CN113110528A - Automatic return method and device for underwater autonomous navigation robot - Google Patents
Automatic return method and device for underwater autonomous navigation robot Download PDFInfo
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- CN113110528A CN113110528A CN202110398938.3A CN202110398938A CN113110528A CN 113110528 A CN113110528 A CN 113110528A CN 202110398938 A CN202110398938 A CN 202110398938A CN 113110528 A CN113110528 A CN 113110528A
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Abstract
The invention provides an automatic returning method and device for an underwater autonomous navigation robot, wherein the method comprises the following steps: after the underwater autonomous navigation robot finishes task execution, a position sensor carries out map creation and positioning on the surrounding environment; establishing a corresponding grid map; performing expansion operation on a plurality of obstacles in the raster map to generate a path of a raster Dirichlet map forming a return path, and then performing path vectorization and topology to obtain a topological path network; performing path planning by adopting a Dijkstra algorithm, and selecting an optimal return path; and carrying out real-time marine environment benchmark test on the grid map by adopting an artificial path planning algorithm, if the grid map is valid, adopting the optimal return route, and if the grid map is invalid, repeating the steps from the step of establishing the grid map. The method adopts Dijkstra algorithm similar to natural language to carry out path planning, and can better process data uncertainty and inaccuracy.
Description
Technical Field
The invention belongs to the technical field of underwater navigation, and particularly relates to an automatic return method and device of an underwater autonomous navigation robot.
Background
The underwater autonomous navigation robot (hereinafter referred to as AUV) is also called a cableless underwater robot, which autonomously navigates with energy and can execute a large-range detection task, has strong environmental adaptability and flexible movement, and is an important tool for ocean resource detection and environmental observation. In the civil field, the system can be used for submarine investigation, marine data acquisition, underwater pipeline investigation, submarine sunken ship search and the like; in the military field, the device can be used for underwater target reconnaissance, rescue and lifesaving and the like. Because the no cable underwater robot has the home range and does not receive the cable restriction, generally speaking, no cable underwater robot is after the executive task, because there is the barrier in complicated changeable waters and the return journey in-process, can't return the journey to retrieve to become present huge problem accurately.
Disclosure of Invention
Aiming at the defects, the invention provides a method for solving the problem that the current underwater AUV cannot accurately return and recover in the actual application of autonomous return. The invention aims to solve the technical problems and provides an AUV automatic return method and device for constructing a grid map based on a grid method, planning an optimal return path by adopting a Dijkstra algorithm and detecting the effectiveness of the optimal return path by using an actual marine environment reference through a manual path planning algorithm.
The invention provides the following technical scheme: an automatic returning method of an underwater autonomous navigation robot comprises the following steps:
s1: after the underwater autonomous navigation robot finishes task execution, a position sensor in the underwater autonomous navigation robot device carries out map creation and positioning on the surrounding environment;
s2: establishing a grid map corresponding to the map obtained in the step S1;
s3: performing expansion operation on the plurality of obstacles described in the grid map obtained in the step S2 by using an expansion operator in mathematical morphology to generate a path of a grid Dirichlet diagram formed by a plurality of parallel boundaries, where the path of the grid Dirichlet diagram formed by the plurality of parallel boundaries constitutes a return path;
s4: performing path vectorization on the return path obtained in the step S3 to obtain a vector path network;
s5: in order to reduce the data storage space and improve the path planning efficiency, the vector path network obtained in the step S4 is subjected to topology to obtain a topology path network;
s6: performing path planning on the topological path network by adopting a Dijkstra algorithm, and selecting an optimal return path;
s7: and performing real-time marine environment benchmark test on the optimal return route obtained in the step S6 by adopting a manual route planning algorithm, if the optimal return route is valid, adopting the optimal return route, and if the optimal return route is invalid, repeating the steps S2-S6.
Furthermore, the position sensor adopts a differential positioning system, a speedometer and a strapdown inertial navigation combination to establish and position a map of the surrounding environment.
Further, in the step S3, when performing dilation operation on the plurality of obstacles described in the grid map obtained in the step S2 by using a dilation operator in mathematical morphology, in order to clarify the distance between the dilation boundary of the underwater autonomous navigation robot and the origin of the path terminal, and to make it easier to perform boundary extraction, a distance measurement structure element mathematical model based on a square form is adopted:
D(m,j)＝min[D(m-1,n-1)＝b,
D(m-1,n)+a，D(m-1,n+1)+b，
D(m,n-1)+a，D(m,n)+0，
D(m,n+1)+a，D(m+1,n-1)+b，
D(m+1,n)+a，D(m+1,n+1)+b]；
wherein, m and n are grid numbers, and a and b are grid distance parameters.
Further, in the step S3, the grid map obtained in the step 2 is processed by using a ranging structure element based on an expansion operation, each grid on the map is searched through repeated circulation, and when the grid is blank, 8 grids around the grid where the underwater autonomous navigation robot is located are scanned by using a mathematical model of the ranging structure element; if the surrounding has obstacles, assigning a value to the blank grid according to the distance measurement structure element mathematical model calculation value, otherwise, not operating the blank grid, and performing the next grid operation; if the next grid of the blank grid has an initial value, the next grid is not processed, and the next grid is continuously scanned to finish the search of the obstacles in the grid map for one time;
each time the obstacle searching is completed, the related obstacle and the origin of the terminal station are uniformly expanded for one circle; and when the whole grid map has no idle grid after the obstacles are searched for many times, the expansion operation is finished.
Further, the artificial path planning algorithm includes calculation of an attraction potential and an attraction energy of the origin of the path terminal station to the underwater autonomous navigation robot, a repulsion potential and a repulsion energy of the obstacle in the path to the underwater autonomous navigation robot, and a resultant potential and a resultant energy of the origin of the path terminal station and the obstacle to the underwater autonomous navigation robot.
Further, the attraction potential U of the origin of the path terminal station to the underwater autonomous navigation robotatt(x) The calculation formula is as follows:
wherein, the x is the position of the underwater autonomous navigation robot, and the G is the position of the origin of the path terminal station; said | is a euclidean distance calculation function; k isattCalculating a proportionality constant for the attraction potential;
attraction energy F of the origin of the path terminal station to the underwater autonomous navigation robotatt(x) The calculation formula of (a) is as follows:
wherein u (·) is a directional unit vector.
Further, an obstacle in the pathRepulsive potential to the underwater autonomous navigation robot
The calculation formula of (a) is as follows:
wherein x is the position of the underwater autonomous navigation robot, oiIs the position of the obstacle; the |) is a Euclidean distance calculation function, the krepCalculating a proportionality constant for the repulsive potential;
total repulsive potential formed by multiple obstacles
Rejection energy of the obstacle in the path to the underwater autonomous navigation robot
The calculation formula of (a) is as follows:
wherein u (·) is a directional unit vector;
total repulsive energy formed by a plurality of obstacles
Further, a calculation formula of a resultant potential U of the origin of the path terminal and the obstacle to the underwater autonomous navigation robot is as follows:
U＝Uatt(x)+Urep；
the calculation formula of the synthetic energy F of the underwater autonomous navigation robot by the origin of the path terminal station and the obstacle is as follows:
the invention also provides an automatic returning device of the underwater autonomous navigation robot by adopting the method, which sequentially comprises a propeller, a chain motion device, a rear steering engine, a sealed cabin, a control circuit, a front steering engine, a lifting rudder connected with the front steering engine, a camera and a position sensor from back to front.
Further, the linkage device comprises a motor connected with the propeller, a battery connected with the motor and used for providing power for the motor, and a rudder positioned at the lower side of the device and used for changing the sailing direction of the device.
The invention has the beneficial effects that:
1. the invention aims at the problem that the automatic return path planning is actually the path planning and control from one place to another place, and the main purpose is to plan the optimal path for an underwater AUV (autonomous Underwater vehicle) so that the underwater AUV can safely and efficiently reach a destination; and the expansion operation is carried out on the barrier on the basis of the grid map, so that higher probability search and global optimal solution are provided.
2. The method provided by the invention adds a grid map into the original algorithm to form the grid map, and simultaneously injects a grid map control method to better finish the obstacle expansion operation of the established grid map, extracts a vector path by using a path vectorization method, and performs topology on the path to finally finish the accurate autonomous return of the AUV. The method adopts Dijkstra algorithm similar to natural language to plan the path of the topological path network, and can better process the uncertainty and inaccuracy of data.
3. The optimal return path of the underwater autonomous navigation robot is based on a Dijkstra algorithm with high calculation efficiency, the algorithm is used for searching a path between a source node and a target node on a grid map, the distance between an underwater AUV and an obstacle is maximized, and the motion trail of the underwater AUV has high performability.
4. According to the method, after the Dijkstra algorithm is adopted to carry out path planning on the topological path network to obtain the optimal return path, the manual planning algorithm is carried out for benchmark test according to the actual marine environment, the problem that all obstacles are assumed to be sources of repulsive potential is solved, and when a target attracts the obstacles by applying attractive potential, the repulsive potential is inversely proportional to the distance between the AUV (autonomous underwater vehicle) and the obstacles. According to the motion of the underwater AUV, the finally obtained derivative of the composite potential comprising the attraction potential and the repulsion potential gives a value of the virtual force applied to the underwater autonomous navigation robot, so that the motion potential of the optimal return path finally selected by the underwater autonomous navigation robot is minimized.
Drawings
The invention will be described in more detail hereinafter on the basis of embodiments and with reference to the accompanying drawings. Wherein:
FIG. 1 is a flow chart of an automatic return method of an underwater autonomous navigation robot provided by the invention;
FIG. 2 is a schematic diagram of a ranging structure element constructed during the dilation operation according to the present invention;
fig. 3 is a schematic diagram of a double boundary path of a grid Dirichlet graph formed by processing 2 adjacent obstacles by using dilation arithmetic;
FIG. 4 is a schematic diagram of repulsive and attractive energies of an obstacle in a return path and an origin of a final return station in the method provided by the present invention;
fig. 5 is an overall schematic view of the automatic return device of the underwater autonomous navigation robot provided by the invention.
Detailed description of the preferred embodiments
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
As shown in fig. 1, a flow chart of an automatic returning method of an underwater autonomous navigation robot provided by the invention includes the following steps:
s1: after the underwater autonomous navigation robot finishes task execution, a position sensor in the underwater autonomous navigation robot device carries out map creation and positioning on the surrounding environment;
s2: establishing a grid map corresponding to the map obtained in step S1;
s3: performing expansion operation on the plurality of obstacles described in the grid map obtained in step S2 by using an expansion operator in mathematical morphology to generate paths of a grid Dirichlet diagram formed by a plurality of parallel boundaries, wherein the paths of the grid Dirichlet diagram formed by the plurality of parallel boundaries form a return path;
s4: performing path vectorization on the return path obtained in the step S3 to obtain a vector path network so as to facilitate the operation of an underwater AUV motion control system;
s5: in order to reduce the data storage space and improve the path planning efficiency, the vector path network obtained in the step S4 is subjected to topology to obtain a topological path network;
s6: performing path planning on the topological path network by adopting a Dijkstra algorithm, and selecting an optimal return path;
s7: and (4) carrying out real-time marine environment benchmark test on the optimal return route obtained in the step S6 by adopting a manual route planning algorithm, if the optimal return route is effective, adopting the optimal return route, and if the optimal return route is ineffective, repeating the steps S2-S6.
The position sensor 9 uses a differential positioning system, a speedometer and a strapdown inertial navigation combination to create and position a map of the surrounding environment.
In the step S3, when performing dilation operation on the plurality of obstacles described in the grid map obtained in the step S2 by using a dilation operator in mathematical morphology, in order to clarify the distance between the dilation boundary of the underwater autonomous navigation robot and the origin of the destination of the route, and to make it easier to extract the boundary, a ranging structure element based on a square form is used, as shown in fig. 2, the origin of the ranging structure element is the geometric center of the ranging structure element, 8 grids around the ranging structure element indicate that the distance between the origin of the route is 1, 16 grids at the 2 nd layer indicate that the distance between the origin of the route is 2, and also indicate the number of layers of dilation operator dilation target grids. The adopted distance measurement structure element mathematical model based on the square form is as follows:
D(m,j)＝min[D(m-1,n-1)＝b,
D(m-1,n)+a，D(m-1,n+1)+b，
D(m,n-1)+a，D(m,n)+0，
D(m,n+1)+a，D(m+1,n-1)+b，
D(m+1,n)+a，D(m+1,n+1)+b]；
wherein m and n are grid numbers, and a and b are grid distance parameters.
S3, processing the grid map obtained in the step 2 by using a ranging structure element based on expansion operation, searching each grid on the map through repeated circulation, and scanning 8 grids around the grid where the underwater autonomous navigation robot is located by using a ranging structure element mathematical model when the grids are blank; if the surrounding has obstacles, assigning a value to the blank grid according to the distance measurement structure element mathematical model calculation value, otherwise, not operating the blank grid, and performing the next grid operation; if the next grid of the blank grid has an initial value, no processing is carried out, the next grid is continuously scanned, and the obstacle search in the grid map is completed for one time;
when the obstacle searching is completed once, the related obstacle and the origin of the terminal station are uniformly expanded for one circle; and when the whole grid map has no idle grid after the barriers are searched for many times, the expansion operation is finished.
Due to the self-calculated property of the Dirichlet diagram, the determined path maximizes the distance between the underwater autonomous navigation robot AUV and the obstacle, so that the path has high performability for the movement control of the underwater autonomous navigation robot AUV.
As shown in fig. 3, since 2 adjacent obstacles are processed by the dilation arithmetic operation, a path of the raster Dirichlet diagram is generated by 2 parallel boundaries, and the path is also called a double-boundary path. The obtained boundary underwater AUV control system cannot be identified and needs to be subjected to vectorization, and the vectorization method adopts a 2 x 2 grid array as a detection window to scan the generated grid Dirichlet image along the row and column directions. The scanning pattern is shown in fig. 4, in which the grid is a double boundary path formed by a grid Dirichlet diagram. Wherein m and n are grid serial numbers; x and y are the length and width distances of the grid; the numbers in the grid are the values assigned to the grid when the ranging structure element expands the obstacle. When 4 windows in a 2 x 2 grid array all have values, a vector coordinate is determined. And then, after the UV is optimized by a vector path and a Dijkstra algorithm, obtaining an optimal path consisting of a plurality of nodes. And recording and numbering the coordinates of the nodes in the vector path topological process, and sequentially sending the coordinates of each node to an underwater AUV motion control system in a table look-up form, namely completing the control of autonomous return of the underwater AUV.
As shown in fig. 4, the artificial path planning algorithm includes calculation of attraction potential and attraction energy of the origin of the path terminal station to the underwater autonomous navigation robot, calculation of repulsion potential and repulsion energy of the obstacle in the path to the underwater autonomous navigation robot, and calculation of synthesis potential and synthesis energy of the origin of the path terminal station and the obstacle to the underwater autonomous navigation robot.
Attraction U of origin of path terminal station to underwater autonomous navigation robotatt(x) The calculation formula is as follows:
wherein x is the position of the underwater autonomous navigation robot, and G is the position of the origin of the path terminal station; | is a euclidean distance calculation function; k is a radical ofattCalculating a proportionality constant for the attraction potential;
the attraction of the origin of the path terminal station to the underwater autonomous navigation robot is a single target, and the underwater autonomous navigation robot is attracted to return to the origin of the path terminal station. There are a number of ways to model the potential for attractiveness, depending on the application and environment. The general approach is to keep the attraction potential proportional to the distance between the current position of the underwater autonomous navigation robot and the target. This causes the potential to approach zero as the robot approaches the target, so the robot decelerates near the target, avoiding overshoot after reaching the target. The proportionality and proportionality constants are also algorithm parameters that can be adjusted for different purposes, such as maintaining high gap and short path length.
Attraction energy F of origin of path terminal station to underwater autonomous navigation robotatt(x) The calculation formula of (a) is as follows:
where u (·) is a directional unit vector.
The force generated by the attraction energy is given by a directional unit vector, the magnitude of which is the potential function and the derivative of the direction, the vector being the line that maximizes the change in potential, which is the line that connects the underwater autonomous navigation robot directly to the path terminal.
Repulsive potential of obstacle in path to underwater autonomous navigation robot
The calculation formula of (a) is as follows:
wherein x is the position of the underwater autonomous navigation robot, oiIs the position of the obstacle; II is a Euclidean distance calculation function, krepCalculating a proportionality constant for the repulsive potential;
total repulsive potential formed by multiple obstacles
The repulsive potential is imposed by all obstacles that attempt to repel underwater autonomous navigation robots from approaching them and possibly causing collisions. Assuming that the boundaries of all obstacles are filled with small obstacles, each obstacle generates a repulsive potential. Consider the position oiAny small obstacle of (2). The repulsive potential can also be simulated in a number of ways. The potential energy is inversely proportional to the distance, and thus if the underwater autonomous traveling robot approaches an obstacle and the underwater autonomous traveling robot is repelled in the same proportion, the potential energy tends to infinity. The obstacle at a distance rarely draws the attention of the robot. Therefore, the obstacle is only considered before the distance d. Distant obstacles have no effect and thus the repulsive potential is 0. Thereby simplifying the calculation.
Rejection energy of obstacle in path to underwater autonomous navigation robot
The calculation formula of (a) is as follows:
wherein u (·) is a directional unit vector;
total repulsive energy formed by a plurality of obstacles
The force is given by the derivative of the repulsive potential in the opposite direction to the obstacle.
The calculation formula of the synthetic potential U of the underwater autonomous navigation robot by the origin where the path terminal station is located and the obstacle is as follows:
U＝Uatt(x)+Urep；
the calculation formula of the synthetic energy F of the underwater autonomous navigation robot by the origin where the path terminal station is located and the obstacle is as follows:
example 2
As shown in fig. 3, the automatic returning device of the underwater autonomous navigation robot using the method provided in embodiment 1 sequentially includes, from back to front, a propeller 1, a linkage device 2, a rear steering engine 3, a sealed cabin 4, a control circuit 5, a front steering engine 6, an elevator 7 connected to the front steering engine, a camera 8, and a position sensor 9.
The linkage device 2 comprises a motor 2-1 connected with the propeller 1, a battery 2-2 connected with the motor 2-1 and supplying power to the motor 2-1, and a rudder 2-3 positioned at the lower side of the device and used for changing the sailing direction of the device.
While the invention has been described with reference to a preferred embodiment, various changes may be made and equivalents may be substituted for elements thereof without departing from the scope of the invention. In particular, the technical features mentioned in the embodiments can be combined in any manner as long as there is no technical solution conflict. It is intended that the invention not be limited to the particular embodiments disclosed, but that the invention will include all embodiments falling within the scope of the appended claims.
Claims (10)
1. An automatic returning method of an underwater autonomous navigation robot is characterized by comprising the following steps:
s1: after the underwater autonomous navigation robot finishes task execution, a position sensor (9) in the underwater autonomous navigation robot device carries out map creation and positioning on the surrounding environment;
s2: establishing a grid map corresponding to the map obtained in the step S1;
s3: performing expansion operation on the plurality of obstacles described in the grid map obtained in the step S2 by using an expansion operator in mathematical morphology to generate a path of a grid Dirichlet diagram formed by a plurality of parallel boundaries, where the path of the grid Dirichlet diagram formed by the plurality of parallel boundaries constitutes a return path;
s4: performing path vectorization on the return path obtained in the step S3 to obtain a vector path network;
s5: in order to reduce the data storage space and improve the path planning efficiency, the vector path network obtained in the step S4 is subjected to topology to obtain a topology path network;
s6: performing path planning on the topological path network by adopting a Dijkstra algorithm, and selecting an optimal return path;
s7: and performing real-time marine environment benchmark test on the optimal return route obtained in the step S6 by adopting a manual route planning algorithm, if the optimal return route is valid, adopting the optimal return route, and if the optimal return route is invalid, repeating the steps S2-S6.
2. The automatic return voyage method of the underwater autonomous voyage robot as claimed in claim 1, wherein the position sensor (9) adopts a combination of a differential positioning system, a speedometer and a strapdown inertial navigation to map and position the surrounding environment.
3. The method as claimed in claim 1, wherein in the step S3, when the dilation operations are performed on the plurality of obstacles described in the grid map obtained in the step S2 by using dilation operators in mathematical morphology, in order to clarify the distance between the dilation boundary of the underwater autonomous navigation robot and the origin of the path terminal, and to make the boundary extraction easier, a distance measurement structure element mathematical model based on a square form is adopted:
D(m,j)＝min[D(m-1,n-1)＝b,
D(m-1,n)+a，D(m-1,n+1)+b，
D(m,n-1)+a，D(m,n)+0，
D(m,n+1)+a，D(m+1,n-1)+b，
D(m+1,n)+a，D(m+1,n+1)+b]；
wherein, m and n are grid numbers, and a and b are grid distance parameters.
4. The automatic returning method of the underwater autonomous navigation robot of claim 3, wherein in the step S3, the grid map obtained in the step 2 is processed by using a ranging structure element based on an expansion operation, each grid on the map is searched by repeating a cycle, and when a grid is blank, 8 grids around the grid where the underwater autonomous navigation robot is located are scanned by using a mathematical model of the ranging structure element; if the surrounding has obstacles, assigning a value to the blank grid according to the distance measurement structure element mathematical model calculation value, otherwise, not operating the blank grid, and performing the next grid operation; if the next grid of the blank grid has an initial value, the next grid is not processed, and the next grid is continuously scanned to finish the search of the obstacles in the grid map for one time;
each time the obstacle searching is completed, the related obstacle and the origin of the terminal station are uniformly expanded for one circle; and when the whole grid map has no idle grid after the obstacles are searched for many times, the expansion operation is finished.
5. The automatic return voyage method of the underwater autonomous navigation robot of claim 1, wherein the manual path planning algorithm comprises the attraction potential and the attraction energy of the origin of the path terminal station to the underwater autonomous navigation robot, the repulsion potential and the repulsion energy of the obstacle in the path to the underwater autonomous navigation robot, and the resultant potential and the resultant energy of the origin of the path terminal station and the obstacle to the underwater autonomous navigation robot.
6. The automatic returning method of underwater autonomous navigation robot of claim 5, wherein the origin of the path terminal station is the attraction potential U of the underwater autonomous navigation robotatt(x) The calculation formula is as follows:
wherein, the x is the position of the underwater autonomous navigation robot, and the G is the position of the origin of the path terminal station; said | is a euclidean distance calculation function; k isattCalculating a proportionality constant for the attraction potential;
attraction energy F of the origin of the path terminal station to the underwater autonomous navigation robotatt(x) The calculation formula of (a) is as follows:
wherein u (·) is a directional unit vector.
7. The automatic return voyage method of the underwater autonomous voyage robot of claim 5, wherein the obstacle in the path has a repulsive potential to the underwater autonomous voyage robot
The calculation formula of (a) is as follows:
wherein x is the position of the underwater autonomous navigation robot, oiIs the position of the obstacle; the |) is a Euclidean distance calculation function, the krepCalculating a proportionality constant for the repulsive potential;
total repulsive potential formed by multiple obstacles
Rejection energy of the obstacle in the path to the underwater autonomous navigation robot
The calculation formula of (a) is as follows:
wherein u (·) is a directional unit vector;
total repulsive energy formed by a plurality of obstacles
8. The automatic returning method of the underwater autonomous navigation robot of claim 5, wherein the calculation formula of the synthetic potential U of the obstacle to the underwater autonomous navigation robot and the origin of the path terminal is as follows:
U＝Uatt(x)+Urep；
the calculation formula of the synthetic energy F of the underwater autonomous navigation robot by the origin of the path terminal station and the obstacle is as follows:
9. the automatic returning device of the underwater autonomous navigation robot according to the method of any one of claims 1 to 8, characterized by comprising a propeller (1), a chain device (2), a rear steering engine (3), a sealed cabin (4), a control circuit (5), a front steering engine (6), a lifting rudder (7) connected with the front steering engine, a camera (8) and a position sensor (9) from back to front in sequence.
10. The underwater autonomous navigation robot automatic return device according to claim 9, wherein the linkage device (2) includes a motor (2-1) connected to the propeller (1), a battery (2-2) connected to the motor (2-1) and supplying power to the motor (2-1), and a rudder (2-3) on a lower side of the device for changing a navigation direction of the device.
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