A Dynamic Suspension Control Method for a Terrain-Adaptive Underwater Silt Cleaning Robot

Abstract

The present invention relates to the technical field of robot suspension control, and discloses a dynamic suspension control method for a terrain-adaptive underwater dredging robot. By comprehensively considering the first expected value and the second expected value of the air volume of the airbag, and combining various key parameters such as water temperature, water depth, robot posture angle, hardness coefficient, and robot speed, the air volume for adjusting the airbag is accurately calculated, and finally the final air volume of the airbag is obtained based on the initial air volume of the airbag. First of all, by dynamically adjusting the air volume of the airbag, the robot can maintain the best working state under different terrain conditions, ensuring that the dredging tool can effectively contact and clean the sediment. Especially in the area of soft sediment, increasing the air volume of the airbag can prevent the robot from sinking into the sediment and avoid jamming or stagnation caused by insufficient buoyancy; while on the hard bottom, reducing the air volume of the airbag can make the robot contact the sediment more closely and improve the working efficiency of the dredging tool.

Description

Technical Field

[0001] The present invention relates to the technical field of robot suspension control, and particularly to a dynamic suspension control method for an underwater dredging robot with terrain adaptability. Background Art

[0002] The traditional method for maintaining the water depth of port channels mainly relies on dredging with dredgers. As the mud dumping distance increases to dozens of kilometers, the dredging cost 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 water depth guarantee technology for port channels based on natural power is: by artificially disturbing, increasing the turbulent energy of the water body, compensating for the reduction in the sediment-carrying capacity of the water flow caused by the excavation of the port basin channel or the water-blocking of buildings, and then achieving the water depth maintenance goal of "no sediment deposition and natural balance" in the water body. Compared with the traditional dredging method of "regular dredging", it has the following advantages: First, it uses natural water flow instead of the long-distance transportation of dredgers, significantly reducing the water depth maintenance cost and carbon emissions; second, the disturbance is completed at the bottom of the water, enabling zero impact on ship operations; third, the disturbance is normalized to avoid the enrichment of pollutants caused by sediment settlement and consolidation. In order to meet the water depth maintenance goal, the prior art designs and develops a set of underwater dredging robots, which can be remotely controlled to sink and rise through airbags. After sinking to the bottom, it can continuously move forward for operation. The power cable and signal cable are connected to the equipment through a winch to provide power and signal transmission; the silt reduction operation is carried out through a mobile equipment platform. After plowing and brushing the silt, the sediment is pushed to the water flow exchange layer by a push-flow pump.

[0003] However, the adjustment range of the buoyancy of existing underwater dredging equipment is limited. Moreover, the underwater dredging robot has a large self-weight and is prone to sinking in soft sediment areas. The prior art designs a floating and sinking box (a water pressure tank in the submarine field) with a floating and sinking control mechanism. An airbag is arranged in the floating and sinking box, and air is filled into the airbag. Existing suspension control systems usually only consider the influence of a single factor on the air volume of the airbag, 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 act together to affect the change in the air volume of the airbag. For example, when the terrain becomes soft, the robot may increase the air volume of the airbag to provide more buoyancy. However, if the water temperature rises or the inclination angle of the robot increases at this time, the elasticity of the airbag material will change, further affecting the buoyancy and resulting in a decrease in control accuracy. Summary of the Invention

[0004] To solve the above technical problems, the present invention provides a dynamic suspension control method for an underwater dredging robot with terrain adaptability, including:

[0005] Step 1, the robot performs underwater silt cleaning 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 several key underwater terrain parameters;

[0006] Step 2, according to the real-time terrain scanning results and the real-time power of the transmission device, dynamic suspension control is performed on the air volume of the airbag of the floating and sinking box; the dynamic suspension control methods include the first control method and the second control method; the first control method is: when the robot scans that the terrain becomes soft 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 hard and / or the power of the transmission device decreases, the air volume of the airbag is automatically decreased;

[0007] Performing dynamic suspension control on the air volume of the airbag of the floating and sinking box according to the real-time terrain scanning results and the real-time power of the transmission device specifically includes:

[0008] Step 21, calculate the first expected value of the air volume of the airbag through the real-time terrain scanning results;

[0009] Step 22, calculate the second expected value of the air volume of the airbag through the real-time power of the transmission device;

[0010] Step 23, according to the first expected value of the air volume of the airbag and the second expected value of the air volume of the airbag, construct a relationship model to obtain the adjusted air volume of the airbag;

[0011] Step 24, obtain the original air volume of the airbag, and according to the original air volume of the airbag and the adjusted air volume of the airbag, obtain the final air volume of the airbag, and control the robot to perform floating and sinking silt cleaning underwater through the final air volume of the airbag.

[0012] Further, calculating the first expected value of the air volume of the airbag through the real-time terrain scanning results specifically includes:

[0013] Step 211, obtain the terrain hardness index at the current moment according to the real-time terrain scanning results;

[0014] Step 212, preset a reference hardness index;

[0015] Step 213, calculate the first expected value of the air volume of the airbag at the current moment according to the terrain hardness index and the reference hardness index at the current moment.

[0016] Further, calculating the second expected value of the air volume of the airbag through the real-time power of the transmission device specifically includes:

[0017] Step 221, obtain the real-time power of the transmission device in the past period of time to obtain a power index;

[0018] Step 222, preset a reference power index;

[0019] Step 223, calculate a second expected value of the air volume of the airbag at the current moment according to the power index and the reference power index at the current moment.

[0020] Furthermore, construct a relationship model based on the first expected value of the airbag air volume and the second expected value of the airbag air volume to obtain the adjusted air volume of the airbag, specifically including:

[0021] Step 231, calculate the correlation coefficient between the first expected value of the airbag air volume and the second expected value of the airbag air volume;

[0022] Step 232, obtain a relationship model according to the correlation coefficient, the first expected value of the airbag air volume and the second expected value of the airbag air volume, and calculate the adjusted air volume of the airbag through the relationship model.

[0023] Furthermore, the calculation formula for the adjusted air volume of the airbag is:

[0024] ;

[0025] In the formula, represents the adjusted air volume of the airbag, represents the first expected value of the airbag air volume at the current moment t, represents the second expected value of the airbag air volume at the current moment t, represents the correlation coefficient.

[0026] Furthermore, the calculation formula for the final air volume of the airbag is:

[0027] ;

[0028] In the formula, represents the final air volume of the airbag, represents the original air volume of the airbag, represents the adjusted air volume of the airbag, represents the time constant, .

[0029] Furthermore, obtain all the key parameter values at the current moment, obtain the average value of the key parameters according to the historical key parameter values, and obtain the terrain hardness index according to the key parameter values and the average value of the key parameters:

[0030] ;

[0031] The calculation formula for the first expected value of the airbag air volume is:

[0032] ;

[0033] In the formula, represents the terrain hardness index at the current moment t, represents the value of the i-th key parameter at the current moment t, represents the average value of the key parameters, and n represents the total number of key parameters; represents the first expected value of the air volume of the airbag at the current moment t, represents the first expected coefficient, represents a non-linear function, represents the reference hardness index.

[0034] Further, the calculation formula for the second expected value of the air volume of the airbag is:

[0035] ;

[0036] In the formula, represents the second expected value of the air volume of the airbag at the current moment t, represents the power index at the current moment t, represents the reference power index, represents a non-linear function, represents the second expected coefficient.

[0037] Further, a preset initial hardness coefficient is set, the water temperature at the position where the robot is located at the current moment t and the robot posture angle are obtained; the first expected coefficient is calculated according to the initial hardness coefficient, the water temperature, and the robot posture angle.

[0038] Further, a preset initial power coefficient is set, the robot speed and the water depth of the robot at the current moment t are obtained; the second expected coefficient is calculated according to the initial power coefficient, the robot speed, and the water depth.

[0039] The embodiments of the present invention have the following technical effects:

[0040] By comprehensively considering the first expected value and the second expected value of the air volume of the airbag, and combining various key parameters such as water temperature, water depth, robot posture angle, hardness coefficient, and robot speed, the present invention accurately calculates the adjusted air volume of the airbag, and finally obtains the final air volume of the airbag according to the initial air volume of the airbag. First, by dynamically adjusting the air volume of the airbag, the robot can maintain the best working state under different terrain conditions, ensuring that the dredging tool can effectively contact and clean the sediment. Especially in the area of soft sediment, increasing the air volume of the airbag can prevent the robot from sinking into the sediment and avoid jamming or stagnation caused by insufficient buoyancy; while on the hard bottom, reducing the air volume of the airbag can make the robot contact the sediment more closely and improve the working efficiency of the dredging tool. This adaptive suspension control mechanism significantly improves the overall efficiency of the dredging operation.

[0041] Secondly, reasonable adjustment of the air volume in 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 operation time of the robot and lower the maintenance cost.

[0042] Thirdly, dynamic suspension control enables the robot to move freely in waters at different depths and flexibly respond to complex underwater environments. Whether in shallow waters or deep waters, the robot can maintain a stable posture, ensuring the continuity and efficiency of the dredging operation. Especially under variable water flow conditions, the robot can maintain stability by adjusting the air volume in the airbag in real time, avoiding postural out-of-control caused by water flow impact, and further improving the safety and reliability of the operation. Description of the Drawings

[0043] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0044] Figure 1 It is a flowchart of a dynamic suspension control method for a terrain-adaptive underwater dredging robot provided by an embodiment of the present invention. Specific Embodiments

[0045] To make the objectives, technical solutions, and advantages of the present invention clearer, the following will clearly and completely describe the technical solutions of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0046] This embodiment integrates high-precision sensors to continuously monitor key environmental parameters such as water depth, terrain features, and robot features in real time, processes the data of these features, automatically adjusts the operation mode, and through continuous optimization, improves the response ability of this embodiment to different dredging tasks and environments, and adjusts the operation strategy in real time, thereby achieving efficient and precise maintenance operations and reducing the dependence on manual intervention.

[0047] The application scenarios of this embodiment: mainly applied to the daily water depth dredging operations of coastal and inland river port channels. Especially in those port channel areas where the sediment-carrying capacity of the water flow decreases, sediment deposition is severe, and the cost of traditional dredging operations is high and the efficiency is low, this system can play a significant role.

[0048] Application environment of this embodiment: applicable to port channels with water depths ranging from several meters to dozens of meters, including open waters, narrow channels, complex terrains, etc.; applicable to natural water flow environments with different flow velocities and directions, including tides, rivers, etc.; applicable to various types of sediment, including suspended sediment, bed load sediment, and deposited and consolidated sediment.

[0049] Figure 1 It is a flowchart of a dynamic suspension control method for a terrain-adaptive underwater dredging robot provided by an embodiment of the present invention. Refer to Figure 1 , specifically including:

[0050] Step 1, the robot conducts 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 several key underwater terrain parameters.

[0051] Start the robot to enter the working area and immediately conduct terrain scanning to obtain terrain scanning results. The following sensors are used to obtain real-time terrain scanning results: a three-dimensional map of the underwater terrain is drawn through a multibeam sonar to obtain information such as substrate type and substrate thickness. The surface features of the underwater terrain, such as slope and undulation, are measured with high precision through a lidar. The water depth is measured through a pressure sensor. The substrate hardness is sensed through a tactile sensor. The water temperature is measured through a temperature sensor. The water flow velocity is measured through a flow velocity sensor. The posture angle of the robot underwater is monitored through an attitude sensor. The action speed of the robot is monitored through an angle sensor.

[0052] The key underwater terrain parameters include at least substrate type, substrate thickness, substrate density, and substrate hardness.

[0053] This embodiment conducts dredging on terrains with soft and hard substrate types. Preferably, the soft substrate type is: bed load sediment, and the hard substrate type is: deposited and consolidated sediment.

[0054] The robot is also equipped with a power sensor to monitor the power consumption of the transmission device (such as thrusters, dredging tools, etc.) in real time.

[0055] Step 2, perform dynamic suspension control on the air volume of the airbag of the floating and sinking 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, automatically increase the air volume of the airbag; the second adjustment control method is: when the robot detects that the terrain becomes harder and / or the power of the transmission device decreases, automatically reduce the air volume of the airbag.

[0056] Perform dynamic suspension control on the air volume of the airbag of the floating and sinking box according to the real-time terrain scanning results and the real-time power of the transmission device, specifically including:

[0057] Step 21, calculate the first expected value of the air volume of the airbag based on the real-time terrain survey result.

[0058] Step 211, obtain the terrain hardness index according to the real-time terrain survey result.

[0059] Obtain all the key parameter values at the current moment t according to the real-time terrain survey result, get the average value of the key parameters according to the historical key parameter values, and obtain the terrain hardness index according to the key parameter values and the average value of the key parameters:

[0060] ;

[0061] represents the terrain hardness index at the current moment t, represents the i-th key parameter value at the current moment t, represents the average value of the key parameters, and n represents the total number of key parameters.

[0062] Step 212, preset the reference hardness index.

[0063] Obtain the operation data of the robot under different terrain conditions according to the historical data. By analyzing the operation data of the robot under different terrain conditions, statistically obtain the average hardness indices of different types of substrates (such as sediment, clay, gravel, etc.), and use the average hardness index as the reference hardness index.

[0064] Step 213, calculate the first expected value of the air volume of the airbag at the current moment according to the terrain hardness index and the reference hardness index at the current moment.

[0065] ;

[0066] ;

[0067] In the formula, represents the first expected value of the air volume of the airbag at the current moment t, represents the first expected coefficient, represents a non-linear function, represents the reference hardness index, , , are adjustment factors respectively, and is a constant.

[0068] Specifically, is used to adjust the first expected value, , meaning 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, decreases, decreases, i.e., the air volume of the airbag decreases. , meaning that the terrain hardness index at the current moment is lower than the reference hardness index , the geology becomes softer, is the difference between the terrain hardness index and the reference hardness index at the current moment between times.

[0069] Furthermore, the first formula of the formula group is adjusted for the first expected value of geological hardening (sediment consolidation sediment in hard geological types), improving the mobility and stability of the robot on soft substrates, reducing operation interruptions caused by getting stuck in sediment, increasing the dredging efficiency. Especially in areas covered with a large area of bed load sediment, dredging operations can be carried out quickly and effectively. The sediment reduction efficiency of suspended sediment can reach 1800 m³ / h, significantly improving the overall operation efficiency. The second formula is adjusted for the first expected value of geological softening (bed load sediment in soft substrate types). In hard substrate areas, the robot can better fit the ground, improving the cutting and excavation efficiency of the dredging tool, reducing unnecessary buoyancy, lowering energy consumption, extending the operation time, and at the same time improving the dredging accuracy. The sediment reduction efficiency of sediment consolidation sediment is 450 m³ / h. Although the efficiency is lower than that of suspended sediment, high operation quality can still be ensured in complex environments. Preferably, , .

[0070] Preset the initial hardness coefficient, obtain the water temperature at the position where the robot is located at the current moment t and the robot posture angle; calculate the first expected coefficient according to the initial hardness coefficient, water temperature, and robot posture angle.

[0071] Obtain the real-time terrain survey results of each historical moment. For each historical moment, calculate several terrain hardness indices according to the terrain hardness index formula, and normalize the average of all terrain hardness indices as the initial hardness coefficient.

[0072] ;

[0073] represents the initial hardness coefficient, represents the water temperature at the position where the robot is located at the current moment t, represents the robot posture angle of the robot at the current moment t, , respectively represent the weight coefficients.

[0074] Step 22, calculate the second expected value of the airbag air volume through the real-time power of the transmission device.

[0075] Step 221: Obtain the real-time power of the transmission device over a past period of time to get the power index.

[0076] When it is necessary to obtain the power index at the current moment, respectively obtain the real-time power of the transmission device over a past period of time and the real-time power at the current moment to get the power index at the current moment. of the power index.

[0077] ;

[0078] represents the real-time power at the m-th moment, and Z represents the total number of acquisition moments.

[0079] Step 222: Preset the reference power index.

[0080] Obtain the operation data of the robot under different terrain conditions from historical data. By analyzing the operation data of the robot under different terrain conditions, statistically calculate the average power index of different types of bottom sediments (such as sediment, clay, gravel, etc.), and use the average power index as the reference power index.

[0081] Step 223: Calculate the second expected value of the air volume in the airbag at the current moment according to the power index at the current moment and the reference power index.

[0082] ;

[0083] In the formula, represents the second expected value of the air volume in the airbag at the current moment t, represents the power index at the current moment t, represents the reference power index, represents a non-linear function, represents the second expected coefficient.

[0084] Preset the initial power coefficient, obtain the robot speed and water depth of the robot at the current moment t; calculate the second expected coefficient according to the initial power coefficient, robot speed, and water depth.

[0085] Obtain the real-time power at each historical moment and get the power index. After averaging all the power indexes and normalizing them, use them as the initial power coefficient.

[0086] ;

[0087] represents the initial power coefficient, represents the water depth of the robot underwater at the current moment t, represents the robot speed of the robot at the current moment t, 、 respectively represent the weight factors.

[0088] Through a dual - path prediction mechanism based on terrain hardness index and power index respectively, the robot can more accurately adjust the air volume of the airbag, thus achieving optimal suspension control and dredging efficiency. Specifically, the first expected value is obtained based on the initial hardness index, reference hardness index, and the first expected coefficient at the current moment. The first expected coefficient takes into account water temperature and posture angle, which enables the robot to dynamically adjust buoyancy under different bottom conditions, ensuring stable postures and effective contact of the dredging tools on both soft and hard bottoms. The second expected value is based on the power index, reference power index, and the second expected coefficient at the current moment. The second expected coefficient takes into account both water depth and robot speed, 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 greatest advantage of this dual - path design is that it can comprehensively consider terrain characteristics and power requirements, providing 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 adjusted volume, which not only ensures the stability and efficiency of the dredging operation but also maximally saves energy and extends the operation time. This design not only improves the adaptability and flexibility of the robot but also enhances its safety and reliability in complex environments, providing a solid guarantee for the successful implementation of underwater dredging tasks.

[0089] Step 23: Construct a relationship model based on the first expected value of the airbag air volume and the second expected value of the airbag air volume to obtain the adjusted air volume of the airbag.

[0090] Step 231: Calculate the correlation coefficient between the first expected value of the airbag air volume and the second expected value of the airbag air volume.

[0091] Collect the first expected value of the airbag air volume and the second expected value of the airbag air volume calculated at each moment based on the above steps. Calculate the mean value of the first expected value of the airbag air volume and the mean value of the second expected value of the airbag air volume respectively according to these data. Finally, calculate the correlation coefficient based on the first expected value of the airbag air volume, the second expected value of the airbag air volume, the mean value of the first expected value of the airbag air volume, and the mean value of the second expected value of the airbag air volume at each moment to represent the correlation strength between the first expected value of the airbag air volume and the second expected value of the airbag air volume.

[0092] Step 232: Obtain the relationship model based on the correlation coefficient, the first expected value of the airbag air volume, and the second expected value of the airbag air volume, and calculate the adjusted air volume of the airbag through the relationship model.

[0093] ;

[0094] In the formula, represents the air volume adjusted by the airbag, represents the first expected value of the airbag air volume at the current moment t, represents the second expected value of the airbag air volume at the current moment t, represents the correlation coefficient.

[0095] Based on the first expected value and the second expected value, and calculating the air volume adjusted by the airbag through 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 precisely. This not only ensures the stable suspension and efficient operation of the robot under different bottom conditions, but also enables it to maintain the optimal buoyancy state under changing water depths and water flow conditions, thus improving the overall operation efficiency and safety.

[0096] Step 24: Obtain the original air volume of the airbag. Based on the original air volume of the airbag and the air volume adjusted by the airbag, obtain the final air volume of the airbag, and control the robot to float and sink for silt cleaning underwater through the calculated final air volume of the airbag.

[0097] ;

[0098] In the formula, represents the final air volume of the airbag, represents the original air volume of the airbag, represents the air volume adjusted by the airbag, represents the time constant, .

[0099] The time constant is used to control the change rate of the final air volume of the airbag. Specifically, the time constant determines the transition speed from the airbag air volume at the current moment to the air volume adjusted by the airbag. In this embodiment, preferably .

[0100] By comprehensively considering the airbag air volume at the current moment and the air volume adjusted by the airbag, and introducing the time constant to calculate the final volume, this design can significantly improve the adaptability and operation efficiency of the underwater silt cleaning robot. The suspended sediment reduction efficiency is 1800 m 3 / h, and the deposited and consolidated sediment reduction efficiency is 450 m 3 / h. Specifically, this method not only ensures the stable suspension of the robot under different substrate conditions and power requirements, but also enables smooth adjustment of buoyancy under changing water depths and flow conditions, avoiding the impact of sharp changes on the robot's attitude and the operation of the dredging tool. By gradually approaching the target volume, the robot can more precisely control its buoyancy state, 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, thus enhancing the safety and reliability of the robot. Finally, this dual-factor control strategy based on the current volume and the adjusted volume provides a more intelligent and efficient suspension control mechanism for the robot in complex underwater environments, ensuring that it can maintain the best working state under various working conditions.

[0101] 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 them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the technical solutions of the embodiments of the present invention.

Claims

1. A dynamic suspension control method for a terrain-adaptive underwater dredging robot, characterized in that, Including: Step 1, the robot conducts underwater silt cleaning 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 several key underwater terrain parameters; Step 2, according to the real-time terrain scanning results and the real-time power of the transmission device, dynamic suspension control is performed on the air volume of the airbag of the floating and sinking box; the dynamic suspension control methods include the first control method and the second control method; the first control method is: when the robot scans that the terrain becomes soft and / or the power of the transmission device increases, automatically increase the air volume of the airbag; the second adjustment control method is: when the robot detects that the terrain becomes hard and / or the power of the transmission device decreases, automatically decrease the air volume of the airbag; Performing dynamic suspension control on the air volume of the airbag of the floating and sinking box according to the real-time terrain scanning results and the real-time power of the transmission device specifically includes: Step 21, calculating the first expected value of the air volume of the airbag through the real-time terrain scanning results; Step 211, obtaining the terrain hardness index at the current moment according to the real-time terrain scanning results; Step 212, presetting the reference hardness index; Step 213, calculating the first expected value of the air volume of the airbag at the current moment according to the terrain hardness index and the reference hardness index at the current moment; Obtaining all the key parameter values at the current moment, obtaining the average value of the key parameters according to the historical key parameter values, and obtaining the terrain hardness index according to the key parameter values and the average value of the key parameters: ； The calculation formula for the first expected value of the air volume of the airbag is: ； Wherein, represents the terrain hardness index at the current moment t, represents the value of the i-th key parameter at the current moment t, represents the average value of the key parameters, and n represents the total number of key parameters; represents the first expected value of the air volume of the airbag at the current moment t, represents the first expected coefficient, represents a non-linear function, represents the reference hardness index; Step 22, calculating the second expected value of the air volume of the airbag through the real-time power of the transmission device; specifically including: Step 221, obtaining the real-time power of the transmission device in the past period of time to obtain the power index; Step 222, presetting the reference power index; Step 223, calculating the second expected value of the air volume of the airbag at the current moment according to the power index and the reference power index at the current moment; The calculation formula for 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 moment t, represents the power exponent at the current moment t, represents the reference power exponent, represents the non-linear function, represents the second expected coefficient; Step 23, constructing a relationship model according to the first expected value of the air volume of the airbag and the second expected value of the air volume of the airbag to obtain the adjusted air volume of the airbag; Step 24, obtaining the original air volume of the airbag, and 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 float and sink and clean silt underwater through the final air volume of the airbag.

2. The dynamic suspension control method of a terrain-adaptive underwater dredging robot according to claim 1, wherein, Constructing a relationship model according to the first expected value of the air volume of the airbag and the second expected value of the air volume of the airbag to obtain the adjusted air volume of the airbag specifically includes: Step 231, calculating the correlation coefficient between the first expected value of the air volume of the airbag and the second expected value of the air volume of the airbag; Step 232, obtaining the relationship model according to the correlation coefficient, the first expected value of the air volume of the airbag and the second expected value of the air volume of the airbag, and calculating the adjusted air volume of the airbag through the relationship model.

3. A dynamic suspension control method for a terrain-adaptive underwater dredging robot according to claim 2, characterized in that, The calculation formula for the adjusted air volume of the airbag is: ； Wherein, represents the air volume for airbag adjustment, 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.

4. A dynamic suspension control method for a terrain-adaptive underwater dredging robot according to claim 1, characterized in that 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 adjusted air volume of the airbag, represents the time constant, .

5. A dynamic suspension control method for a terrain-adaptive underwater dredging robot according to claim 1, characterized in that, Presetting the initial hardness coefficient, obtaining the water temperature at the position where the robot is located at the current moment t and the robot posture angle; calculating the first expected coefficient according to the initial hardness coefficient, the water temperature, and the robot posture angle.

6. The dynamic suspension control method of a terrain-adaptive underwater dredging robot according to claim 1, characterized in that, Preset an initial power coefficient, obtain the robot speed and water depth of the robot at the current moment t; calculate a second expected coefficient based on the initial power coefficient, the robot speed, and the water depth.

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