CN111307143B - Bionic navigation algorithm for multi-target evolution search based on geomagnetic gradient assistance - Google Patents

Abstract

The bionic navigation algorithm based on multi-objective evolutionary search aided by geomagnetic gradient first obtains the geomagnetic parameter information of the carrier's current location and destination; at the initial time of navigation, the carrier walks in the east and north directions to obtain the geomagnetic parameter gradient information, and then The heading angle is predicted according to the principle of simultaneous and co-located convergence of geomagnetic parameters. In order to reduce the invalid search process, in the evolutionary algorithm, the population sample space is constrained according to the predicted heading angle to improve its search efficiency. Secondly, inspired by the parallel approach method in missile tracking, the evaluation criteria of the samples are improved to make the sample evaluation more accurate, and then optimize the navigation search path. The present application takes the multi-parameters of the geomagnetic field at the end point as the target value, and in the absence of a prior geomagnetic map, performs efficient and fast path search, and realizes the long-duration geomagnetic autonomous navigation of the autonomous underwater vehicle.

Description

Bionic Navigation Algorithm Based on Geomagnetic Gradient Aided Multi-objective Evolutionary Search

technical field

The invention relates to a bionic navigation algorithm based on multi-objective evolutionary search assisted by geomagnetic gradients, which is suitable for the realization of long-duration geomagnetic autonomous navigation of autonomous underwater vehicles by taking multi-parameters of the geomagnetic field at the end point as target values without prior geomagnetic field. .

Background technique

Autonomous underwater vehicle integrates advanced technologies such as underwater acoustic communication, intelligent control, energy storage, multi-sensor measurement and information fusion. It can be applied to petroleum resource survey, submarine pipeline survey, marine environment survey and underwater equipment maintenance, etc. It is one of the important tools for future ocean exploration and development. Different from the navigation systems of land or air carriers, the rapid attenuation of radio waves in the underwater environment makes the radio navigation systems represented by the global navigation satellite system unsuitable for autonomous underwater vehicles. At present, the commonly used underwater navigation technologies mainly include inertial navigation, underwater acoustic positioning and navigation and geophysical navigation. Among them, inertial navigation system has the characteristics of autonomy, continuity, concealment, etc., and is often used as the main navigation system of AUV, but its error accumulates over time, and it is not suitable for long-distance and long-distance navigation. Acoustic navigation can be divided into three types: ultra-short baseline, short baseline and long baseline. Among them, the deployment and recovery of underwater acoustic arrays are cumbersome, and the range and accuracy of ultra-short baseline and short baseline are limited, and active active navigation is used. The concealment is poor. Geophysical navigation system is a navigation technology using the physical characteristics of the earth itself. It is mainly based on terrain matching, geomagnetic matching and gravity matching. It has the characteristics of strong autonomy, good concealment, and no geographical and time constraints, but its navigation The accuracy mainly depends on the accuracy of the prior map, and the acquisition of the prior map has become a constraint condition for navigation in the geophysical field.

Various studies in recent years have shown that many creatures on earth can locate and navigate based on the information of the earth's magnetic field. For example, pigeons released in unfamiliar places can travel hundreds of kilometers to return home; Pacific salmon can use geomagnetic cues to navigate from the open ocean to the correct coastal area during spawning migration; adult green sea turtles can also help by detecting geomagnetic information They return to spawn sites, etc. The experimental verification and research analysis on the behavior of various animals using geomagnetic navigation show that the geomagnetic field is a reliable source of navigation information for long-distance movement of animals. Obviously, it is less likely to store a complete magnetic map in their brains, which provides a biological basis for navigation without relying on a priori magnetic map. At the same time, the unique correspondence between the earth's magnetic field vector and each point in the near-Earth space also provides a sufficient theoretical basis for geomagnetic navigation. Therefore, in the absence of a priori geomagnetic field, a bionic geomagnetic navigation method can be constructed by taking the multi-parameters of the geomagnetic field at the end point as the target value and combining the multi-target solution with the navigation motion. From the perspective of biomagnetic trend sensitivity, some researchers have proposed a geomagnetic biomimetic navigation method that does not rely on evolutionary search of prior geomagnetic data, but the existing methods have the problems of long navigation time, low efficiency, and strong pathability.

SUMMARY OF THE INVENTION

In order to solve the above problems. The invention provides a bionic navigation algorithm based on geomagnetic gradient-assisted multi-target evolutionary search. In the absence of a priori geomagnetic map, in view of the strong randomness and low search efficiency in the evolutionary search strategy, a bionic navigation algorithm based on geomagnetic gradient-assisted multi-objective evolutionary search is proposed to improve its search efficiency and make the Navigation paths are more reliable and accurate. For this purpose:

The present invention provides a bionic navigation algorithm based on geomagnetic gradient-assisted multi-target evolutionary search, and the specific steps are as follows:

Step 1: Obtain the geomagnetic parameters of the current position of the carrier and the target position;

Step 2: Calculate the gradient information of the geomagnetic parameters, predict the heading angle according to the principle of "simultaneous and co-located convergence" of multiple geomagnetic parameters, and then constrain the sample space of the evolutionary algorithm;

Step 3: Judging whether the destination has been reached: construct an objective function according to the geomagnetic parameters of the current position and the destination, and judge whether the carrier has reached the destination by observing the current objective function. If the destination is reached, stop the search and complete the navigation; otherwise, jump to step 4;

Step 4: Select the next movement direction according to the evolutionary algorithm, perform a posteriori evaluation on the executed samples, and update the sample space in real time; repeat the above steps until the navigation is completed.

As a further improvement of the present invention, the geomagnetic parameter element includes three components of the magnetic field, that is, some or all of the north component, the east component and the vertical component, the total strength of the magnetic field, the horizontal component of the magnetic field, the magnetic declination, and the magnetic dip.

As a further improvement of the present invention, in step 4, in order to guide the carrier to the destination as soon as possible, in the process of path search, try to keep multiple parameters and converge at the same place at the same time, that is, satisfy:

Among them, B _i,k ,B _j,k are two different geomagnetic parameters at time k respectively, B _i,T B _j,T are two geomagnetic parameters at the target position, in the above formula, the geomagnetic parameters at time k+1 The parameters B _i,k+1 B _j,k+1 can be expressed as:

Solve the above two equations simultaneously to obtain the heading angle γ _k as:

The evolutionary search algorithm is constrained by the sample space based on the predicted heading angle.

As a further improvement of the present invention, considering the magnitude and unit between the geomagnetic parameters, the loss function constructed by the current position of the carrier and the target position is normalized as follows:

Among them, B _i,0 , B _i,T , B _i,k are the initial position of the carrier, the destination and the ith geomagnetic parameter information of the carrier position at time k, respectively. When the carrier travels to the destination, the loss function value is theoretically is 0, therefore, when the loss function value of the magnetic parameter at the current position is small, that is, if F(B _k )≤ε, it can be considered that the navigator has reached the destination, where ε is a very small amount close to 0, which is set according to the navigation accuracy .

As a further improvement of the present invention, step 3 is to search as close as possible to the shortest path, and improve the sample evaluation function. In order to make the geomagnetic parameters converge as soon as possible, the input heading angle is required to satisfy:

(B _aim -B _k )//(B _k+1 -B _k )

进行观测，从而评价样本的优劣:Therefore, the angle between the vectors (B _aim -B _k ) and (B _k+1 -B _k ) can be calculated

Make observations to evaluate the pros and cons of the sample:

As a further improvement of the present invention, the sample evaluation function is improved, and based on the improved evaluation criteria, the corresponding population update rules are formulated as follows:

Among them, P _p is the reproduction ratio of the sample. When the sample does not meet the reproduction conditions, the randomly generated sample is used to replace the current sample to realize the real-time update of the population.

Compared with the prior art, the present invention has the advantages that the geomagnetic bionic navigation algorithm provided by the present application does not need to rely on a priori geomagnetic map, continuously adjusts the optimal travel direction according to the search strategy, and completes the navigation path search quickly and efficiently. Compared with the existing time series evolutionary search method, the randomness in the traveling process is greatly reduced, and the heading angle predicted by the geomagnetic gradient information is used to constrain the sample space, avoiding many invalid searches, making the navigation path more stable, and facilitating the realization of engineering applications; Secondly, the evaluation criteria in the evolutionary algorithm are modified to evaluate the samples more accurately, so that the navigation path search is close to the shortest path.

Description of drawings

Fig. 1 is the flow chart of the navigation method of the present application;

FIG. 2 is a schematic diagram of the evaluation strategy of the application;

3 is a schematic comparison diagram of a navigation path without geomagnetic anomaly according to an embodiment of the present application;

FIG. 4 is a schematic diagram of the convergence of geomagnetic parameters of a path without geomagnetic anomalies before improvement according to an embodiment of the present application;

FIG. 5 is a schematic diagram of the convergence of geomagnetic parameters of the improved path without geomagnetic anomalies according to an embodiment of the present application;

6 is a schematic comparison diagram of a navigation path with geomagnetic anomalies according to an embodiment of the present application;

FIG. 7 is a schematic diagram of the convergence of geomagnetic parameters with a path before geomagnetic anomaly improvement according to an embodiment of the present application;

FIG. 8 is a schematic diagram of the convergence of geomagnetic parameters of a path with an improved geomagnetic anomaly according to an embodiment of the present application.

Detailed ways

The present invention will be described in further detail below in conjunction with the accompanying drawings and specific embodiments:

The invention provides a bionic navigation algorithm based on geomagnetic gradient-assisted multi-target evolutionary search. In the absence of a priori geomagnetic map, in view of the strong randomness and low search efficiency in the evolutionary search strategy, a bionic navigation algorithm based on geomagnetic gradient-assisted multi-objective evolutionary search is proposed to improve its search efficiency and make the Navigation paths are more reliable and accurate.

The geomagnetic field contains multiple geomagnetic characteristic quantities. From the perspective of bionics, the biological movement behavior is sensitive to the changing trend of the geomagnetic field. Therefore, the process of geomagnetic biomimetic navigation can be regarded as a combination of multiple characteristic parameters of the geomagnetic field from the starting position. The process of searching for the convergence of each feature parameter of the target position, as shown in Figure 1, the specific steps of the implementation method are as follows:

1) Establishment of carrier motion model

In the process of bionic navigation of underwater submersibles based on geomagnetic parameters, the submersible can be regarded as a mass point, and its motion equation can be expressed as:

Among them, (x _k , y _k ) represents the position of the submersible at time k, and u is the input of the system, which is related to the heading angle θ of the submersible and the speed v. Assuming that the vehicle moves at a uniform speed within ΔT, v can be represented by a constant V, and the above formula is simplified to:

Among them, L represents the motion step length, and L=ΔT×V.

2) Heading angle prediction

Accurate geomagnetic gradient information is difficult to obtain. Here, the gradient information of geomagnetic parameters is approximated by simply decomposing the changes of geomagnetic parameters between two consecutive sampling points, east and north. At the start of navigation, set the carrier heading angles of the first step and the second step to 0° and 90° respectively, and use the magnetic sensor to measure and record the geomagnetic parameters of each position, and calculate the geomagnetic gradient information by the following formula:

分别为k时刻地磁参量B_i,k与B_j,k在x轴与y轴向的地磁梯度。Among them, B _i,k , B _j,k are two different geomagnetic parameters at time k, L is the movement step of the carrier,

are the geomagnetic gradients of the geomagnetic parameters B _i,k and B _j,k in the x-axis and the y-axis at time k, respectively.

Secondly, in order to optimize the navigation path and improve the search efficiency, the geomagnetic multi-parameters in the navigation path search process should try to satisfy the principle of "simultaneous and co-located convergence", namely:

Among them, B _i,k , B _j,k , B _i,T , B _j,T are two different geomagnetic parameters at time k and the target position, respectively. In the above formula, the geomagnetic parameters B _i,k+1 B _{j,k+1 at time k+1} can be expressed as:

Solving the above two equations simultaneously, the heading angle can be obtained as:

Due to the error in the calculation of the geomagnetic gradient information, the error of the predicted heading angle is inevitable, thus reducing the navigation accuracy. Moreover, the anti-interference of the geomagnetic gradient information prediction method is poor and cannot overcome the interference caused by the geomagnetic anomaly area. Therefore, this method does not completely rely on the predicted heading angle to travel, but based on the predicted heading angle to carry out the constraints of the sample space in the evolutionary search strategy.

3) Sample space initialization

In order to reduce the randomness and invalid search in the search process, the predicted heading angle is used to constrain the sample space. It can be known from the carrier motion model that the carrier heading angle is used as its only input, and the selection of the heading angle determines the navigation search path. Therefore, the carrier heading angle is selected as the population sample.

The carrier heading angle is discretely sampled, the sample space is initialized, and the sample space is constrained according to the predicted heading angle. Therefore, the sample space is initialized as follows:

Among them, θ _i is the carrier heading angle, γ _k is the predicted heading angle, D _θ is the sampling interval, N is the population sample size, β is the constraint threshold of the sample space, and R _i is [(γ _k -β)/D _θ , (γ _k +β)/D _θ ] any random integer.

4) Determine whether to reach the end point

The geomagnetic parameter environment of the current location can be described as:

B={B ₁ ,B ₂ ,...B _n }

Among them, B ₁ , B ₂ ,...B _n are the parameter elements of the geomagnetic field, which can be three components of the magnetic field (geomagnetic north component, geomagnetic east component, geomagnetic vertical component), total magnetic field intensity, magnetic declination, magnetic dip, magnetic field Some or all of the parameters such as the horizontal component. The objective function of constructing the i-th geomagnetic parameter according to the geomagnetic information of the current location and the destination is:

f _i,k (B)=(B _i,k -B _i,T ) ²

Among them, B _i,k and B _i,T are the ith geomagnetic parameter information of the carrier position and destination at time k, respectively. Determine whether the carrier has reached the destination by observing the current objective function. Considering the magnitude and unit between the geomagnetic parameters, the objective function is normalized to obtain:

Among them, B _i.0 and B _i,T are the ith geomagnetic parameter information of the carrier's initial position and the destination, respectively. When the carrier travels to the destination, the objective function value is theoretically 0. Therefore, the current position of the magnetic parameter When the objective function value is small, that is, if F(B _k )≤ε, it can be considered that the navigator has reached the destination, where ε is a very small amount close to 0, which is set according to the navigation accuracy. If the above conditions are not met, skip to the next step.

5) Sample selection

In the absence of historical information, a sample is randomly selected from the sample space as the carrier heading angle in an unbiased manner, and each sample has the same probability of being selected. Therefore, the probability that a certain heading angle is selected depends on the proportion of the same number of samples. At time K, the probability that the population sample θ _i is selected is:

6) Sample posterior evaluation

The effective evaluation of the sample is very important to the search of the navigation path, which is directly related to the correctness of the search path. In the current evolutionary algorithm, the objective function is monotonically decreasing as the evaluation criterion. If the objective function of the k+1 step is smaller than the kth step, the corresponding sample is considered to be better, and the breeding operation is performed. However, in this case, the possibly obtained navigation path follows the direction of the greatest change of a certain geomagnetic parameter, and deviates from the shortest navigation path. In order to search as close as possible to the shortest path, the sample evaluation function is improved here. As shown in Figure 2, in order to make the geomagnetic parameters converge as soon as possible, the input heading angle is required to satisfy:

(B _aim -B _k )//(B _k+1 -B _k )

进行观测，从而评价样本的优劣。Therefore, the angle between the vectors (B _aim -B _k ) and (B _k+1 -B _k ) can be calculated

Make observations to evaluate the quality of the sample.

7) Sample population update

Update the population samples based on the above evaluation criteria:

Among them, P _p is the reproduction proportion of the sample. When the sample does not meet the breeding conditions, the randomly generated sample is used to replace the current sample to realize the real-time update of the population. and repeat the above steps until the navigation is complete.

The embodiments of the present invention are described in detail below, and the embodiments are exemplary and intended to explain the present invention, but should not be construed as a limitation of the present invention.

In order to verify the effectiveness of the present invention, simulation experiments are carried out under MATLAB. Use the International Geomagnetic Reference Field IGRF-12 to simulate the actual geomagnetic field environment. In this embodiment, the magnetic inclination angle and the magnetic declination angle are selected as the magnetic parameters for heading angle prediction, the geomagnetic northing component, the geomagnetic easting component and the geomagnetic vertical component are selected as the parameter elements of the navigation search, and the coordinates of the starting point are set to be 5° north latitude and 5° east longitude. °, the geomagnetic parameters of the starting position are B _0,x = 31165nT, B _0,y =-1798.6nT, B _0,z =-9342.8nT; set the destination coordinates as 10° north latitude and 10° east longitude, corresponding to the geomagnetic parameters is B _T,x =33756nT,B _T,y =-680.8nT,B _T,z =-1939.5nT; population size N is 35; sample sampling interval D _θ is 1°; in order to improve the accuracy of navigation, the initial stage The carrier motion step is 3 nautical miles, that is, the heading angle is updated every 3 nautical miles. When the objective function is less than 0.005, the carrier motion step is 1.5 nautical miles; the sample constraint threshold is 40°; when it is satisfied, it is considered that the carrier reaches the destination, End navigation.

In order to illustrate the effectiveness of the algorithm proposed by the present invention, the evolutionary search algorithm after improvement and before improvement are compared under the condition of no geomagnetic anomaly area and existence of geomagnetic anomaly area respectively.

For navigation in an area without geomagnetic anomalies, the search trajectories of the two algorithms are shown in Figure 3, and the convergence process of the three geomagnetic navigation parameters is shown in Figures 4 and 5. It can be seen from the navigation trajectories in Figure 3 that the trajectories of the two navigation algorithms are quite different. The evolutionary search strategy before the improvement has strong randomness in the initial stage, and the optimal path is found through continuous trial and error. The angle changes greatly, which is not suitable for engineering applications. At the same time, due to the inaccuracy of its evaluation criteria, it cannot guarantee that multiple parameters converge at the same time, which makes the navigation search path deviate from the optimal path, resulting in a waste of time and resources. The sample space is constrained in the improved algorithm, which greatly reduces the randomness of the carrier motion and speeds up the search efficiency. The improvement of the evaluation function makes the navigation path close to the optimal path, and the search of the path can also be adjusted in real time during the traveling process of the carrier. The anti-interference ability is strong, and the navigation trajectory is relatively straight, which is convenient for engineering applications. It can also be seen from the convergence of geomagnetic parameters that, with time accumulation, the convergence curves of the two algorithms can gradually tend to 0, and guide the carrier to continuously approach the target position. However, it can be seen from the comparison that in the improved algorithm, the convergence speed of the geomagnetic multi-parameter is faster, the convergence curve is smoother, the corresponding navigation path is relatively stable, and the multi-parameter simultaneous and co-located convergence can basically be achieved, which proves the effectiveness of the improved algorithm. sex.

The geomagnetic anomalous field is caused by the uneven distribution of magnetic rocks in the crust, resulting in different sizes and strengths of the geomagnetic anomalous field in space. The strength of the weak anomalous field is less than 1nT, while the strength of some strong anomalous fields can even reach several times that of the main magnetic field. Geomagnetic anomalies are inevitably encountered during long-endurance navigation. In the simulation experiment, the geomagnetic anomaly area is constructed by the normal geomagnetic field and multi-peak function. The geomagnetic anomaly area is 6°~8°N north latitude and 5.5°~7.5° east longitude. The search trajectories of the two algorithms are shown in FIG. 6 , and the convergence processes of the three geomagnetic navigation parameters are shown in FIGS. 7 and 8 . It can be seen from the navigation trajectory in Figure 6 that due to the change in the constraint relationship between the geomagnetic parameters and the carrier motion in the geomagnetic anomaly area, the algorithm before the improvement falls into a disordered and chaotic state during the search, cannot pass through the anomaly area smoothly, and has poor anti-interference performance. . From the path searched by the improved algorithm, it can be seen that when the carrier passes through the abnormal area, although the path will fluctuate, it will not fall into the local abnormal area. After the carrier walks out of the abnormal area, it can quickly re-find the optimal heading and guide the carrier reach the destination. Similarly, it can be seen from the convergence curve of the three-component geomagnetic field that when the carrier reaches the abnormal area, the algorithm before the improvement still searches for the path according to the constraint relationship between the geomagnetic component and the path in the non-anomalous area, which causes the carrier to fall into a chaotic search state and cannot The improved algorithm, due to the constraints of the sample space, enables the algorithm to break through the constraint relationship between the geomagnetic component and the path, so as to get out of the abnormal area smoothly, and can quickly realize the re-search of the optimal path, with better anti-interference performance. powerful.

The above are only preferred embodiments of the present invention, and are not intended to limit the present invention in any other form, and any modifications or equivalent changes made according to the technical essence of the present invention still fall within the scope of protection of the present invention. .

Claims (1)

1. The bionic navigation algorithm for the multi-target evolutionary search based on the assistance of the geomagnetic gradient comprises the following specific steps and is characterized in that:

step 1: acquiring geomagnetic parameters of a position where a carrier is located at the current moment and a target position;

the elements of the geomagnetic parameters comprise three components of a magnetic field, namely a north component, an east component, a vertical component, the total intensity of the magnetic field, a horizontal component of the magnetic field, a declination angle and a dip angle;

step 2: calculating gradient information of geomagnetic parameters, predicting a course angle according to a simultaneous and same-ground convergence principle of multiple geomagnetic parameters, and further constraining a sample space of an evolutionary algorithm;

and step 3: judging whether the destination is reached: constructing an objective function according to the current position and geomagnetic parameters of the destination, judging whether the carrier reaches the destination or not by observing the current objective function, and stopping searching to finish navigation if the carrier reaches the destination; otherwise, jumping to the step 4;

step 3 is to guide the carrier to the destination as soon as possible, and multiple parameters should be kept to be converged simultaneously and simultaneously as much as possible in the path searching process, that is, the following conditions are met:

wherein, B_i,k,B_j,kTwo different geomagnetic parameters at time k, B_i,TB_j,TTwo geomagnetic parameters at the target position, in the above formula, the geomagnetic parameter B at the time of k +1_i,k+1B_j,k+1The geomagnetic parameter and the geomagnetic gradient information at the previous time can be expressed as:

the course angle gamma is obtained by simultaneously solving the two formulas_kComprises the following steps:

carrying out sample space constraint on the evolutionary search algorithm based on the predicted course angle;

considering the magnitude and unit between geomagnetic parameters, normalizing the loss function constructed by the current position of the carrier and the target position is as follows:

wherein, B_i,0、B_i,T、B_i,kI-th geomagnetic parameter information including initial position, destination and k-th carrier position of the carrier, loss function value when the carrier moves to the destinationTheoretically 0, therefore, when the value of the loss function of the magnetic parameter at the current position is small, F (B) is satisfied_k) If the navigation precision is less than or equal to epsilon, the navigator can be considered to reach the destination, wherein epsilon is a very small quantity close to 0, and the navigation precision is set;

and 4, step 4: selecting the motion direction of the next step according to an evolutionary algorithm, performing posterior evaluation on the executed sample, and updating the sample space in real time; repeating the steps until the navigation is completed;

and 4, searching the shortest path as close as possible, improving the sample evaluation function, and requiring the input heading angle to meet the following requirements in order to enable all geomagnetic parameters to be converged as soon as possible:

(B_aim-B_k)//(B_k+1-B_k)

thus, it is possible to align the vector (B)_aim-B_k) And (B)_k+1-B_k) Angle therebetween

Observations were made to evaluate the quality of the samples:

the sample evaluation function is improved, and a corresponding population updating rule is formulated based on the improved evaluation criterion as follows:

wherein, P_pAnd (4) for the reproduction proportion of the sample, when the sample does not meet the reproduction condition, replacing the current sample with the randomly generated sample to realize the real-time update of the population.

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