AUV (Autonomous Underwater Vehicle) state estimation method based on Flexray protocol

Abstract

An AUV state estimation method based on a Flexray protocol comprises the following steps: S1, constructing an underwater vehicle system of a 6-DOF model, S2, designing the FlexRay protocol for the underwater vehicle system, S3, designing an augmentation system based on a mechanism of the FlexRay protocol, S4, establishing set membership filtering of a holosymmetric multi-cell body, and S5, estimating the state of the AUV based on the set membership filtering of the holosymmetric multi-cell body. A set membership estimator using a holosymmetric multi-cell shell provides high precision state set estimation. The invention aims to provide an AUV (Autonomous Underwater Vehicle) state estimation method based on a Flexray protocol, and high-precision positioning of the AUV in complex, unknown and bounded noise is realized.

Description

Technical Field

[0001] The present invention relates to the technology of underwater vehicles, and particularly to a method for AUV state estimation based on the Flexray protocol. Background Art

[0002] When an AUV (Autonomous Underwater Vehicle) performs tasks in an underwater environment, it needs to accurately estimate its own trajectory to ensure that it can complete the tasks and return to the predetermined position. However, due to the special challenges of the underwater environment, the trajectory estimation of AUVs faces a series of technical difficulties, mainly including the following aspects:

[0003] Factors such as water flow, turbulence, and temperature changes in the underwater environment all affect the navigation of AUVs. These factors make the movement trajectory of AUVs very difficult to predict; the optical signals underwater attenuate severely, restricting the use of the visual navigation and imaging systems of AUVs, which further increases the difficulty of trajectory estimation. In an unknown or dynamically changing underwater environment, an AUV must be able to estimate its own trajectory in real time and perform path planning and dynamic obstacle avoidance according to environmental changes. However, due to the complexity and dynamics of the underwater environment, performing accurate trajectory estimation in real time is a huge challenge.

[0004] Set membership estimation has become a suitable and effective state estimation method for dealing with noise, where unknown but bounded noise is considered. However, in classical set membership filtering, the set membership algorithm with an ellipsoid as the boundary is considered, and in the set operation of the ellipsoid, the step of the Minkowski sum often requires a more conservative circumscribed ellipsoid to envelope, which to a certain extent leads to a decrease in estimation accuracy; while in another ellipsoid algorithm, the linear matrix inequality method LMIs is used, which is usually computationally complex in a high-dimensional system and has the disadvantage of a slow real-time response speed. Therefore, designing a set membership estimation algorithm with a more compact estimation set and a faster response speed has become a topic.

[0005] With the increasing demand for AUVs to perform complex tasks underwater, the application of network technology plays an important role in improving the system communication ability, data transmission efficiency, etc. However, the special environment of underwater communication and the limited resources bring challenges to the communication network. When an AUV navigates underwater, it usually needs to perform operations such as high-precision sensor data sharing, real-time state update, path planning, and cooperative control. These operations require a large amount of data transmission. However, due to limited underwater resources, the communication bandwidth and computing resources between AUVs cannot meet the requirements, which may lead to a decline in system performance or the failure of tasks to be completed as expected. In order to effectively solve data conflicts, a reasonable communication scheduling protocol must be designed to ensure that data can be exchanged orderly between multiple AUVs, thereby avoiding frequent signal interference and conflicts.

[0006] In the fields of network communication and multi-task scheduling, the Round Robin Protocol (RRP) and the Try Once Discard Protocol (TODP) are often used to handle the scheduling problems of resource sharing and data transmission. However, as a fixed scheduling method, RRP lacks flexibility. Especially when some devices have no data to transmit for a long time, the polling protocol still wastes time checking these devices. While TODP improves the flexibility of scheduling, it cannot guarantee the stability of the system under estimation / control under certain competition rules.

[0007] Therefore, it is urgent to study a set membership estimation algorithm for AUV under a new communication scheduling protocol to achieve accurate trajectory estimation of AUV in the complex underwater environment. Summary of the Invention

[0008] Aiming at the above defects, the purpose of the present invention is to propose an AUV state estimation method based on the FlexRay protocol, which realizes high-precision positioning of AUV in complex unknown but bounded noise by effectively improving the utilization efficiency of underwater network resources and achieving flexible and stable data transmission.

[0009] To achieve this purpose, the present invention adopts the following technical solutions:

[0010] An AUV state estimation method based on the FlexRay protocol, comprising the following steps:

[0011] S1. Construct an underwater vehicle system with a 6-DOF model, model it according to the dynamic equations of the position information and Euler angles of the underwater vehicle, and based on the dynamic equations of the continuous-time system, convert the continuous-time system into a discrete-time system by the method of zero-order hold.

[0012] S2. Based on the FlexRay protocol, comparing the core part of the transmission protocol, roughly divide the transmission protocol into two categories, one based on time division multiple access mode and the other based on flexible time division multiple access mode, and thus design a FlexRay protocol for the underwater vehicle system, and divide the protocol into a polling protocol for the static segment and a try once discard protocol for the dynamic segment.

[0013] S3. Since the Flexray protocol is used, there is a unit estimator working interval in the arrival time of data packets, and this phenomenon is regarded as a partial time delay problem; aiming at the partial time delay problem that appears in the mechanism based on the FlexRay protocol, augment the system state to become a higher-dimensional discrete system, and solve the problems of synchronization difficulty, error accumulation, and algorithm stability decline of the general state estimation algorithm under hybrid time delay.

[0014] S4. Since the dimension of the augmented system is very large, it involves the problem of large computational complexity; establish a set membership filter of a fully symmetric polytope, and use the outer shell of the fully symmetric polytope to provide a higher-precision state set estimation.

[0015] Preferably, in step S1, the dynamic model of the AUV is described as a 6-DOF model based on the body-fixed coordinate system and the earth-fixed coordinate system;

[0016] Since the influence of roll on translational motion is very small, the roll velocity is not considered; the following are the kinematic model and dynamic model of the AUV in the discrete case:

[0017]

[0018] In Equation 1, s = [x, y, z] T and θ = [φ, ψ] T represent the 3D position state and Euler angles of the AUV respectively, and and represent the velocities in the corresponding components; define the system state as;

[0019]

[0020] In Equation 2, x k represents the state parameter of the system at time k;

[0021] Thus, the dynamic equation of the initial AUV system can be obtained,

[0022] x k+1 = Ax k + w k Equation (3)

[0023] and the original measurement is obtained by sensor measurement

[0024]

[0025] where the unknown but bounded noise w k and v k are limited by the following polyhedron sets; C is the measurement matrix, and sensors usually cannot directly obtain the data of state x, so C is the physical parameter of the sensor:

[0026]

[0027] where <c, P> represents a polyhedron set with the center as vector c and the generation matrix as P; so w k and v k are respectively located in the polyhedron sets with the center at the origin and the generation matrices as W and V.

[0028] Preferably, in step 2, the uniform quantizer sets a scalar b such that the interval [-b, b] can be divided into 2 according to the allocated bits rbpsr -1 quantization intervals. Based on the size of the received information, it is determined which sub-interval it falls into, and the corresponding interval code is output;

[0029] We define the quantized measurement as:

[0030] y k = Cx k + v k + d k Equation (6)

[0031] In Equation 6, represents the quantization error, and the quantization error is related to the number of data bits of the data packet; the quantization error is defined as a polytope:

[0032] d k ∈ <0, D k >; Equation (7)

[0033] In Equation 7, <0, D k > represents the set of polytopes with the origin as the center and the generating matrix as D k ;

[0034] In automotive network communication, the dynamic segment is widely used for diagnostic flashing; therefore, the dynamic timing parameters are inconsistent with the static cycle timing parameters; here, the rate of change of the Euler angles of the AUV is defined as the data packet transmitted by the one-shot discard protocol in the dynamic segment, that is, flashing is performed on in the dynamic segment; considering that only one data packet can access the channel resource during the transmission period, we divide the measurement into N data packets and divide them into two sets and Among them, the nodes belonging to set l1 are scheduled by the polling protocol, and the nodes belonging to l2 are scheduled by the one-shot discard protocol. The scheduling rules are as follows:

[0035] A. Polling protocol:

[0036] λ(j) = mod(j - 1, h) + 1 (8)

[0037] In Equation 8, λ(j) ∈ l1 represents the node that obtains the transmission right at time j, and mod() is the remainder function;

[0038] B. One-shot discard protocol:

[0039]

[0040] In Equation 9, y′ j，k represents the information of node j in the last transmission before time k, and α k ∈ l2 represents the node that most needs to transmit under the "maximum error first" rule at time k; is a given positive definite weight matrix;

[0041] Therefore, based on the FlexRay protocol in a high-speed network, the measurements can be divided into data packets to be scheduled in the static segment and the dynamic segment:

[0042]

[0043] Similarly, define the measurement matrix:

[0044]

[0045] Define the measurement received at the receiver as:

[0046] Based on the assumption of a high-speed network, we can divide a communication cycle of the FlexRay protocol into transmission cycles, because the transmission speed has been greatly improved due to the update of hardware technology; at the same time, the state update cycle has not changed due to the change of the transmission speed, which makes it possible to receive at most data packets in each state update cycle; furthermore, the received measurement is defined as:

[0047]

[0048] where and represent the quantization error of the received data packet and the measurement noise respectively.

[0049] Furthermore, in step 3, under the communication scheduling using the FlexRay protocol, the problem that the static segment data packet and the dynamic segment data packet are at different times will naturally occur, that is, the receiver faces the mixture of the measurement at the instantaneous moment and the measurement with a unit time delay, which makes it necessary to perform preprocessing before estimation to convert it into a standard time-delay-free system;

[0050] We set the augmented state as We have the following augmented system:

[0051]

[0052] where

[0053]

[0054] Furthermore, in step 4, using the fully symmetric polytope as the set outer shell, we define the initial state based on prior knowledge to be located in the polytope Compared with the existing ellipsoid algorithm, the Minkowski sum of the polytope satisfies the closure of set operations, which means that it is still a polytope after the operation, that is:

[0055]

[0056] This reduces the conservativeness in various steps of the estimation, making the size of the estimated set smaller and the error of the final estimation result smaller;

[0057] For the state at time k, first perform a one-step prediction on the augmented system to obtain the one-step prediction polytope where:

[0058]

[0059] In Equation 14, and represent the center and the generation matrix of the one-step prediction state respectively;

[0060] Next, the estimated polytope where:

[0061]

[0062] In Equation 15, and represent the center and the generation matrix of the final estimated state set respectively.

[0063] Furthermore, in Step 4, the specific steps are as follows:

[0064] S41. Initialization, input the initial state using prior knowledge generation matrix

[0065] S42. Calculate the set of one-step prediction polytopes, and find the set center of the one-step prediction set according to the following formula and the generation matrix

[0066]

[0067] Thus, we obtain:

[0068]

[0069] S43. Calculate the set of estimated polytopes, and find the set center of the one-step prediction set according to the following formula and the generation matrix

[0070]

[0071] Thus, we obtain:

[0072]

[0073] S44. Design gain G based on minimizing the Frobenius radius of matrix k+1 To facilitate minimizing the matrix Frobenius radius We define the covariance matrix of the generating matrix as:

[0074] P = cov(<c, R>) = RR T Equation (20)

[0075] Therefore, a similar covariance matrix can be expressed as:

[0076]

[0077] According to the optimality theorem, it can be obtained that

[0078]

[0079] S45. Iteration. Finally, based on the dynamic model of the AUV and the measurements obtained from the FlexRay protocol, we obtain the navigation trajectory of the AUV in three-dimensional space at each step.

[0080] One of the above technical solutions includes the following beneficial effects:

[0081] The 3D trajectory estimation algorithm based on the FlexRay protocol designed in this patent can solve the problem of limited resources in underwater networks. It is achieved by transmitting only one packet in the shared network channel at a time, effectively avoiding data conflicts. And due to the use of the hybrid protocol, it not only retains the stability advantages of the RRP but also has a certain flexibility due to the TODP. Compared with existing methods, it can effectively improve the utilization efficiency of underwater network resources and achieve flexible and stable data transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] Figure 1 is a schematic diagram of the overall steps of an embodiment of the present invention;

[0083] Figure 2 is the AUV model under 6-DOF of an embodiment of the present invention;

[0084] Figure 3 is a schematic diagram of the composition within a unit communication cycle of the Flexray protocol;

[0085] Figure 4 is the dynamic segment schedule under the Flexray protocol;

[0086] Figure 5 is the navigation trajectory diagram of the AUV of an embodiment of the present invention;

[0087] Figure 6 It is the membership estimation envelope diagram of the relevant state of the AUV in an embodiment of the present invention. Detailed implementation manners

[0088] Embodiments of the present invention will be described in detail below. Examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention and should not be construed as a limitation to the present invention.

[0089] As Figure 1 shown, an AUV state estimation method based on the FlexRay protocol includes the following steps:

[0090] S1. Construct an underwater vehicle system with a 6-DOF model, perform modeling according to the dynamic equations of the position information and Euler angles of the underwater vehicle, and based on the dynamic equations of the continuous-time system, convert the continuous-time system into a discrete-time system by means of a zero-order hold.

[0091] S2. Based on the FlexRay protocol, with reference to the core part of the transmission protocol, roughly divide the transmission protocol into two categories, one based on time division multiple access and the other based on flexible time division multiple access, and thus design a FlexRay protocol for the underwater vehicle system, and divide the protocol into a polling protocol for the static segment and a try-once discard protocol for the dynamic segment.

[0092] S3. Since the Flexray protocol is used, there is a unit estimator working interval in the arrival time of data packets, and this phenomenon is regarded as a partial time delay problem; for the partial time delay problem that appears in the mechanism based on the FlexRay protocol, augment the system state to become a higher-dimensional discrete system, and solve the problems of synchronization difficulty, error accumulation, and algorithm stability decline of the general state estimation algorithm under hybrid time delays.

[0093] S4. Since the dimension of the augmented system is very large, it involves the problem of large computational complexity; establish a membership filter of a fully symmetric polytope and use the outer shell of the fully symmetric polytope to provide a state set estimation with higher accuracy.

[0094] S1: For the 6-DOF underwater vehicle in this patent, through the dynamic analysis of the continuous-time system and the method of zero-order hold, a general discrete model of the 6-DOF underwater vehicle within the corresponding sampling time is obtained;

[0095] S2: A transmission mechanism for underwater networks is designed by referring to the FlexRay protocol in automotive networks. By transmitting only one packet in the shared network channel at a time, data conflicts are effectively avoided, and the problem of limited resources in underwater networks is solved. Moreover, due to the use of the hybrid protocol, the stability advantages of the polling protocol are retained, and certain flexibility is obtained due to the use of the try-once-discard protocol. Compared with existing methods, the utilization efficiency of underwater network resources can be effectively improved, and flexible and stable data transmission can be achieved. S3: Regarding the problem of hybrid delay measurement that occurs when applying the FLexRay protocol, problems such as synchronization difficulties, error accumulation, decreased algorithm stability, and increased computational burden will occur in general state estimation algorithms. Therefore, a method of augmenting the system state is proposed, which can unify the hybrid delay measurement into a delay-free measurement system, and thus be converted into the standard trajectory estimation of AUV, avoiding the error accumulation caused by time inconsistency and the impact on convergence. S4: Design a set membership estimation algorithm based on Zonotope. By using Zonotope as the outer bound of the set, it is closed during the set operation of Minkowski sum, which makes the obtained set still a polytope. And during the operation of the entire estimation process, we use a more concise Zonotope Kalman Filter (ZKF) algorithm that only involves simple linear affine and matrix operations, greatly reducing the computational complexity and improving the real-time speed of response. Compared with the existing ellipsoidal set membership filtering, it provides a tighter set in some directions, making the error of the final estimation result smaller, and there is an explicit optimization regarding the set norm. TDMA is the time division multiple access method, Flexible TDMA is the flexible time division multiple access method, zonotope is the fully symmetric polytope, RRP is the polling protocol, and TODP is the try-once-discard protocol.

[0096] As Figure 2 shown, in step S1, the dynamic model of the AUV is described as a 6-DOF model based on the body-fixed coordinate system and the earth-fixed coordinate system;

[0097] Since the influence of roll on translational motion is very small, the roll speed is not considered; the following are the kinematic model and dynamic model of the AUV in the discrete case:

[0098]

[0099] In Equation 1, s = [x, y, z] T and θ = [φ, ψ] T respectively represent the 3D position state and Euler angles of the AUV, and and represent the velocities in the corresponding components; define the system state as;

[0100]

[0101] In Equation 2, x k represents the state parameter of the system at time k;

[0102] Thus, the dynamic equation of the initial AUV system can be obtained.

[0103] x k+1 = Ax k + w k Equation (3)

[0104] And the original measurement is obtained by sensor measurement

[0105]

[0106] where the unknown but bounded noise w k and v k are bounded by the following polyhedron set; C is the measurement matrix. Since sensors usually cannot directly obtain the data of state x, C is the physical parameter of the sensor:

[0107]

[0108] where <c, P> represents a polyhedron set with the center as vector c and the generating matrix as P; thus w k and v k are respectively located in the polyhedron sets with the center at the origin and the generating matrices as W and V.

[0109] Defining the perturbation as an unknown but bounded set variable, compared with the traditional Kalman filter, it does not require additional prior knowledge of statistical distribution, so it is more in line with engineering practice; secondly, in set membership filtering, compared with the ellipsoidal-shaped set, the fully symmetric polyhedron can better solve the over-packing problem in the operation. This reduces the time required for feedback and improves the real-time response rate.

[0110] Specifically, in Step 2, the uniform quantizer sets a scalar b such that the interval [-b, b] can be divided into 2 r -1 quantization intervals according to the allocated bit rate r bps, and determines which small interval it falls into according to the size of the received information, and outputs the corresponding interval code;

[0111] We define the quantized measurement as:

[0112] y k = Cx k + v k + d k Equation (6)

[0113] In Equation 6, represents the quantization error, and the quantization error is related to the number of data bits of the data packet; the quantization error is defined as a polytope:

[0114] d k ∈ <0, D k >; Equation (7)

[0115] In Equation 7, <0, D k > represents the set of polytopes with the origin as the center and the generating matrix as D k ;

[0116] In automotive network communication, the dynamic segment is widely used for diagnostic flashing; therefore, the dynamic timing parameters are inconsistent with the static cyclic timing parameters; here, the rate of change of the Euler angles of the AUV is defined as the data packet discarded in one attempt during transmission in the dynamic segment, that is, flashing is performed on in the dynamic segment; considering that only one data packet can access the channel resources during the transmission period, we divide the measurement into N data packets and divide them into two sets and where the nodes belonging to set l1 are scheduled by the polling protocol, and the nodes belonging to l2 are scheduled by the one-shot discard protocol. The scheduling rules are as follows:

[0117] A. Polling protocol:

[0118]

[0119] In Equation 8, λ(j) ∈ l1 represents the node that obtains the transmission right at time j, and mod() is the remainder function;

[0120] B. One-shot discard protocol:

[0121]

[0122] In Equation 9, y′ j，k represents the information of node j in the last transmission before time k, and α k ∈ l2 represents the node that most needs to transmit under the "maximum error first" rule at time k; is a given positive definite weight matrix;

[0123] Therefore, based on the Flexray protocol in high-rate networks, the measurement can be divided into data packets to be scheduled in the static segment and the dynamic segment:

[0124]

[0125] Similarly, the measurement matrix is defined:

[0126]

[0127] Define the measurement received at the receiving end as:

[0128] Based on the assumption of a high-speed network, we divide a communication cycle of the Flexray protocol into transmission cycles. This is because the transmission speed has been greatly improved with the update of hardware technology. At the same time, the state update cycle has not changed due to the change of the transmission speed, which enables at most data packets to be received in each state update cycle. Furthermore, the received measurement is defined as:

[0129]

[0130] where and represent the quantization error of the received data packet and the measurement noise respectively.

[0131] The Flexray protocol is a type of periodic transmission protocol, where each communication cycle consists of a static segment, a dynamic segment, a symbol window, and a network idle time (NIT), as shown in Figure 3 . The static segment implemented by the RRP consists of time slots of equal length. On the other hand, the dynamic segment implemented by the TODP is composed of small slots that are much shorter than the static slots. The length of the dynamic slot depends on the information that different frame length nodes need to transmit. In addition, the symbol window and NIT play key roles in ensuring the stability and synchronization of the communication cycle within the FRP respectively. Ignoring the time lengths of the symbol window and NIT will not affect the efficacy of the protocol itself. Compared with the existing methods, the Flexray protocol is mostly used in automotive networks. This patent applies it to AUVs in the underwater environment for the first time. Figure 4 is the dynamic segment schedule, and the protocol-related content in the table can be seen.

[0132] In addition, in step 3, under the communication scheduling of the Flexray protocol, the problem that the moments of the static segment data packet and the dynamic segment data packet are different will naturally occur, that is, the receiving end faces the mixture of the measurement at the instantaneous moment and the measurement with a unit time delay, which makes it necessary to perform preprocessing before estimation to convert it into a standard system without time delay;

[0133] We set the augmented state as We have the following augmented system:

[0134]

[0135] where

[0136]

[0137] In addition, in step 4, using the fully symmetric polytope as the set enclosure, we define the initial state based on prior knowledge to be located within the polytope Compared with the existing ellipsoid algorithm, the Minkowski sum of polytopes satisfies the closure of set operations, which means that the result after the operation is still a polytope, that is:

[0138]

[0139] This reduces the conservatism in various steps of the estimation, making the size of the estimated set smaller and the error of the final estimation result smaller;

[0140] For the state at time k, first perform a one-step prediction on the augmented system to obtain a one-step prediction polytope where:

[0141]

[0142] In Equation 14, and represent the center and the generation matrix of the one-step prediction state respectively;

[0143] Next, the estimated polytope where:

[0144]

[0145] In Equation 15, and represent the center and the generation matrix of the final estimated state set respectively.

[0146] Compared with the existing ellipsoidal set membership filtering, it provides an estimated result with a smaller set size and smaller error, and has an explicit optimization regarding the set norm, reducing the computational complexity and improving the real-time speed of the response. It realizes high-precision positioning of the trajectory of the AUV in the complex underwater environment.

[0147] In addition, in step 4, the specific steps are as follows:

[0148] S41. Initialization, input the initial state using prior knowledge generation matrix

[0149] S42. Calculate the one-step prediction polytope set, and find the set center of the one-step prediction set according to the following formula and the generation matrix

[0150]

[0151] Thus, we obtain:

[0152]

[0153] S43. Calculate and estimate the polytope set, and find the set center of the one-step prediction set according to the following formula: and the generator matrix

[0154]

[0155] Thus we get:

[0156]

[0157] S44, Matrix-based F-radius minimization design gain G k+1 , in order to minimize the matrix F radius We define the generator matrix The covariance matrix of is:

[0158] P = cov(<c,R> )=RR T Formula (20)

[0159] Therefore, a similar covariance matrix can be expressed as:

[0160]

[0161] According to the optimality theorem, we can get

[0162]

[0163] S45. Iteration. Finally, we obtain the navigation trajectory of the AUV in three-dimensional space at each step based on the AUV's dynamic model and the measurements based on the Flexray protocol.

[0164] Beneficial effect: Based on the evaluation function of segment minimization, we obtained an explicit optimization solution for the final optimization result. Therefore, we avoided the use of optimization algorithms in the program, greatly reducing the computational complexity. In terms of state estimation, it improves the real-time response speed and ensures the stability of the estimation system. Finally, we obtained good results based on simulation, proving the effectiveness of the proposed algorithm. Figure 4 , Figure 5 shown.

[0165] The technical principle of the present invention is described above in conjunction with specific embodiments. These descriptions are only for explaining the principle of the present invention and cannot be interpreted as limiting the scope of protection of the present invention in any way. Based on the explanations herein, those skilled in the art can associate other specific implementations of the present invention without paying creative labor, and these methods will fall within the scope of protection of the present invention.

Claims

1. A method for estimating the state of an AUV based on the Flexray protocol, characterized in that: The following steps are involved: S1. Construct a 6-DOF model of the underwater vehicle system, model it according to the position information of the underwater vehicle and the dynamic equation of the Euler angle, and transform the continuous time system into a discrete time system by the method of the zero-order holder based on the dynamic equation of the continuous time system; S2. Based on the FlexRay protocol, the transmission protocol is roughly divided into two categories by comparing the core part of the transmission protocol, one is based on the time multiple access connection mode, and the other is based on the flexible time multiple access connection mode. Based on this, the FlexRay protocol for the underwater vehicle system is designed, and the protocol is divided into a polling protocol for the static segment and an attempt-once-discard protocol for the dynamic segment; S3. Due to the use of the FlexRay protocol, there is a unit of estimator working interval between the arrival time of the data packet, which is regarded as a partial delay problem. To address the partial delay problem caused by the mechanism based on the FlexRay protocol, the system state is augmented to become a higher-dimensional discrete system, solving the problems of synchronization difficulty, error accumulation, and decreased algorithm stability of general state estimation algorithms under mixed delays. S4. Since the augmented system has many dimensions, it involves the problem of high computational complexity; establish a set membership filter for a fully symmetric polyhedron and use the shell of the fully symmetric polyhedron to provide a higher precision state set estimate.

2. The AUV state estimation method based on the Flexray protocol according to claim 1, characterized in that: In step S1, the dynamic model of the AUV is described as a 6-DOF model based on the body-fixed coordinate system and the earth-fixed coordinate system; Since the roll has little effect on the translational motion, the roll speed is not considered; the following is the kinematic model and dynamic model of the AUV in the discrete case: In formula 1, s = [x, y, z] T and θ=[φ,ψ] T represent the 3D position state and Euler angle of the AUV respectively, and and represents the velocity on the corresponding component; the system state is defined as; In formula 2, x k It represents the state parameters of the system at time k; From this, the dynamic equation of the initial AUV system can be obtained: x k+1 = Ax k + w k Equation (3) The original measurement is obtained by the sensor where the unknown but bounded noise w k and v k Restricted to the following polytope set; C is the measurement matrix. The sensor usually cannot directly obtain the data of state x, so C is the physical parameter of the sensor: in,<c,P> represents a polyhedral set with a center vector c and a generating matrix P; so w k and v k They are located in a polyhedral set with the origin at the center and the generating matrices W and V respectively.

3. The AUV state estimation method based on Flexray protocol according to claim 2 is characterized in that: In step 2, the uniform quantizer sets a scalar b so that the interval [-b, b] can be divided into 2 according to the allocated bits r bps. r -1 quantization interval, according to the size of the received information, to determine which small interval it falls into, and output the corresponding interval code; We define the quantized measurement as: y k =Cx k +in k +d k formula(6) In formula 6, Represents the quantization error, and the quantization error is related to the number of data bits in the data packet; the quantization error is defined as a polyhedron: d k ∈<0, Dk>; Formula (7) In formula 7, <0, D k >The center is the origin, and the generated matrix is ​​D k A polytopic collection of In automotive network communications, dynamic segments are widely used for diagnostic flashing; therefore, the timing parameters of the dynamic segment are inconsistent with the timing parameters of the static cycle; here, the rate of change of the Euler angle of the AUV is defined as the attempt to discard the data packet transmitted by the protocol in the dynamic segment, that is, in the dynamic segment Considering that only one data packet can access the channel resources during the transmission period, we divide the measurement into N data packets and divide them into two sets and The nodes belonging to set l1 are scheduled by the polling protocol, and the nodes belonging to l2 are scheduled by the try once and discard protocol. The scheduling rules are as follows: A. Polling protocol: In formula 8, λ(j)∈l1 represents the node that obtains the transmission right at time j, and mod() is the remainder function; B. Try a discard protocol: In formula 9, y′ j，k represents the information of node j that was last transmitted before time k, α k ∈l2 represents the node that needs the most transmission at time k under the rule of "maximum error first"; is a given positive definite weight matrix; Therefore, based on the Flexray protocol in a high-speed network, the measurement can be divided into static and dynamic segments of data packets to be scheduled: Similarly, define the measurement matrix: The measurement received at the receiving end is defined as: Based on the assumption of a high-speed network, we divide a Flexray protocol communication cycle into This is because the transmission speed has been greatly improved due to the update of hardware technology; at the same time, the status update cycle has not changed due to the change of transmission speed, so that at most packets; further, the receive measurement is defined as: in, and They represent the quantization error and measurement noise of the received data packet respectively.

4. The AUV state estimation method based on Flexray protocol according to claim 3 is characterized in that: In step 3, the problem of different times for static and dynamic packets will naturally occur when using the Flexray protocol for communication scheduling. That is, the receiver faces a mixture of instantaneous measurements and measurements with a unit delay. This requires preprocessing before estimation to convert it into a standard delay-free system. We set the augmented state to We have the following augmented system: in, 5. The AUV state estimation method based on Flexray protocol according to claim 4 is characterized in that: In step 4, using the fully symmetric polyhedron as the collective shell, we define the initial state to be located in the polyhedron based on prior knowledge. Compared with the existing ellipsoid algorithm, the Minkowski sum of the polyhedron satisfies the closed property of set operation, which means that it is still a polyhedron after the operation, that is: This reduces the conservatism of the estimates in various steps, making the size of the estimated set smaller and the final estimate result less error-prone; For the state at time k, first make a one-step prediction of the augmented system to obtain a one-step prediction polytope in: In formula 14, and Represent the center and generation matrix of the one-step prediction state respectively; Next, the estimated polytope in: In formula 15, and They represent the center and generation matrix of the final estimated state set respectively.

6. The AUV state estimation method based on Flexray protocol according to claim 5 is characterized in that: In step 4, the specific steps are: S41, initialization, using prior knowledge to input the initial state Generate Matrix S42. Calculate the one-step prediction polytope set and find the set center of the one-step prediction set according to the following formula and the generator matrix Thus we get: S43. Calculate and estimate the polytope set, and find the set center of the one-step prediction set according to the following formula: and the generator matrix Thus we get: S44, Matrix-based F-radius minimization design gain G k+1 , in order to minimize the matrix F radius We define the generator matrix The covariance matrix of is: P = cov(<c,R> )=RRT Formula (20) Therefore, a similar covariance matrix can be expressed as: According to the optimality theorem, we can get S45. Iteration. Finally, we obtain the navigation trajectory of the AUV in three-dimensional space at each step based on the AUV's dynamic model and the measurements based on the Flexray protocol.

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