CN108681321B - Underwater detection method for unmanned ship cooperative formation - Google Patents
Underwater detection method for unmanned ship cooperative formation Download PDFInfo
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- CN108681321B CN108681321B CN201810318058.9A CN201810318058A CN108681321B CN 108681321 B CN108681321 B CN 108681321B CN 201810318058 A CN201810318058 A CN 201810318058A CN 108681321 B CN108681321 B CN 108681321B
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Abstract
The invention discloses an underwater detection method for unmanned ship cooperative formation. The method comprises the following main processes: establishing a grid map based on environmental information, forming unmanned ships, then detecting region coverage data, searching a nearest uncovered region when the unmanned ships fall into a dead angle or the coverage of the unmanned ships is finished, and planning a path to reach the region for continuing a task; meanwhile, each unmanned ship is searched for, the obstacle avoidance function is achieved, and a solution is achieved when the unmanned ship is obstructed. The invention uses a small unmanned ship for detection, and can freely pass in a shallow water area; the invention adopts the unmanned ship formation method for detection, can detect from different angles, and overcomes the misjudgment caused by the limitation of the angle of a single unmanned ship and an ultrasonic probe; the invention uses the ultrasonic probe on the water surface and underwater, the obstacle avoidance range is not limited to the obstacle on the water surface, and the underwater obstacle can be detected, thus avoiding the collision caused by the underwater submerged reef; when one unmanned ship breaks down suddenly, the rest unmanned ships can be replanned to continue the task.
Description
Technical Field
The invention belongs to the technical field of robot path planning, and relates to an underwater detection method for collaborative formation of various unmanned ships.
Background
Nowadays, the science and technology in the world are changing day by day, and the robot technology is also rapidly developed as an emerging technology. The robot equipment mainly comprises three types of sea, land and air, namely an unmanned aerial vehicle, a land robot and a water robot. The unmanned ship is used as a water robot, and can effectively avoid casualties and reduce cost under severe conditions due to the intelligent advantage, so that the unmanned ship can be widely applied to the fields of oil exploration, cargo transportation, meteorological monitoring, water area survey and the like. China is rich and vast, has numerous rivers and seas and rich resources, and is a strategic space with development potential. Therefore, the government has started the sea river as one of the key strategies for the development of the current society. It is now a great need to perform water surveys using unmanned vessels to map underwater topography for use. In addition, China plans to implement lake growth in rivers and lakes nationwide, and carries out gridding management on the lakes so as to strictly control the problems of lake water area space and the like. These strategic requirements predict that water area surveys will have relatively large application space and application requirements.
For shallow water areas such as offshore areas and lakes, unmanned ship survey has some problems at present: the large unmanned ship is heavy in hull, deep in draft and easy to cause stranding problem in shallow water areas due to large volume and heavy accessory equipment caused by large transmitting power of detection equipment aiming at deep water detection. For some narrow areas, a large unmanned ship cannot pass through the narrow areas, so that measurement data cannot be obtained.
When a water area is surveyed and meets complex terrains such as a sand cave or a cliff type, signals sent by an ultrasonic probe used for distance measurement of the unmanned ship cannot be recovered, and the unmanned ship can mistakenly think that no obstacle exists in the direction or the depth is extremely deep. Therefore, for complex terrain, a single unmanned ship may acquire inaccurate data under low repetition rate conditions and may not perform the water area survey task well.
The problem of area coverage is involved in conducting a survey of a body of water, requiring the unmanned ship to travel in a manner that covers the entire area. Area coverage is a special global path plan that refers to letting the robot walk quickly and efficiently all over an area except for obstacles. Traditional collaborative area coverage involves more land robots, aerial drones and underwater unmanned ships, and involves less for the surface unmanned ship part. Unmanned aerial vehicles and underwater unmanned ships cover in three-dimensional space, land robots cover in two-dimensional space, and water unmanned ships cover in two-dimensional space except water, and need to consider underwater conditions. Since the underwater situation is not known in advance in the water area survey, the obstacle avoidance function needs to be added in combination with the real-time underwater detection situation while the area is covered, so that the water area survey can be safely and efficiently realized.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides an underwater detection method for unmanned ship cooperative formation, aiming at solving the problem that a large unmanned ship is not suitable for shallow water surveying, the small and cheap unmanned ship is used for forming formation cooperative detection, and the underwater detection method can meet the advancing requirement of a special area and the economic advantage of most unmanned ships.
The invention is realized by the following technical scheme.
An underwater detection method for unmanned ship collaborative formation comprises the following steps:
(1) the background processor collects environmental information, acquires GPS coordinates of a region to be detected, and simultaneously establishes a database; if the area is subjected to a water area surveying task before, calling the obstacle information detected before for later grid modeling; the database is established for storing the detection data information obtained by surveying;
(2) establishing a grid map by using the environmental information in the step (1) to prepare for subsequent regional coverage path and point-to-point global path planning; the grid map stores information about whether obstacles exist and whether the obstacles are covered, and the information is updated along with detection during the task;
(3) the N unmanned searching ships are formed, and serial numbers 1-N are given in sequence and cannot be changed in subsequent tasks; then, establishing the relative position of the unmanned ship according to the formation, initializing the position of each unmanned ship, and preparing to execute a detection task; selecting a covering method to obtain a cruise path, wherein the cruise path gives a series of sub-target grid cells; if one of the unmanned searching ships breaks down in the cooperative detection, the formation is adapted to the number of the new unmanned searching ships to continue the task;
(4) the unmanned ships in the same formation are used as a whole to start to detect; firstly, selecting a cruise path, wherein the cruise path gives a series of sub-target grid units, and then the whole formation starts to detect from a starting point grid of the cruise path; in the process that the unmanned ship formation moves from one grid to another grid, the ultrasonic probe of each unmanned ship carries out measurement in real time, and the obtained data and the coordinate position given by the GPS are transmitted back to the background processor through the communication module and are stored in the database of the background processor; the background processor performs fusion analysis on the detection data of the whole unmanned ship formation, and when a detection task is executed, if an ultrasonic probe of one unmanned ship detects that an obstacle exists near the water surface or the underwater terrain is insufficient for the unmanned ship to pass through, the detection task is suspended firstly, the obstacle is avoided, the grid information is updated, and the point can be bypassed in the subsequent path planning; after the unmanned ship leaves the obstacle, whether the unmanned ship keeps formation of a formation is detected, if the formation is broken up, the unmanned ship is firstly re-formed and then is re-detected;
(5) after the first covering is finished, namely after traversing for one time according to the cruise path, detecting whether an uncovered area exists in the map; if yes, continuing to execute the next step, if not, proving that the area is completely detected, and ending the detection task;
(6) when one or more uncovered areas exist in the area, finding out the nearest uncovered area, if a plurality of uncovered areas exist, randomly selecting one uncovered area, then planning a better path by adopting a point-to-point global path algorithm, and enabling the unmanned ship formation to reach the uncovered area according to the path and execute a point-to-point task; when the unmanned ship drives to an uncovered area, whether obstacles exist on the water surface or not and whether the underwater terrain can be passed by the unmanned ship or not are also detected; if the task can not pass through the obstacle avoidance system, the task from point to point needs to be paused firstly, and the obstacle avoidance is carried out; updating the raster information at the same time, and bypassing the point in the subsequent path planning; after the unmanned ship leaves the obstacle, whether the unmanned ship keeps formation of a formation is detected, if the formation is broken up, the unmanned ship is firstly re-formed and then is re-detected; and (4) after the uncovered area is reached, continuing the detection task according to the method in the step (4), and then switching to the step (5).
Further, in the step (3), the multiple unmanned ships in the formation team adopt a centralized control mode or a distributed control mode; there is one pilot in the centralized control approach.
Further, in the step (3), the unmanned ship in the formation in the cooperative detection is subjected to fault processing in a centralized control mode, a pilot is told after the unmanned ship randomly follows the unmanned ship to have fault, then the pilot starts to perform tasks by re-formation according to the number of the left unmanned ships, and the unmanned ship of the pilot is unchanged in the re-formation; if the unmanned ship is in a distributed control mode, the background server is told after any unmanned ship fails, and then the unmanned ship starts to form a formation again according to the number of the remaining unmanned ships.
Furthermore, in the step (4), whether obstacles exist nearby or whether underwater topography can be passed through by the unmanned ship is detected by using the ultrasonic probe, so that the ultrasonic probes are arranged in the front, left and right directions below the hull of each unmanned ship to avoid stranding and colliding with the underwater obstacles; meanwhile, ultrasonic probes are arranged in the front direction, the left direction and the right direction above the ship body and used for measuring data of 180 degrees in front of the ship body and avoiding colliding with barriers around the ship body.
Further, in the obstacle avoidance process in the step (4) and the step (6), if the control mode is centralized, any unmanned ship detects an obstacle, the unmanned ship informs a pilot of the information, then stops the formation task, starts to autonomously execute the obstacle avoidance task until the unmanned ship is far away from the obstacle, informs the pilot, and resumes the formation task; when the unmanned ship detects the obstacle, after obtaining the information, a pilot ignores the unmanned ship, and continues to keep the formation to decelerate or even stop, after waiting for the unmanned ship to avoid the obstacle, the pilot informs the pilot of the GPS position coordinate required to arrive at present, and continues to perform the task at the previous speed after the unmanned ship recovers the formation; if the unmanned ship detects the obstacle in the distributed control mode, informing the background server of the message, stopping the formation task, starting to autonomously execute the obstacle avoidance task, informing the background server until the unmanned ship is far away from the obstacle, and recovering the formation task; when the unmanned ship detects an obstacle, the background server knows that the message informs the rest unmanned ships to continue to keep the formation to slow down and move forward or even stop, waits for the unmanned ship to avoid the obstacle, informs the background server of the GPS position coordinate required to arrive at present, and continues to perform tasks at the previous speed after the unmanned ship recovers the formation.
The ultrasonic probe is used for distance measurement, and compared with other sensors, the ultrasonic probe has good ultrasonic propagation directivity and strong penetration capacity. In order to meet the requirements of acquiring detection data and avoiding stranding of unmanned ships, probes are required to be arranged in the front direction, the left direction and the right direction below a ship body. Meanwhile, in order to acquire data of obstacles around the ship body, probes are required to be arranged in the front, left and right directions above the ship body and used for measuring data 180 degrees in front of the ship body.
The GPS is used for positioning the position of the unmanned ship, the positioning data return frequency can be freely set, and the position of the unmanned ship can be recorded in real time. This data, together with the survey data from the survey, provides coordinate data for later terrain modeling.
The communication module is used for information communication between unmanned ships, completes the function of collaborative obstacle avoidance, and simultaneously needs to transmit the measurement data back to the background processor. The unmanned ship information exchange system is the basis for the cooperative operation of the whole system, and can realize unmanned ship information exchange only on the premise of meeting the real-time communication, thereby realizing cooperative work.
The architecture of the unmanned ships in the formation team can be in a centralized control mode or a distributed control mode. If the unmanned ships are in a centralized control mode, one control center unmanned ship in the formation serves as a pilot to perform centralized control on other unmanned ships, sub-target point information of the other unmanned ships is given by the pilot, and barrier information obtained by the other unmanned ships is transmitted to the pilot to perform centralized analysis processing; if the unmanned ship is in a distributed control mode, the unmanned ship is equal in status, a background server exists, the position of each unmanned ship is given by the background server, and if the obstacle information is detected, the obstacle information is transmitted to the background server. However, no matter which architecture is adopted, the detection data acquired by each unmanned ship is directly transmitted to the database of the background processor for storage.
Further, in the step (4), if the data difference of a certain detection point measured from different directions by the unmanned ship formation is larger than a set value, the point terrain is determined to belong to a complex terrain, and detection needs to be emphasized; this point is therefore recorded in the database of the background processor.
Compared with the prior device and technology, the invention has the following advantages:
(1) the invention utilizes the economic, applicable, small and efficient unmanned ship to form the unmanned ship formation to carry out cooperative detection, so that the unmanned ship can freely pass in shallow water areas, and the measurable range is expanded. In addition, the cost of a single ship is low, so that the detection cost is greatly reduced, and the economical efficiency is more outstanding when the detection workload is large.
(2) The invention adopts the unmanned ship cooperative formation method to carry out coverage detection, the whole formation can detect the same detection point from different directions and different angles, the detection results obtained from different angles are comprehensively judged, the misjudgment caused by the limitation of the angle of a single unmanned ship and a sensor is overcome, and the underwater topography and geomorphologic map can be more accurately drawn. Meanwhile, the whole formation can be detected in multiple directions, so that the stranding problem caused by an underwater slope can be avoided,
(3) the invention adopts the method of unmanned ship cooperative formation to carry out coverage detection, the multiple ships work cooperatively and simultaneously, thus improving the working efficiency, simultaneously enlarging the monitoring range of the water area by more sensors and improving the capability of discovering potential danger. When a single unmanned ship suddenly breaks down, other unmanned ships can be replanned to replace the accident unmanned ship to continue the task, and the robustness of the system is improved.
(4) The obstacle avoidance range of the invention is not limited to the traditional water surface obstacles, and because the obstacle avoidance range can detect the underwater condition, whether the underwater obstacle exists or not can be easily detected, and the collision caused by the underwater submerged reef can be avoided.
Drawings
Fig. 1 is a flowchart of an underwater exploration method for unmanned ship cooperative formation in an example.
Fig. 2 is a schematic view of area coverage.
Fig. 3 is an obstacle avoidance flow chart.
Fig. 4 is a schematic view of an unmanned ship ultrasonic probe.
FIG. 5 is a diagram illustrating a membership function of distance information.
FIG. 6 is a diagram of a direction angle membership function.
Detailed Description
The following examples are presented to further illustrate the practice of the present invention, but the practice and protection of the present invention is not limited thereto, and it is noted that the following procedures and symbols, if not specifically described in detail, are understood or implemented by those skilled in the art with reference to the prior art. The following examples are described as unmanned ship cooperative formation underwater detection method flow and obstacle avoidance flow.
The unmanned ship related to the scheme adopted by the embodiment is a kayak which is designed and assembled by devices such as water pipes and the like, and various devices are purchased and installed on the kayak. The core module required in the method comprises an ultrasonic probe, a GPS module and a communication module, and each unmanned ship is provided with the equipment. In addition, data collected by each unmanned ship is transmitted to a background processor through a communication module, and the background processor implements the software part of the method. The background processor can be a large cloud platform data center and the like.
1. Unmanned ship cooperative formation underwater detection method process
Fig. 1 shows a flow chart of an underwater detection method for unmanned ship formation, which comprises the following steps:
the method comprises the following steps: the background processor collects the environmental information and establishes corresponding database storage information.
The method is based on known or semi-known environment information, and if the area is subjected to tasks such as water area survey and the like before, the obstacle information detected before is called for later grid modeling. The invention needs to establish a database in the background processor for storing the subsequently detected detection data or the obstacle information, and the GPS longitude and latitude is taken as a key value for storage. The database is dynamically updated throughout the task. And after the final task is finished, the data required for establishing the underwater topographic map is obtained from the database of the background processor.
Step two: and establishing a grid map by using the environment information in the steps to prepare for subsequent regional coverage path and point-to-point global path planning.
The whole environment is divided into a series of grid units, and the selection of the grid size influences the performance of the method. The grid selection is small, the environmental resolution is high, the anti-interference is weak, and the amount of stored information is large; the grid selection is large, the anti-interference capability is strong, the stored information is small, and the resolution ratio is reduced. In the present embodiment, it is known from the subsequent steps that three diamond formation modes are adopted, and thus the grid map size is set to be four times the unmanned ship size in combination with the search speed and the data complexity.
After the known environment information is obtained, a two-dimensional Cartesian rectangular grid is adopted to represent the environment, and grid units are taken as units when the area coverage task is carried out. And setting the upper left corner as a global coordinate origin, and horizontally and rightwards as an X-axis increasing direction and vertically and downwards as a Y-axis increasing direction. Row-column encoding is performed on each grid cell, and CV values and the number of times of coverage are initialized for the grid. The CV value indicates whether the grid cell is a free-passage area or an obstacle, and when the CV value is 1, the obstacle cannot pass through the grid cell at the point, and an initial value is given according to known environment information. The number of coverages indicates the number of times the drone walks through the grid, initially zero. And dynamically updating the CV value and the covering times in the process of the unmanned ship. And taking a central point GPS as the GPS coordinate of each code.
Step three: and (3) the unmanned 3-search ship is formed into a team by adopting a leader-follower method, and sequence numbers 1-3 are given in sequence and cannot be changed in the subsequent tasks.
Wherein 1 is the pilot number. The formation is guaranteed using the distance L and the angle phi. The position of each ship is based on the center GPS of the ship. Establishing a relative coordinate system, setting the position of the pilot as an original point, and taking the advancing direction of the pilot as a Y-axis negative axis and the right position of the pilot as an X-axis positive axis. Taking L as the length of the ship and phi as 45 degrees. Then the coordinate of number 2 in the relative coordinate system is
The coordinate of No. 3 in the relative coordinate system is
In the area coverage, the GPS coordinates which the follower needs to arrive can be obtained only by giving the GPS coordinates which the pilot needs to arrive.
And if the unmanned ship fails in the task process, the formation is adapted to the number of the new unmanned ships. When one unmanned searching ship fails, the remaining two ships form a transverse formation, and the left ship is used as a pilot. Establishing a relative coordinate system, setting the position of the navigator as an original point, taking the advancing direction of the navigator as a Y-axis positive axis, and setting the right position of the navigator as an X-axis positive axis. Then the follower's coordinates in the relative coordinate system are (L, 0). When two unmanned ships have faults, the remaining unmanned ships directly carry out the task of the single unmanned ship without formation.
Step four: unmanned ships in the same formation start to survey as a whole. When the unmanned ships form a team and move from one grid to another grid, the ultrasonic probe of each unmanned ship carries out measurement in real time, and the obtained data and the coordinate positions given by the GPS are transmitted back to the background processor one by one through the communication module and are stored in the database of the background processor. And the background processor performs fusion analysis on the detection data of the whole unmanned ship formation, and if the data difference of certain detection point data measured from different directions by the unmanned ship formation is found to be large (larger than a set value), the point terrain is determined to belong to a complex terrain, and detection needs to be emphasized. This point is therefore recorded in the database of the background processor. When a detection task is executed, if an ultrasonic probe of a certain unmanned ship detects that an obstacle exists near the water surface or the underwater topography is insufficient for the unmanned ship to pass through, the detection task is firstly suspended, and an obstacle avoidance flow in the method is entered for obstacle avoidance. The raster information is updated at the same time, which point is bypassed later in the path planning. After the unmanned ship is far away from the obstacle, whether the unmanned ship keeps formation is detected, and if the formation is broken up, the unmanned ship is firstly re-formed and then starts to detect again.
As shown in fig. 2, the present invention adopts a schematic view of coverage of an area, and the present invention adopts a bull back-and-forth algorithm to allow an unmanned ship to cover the area to be measured in all directions for data acquisition. In the covering process, for an uncovered area, a point-to-point global path algorithm needs to be found and used for reaching the uncovered area, and the specific steps are as follows:
the unmanned ship is placed at the origin of coordinates, the coverage task is started along the X axis, when the unmanned ship touches an obstacle or a boundary, the unmanned ship moves a distance of one grid unit on the Y axis, and then the new coverage is started in the reverse direction. Before moving on the Y axis, it is necessary to determine whether there is an obstacle in front. If there is an obstacle, the robot cannot move on the Y axis directly, and needs to first perform a retraction operation until there is no obstacle in the Y axis direction and then move forward in the Y axis direction to continue covering. The specific rollback operation is as follows: and inquiring the code of the point where the code is positioned, after increasing one for the code column, inquiring the code which is not covered and has the first CV value of 1 in the reverse direction, and after reducing one for the code column code, determining the end point of the rollback.
Step five: and after the first covering is finished, namely after traversing for one time according to the cruise path, detecting whether an uncovered area exists in the map. If yes, continuing to execute the next step, and if not, proving that the area is completely detected, and ending the detection task.
According to the cattle farming algorithm, when the unmanned ship drives to the position where the Y axis of the area is maximum, the first round of coverage is proved to be completed. Due to obstacles, many uncovered areas are created, which need to be found. And when the data of the areas to be detected are detected, ending the task. And (4) taking out detection data from a database of the background processor to draw a topographic map, and for some characteristic points such as sand holes and the like, performing important detection later.
Step six: when one or more uncovered areas exist in the area, finding out the nearest uncovered area by using a wildfire method, randomly selecting one uncovered area if a plurality of uncovered areas exist, planning a better path by adopting an A-algorithm, and enabling the unmanned ship formation to reach the uncovered area according to the path to execute a point-to-point task. When driving to an uncovered area, whether the water surface has obstacles or not and whether the underwater terrain can be passed by the unmanned ship or not are also detected. If the task can not pass through the method, the point-to-point task needs to be paused first, and the obstacle avoidance process in the method is entered for obstacle avoidance. The raster information is updated at the same time, which point is bypassed in the subsequent path planning. After the unmanned ship is far away from the obstacle, whether the unmanned ship keeps formation is detected, and if the formation is broken up, the unmanned ship is firstly re-formed and then starts to detect again. And after the uncovered area is reached, continuing the detection task according to the cattle farming method mentioned in the step four, and then turning to the step five.
The following is only an example reference, and the related code portions are clear to those skilled in the art and are not described herein again. The method for realizing the wild fire method comprises the following steps: querying the point code, if (x, y), and if c is 1, then the upper unit code is (x, y-c), the lower unit code is (x, y + c), the left unit code is (x-c, y), the right unit code is (x + c, y), the upper left unit code is (x-c, y-c), the upper right unit code is (x + c, y-c), the lower left unit code is (x-c, y + c), the lower left unit code is (x + c, y + c), and the query is whether covered, if all covered, the c value is increased until the nearest uncovered unit is queried, and the pseudo code is as follows:
thus, the closest uncovered point is found to be (x0, y 0).
At this time, after searching the uncovered area nearest to the current position, an optimal or suboptimal path needs to be planned to reach the area, and an a-algorithm is used here. The algorithm A is a heuristic algorithm, namely when the next node is searched from the current node, the environment map information is used for setting a heuristic function for selection, and the node with the minimum cost function is selected and is not the next node. Cost function f (n) g (n) h (n), f (n) represents the lowest estimated cost value from the start bit to the target location through node n. g (n) is the actual cost value from the starting location to the current location, and h (n) is the estimated cost value from the current location to the target location. How to choose h (n) is a key point of the a-x algorithm. Taking 10 for h (n) of four points of upper, lower, left and right; for h (n) at four oblique angles, 14 is taken. The specific steps of the algorithm are as follows:
(1) creating an opening list and a closing list, initializing a search list and a cost function, adding an initial node into the opening list, and setting the initial node as a current node;
(2) inquiring nodes adjacent to the current node, and judging as follows:
if the node is an obstacle area or has been selected to be placed in a closed list, the node is ignored. Otherwise, the following is done.
If the node is not in the open list, it is first added to the open list. The current node is then set as the parent node of the node, whose values f (n), g (n), h (n) are computed and saved.
If the node already exists in the open list, it is determined whether the path is shorter using g (n) as a reference. If the new g (n) calculated cost value is smaller, the parent node of the node is changed into the current node, and then the values of f (n) and g (n) are recalculated. Finally, the order of the open list is updated according to the f (n) size.
(3) And (4) inquiring whether the opening list is empty, if the opening list is empty and the current node is not the target node, finding no path to reach the target point, outputting a failure signal, and turning to the step (6).
And if the opening list is not empty, removing the current node from the opening list, adding the current node into the closing list, and finding out the node needing to be moved next. In this process, when the vehicle is inclined (upper left, upper right, lower left, and lower left), if there are obstacles on the left and right sides of the path, a collision may be caused, so the following operations are required to optimize the search algorithm:
searching the current node as (x, y), and when expanding to the next node, judging the position of the next node and executing different operations, wherein the specific judgment is as follows:
when the next node is (x-1, y +1), judging whether (x, y +1) and (x-1, y) are free to pass through the area (without obstacles), if not, the next node is not available, updating the search node, and removing the next node from the opening list;
when the next node is (x-1, y-1), judging whether (x, y-1) and (x-1, y) are free to pass through the area (without obstacles), if not, the next node is not available, updating the search node, and removing the next node from the opening list;
when the next node is (x +1, y +1), judging whether (x, y +1) and (x +1, y) are free to pass through the area (without obstacles), if not, the next node is not available, updating the search node, and removing the next node from the opening list;
when the next node is (x +1, y-1), judging whether (x, y-1) and (x +1, y) are free to pass through the area (without obstacles), if not, the next node is not available, updating the search node, and removing the next node from the opening list;
the next node is not the above node and does not operate.
And after the operation is finished, selecting the node with the minimum cost function as the current node.
(4) Adding the current node into a closing list to indicate that the node is selected, then judging whether the current node is a target node, if so, turning to a step 6, otherwise, turning to a step 3;
(5) starting from the target node, sequentially connecting nodes along the direction of a father node, wherein the path is an optimal path selected by an A-x algorithm, each node is a sub-target point, and storing and outputting the nodes;
(6) ending the path finding;
2. obstacle avoidance process
As shown in fig. 3, the obstacle avoidance flow chart of the method of the present invention includes functions of obstacle avoidance on the water surface and obstacle avoidance under water (anti-grounding), and in the course of walking, when any unmanned ship detects an obstacle, the distance information of the obstacle is synchronized to the pilot, and the pilot judges the direction of the obstacle and then performs overall obstacle avoidance by using a fuzzy algorithm. The main method is to achieve the effect of steering by controlling the rotating speed of the left wheel and the right wheel of the unmanned ship, thereby keeping away from the obstacle. Since the ultrasonic probe of the unmanned ship has two types, namely water and underwater, but the obstacle avoidance directions are consistent no matter whether the ultrasonic probe is water or underwater, if an obstacle appears on the left of the water surface and an obstacle appears on the left of the water and the underwater, the operation is carried out by enabling the unmanned ship to turn right to bypass the obstacle, so that the ultrasonic probes on the water and the underwater in the same direction are judged together, and as shown in fig. 4, the ultrasonic probe of the unmanned ship is a schematic diagram. The obstacle avoidance specifically operates as follows:
the method comprises the following steps: first fuzzy inputs and outputs need to be established. Taking the distance (LD, FD, RD) of the obstacle collected by the ultrasonic probe at the left, the front and the right in the range of 180 degrees in front of the unmanned ship (including underwater) and the target direction angle (angle) obtained by the compass as the input of a fuzzy controller; unmanned ship speed (speed) and steering (turning) are taken as fuzzy outputs.
Step two: and establishing a membership function according to the fuzzy input. Fuzzy variables of input variables LD, FD, RD are defined as { "near", "middle", "far" } { "N", "M", "F" }, the domain of discourse is [0, 3L ], where 3L denotes 3 unmanned ship lengths, and the membership function is as shown in fig. 5. The fuzzy variables of the input variable angle are defined as { "left", "front", "right" } { "L", "Z", "R" }, the domain of discourse is [0, 180 ° ], and the membership function is as shown in fig. 6. Fuzzy variables of the output variable speed are defined as { "fast", "uniform speed", "slow" } { "F", "M", "S" }, and fuzzy variables of the output variable turning are defined as { "left", "front", "right" } { "L", "Z", "R" }.
Step three: establishing fuzzy rules, and inquiring different rule tables according to different states to determine each output probability. When the unmanned ship is in a safe state, namely surrounding obstacles are far away, the task of driving to a target point is selected to be performed. At this time, the position of the target point needs to be judged, and the specific rule is shown in table 1; when obstacles exist around the unmanned ship, an obstacle avoidance task needs to be executed, and the specific rule is shown in table 2; here, if the forward distance measurement is not F, only the obstacle avoidance task is considered. When the distance measurement from the front side and the distance measurement from the other side are F, the obstacle avoidance is considered firstly, and meanwhile, the approach to the target is also considered, and the specific rule is shown in a table 3; other cases are shown in table 4.
TABLE 1
TABLE 2
TABLE 3
TABLE 4
Step four: defuzzification is the conversion of a fuzzy variable into a defined quantity. The speed is controlled by the propeller speed, and the steering is controlled by the difference between the left and right propeller speeds. The speed of the left wheel is less than that of the right wheel to realize left turning, and the speed of the left wheel is greater than that of the right wheel to realize right turning. The specific numerical value is determined according to the speed of the physical hardware.
The method is the integral action of the unmanned ship after formation, when forming and avoiding the obstacle, both sides of the unmanned ship may meet the obstacle at the same time, and at the moment, a pilot gives an obstacle avoiding scheme after comprehensive judgment according to transmitted information. The specific obstacle avoidance principle is as follows:
when the width of the passable area formed by the left and right obstacles is larger than 1.5 times of the formation width: the left side can not pass through, and when the right side can pass through, the pilot is controlled to move to the right, so that the follower also moves to the right integrally, and the follower moves back to the previous route from the left after obstacle avoidance is finished; the right side can not pass through, and when the left side can pass through, the pilot is controlled to move leftwards, so that the follower also moves leftwards integrally, and the follower moves back to the previous route right again after obstacle avoidance is finished; when the left side and the right side can pass through, the formation is kept forward.
When the passing area is narrow, the notice is not needed to keep the formation, and the notice can be formed into a column of longitudinal rows first, so that the obstacle avoidance is prioritized. And after the obstacle avoidance is finished, the pilot decelerates or stops moving forwards, and the task is continued after the followers return to the corresponding positions to form the formation.
The above description is only a preferred embodiment of the present invention, and it should be understood by those skilled in the art that the present invention is not limited to the principle of the present invention, and other changes, modifications, substitutions, combinations and simplifications which are made without departing from the spirit and principle of the present invention and which are equivalent to each other and are included in the protection scope of the present invention.
Claims (5)
1. An underwater detection method for unmanned ship collaborative formation is characterized by comprising the following steps:
(1) the background processor collects environmental information, acquires GPS coordinates of a region to be detected, and simultaneously establishes a database; if the area is subjected to a water area surveying task before, calling the obstacle information detected before for later grid modeling; the database is established for storing the detection data information obtained by surveying;
(2) establishing a grid map by using the environmental information in the step (1) to prepare for subsequent regional coverage path and point-to-point global path planning; the grid map stores information about whether obstacles exist and whether the obstacles are covered, and the information is updated along with detection during the task;
(3) the N unmanned searching ships are formed, and serial numbers 1-N are given in sequence and cannot be changed in subsequent tasks; then, establishing the relative position of the unmanned ship according to the formation, initializing the position of each unmanned ship, and preparing to execute a detection task; selecting a covering method to obtain a cruise path, wherein the cruise path gives a series of sub-target grid cells; if one of the unmanned searching ships breaks down in the cooperative detection, the formation is adapted to the number of the new unmanned searching ships to continue the task;
(4) the unmanned ships in the same formation are used as a whole to start to detect; firstly, selecting a cruise path, wherein the cruise path gives a series of sub-target grid units, and then the whole formation starts to detect from a starting point grid of the cruise path; in the process that the unmanned ship formation moves from one grid to another grid, the ultrasonic probe of each unmanned ship carries out measurement in real time, and the obtained data and the coordinate position given by the GPS are transmitted back to the background processor through the communication module and are stored in the database of the background processor; the background processor performs fusion analysis on the detection data of the whole unmanned ship formation, and when a detection task is executed, if an ultrasonic probe of one unmanned ship detects that an obstacle exists near the water surface or the underwater terrain is insufficient for the unmanned ship to pass through, the detection task is suspended firstly, the obstacle is avoided, the grid information is updated, and the point can be bypassed in the subsequent path planning; after the unmanned ship leaves the obstacle, whether the unmanned ship keeps formation of a formation is detected, if the formation is broken up, the unmanned ship is firstly re-formed and then is re-detected;
(5) after the first covering is finished, namely after traversing for one time according to the cruise path, detecting whether an uncovered area exists in the map; if yes, continuing to execute the next step, if not, proving that the area is completely detected, and ending the detection task;
(6) when one or more uncovered areas exist in the area, finding out the nearest uncovered area, if a plurality of uncovered areas exist, randomly selecting one uncovered area, then planning a better path by adopting a point-to-point global path algorithm, and enabling the unmanned ship formation to reach the uncovered area according to the path and execute a point-to-point task; when the unmanned ship drives to an uncovered area, whether obstacles exist on the water surface or not and whether the underwater terrain can be passed by the unmanned ship or not are also detected; if the task can not pass through the obstacle avoidance system, the task from point to point needs to be paused firstly, and the obstacle avoidance is carried out; updating the raster information at the same time, and bypassing the point in the subsequent path planning; after the unmanned ship leaves the obstacle, whether the unmanned ship keeps formation of a formation is detected, if the formation is broken up, the unmanned ship is firstly re-formed and then is re-detected; and (4) after the uncovered area is reached, continuing the detection task according to the method in the step (4), and then switching to the step (5).
2. The underwater detection method for unmanned ship cooperative formation according to claim 1, wherein in the step (3), the architecture of the unmanned ships in the formation group is in a centralized control mode or a distributed control mode; there is one pilot in the centralized control approach.
3. The underwater detection method for unmanned ship collaborative formation according to claim 2, wherein in the step (3), the unmanned ship in formation in collaborative detection is processed by informing a pilot in a centralized control mode after the unmanned ship arbitrarily follows to have a fault, and then the pilot carries out a task by forming again with the number of the remaining unmanned ships and the unmanned ship of the pilot in forming again is unchanged; if the unmanned ship is in a distributed control mode, the background server is told after any unmanned ship fails, and then the unmanned ship starts to form a formation again according to the number of the remaining unmanned ships.
4. The underwater detection method for unmanned ship cooperative formation according to claim 1, wherein in the step (4), an ultrasonic probe is used to detect whether there is an obstacle nearby or whether the underwater terrain can be passed by the unmanned ship, so that the ultrasonic probe is installed in the front, left and right directions below the hull of each unmanned ship to avoid grounding and collision with the underwater obstacle; meanwhile, ultrasonic probes are arranged in the front direction, the left direction and the right direction above the ship body and used for measuring data of 180 degrees in front of the ship body and avoiding colliding with barriers around the ship body.
5. The underwater detection method for unmanned ship cooperative formation according to claim 2, wherein in the obstacle avoidance process in the step (4) and the step (6), if the obstacle avoidance process is in a centralized control mode, any unmanned ship detects an obstacle, informs a pilot of the obstacle avoidance message, then stops the formation task, starts to autonomously execute the obstacle avoidance task, informs the pilot until the obstacle is far away from the obstacle, and resumes the formation task; when the unmanned ship detects the obstacle, after obtaining the information, a pilot ignores the unmanned ship, and continues to keep the formation to decelerate or even stop, after waiting for the unmanned ship to avoid the obstacle, the pilot informs the pilot of the GPS position coordinate required to arrive at present, and continues to perform the task at the previous speed after the unmanned ship recovers the formation; if the unmanned ship detects the obstacle in the distributed control mode, informing the background server of the message, stopping the formation task, starting to autonomously execute the obstacle avoidance task, informing the background server until the unmanned ship is far away from the obstacle, and recovering the formation task; when the unmanned ship detects an obstacle, the background server knows that the message informs the rest unmanned ships to continue to keep the formation to slow down and move forward or even stop, waits for the unmanned ship to avoid the obstacle, informs the background server of the GPS position coordinate required to arrive at present, and continues to perform tasks at the previous speed after the unmanned ship recovers the formation.
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Families Citing this family (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109520504B (en) * | 2018-11-27 | 2022-07-22 | 北京航空航天大学 | Grid discretization-based unmanned aerial vehicle patrol route optimization method |
| CN109856668B (en) * | 2019-03-21 | 2020-09-01 | 西南科技大学 | Multi-machine cooperative monitoring method for ocean radioactive pollution |
| CN110097212B (en) * | 2019-04-08 | 2022-03-29 | 华南理工大学 | Unmanned ship high-energy-efficiency cooperative area detection method |
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| CN114371692B (en) * | 2021-11-26 | 2023-10-13 | 中国人民解放军军事科学院国防科技创新研究院 | Patrol ship area coverage path planning method, system and device under energy constraint |
| CN114428512A (en) * | 2021-12-30 | 2022-05-03 | 珠海云洲智能科技股份有限公司 | Cross-domain cluster collaborative search method and cross-domain cluster collaborative search system |
| CN116225032B (en) * | 2023-05-09 | 2023-07-25 | 陕西欧卡电子智能科技有限公司 | Unmanned ship cluster collaborative obstacle avoidance planning method based on known flight path |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2014171999A3 (en) * | 2013-02-04 | 2014-12-31 | Vanderbilt University | Method and system for high-accuracy differential tracking of global positioning system (gps) receivers |
| WO2017092905A1 (en) * | 2015-12-03 | 2017-06-08 | Graf Plessen Mogens Max Sophus Edzard | System and method for navigation guidance of a vehicle in an agricultural field |
| CN106840168A (en) * | 2017-03-16 | 2017-06-13 | 苏州大学 | Complete coverage path planning method under clean robot and its dynamic environment |
| CN106919184A (en) * | 2016-12-20 | 2017-07-04 | 北京理工大学 | Double unmanned plane coordinated operation systems and double unmanned plane work compound methods |
| CN106979785A (en) * | 2017-03-24 | 2017-07-25 | 北京大学深圳研究生院 | A kind of complete traverse path planing method of multi-robot system |
| CN107728642A (en) * | 2017-10-30 | 2018-02-23 | 北京博鹰通航科技有限公司 | A kind of UAV Flight Control System and its method |
| CN107843262A (en) * | 2017-10-30 | 2018-03-27 | 洛阳中科龙网创新科技有限公司 | A kind of method of farm machinery all standing trajectory path planning |
Family Cites Families (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103674029B (en) * | 2013-12-19 | 2017-01-04 | 哈尔滨工程大学 | A kind of method of many ships collaborative navigation formation configuration based on underwater sound communication |
| US20160357193A1 (en) * | 2015-06-02 | 2016-12-08 | 5D Robotics, Inc. | Mobile ultra wide band constellations |
| KR20170004407A (en) * | 2015-07-02 | 2017-01-11 | 김성훈 | system and method for automated reconnaissance |
| CN105584599B (en) * | 2016-01-25 | 2017-10-31 | 大连海事大学 | A kind of marine environmental monitoring system for motion of being formed into columns based on unmanned boat |
| US9711851B1 (en) * | 2016-02-04 | 2017-07-18 | Proxy Technologies, Inc. | Unmanned vehicle, system and method for transmitting signals |
| CN105892480B (en) * | 2016-03-21 | 2018-12-11 | 南京航空航天大学 | Isomery multiple no-manned plane systematic collaboration, which is examined, beats task self-organizing method |
| US10328577B2 (en) * | 2016-04-19 | 2019-06-25 | Xiaoyu Arasive Inc. | Autonomous navigational system for floor preparation and maintenance equipment |
| JP6302956B2 (en) * | 2016-06-15 | 2018-03-28 | 株式会社Ｓｕｂａｒｕ | Formation setting device, formation setting method and formation setting program |
| US10479455B2 (en) * | 2016-06-29 | 2019-11-19 | Pgs Geophysical As | Performing geophysical surveys using unmanned tow vessels |
| CN106406354A (en) * | 2016-11-29 | 2017-02-15 | 中山大学 | Distributed aircraft formation method based on three-dimensional dynamic obstacle avoidance |
| CN107085427B (en) * | 2017-05-11 | 2019-06-18 | 华南理工大学 | A kind of unmanned water surface ship formation control method following structure based on leader |
| CN107121986A (en) * | 2017-05-24 | 2017-09-01 | 浙江大学 | The method that a kind of unmanned plane flight pattern of Behavior-based control is kept |
| CN107037816A (en) * | 2017-06-15 | 2017-08-11 | 华中科技大学 | A kind of many unmanned boat fleet systems |
| CN107608347B (en) * | 2017-09-04 | 2021-02-05 | 广东华中科技大学工业技术研究院 | Distributed control unmanned ship cluster enclosure tracking method |
-
2018
- 2018-04-10 CN CN201810318058.9A patent/CN108681321B/en not_active Expired - Fee Related
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2014171999A3 (en) * | 2013-02-04 | 2014-12-31 | Vanderbilt University | Method and system for high-accuracy differential tracking of global positioning system (gps) receivers |
| WO2017092905A1 (en) * | 2015-12-03 | 2017-06-08 | Graf Plessen Mogens Max Sophus Edzard | System and method for navigation guidance of a vehicle in an agricultural field |
| CN106919184A (en) * | 2016-12-20 | 2017-07-04 | 北京理工大学 | Double unmanned plane coordinated operation systems and double unmanned plane work compound methods |
| CN106840168A (en) * | 2017-03-16 | 2017-06-13 | 苏州大学 | Complete coverage path planning method under clean robot and its dynamic environment |
| CN106979785A (en) * | 2017-03-24 | 2017-07-25 | 北京大学深圳研究生院 | A kind of complete traverse path planing method of multi-robot system |
| CN107728642A (en) * | 2017-10-30 | 2018-02-23 | 北京博鹰通航科技有限公司 | A kind of UAV Flight Control System and its method |
| CN107843262A (en) * | 2017-10-30 | 2018-03-27 | 洛阳中科龙网创新科技有限公司 | A kind of method of farm machinery all standing trajectory path planning |
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