Dynamic suspension control method for terrain-adaptive underwater dredging robot

Abstract

The invention relates to the technical field of robot suspension control, and discloses a terrain-adaptive underwater dredging robot dynamic suspension control method, which comprehensively considers a first expected value and a second expected value of the air volume of an air bag, and combines various key parameters such as water temperature, water depth, robot posture angle, hardness coefficient, robot speed and the like. And accurately calculating the adjusted air volume of the airbag, and finally obtaining the final air volume of the airbag according to the initial air volume of the airbag. Firstly, by dynamically adjusting the air volume of the air bag, the robot can keep the optimal working state under different topographic conditions, and it is ensured that the dredging tool can effectively make contact with and clean sediments. Particularly in a soft sediment area, the air volume of the air bag is increased, so that the robot can be prevented from falling into the sediment, and jamming or stagnation caused by insufficient buoyancy is avoided; and on the hard substrate, the robot can make contact with the sediment more tightly by reducing the air volume of the air bag, and the working efficiency of the desilting tool is improved.

Description

Technical Field

[0001] The invention relates to the technical field of robot suspension control, and in particular to a terrain-adaptive underwater dredging robot dynamic suspension control method. Background Art

[0002] The traditional way of maintaining the depth of port channels is mainly to use dredgers to clear the silt. As the distance of mud dumping increases to dozens of kilometers, the cost of clearing the silt increases sharply. In addition, there are problems such as low efficiency, high cost, and environmental pollution, which cannot meet the requirements. The basic idea of ​​the intelligent guarantee technology for the depth of port channels based on natural power is to increase the turbulent energy of the water body through artificial disturbance, compensate for the reduction of the water flow carrying capacity caused by the excavation of the port channel or the water blocking of the building, and then achieve the depth maintenance goal of "transit without falling and natural balance" of the silt in the water body. Compared with the traditional "regular dredging" dredging method, it has the following advantages: First, it uses natural water flow instead of long-distance transportation of dredgers, which significantly reduces the cost of water depth maintenance and carbon emissions; second, the disturbance is completed at the bottom of the water, which can achieve zero impact on ship operations; third, normalized disturbance avoids pollutant enrichment caused by sediment sedimentation and consolidation. In order to meet the water depth maintenance target, a set of underwater dredging robots has been designed and developed with existing technology. The robot can be remotely controlled to rise and fall through air bags, and can continue to operate after sinking to the bottom. The power cable and signal cable are connected to the equipment through a winch to provide power supply and signal transmission. The silt reduction operation is completed through a mobile equipment platform, and the silt is plowed and brushed, and then the silt is pushed to the water exchange layer by a plug pump.

[0003] However, the buoyancy adjustment range of existing underwater dredging equipment is limited. Moreover, the underwater dredging robot has a heavy weight and is easy to sink in the soft siltation area. The existing technology designs a buoyancy box with a buoyancy control mechanism (a water ballast tank in the submarine field), and an air bag is arranged in the buoyancy box, and air is filled into the air bag. The existing suspension control system usually only considers the influence of a single factor on the air volume of the air bag, while ignoring the coupling effect between multiple factors. In fact, multiple factors such as terrain hardness, power change, water temperature, robot posture angle, speed and water depth will work together to affect the change of the air volume of the air bag. For example, when the terrain becomes soft, the robot may increase the air volume of the air bag to provide more buoyancy, but if the water temperature rises or the robot's tilt angle increases at this time, the elasticity of the air bag material will change, further affecting the buoyancy, resulting in a decrease in control accuracy. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a terrain-adaptive underwater dredging robot dynamic suspension control method, comprising: Step 1: The robot performs underwater dredging and terrain scanning to obtain real-time terrain scanning results; the real-time power of the transmission device is obtained through the power sensor of the robot; the real-time terrain scanning results include one or more key parameters of the underwater terrain; Step 2, dynamically suspending the air volume of the airbag of the floating caisson according to the real-time terrain scanning result and the real-time power of the transmission device; the dynamic suspension control mode includes a first control mode and a second control mode; the first control mode is: when the robot scans that the terrain becomes softer and / or the power of the transmission device increases, the air volume of the airbag is automatically increased; the second adjustment control mode is: when the robot detects that the terrain becomes harder and / or the power of the transmission device decreases, the air volume of the airbag is automatically reduced; According to the real-time terrain scanning results and the real-time power of the transmission device, the air volume of the airbag of the floating caisson is dynamically controlled, including: Step 21, calculating a first expected value of the air volume of the airbag through the real-time terrain scanning result; Step 22, calculating a second expected value of the air volume of the air bag by using the real-time power of the transmission device; Step 23, constructing a relationship model according to the first expected value of the air volume of the air bag and the second expected value of the air volume of the air bag to obtain the adjusted air volume of the air bag; Step 24, obtaining the original air volume of the airbag, obtaining the final air volume of the airbag according to the original air volume of the airbag and the adjusted air volume of the airbag, and controlling the robot to perform floating and sinking dredging underwater by the final air volume of the airbag.

[0005] Further, calculating the first expected value of the air volume of the airbag according to the real-time terrain scanning result specifically includes: Step 211, obtaining the terrain hardness index at the current moment according to the real-time terrain scanning result; Step 212, preset a reference hardness index; Step 213, calculating a first expected value of the air volume of the airbag at the current moment according to the terrain hardness index at the current moment and the reference hardness index.

[0006] Further, calculating the second expected value of the air volume of the airbag by the real-time power of the transmission device specifically includes: Step 221, obtaining the real-time power of the transmission device over a period of time in the past to obtain a power index; Step 222, preset a reference power index; Step 223, calculating a second expected value of the air volume of the airbag at the current moment according to the power index at the current moment and the reference power index.

[0007] Further, according to the first expected value of the air volume of the air bag and the second expected value of the air volume of the air bag, a relationship model is constructed to obtain the air volume of the air bag adjustment, which specifically includes: Step 231, calculating the correlation coefficient between the first expected value of the air volume of the air bag and the second expected value of the air volume of the air bag; Step 232, obtaining a relationship model according to the correlation coefficient, the first expected value of the air volume of the air bag and the second expected value of the air volume of the air bag, and calculating the air volume of the air bag through the relationship model.

[0008] Furthermore, the calculation formula for adjusting the air volume of the airbag is: ; In the formula, Represents the airbag adjusting the air volume, represents the first expected value of the airbag air volume at the current time t, represents the second expected value of the airbag air volume at the current time t, Represents the correlation coefficient.

[0009] Furthermore, the calculation formula for the final air volume of the airbag is: ; In the formula, represents the final air volume of the airbag, represents the original air volume of the airbag, Represents the airbag adjusting the air volume, represents the time constant, .

[0010] Furthermore, all key parameter values ​​at the current moment are obtained, the key parameter average values ​​are obtained according to the historical key parameter values, and the terrain hardness index is obtained according to the key parameter values ​​and the key parameter average values: ; The calculation formula for the first expected value of the air volume of the air bag is: ; In the formula, Represents the terrain hardness index at the current time t, Represents the value of the i-th key parameter at the current time t, represents the average value of key parameters, and n represents the total number of key parameters; represents the first expected value of the airbag air volume at the current time t, represents the first expected coefficient, represents a nonlinear function, Represents the reference hardness index.

[0011] Furthermore, the calculation formula of the second expected value of the air volume of the airbag is: ; In the formula, represents the second expected value of the airbag air volume at the current time t, represents the power index at the current time t, represents the reference power index, represents a nonlinear function, represents the second expected coefficient.

[0012] Furthermore, an initial hardness coefficient is preset, and the water temperature of the robot's position at the current time t and the robot's posture angle are obtained; and a first expected coefficient is calculated based on the initial hardness coefficient, the water temperature, and the robot's posture angle.

[0013] Furthermore, an initial power coefficient is preset, and the robot speed and water depth of the robot at the current time t are obtained; and a second expected coefficient is calculated based on the initial power coefficient, the robot speed, and the water depth.

[0014] The embodiments of the present invention have the following technical effects: The present invention comprehensively considers the first expected value and the second expected value of the air volume of the air bag, and combines multiple key parameters such as water temperature, water depth, robot posture angle, hardness coefficient, robot speed, etc., to accurately calculate the adjusted air volume of the air bag, and finally obtains the final air volume of the air bag according to the initial air volume of the air bag. First, by dynamically adjusting the air volume of the air bag, the robot can maintain the best working state under different terrain conditions, ensuring that the dredging tools can effectively contact and clean the sediment. Especially in soft sediment areas, increasing the air volume of the air bag can prevent the robot from sinking into the sediment and avoid jamming or stagnation due to insufficient buoyancy; on hard substrates, reducing the air volume of the air bag can make the robot contact the sediment more closely, thereby improving the working efficiency of the dredging tools. This adaptive suspension control mechanism significantly improves the overall efficiency of dredging operations.

[0015] Secondly, reasonable air volume adjustment of the airbag not only improves the dredging efficiency, but also significantly reduces energy consumption. By reducing unnecessary inflation or deflation operations, the system can extend the robot's operating time and reduce maintenance costs.

[0016] Third, dynamic suspension control enables the robot to move freely in waters of different depths and flexibly respond to complex underwater environments. Whether in shallow or deep waters, the robot can maintain a stable posture to ensure the continuity and efficiency of dredging operations. Especially under variable water flow conditions, the robot can maintain stability by adjusting the air volume of the airbag in real time to avoid posture loss caused by water flow impact, further improving the safety and reliability of the operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 The present invention provides a flow chart of a method for controlling the dynamic suspension of an underwater dredging robot that is adaptive to terrain. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work belong to the scope of protection of the present invention.

[0020] This embodiment integrates high-precision sensors for real-time monitoring of key environmental parameters such as water depth, terrain features, and robot features, and processes data on these features to automatically adjust the operating mode. Through continuous optimization, the ability of this embodiment to respond to different dredging tasks and environments is improved, and the operating strategy is adjusted in real time, thereby achieving efficient and accurate maintenance operations and reducing dependence on human intervention.

[0021] Application scenarios of this embodiment: Mainly used in daily deep dredging operations in coastal and inland ports and waterways. This system can play a significant role, especially in port and waterway areas where the sand-carrying capacity of the water flow is reduced, the sediment is seriously deposited, and the traditional dredging operation is costly and inefficient.

[0022] The application environment of this embodiment is as follows: it is suitable for port channels with a water depth ranging from several meters to tens of meters, including open waters, narrow channels, complex terrains, etc.; it is suitable for natural water flow environments with different flow rates and directions, including tides, rivers, etc.; it is suitable for various types of sediment, including suspended sediment, bedload sediment and deposited consolidated sediment.

[0023] Figure 1 Flowchart of a terrain-adaptive underwater dredging robot dynamic suspension control method provided by an embodiment of the present invention. Figure 1 , including: Step 1: The robot performs underwater dredging and terrain scanning to obtain real-time terrain scanning results; the real-time power of the transmission device is obtained through the power sensor of the robot; the real-time terrain scanning results include one or more key parameters of the underwater terrain.

[0024] Start the robot and enter the work area to immediately scan the terrain and obtain the terrain scanning results. Use the following sensors to obtain real-time terrain scanning results: Use multi-beam sonar to draw a three-dimensional map of the underwater terrain and obtain information such as the type and thickness of the bottom. Use lidar to measure the surface features of the underwater terrain with high precision, such as slope and undulation. Use pressure sensors to measure water depth. Use tactile sensors to sense the hardness of the bottom. Use temperature sensors to measure water temperature. Use flow sensors to measure water flow speed. Use posture sensors to monitor the robot's posture angle underwater. Use angle sensors to monitor the robot's movement speed.

[0025] The key parameters of underwater topography include at least substrate type, substrate thickness, substrate density, and substrate hardness.

[0026] This embodiment is for dredging soft and hard bottom types of terrain. Preferably, the soft bottom type is bedload silt, and the hard bottom type is sedimentary consolidation silt.

[0027] The robot is also equipped with power sensors to monitor the power consumption of transmission devices (such as thrusters, dredging tools, etc.) in real time.

[0028] Step 2, dynamically suspending the air volume of the airbag of the floating box according to the real-time terrain scanning results and the real-time power of the transmission device; the dynamic suspension control method includes a first control method and a second control method; the first control method is: when the robot scans that the terrain becomes softer and / or the power of the transmission device increases, the air volume of the airbag is automatically increased; the second adjustment control method is: when the robot detects that the terrain becomes harder and / or the power of the transmission device decreases, the air volume of the airbag is automatically reduced.

[0029] According to the real-time terrain scanning results and the real-time power of the transmission device, the air volume of the airbag of the floating caisson is dynamically controlled, including: Step 21, calculating a first expected value of the air volume of the airbag through real-time terrain scanning results.

[0030] Step 211, obtaining a terrain hardness index according to the real-time terrain scanning result.

[0031] The real-time terrain scanning results are used to obtain all key parameter values ​​at the current time t, the key parameter average values ​​are obtained based on the historical key parameter values, and the terrain hardness index is obtained based on the key parameter values ​​and the key parameter average values: ; Represents the terrain hardness index at the current time t, Represents the value of the i-th key parameter at the current time t, represents the average value of key parameters, and n represents the total number of key parameters.

[0032] Step 212: preset a reference hardness index.

[0033] The robot's operating data under different terrain conditions are obtained based on historical data. By analyzing the robot's operating data under different terrain conditions, the average hardness index of different types of substrates (such as mud, clay, gravel, etc.) is statistically calculated, and the average hardness index is used as a reference hardness index.

[0034] Step 213, calculating a first expected value of the air volume of the airbag at the current moment according to the terrain hardness index at the current moment and the reference hardness index.

[0035] ; ; In the formula, represents the first expected value of the airbag air volume at the current time t, represents the first expected coefficient, represents a nonlinear function, represents the reference hardness index, , , are adjustment factors and are constants.

[0036] Specifically, Used to adjust the first expected value, , which means that the terrain hardness index at the current moment is higher than the reference hardness index , the geology gradually hardens, is an exponential decay function, Reduce, Decrease, that is, the air volume of the airbag decreases. , which means that the terrain hardness index at the current moment is higher than the reference hardness index , the geology becomes soft, is the terrain hardness index at the current moment and the reference hardness index The difference between times.

[0037] Furthermore, the formula group The first formula is adjusted for the first expected value of geological hardening (deposited and consolidated sediment in hard geological types), which improves the mobility and stability of the robot on soft substrates, reduces operation interruptions caused by getting stuck in sediment, and increases dredging efficiency, especially in areas with large areas of bedload sediment. Dredging operations can be carried out quickly and effectively, and the silt reduction efficiency of suspended sediment can reach 1800m³ / h, significantly improving overall operation efficiency. The second formula is adjusted for the first expected value of geological softening (bedload sediment in soft substrate types). In hard substrate areas, the robot can better fit the ground, improve the cutting and excavation efficiency of dredging tools, reduce unnecessary buoyancy, reduce energy consumption, extend operation time, and improve dredging accuracy. The silt reduction efficiency of deposited and consolidated sediment is 450m³ / h. Although the efficiency is lower than that of suspended sediment, it can still ensure a high operation quality in complex environments. Preferably, , .

[0038] An initial hardness coefficient is preset, and the water temperature at the robot's position at the current time t and the robot's posture angle are obtained; a first expected coefficient is calculated based on the initial hardness coefficient, water temperature, and robot posture angle.

[0039] The real-time terrain scanning results at each historical moment are obtained, and several terrain hardness indices are calculated according to the terrain hardness index formula for each historical moment. All terrain hardness indices are averaged and normalized as the initial hardness coefficient.

[0040] ; represents the initial hardness coefficient, represents the water temperature of the robot at the current time t, represents the robot posture angle at the current time t, , Represent the weight coefficients respectively.

[0041] Step 22, calculating a second expected value of the air volume of the airbag by using the real-time power of the transmission device.

[0042] Step 221, obtaining the real-time power of the transmission device over a period of time in the past, and obtaining a power index.

[0043] When you need to obtain the power index at the current moment, obtain the real-time power of the transmission device in the past period of time and the real-time power at the current moment, and get the current moment The power index.

[0044] ; represents the real-time power at the mth moment, and Z represents the total number of acquisition moments.

[0045] Step 222: preset a reference power index.

[0046] The robot's operating data under different terrain conditions are obtained from historical data. By analyzing the robot's operating data under different terrain conditions, the average power index of different types of bottom materials (such as silt, clay, gravel, etc.) is calculated, and the average power index is used as the reference power index.

[0047] Step 223, calculating a second expected value of the air volume of the airbag at the current moment according to the power index at the current moment and the reference power index.

[0048] ; In the formula, represents the second expected value of the airbag air volume at the current time t, represents the power index at the current time t, represents the reference power index, represents a nonlinear function, represents the second expected coefficient.

[0049] The initial power coefficient is preset, and the robot speed and water depth of the robot at the current time t are obtained; the second expected coefficient is calculated according to the initial power coefficient, the robot speed, and the water depth.

[0050] The real-time power at each historical moment is obtained and the power index is obtained. All power indexes are averaged and normalized as the initial power coefficient.

[0051] ; represents the initial power factor, represents the water depth of the robot at the current time t, represents the robot speed at the current time t, , Represent weight factors respectively.

[0052] Through the dual-path prediction mechanism based on the terrain hardness index and power index, the robot can adjust the air volume of the airbag more accurately, thereby achieving optimal suspension control and dredging efficiency. Specifically, the first expected value is obtained based on the initial hardness index, the reference hardness index and the first expected coefficient at the current moment. The first expected coefficient takes into account the water temperature and posture angle, which enables the robot to dynamically adjust the buoyancy under different bottom conditions to ensure that it can maintain a stable posture and effective contact with the dredging tools on soft or hard bottoms. The second expected value is based on the power index and the reference power index at the current moment and the second expected coefficient. The second expected coefficient takes into account the water depth and robot speed at the same time, aiming to optimize energy consumption and propulsion efficiency, ensuring that the robot can still operate efficiently in complex water flows and waters of different depths. The biggest advantage of this dual-path design is that it can comprehensively consider the terrain characteristics and power requirements, and provide a more comprehensive and accurate airbag air volume adjustment strategy. Finally, by comprehensively analyzing the first expected value and the second expected value of the airbag air volume, the robot can obtain a more reasonable final adjustment volume, which not only ensures the stability and efficiency of the dredging operation, but also saves energy to the maximum extent and prolongs the operation time. This design not only improves the robot's adaptability and flexibility, but also enhances its safety and reliability in complex environments, providing a solid guarantee for the successful implementation of underwater dredging tasks.

[0053] Step 23, constructing a relationship model according to the first expected value of the air volume of the air bag and the second expected value of the air volume of the air bag to obtain the adjusted air volume of the air bag.

[0054] Step 231, calculating the correlation coefficient between the first expected value of the air volume of the air bag and the second expected value of the air volume of the air bag.

[0055] Collect the first expected value of the airbag air volume and the second expected value of the airbag air volume at each moment calculated based on the above steps, and calculate the mean of the first expected value of the airbag air volume and the mean of the second expected value of the airbag air volume based on these data. Finally, based on the first expected value of the airbag air volume, the second expected value of the airbag air volume, the mean of the first expected value of the airbag air volume, and the mean of the second expected value of the airbag air volume at each moment, calculate the correlation coefficient to express the correlation strength between the first expected value of the airbag air volume and the second expected value of the airbag air volume.

[0056] Step 232, obtaining a relationship model according to the correlation coefficient, the first expected value of the air volume of the air bag and the second expected value of the air volume of the air bag, and calculating the air volume of the air bag through the relationship model.

[0057] ; In the formula, Represents the airbag adjusting the air volume, represents the first expected value of the airbag air volume at the current time t, represents the second expected value of the airbag air volume at the current time t, Represents the correlation coefficient.

[0058] Based on the first expected value and the second expected value, the air volume of the airbag is adjusted by calculating the correlation coefficient. This calculation method can significantly improve the adaptability and stability of the robot in complex underwater environments. Specifically, by comprehensively considering the terrain hardness and power requirements, and introducing the correlation coefficient to measure the interaction strength between the two, the air volume of the airbag can be adjusted more accurately. This not only ensures the stable suspension and efficient operation of the robot under different bottom conditions, but also maintains the optimal buoyancy state under changing water depth and water flow conditions, thereby improving overall operation efficiency and safety.

[0059] Step 24, obtain the original air volume of the airbag, obtain the final air volume of the airbag according to the original air volume of the airbag and the adjusted air volume of the airbag, and control the robot to perform floating and sinking dredging underwater by the calculated final air volume of the airbag.

[0060] ; In the formula, represents the final air volume of the airbag, represents the original air volume of the airbag, Represents the airbag adjusting the air volume, represents the time constant, .

[0061] The time constant is used to control the rate of change of the final air volume of the airbag. Specifically, the time constant determines the transition speed from the current air volume of the airbag to the adjusted air volume of the airbag. .

[0062] By comprehensively considering the current air volume of the airbag and the air volume adjusted by the airbag, and introducing a time constant to calculate the final volume, this design can significantly improve the adaptability and operating efficiency of the underwater dredging robot, and the suspended sediment reduction efficiency is 1800m 3 / h, the sedimentation and consolidation silt reduction efficiency is 450m 3 / h. Specifically, this method not only ensures the stable suspension of the robot under different bottom conditions and power requirements, but also can smoothly adjust the buoyancy under changing water depth and water flow conditions, avoiding the impact of drastic changes on the robot's posture and the operation of the dredging tools. By gradually approaching the target volume, the robot can control its buoyancy state more accurately, reduce energy consumption, extend the operation time, and improve the dredging effect. In addition, the introduction of the time constant makes the adjustment process smoother, avoiding instability or overshoot caused by rapid changes, thereby enhancing the safety and reliability of the robot. Ultimately, this dual-factor control strategy based on current volume and adjusted volume provides the robot with a more intelligent and efficient suspension control mechanism in complex underwater environments, ensuring that it can maintain the best working state under various working conditions.

[0063] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the technical solutions of the embodiments of the present invention.

Claims

1. A terrain-adaptive underwater dredging robot dynamic suspension control method, characterized in that: include: Step 1: The robot performs underwater dredging and terrain scanning to obtain real-time terrain scanning results; the real-time power of the transmission device is obtained through the power sensor of the robot; the real-time terrain scanning results include one or more key parameters of the underwater terrain; Step 2, dynamically suspending the air volume of the airbag of the floating caisson according to the real-time terrain scanning result and the real-time power of the transmission device; the dynamic suspension control mode includes a first control mode and a second control mode; the first control mode is: when the robot scans that the terrain becomes softer and / or the power of the transmission device increases, the air volume of the airbag is automatically increased; the second adjustment control mode is: when the robot detects that the terrain becomes harder and / or the power of the transmission device decreases, the air volume of the airbag is automatically reduced; According to the real-time terrain scanning results and the real-time power of the transmission device, the air volume of the airbag of the floating caisson is dynamically controlled, including: Step 21, calculating a first expected value of the air volume of the airbag through the real-time terrain scanning result; Step 22, calculating a second expected value of the air volume of the air bag by using the real-time power of the transmission device; Step 23, constructing a relationship model according to the first expected value of the air volume of the air bag and the second expected value of the air volume of the air bag to obtain the adjusted air volume of the air bag; Step 24, obtaining the original air volume of the airbag, obtaining the final air volume of the airbag according to the original air volume of the airbag and the adjusted air volume of the airbag, and controlling the robot to perform floating and sinking dredging underwater by the final air volume of the airbag.

2. The method for dynamic suspension control of a terrain-adaptive underwater dredging robot according to claim 1, characterized in that: The first expected value of the air volume of the airbag is calculated based on the real-time terrain scanning result, specifically including: Step 211, obtaining the terrain hardness index at the current moment according to the real-time terrain scanning result; Step 212, preset a reference hardness index; Step 213, calculating a first expected value of the air volume of the airbag at the current moment according to the terrain hardness index at the current moment and the reference hardness index.

3. The method for dynamic suspension control of a terrain-adaptive underwater dredging robot according to claim 1, characterized in that: Calculating the second expected value of the air volume of the airbag through the real-time power of the transmission device specifically includes: Step 221, obtaining the real-time power of the transmission device over a period of time in the past to obtain a power index; Step 222, preset a reference power index; Step 223, calculating a second expected value of the air volume of the airbag at the current moment according to the power index at the current moment and the reference power index.

4. The method for dynamic suspension control of a terrain-adaptive underwater dredging robot according to claim 1, characterized in that: According to the first expected value of the air volume of the air bag and the second expected value of the air volume of the air bag, a relationship model is constructed to obtain the air volume of the air bag adjustment, specifically including: Step 231, calculating the correlation coefficient between the first expected value of the air volume of the air bag and the second expected value of the air volume of the air bag; Step 232, obtaining a relationship model according to the correlation coefficient, the first expected value of the air volume of the air bag and the second expected value of the air volume of the air bag, and calculating the air volume of the air bag through the relationship model.

5. The method for dynamic suspension control of a terrain-adaptive underwater dredging robot according to claim 4, characterized in that: The calculation formula for the airbag adjustment air volume is: ； In the formula, Represents the airbag adjusting the air volume, represents the first expected value of the airbag air volume at the current time t, represents the second expected value of the airbag air volume at the current time t, Represents the correlation coefficient.

6. The method for dynamic suspension control of a terrain-adaptive underwater dredging robot according to claim 1, characterized in that: The formula for calculating the final air volume of the airbag is: ； In the formula, represents the final air volume of the airbag, represents the original air volume of the airbag, Represents the airbag adjusting the air volume, represents the time constant, .

7. The method for dynamic suspension control of a terrain-adaptive underwater dredging robot according to claim 2, characterized in that: Get all the key parameter values ​​at the current moment, get the key parameter average value based on the historical key parameter values, and get the terrain hardness index based on the key parameter values ​​and the key parameter average value: ； The calculation formula for the first expected value of the air volume of the air bag is: ； In the formula, Represents the terrain hardness index at the current time t, Represents the value of the i-th key parameter at the current time t, represents the average value of key parameters, and n represents the total number of key parameters; represents the first expected value of the airbag air volume at the current time t, represents the first expected coefficient, represents a nonlinear function, Represents the reference hardness index.

8. The method for dynamic suspension control of a terrain-adaptive underwater dredging robot according to claim 3, characterized in that: The calculation formula for the second expected value of the air volume of the air bag is: ； In the formula, represents the second expected value of the airbag air volume at the current time t, represents the power index at the current time t, represents the reference power index, represents a nonlinear function, represents the second expected coefficient.

9. The method for dynamic suspension control of a terrain-adaptive underwater dredging robot according to claim 7, characterized in that: An initial hardness coefficient is preset, and the water temperature at the robot's position at the current time t and the robot's posture angle are obtained; a first expected coefficient is calculated based on the initial hardness coefficient, water temperature, and robot posture angle.

10. The method for dynamic suspension control of a terrain-adaptive underwater dredging robot according to claim 8, characterized in that: The initial power coefficient is preset, and the robot speed and water depth of the robot at the current time t are obtained; the second expected coefficient is calculated according to the initial power coefficient, the robot speed, and the water depth.

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