CN114779801B - Autonomous remote control underwater robot path planning method for target detection - Google Patents
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Abstract
The invention relates to a path planning method of an autonomous remote control underwater robot for target detection, which comprises the steps of carrying sensors such as side-scan sonar, forward-looking sonar, 3D cameras and the like on an ARV, and searching, tracking and identifying underwater targets: firstly, acquiring prior probability distribution of a target position, and determining suspicious sub-areas; between the subareas, automatically planning an optimal path based on an energy consumption minimum principle; and finally, in the subarea, carrying out multi-source information fusion and self-adaptive detection of the target to obtain information such as the position and appearance of the target. The method can detect the underwater unknown target in a large range, the convergence speed of path planning is high, meanwhile, the planned path accords with the actual motion trail of the robot, the average time of detecting the target can be reduced under the condition of limited energy, and the probability of finding the target is improved. The method is convenient to transplant, has strong expansibility, and is also suitable for underwater detection of unmanned surface vessels, autonomous underwater robots, remote underwater robots and other marine robots.
Description
Technical Field
The invention relates to the technical field of underwater robots, in particular to a path planning method for underwater target detection based on an autonomous remote control underwater robot.
Background
The exploration of the underwater world by human beings is never stopped, and the 21 st century is considered as the century of the arming of human beings to the ocean, and the exploration of the underwater by human beings becomes very challenging due to the characteristics of low underwater visibility, complex and severe environment, difficult communication and the like. In recent years, the autonomous remote control underwater vehicle (abbreviated as ARV) has the technical characteristics of good underwater communication capability, flexible underwater mobility, economic applicability, certain underwater operation capability and the like, and is focused on converging high-tech means. The underwater target detection is an important application field of autonomous remote control underwater robots, civil applications mainly comprise ocean landform detection, sunken ship or wreck plane searching, underwater archaeology and the like, and military applications comprise important sea area information detection, capturing of invasive targets, mine detection and the like. However, most of the traditional underwater target detection only adopts comb search, which has contradiction in indexes such as time, space and recognition precision, and especially has low detection success rate for dynamic targets.
Disclosure of Invention
The invention relates to the technical field of underwater robots, in particular to a path planning method for underwater target detection based on an autonomous remote control underwater robot (ARV for short). The invention solves the problem of underwater target detection, and the ARV can efficiently, rapidly and accurately detect the target in an unknown area through the steps of searching, tracking, identifying and the like, can obtain the position and appearance information of the target, is suitable for engineering application of most underwater target detection, and has certain prospect and innovation. The method has high path planning convergence speed, can reduce the average time of detecting the target under the condition of limited energy, and improves the probability of finding the target.
The invention adopts the following technical scheme: an autonomous remote control underwater robot path planning method for target detection is provided, wherein side-scan sonar, forward-looking sonar and 3D cameras are carried on an ARV, and the method comprises the following steps of searching, tracking and identifying underwater targets:
Step 1, describing and modeling environment information of an area to be planned of underwater detection based on a search graph method, and obtaining prior probability distribution of a target in the area through prediction and updating of a search graph model to determine suspicious sub-areas;
step 2, traversing optimal paths of suspicious subareas in sequence by autonomous planning based on an energy consumption minimum principle among subareas;
And 3, in the subarea, carrying out multi-source information fusion and self-adapting detection of the target, and obtaining the position and appearance outline of the target.
The method for modeling, calculating the prior probability distribution of the target and determining the suspicious sub-region based on the search graph comprises the following steps:
step 1-1, describing the position state of a target by adopting a probability model, gridding an unknown region, establishing an environment information graph to represent the current target and the environment state, continuously updating the probability value of each grid based on side-scan sonar data, and calculating the prior probability distribution of the target position through formulas (1) and (2);
The probability model is as follows:
Ψ＝{Pxy|x∈{1,2,...,Mx},y∈{1,2,...,Ny}} (1)
Wherein, ψ is the whole set of the existence probability of the current grid target; (M x,Ny) is a small grid obtained by gridding; p xy is the probability value of the presence of the target at the (M x,Ny) grid; p xy (k) is the probability value obtained by detecting the target at (M x,Ny) the kth time, P xy(0)＝0,0≦Pxy (k) +.1, (k=0, 1, …, L), L is the total number of times the target is detected; q d(xy) is a probability weight function; d (xy) is the Euclidean distance of the ARV to the grid particle (M x,Ny);
step 1-2, determining suspicious subregions:
wherein Θ represents the complete set of subregions; Representing a probability threshold when the target probability is greater than/> When it is considered that there may be a target in the vicinity of this grid; two times g, h represent the size of the current ith suspicious sub-region; m is the number of suspicious subregions.
The acquisition of the optimal path comprises the following steps:
step 2-1, establishing an energy cost function of a path:
E (S) is the energy consumption of any two suspicious subareas i and j; xij=0 or 1,0 means that the two sub-regions are not connected, 1 means that the two sub-regions are connected; h ij denotes the euclidean distance between the two sub-regions; e con denotes a constant energy consumption amount irrespective of the path;
And 2-2, based on the energy consumption minimum principle, sequentially acquiring the traversal sequence of any suspicious sub-region i and j to obtain the sequence connected into the autonomous planning optimal path.
The multi-source information fusion and self-adaptive detection target, and the acquisition of the target position and the appearance outline comprises the following steps:
Step 3-1, in the sub-region detection process, performing Bayesian filtering on forward looking sonar data by the ARV, estimating posterior probability of the moving target position serving as a target state variable according to the processed acoustic information, and controlling the ARV to approach the moving target position;
Step 3-2, in the detection process of the ARV in the subarea according to the obtained posterior probability, describing the condition of the ARV motion by using an objective function pi (n) based on the information gain, and calculating the information gain of the ARV for the next detection; the heading at the next moment is decided, so that the energy consumption is minimum while the information gain acquired by the ARV is maximum: ARV records the course position and autonomously plans to run according to the decided course position;
And 3-3, performing information interaction between the shore-based control center and the underwater ARV, sending a control instruction to switch the autonomous planning running state of the ARV into a remote control state, and acquiring a 3D image by the ARV to accurately identify the outline of the target so as to finish the detection task of the current suspicious subarea.
In the sub-region detection process in step 3-1, the ARV predicts the position of the moving target according to the processed acoustic information, and the method comprises the following steps:
the confidence of the moving target is expressed by using conditional probability, and a target position state probability model is established as follows:
In practice, the ARV assigns a probability to each possible position of the target, the confidence of the ARV being a posterior probability with respect to the target position variable x t, conditioned on the forward looking sonar measurement information m t and the self control output u t; the confidence level conf (x t) of the position variable x t at time t is denoted by conf (x t), which is an abbreviation for the posterior probability of the formula:
conf(xt)＝p(xt|mt,ut) (6)
The posterior probability is the conditional probability of the state x t at the moment t, and the initial confidence is given by the comb scanning of the side scan sonar under the condition that the measured quantity m t at the moment t and the control quantity u t at the moment t are taken as the conditions;
Based on the posterior probability of the measurement quantity m t-1 at the previous time immediately after the control quantity u t is executed, the state at time t is predicted before the measurement at time t, as follows:
a. the objective function based on the information gain:
π(n)＝α(Hn(xt-1)-Em[Hn(xt|mt-1,u)])+β∫r(x,u)N(x)dx (8)
Wherein, H n(xt-1) is the information entropy of the confidence level conf (x t-1) at the time t-1, H n(xt|mt-1,ut) is the information entropy on the condition that the measurement quantity at the time t-1 and the control quantity at the time t are taken as the conditions entropy integration, E m is the information gain obtained by the action of the ARV in the next step, the energy consumption required by the decision of the ARV is taken by the ∈r (x, u) N (x) dx, and alpha and beta are balance factors;
b. the course of the next moment of decision comprises the following steps:
and solving an optimal solution of an objective function based on the information gain by using a greedy algorithm, and enabling the ARV to change the course to approach the target according to the optimal solution x best.
A. The ARV autonomous planning driving state is a remote control state, a threshold value phi is set on the basis of a function pi (h) of information entropy, and after pi (h) exceeds the threshold value, a shore-based control center sends an instruction to control the ARV movement state, and the ARV autonomous planning driving state is switched from an autonomous mode to a remote control mode;
b. in a remote control mode, the 3D camera accurately identifies the target, if the target is identified or the target is not identified for a long time, the shore-based control center sends an instruction to control the ARV to adaptively detect the next suspicious region according to the path autonomously planned in the step 2, the step3 is repeated until the ARV sequentially completes the detection task of each suspicious region and returns to the home, and the detection task is ended.
The function based on the information entropy is as follows: pi (H) =h n(xt-1)-Em[Hn(xt|mt-1, u) ] (9
And judging the ARV motion state:
The judgment of the motion change area is as follows:
wherein MOV represents a motion state, mov=0 represents an ARV in an autonomous mode, and mov=1 represents an ARV in a remote control mode; pi (h) represents an objective function of information entropy; phi represents the threshold of the objective function; alt=0 indicates a region where motion is not changed, and alt=1 indicates a region where motion is changed.
The invention has the following beneficial effects and advantages:
1. compared with the traditional underwater target detection method, the method solves the contradiction of indexes such as time, space, precision and the like in the underwater robot target detection process, can improve the detection efficiency, and can also improve the detection success rate.
2. The method has the advantages that a large-range area is decomposed into a plurality of suspicious subareas, paths can be planned independently among the subareas, the effective detection distance of each voyage of the ARV is improved, and more efficient operation can be realized.
3. The method of combining the acoustic information and the visual information is adopted to detect the target, the target is acoustically identified at a long distance, and the target is visually confirmed at a short distance, so that the success rate of detecting the target is improved.
4. The invention has convenient transplanting and strong expansibility, and is also suitable for underwater detection of unmanned surface vessels (USV for short), autonomous underwater robots (AUV for short), remote underwater robots (ROV for short) and other marine robots.
Drawings
FIG. 1 is a schematic diagram of the composition of the present invention.
Fig. 2 is an example diagram of autonomous path planning between sub-regions.
Detailed Description
In order that the above objects, features and advantages of the invention will be readily understood, a more particular description of the invention will be rendered by reference to the appended drawings. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. The invention may be embodied in many other forms than described herein and similarly modified by those skilled in the art without departing from the spirit or scope of the invention, which is therefore not limited to the specific embodiments disclosed below.
Unless defined otherwise, 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 belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.
The hardware requirement of the invention is at least one autonomous remote control underwater robot (ARV for short), the ARV is provided with a depth gauge for measuring depth, a Doppler log for measuring navigation speed, an electronic compass for measuring attitude angles (including course angles, pitch angles and roll angles), an underwater acoustic communication machine for underwater acoustic communication, an iridium satellite and GPS module for communication with a shore-based center, a side-scan sonar and a forward-looking sonar for searching underwater targets, and a 3D camera for target confirmation.
The main content of the invention is a path planning method for underwater target detection based on ARV. The method comprises three steps: firstly, describing and modeling regional environment information based on a search graph method, and obtaining prior probability distribution of a target in a region through prediction and updating of a search graph model to determine suspicious subregions; secondly, between the subareas, automatically planning an optimal path based on an energy consumption minimum principle; thirdly, in the subarea, multi-source information fusion and self-adaptive detection of the target are carried out, and information such as the position and appearance of the target are obtained.
The first step: describing and modeling the regional environment information based on a search graph method, and obtaining prior probability distribution of a target in a region through prediction and updating of a search graph model to determine suspicious subregions.
The complex environment under water often affects the ARV's performance of target detection tasks, and thus describing this uncertainty, the environment must first be modeled and described, discretizing the entire area into M x×Ny grids. The uncertainty of the target and the environment determines the randomness of the detection problem, and the search problem can also be regarded as a random problem, so that a probability model is used for describing the position state of the underwater target, and the probability value of the existence of the target at the grid of P xy is set as (M x,Ny). The ARV searches the target in a comb shape in the whole area, along with the movement of the ARV, the position of the ARV is changed continuously, the ARV searches the area continuously, the ARV detects the unknown area continuously through sonar, the probability of existence of each grid target is calculated based on the sonar detection result, P xy is updated continuously, and finally a total set ψ of the existence probabilities of the targets is obtained.
Ψ＝{Pxy|x∈{1,2,...,Mx},y∈{1,2,...,Ny}} (2)
Wherein ψ is the complete set of grid target existence probabilities; (M x,Ny) is a small grid obtained by gridding; p xy is the probability value of the presence of the target at the (M x,Ny) grid; p xy (k) is a probability value obtained by detecting the target by the side-scan sonar at (M x,Ny) the kth time (P xy(0)＝0,0≦Pxy (k) +.1, k=0, 1, …, L is the total number of times the target is detected); As a probability weight function with respect to distance; d (xy) is the Euclidean distance of the ARV to the grid (M x,Ny) (grid (M x,Ny) is considered a particle and is at its geometric center).
Dividing suspicious subareas according to the obtained target position prior probability distribution:
wherein Θ represents the complete set of subregions; Representing a probability threshold when the target probability is greater than/> When it is considered that there may be a target in the vicinity of this grid; two times g, h represent the size of the current ith suspicious sub-region; m is the number of suspicious subregions.
And a second step of: and between the subareas, an optimal path is planned autonomously based on the energy consumption minimum principle.
The ARV moves between the various sub-regions, eventually detecting all suspicious sub-regions. In order to shorten the detection time and improve the detection efficiency, a path with minimum energy consumption needs to be planned, and the effective distance of detection is maximum under the condition of limited energy. The energy cost function of the path is established, the optimal path is planned autonomously based on the energy consumption minimum principle, and the energy cost function and constraint conditions are as follows:
Wherein E (S) is an energy consumption function, and the smaller the energy consumption is, the longer the effective detection distance of each voyage of the ARV is, the higher the detection efficiency is; x ij = 0 or 1,0 indicating that the two sub-regions are not connected, 1 indicating that the two sub-regions are connected; h ij denotes the euclidean distance between the two sub-regions; e con denotes a constant energy consumption amount irrespective of the path; z is all non-empty subsets of m; the |z| is the total number of vertices including H in the set m.
And (3) solving the formulas (13) and (14) by using a genetic algorithm, and performing iterative optimization to approach an optimal solution so as to obtain an optimal route. Fig. 2 is a discretized grid of the area M x＝100,Ny =100, in which, in the first step, the prior probability distribution of the target position is obtained, 5 suspicious sub-areas are determined, and in the second step, the optimal path is planned autonomously based on the minimum energy consumption between the sub-areas.
And a third step of: and in the subarea, carrying out multi-source information fusion and self-adapting detection of the target to obtain information such as the position and appearance of the target.
In each subarea, the ARV carries out Bayesian filtering on the forward-looking sonar data, removes unreliable information such as noise and the like, and obtains the posterior probability of the target state variable. The ARV plans the movement route according to the obtained posterior probability and taking the uncertainty of the target reduction as a criterion. The shore-based control center performs information fusion with the underwater ARV, sends a control instruction to switch the ARV motion state, and accurately identifies the target. The ARV sequentially completes the detection task of each suspicious subarea and then returns to the home, and the detection task is finished.
First, a probability model about a target position variable is established, and Bayesian filtering is performed on data measured by forward looking sonar. The Bayesian filtering is recursive, that is, the confidence level conf (x t) at time t is calculated from the confidence level conf (x t-1) at time t-1. The inputs are the confidence conf (x t-1) at time t-1, and the last control action u t, and the last measurement m t. The output is the confidence conf (x t) at time t. The pseudo-code form of the Bayesian filtering is given below:
Filtering algorithm: input conf (x) t-1)、ut、mt
For all xt do
End for
Return conf(xt)
Conf (x t-1) is the confidence of the position at the previous time, and the initial confidence conf (x 0) is given by the side-scan sonar information at the time of comb scanning. The bayesian filtering algorithm first processes the control variable u t, and calculates the confidence of the state x t by based on the confidence of the state x t-1 and the control quantity u t. The confidence conf (x t) that the ARV assigns to the target position state x t is obtained by integration of the two distributions (confidence assigned to x t-1 and transition probability from x t-1 to x t caused by control u t). The Bayesian filtering algorithm then multiplies the confidence level by the probability of the measured variable m t that has been observedThe product results are summed to 1 by a normalization constant η.
And then carrying out self-adaptive detection based on the information gain maximum and energy consumption minimum principles. The case of ARV motion is described by an objective function pi (n) based on the information gain. By establishing an objective function, the detection problem is expressed as a decision problem, namely, how the ARV decides the heading at the next moment so that the information gain acquired by the ARV is maximum and the energy consumption is minimum, and the balance between the information gain and the energy consumption is obtained. The optimal solution direction x best is solved through a greedy algorithm to maximize its information gain or reduce the uncertainty of the target position maximally, and at the same time, the energy consumption is minimal. The ARV records the heading position x best and takes the optimal solution direction x best as the heading position of the next moment to carry out autonomous planning driving.
π(n)＝α(Hn(xt-1)-Em[Hn(xt|mt-1,u)])+β∫r(x,u)N(x)dx (8)
Where H n(xt-1) is the information entropy of the time t-1 with respect to the confidence level conf (x t-1), H n(xt|mt-1,ut) is the information entropy conditioned on the measurement quantity at time t-1 and the control quantity at time t, E m represents the integration of the conditional entropy, and the difference between the two is the information gain obtained by the next action of the ARV. And r (x, u) N (x) dx is the energy consumption required by the ARV to perform the decision. Alpha and beta are balance factors.
And finally, the shore-based control center performs information fusion with the underwater ARV, sends a control instruction to switch the ARV motion state, and accurately identifies the target. Setting an objective function threshold phi based on information entropy, and sending an instruction to control the ARV motion state after the objective function pi (h) exceeds the threshold value, switching from an autonomous mode to a remote control mode, and accurately identifying a target by using a 3D camera in the remote control mode. If the target is identified or the target is not identified for a long time, the shore-based control center sends an instruction to control the ARV to adaptively detect the next suspicious region according to the path autonomously planned in the step two, and the step three is repeated until the ARV sequentially completes the detection task of each suspicious sub-region and returns to the navigation, and the detection task is ended.
The ARV motion state is switched according to equation (9), and equation (10) controls the ARV motion to the next suspicious sub-region.
π(h)＝Hn(xt-1)-Em[Hn(xt|mt-1,u)] (9)
Where MOV represents a motion state, mov=0 represents an ARV in autonomous mode, and mov=1 represents an ARV in remote mode; pi (h) represents an objective function based on information entropy; phi represents the threshold of the objective function; alt=0 indicates a region where motion is not changed, and alt=1 indicates a region where motion is changed.
The foregoing description is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and any simple modification, variation and equivalent structural changes made to the above embodiment according to the technical substance of the present invention still fall within the scope of the technical solution of the present invention.
Claims (6)
1. The autonomous remote control underwater robot path planning method for target detection is characterized in that side-scan sonar, forward-looking sonar and 3D cameras are carried on an ARV, and underwater targets are searched, tracked and identified according to the following steps:
Step 1, describing and modeling environment information of an area to be planned of underwater detection based on a search graph method, and obtaining prior probability distribution of a target in the area through prediction and updating of a search graph model to determine suspicious sub-areas;
step 1-1, describing the position state of a target by adopting a probability model, gridding an unknown region, establishing an environment information graph to represent the current target and the environment state, continuously updating the probability value of each grid based on side-scan sonar data, and calculating the prior probability distribution of the target position through formulas (1) and (2);
The probability model is as follows:
Ψ＝{Pxy|x∈{1,2,...,Mx},y∈{1,2,...,Ny}} (1)
Wherein, ψ is the whole set of the existence probability of the current grid target; (M x,Ny) is a small grid obtained by gridding; p xy is the probability value of the presence of the target at the (M x,Ny) grid; p xy (k) is a probability value obtained by detecting the target at (M x,Ny) the kth time, P xy(0)＝0,0≦Pxy (k) +.1, k=0, 1, …, L is the total number of times the target is detected; q d(xy) is a probability weight function; d (xy) is the Euclidean distance of the ARV to the grid particle (M x,Ny);
step 1-2, determining suspicious subregions:
wherein Θ represents the complete set of subregions; Representing a probability threshold when the target probability is greater than/> When it is considered that there may be a target in the vicinity of this grid; two times g, h represent the size of the current ith suspicious sub-region; m is the number of suspicious subregions; (M x±g)×(Ny±h) is the ith suspicious sub-region centered on grid particle (M x,Ny), wide and high at 2g, 2 h;
step 2, traversing optimal paths of suspicious subareas in sequence by autonomous planning based on an energy consumption minimum principle among subareas;
step 2-1, establishing an energy cost function of a path:
E (S) is the energy consumption of any two suspicious subareas i and j; xij=0 or 1,0 means that the two sub-regions are not connected, 1 means that the two sub-regions are connected; h ij denotes the euclidean distance between the two sub-regions; e con denotes a constant energy consumption amount irrespective of the path;
Step 2-2, based on the energy consumption minimum principle, sequentially acquiring the traversal sequence of any suspicious sub-region i, j to obtain a sequence connected into an autonomous planning optimal path;
And 3, in the subarea, carrying out multi-source information fusion and self-adapting detection of the target, and obtaining the position and appearance outline of the target.
2. The method for planning a path of an autonomous remote underwater robot for target-oriented exploration according to claim 1, wherein the steps of merging the multi-source information and adaptively exploration targets, and acquiring the positions and the appearance contours of the targets include:
Step 3-1, in the sub-region detection process, performing Bayesian filtering on forward looking sonar data by the ARV, estimating posterior probability of the moving target position serving as a target state variable according to the processed acoustic information, and controlling the ARV to approach the moving target position;
Step 3-2, in the detection process of the ARV in the subarea according to the obtained posterior probability, describing the condition of the ARV motion by using an objective function pi (n) based on the information gain, and calculating the information gain of the ARV for the next detection; the heading at the next moment is decided, so that the energy consumption is minimum while the information gain acquired by the ARV is maximum: ARV records the course position and autonomously plans to run according to the decided course position;
And 3-3, performing information interaction between the shore-based control center and the underwater ARV, sending a control instruction to switch the autonomous planning running state of the ARV into a remote control state, and acquiring a 3D image by the ARV to accurately identify the outline of the target so as to finish the detection task of the current suspicious subarea.
3. The method for planning a path of an autonomous remote underwater vehicle (autonomous remote control) for target detection according to claim 2, wherein in the step 3-1, the ARV estimates the position of the moving target according to the processed acoustic information during the detection of the sub-area, and the method comprises:
the confidence of the moving target is expressed by using conditional probability, and a target position state probability model is established as follows:
In practice, the ARV assigns a probability to each possible position of the target, the confidence of the ARV being a posterior probability with respect to the target position variable x t, conditioned on the forward looking sonar measurement information m t and the self control output u t; the confidence level conf (x t) of the position variable x t at time t is denoted by conf (x t), which is an abbreviation for the posterior probability of the formula:
conf(xt)＝p(xt|mt,ut) (6)
The posterior probability is the conditional probability of the state x t at the moment t, and the initial confidence is given by the comb scanning of the side scan sonar under the condition that the measured quantity m t at the moment t and the control quantity u t at the moment t are taken as the conditions;
Based on the posterior probability of the measurement quantity m t-1 at the previous time immediately after the control quantity u t is executed, the state at time t is predicted before the measurement at time t, as follows:
4. the method for planning the path of the autonomous and remotely controlled underwater vehicle for target exploration according to claim 2, wherein,
A. the objective function based on the information gain:
π(n)＝α(Hn(xt-1)-Em[Hn(xt|mt-1,u)])+β∫r(x,u)N(x)dx (8)
Wherein, H n(xt-1) is the information entropy of the confidence level conf (x t-1) at the time t-1, H n(xt|mt-1,ut) is the information entropy on the condition that the measurement quantity at the time t-1 and the control quantity at the time t are taken as the conditions entropy integration, E m is the information gain obtained by the action of the ARV in the next step, the energy consumption required by the decision of the ARV is taken by the ∈r (x, u) N (x) dx, and alpha and beta are balance factors;
b. the course of the next moment of decision comprises the following steps:
and solving an optimal solution of an objective function based on the information gain by using a greedy algorithm, and enabling the ARV to change the course to approach the target according to the optimal solution x best.
5. The method for planning the path of the autonomous and remotely controlled underwater vehicle for target exploration according to claim 2, wherein,
A. The ARV autonomous planning driving state is a remote control state, a threshold value phi is set on the basis of a function pi (h) of information entropy, and after pi (h) exceeds the threshold value, a shore-based control center sends an instruction to control the ARV movement state, and the ARV autonomous planning driving state is switched from an autonomous mode to a remote control mode;
b. in a remote control mode, the 3D camera accurately identifies the target, if the target is identified or the target is not identified for a long time, the shore-based control center sends an instruction to control the ARV to adaptively detect the next suspicious region according to the path autonomously planned in the step 2, the step3 is repeated until the ARV sequentially completes the detection task of each suspicious region and returns to the home, and the detection task is ended.
6. The method for planning a path of an autonomous remote underwater robot for target exploration according to claim 5, wherein,
The function based on the information entropy is as follows: pi (H) =h n(xt-1)-Em[Hn(xt|mt-1, u) ] (9
And judging the ARV motion state:
the determination of the area of change motion is:
wherein MOV represents a motion state, mov=0 represents an ARV in an autonomous mode, and mov=1 represents an ARV in a remote control mode; pi (h) represents an objective function of information entropy; phi represents the threshold of the objective function; alt=0 indicates a region where motion is not changed, and alt=1 indicates a region where motion is changed.
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