Deep-sea hydrothermal sampling system benthic submersible and state control method

Abstract

The application provides a deep-sea hydrothermal sampling system benthic submersible and a state control method, the method comprises the following steps: planning a motion trajectory according to a task requirement, and dynamically adjusting control force distribution weights of a propeller and a mechanical leg system according to errors between an actual motion state and an expected value, so that motion performance is optimized, thrust and speed of each propeller and joint torque of the mechanical leg are accurately controlled through thrust distribution and inverse dynamics decomposition, the benthic submersible is kept stable in a working process through joint action of the mechanical leg and the propeller, the benthic submersible has the ability to resist ocean current interference and adapt to rugged terrain, so that position and posture accuracy of the hydrothermal sampling system is ensured, operation efficiency is ensured, sensor data is fused in a closed-loop control system, the actual state of the submersible is fed back in real time through a state observer / filter processing, the accuracy and stability of motion control are improved, and the operation ability and adaptability of the submersible in a complex environment are enhanced.

Description

Technical Field

[0001] This invention relates to the field of deep-sea submersible technology, and in particular to a deep-sea hydrothermal sampling system bottom submersible and its state control method. Background Technology

[0002] The deep seabed contains numerous hydrothermal vent areas, with supercritical seawater at hydrothermal vents generating total heat up to gigawatt levels. Sampling hydrothermal vents with high heat flux density and direct usability allows for understanding their specific composition and characteristics, which is beneficial for further targeted and efficient utilization of seabed hydrothermal vents. However, due to the accumulation of sediment from long-term eruptions, the area around seabed hydrothermal vents is characterized by jagged rocks and towering peaks, making it difficult for traditional submersibles such as remotely operated vehicles (ROVs), autonomous underwater vehicles (AUVs), and manned submersibles to reach the bottom. At the same time, the narrow diameter of hydrothermal vents and the environmental characteristics of high temperature differences, high flow velocities, and high corrosiveness pose significant challenges to the precise deployment of sampling systems on the hydrothermal vents.

[0003] CN118857856B discloses a device and method for detecting the location of deep-sea hydrothermal vents based on a submersible. The device includes a main chamber with an upward-facing, barrel-shaped structure. A connector is fixed between the outer wall of the main chamber and the outer wall of the submersible's head. A transition post is inserted into the outer circumference of the main chamber, away from the connector. Two fixed tubes, one extending upwards and the other downwards, are fixed to the outer circumference of the transition post. A closed convex cover is provided at the top of the main chamber. The extended detection chamber detects the location of the heat source immediately and then performs sample extraction at different locations as needed. The extraction power is located above the extraction tank inside the main chamber, and the required amount is extracted to avoid over-extraction due to excessive water pressure.

[0004] The aforementioned hydrothermal sampling system needs to maintain a stable position during operation to ensure maximum sampling efficiency. However, the system is relatively large and difficult to mount directly. Currently, it is impossible to accurately deploy the hydrothermal sampling system at the hydrothermal vent and maintain a stable position under the interference of the vent jet, thus reducing operational efficiency. Summary of the Invention

[0005] In view of this, the present invention proposes a deep-sea hydrothermal vent sampling system with a benthic submersible and a state control method. By carrying hydrothermal vent sampling on a benthic submersible and using a multimodal motion system based on thrusters and mechanical legs, combined with real-time sensor monitoring and error feedback mechanisms, the benthic submersible maintains stable position and attitude during operation, has the ability to resist ocean current interference and adapt to rugged terrain, thereby ensuring the position and attitude accuracy of the hydrothermal vent sampling system and ensuring operational efficiency.

[0006] The technical solution of the present invention is implemented as follows: In a first aspect, the present invention provides a state control method for a bottom-dwelling submersible in a deep-sea hydrothermal sampling system, the submersible including thrusters and mechanical legs for adjusting the submersible's attitude, the method comprising the following steps:

[0007] S1. Using a path planning algorithm combined with seabed topographic data, the desired motion trajectory of the submersible from its current position to the target hydrothermal vent is generated, and the desired motion state data of the submersible's movement trajectory is obtained.

[0008] S2 uses an inertial navigation IMU, a depth gauge, and a DVL sensor to collect the submersible's motion state data in real time, and uses an extended Kalman filter to fuse the multi-sensor data to obtain the submersible's actual motion state feedback.

[0009] S3 compares the actual motion state feedback of the submersible with the planned expected motion state data to calculate the position error and attitude error;

[0010] S4. Based on the position error and attitude error, the translational force and rotational torque required to eliminate the position and attitude errors are calculated using the PID control algorithm and backstepping method.

[0011] S5. Establish the optimization objective function, and assign weights to the thruster module and the mechanical leg module according to the translational force required for the submersible to eliminate position error, so as to obtain the mechanical leg force and the thruster force.

[0012] S6. Based on the number and layout of the submersible's mechanical legs, the forces of the mechanical legs are decomposed inversely to obtain the torque of each joint of the mechanical leg, and the movement of the mechanical leg is controlled by the actuator.

[0013] S7. Based on the number and layout of the submersible thrusters, a thrust distribution model is established. The thruster force is distributed using the pseudo-inverse method to obtain the rotational speed of each thruster. The thruster movement is then controlled by the actuator.

[0014] S8 uses the movement of mechanical legs and thrusters to collect real-time motion state data of the submersible and generate feedback on the actual motion state of the submersible, forming a closed-loop control.

[0015] Based on the above technical solutions, preferably, step S2 involves using an inertial navigation IMU, an altitude sensor, and a DVL sensor to collect real-time motion state data of the submersible, and then fusing the multi-sensor data using an extended Kalman filter to obtain the actual motion state feedback of the submersible. This includes the following sub-steps:

[0016] Submersible motion data includes position, velocity, attitude, and sensor bias data, expressed as:

[0017]

[0018] In the formula, p n =[x,y,z] T , representing the position in the geodetic coordinate system; v b =[u,v,w] T θ represents the velocity in the carrier coordinate system; θ = [φ, θ, ψ] T , which is represented by the attitude angle; This is represented as zero bias of the IMU accelerometer. This is represented as zero bias of the IMU gyroscope;

[0019] The state equation for the submersible's state information changing over time is established, and its expression is:

[0020]

[0021] In the formula, x is the motion state vector of the submersible, and u is the acceleration measurement value of the IMU. m and angular velocity measurement ω m w represents process noise, modeled as zero-mean Gaussian noise, with covariance matrix Q.

[0022] The state equations expand as follows:

[0023]

[0024] In the formula, Let T(θ) be the rotation matrix from the carrier coordinate system to the geodetic coordinate system, and T(θ) be the matrix representing the angular velocity to the Euler angular rate. The expression is:

[0025]

[0026] g n =[0,0,g] T ≈[0,0,9.81] T , represented as the gravity vector; w a To measure noise with an accelerometer, w g The gyroscope angular velocity is represented by random white noise; w ba For accelerometer zero-bias random walk noise; w bg This refers to the zero-bias random walk noise of the gyroscope.

[0027] The state equations are discretized using the first-order Euler method, and the expression is as follows:

[0028]

[0029] In the formula, Δt is the sampling time;

[0030] Based on the sensor observations and the discretized state equation, the sensor observation equation is constructed as follows:

[0031] z k =h(x k )+v k

[0032] In the formula, z k h(x) represents the observed value, indicating the data actually measured by the sensor at time k; k Let be the observation function, representing the state x. k A deterministic function mapped to the observations; v k Observational noise represents the random error introduced by sensor measurements;

[0033] Based on the depth gauge and sensor observation equations, the depth gauge observation equation is obtained, and its expression is:

[0034]

[0035] In the formula, p z The depth gauge directly measures the value, v depth This refers to the observation noise of the depth gauge;

[0036] Based on the DVL and sensor observation equations, the DVL observation equation is obtained, and its expression is:

[0037] v d =z DVL =v b +v DVL ,v DVL ~N(0,R DVL )

[0038] In the formula, v b For DVL, the velocity value, v DVL This refers to the observation noise in DVL;

[0039] Based on the IMU and sensor observation equations, the IMU observation equation is obtained, and its expression is:

[0040]

[0041] Based on the optimal estimated state and IMU measurements from the previous time step, the extended Kalman filter (EKF) method is used to predict the current state and covariance matrix.

[0042] When DVL or depth gauge data is refreshed, the predicted state is corrected using the observed values ​​and observation equations, the Kalman gain is calculated, and the state estimate and covariance matrix are updated.

[0043] When IMU data is refreshed, the current state is predicted through the state equation without observation correction.

[0044] When the DVL or depth gauge data is refreshed, the predicted state at the current moment is fused with the DVL and depth gauge observations, and the optimal estimated state information of the submersible is calculated through a filtering algorithm to obtain the actual motion state feedback of the submersible.

[0045] Based on the above technical solution, preferably, step S3, which compares the actual motion state feedback of the submersible with the planned expected motion state data to calculate the position error and attitude error, includes the following sub-steps:

[0046] The planned expected motion state data includes the expected position and the expected attitude;

[0047] The position error is calculated by comparing the actual position fed back from the submersible's actual motion state with the expected position, and the expression is as follows:

[0048] e p =x d -x

[0049] In the formula, e p Let X be the position error vector. d Let X be the desired position vector, and let X be the real-time monitoring position vector.

[0050] The attitude error is calculated by comparing the actual attitude feedback from the submersible's actual motion state with the corresponding desired attitude, and the expression is as follows:

[0051] e a =θ d -θ

[0052] In the formula, e a Let θ be the attitude error vector. d Let θ represent the desired attitude information, and let θ represent the real-time monitored attitude information.

[0053] Based on the above technical solutions, preferably, step S4, which involves using a PID control algorithm to make preliminary adjustments based on position and attitude errors, and calculating the translational force and rotational torque required to eliminate position and attitude errors using a backstepping method, includes the following sub-steps:

[0054] Based on the position and attitude errors, PID control is used to calculate the compensation control force and rotational torque, which are expressed as follows:

[0055]

[0056] In the formula, To compensate for the lack of control, These are the proportional gain, integral gain, and derivative gain for position control, respectively. These represent the proportional gain, integral gain, and derivative gain for attitude control, respectively; m is the submersible mass; g is the gravitational acceleration vector; and τ is the differential gain. d To compensate for rotational torque;

[0057] S32, based on the compensation control force, the compensation translational force is obtained using the backstepping method and its expression is:

[0058]

[0059] In the formula, F d To compensate for the translational force, V d Let V be the desired velocity. d Let ξ be the first derivative of the desired position with respect to time, and V be the actual velocity; p α is a dummy control variable. p This is the dynamic surface attenuation coefficient.

[0060] Based on the above technical solutions, preferably, step S5, which involves establishing an optimization objective function and allocating weights to the thruster module and the mechanical leg module according to the translational force required for the submersible to eliminate position errors, to obtain the mechanical leg force and the thruster force, includes the following sub-steps:

[0061] Construct an optimization objective function to minimize the weighted execution cost of the thrusters and robotic legs when performing compensating translational forces, expressed as:

[0062]

[0063] In the formula, F leg For mechanical leg force, F thr For the thruster force, W1 is the force mapping diagonal weight matrix of the mechanical leg, W2 is the force mapping diagonal weight matrix of the mechanical thruster, and st represents the constraint condition;

[0064] Using the pseudo-inverse distribution method, the compensating translational force is substituted into the pseudo-inverse formula to calculate the mechanical leg force and the propeller force, as expressed in the following expressions:

[0065]

[0066] F thr =F d -F leg

[0067] In the formula, F leg For mechanical leg force, F thr For propulsion force.

[0068] Based on the above technical solutions, preferably, step S6, which involves decomposing the mechanical leg forces inversely according to the number and layout of the submersible's mechanical legs to obtain the torque magnitude of each joint of the mechanical leg, and controlling the movement of the mechanical leg through an actuator, includes the following sub-steps:

[0069] An inverse dynamics model of the robotic leg is established. Using this model, the driving torque of each joint of the robotic leg is calculated, and the expression is as follows:

[0070]

[0071] In the formula, τ i Let M be the driving torque of the i-th joint of the robotic leg, q be the current joint angle of the robotic leg, and M be the driving torque of the i-th joint. i (q) is the inertia matrix of joint i. G represents the Coriolis force and centrifugal force terms for joint i. i (q) represents the gravity term of joint i. F is the transpose of the Jacobian matrix of joint i. leg For mechanical leg force;

[0072] Based on the driving torque τ of each joint i The system generates corresponding actuator control commands, and the actuators generate corresponding driving torques according to the control commands to drive the movement of each joint of the mechanical leg, adjust the posture and position of the mechanical leg, and realize the control of the submersible's posture.

[0073] Based on the above technical solutions, preferably, step S7 involves establishing a thrust distribution model according to the number and layout of the submersible thrusters, distributing the thruster force using a pseudo-inverse method to obtain the rotational speed of each thruster, and controlling the thruster movement through actuators. This includes the following sub-steps:

[0074] Based on the number and layout of the submersible's thrusters, a thrust distribution model is established, and the thruster forces are substituted into the thrust model. The pseudo-inverse method is used to solve for the speed commands of each thruster, and the expression is:

[0075] F thr =T·K t ·n 2

[0076]

[0077] In the formula, T represents the thruster configuration matrix, K t =diag(k) t1 ,...,k tm ), K t This represents the thrust coefficient matrix, n = [n1, ..., n]. m ]T , n represents the thruster speed command; T + K represents the pseudo-inverse of the thrust configuration matrix. t -1 The matrix representing the inverse of the thrust coefficient matrix K;

[0078] The saturation limiting algorithm is used to saturate and limit the speed commands of each thruster, resulting in the saturated speed command, expressed as:

[0079] n cmd =sat(n,n min ,n max )

[0080] In the formula, n cmd This is the speed command after saturation limiting, where sat(·) is the speed saturation function, and n min To limit the minimum rotational speed of the thruster, n max This is the maximum rotational speed limit for the thruster;

[0081] Based on the speed command after saturation limitation, a corresponding actuator control command is generated. The actuator generates a corresponding speed according to the control command, drives the thruster to rotate and generate thrust, and adjusts the attitude and position of the submersible.

[0082] Secondly, the present invention also provides a deep-sea hydrothermal vent sampling system benthic submersible for executing the state control method of the deep-sea hydrothermal vent sampling system benthic submersible, comprising a hydrothermal vent sampling mechanism and a benthic submersible mechanism, wherein...

[0083] The bottom-diving submersible has a circular groove in the middle, and the hydrothermal sampling mechanism is set in the circular groove for extracting and sampling deep-sea hydrothermal fluids.

[0084] The benthic submersible mechanism includes four thrusters and four mechanical legs. The four thrusters and four mechanical legs are located at the four corners of the benthic submersible mechanism, and adjacent thrusters and adjacent mechanical legs are symmetrically arranged. The thrusters are arranged horizontally with respect to the benthic submersible mechanism, and the mechanical legs are arranged perpendicularly with respect to the benthic submersible mechanism. The four thrusters and four mechanical legs are used to adjust the attitude of the submersible.

[0085] Based on the above technical solutions, preferably, the hydrothermal sampling mechanism includes an extractor, a sampling sensor, and a hydrothermal storage device. The hydrothermal storage device is disposed in a circular trough for storing hydrothermal samples, and the sampling sensor is disposed in the hydrothermal storage device for testing and recording the physicochemical properties of the hydrothermal samples. The extractor is disposed on the side of the hydrothermal storage device near the bottom-diving submersible mechanism, and one end of the extractor is connected to the hydrothermal storage device for extracting hydrothermal samples into the hydrothermal storage device.

[0086] Based on the above technical solutions, preferably, the mechanical leg includes a bearing bracket, a housing, a thigh support arm, a lower leg support arm, a connecting rod, a lower leg joint motor, a thigh joint motor, and a hip joint motor, wherein...

[0087] The hip joint motor is fixed to the bottom submersible mechanism, and the output shaft of the hip joint motor is fixedly connected to the top of the bearing bracket to drive the mechanical leg to swing in and out.

[0088] The bearing bracket has two rotating sections, the housing is rotatably connected between the two rotating sections, and the thigh joint motor is fixed on the side of one rotating section. The output shaft of the thigh joint motor is fixedly connected to the housing to drive the housing to swing back and forth along the central axis of the two rotating sections.

[0089] The lower leg joint motor is rotatably connected to the rotating cylinder on the side away from the thigh joint motor, and the lower leg joint motor is fixedly connected to the housing. The output shaft of the lower leg joint motor is hinged to one end of the thigh support arm, and the other end of the thigh support arm is hinged to the lower leg support arm on the housing. One end of the connecting rod is hinged to the drive plate of the lower leg joint motor, and the other end of the connecting rod is hinged to one end of the lower leg support arm. The other end of the lower leg support arm serves as the support point between the mechanical leg and the ground.

[0090] The connecting rod forms a parallelogram with the lower leg support arm and the thigh support arm. The lower leg joint motor controls the back-and-forth swing of the lower leg support arm through the parallelogram connecting rod.

[0091] The deep-sea hydrothermal sampling system, benthic submersible, and state control method of the present invention have the following advantages over the prior art:

[0092] (1) By planning the motion trajectory according to the task requirements and dynamically adjusting the control force distribution weight of the thruster and mechanical leg system based on the error between the actual motion state and the expected value, the motion performance is optimized; by thrust distribution and inverse dynamics decomposition, the thrust and speed of each thruster and the joint torque of the mechanical leg are precisely controlled. Through the combined action of the mechanical leg and the thruster, the position and attitude of the bottom submersible are kept stable during the operation, and it has the ability to resist ocean current interference and adapt to rugged terrain, thereby ensuring the position and attitude accuracy of the hydrothermal sampling system and ensuring the operation efficiency.

[0093] (2) The extended Kalman filter (EKF) realizes the spatiotemporal alignment and optimal fusion of multi-sensor data, and after processing by the state observer / filter, the actual state of the submersible is fed back in real time, which significantly improves the accuracy and stability of motion control and enhances the submersible's ability to operate and adapt in complex environments.

[0094] (3) By constructing the objective function, the optimal force distribution scheme can be found under the premise of satisfying the constraints, so that the total cost of the thruster and mechanical leg when performing compensating translational force is minimized, thereby improving the motion control efficiency of the submersible, reducing energy consumption, and enhancing the stability of the system; and by adopting the pseudo-inverse distribution method, the values ​​of mechanical leg force and thruster force can be obtained, thereby eliminating position error and maintaining stable attitude. Attached Figure Description

[0095] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0096] Figure 1 This is a flowchart of the state control method for the bottom submersible of the deep-sea hydrothermal sampling system of the present invention;

[0097] Figure 2 This is a system block diagram of the state control method for the deep-sea hydrothermal sampling system's bottom submersible according to the present invention;

[0098] Figure 3 A three-dimensional structural view of the deep-sea hydrothermal sampling system benthic submersible of the present invention;

[0099] Figure 4 A perspective view of the bottom submersible mechanism of the deep-sea hydrothermal sampling system of the present invention;

[0100] Figure 5 Cross-sectional view of the hydrothermal sampling mechanism of the deep-sea hydrothermal sampling system of the present invention (bottom submersible);

[0101] Figure 6 A perspective view of the mechanical leg structure of the bottom-diving submersible in the deep-sea hydrothermal sampling system of the present invention;

[0102] Figure 7 A schematic diagram illustrating the mechanical leg movement principle of the benthic submersible in the deep-sea hydrothermal sampling system of this invention.

[0103] Figure 8 A schematic diagram of the operation process of the deep-sea hydrothermal vent sampling system's bottom submersible according to the present invention;

[0104] Figure 9 A schematic diagram of the horizontal layout of the thrusters of the deep-sea hydrothermal sampling system benthic submersible of the present invention. Detailed Implementation

[0105] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0106] like Figure 3 As shown, a deep-sea hydrothermal vent sampling system of the present invention includes a bottom-diving submersible, comprising a hydrothermal vent sampling mechanism 1 and a bottom-diving submersible mechanism 2. The bottom-diving submersible mechanism 2 has a circular groove 200 in the middle, and the hydrothermal vent sampling mechanism 1 is disposed in the circular groove 200 for extracting and sampling deep-sea hydrothermal vents.

[0107] It should be noted that this benthic submersible adopts a semi-enclosed symmetrical overall configuration, with the hydrothermal sampling mechanism 1 installed at the center of the benthic submersible mechanism. This ensures that the core electromechanical components are kept away from the hydrothermal vents to avoid high-temperature damage, while also ensuring the overall stability of the submersible.

[0108] like Figure 5 As shown, the hydrothermal vent sampling mechanism 1 in this embodiment includes an extractor 11, a sampling sensor 12, and a hydrothermal vent storage device 13. The hydrothermal vent storage device 13 is disposed in a circular trough 200 for storing hydrothermal vent samples. The sampling sensor 12 is disposed in the hydrothermal vent storage device 13 for testing and recording the physicochemical properties of the hydrothermal vent samples. The extractor 11 is disposed on the side of the hydrothermal vent storage device 13 near the bottom-diving submersible mechanism 2, and one end of the extractor 11 is connected to the hydrothermal vent storage device 13 for extracting hydrothermal vent samples into the hydrothermal vent storage device 13.

[0109] It should be noted that both the extractor 11 and the sampling sensor 12 can be remotely or autonomously controlled by the control system; the extractor 11 is made of high-temperature resistant and corrosion-resistant materials, and can be directly inserted into the hydrothermal flow to quickly collect samples of liquid and solid substances under high temperature and high pressure conditions; it can improve operational efficiency, reduce pollution risks, lower operating costs, and promote research on deep-sea hydrothermal sampling operations and exploration analysis.

[0110] like Figure 4 and Figure 9 As shown, the bottom-diving submersible mechanism 2 includes four thrusters 21 and four mechanical legs 22. The four thrusters 21 and four mechanical legs 22 are respectively located at the four corners of the bottom-diving submersible mechanism 2, and two adjacent thrusters 21 and two adjacent mechanical legs 22 are symmetrically arranged. The thrusting direction of the thrusters 21 is horizontal with respect to the bottom-diving submersible mechanism 2; the mechanical legs 22 are arranged perpendicular to the bottom-diving submersible mechanism 2. The four thrusters 21 and four mechanical legs 22 are used to adjust the attitude of the submersible.

[0111] In addition, the benthic submersible mechanism 2 also includes a main frame, buoyancy material, underwater communication system, underwater navigation and positioning system, underwater detection system, control system, and energy system. Among them, four thrusters 21 and four mechanical legs 22 are all set on the main frame. The four thrusters 21 are horizontally set on the main frame, and the four mechanical legs 22 are centrally symmetrically set at the bottom of the main frame. The buoyancy material is set on the top of the main frame. Due to the huge pressure of the deep sea environment, the role of the buoyancy material is to provide a certain buoyancy, reduce the weight of the submersible, reduce the difficulty of the submersible sinking in the deep sea, and also help the submersible adjust its attitude during operation. The underwater communication system, underwater navigation and positioning system, underwater detection system, control system, and energy system are all set on the main frame.

[0112] The bottom-diving submersible in this embodiment adopts a multi-mode motion system combining mechanical legs 22 and thrusters 21. Through redundant motion capability design, it has the ability to resist ocean current interference and adapt to rugged terrain, thereby ensuring the position and attitude accuracy of the hydrothermal vent sampling system and ensuring operational efficiency. In addition, it is equipped with a high-precision inertial navigation and positioning system to ensure that the submersible can accurately locate and approach the hydrothermal vent in complex deep-sea terrain, and adjust the sampling position in real time to adapt to the hydrothermal vent environment. At the same time, it is equipped with an energy system and an underwater communication system. To ensure long-term operation capability, the system integrates an efficient energy management system that supports the combined use of batteries and renewable energy sources, such as thermoelectric power generation and tidal power generation. The underwater communication module ensures stable data transmission between the submersible and the mother ship, realizing real-time monitoring and command transmission and reception.

[0113] like Figure 6 and Figure 7As shown, the mechanical leg 22 includes a bearing bracket 201, a housing 202, a thigh support arm 203, a lower leg support arm 204, a connecting rod 205, a lower leg joint motor 206, a thigh joint motor 207, and a hip joint motor 208. The hip joint motor 208 is fixed to the bottom-diving submersible mechanism 2, and its output shaft is fixedly connected to the top of the bearing bracket 201, driving the mechanical leg 22 to swing in and out. The bearing bracket 201 has two rotating cylinders 209, and the housing 202 is rotatably connected between the two rotating cylinders 209. The thigh joint motor 207 is fixed to the side of one rotating cylinder 209, and its output shaft is fixedly connected to the housing 202, driving the housing 202 along the central axis of the two rotating cylinders 209. The leg swings back and forth. The lower leg joint motor 206 is rotatably connected to the rotating cylinder 209 on the side away from the thigh joint motor 207, and the lower leg joint motor 206 is fixedly connected to the housing 202. The output shaft of the lower leg joint motor 206 is hinged to one end of the thigh support arm 203, and the other end of the thigh support arm 203 is hinged to the lower leg support arm 204 on the housing 202. One end of the connecting rod 205 is hinged to the drive plate of the lower leg joint motor 206, and the other end of the connecting rod 205 is hinged to one end of the lower leg support arm 204. The other end of the lower leg support arm 204 serves as the support point between the mechanical leg and the ground. The connecting rod 205, the lower leg support arm 204, and the thigh support arm 203 form a parallelogram. The lower leg joint motor 206 controls the back and forth swing of the lower leg support arm 204 through the parallelogram connecting rod.

[0114] It should be noted that when the horizontal extension direction of the mechanical leg 22 needs to be adjusted, the hip joint motor 208 is activated, and its output shaft drives the bearing bracket 201 to rotate. Since other components of the mechanical leg are connected to the bearing bracket 201, the mechanical leg 22 as a whole can swing inward and outward relative to the bottom-diving submersible mechanism 2. When the thigh joint motor 207 is working, its output shaft drives the housing 202 to swing back and forth along the central axis of the rotating cylinder 209, causing the thigh support arm 203 and the lower leg support arm 204 in the housing 202 to move accordingly, realizing the forward and backward movement of the thigh part of the mechanical leg. This allows the mechanical leg to adjust its position in the forward and backward direction to adapt to different terrain undulations. The lower leg joint motor 206 drives its output shaft to move the connecting rod 205. Since the connecting rod 205, the lower leg support arm 204, and the thigh support arm 203 form a parallelogram structure, according to the characteristics of a parallelogram, the lower leg support arm 204 will swing back and forth with the movement of the connecting rod 205, making the movement of the lower leg support arm 204 more stable and precise, better adapting to changes in terrain, and providing stable support for the mechanical leg.

[0115] In addition, the hip joint motor 208, thigh joint motor 207, and lower leg joint motor 206 are integrated motors, sealed with a metal shell. The output shaft is sealed with two Glyd rings to ensure that the motors can work normally underwater and output a large torque. Furthermore, all three joint motors are located at the base, which facilitates the overall wiring and reduces the inertia of the mechanical leg during movement. The mechanical leg is also long, providing a large working space and making it easier for the robot to traverse rugged terrain.

[0116] like Figure 8 As shown in the diagram, in this example, the hydrothermal vent sampling system's bottom-dwelling submersible is deployed from the mother ship. Based on the pre-detected location of the hydrothermal vent, guided by a high-precision navigation and positioning system, it uses thrusters 22 to cruise towards the vicinity of the vent. The underwater detection system detects and senses the specific location of the vent, and the submersible's status information and the vent's location information are fed back to the mother ship's control unit in real time via an underwater communication system. The submersible then uses remote control or automatic control to accurately deploy the hydrothermal vent sampling mechanism 1 to the vent. Based on the actual environment near the vent, the mechanical legs 22 are deployed to support the sampling mechanism 1 around the vent, ensuring its normal operation. During operation, the sampling mechanism 1 is subject to interference from ocean currents and the vent's jet stream. The navigation and positioning system monitors the submersible's actual motion as feedback, and the mechanical legs 22 and thrusters 21 work together to maintain the submersible's stable position during operation.

[0117] like Figure 1 and Figure 2 As shown, in a second aspect, the present invention also provides a state control method for a deep-sea hydrothermal sampling system's bottom submersible, the method comprising the following steps:

[0118] S1. Using a path planning algorithm combined with seabed topographic data, the desired motion trajectory of the submersible from its current position to the target hydrothermal vent is generated, and the desired motion state data of the submersible's movement trajectory is obtained.

[0119] It should be noted that, based on the complexity of the seabed topography and the motion characteristics of the submersible, the RRT path planning algorithm is selected. Seabed topography data, including seabed depth and obstacle distribution, is acquired through sensors such as sonar and lidar. The acquired topography data undergoes preprocessing, such as filtering, denoising, and interpolation, to improve data accuracy and usability. Simultaneously, the topography data is converted into a format recognizable by the path planning algorithm. The processed seabed topography data is then input into the path planning algorithm for path search. The algorithm generates one or more candidate paths based on the submersible's starting position, target position, and topographic constraints. The candidate paths are evaluated and optimized, and the optimal path is selected as the desired motion trajectory. The forces acting on the benthic submersible during attitude stabilization control are analyzed, and a submersible dynamic model is established. Based on the generated desired motion trajectory and the submersible dynamic model, the desired motion state data of the submersible's movement trajectory is obtained. The desired motion state data, such as velocity, acceleration, and attitude, of the submersible at different positions are extracted. This data will be used in subsequent control steps to ensure that the submersible can reach the target position according to the desired trajectory and state.

[0120] This embodiment analyzes the force situation during the attitude stabilization control of the benthic submersible and establishes a dynamic model of the submersible. This model includes defining an inertial coordinate system and a body coordinate system. The inertial coordinate system is fixed to the seabed, with the Z-axis pointing vertically downward, the X-axis pointing due north, and the Y-axis pointing due east. The body coordinate system is fixed to the center of mass of the body, with the X-axis pointing forward, the Y-axis pointing to the right, and the Z-axis pointing vertically downward.

[0121] The kinematic equations are constructed for the transformation between the submersible's inertial coordinate system and the body coordinate system. The expression is as follows:

[0122]

[0123] In the formula, η is the generalized position vector in the inertial coordinate system, which includes the position and attitude information of the submersible; Let be the generalized velocity vector, represent the derivative of the generalized position vector in the inertial coordinate system with respect to time, and J(η) be the kinematic transformation matrix, where

[0124]

[0125] In the formula, T(Θ) is the rotation matrix from the fuselage coordinate system to the inertial coordinate system, and T(Θ) is the attitude angular velocity transformation matrix.

[0126] Based on the analysis of the forces acting on the submersible for attitude stability control using inertial and body coordinate systems, a dynamic model of the submersible is established, expressed as follows:

[0127]

[0128] In the formula, M is the inertia matrix, ν is the velocity vector in the body coordinate system, C(v) is the Coriolis centripetal matrix, D(v) is the hydrodynamic damping matrix, g(η) is the restoring force matrix, and τ 腿 τ is the torque generated by the mechanical leg. 推 The torque generated by the thruster.

[0129] Where M is the inertia matrix, M = M R +M A M R M is the rigid body inertia matrix. A For the additional mass matrix; for the rigid body inertia matrix M R The expression is:

[0130]

[0131] In the formula, m is the total mass of the robot, I3 is the 3×3 identity matrix, and I G Let be the moment of inertia tensor about the center of mass;

[0132] Additional mass matrix M A The expression is:

[0133]

[0134] In the formula, This represents the additional mass coefficient in each direction of the linear velocity. The additional mass coefficient in the direction of angular velocity;

[0135] v is the velocity vector in the submersible's coordinate system, including the submersible's linear velocity and angular velocity, expressed as:

[0136]

[0137] In the formula, u, v, w are the linear velocities of the submersible, u is the forward velocity of the submersible, v is the lateral velocity of the submersible, and w is the vertical velocity of the submersible; p, q, r are the angular velocities of the submersible, p is the roll angular velocity of the submersible, q is the pitch angular velocity, and r is the yaw angular velocity of the submersible.

[0138] C(v) is the Coriolis centripetal matrix, which includes the Coriolis effect terms for the rigid body and hydrodynamic added mass, and satisfies antisymmetry. Its expression is:

[0139] C(v)=-C T (v)

[0140]

[0141] In the formula, S(·) is the skew-symmetric matrix of the cross product of vectors, 03 is the skew-symmetric matrix of the cross product of vectors, and v1=[u,v,w] ]T v2 = [p, q, r ] T I G Let M be the rotational inertia tensor about the center of mass. R11 Let M be the inertia of the submersible along the x-axis. A11 The inertial drag of the fluid on the submersible's x-axis motion;

[0142] D(v) is the hydrodynamic damping matrix, expressed as:

[0143] D(v)=diag(D u |u|,D v |v|,D w |w|,D p |p|,D q |q|,D r |r|)

[0144] In the formula, D u D v D w D is the second-order damping coefficient for linear velocity. p D q D r The second-order damping coefficient for angular velocity;

[0145] η is the position vector in the inertial coordinate system, including the submersible's position and attitude angle, expressed as:

[0146]

[0147] In the formula, x, y, z are the coordinates of the submersible's position. θ and ψ are the attitude angles of the submersible;

[0148] g(η) is the restoring force matrix, expressed as:

[0149]

[0150] In the formula, B is the magnitude of the buoyant force, mg is the weight, and r B Let r be the position vector of the center of buoyancy in the fuselage coordinate system. G Let be the position vector of the center of gravity in the fuselage coordinate system.

[0151] The submersible has four mechanical legs: a left front leg, a right front leg, a left rear leg, and a right rear leg. The supporting forces at the ends of the mechanical legs are mapped to the center of mass of the submersible using a Jacobian matrix, yielding the equivalent torque, expressed as:

[0152]

[0153] In the formula, J iLet F be the Jacobian matrix of the i-th leg, representing the mapping from joint velocity to foot velocity. i The contact forces are at the foot ends, where F1 is the left front, F2 is the right front, F3 is the left rear, and F4 is the right rear leg. The systemic positions of the four legs relative to their centers of mass are as follows: in:

[0154]

[0155] The contact force at the foot end satisfies the conical constraint of friction, and its expression is:

[0156]

[0157] In the formula, μ is the friction coefficient, and f ix ,f iy ,f iz For F i Components in the geodetic coordinate system;

[0158] The equivalent force / torque of the thruster mapped to the center of mass is expressed as:

[0159] τ 推 =B t T

[0160] In the formula, B t The thrust allocation matrix for the submersible is given, where T represents the thrust of a single thruster.

[0161]

[0162] In the formula, the symbol c represents the cosine function cos(·), and the symbol s represents the sine function sin(·). h y h Represents the horizontal thruster and the carrier coordinate system x B y B lever arm, x v y v Represents the vertical thruster and the carrier coordinate system x B y B The lever arm.

[0163] S2 uses an inertial navigation IMU, an altitude sensor, and a DVL sensor to collect real-time motion state data of the submersible, and then uses an extended Kalman filter to fuse the multi-sensor data to obtain the actual motion state feedback of the submersible.

[0164] In this embodiment, step S2 includes the following sub-steps:

[0165] Submersible motion data includes position, velocity, attitude, and sensor bias data, expressed as:

[0166]

[0167] In the formula, p n =[x,y,z] T , representing the position in the geodetic coordinate system; v b =[u,v,w] T θ represents the velocity in the carrier coordinate system; θ = [φ, θ, ψ] T , which is represented by the attitude angle; This is represented as zero bias of the IMU accelerometer. This is represented as zero bias of the IMU gyroscope;

[0168] The state equation for the submersible's state information changing over time is established, and its expression is:

[0169]

[0170] In the formula, x is the motion state vector of the submersible, and u is the acceleration measurement value of the IMU. m and angular velocity measurement ω m w represents process noise, modeled as zero-mean Gaussian noise, with covariance matrix Q.

[0171] The state equations expand as follows:

[0172]

[0173] In the formula, Let T(θ) be the rotation matrix from the carrier coordinate system to the geodetic coordinate system, and T(θ) be the matrix representing the angular velocity to the Euler angular rate. The expression is:

[0174]

[0175] g n =[0,0,g] T ≈[0,0,9.81] T , represented as the gravity vector; w a To measure noise with an accelerometer, w g The gyroscope angular velocity is represented by random white noise; w ba For accelerometer zero-bias random walk noise; w bg This refers to the zero-bias random walk noise of the gyroscope.

[0176] The state equations are discretized using the first-order Euler method, and the expression is as follows:

[0177]

[0178] In the formula, Δt is the sampling time;

[0179] Based on the sensor observations and the discretized state equation, the sensor observation equation is constructed as follows:

[0180] z k =h(x k )+v k

[0181] In the formula, z k h(x) represents the observed value, indicating the data actually measured by the sensor at time k; k Let be the observation function, representing the state x. k A deterministic function mapped to the observations; v k Observational noise represents the random error introduced by sensor measurements;

[0182] Based on the depth gauge and sensor observation equations, the depth gauge observation equation is obtained, and its expression is:

[0183]

[0184] In the formula, p z The depth gauge directly measures the value, v depth This refers to the observation noise of the depth gauge;

[0185] Based on the DVL and sensor observation equations, the DVL observation equation is obtained, and its expression is:

[0186] v d =z DVL =v b +v DVL ,v DVL ~N(0,R DVL )

[0187] In the formula, v b For DVL, the velocity value, v DVL This refers to the observation noise in DVL;

[0188] Based on the IMU and sensor observation equations, the IMU observation equation is obtained, and its expression is:

[0189]

[0190] Based on the optimal estimated state and IMU measurements from the previous time step, the extended Kalman filter (EKF) method is used to predict the current state and covariance matrix.

[0191] When DVL or depth gauge data is refreshed, the predicted state is corrected using the observed values ​​and observation equations, the Kalman gain is calculated, and the state estimate and covariance matrix are updated.

[0192] When IMU data is refreshed, the current state is predicted through the state equation without observation correction.

[0193] When the DVL or depth gauge data is refreshed, the predicted state at the current moment is fused with the DVL and depth gauge observations, and the optimal estimated state information of the submersible is calculated through a filtering algorithm to obtain the actual motion state feedback of the submersible.

[0194] It should be noted that, based on the optimal estimated state and IMU measurements from the previous time step, the Extended Kalman Filter (EKF) method is used to predict the current state and covariance matrix.

[0195] State prediction expression:

[0196]

[0197] In the formula, The predicted state indicates that the state at time k is based on the data from the previous time k-1. The state is the optimal estimate of the state at the previous time step, and f(·) is the nonlinear state equation; u k For control input, Δt is the time step, and 0 means ignoring process noise.

[0198] Covariance matrix prediction expression:

[0199]

[0200] In the formula, P k|k-1 To predict the covariance matrix, F k-1 Let Jacobian be the state transition matrix, in Linearization is achieved, and the expression is:

[0201]

[0202] P k-1|k-1 Let Q be the covariance matrix of the previous time step. k-1 The process noise covariance matrix contains w a w g w ba w bg Statistical characteristics.

[0203] When DVL or depth gauge data is refreshed, the predicted state is corrected using the observed values ​​and observation equations, the Kalman gain is calculated, and the state estimate and covariance matrix are updated.

[0204] The expression for calculating the Kalman gain is:

[0205]

[0206] In the formula, K k H is the Kalman gain, used to weigh the predictions against the observations. k To observe the Jacobian matrix, in Linearization: The expression is:

[0207]

[0208] R k To observe the noise covariance matrix.

[0209] The state estimation correction expression is:

[0210]

[0211] In the formula, For the corrected optimal estimated state, z k The actual observed value is given, and h(·) is the nonlinear observation equation. The new information represents the difference between observation and prediction.

[0212] Covariance correction expression:

[0213] P k|k =(IK k H k )P k|k-1

[0214] In the formula, P k|k Let I be the corrected covariance matrix, and let I be the identity matrix.

[0215] When IMU data is refreshed, the current state is predicted using the state equation, without observation correction; the expression is:

[0216]

[0217] In the formula, u k Input to the IMU;

[0218] When DVL or depth gauge data is refreshed, the predicted state at the current moment is fused with the DVL and depth gauge observations, and the optimal estimated state information of the submersible is calculated using a filtering algorithm to obtain the actual motion state feedback of the submersible; the expression is:

[0219]

[0220] In the formula, K k Let h(·) be the Kalman gain, and h(·) be the observation function.

[0221] It should be noted that EKF enables spatiotemporal alignment and optimal fusion of multi-sensor data, significantly improving the accuracy, robustness, and environmental adaptability of submersible motion state estimation while ensuring real-time performance, thus providing a reliable state feedback basis for complex underwater missions.

[0222] S3 compares the actual motion state feedback of the submersible with the planned expected motion state data to calculate the position error and attitude error.

[0223] Step S3 in this embodiment includes the following sub-steps:

[0224] The planned expected motion state data includes the expected position and the expected attitude;

[0225] The position error is calculated by comparing the actual position fed back from the submersible's actual motion state with the expected position, and the expression is as follows:

[0226] e p =x d -x

[0227] In the formula, e p Let X be the position error vector. d Let X be the desired position vector, and let X be the real-time monitoring position vector.

[0228] The attitude error is calculated by comparing the actual attitude feedback from the submersible's actual motion state with the corresponding desired attitude, and the expression is as follows:

[0229] e a =θ d -θ

[0230] In the formula, e a Let θ be the attitude error vector. d Let θ represent the desired attitude information, and let θ represent the real-time monitored attitude information.

[0231] S4. Based on the position error and attitude error, the translational force and rotational torque required to eliminate the position and attitude errors are calculated using the PID control algorithm and backstepping method.

[0232] Step S4 in this embodiment includes the following sub-steps:

[0233] Based on the position and attitude errors, PID control is used to calculate the compensation control force and rotational torque, which are expressed as follows:

[0234]

[0235] In the formula, To compensate for the lack of control, These are the proportional gain, integral gain, and derivative gain for position control, respectively. These represent the proportional gain, integral gain, and derivative gain for attitude control, respectively; m is the submersible mass; g is the gravitational acceleration vector; and τ is the differential gain. d To compensate for rotational torque;

[0236] S32, based on the compensation control force, the compensation translational force is obtained using the backstepping method and its expression is:

[0237]

[0238] In the formula, F d To compensate for the translational force, V d Let V be the desired velocity. d Let ξ be the first derivative of the desired position with respect to time, and V be the actual velocity; p α is a dummy control variable. p This is the dynamic surface attenuation coefficient.

[0239] It should be noted that by calculating position and attitude errors, the difference between the actual motion state and the desired motion state of the submersible can be quantified. This allows the control algorithm to accurately know the gap between the current state and the desired state, thus providing an accurate basis for subsequent control. Furthermore, position and attitude errors are important input parameters for PID control algorithms and backstepping methods. Based on the magnitude and direction of the error, the control algorithm calculates the control force required to eliminate the error, thereby driving the submersible's thrusters and mechanical legs to adjust, making the submersible approach the desired motion state.

[0240] S5. Establish the optimization objective function, and assign weights to the thruster module and mechanical leg module according to the translational force required for the submersible to eliminate position errors, so as to obtain the mechanical leg force and thruster force.

[0241] Step S5 in this embodiment includes the following sub-steps:

[0242] Construct an optimization objective function to minimize the weighted execution cost of the thrusters and robotic legs when performing compensating translational forces, expressed as:

[0243]

[0244] In the formula, F leg For mechanical leg force, F thr For the thruster force, W1 is the force mapping diagonal weight matrix of the mechanical leg, W2 is the force mapping diagonal weight matrix of the mechanical thruster, and st represents the constraint condition;

[0245] Using the pseudo-inverse distribution method, the compensating translational force is substituted into the pseudo-inverse formula to calculate the mechanical leg force and the propeller force, as expressed in the following expressions:

[0246]

[0247] F thr =F d -F leg

[0248] In the formula, Fleg For mechanical leg force, F thr For propulsion force.

[0249] It should be noted that by constructing an objective function, an optimal force distribution scheme can be found under the premise of satisfying the constraints, so as to minimize the total cost of the thrusters and mechanical legs when performing compensating translational forces, thereby improving the motion control efficiency of the submersible, reducing energy consumption, and enhancing the stability of the system. Furthermore, by using a pseudo-inverse distribution method, the specific values ​​of the mechanical leg forces and thruster forces can be obtained. These values ​​will be used as inputs to subsequent control algorithms to drive the mechanical legs and thrusters to move, thereby eliminating position errors and maintaining a stable attitude.

[0250] S6, based on the number and layout of the submersible's mechanical legs, performs inverse dynamics decomposition of the mechanical leg forces to obtain the torque magnitude of each joint of the mechanical leg, and controls the movement of the mechanical leg through the actuator.

[0251] Step S6 includes the following sub-steps:

[0252] An inverse dynamics model of the robotic leg is established. Using this model, the driving torque of each joint of the robotic leg is calculated, and the expression is as follows:

[0253]

[0254] In the formula, τ i Let M be the driving torque of the i-th joint of the robotic leg, q be the current joint angle of the robotic leg, and M be the driving torque of the i-th joint. i (q) is the inertia matrix of joint i. G represents the Coriolis force and centrifugal force terms for joint i. i (q) represents the gravity term of joint i. F is the transpose of the Jacobian matrix of joint i. leg For mechanical leg force;

[0255] Based on the driving torque τ of each joint i The system generates corresponding actuator control commands, and the actuators generate corresponding driving torques according to the control commands to drive the movement of each joint of the mechanical leg, adjust the posture and position of the mechanical leg, and realize the control of the submersible's posture.

[0256] It should be noted that by establishing a support force distribution model and a mechanical leg inverse dynamics model, the driving torque of each joint of the mechanical leg can be accurately calculated, thereby improving the control accuracy of the submersible's attitude. Reasonable support force distribution and mechanical leg motion control can maintain the stable attitude of the submersible and reduce motion instability caused by external interference or changes in internal parameters.

[0257] S7. Based on the number and layout of the submersible thrusters, a thrust distribution model is established. The thruster force is distributed using a pseudo-inverse method to obtain the rotational speed of each thruster. The thruster movement is then controlled by the actuator.

[0258] In this embodiment, step S7 includes the following sub-steps:

[0259] Based on the number and layout of the submersible's thrusters, a thrust distribution model is established, and the thruster forces are substituted into the thrust model. The pseudo-inverse method is used to solve for the speed commands of each thruster, and the expression is:

[0260] F thr =T·K t ·n 2

[0261]

[0262] In the formula, T represents the thruster configuration matrix, K t =diag(k) t1 ,...,k tm ), K t This represents the thrust coefficient matrix, n = [n1, ..., n]. m ] T , n represents the thruster speed command; T + K represents the pseudo-inverse of the thrust configuration matrix. t -1 The matrix representing the inverse of the thrust coefficient matrix K;

[0263] The saturation limiting algorithm is used to saturate and limit the speed commands of each thruster, resulting in the saturated speed command, expressed as:

[0264] n cmd =sat(n,n min ,n max )

[0265] In the formula, n cmd This is the speed command after saturation limiting, where sat(·) is the speed saturation function, and n min To limit the minimum rotational speed of the thruster, n max This is the maximum rotational speed limit for the thruster;

[0266] Based on the speed command after saturation limitation, a corresponding actuator control command is generated. The actuator generates a corresponding speed according to the control command, drives the thruster to rotate and generate thrust, and adjusts the attitude and position of the submersible.

[0267] It should be noted that by solving the thruster speed command using the pseudo-inverse method and combining it with the saturation limit algorithm, the reasonable distribution of thrust force between the thrusters can be ensured, thereby improving the control accuracy of the submersible's attitude and position. Reasonable thrust distribution and saturation limit can avoid performance degradation or failure of the thrusters due to overload or underload, and enhance the stability of the system.

[0268] S8 uses the movement of mechanical legs and thrusters to collect real-time motion state data of the submersible and generate feedback on the actual motion state of the submersible, forming a closed-loop control.

[0269] In this embodiment, the benthic submersible employs a multimodal motion system based on thrusters and robotic legs to achieve overall attitude stability. The overall motion trajectory of the submersible is planned according to mission requirements. During motion, sensors such as inertial navigation, depth gauges, and DVL (Displacement Volume Level) sensors monitor the submersible's motion status in real time. When the actual motion status deviates from the planned desired position and attitude angle, the attitude controller distributes the total control force of the submersible body to the thruster system and robotic leg system according to the magnitude of the error. The distribution weight is determined by the submersible's current motion stage and motion status. Combined with the submersible's thruster layout, the thrust commands of the thruster system are distributed to obtain... The thrust and rotational speed of each thruster are determined by the electronic control system, which issues a speed command to control the thruster's movement. Combined with the layout of the benthic submersible's mechanical legs, the support force command of the mechanical leg system is decomposed using inverse dynamics to obtain the torque magnitude of each joint. The electronic control system then issues current commands to the joints to control the mechanical leg's movement. During the operation of the thruster and mechanical leg systems, the output data from various sensors are fused and processed through a state observer / filter to obtain the submersible's actual state feedback, thus forming a closed-loop control system. This enhances the system's robustness and reliability, improves load balancing capabilities and safety, and ensures the submersible's ability to withstand seabed turbulence and adapt to rugged terrain.

[0270] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A state control method for a deep-sea hydrothermal sampling system's bottom-diving submersible, characterized in that: The submersible includes thrusters and mechanical legs for adjusting the submersible's attitude, and the method includes the following steps: S1. Using a path planning algorithm combined with seabed topographic data, the desired motion trajectory of the submersible from its current position to the target hydrothermal vent is generated, and the desired motion state data of the submersible's movement trajectory is obtained. S2 uses an inertial navigation IMU, a depth gauge, and a DVL sensor to collect the submersible's motion state data in real time, and uses an extended Kalman filter to fuse the multi-sensor data to obtain the actual motion state feedback of the submersible. S3 compares the actual motion state feedback of the submersible with the planned expected motion state data to calculate the position error and attitude error; S4. Based on the position error and attitude error, the translational force and rotational torque required to eliminate the position and attitude errors are calculated using the PID control algorithm and backstepping method. S5. Establish the optimization objective function, and assign weights to the thruster module and the mechanical leg module according to the translational force required for the submersible to eliminate position error, so as to obtain the mechanical leg force and the thruster force. S6. Based on the number and layout of the submersible's mechanical legs, the forces of the mechanical legs are decomposed inversely to obtain the torque of each joint of the mechanical leg, and the movement of the mechanical leg is controlled by the actuator. S7. Based on the number and layout of the submersible thrusters, a thrust distribution model is established. The thruster force is distributed using the pseudo-inverse method to obtain the rotational speed of each thruster. The thruster movement is then controlled by the actuator. S8 uses the movement of mechanical legs and thrusters to collect real-time motion state data of the submersible and generate feedback on the actual motion state of the submersible, forming a closed-loop control.

2. The state control method for a bottom-dwelling submersible in a deep-sea hydrothermal sampling system as described in claim 1, characterized in that, Step S2 involves using an inertial navigation IMU, an altitude sensor, and a DVL sensor to collect real-time motion state data of the submersible, and then fusing the multi-sensor data using an extended Kalman filter to obtain the actual motion state feedback of the submersible. This includes the following sub-steps: Submersible motion data includes position, velocity, attitude, and sensor bias data, expressed as: In the formula, p n =[x,y,z] T , representing the position in the geodetic coordinate system; v b =[u,v,w] T θ represents the velocity in the carrier coordinate system; θ = [φ, θ, ψ] T , which is represented by the attitude angle; This is represented as zero bias of the IMU accelerometer. This is represented as zero bias of the IMU gyroscope; The state equation for the submersible's state information changing over time is established, and its expression is: In the formula, x is the motion state vector of the submersible, and u is the acceleration measurement value of the IMU. m and angular velocity measurement value ω m w represents process noise, modeled as zero-mean Gaussian noise, with covariance matrix Q. The state equations expand as follows: In the formula, Let T(θ) be the rotation matrix from the carrier coordinate system to the geodetic coordinate system, and T(θ) be the matrix representing the angular velocity to the Euler angular rate. The expression is: g n =[0,0,g] T ≈[0,0,9.81] T , represented as the gravity vector; w a To measure noise with an accelerometer, w g The gyroscope angular velocity is represented by random white noise; w ba For accelerometer zero-bias random walk noise; w bg This refers to the zero-bias random walk noise of the gyroscope. The state equations are discretized using the first-order Euler method, and the expression is as follows: In the formula, Δt is the sampling time; Based on the sensor observations and the discretized state equation, the sensor observation equation is constructed as follows: z k =h(x k )+v k In the formula, z k h(x) represents the observed value, indicating the data actually measured by the sensor at time k; k Let be the observation function, representing the state x. k A deterministic function mapped to the observations; v k Observational noise represents the random error introduced by sensor measurements; Based on the depth gauge and sensor observation equations, the depth gauge observation equation is obtained, and its expression is: In the formula, p z For the depth gauge's direct measurement, v depth This refers to the observation noise of the depth gauge; Based on the DVL and sensor observation equations, the DVL observation equation is obtained, and its expression is: v d =z DVL =v b +v DVL ,v DVL ~N(0,R DVL ) In the formula, v b For DVL, the velocity value, v DVL This refers to the observation noise in DVL; Based on the IMU and sensor observation equations, the IMU observation equation is obtained, and its expression is: Based on the optimal estimated state and IMU measurements from the previous time step, the extended Kalman filter (EKF) method is used to predict the current state and covariance matrix. When DVL or depth gauge data is refreshed, the predicted state is corrected using the observed values ​​and observation equations, the Kalman gain is calculated, and the state estimate and covariance matrix are updated. When IMU data is refreshed, the current state is predicted through the state equation without observation correction. When DVL or depth gauge data is refreshed, the predicted state at the current moment is fused with the DVL and depth gauge observations, and the optimal estimated state information of the submersible is calculated through a filtering algorithm to obtain the actual motion state feedback of the submersible.

3. The state control method for the bottom submersible of the deep-sea hydrothermal sampling system as described in claim 2, characterized in that, Step S3, which compares the actual motion state feedback of the submersible with the planned expected motion state data to calculate the position error and attitude error, includes the following sub-steps: The planned expected motion state data includes the expected position and the expected attitude; The position error is calculated by comparing the actual position fed back from the submersible's actual motion state with the expected position, and the expression is as follows: e p =x d -x In the formula, e p Let X be the position error vector. d Let X be the desired position vector, and let X be the real-time monitoring position vector. The attitude error is calculated by comparing the actual attitude feedback from the submersible's actual motion state with the corresponding desired attitude, and the expression is as follows: e a =θ d -θ In the formula, e a Let θ be the attitude error vector. d Let θ represent the desired attitude information, and let θ represent the real-time monitored attitude information.

4. The state control method for the bottom submersible of the deep-sea hydrothermal sampling system as described in claim 3, characterized in that: Step S4, which involves using a PID control algorithm for initial adjustment based on position and attitude errors, and calculating the translational force and rotational torque required to eliminate these errors using a backstepping method, includes the following sub-steps: Based on the position and attitude errors, PID control is used to calculate the compensation control force and rotational torque, which are expressed as follows: In the formula, To compensate for the lack of control, These are the proportional gain, integral gain, and derivative gain for position control, respectively. These represent the proportional gain, integral gain, and derivative gain for attitude control, respectively; m is the submersible mass; g is the gravitational acceleration vector; and τ is the differential gain. d To compensate for rotational torque; S32, based on the compensation control force, the compensation translational force is obtained using the backstepping method and its expression is: In the formula, F d To compensate for the translational force, V d Let V be the desired velocity. d Let ξ be the first derivative of the desired position with respect to time, and V be the actual velocity; p α is a dummy control variable. p This is the dynamic surface attenuation coefficient.

5. The state control method for a bottom-diving submersible in a deep-sea hydrothermal sampling system as described in claim 4, characterized in that, Step S5 involves establishing an optimization objective function and allocating weights to the thruster module and the robotic leg module based on the translational force required for the submersible to eliminate position errors, thereby obtaining the robotic leg force and thruster force. This includes the following sub-steps: Construct an optimization objective function to minimize the weighted execution cost of the thrusters and robotic legs when performing compensating translational forces, expressed as: In the formula, F leg For mechanical leg force, F thr For the thruster force, W1 is the force mapping diagonal weight matrix of the mechanical leg, W2 is the force mapping diagonal weight matrix of the mechanical thruster, and st represents the constraint condition; Using the pseudo-inverse distribution method, the compensating translational force is substituted into the pseudo-inverse formula to calculate the mechanical leg force and the propeller force, as expressed in the following expressions: F thr =F d -F leg In the formula, F leg For mechanical leg force, F thr For propulsion force.

6. The state control method for a bottom-dwelling submersible in a deep-sea hydrothermal sampling system as described in claim 5, characterized in that: Step S6 describes the inverse dynamics decomposition of the mechanical leg forces based on the number and layout of the submersible's mechanical legs, obtaining the torque magnitude of each joint of the mechanical leg, and controlling the movement of the mechanical leg through actuators. This includes the following sub-steps: An inverse dynamics model of the robotic leg is established. Using this model, the driving torque of each joint of the robotic leg is calculated, and the expression is as follows: In the formula, τ i Let M be the driving torque of the i-th joint of the robotic leg, q be the current joint angle of the robotic leg, and M be the driving torque of the i-th joint. i (q) is the inertia matrix of joint i. G represents the Coriolis force and centrifugal force terms for joint i. i (q) represents the gravity term of joint i. F is the transpose of the Jacobian matrix of joint i. leg For mechanical leg force; Based on the driving torque τ of each joint i The system generates corresponding actuator control commands, and the actuators generate corresponding driving torques according to the control commands to drive the movement of each joint of the mechanical leg, adjust the posture and position of the mechanical leg, and realize the control of the submersible's posture.

7. The state control method for a deep-sea hydrothermal sampling system bottom submersible as described in claim 5, characterized in that: Step S7 describes establishing a thrust distribution model based on the number and layout of the submersible thrusters, distributing the thruster force using a pseudo-inverse method to obtain the rotational speed of each thruster, and controlling the thruster movement through actuators. This includes the following sub-steps: Based on the number and layout of the submersible's thrusters, a thrust distribution model is established, and the thruster forces are substituted into the thrust model. The pseudo-inverse method is used to solve for the speed commands of each thruster, and the expression is: F thr =T·K t ·n 2 In the formula, T represents the thruster configuration matrix, K t =diag(k) t1 ,...,k tm ), K t This represents the thrust coefficient matrix, n = [n1, ..., n]. m ] T , n represents the thruster speed command; T + K represents the pseudo-inverse of the thrust configuration matrix. t -1 The matrix representing the inverse of the thrust coefficient matrix K; The saturation limiting algorithm is used to saturate and limit the speed commands of each thruster, resulting in the saturated speed command, expressed as: n cmd =sat(n,n min ,n max ) In the formula, n cmd This is the speed command after saturation limiting, where sat(·) is the speed saturation function, and n min To limit the minimum rotational speed of the thruster, n max This is the maximum rotational speed limit for the thruster; Based on the speed command after saturation limitation, a corresponding actuator control command is generated. The actuator generates a corresponding speed according to the control command, drives the thruster to rotate and generate thrust, and adjusts the attitude and position of the submersible.

8. A deep-sea hydrothermal vent sampling system bottom submersible, characterized in that: A method for controlling the state of a bottom-dwelling submersible in a deep-sea hydrothermal sampling system as described in any one of claims 1-7, comprising a hydrothermal sampling mechanism (1) and a bottom-dwelling submersible mechanism (2), wherein, A circular groove (200) is provided in the middle of the bottom submersible mechanism (2), and a hydrothermal sampling mechanism (1) is set in the circular groove (200) for extracting and sampling deep-sea hydrothermal fluids; The bottom-diving submersible mechanism (2) includes four thrusters (21) and four mechanical legs (22). The four thrusters (21) and four mechanical legs (22) are located at the four corners of the bottom-diving submersible mechanism (2), and two adjacent thrusters (21) and two adjacent mechanical legs (22) are symmetrically arranged. The thruster (21) is arranged horizontally with the bottom-diving submersible mechanism (2), and the mechanical legs (22) are arranged vertically with the bottom-diving submersible mechanism (2). The four thrusters (21) and four mechanical legs (22) are used to adjust the attitude of the submersible.

9. The deep-sea hydrothermal sampling system bottom submersible as described in claim 8, characterized in that: The hydrothermal sampling mechanism (1) includes an extractor (11), a sampling sensor (12), and a hydrothermal storage device (13). The hydrothermal storage device (13) is located in a circular trough (200) for storing hydrothermal samples. The sampling sensor (12) is located in the hydrothermal storage device (13) for testing and recording the physicochemical properties of the hydrothermal samples. The extractor (11) is located on the side of the hydrothermal storage device (13) near the bottom submersible mechanism (2), and one end of the extractor (11) is connected to the hydrothermal storage device (13) for extracting hydrothermal samples into the hydrothermal storage device (13).

10. The deep-sea hydrothermal sampling system bottom submersible as described in claim 8, characterized in that: The mechanical leg (22) includes a bearing bracket (201), a housing (202), a thigh support arm (203), a lower leg support arm (204), a connecting rod (205), a lower leg joint motor (206), a thigh joint motor (207), and a hip joint motor (208), wherein, The hip joint motor (208) is fixed on the bottom submersible mechanism (2), and the output shaft of the hip joint motor (208) is fixedly connected to the top of the bearing bracket (201) to drive the mechanical leg (22) to swing in and out. The bearing bracket (201) has two rotating cylinders (209), the housing (202) is rotatably connected between the two rotating cylinders (209), and the thigh joint motor (207) is fixed on the side of one of the rotating cylinders (209), and the output shaft of the thigh joint motor (207) is fixedly connected to the housing (202) to drive the housing (202) to swing back and forth along the central axis of the two rotating cylinders (209); The lower leg joint motor (206) is rotatably connected to the rotating cylinder (209) on the side away from the thigh joint motor (207), and the lower leg joint motor (206) is fixedly connected to the housing (202). The output shaft of the lower leg joint motor (206) is hinged to one end of the thigh support arm (203), and the other end of the thigh support arm (203) is hinged to the lower leg support arm (204) on the housing (202). A drive disk is fixed on the outside of the output shaft of the lower leg joint motor (206). One end of the connecting rod (205) is hinged to the drive disk of the lower leg joint motor (206), and the other end of the connecting rod (205) is hinged to one end of the lower leg support arm (204). The other end of the lower leg support arm (204) serves as the support point between the mechanical leg and the ground. The connecting rod (205) forms a parallelogram with the lower leg support arm (204) and the thigh support arm (203). The lower leg joint motor (206) controls the lower leg support arm (204) to swing back and forth through the parallelogram connecting rod.

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