AUV cluster underwater energy rescue method based on wireless charging technology

Abstract

The present invention discloses an AUV cluster underwater energy rescue method based on wireless charging technology, which fully considers the actual situation of the AUV underwater rescue system, for example: the AUV at the rescue end uses speed as a constraint to achieve rapid rescue; the AUV at the demand end selects the best charging point with the least energy consumption; and the optimal path planning method under dynamic interference (such as fish school) and other environments. By combining the RRT and DWA path planning methods, fast and effective AUV underwater energy rescue is achieved.

Description

Technical Field

[0001] The present invention relates to the field of underwater wireless rescue technology, and in particular to an AUV cluster underwater energy rescue method based on wireless charging technology. Background Art

[0002] my country has a vast sea area. With the rapid economic development of coastal areas, the gradual increase in population and the continuous expansion of marine development, the protection and sustainable development of the ocean has become a key development strategic direction of the country. Marine scientific and technological innovation is an important support for achieving high-quality development of the marine economy.

[0003] With the development of science and technology, underwater robots (Autonomous underwater vehicle, AUV) have shown a particularly important strategic position in deep-sea exploration, underwater rescue, etc. However, due to the limitations of battery energy storage, the operation time of underwater robots is usually short, and charging in underwater environments is relatively difficult, requiring a closed (waterproof) state. Wireless charging technology can well solve the problem of underwater AUV energy supply. Energy interaction between AUV clusters through wireless charging can realize energy rescue of AUV individuals.

[0004] In the underwater wireless rescue system of the AUV cluster, in addition to the energy matching between the AUV on the demand side and the AUV on the rescue side, path planning is also required for the matched AUV on the demand side and the AUV on the rescue side. The current research on path planning of underwater mobile robots is relatively scarce, mainly because the underwater environment is relatively complex. The present invention fully considers the actual situation based on the AUV underwater rescue system, and realizes fast and effective AUV underwater energy rescue by combining the path planning method based on dynamic bidirectional heuristic RRT* (Rapidly-exploring Random Trees) and DWA (Dynamic window approach). Summary of the invention

[0005] In view of the above problems, the present invention proposes an AUV cluster underwater energy rescue method and device based on wireless charging technology.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] On the one hand, the present invention provides an AUV cluster underwater energy rescue method based on wireless charging technology, comprising the following steps:

[0008] An AUV cluster underwater energy rescue method based on wireless charging technology includes the following steps:

[0009] S1, the demand-side AUV sends a charging request through its own equipment;

[0010] S2, the cloud controller extracts the demand-side AUV information, which includes the underwater geographical location, the estimated mileage to the destination, and the AUV's driving efficiency;

[0011] S3. According to the location of the demand-side AUV, the cloud controller searches for the rescue-side AUV information that can provide charging services within a certain range from the demand-side AUV; the rescue-side AUV information includes the underwater geographical location, the expected mileage, the AUV's driving efficiency, and the AUV's battery information;

[0012] S4, calculating the distance between the demand-side AUV and each rescue-side AUV in the sample;

[0013] S5. Calculate the energy required by the AUV on the demand side;

[0014] S6. Calculate the difference between the energy that can be provided by the rescue end AUV and the energy required by the demand end AUV;

[0015] S7, filtering out sample information of all rescue-end AUVs whose energy difference is greater than 0 as samples of the rescue-end AUV to be selected;

[0016] S8. Use 3D-DBH-RRT* algorithm for global path planning and DWA algorithm for local path planning to avoid obstacles in real time and find the optimal rescue path.

[0017] Furthermore, the demand-side AUV information in step S2 also includes: the amount of electricity required to reach the destination, the AUV number, speed and attitude.

[0018] Furthermore, in step S3, the rescue end AUV information includes: available power, AUV number, speed and attitude.

[0019] Furthermore, in step S8, the global path planning adopts the 3D-DBH-RRT* algorithm, that is, the rescue-end AUV is taken as the starting point and the demand-end AUV is taken as the end point, and the RRT* algorithm is run. Conversely, the demand-end AUV is taken as the starting point and the rescue-end AUV is taken as the end point, and the RRT* algorithm is run. Finally, a heuristic function is added to quickly find a feasible path solution, and then the path is continuously optimized so that the path continues to approach the shortest path; the rescue-end AUV and the demand-end AUV share maps and path information.

[0020] Furthermore, in step S8, the DWA algorithm is deployed on the demand-side AUV and the rescue-side AUV respectively. The DWA algorithm deployed on the rescue-side AUV is divided into a fast mode and a power-saving mode. The DWA algorithm deployed on the demand-side AUV is in a power-saving mode and is only run when the rescue-side AUV cannot search for a new feasible path within the limit distance through the 3D-DBH-RRT* algorithm. The limit distance is expressed as:

[0021]

[0022] d1 is the diameter of the AUV model, which is the minimum circle diameter that can contain the AUV model, and the center of the circle is the geometric midpoint P of the AUV model. AUV =(A x ,A y );d2 is the obstacle expansion distance, d2=r1+r2, r1 is the minimum circle radius that can contain the obstacle, and the center of the circle is set to P OB＝ (O x ,O y ), r2 is the AUV model radius.

[0023] Furthermore, in step S8, the rescue-side AUV deploys the 3D-DBH-RRT* algorithm as follows: whenever the path planned by the rescue-side AUV is blocked by an obstacle, the path will be replanned with the current coordinates as the starting point and the demand-side AUV as the end point; if no feasible solution is found before the rescue-side AUV reaches the obstacle, the DWA algorithm will be executed to complete dynamic obstacle avoidance; if a feasible path solution is found, it will replace the original path.

[0024] Furthermore, before the rescue AUV sets out, the 3D-DBH-RRT algorithm is initialized.

[0025] Furthermore, in step S8, the 3D-DBH-RRT* algorithm for the deployment of the demand-side AUV is as follows: adopting the power-saving mode, sampling and searching only when the AUV is within the rescue radius range (the maximum distance that the demand-side AUV can continue to move based on the current power) from the rescue-side AUV, dynamically improving the path, avoiding dynamic obstacles, and synchronizing the map and path information with the rescue-side AUV. Where W is the current power of the AUV battery, P is the minimum power, and V is the speed corresponding to the minimum power.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] The AUV cluster underwater energy rescue method based on wireless charging technology based on decoupled representation proposed in this invention fully considers the actual situation based on the AUV underwater rescue system, for example: the AUV at the rescue end uses speed as a constraint to achieve rapid rescue; the AUV at the demand end selects the best charging point with the least energy consumption; and the optimal path planning method under dynamic interference (such as fish schools) and other environments. By combining the RRT and DWA path planning methods, fast and effective AUV underwater energy rescue is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention, and for those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0029] Figure 1 This is a diagram of an application scenario of the AUV cluster underwater energy rescue method based on wireless charging technology provided in an embodiment of the present invention.

[0030] Figure 2 A flow chart of an AUV cluster underwater energy rescue method based on wireless charging technology provided in an embodiment of the present invention.

[0031] Figure 3 This is an overall flow chart of the path planning algorithm for the AUV cluster underwater energy rescue method provided by an embodiment of the present invention.

[0032] Figure 4 A flow chart of the DWA algorithm of the AUV cluster underwater energy rescue method provided in an embodiment of the present invention.

[0033] Figure 5 This is a diagram of the AUV path planning under ideal working conditions provided by an embodiment of the present invention.

[0034] Figure 6 This is a path planning situation in which the demand-side AUV fails to avoid obstacles in time (is trapped by obstacles) according to an embodiment of the present invention.

[0035] Figure 7 A schematic diagram of the limit distance provided by an embodiment of the present invention.

[0036] Figure 8 This is a diagram showing the effect of the three-dimensional dynamic bidirectional heuristic RRT* algorithm provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0037] In order to better understand the technical solution, the method of the present invention is described in detail below with reference to the accompanying drawings.

[0038] The present invention proposes an underwater wireless charging energy interaction model based on AUV clusters and a path planning optimization method in a dynamic interference environment (such as a school of fish). Figure 1 As shown in the figure, the demand-side AUV wants to reach the destination, but the power of the demand-side AUV is lower than the threshold (such as only 20%), which is not enough to reach the destination. A charging request can be issued by the device itself. The energy interaction model can search and match a suitable charging end AUV that can provide charging according to the geographical location (underwater location) of the client AUV, and provide energy replenishment to the client AUV nearby.

[0039] The present invention proposes an AUV cluster underwater energy rescue method based on wireless charging technology. Figure 2 As shown, the steps are as follows:

[0040] S1. When the power of the demand-side AUV is insufficient (lower than a threshold, such as 20%), the demand-side AUV sends a charging request through its own device.

[0041] S2. The cloud controller extracts the demand-side AUV information.

[0042] Demand-side AUV information includes:

[0043] Underwater geographic location (latitude and longitude, depth), estimated mileage to the destination, power required to reach the destination, AUV number, speed, attitude and AUV driving efficiency.

[0044] S3. According to the location of the demand-side AUV, the cloud controller searches for information of the rescue-side AUV that can provide charging services within a certain range from the demand-side AUV.

[0045] The rescue end AUV information includes:

[0046] Underwater geographic location (latitude and longitude, depth), estimated mileage, available power, AUV number, speed, attitude, AUV driving efficiency and AUV battery information (total battery storage capacity, discharge depth, cycle life).

[0047] Energy rescue model custom variables:

[0048] Charging Mode: Wireless Charging Efficiency.

[0049] Then the energy rescue model is used to match the appropriate rescue end AUV. The specific process is as follows:

[0050] S4, calculating the distance between the demand-side AUV and each rescue-side AUV in the sample;

[0051] S5. Calculate the energy required by the AUV on the demand side;

[0052] S6. Calculate the difference between the energy that can be provided by the rescue end AUV and the energy required by the demand end AUV;

[0053] S7, filtering out sample information of all rescue-end AUVs whose energy difference is greater than 0 as samples of the rescue-end AUV to be selected;

[0054] S8. Use 3D-DBH-RRT* algorithm for global path planning and DWA algorithm for local path planning to avoid obstacles in real time and find the optimal rescue path.

[0055] like Figure 3 As shown in the figure, based on the path planning of the AUV cluster underwater energy rescue system, the DWA algorithm is combined with the dynamic bidirectional heuristic RRT* algorithm to perform multi-target path planning in the complex dynamic interference environment underwater, and combined with the 3D dynamic heuristic optimization algorithm, the optimal path selection that meets the underwater wireless energy rescue is realized. The power of the AUV on the demand side is used as the constraint condition for the path planning of the AUV on the demand side, so that the AUV on the demand side can meet the AUV on the rescue side in the optimal path within the remaining energy range; the speed of the AUV on the rescue side is used as the constraint condition for the path planning of the AUV on the charging side, so that the AUV on the rescue side can provide rescue at the fastest speed. In this process, both the AUV on the rescue side and the AUV on the demand side must perfectly avoid obstacles (fish schools, etc.).

[0056] Specifically, path planning is divided into global path planning and local path planning. Specifically, the global path planning adopts the 3D-DBH-RRT* algorithm, that is, the rescue end AUV is used as the starting point and the demand end AUV is used as the end point, and the RRT* algorithm is run. Conversely, the demand end AUV is used as the starting point and the rescue end AUV is used as the end point, and the RRT* algorithm is run. Finally, a heuristic function is added to quickly find a feasible path solution, and then the path is continuously optimized to make the path approach the shortest path. The local path planner adopts the DWA algorithm, follows the feasible path found by the global path planner, and has the function of real-time obstacle avoidance. The rescue end AUV and the demand end AUV share maps and path information.

[0057] DWA is used by underwater AUVs to cope with underwater dynamic interference environments and avoid obstacles such as schools of fish in real time. It is deployed on the demand side and rescue side. Figure 4 As shown in Figure 2, the DWA algorithm is deployed on the demand-side AUV and the rescue-side AUV respectively.

[0058] DWA algorithm deployed by the rescue AUV:

[0059] According to the different requests of the AUV on the demand side, it is divided into fast mode and power saving mode. The fast mode takes into account the performance limitations of the rescue AUV itself (such as maximum and minimum speeds, maximum acceleration, etc.), with good acceleration performance and fast speed. In the power saving mode, energy consumption limitations are considered, which will reduce acceleration and maximum speed, and manifest as low power and low speed. Ideal working conditions are as follows: Figure 5 shown.

[0060] DWA algorithm for AUV deployment on the demand side:

[0061] The demand-side AUV has a low battery level of only 20%, so only the power saving mode is considered.

[0062] The demand-side AUV only runs the DWA algorithm when the rescue-side AUV cannot find a new feasible path within the limit distance through the 3D-DBH-RRT* algorithm, so as to avoid obstacles in time and combine with the rescue-side AUV. This avoids the situation where the rescue-side AUV cannot combine with the demand-side AUV for charging because it is trapped by obstacles (such as Figure 6 As shown in the figure, the rescue AUV will make many circles to wait for the demand AUV to get out of trouble.

[0063] Since the operating condition of the 3D-DBH-RRT* algorithm deployed on the demand side is within the rescue radius, the rescue radius (i.e., the maximum distance that the AUV can continue to move) s is first defined as the current power W of the AUV battery, the minimum power P, and the corresponding speed V at this power:

[0064]

[0065] Then define the limit distance. First, define the AUV model diameter, which is the minimum circle diameter d1 that can contain the AUV model, and the center of the circle is the geometric midpoint P of the AUV model. AUV =(A x ,A y ). Secondly, the obstacle expansion distance is defined, which is to expand a distance outward from the original obstacle, and define the obstacle expansion distance d2: Let the minimum circle radius that can contain the obstacle be r1, and the center of the circle be P OB＝ (O x ,O y ). The radius of the AUV model is r2, then d2 = r1 + r2. The expansion distance is set to leave a certain margin for the operation of the DWA algorithm. If there is no such distance, when switching from the 3D-DBH-RRT* algorithm to the DWA algorithm, it will not be able to turn in time and collide with obstacles. Figure 7 As shown, the final limit distance is defined as:

[0066]

[0067] The three-dimensional dynamic bidirectional heuristic RRT* algorithm (referred to as 3D-DBH-RRT* algorithm) is as follows:

[0068] 3D-DBH-RRT algorithm initialization:

[0069] Before the rescue AUV sets out, the algorithm will be run for a period of time. The reasons are: ① to obtain a feasible path solution from the rescue AUV to the demand AUV. ② to continuously optimize the path to make it close to the shortest path. Secondly, the rescue AUV and the demand AUV share maps and path information to maintain algorithm efficiency.

[0070] The rescue end AUV deploys the 3D-DBH-RRT* algorithm as follows:

[0071] Because the position of the rescue AUV is constantly changing, and the planned path may be blocked by dynamic obstacles in the water, whenever the path planned by the rescue AUV is blocked by an obstacle, it will replan the path with the current coordinates as the starting point and the demand AUV as the end point. Since the initialization operation has been completed, the process will not take too much time; if no feasible solution is found before the rescue AUV reaches the obstacle, the DWA algorithm will be executed to complete dynamic obstacle avoidance; if a feasible path solution is found, it will replace the original path.

[0072] The 3D-DBH-RRT* algorithm for AUV deployment on the demand side is:

[0073] Since the demand-side AUV has low battery, it will only start moving within the rescue radius of the rescue-side AUV (the maximum distance that the demand-side AUV can continue to move based on the current battery), so it must adopt power saving mode and the algorithm will not run before this. When running the algorithm, it is necessary to maintain the same short path as the rescue-side AUV, that is, synchronize the map and path information with the rescue-side AUV.

[0074] The effect of the three-dimensional dynamic bidirectional heuristic RRT* algorithm is shown in the figure below: Figure 8 shown.

[0075] The AUV cluster underwater energy rescue method based on wireless charging technology based on decoupled representation proposed in this invention fully considers the actual situation based on the AUV underwater rescue system, for example: the AUV at the rescue end uses speed as a constraint to achieve rapid rescue; the AUV at the demand end selects the best charging point with the least energy consumption; and the optimal path planning method under dynamic interference (such as fish schools) and other environments. By combining the RRT and DWA path planning methods, fast and effective AUV underwater energy rescue is achieved.

[0076] The above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention are included in the protection scope of the present invention.

Claims

1. An AUV cluster underwater energy rescue method based on wireless charging technology, characterized in that: The following steps are involved: S1, the demand-side AUV sends a charging request through its own equipment; S2, the cloud controller extracts the demand-side AUV information, which includes the underwater geographical location, the estimated mileage to the destination, and the AUV's driving efficiency; S3. According to the location of the demand-side AUV, the cloud controller searches for the rescue-side AUV information that can provide charging services within a certain range from the demand-side AUV; the rescue-side AUV information includes the underwater geographical location, the expected mileage, the AUV's driving efficiency, and the AUV's battery information; S4, calculating the distance between the demand-side AUV and each rescue-side AUV in the sample; S5. Calculate the energy required by the AUV on the demand side; S6. Calculate the difference between the energy that can be provided by the rescue end AUV and the energy required by the demand end AUV; S7, filtering out sample information of all rescue-end AUVs whose energy difference is greater than 0 as samples of the rescue-end AUV to be selected; S8, use 3D-DBH-RRT* algorithm for global path planning, and use DWA algorithm for local path planning to avoid obstacles in real time and find the optimal rescue path; In step S8, the global path planning adopts the 3D-DBH-RRT* algorithm, that is, the rescue end AUV is taken as the starting point and the demand end AUV is taken as the end point, and the RRT* algorithm is run. Conversely, the demand end AUV is taken as the starting point and the rescue end AUV is taken as the end point, and the RRT* algorithm is run. Finally, a heuristic function is added to quickly find a feasible solution, and then the path length is continuously optimized to make the path approach the shortest path. The rescue end AUV and the demand end AUV share the map and path information; In step S8, the DWA algorithm is deployed on the demand-side AUV and the rescue-side AUV respectively; the DWA algorithm deployed on the rescue-side AUV is divided into a fast mode and a power-saving mode; the DWA algorithm deployed on the demand-side AUV is in power-saving mode and is only run when the rescue-side AUV cannot search for a new feasible path within the limit distance through the 3D-DBH-RRT* algorithm, where the limit distance is expressed as: d1 is the diameter of the AUV model, which is the minimum circle diameter that can contain the AUV model, and the center of the circle is the geometric midpoint P of the AUV model. AUV =(A x ,A y );d2 is the obstacle expansion distance, d2=r1+r2, r1 is the minimum circle radius that can contain the obstacle, and the center of the circle is set to P OB =(O X ,O y ), r2 is the AUV model radius.

2. The AUV cluster underwater energy rescue method based on wireless charging technology according to claim 1 is characterized in that: The demand-side AUV information in step S2 also includes: the amount of electricity required to reach the destination, the AUV number, speed and attitude.

3. The AUV cluster underwater energy rescue method based on wireless charging technology according to claim 1 is characterized in that: In step S3, the rescue end AUV information includes: available power, AUV number, speed and attitude.

4. The AUV cluster underwater energy rescue method based on wireless charging technology according to claim 1 is characterized in that: In step S8, the rescue AUV also deploys the 3D-DBH-RRT* algorithm: whenever the path planned by the rescue AUV is blocked by an obstacle, it will replan the path with the current coordinates as the starting point and the demand AUV as the end point; if no feasible solution is found before the rescue AUV reaches the obstacle, the DWA algorithm will be executed to complete dynamic obstacle avoidance; if a feasible path solution is found, it will replace the original path.

5. The AUV cluster underwater energy rescue method based on wireless charging technology according to claim 4 is characterized in that: Before the rescue AUV sets out, the 3D-DBH-RRT algorithm is initialized.

6. The AUV cluster underwater energy rescue method based on wireless charging technology according to claim 1 is characterized in that: In step S8, the demand-side AUV also deploys the 3D-DBH-RRT* algorithm: adopting power saving mode, only starting to move within the rescue radius of the rescue-side AUV, and synchronizing the map and path information with the rescue-side AUV. Where W is the current power of the AUV battery, P is the minimum power, and V is the speed corresponding to the minimum power.

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