor mobile robot positioning. Attached Figure Description

[0024] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0025] Figure 1 This is a flowchart illustrating the process of the method described in an embodiment of the present invention;

[0026] Figure 2 This is the kinematic model of the mobile robot in this embodiment of the invention;

[0027] Figure 3 This is the movement trajectory of the mobile robot in this embodiment of the invention, which is located solely by encoder data;

[0028] Figure 4 This is the movement trajectory of the mobile robot in this embodiment of the invention, which is located by encoder and gyroscope data.

[0029] Figure 5 This refers to the positioning error in the X direction of the mobile robot in this embodiment of the invention when it is not attacked;

[0030] Figure 6This refers to the positioning error in the Y direction of the mobile robot in this embodiment of the invention when it is not attacked.

[0031] Figure 7 This refers to the positioning error in the X direction of the mobile robot when it is attacked in this embodiment of the invention.

[0032] Figure 8 This refers to the positioning error in the Y direction of the mobile robot when it is attacked in this embodiment of the invention.

[0033] Figure 9 This is the movement trajectory of the mobile robot in this embodiment of the invention, which is located by encoder and gyroscope data when the robot is attacked.

[0034] Figure 10 This is the online chi-square detection effect when the mobile robot is attacked in an embodiment of the present invention. Detailed Implementation

[0035] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0036] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0037] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0038] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0039] Example 1

[0040] like Figure 1As shown, this embodiment provides an indoor mobile robot positioning method with online attack detection function. This embodiment uses the application of this method to a server as an example for illustration. It can be understood that this method can also be applied to a terminal, and can also be applied to a system including a terminal, a server, and a system, and can be implemented through the interaction between the terminal and the server. The server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network servers, cloud communication, middleware services, domain name services, CDN security services, and big data and artificial intelligence platforms. The terminal can be a smartphone, tablet computer, laptop computer, desktop computer, smart speaker, smartwatch, etc., but is not limited to these. The terminal and the server can be directly or indirectly connected through wired or wireless communication, which is not limited in this application. In this embodiment, the method includes the following steps:

[0041] Based on the kinematic model of the mobile robot, a discrete state-space expression for the mobile robot is established, and a recursive formula for the relative pose of the mobile robot is obtained.

[0042] The position signal of the mobile robot is acquired, and the optimal pose of the mobile robot is estimated using the Kalman filter algorithm in the discrete state space expression of the mobile robot, so as to realize the relative positioning of the mobile robot.

[0043] By comparing the optimal pose estimate of the mobile robot with the actual pose value, the residual between the two is calculated, and the chi-square test is used to detect whether the residual is abnormal, thus realizing online attack detection for mobile robot localization.

[0044] The discrete state-space expression of the mobile robot is specifically as follows:

[0045]

[0046] Where X(k)=[v L (k),v R (k),w(k)] T Z(k) = [v L (k),v R (k),θ(k)] T ;v L (k) represents the linear velocity of the robot's left wheel, v R w(k) represents the linear velocity of the robot's right wheel, w(k) represents the angular velocity at the robot's centroid, and θ(k) represents the heading angle of the mobile robot; W(k) and V(k) are uncorrelated white noises with zero mean and variance matrices of Q and R, respectively; F is the state transition matrix; and H is the measurement matrix.

[0047] The recursive formula for the relative pose of the mobile robot is:

[0048]

[0049] Where v(k) is the linear velocity at the centroid of the mobile robot; p(k) = [x(k), y(k), θ(k)] T x(k) is the position of the centroid of the mobile robot on the X-axis in the XOY coordinate system, y(k) is the position of the centroid of the mobile robot on the Y-axis in the XOY coordinate system, θ(k) is the heading angle of the mobile robot; T is the sampling period; This represents the estimated value of *.

[0050] The process of acquiring the position signal of the mobile robot and estimating its optimal pose using a Kalman filter algorithm within the discrete state-space expression of the mobile robot is specifically as follows:

[0051] The observed position signal of the mobile robot is sent to the filter and sensor attack detection system in real time;

[0052] The position signals of the mobile robot are fused using a Kalman filter to calculate the state estimate of the mobile robot at time k, and then the pose estimate of the mobile robot at time k is obtained. The estimate is then sent to the attack detection system.

[0053] The process of fusing the mobile robot's position signal using a Kalman filter to calculate the mobile robot's state estimate at time k, further obtaining the mobile robot's pose estimate at time k, and then sending the estimate to the attack detection system includes:

[0054] The state at the current moment X(k) is predicted using the state one-step prediction equation, as follows:

[0055]

[0056] in, and Let represent the predicted value of the first estimator at time k+1 and the estimated value at time k, respectively.

[0057] The covariance at the current time step is used to predict the covariance at the next time step using a one-step covariance prediction equation, as follows:

[0058] P1(k+1|k)=FP1(k)F T +Q

[0059] Where P1(k+1|k) and P1(k) represent the predicted covariance of the first estimator at time k+1 and the estimated covariance at time k, respectively.

[0060] The calculated Kalman gain K(k+1) is as follows:

[0061] K(k+1)=P1(k+1∣k)H T / (HP1(k+1∣k)H T +R)

[0062] The observation value Z(k+1) for the next time step is calculated using the observation equation, and the state and covariance for the next time step are estimated based on the state and covariance update equation, as follows:

[0063] Z(k+1)=HX(k+1)+V(k+1)

[0064]

[0065] P1(k+1)=(IK(k+1)H)P1(k+1|k)

[0066] in, This represents the estimate of the first estimator at time k+1;

[0067] The estimated pose of the mobile robot is sent to the attack detection system.

[0068] The optimal pose estimate of the mobile robot is compared with the actual pose value, the residual between the two is calculated, and the chi-square test is used to detect whether the residual is abnormal, thereby realizing online attack detection for mobile robot localization, including:

[0069] Based on the comparison between the optimal pose estimate and the actual pose of the mobile robot, two state estimation errors, e1(k) and e2(k), are defined as follows:

[0070]

[0071] in, It is the estimate of the first estimator at time k. X(k) is the estimate of the second estimator at time k, and X(k) is the state at the current time.

[0072] Based on the estimation errors of the two states, a new index with a mean of zero is defined. Its covariance matrix M(k) is as follows:

[0073] φ(k)=e1(k)-e2(k)

[0074]

[0075] Among them, P 12 (k) represents the cross covariance of e1(k) and e2(k). It is P12 Transpose of (k);

[0076] Using a new index with zero mean and its covariance matrix, the attack detection function λ(k) is defined as:

[0077] λ(k)=φ T (k)M -1 (k)φ(k);

[0078] Utilize attack detection functions to identify online attacks targeting mobile robot positioning.

[0079] The attack detection function is used to identify online attacks targeting mobile robot positioning, specifically:

[0080] Since the attack detection function follows a chi-square distribution, the threshold T is obtained by querying the chi-square distribution table. D ;

[0081] The attack detection function λ(k) is compared with the threshold T. D In comparison, if λ(k) is greater than T D If the attack occurs, then an attack exists; otherwise, no attack exists.

[0082] This invention provides an indoor mobile robot localization method with online attack detection capability. The method includes: constructing a discrete state-space expression for the mobile robot based on its kinematic model; deriving a recursive formula for the robot's relative pose; acquiring the robot's position signal through an encoder and gyroscope; fusing the encoder and gyroscope data using a Kalman filter to estimate the robot's optimal pose and achieve relative localization; and employing a novel online attack detection method to detect spoofed data injection attacks during the robot's movement, ensuring accurate system localization.

[0083] It is understood that the method described in this embodiment is to locate first and then detect; the initial state space expression and relative pose recursive formula are the basis for realizing the localization of the mobile robot, and subsequent state estimation uses the previously established state space expression and relative pose recursive formula.

[0084] After recursive calculation based on the state space expression and the relative pose recursive formula, the optimal position estimate is obtained. The system is then checked for attacks by an attack detection algorithm. If no attack is found, the final motion position can be obtained. If an attack is found, the optimal position estimate is not the final motion position of the mobile robot, and the localization fails. The previous process is repeated for state estimation, localization, and detection.

[0085] Please see Figure 2This invention provides an indoor mobile robot localization method with online attack detection function. Based on the kinematic model of the mobile robot, the state equation and observation equation of the mobile robot's motion process are established:

[0086]

[0087] Where X(k)=[v L (k),v R (k),w(k)] T , representing the system's state variables; Z(k) = [v L (k),v R (k),θ(k)] T , representing the system's measured value; v L (k) represents the linear velocity of the robot's left wheel, v R w(k) represents the linear velocity of the robot's right wheel, w(k) represents the angular velocity at the robot's centroid, and θ(k) represents the heading angle of the mobile robot; W(k) and V(k) are uncorrelated white noises with zero mean and variance matrices of Q and R, respectively; F is the state transition matrix; and H is the measurement matrix.

[0088] Based on the established state equation and observation equation, the recursive formula for the relative pose of the mobile robot is obtained:

[0089]

[0090] Where v(k) is the linear velocity at the centroid of the mobile robot; p(k) = [x(k), y(k), θ(k)] T Let represent the pose of the mobile robot, x(k) be the position of the robot's centroid on the X-axis in the XOY coordinate system, y(k) be the position of the robot's centroid on the Y-axis in the XOY coordinate system, and θ be the position of the robot's centroid on the Y-axis in the XOY coordinate system. k The heading angle of the mobile robot; T is the sampling period; This represents the estimated value of *. It is an estimate of w(k).

[0091] Indoor mobile robot positioning includes the following steps:

[0092] S1. The sensor sends the observed data to the filter and the sensor attack detection system in real time;

[0093] S2. Using a Kalman filter, the data obtained from the encoder and gyroscope are fused to calculate the state estimate of the mobile robot at time k. The pose estimate of the mobile robot at time k is then obtained and sent to the attack detection system.

[0094] S3. The attack detection system compares the received pose estimate with the actual pose value and calculates the residual between the two. Chi-square test is used to detect whether the residual is abnormal, enabling online attack detection for mobile robot positioning.

[0095] Please see Figure 1 The Kalman filtering process, specifically step S2, works as follows:

[0096] S201. The state at the next moment is predicted from the current state X(k) using the state one-step prediction equation, as follows:

[0097]

[0098] in, and Let represent the predicted value of the first estimator at time k+1 and the estimated value at time k, respectively.

[0099] S202. The covariance at the current time is used to predict the covariance at the next time step through the covariance one-step prediction equation, using the Kalman filter algorithm, as follows:

[0100] P1(k+1|k)=FP1(k)F T +Q

[0101] Where P1(k+1|k) and P1(k) represent the prediction covariance of the first estimator at time k+1 and the estimated covariance at time k, respectively, and Q represents the variance of the process noise.

[0102] S203. The calculated Kalman gain K(k+1) is as follows:

[0103] in,

[0104] K(k+1)=P1(k+1∣k)H T / (HP1(k+1∣k)H T +R)

[0105] S204. Calculate the observed value Z(k+1) at the next time step using the observation equation, and estimate the state and covariance at the next time step based on the state and covariance update equation, as follows:

[0106] Z(k+1)=HX(k+1)+V(k+1)

[0107]

[0108] P1(k+1)=(IK(k+1)H)P1(k+1|k)

[0109] in, P1(k+1) and P1(k+1) represent the state estimate and covariance estimate of the first estimator at time k+1, respectively.

[0110] S205. Send the estimated pose of the mobile robot to the attack detection system.

[0111] Design an estimator for the state variables in the absence of attacks. The designed state estimator is as follows:

[0112]

[0113] P2(k+1)=FCov{X(k)}F T +Q

[0114] in, P2(k+1) and P2(k+1) represent the state estimate and covariance estimate of the second estimator at time k+1, respectively; E{X(k)} and Cov{X(k)} represent the mean and covariance of X(k), respectively.

[0115] It should be noted that the first estimator only uses the state from the previous time step to estimate the current state, while the second estimator uses all state values ​​up to the current time step, resulting in a more reliable estimate. Like the first estimator, the second state estimator utilizes the state-space expression and the relative pose recursive formula for estimation.

[0116] Based on the designed state estimator, the system detects whether an attack exists during the movement of the mobile robot. Step S3 is as follows:

[0117] S301. Define two state estimation errors, e1(k) and e2(k), as follows:

[0118]

[0119] S302. Define a new index φ(k) with a mean of zero and its covariance matrix M(k) as follows:

[0120] φ(k)=e1(k)-e2(k)

[0121]

[0122] Among them, P 12 (k) represents the cross covariance of e1(k) and e2(k). It is P 12 The transpose of (k).

[0123] S303. Define the attack detection function λ(k) as follows:

[0124] λ(k)=φ T (k)M -1(k)φ(k)

[0125] Since the attack detection function follows a chi-square distribution, the threshold T is obtained by querying the chi-square distribution table. D The attack detection function λ(k) is compared with the threshold T. D In comparison, if λ(k) is greater than T D If the attack occurs, then an attack exists; otherwise, no attack exists.

[0126] To demonstrate the effectiveness of this embodiment, the following simulation verification was performed:

[0127] In this simulation experiment, it is assumed that the mobile robot performs circular motion, the distance between the two wheels of the mobile robot is set to l = 0.2m, and the linear velocities of the left and right wheels are set to v. L (0) = 0.35, v R (0)=0.4,ω(0)=0.25。 Assume that the system process noise and observation noise are Gaussian white noise with a mean of 0, and their variance and mean are: Q=diag([0.00001,0.00001,0.00001]) ,R=diag([0.001,0.001,0.0001]).

[0128] System Matrix The observation matrix is ​​H = E, where E is the identity matrix. The sampling period is T = 0.1, and the simulation time is 27s.

[0129] To verify the effectiveness of the attack detection mechanism and control method proposed in this invention, simulation software was used to obtain... Figures 3-10 . Figure 3 This indicates the movement trajectory of a mobile robot, which is located solely by encoder data. Figure 4 This represents the movement trajectory of a mobile robot, determined by the fusion of encoder and gyroscope data. Figure 3 and Figure 4 It can be seen that the positioning effect of the fused encoder and gyroscope data is good, almost coinciding with the actual trajectory. During the simulation, a false data attack was injected at t=11s, and the movement trajectory relying on the fused encoder and gyroscope data was as follows: Figure 9 As shown. Figure 10 This indicates the effectiveness of the attack detection mechanism when an attack occurs. Figure 5 This represents the positioning error in the X direction of the mobile robot when it is not under attack. Figure 6 This represents the positioning error in the Y direction of the mobile robot when it is not under attack. Figure 7 This represents the positioning error in the X direction when the mobile robot is attacked. Figure 8 This represents the positioning error in the Y direction when the mobile robot is attacked. Figure 5 , Figure 6 , Figure 7 , Figure 8 It can be seen that the positioning error increases significantly when the system is attacked, reducing the system's positioning accuracy. Figure 9 It can be seen that when attacked, the mobile robot's trajectory begins to deviate from its actual trajectory. If the adverse effects of the attack on the system are not eliminated in time, the mobile robot's trajectory will diverge and fail to converge back to its original circular trajectory. Figure 10 It can be seen that the attack detection mechanism detected the existence of the attack at t=16s.

[0130] In summary, this invention not only improves the robustness of mobile robots to external interference during the localization process and reduces the localization error of mobile robots, but also enables real-time online detection of whether mobile robots are under network attacks, thereby improving localization accuracy and reducing false alarm rates.

[0131] To accurately detect random attacks, reduce false alarm rates, and improve system robustness, this embodiment designs an indoor mobile robot localization method with online attack detection functionality. This method utilizes two state estimation errors, e1(k) and e2(k), to define a new index φ(k) and an attack detection function λ(k). Here, φ(k) depends on the attack strength. If the attack strength is sufficiently high, then the estimated value... Will be compared with the estimated value There is a significant deviation, therefore φ(k) will change noticeably. If the attack strength is small, φ(k) will be small, and the impact on the state estimation of the Kalman filter will also be small. When the mobile robot suffers a strong hidden attack, the measured value will deviate from the actual value, thus interfering with the result of the state estimator. φ(k) and λ(k) will change significantly, causing the chi-square test value to exceed the threshold, triggering an alarm, and thus realizing attack detection during the mobile robot localization process.

[0132] Example 2

[0133] This embodiment provides an indoor mobile robot positioning system with online attack detection function, including:

[0134] The mobile robot state space representation determination module is configured to establish a discrete state space expression for the mobile robot based on the kinematic model of the mobile robot, and obtain the recursive formula for the relative pose of the mobile robot.

[0135] The mobile robot localization module is configured to acquire the position signal of the mobile robot, and use the Kalman filter algorithm to estimate the optimal pose of the mobile robot in the discrete state space expression of the mobile robot, thereby realizing the relative localization of the mobile robot.

[0136] The mobile robot localization online attack detection module is configured to compare the optimal pose estimate of the mobile robot with the actual pose value, calculate the residual between the two, and use chi-square test to detect whether the residual is abnormal, thereby realizing online attack detection for mobile robot localization.

[0137] The examples and application scenarios implemented by the above modules and corresponding steps are the same, but are not limited to the content disclosed in Embodiment 1 above. It should be noted that the above modules, as part of the system, can be executed in a computer system such as a set of computer-executable instructions.

[0138] The descriptions of each embodiment in the above embodiments have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0139] The proposed system can be implemented in other ways. For example, the system embodiments described above are merely illustrative, and the division of modules described above is only a logical functional division. In actual implementation, there may be other division methods. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not executed.

[0140] Example 3

[0141] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in the indoor mobile robot positioning method with online attack detection function as described in Embodiment 1 above.

[0142] Example 4

[0143] This embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps in the indoor mobile robot positioning method with online attack detection function as described in Embodiment 1 above.

[0144] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of hardware embodiments, software embodiments, or embodiments combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0145] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0146] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0147] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0148] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0149] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for locating an indoor mobile robot with online attack detection capability, characterized in that, include: Based on the kinematic model of the mobile robot, a discrete state-space expression for the mobile robot is established, resulting in a recursive formula for the relative pose of the mobile robot. Specifically, the discrete state-space expression for the mobile robot is as follows: in, ; ; Indicates the linear velocity of the robot's revolver, The linear velocity of the robot's right wheel, Represents the angular velocity at the centroid of the robot. Indicates the heading angle of the mobile robot; and It has a mean of zero and a variance matrix of... and Uncorrelated white noise; This is the state transition matrix; For measurement matrix; The recursive formula for the relative pose of the mobile robot is: in, Let be the linear velocity at the centroid of the mobile robot; yes The estimated value, , In order to be in The centroid of the mobile robot in the coordinate system is Position on the axis In order to be in The centroid of the mobile robot in the coordinate system is Position on the axis The heading angle of the mobile robot; The sampling period; express The estimated value; The position signal of the mobile robot is acquired, and the optimal pose of the mobile robot is estimated using the Kalman filter algorithm in the discrete state space expression of the mobile robot to achieve relative localization of the mobile robot. Specifically: The observed position signal of the mobile robot is sent to the filter and sensor attack detection system in real time; The position signals of the mobile robot are fused using a Kalman filter to calculate the position of the mobile robot in the [missing information - likely a specific location or phase]. The state estimate at time t is used to further obtain the mobile robot's state estimate at time t. The pose estimate at time t is then sent to the attack detection system, including: From the current state The state at the next time step is predicted using the state-one-step prediction equation, as follows: in, and These represent the first estimator at the 1st... The predicted value at time 1 and at the 2nd The estimated value at time; The covariance at the current time step is used to predict the covariance at the next time step using a one-step covariance prediction equation, as follows: in, and These represent the first estimator at the 1st... The prediction covariance at time 1 and the first The estimated covariance at time; Calculated Kalman gain ,as follows: Calculate the observation value at the next time step using the observation equation. The state and covariance at the next time step are estimated based on the state and covariance update equation, as follows: in, Indicates the first estimator at the 1st... The estimated value at time; The estimated pose of the mobile robot is sent to the attack detection system; By comparing the optimal pose estimate of a mobile robot with the actual pose value, calculating the residual between the two, and using chi-square test to detect whether the residual is abnormal, online attack detection for mobile robot localization is achieved, including: Based on the comparison between the optimal pose estimate and the actual pose of the mobile robot, two types of state estimation errors are defined. and for: ， in, It is the estimate of the first estimator at time k. It is the estimate of the second estimator at time k. It is the current state; Based on the estimation errors of the two states, a new index with a mean of zero is defined. and its covariance matrix as follows: in, express and Cross covariance, yes Transpose of; Using a new index with a mean of zero and its covariance matrix, an attack detection function is defined. for: ； Utilize attack detection functions to identify online attacks targeting mobile robot positioning.

2. The indoor mobile robot positioning method with online attack detection function as described in claim 1, characterized in that, The attack detection function is used to identify online attacks targeting mobile robot positioning, specifically: Since the attack detection function follows a chi-square distribution, the threshold is obtained by querying the chi-square distribution table. ; attack detection function With threshold In comparison, if Greater than If the attack occurs, then an attack exists; otherwise, no attack exists.

3. An indoor mobile robot positioning system with online attack detection function, characterized in that, include: The mobile robot state space representation determination module is configured to establish a discrete state space expression for the mobile robot based on its kinematic model, thereby obtaining a recursive formula for the mobile robot's relative pose. Specifically, the discrete state space expression for the mobile robot is as follows: in, ; ; Indicates the linear velocity of the robot's revolver, The linear velocity of the robot's right wheel, Represents the angular velocity at the centroid of the robot. Indicates the heading angle of the mobile robot; and It has a mean of zero and a variance matrix of... and Uncorrelated white noise; This is the state transition matrix; For measurement matrix; The recursive formula for the relative pose of the mobile robot is: in, Let be the linear velocity at the centroid of the mobile robot; yes The estimated value, , In order to be in The centroid of the mobile robot in the coordinate system is Position on the axis In order to be in The centroid of the mobile robot in the coordinate system is Position on the axis The heading angle of the mobile robot; The sampling period; express The estimated value; The mobile robot localization module is configured to acquire the position signal of the mobile robot, and estimate the optimal pose of the mobile robot using a Kalman filter algorithm in the discrete state space expression of the mobile robot to achieve relative localization of the mobile robot. Specifically: The observed position signal of the mobile robot is sent to the filter and sensor attack detection system in real time; The position signals of the mobile robot are fused using a Kalman filter to calculate the position of the mobile robot in the [missing information - likely a specific location or phase]. The state estimate at time t is used to further obtain the mobile robot's state estimate at time t. The pose estimate at time t is then sent to the attack detection system, including: From the current state The state at the next time step is predicted using the state-one-step prediction equation, as follows: in, and These represent the first estimator at the 1st... The predicted value at time 1 and at the 2nd The estimated value at time; The covariance at the current time step is used to predict the covariance at the next time step using a one-step covariance prediction equation, as follows: in, and These represent the first estimator at the 1st... The prediction covariance at time 1 and the first The estimated covariance at time; Calculated Kalman gain ,as follows: Calculate the observation value at the next time step using the observation equation. The state and covariance at the next time step are estimated based on the state and covariance update equation, as follows: in, Indicates the first estimator at the 1st... The estimated value at time; The estimated pose of the mobile robot is sent to the attack detection system; The mobile robot localization online attack detection module is configured to compare the optimal pose estimate of the mobile robot with the actual pose value, calculate the residual between the two, and use chi-square test to detect whether the residual is abnormal, thereby realizing online attack detection for mobile robot localization, including: Based on the comparison between the optimal pose estimate and the actual pose of the mobile robot, two types of state estimation errors are defined. and for: ， in, It is the estimate of the first estimator at time k. It is the estimate of the second estimator at time k. It is the current state; Based on the estimation errors of the two states, a new index with a mean of zero is defined. and its covariance matrix as follows: in, express and Cross covariance, yes Transpose of; Using a new index with a mean of zero and its covariance matrix, an attack detection function is defined. for: ； Utilize attack detection functions to identify online attacks targeting mobile robot positioning.

4. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps in the indoor mobile robot localization method with online attack detection function as described in any one of claims 1-2.

5. A computer device, 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 program, it implements the steps in the indoor mobile robot localization method with online attack detection function as described in any one of claims 1-2.

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