An underwater robot propeller anti-stall protection and autonomous self-rescue method

Abstract

The application discloses an underwater robot propeller anti-blocking protection and autonomous self-help method, which comprises a robot body, a propeller, a multi-source information cooperative detection module, an intelligent analysis module and a ballast and energy storage airbag module; the multi-source information cooperative detection module collects input voltage, input current, measured propeller speed, vibration signal and sonar image of the propeller in real time, the intelligent analysis module calculates power residual error, vibration abnormality, foreign matter shielding ratio and current abnormality based on propeller input effective power, propeller absorbed power, measured propeller speed and theoretical reference propeller speed, and obtains a blocking risk index; and corresponding graded response actions are executed according to the blocking risk index. The method combines propeller edge protection, propeller state recognition and graded self-help control in the same control process, can reduce the risk of propeller winding, jamming and overload failure in complex water areas, and improve the reliability of continuous operation and fault recovery of the underwater robot.

Description

Technical Field

[0001] This invention relates to the field of safety control technology for underwater robot propulsion systems, specifically to a method for preventing stalling and enabling autonomous self-rescue of underwater robot thrusters based on multi-source state recognition, blade edge composite protection, and graded response control. Background Technology

[0002] With the development of marine development and deep-sea operation equipment, underwater robots have been widely used in complex mission scenarios such as marine resource exploration, subsea pipeline inspection, deep-sea sampling, marine environmental monitoring, underwater structure maintenance, and underwater reconnaissance. In actual operations, underwater robots often need to navigate complex waters containing fishing nets, aquaculture cables, seaweed, seabed mud, and suspended particles. The reliability of their power systems directly affects mission safety and equipment recovery success rates.

[0003] As the core actuator of an underwater robot, the thruster directly determines its power output, attitude maintenance, and trajectory control capabilities. In complex underwater environments, the thruster propeller, drive shaft, and their support clearances are susceptible to stalling due to entanglement of fibrous foreign objects, jamming of hard particles, sediment intrusion, and external collisions. Thruster stalling causes a sharp drop in effective thrust, reducing motion control accuracy; in severe cases, it can lead to drive motor overload, abnormal winding temperature rise, controller protection failure, or even complete loss of control and communication. Therefore, achieving real-time identification, active extrication, and failure recovery of thruster stalling is a key technical challenge for improving the reliability of underwater robot operations in complex waters.

[0004] Currently, existing technologies for preventing stalling of underwater robot thrusters mainly focus on structural protection and fault identification, but these are still insufficient in complex marine environments.

[0005] First, regarding structural protection, some technologies reduce the probability of direct impact from large, hard foreign objects onto the propeller blades by installing a ducted shell around the propeller. However, this type of structure is insufficient to prevent slender, flexible debris such as fishing net fibers and seaweed from entering the duct and entangled around the blade roots or drive shaft. Furthermore, the duct structure increases hydrodynamic drag, reducing propulsion efficiency and shortening endurance. Other technologies design a moving shell that completely encloses the drive shaft end and reduces the clearance between the moving and stationary shells to reduce the probability of fibrous debris entering the moving pair; however, fine deposits can still enter through the gaps and accumulate over time, and collisions with hard objects can cause shell deformation and further narrowing of the clearance, increasing the risk of stalling. Still other technologies use carbon fiber-fiberglass hybrid materials to fabricate lightweight blades, but these lack an edge-cutting and impact-resistant composite structure specifically designed to prevent fibrous entanglement, making it difficult to actively clean entangled debris from the blade's rotation area.

[0006] Secondly, regarding stall fault identification, some technologies determine the stall state by monitoring sudden changes in drive motor current. However, when underwater robots operate under different water depths, current velocities, and water densities, the propeller load itself undergoes normal fluctuations. A single current threshold method is insufficient to distinguish between normal load changes and abnormal changes caused by stall, easily leading to false alarms or missed alarms. Other technologies utilize hydrophones to collect the propeller operating noise spectrum to identify faults such as missing propeller blades, shaft misalignment, and stall. However, ocean background noise, noise from other equipment, and current noise significantly affect the stability of the acoustic spectrum identification, making it difficult to meet the requirements for rapid identification and timely response to stall faults.

[0007] Therefore, there is an urgent need to propose a method for underwater robot thruster anti-jamming protection and autonomous self-rescue that can take into account structural protection, state recognition, active escape and failure recovery, so as to improve the safety, stability and economy of operations in complex waters. Summary of the Invention

[0008] This invention aims to address the problems of high false alarm rate in stall detection, insufficient active escape capability after stalling, and difficulty in recovery after severe failure in existing underwater robot thrusters. To this end, this invention provides an underwater robot thruster anti-stall protection and autonomous self-rescue method based on multi-source state recognition, blade edge composite protection, and graded response control. This method aims to improve the reliability and economy of underwater robots operating in complex marine environments and reduce the overall cost of deep-sea resource extraction.

[0009] To achieve the above objectives, the present invention adopts the following technical solution: a method for preventing stalling and protecting the underwater robot's thruster, and for autonomous self-rescue, comprising the following steps: The underwater robot is mainly composed of the underwater robot body, thrusters, multi-source information collaborative detection module, intelligent analysis module, and ballast and energy storage airbag module. The multi-source information collaborative detection module collects the input voltage, input current, measured blade speed, vibration signal and sonar image of the thruster in real time, and transmits the collected data to the intelligent analysis module. The intelligent analysis module calculates the effective input power of the thruster based on the input voltage, input current and transmission system efficiency, and calculates the theoretical reference blade speed based on the effective input power of the thruster; at the same time, it establishes a blade absorption power model based on the blade power coefficient, blade diameter and water density, and substitutes the measured blade speed into the blade absorption power model to calculate the power residual. The intelligent analysis module performs bandpass filtering and spectrum analysis on the vibration signal of the thruster, extracts the fundamental frequency, harmonics and abnormal high-frequency components, and calculates the vibration anomaly degree based on the vibration amplitude of the abnormal frequency band and the reference vibration amplitude. The intelligent analysis module identifies the blade region in the sonar image of the thruster, extracts the blade outline integrity, foreign object obstruction area and edge anomaly features, and calculates the foreign object obstruction ratio based on the foreign object obstruction area and the blade identification area. The intelligent analysis module calculates the speed deviation based on the theoretical reference blade speed and the measured blade speed. At the same time, it calculates the current anomaly based on the measured current and the reference current. It also calculates the stall risk index by combining the power residual, vibration anomaly and foreign object obstruction ratio. The stall risk level is identified based on the calculated stall risk index. The intelligent analysis module identifies the level of blockage risk based on the blockage risk index and outputs graded response commands according to the level of blockage risk, enabling the underwater robot to perform corresponding graded response actions.

[0010] Preferably, a composite blade protection structure is provided on the propeller. The composite blade protection structure includes a blade body, a reinforcing edge member, a transition bonding layer, and an interface locking structure. The reinforcing edge member is continuously or intermittently disposed on the leading edge, outer edge, and / or tip region of the blade body. The transition bonding layer is located between the blade body and the reinforcing edge member. The interface locking structure is used to restrict the reinforcing edge member from peeling, slipping, or fatigue loosening relative to the blade body. The composite blade protection structure is used to cut, peel, or remove fibrous foreign matter entering the blade rotation area and improve the impact resistance and fatigue resistance of the blade edge.

[0011] Preferably, the blade body is made of carbon fiber reinforced engineering plastic, glass fiber reinforced engineering plastic, or lightweight alloy material; the reinforcing edge component is made of precipitation hardened stainless steel, titanium alloy, nickel-titanium alloy, wear-resistant ceramic coated metal strip, or carbon fiber reinforced layer; the interface locking structure is a sawtooth, dovetail, corrugated, or stepped structure; the upstream side of the reinforcing edge component forms a guide slope or micro-cutting edge to cut, peel, or remove fibrous entanglement during blade rotation.

[0012] Preferably, the effective input power of the propeller, the absorbed power of the blades, the theoretical reference blade speed, and the power residual are calculated using the following formulas:

[0013] In the formula, P in To provide effective power to the thruster; U The input voltage of the thruster; I This refers to the input current of the thruster; or For the efficiency of the propulsion system; P abs ( n The blade rotation speed is nThe blade absorbs power at that time; r Density of water; C P The blade power coefficient; D The diameter of the blade; n ref The theoretical reference blade speed; n meas The measured blade speed; E p For power residual; e P This is used to avoid positive numbers with a denominator of zero.

[0014] Preferably, the stall risk index is calculated using the following formula: ; In the formula, R s The congestion risk index; E n , E v , E s , E i and E p These are rotational speed deviation, vibration anomaly, foreign object obstruction ratio in sonar images, current anomaly, and power residual; w 1. w 2. w 3. w 4 and w 5 represents the corresponding weight coefficient, which satisfies: .

[0015] Preferably, the speed deviation, vibration anomaly, foreign object obstruction ratio, and current anomaly are calculated as follows:

[0016] In the formula, n meas The measured blade speed; A abn This refers to the vibration amplitude in the abnormal frequency band. A base This is the reference vibration amplitude under normal operating conditions; S occ The area obstructed by the foreign object in the sonar image; S prop The area of ​​the blade identification region; I This refers to the input current of the thruster; I baseThe reference input current is used under the same speed command, the same water conditions, or the same calibration conditions. e n , e A , e S and e I These are all positive numbers used to avoid the denominator being zero.

[0017] Preferably, the multi-source information collaborative detection module includes a composite sensor network, a power detection module, and a high-speed data acquisition module. The composite sensor network includes a speed sensor, a vibration sensor, a sonar device, and a voltage and current sampling device. The speed sensor is used to acquire the measured blade speed, the vibration sensor is used to acquire vibration signals from the propeller housing, blade support structure, or transmission support node, the sonar device is used to acquire sonar images of the blade and its surrounding area, and the voltage and current sampling device is used to acquire the input voltage and input current of the propeller. The power detection module is used to perform isolated sampling, filtering, and effective power calculation on the input voltage and input current. The high-speed data acquisition module is used to synchronize and buffer the speed, vibration, sonar images, and voltage and current data.

[0018] Preferably, the intelligent analysis module includes a signal processing unit, a model calibration unit, a risk assessment unit, and a response control unit; the signal processing unit is used for denoising, filtering, Fourier transform, image segmentation, and feature normalization; the model calibration unit is used to correct the propeller power coefficient and transmission system efficiency based on bench calibration data, water density, water depth, water temperature, ocean current velocity, and salinity; the risk assessment unit is used to calculate the stall risk index and output the stall risk level; and the response control unit is used to output control commands to the propeller and ballast and energy storage airbag modules.

[0019] Preferably, the ballast and energy storage airbag module includes a fixed bionic ring belt, a high-pressure nitrogen delivery pipe, an airbag chamber, an airbag, a compression spring, an inflation valve, a release mechanism, and a releasable ballast component. The fixed bionic ring belt is used to fix the airbag chamber and the releasable ballast component to the outside of the underwater robot's pressure-resistant shell. The high-pressure nitrogen delivery pipe is used to supply air to the airbag. The airbag chamber is used to house the airbag and limit the airbag's deployment direction. The compression spring is used to assist the airbag in rapid deployment when released. The inflation valve is used to control the entry of high-pressure nitrogen into the airbag. The release mechanism is used to release the locking of the releasable ballast component. The releasable ballast component is used to detach from the underwater robot in a state of severe stall or loss of control, so as to reduce the underwater robot's gravity and form positive buoyancy with the deployment of the airbag, allowing the underwater robot to float and be recovered.

[0020] Preferably, the stall risk level includes four levels: normal, mild stall, moderate stall, and severe stall, and the stall risk level is determined as follows:

[0021] in, T 1 represents the first blocking risk threshold between normal and mild blocking conditions; T 2 represents the second blocking risk threshold between mild and moderate blocking conditions; T 3 is the third blocking risk threshold between moderate and severe blocking conditions, and it satisfies 0. <T 1 < T 2 < T 3.

[0022] Preferably, the graded response actions include, in sequence according to the stall risk level, power reduction protection, short-time forward and reverse pulse escape, pulse disturbance escape, thruster protection shutdown, ballast release, and airbag buoyancy recovery, specifically: When the stall risk level is normal, the thruster outputs thrust normally according to the mission control command, and the intelligent analysis module continuously monitors the stall risk index. When the stall risk level is mild stall, the intelligent analysis module reduces the thruster output power to 60% to 80% of the thruster command power before the stall risk is triggered, and executes short-term forward and reverse pulse control. The mild stall power command can be expressed as: P cmd = α 1 P 0 , 0 . 60≤ α 1≤0 . 80; In the formula, P cmd This is the commanded power after power reduction; P 0 represents the thruster command power before the stall risk is triggered; α 1 represents the power attenuation coefficient for mild stall; forward and reverse pulses are applied alternately to the propeller blades to loosen minor entanglements or small stuck particles; if R s Descend to within the set time T If the power output drops below 1, the intelligent analysis module will exit the escape mode and gradually restore normal power output. When the stall risk level is moderate, the intelligent analysis module limits the maximum thruster current and executes multiple sets of pulse disturbance escape actions. The moderate stall current constraint can be expressed as: I cmd≤ I lim , I lim = βI safe , 0 <β ≤1; In the formula, I cmd In response to the command current permitted by the control unit; I lim This is the rate limiting threshold; I safe For the safe current of the thruster motor; β For safety margin coefficient; in this mode, the intelligent analysis module can drive the propeller according to the preset pulse width, pulse interval and reverse duty cycle to loosen the entangled object or make the stuck particles leave the working area of ​​the blade; if the speed returns to the theoretical range and the vibration abnormality decreases after several consecutive escape attempts, the medium escape mode is exited; otherwise, the current limiting protection is maintained and the temperature rise and stall risk index are monitored. When the stall risk level is severe stall, the intelligent analysis module immediately executes the thruster protection shutdown, cuts off or reduces the thruster drive output to prevent the motor from continuing to overload. At the same time, the ballast and energy storage airbag module triggers the ballast release and airbag floating and recovery program.

[0023] The present invention has the following advantages due to the adoption of the above technical solutions: 1. This invention employs a composite blade edge protection structure, incorporating wear-resistant reinforced edge components and interface locking structures in critical areas such as the leading edge, outer edge, or blade tip. This enhances the blade's impact resistance, fatigue resistance, and anti-entanglement capabilities while ensuring lightweight design. When the propeller encounters fibrous foreign objects or hard particles such as fishing nets or aquatic plants, the reinforced edge components can cut or peel off the entangled material, reducing the risk of blade chipping, breakage, and root entanglement.

[0024] 2. This invention establishes a current-speed-vibration-sonar image fusion diagnostic mechanism. Through a speed-power dynamic matching model, it performs operating condition correlation verification, effectively distinguishing between normal load fluctuations and stall anomalies, thus improving the accuracy of stall risk identification. Compared to identification methods that rely solely on current mutations or acoustic spectrum signals, this invention has stronger anti-interference capabilities and higher engineering adaptability.

[0025] 3. This invention sets up a multi-level dynamic response strategy. When a slight risk of stalling is detected, the controller reduces the thruster output power and executes short-term forward and reverse pulse control, so that the slight entanglement or jamming fault can be automatically resolved without interrupting the mission. When the abnormality persists, it enters a medium-level escape mode and limits the maximum current to avoid prolonged motor overload. When the duration of the abnormality exceeds the set threshold or the motor temperature rise exceeds the safety threshold, the thruster protection shutdown is executed and the ballast release and airbag floating recovery program are triggered, thereby reducing the risks of motor burnout, complete machine disconnection, and difficulty in deep-water salvage.

[0026] 4. This invention, through the deep integration of material strengthening, structural innovation, and intelligent control, forms a complete safety assurance system encompassing "anti-blockage-self-rescue-risk avoidance and recovery." This technical solution is adaptable to various application scenarios such as deep-sea resource exploration, underwater construction of cross-sea engineering projects, marine ecological monitoring, subsea pipeline inspection, and underwater search and rescue, exhibiting good environmental adaptability, mission continuity, and full life-cycle economic efficiency. Attached Figure Description

[0027] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts. In the drawings: Figure 1 A flowchart of an underwater robot thruster anti-jamming protection and autonomous self-rescue method provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of the ballast and energy storage airbag module provided in this embodiment of the present invention; Figure 3 This is a schematic diagram illustrating the working principle of the ballast and energy storage airbag module provided in this embodiment of the present invention. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of the present invention clearer, specific embodiments of the present invention will be further described below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0029] To address the problems of high false alarm rates in existing underwater robot thruster stall detection, insufficient escape capability after stalling, and difficulty in recovery after failure, this invention proposes an underwater robot thruster stall prevention protection and autonomous self-rescue method based on multi-source state recognition and graded response. The method constructs a stall risk assessment model by collecting thruster input effective power, rotational speed response, vibration spectrum, and sonar image features. Based on the stall risk level, it sequentially executes graded response actions such as power reduction protection, short-term reverse escape, pulse disturbance escape, thruster protection shutdown, ballast release, and airbag buoyancy recovery. This enables real-time identification, proactive mitigation, and autonomous recovery of thruster stall risks after failure.

[0030] The following is a detailed description of the underwater robot thruster anti-jamming protection and autonomous self-rescue method provided in the embodiments of the present invention, with reference to the accompanying drawings.

[0031] Please see Figure 1 This embodiment provides a method for preventing stalling and protecting the underwater robot's thruster, as well as for autonomous self-rescue, including: S100. The underwater robot is mainly composed of the underwater robot body, thrusters, multi-source information collaborative detection module, intelligent analysis module and ballast and energy storage airbag module. The composite sensor network in the S200 multi-source information collaborative detection module collects the input voltage, input current, measured blade speed, vibration signal and sonar image of the thruster in real time, and transmits the collected data to the intelligent analysis module via a high-speed data acquisition circuit. The S300 intelligent analysis module calculates the effective input power of the propeller based on the input voltage, input current, and transmission system efficiency, and calculates the theoretical reference blade speed based on the effective input power of the propeller. At the same time, it establishes a blade absorption power model based on the blade power coefficient, blade diameter, and water density, and substitutes the measured blade speed into the blade absorption power model to calculate the power residual. The S400 intelligent analysis module performs bandpass filtering and spectrum analysis on the vibration signal of the thruster, extracts the fundamental frequency, harmonics and abnormal high-frequency components, and calculates the vibration anomaly degree based on the vibration amplitude of the abnormal frequency band and the reference vibration amplitude. The S500 intelligent analysis module identifies the blade region in the sonar image of the thruster, extracts the blade outline integrity, foreign object obstruction area and edge anomaly features, and calculates the foreign object obstruction ratio based on the foreign object obstruction area and the blade identification area. The S600 intelligent analysis module calculates the speed deviation based on the theoretical reference blade speed and the measured blade speed. At the same time, it calculates the current anomaly based on the measured current and the reference current, and calculates the stall risk index by combining the power residual, vibration anomaly and foreign object obstruction ratio. The S700 intelligent analysis module identifies the level of stalling risk based on the stalling risk index and outputs graded response commands according to the level of stalling risk, enabling the underwater robot to execute the corresponding graded response actions.

[0032] In the above embodiments, preferably, a composite blade protection structure is provided on the propeller. The composite blade protection structure includes a blade body, a reinforcing edge member, a transition bonding layer, and an interface locking structure. The reinforcing edge member is continuously or intermittently disposed on the leading edge, outer edge, and / or tip region of the blade body. The transition bonding layer is located between the blade body and the reinforcing edge member. The interface locking structure is used to restrict the reinforcing edge member from peeling, slipping, or fatigue loosening relative to the blade body. The composite blade protection structure is used to cut, peel, or remove fibrous foreign matter entering the blade rotation area and improve the impact resistance and fatigue resistance of the blade edge. The upstream side of the reinforcing edge member can form a guide slope or micro-edge to cut, peel, or remove fibrous foreign matter when the blade rotates. The transition bonding layer is used to reduce the abrupt change in stiffness and interface stress concentration between different materials. The interface locking structure can adopt a serrated, dovetail, corrugated, or stepped structure to improve the peel resistance and fatigue resistance between the reinforcing edge member and the blade body. When flexible entanglements such as fishing nets, aquatic plants, and aquaculture cables enter the blade rotation area, the reinforced edge components preferentially contact and disturb the entanglements, reducing their accumulation at the blade root and near the drive shaft; when hard particles or floating objects impact the blade edge, the reinforced edge components share the local impact load, reducing the risk of blade edge notches and crack propagation.

[0033] In the above embodiments, preferably, the effective input power of the thruster, the absorbed power of the blades, the theoretical reference blade speed, and the power residual are calculated using the following formulas:

[0034] In the formula, P in To provide effective power to the thruster; U The input voltage of the thruster; I This refers to the input current of the thruster; or For the efficiency of the propulsion system; P abs ( n The blade rotation speed is n The blade absorbs power at that time; r Density of water; C P The blade power coefficient; D The diameter of the blade; n ref The theoretical reference blade speed; n meas The measured blade speed; Ep For power residual; e P This is to avoid positive numbers with a denominator of zero. In practical applications, it can be determined based on the thruster bench test. C P and or Calibration is performed, and the model parameters are corrected based on changes in water depth, temperature, salinity, current velocity, and water density. When the test bench database contains data with different rotational speeds, thruster loads, and water conditions, table lookup interpolation or online identification methods can also be used to update the data. C P and or .

[0035] In the above embodiments, preferably, the stall risk index is calculated using the following formula: ; In the formula, R s The congestion risk index; E n , E v , E s , E i and E p These are rotational speed deviation, vibration anomaly, foreign object obstruction ratio in sonar images, current anomaly, and power residual; w 1. w 2. w 3. w 4 and w 5 represents the corresponding weight coefficient, which satisfies:

[0036] The weighting coefficient can be determined by bench calibration tests, typical operating condition data, or safety redundancy requirements, and can also be adjusted according to the operating water area. For example, when the stability of sonar images decreases in turbid water, the weighting coefficient can be appropriately reduced. w 3, and improve w 1. w 4 or w 5. When strong ocean currents cause significant load fluctuations, the weights of power residual and vibration anomaly can be increased to reduce misjudgments.

[0037] In the above embodiments, preferably, the state characteristic quantities in the stall risk index are calculated in the following manner:

[0038] In the formula, n ref The theoretical reference blade speed;n meas The actual blade rotation speed measured by the rotation speed sensor; A abn This refers to the vibration amplitude in the abnormal frequency band. A base This is the reference vibration amplitude under normal operating conditions; S occ The area obstructed by the foreign object in the sonar image; S prop The area of ​​the blade identification region; I This refers to the input current of the thruster; I base The reference input current is used under the same speed command, the same water conditions, or the same calibration conditions. e n , e A , e S and e I These are all positive numbers used to avoid the denominator being zero.

[0039] In the above embodiments, preferably, the multi-source information collaborative detection module includes a composite sensor network, a power detection module, and a high-speed data acquisition module; the composite sensor network includes a speed sensor, a vibration sensor, a sonar device, and a voltage and current sampling device, wherein the speed sensor is used to acquire the measured blade speed, the vibration sensor is used to acquire the vibration signal of the propeller housing, blade support structure, or transmission support node, the sonar device is used to acquire sonar images of the blade and its surrounding area, and the voltage and current sampling device is used to acquire the input voltage and input current of the propeller; the power detection module is used to perform isolated sampling, filtering, and effective power calculation on the input voltage and input current; and the high-speed data acquisition module is used to perform time synchronization and buffering of the speed, vibration, sonar images, and voltage and current data.

[0040] In the above embodiments, preferably, the intelligent analysis module includes a signal processing unit, a model calibration unit, a risk assessment unit, and a response control unit; the signal processing unit is used for denoising, filtering, Fourier transform, image segmentation, and feature normalization; the model calibration unit is used to correct the blade power coefficient and transmission system efficiency based on bench calibration data, water density, water depth, water temperature, ocean current velocity, and salinity; the risk assessment unit is used to calculate the stall risk index and output the stall risk level; and the response control unit is used to output control commands to the thruster and ballast and energy storage airbag modules.

[0041] In the above embodiments, preferably as shown in Figures 2 and 3, the ballast and energy storage airbag module mainly consists of a fixed bionic ring belt 15, a high-pressure nitrogen delivery pipe 16, an airbag chamber 17, an airbag 18, a compression spring 19, an inflation valve, a release mechanism, and a releasable ballast component. A fixed bionic ring 15 is fixed to the outside of the pressure-resistant shell of the underwater robot for mounting the airbag compartment 17 and the releasable ballast component, and for distributing the deployment load of the airbag 18 to the outside of the pressure-resistant shell; a high-pressure nitrogen delivery pipe 16 is connected to the airbag compartment 17 for supplying air to the airbag 18 in self-rescue mode; the airbag compartment 17 is used to store the airbag 18 during normal operation and to limit the deployment direction of the airbag 18 during deployment; the airbag 18 is used to increase the underwater robot's drainage volume and provide positive buoyancy after deployment; a compression spring 19 is used to assist the airbag 18 in rapid ejection or deployment in the deployment state, avoiding delays in deployment due to water pressure, folding resistance, or sediment adhesion; an inflation valve is used to control the entry of high-pressure nitrogen into the airbag 18; a release mechanism is used to release the locking of the releasable ballast component; the releasable ballast component is used to detach from the underwater robot in severe stall or runaway state to reduce system gravity and cooperate with the airbag 18 to form conditions for surfacing and recovery. During normal operation, the airbag 18 is in a retracted state and the releasable ballast remains locked. When the thruster enters a severely stalled state, the intelligent analysis module determines that the underwater robot is unable to continue performing its tasks, or the underwater robot loses effective power for an extended period of time, the release mechanism causes the releasable ballast to detach. At the same time, high-pressure nitrogen enters the airbag chamber 17 through the high-pressure nitrogen delivery pipe 16 and causes the airbag 18 to deploy, thereby increasing the underwater robot's drainage volume and positive buoyancy, thus enabling autonomous ascent and recovery.

[0042] In the above embodiments, preferably, the stall risk level includes four levels: normal, mild stall, moderate stall, and severe stall, wherein the stall risk level is determined as follows:

[0043] in, T 1 represents the first blocking risk threshold between normal and mild blocking conditions; T 2 represents the second blocking risk threshold between mild and moderate blocking conditions; T 3 is the third blocking risk threshold between moderate and severe blocking conditions, and it satisfies 0. <T 1 < T 2 < T 3.

[0044] T 1. T 2 and T 3. It can be preset based on the thruster bench calibration test, typical water area operation data, motor safety current, motor temperature rise limit and mission safety redundancy coefficient, and can also be adaptively corrected under different operating water depth, water density or ocean current conditions.

[0045] In the above embodiments, preferably, the graded response actions include, in sequence according to the stall risk level, power reduction protection, short-term forward and reverse pulse escape, pulse disturbance escape, thruster protection shutdown, ballast release, and airbag buoyancy recovery, specifically as follows: When the stall risk level is normal, the thruster outputs thrust normally according to the mission control command, and the intelligent analysis module continuously monitors the stall risk index. When the stall risk level is mild stall, the intelligent analysis module reduces the thruster output power to 60% to 80% of the current commanded power and executes short-term forward and reverse pulse control. The mild stall power command can be expressed as: P cmd = α 1 P 0 , 0 . 60≤ α 1≤0 . 80; In the formula, P cmd This is the commanded power after power reduction; P 0 represents the thruster command power before the stall risk is triggered; α 1 represents the power attenuation coefficient for mild stall. Forward and reverse pulses are applied alternately to the propeller blades to loosen minor entanglements or small stuck particles; if... R s Descend to within the set time T If the value is below 1, the intelligent analysis module will exit the escape mode and gradually restore normal power output.

[0046] When the stall risk level is moderate, the intelligent analysis module limits the maximum thruster current and executes multiple sets of pulse disturbance escape actions. The moderate stall current constraint can be expressed as: I cmd ≤ I lim , I lim = βI safe , 0 <β ≤1; In the formula, I cmd In response to the command current permitted by the control unit; I lim This is the rate limiting threshold; I safe For the safe current of the thruster motor; βThis is the safety margin factor. In this mode, the intelligent analysis module can drive the propeller according to the preset pulse width, pulse interval, and reverse duty cycle to loosen the entangled object or remove stuck particles from the working area of ​​the blade. If the speed returns to the theoretical range and the vibration abnormality decreases after several consecutive escape attempts, the medium escape mode is exited; otherwise, the current limiting protection is maintained and the temperature rise and stall risk index are continuously monitored.

[0047] When the stall risk level is severe stall, the intelligent analysis module immediately executes the thruster protection shutdown, cuts off or reduces the thruster drive output to prevent the motor from continuing to overload. At the same time, the ballast and energy storage airbag module triggers the ballast release and airbag floating and recovery program.

[0048] Through the above process, this invention expands the underwater robot recovery from the traditional passive towing method to an intelligent closed loop of "autonomous risk avoidance-floating-positioning", effectively alleviating the shortcomings of traditional salvage methods in terms of efficiency, safety and environmental compatibility.

[0049] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and not to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application, and they should all be covered within the scope of the claims and specification of this application. In particular, as long as there is no structural conflict, the various technical features mentioned in the embodiments can be combined in any way. This application is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A method for preventing stalling and protecting the thruster of an underwater robot, and for autonomous self-rescue, characterized in that, Includes the following steps: The underwater robot is composed of an underwater robot body, a thruster, a multi-source information collaborative detection module, an intelligent analysis module, and a ballast and energy storage airbag module. The multi-source information collaborative detection module collects the input voltage, input current, measured blade speed, vibration signal and sonar image of the thruster in real time, and transmits the collected data to the intelligent analysis module. The intelligent analysis module calculates the effective input power of the thruster based on the input voltage, input current and transmission system efficiency, and calculates the theoretical reference blade speed based on the effective input power of the thruster; at the same time, it establishes a blade absorption power model based on the blade power coefficient, blade diameter and water density, and substitutes the measured blade speed into the blade absorption power model to calculate the power residual. The intelligent analysis module performs bandpass filtering and spectrum analysis on the vibration signal of the thruster, extracts the fundamental frequency, harmonics and abnormal high-frequency components, and calculates the vibration anomaly degree based on the vibration amplitude of the abnormal frequency band and the reference vibration amplitude. The intelligent analysis module identifies the blade region in the sonar image of the thruster, extracts the blade outline integrity, foreign object obstruction area and edge anomaly features, and calculates the foreign object obstruction ratio based on the foreign object obstruction area and the blade identification area. The intelligent analysis module calculates the speed deviation based on the theoretical reference blade speed and the measured blade speed. At the same time, it calculates the current anomaly based on the measured current and the reference current. It also calculates the stall risk index by combining the power residual, vibration anomaly and foreign object obstruction ratio. The stall risk level is identified based on the calculated stall risk index. The intelligent analysis module identifies the level of blockage risk based on the blockage risk index and outputs graded response commands according to the level of blockage risk, enabling the underwater robot to perform corresponding graded response actions.

2. The underwater robot thruster anti-jamming protection and autonomous self-rescue method according to claim 1, characterized in that, A composite blade protection structure is provided on the propeller. The composite blade protection structure includes a blade body, a reinforcing edge member, a transition bonding layer, and an interface locking structure. The reinforcing edge member is continuously or intermittently disposed on the leading edge, outer edge, and / or tip region of the blade body. The transition bonding layer is located between the blade body and the reinforcing edge member. The interface locking structure is used to restrict the reinforcing edge member from peeling, slipping, or fatigue loosening relative to the blade body. The composite blade protection structure is used to cut, peel, or remove fibrous foreign matter entering the blade rotation area and improve the impact resistance and fatigue resistance of the blade edge.

3. The underwater robot thruster anti-jamming protection and autonomous self-rescue method according to claim 2, characterized in that, The blade body is made of carbon fiber reinforced engineering plastic, glass fiber reinforced engineering plastic, or lightweight alloy material; the reinforcing edge component is made of precipitation hardened stainless steel, titanium alloy, nickel-titanium alloy, wear-resistant ceramic coated metal strip, or carbon fiber reinforced layer; the interface locking structure is a sawtooth, dovetail, corrugated, or stepped structure; the upstream side of the reinforcing edge component forms a guide slope or micro-cutting edge to cut, peel, or remove fibrous entanglement during blade rotation.

4. The underwater robot thruster anti-jamming protection and autonomous self-rescue method according to claim 1, characterized in that, The effective input power of the thruster, the absorbed power of the blades, the theoretical reference blade speed, and the power residual are calculated using the following formulas: ； In the formula, P in To provide effective power to the thruster; U The input voltage of the thruster; I This refers to the input current of the thruster; η For the efficiency of the propulsion system; P abs ( n The blade rotation speed is n The blade absorbs power at that time; ρ Density of water; C P The blade power coefficient; D The diameter of the blade; n ref The theoretical reference blade speed; n meas The measured blade speed; E p For power residual; ε P This is used to avoid positive numbers with a denominator of zero.

5. The underwater robot thruster anti-jamming protection and autonomous self-rescue method according to claim 4, characterized in that, The stall risk index is calculated using the following formula: ； In the formula, R s The congestion risk index; E n , E v , E s , E i and E p These are rotational speed deviation, vibration anomaly, foreign object obstruction ratio in sonar images, current anomaly, and power residual; w 1. w 2. w 3. w 4 and w 5 represents the corresponding weight coefficient, which satisfies: 。 6. The underwater robot thruster anti-jamming protection and autonomous self-rescue method according to claim 5, characterized in that, The speed deviation, vibration anomaly, foreign object obstruction ratio, and current anomaly are calculated as follows: ； In the formula, n meas The measured blade speed; A abn This refers to the vibration amplitude in the abnormal frequency band. A base This is the reference vibration amplitude under normal operating conditions; S occ The area obstructed by the foreign object in the sonar image; S prop The area of ​​the blade identification region; I This refers to the input current of the thruster; I base The reference input current is used under the same speed command, the same water conditions, or the same calibration conditions. ε n , ε A , ε S and ε I These are all positive numbers used to avoid the denominator being zero.

7. The underwater robot thruster anti-jamming protection and autonomous self-rescue method according to claim 1, characterized in that, The multi-source information collaborative detection module includes a composite sensor network, a power detection module, and a high-speed data acquisition module. The composite sensor network includes a speed sensor, a vibration sensor, a sonar device, and a voltage and current sampling device. The speed sensor is used to acquire the measured blade speed, the vibration sensor is used to acquire vibration signals from the propeller housing, blade support structure, or transmission support node, the sonar device is used to acquire sonar images of the blade and its surrounding area, and the voltage and current sampling device is used to acquire the input voltage and input current of the propeller. The power detection module is used to perform isolated sampling, filtering, and effective power calculation on the input voltage and input current. The high-speed data acquisition module is used to synchronize and buffer the speed, vibration, sonar images, and voltage and current data.

8. The underwater robot thruster anti-jamming protection and autonomous self-rescue method according to claim 6, characterized in that, The intelligent analysis module includes a signal processing unit, a model calibration unit, a risk assessment unit, and a response control unit. The signal processing unit is used for denoising, filtering, Fourier transform, image segmentation, and feature normalization. The model calibration unit is used to correct the propeller power coefficient and transmission system efficiency based on bench calibration data, water density, water depth, water temperature, ocean current velocity, and salinity. The risk assessment unit is used to calculate the stall risk index and output the stall risk level. The response control unit is used to output control commands to the thruster and the ballast and energy storage airbag module.

9. The underwater robot thruster anti-jamming protection and autonomous self-rescue method according to claim 1, characterized in that, The ballast and energy storage airbag module includes a fixed bionic ring belt, a high-pressure nitrogen delivery pipe, an airbag chamber, an airbag, a compression spring, an inflation valve, a release mechanism, and a releasable ballast component. The fixed bionic ring belt is used to fix the airbag chamber and the releasable ballast component to the outside of the underwater robot's pressure-resistant shell. The high-pressure nitrogen delivery pipe is used to supply air to the airbag. The airbag chamber is used to house the airbag and limit its deployment direction. The compression spring is used to assist the airbag in rapid deployment when released. The inflation valve is used to control the entry of high-pressure nitrogen into the airbag. The release mechanism is used to release the locking of the releasable ballast component. The releasable ballast component is used to detach from the underwater robot in a state of severe stall or loss of control, so as to reduce the underwater robot's weight and form positive buoyancy with the deployment of the airbag, allowing the underwater robot to float and be recovered.

10. The underwater robot thruster anti-jamming protection and autonomous self-rescue method according to claim 8, characterized in that, The congestion risk level is divided into four levels: normal, mild congestion, moderate congestion, and severe congestion. The congestion risk level is determined as follows: ； in, T 1 represents the first blocking risk threshold between normal and mild blocking conditions; T 2 represents the second blocking risk threshold between mild and moderate blocking conditions; T 3 is the third blocking risk threshold between moderate and severe blocking conditions, and it satisfies 0. < T 1 < T 2 < T 3.

11. The underwater robot thruster anti-jamming protection and autonomous self-rescue method according to claim 10, characterized in that, The graded response actions include, in sequence according to the stall risk level, power reduction protection, short-term forward and reverse pulse escape, pulse disturbance escape, thruster protection shutdown, ballast release, and airbag buoyancy recovery, specifically as follows: When the stall risk level is normal, the thruster outputs thrust normally according to the mission control command, and the intelligent analysis module continuously monitors the stall risk index. When the stall risk level is mild stall, the intelligent analysis module reduces the thruster output power to 60% to 80% of the thruster command power before the stall risk is triggered, and executes short-term forward and reverse pulse control. The mild stall power command can be expressed as: P cmd = α 1 P 0 ， 0 . 60≤ α 1≤0 . 80； In the formula, P cmd This is the commanded power after power reduction; P 0 represents the thruster command power before the stall risk is triggered; α 1 represents the power attenuation coefficient for mild stall; forward and reverse pulses are applied alternately to the propeller blades to loosen minor entanglements or small stuck particles; like R s Descend to within the set time T If the power output drops below 1, the intelligent analysis module will exit the escape mode and gradually restore normal power output. When the stall risk level is moderate, the intelligent analysis module limits the maximum thruster current and executes multiple sets of pulse disturbance escape actions. The moderate stall current constraint can be expressed as: I cmd ≤ I lim ， I lim = βI safe ，0 <β ≤1 ； In the formula, I cmd In response to the command current permitted by the control unit; I lim This is the rate limiting threshold; I safe For the safe current of the thruster motor; β For safety margin coefficient; in this mode, the intelligent analysis module can drive the propeller according to the preset pulse width, pulse interval and reverse duty cycle to loosen the entangled material or make the stuck particles leave the working area of ​​the blade. If the speed returns to the theoretical range and the vibration abnormality decreases after several consecutive escape attempts, the moderate escape mode is exited; otherwise, the current limiting protection is maintained and the temperature rise and stall risk index are monitored. When the stall risk level is severe stall, the intelligent analysis module immediately executes the thruster protection shutdown, cuts off or reduces the thruster drive output to prevent the motor from continuing to overload. At the same time, the ballast and energy storage airbag module triggers the ballast release and airbag floating and recovery program.

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