Intelligent attitude control method of underwater robot based on machine learning

Abstract

The invention provides an intelligent attitude control method. The control performance of an underwater robot is improved by utilizing a machine learning technology. The method comprises the following steps: acquiring attitude data in an underwater environment through a sensor, such as an attitude angle, a linear velocity and an angular velocity; inputting the attitude data into a machine learning model for analysis and learning; predicting and optimizing an attitude control command of the underwater robot by using the machine learning model; and transmitting the optimized attitude control command to an actuator of the underwater robot so as to realize accurate attitude control. In the method, a machine learning model is trained through a supervised learning algorithm or a reinforcement learning algorithm so as to optimize the prediction and adjustment process of an attitude control command according to historical data and feedback information. The historical data comprises sensor data and corresponding attitude control commands, and the feedback information comprises the attitude, the position and the sensor data of the underwater robot. By continuously updating a machine learning model, the method can adapt to changes of different underwater environments, and attitude control targets such as energy consumption, stability and maneuvering characteristics are optimized. The intelligent attitude control method has the following advantages: the attitude control precision and stability of the underwater robot are improved; the system adapts to changes of different underwater environments; the control performance is continuously improved through learning and optimization of a machine learning model; the device is widely applicable to the fields of ocean exploration, underwater pipeline maintenance, seabed resource development, diving operation and the like.

Description

Technical Field

[0001] This invention relates to the field of underwater robot technology, and in particular to an intelligent attitude control method for underwater robots based on machine learning. Underwater robots are a class of autonomous or semi-autonomous robotic systems capable of performing tasks in underwater environments. They have broad application prospects in fields such as marine exploration, underwater pipeline maintenance, seabed resource development, and diving operations. Machine learning can improve the control performance and adaptability of underwater robots by analyzing and learning from historical data, automatically learning and optimizing control strategies. By utilizing supervised learning or reinforcement learning algorithms, machine learning models can optimize the prediction and adjustment process of attitude control commands based on sensor data and feedback information. This intelligent attitude control method based on machine learning has the potential to improve accuracy, stability, and adaptability, bringing new breakthroughs to the practical applications of underwater robots. Background Technology

[0002] Traditional underwater robot attitude control methods typically rely on sensor data and predefined control algorithms, such as PID controllers. However, due to the complexity and uncertainty of the underwater environment, these methods have several limitations. First, environmental factors such as water flow, water quality, and water depth can affect the underwater robot's attitude, leading to inaccuracies in sensor data. This poses a challenge to traditional attitude control methods, as they depend on accurate sensor data for precise control. Second, traditional methods often use predefined control algorithms to generate control commands, which are usually based on simplified assumptions from experience and models. However, these assumptions may not accurately reflect the complex underwater environment and robot dynamics, resulting in insufficient control accuracy and stability. Furthermore, the variability and uncertainty of the underwater environment limit the adaptability of traditional methods. Underwater robots may face different control requirements in different underwater tasks and environments, and traditional methods often lack the flexibility and adaptability to meet these diverse needs.

[0003] Therefore, machine learning technology has been introduced into the attitude control of underwater robots. Machine learning can automatically learn and optimize control strategies by analyzing and learning from historical data, thereby improving the control performance and adaptability of underwater robots. Supervised learning algorithms and reinforcement learning algorithms, among other machine learning methods, are widely used in attitude control tasks. By training machine learning models to predict and optimize control commands, more accurate, stable, and adaptive attitude control can be achieved. The background of this invention relates to the limitations of traditional underwater robot attitude control methods and the application of machine learning technology in underwater robot attitude control. By utilizing machine learning models to analyze and learn from sensor data, this invention aims to improve the attitude control performance of underwater robots and provide a more accurate, stable, and adaptive attitude control solution. Summary of the Invention

[0004] This invention relates to a machine learning-based intelligent attitude control method for underwater robots, aiming to provide a solution for improving the attitude control performance and adaptability of underwater robots. The following details aspects of the invention: Sensor Data Acquisition: This invention first acquires attitude data from the underwater environment through a sensor system, including attitude angles, linear velocity, and angular velocity. Sensors may include gyroscopes, accelerometers, magnetometers, etc., used to measure the state parameters of the underwater robot.

[0005] Machine learning model training: Using machine learning techniques, sensor data is input into the machine learning model for training. The machine learning model can employ supervised learning or reinforcement learning algorithms, optimizing the prediction and adjustment process of attitude control commands by learning from historical data and feedback information.

[0006] Attitude control command generation: Guided by the trained machine learning model, optimized attitude control commands are generated based on the current state of the underwater robot and the target attitude requirements. These commands may involve thruster power output, servo motor angle control, or hydraulic system pressure control, etc.

[0007] Control command transmission and execution: The generated optimized attitude control commands are transmitted to the underwater robot's actuators, such as thrusters, servos, or hydraulic systems. The actuators then perform corresponding control actions according to the requirements of the control commands, enabling the underwater robot to achieve the desired attitude state.

[0008] Feedback monitoring and updating: During attitude control, the underwater robot's attitude, position, and sensor data are continuously monitored and updated. This feedback information is used to evaluate the control effectiveness and serves as the basis for training the machine learning model. By continuously updating the weight parameters of the machine learning model, the accuracy of attitude control command prediction and adjustment is further optimized.

[0009] Application of reinforcement learning algorithms: Reinforcement learning algorithms are used in the training of machine learning models.

[0010] By learning and interacting with the environment, the attitude control strategy is gradually optimized, thereby improving the control performance and adaptability of the underwater robot. Attached Figure Description

[0011] Figure 1 is a flowchart illustrating the implementation of the intelligent attitude control method for underwater robots based on machine learning according to the present invention.

[0012] The following is a detailed description of a specific embodiment of the invention, used to implement a machine learning-based intelligent attitude control method for underwater robots: Sensor data acquisition: Sensor devices such as gyroscopes, accelerometers, and magnetometers are installed to measure the underwater robot's attitude angles, linear velocities, angular velocities, and other state parameters. The sensor data can be transmitted to the control system via a digital interface.

[0013] Data preprocessing: Preprocessing the sensor data includes operations such as filtering, calibration, and coordinate system transformation. This helps improve the accuracy and stability of the sensor data, providing reliable input for subsequent attitude control algorithms.

[0014] Data Acquisition and Labeling: Collect sensor data of the underwater robot in different posture states and manually label it. The labeled data should include the current posture state and the corresponding posture control commands, serving as training samples for the machine learning model.

[0015] Machine learning model selection: Choose a suitable machine learning model for attitude control. Use supervised learning algorithms such as neural networks, support vector machines, or decision trees.

[0016] Data partitioning and training: The collected and labeled dataset is divided into training and testing sets. The machine learning model is trained using the training set, and the model is optimized and parameters are tuned according to predefined performance metrics to improve the accuracy and stability of attitude control.

[0017] Attitude control command generation: Guided by a trained machine learning model, optimized attitude control commands are generated based on the current state parameters of the underwater robot. Command generation can be achieved using forward propagation of a neural network model or a policy network based on a reinforcement learning algorithm.

[0018] Feedback monitoring and updates: Continuously monitor feedback information such as the underwater robot's attitude, position, and sensor data. This information is used to evaluate the effectiveness of attitude control and serves as the basis for training the machine learning model. Model parameters are updated regularly to adapt to the needs of different underwater environments and tasks.

[0019] Reinforcement learning training: Training is conducted through interaction with the environment. At each time step, the underwater robot receives the current state as input and executes actions based on the attitude control commands predicted by the current policy network. The parameters of the policy network are adjusted based on environmental feedback and reward signals to optimize the control strategy.

[0020] Model Deployment and Real-Time Control: After training, the machine learning model is deployed into the underwater robot's control system for real-time control. Sensor data is fed into the model as input, which generates optimized attitude control commands and transmits these commands to the actuators to adjust the attitude.

[0021] Model updates and optimizations: Regularly update the parameters and training data of the machine learning model to adapt to new underwater environments and mission requirements. Incremental learning or online learning methods can be used to iteratively train and optimize the model using new data to improve the performance and adaptability of attitude control.

[0022] Through the specific implementation methods described above, the machine learning-based intelligent attitude control method for underwater robots can achieve precise, stable, and adaptive control of the robot's attitude. This method fully leverages the advantages of machine learning technology, improving the accuracy and adaptability of attitude control through a data-driven learning and optimization process, thereby providing better support for underwater robots to perform tasks in various application scenarios.

[0023] The above-described specific embodiments are merely preferred embodiments of this invention and are not intended to limit this invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this invention should be included within the protection scope of this invention.

Claims

1. An intelligent attitude control method for an underwater robot, comprising the following steps: a) Acquire attitude data in the underwater environment using sensors; b) Input the attitude data into a machine learning model; c) Use the machine learning model to predict and optimize the attitude control commands of the underwater robot by analyzing and learning historical data; d) Transmit the optimized attitude control commands to the actuators of the underwater robot to achieve attitude control.

2. According to claim 1, the characteristic is that The sensors include an inertial measurement unit (IMU), a depth sensor, and an image sensor; the attitude data includes the underwater robot's attitude angles, linear velocity, and angular velocity. The machine learning model is trained using a supervised learning algorithm to predict and optimize the attitude control commands of the underwater robot based on historical data. The supervised learning algorithm includes support vector machines (SVM), neural networks, or decision trees. The historical data includes sensor data of the underwater robot and corresponding attitude control commands. The goal of attitude control command optimization is to minimize the energy consumption of the underwater robot or optimize the robot's stability and maneuverability.

3. According to claim 1, the characteristic is that The goal of optimizing the attitude control command is to adapt to changes in different underwater environments, including factors such as water flow, water quality, and water depth. The attitude control method further includes the following steps: e) generating appropriate control instructions based on the optimized attitude control command and the current underwater robot state; f) transmitting the control instructions to the actuators of the underwater robot to achieve attitude control; g) monitoring and providing feedback on the current underwater robot state; h) updating the machine learning model based on the feedback information to further optimize the prediction and adjustment process of the attitude control command.

4. According to claim 3, the characteristic is that The feedback information includes the underwater robot's attitude, position, and sensor data; the machine learning model is trained using a reinforcement learning algorithm to optimize the prediction and adjustment process of attitude control commands based on the feedback information; the reinforcement learning algorithm includes deep reinforcement learning algorithms, such as Deep Q-Network (DQN) or Deterministic Policy Gradient (DDPG) algorithm; wherein the feedback information is used to update the weight parameters of the machine learning model to improve the accuracy of attitude control command prediction and adjustment.

5. According to claim 1, the characteristic is that The actuators of the underwater robot include thrusters, servo motors, or hydraulic systems; the attitude control commands include power output of the thrusters, angle control of the servo motors, or pressure control of the hydraulic system; the optimized attitude control commands are adjusted and corrected based on the error between the control commands predicted by the model and the actual measured values.

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