Autonomous obstacle avoidance control system and method for underwater robot

Abstract

The invention relates to the technical field of underwater robots, in particular to an autonomous obstacle avoidance control system and method for an underwater robot. The system comprises a sensor assembly, a controller assembly and an actuator assembly, the sensor assembly is used for acquiring surrounding environment information of the robot, the controller assembly is used for processing sensor data signals and formulating an obstacle avoidance strategy, and the actuator assembly is used for adjusting motion parameters of the robot to achieve obstacle avoidance. The method of the system comprises the steps of acquiring sensor data, formulating an obstacle avoidance strategy, adjusting motion parameters, executing the obstacle avoidance strategy, realizing autonomous obstacle avoidance and the like. The system can avoid obstacles based on various sensors such as a sonar sensor, a camera sensor, a pressure sensor and a positioning system, can also adopt artificial intelligence technologies such as deep learning and reinforcement learning to improve the accuracy and efficiency of obstacle avoidance, and can also perform data exchange and cooperation with other robots through an internal communication system of the robot. And the overall obstacle avoidance effect is improved. The system and the method can be widely applied to the fields of underwater scientific research, seabed exploration, underwater operation and the like, and the autonomy and the application value of the underwater robot are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of underwater robot technology, specifically an autonomous obstacle avoidance control system for underwater robots and a method thereof. An underwater robot is an autonomous control robot capable of performing various tasks underwater, typically including various sensors, controllers, actuators, and other components. In underwater environments, robots need to face various challenges such as complex water flow, various obstacles, unpredictable environmental changes, etc., and need to have good autonomous obstacle avoidance capabilities. Therefore, the present application belongs to the field of underwater robot control technology, involving technologies in sensors, controllers, actuators, and other aspects, aiming to improve the autonomy and application value of underwater robots. BACKGROUND

[0002] With the continuous exploration and development of marine resources, underwater robots have gradually become an important tool in the fields of marine scientific research, seabed exploration, and underwater operations. Underwater robots can perform various tasks such as seabed topography measurement, underwater pipeline maintenance, and marine environment monitoring, with the advantages of long time, stable operation, and good repeatability. However, underwater robots need to face various complex environments and obstacles when performing tasks, so it is crucial to have good autonomous obstacle avoidance capabilities.

[0003] Currently, the obstacle avoidance technology for underwater robots mainly includes two methods: sensor-based obstacle avoidance and map-based obstacle avoidance. The sensor-based obstacle avoidance method is to obtain the surrounding environment information through various sensors carried by the underwater robot itself, such as sonar sensors, camera sensors, and pressure sensors, and then use the controller to control the robot to avoid obstacles. The map-based obstacle avoidance method is to establish a map of the underwater environment in advance, and then plan the path and avoid obstacles for the robot. These two methods have their own advantages and disadvantages, but they all have certain limitations, such as the sensor-based method being affected by environmental noise and sensor accuracy, and the map-based method needing to establish a map in advance and being unable to respond to sudden environmental changes.

[0004] Therefore, the present application aims to provide an autonomous obstacle avoidance control system for underwater robots and a method thereof, which realizes autonomous obstacle avoidance of underwater robots by using various sensors, controllers, actuators, and other components, as well as the application of artificial intelligence technology, and improves the autonomy and application value of robots. SUMMARY

[0005] The underwater robot plays an important role in deep sea exploration, underwater rescue, ocean resource development and other fields, but in the underwater environment, various complex situations are encountered, such as changes in underwater topography, interference of water flow, existence of obstacles and the like, which bring great challenges to the movement of the underwater robot. The traditional sensor-based and map-based obstacle avoidance method is difficult to meet the requirements of autonomous obstacle avoidance of the underwater robot, therefore the present application provides an underwater robot autonomous obstacle avoidance control system and method, which can effectively solve the problem of autonomous obstacle avoidance of the underwater robot.

[0006] The main invention of the present application is an underwater robot autonomous obstacle avoidance control system and method. The system includes various sensors, controllers and actuators and the like components, wherein the sensors include sonar sensors, camera sensors, pressure sensors and the like; the controller adopts artificial intelligence algorithm, and combines the data collected by the sensors to control the robot; the actuator is the power system and rudder of the robot and the like components.

[0007] Specifically, the present application adopts various sensors to collect the environmental information around the underwater robot, including underwater topography, obstacles, water flow and the like information. Then, the collected data are processed by the controller, and artificial intelligence algorithm is adopted for analysis and judgment, the obstacles that may be encountered in the movement process of the robot are identified, and the action of the robot is planned and controlled, so as to realize the autonomous obstacle avoidance of the robot.

[0008] Further, the present application adopts deep learning algorithm to train the data collected by the sensors, establishes the obstacle recognition model, and integrates it into the controller to realize the autonomous obstacle avoidance of the robot. At the same time, the present application also adopts obstacle avoidance optimization algorithm to optimize the path of the robot, so as to ensure that the robot can quickly and efficiently complete the task while avoiding obstacles.

[0009] The present application has the advantages that various sensors, controllers and actuators and the like components are adopted, combined with artificial intelligence algorithm and obstacle avoidance optimization algorithm, the autonomous obstacle avoidance of the underwater robot is realized. Compared with the traditional sensor-based and map-based obstacle avoidance method, the present application has higher obstacle avoidance precision and stronger adaptability, and can better cope with the complex and changeable situation in the underwater environment, and improve the autonomy and application value of the underwater robot. BRIEF DESCRIPTION OF DRAWINGS

[0010] Fig. 1 is a flow chart of an underwater robot autonomous obstacle avoidance control method according to the present application; and Fig. 2 is a schematic diagram of an underwater robot structure according to the present application. DETAILED DESCRIPTION

[0011] The embodiments of the present invention are as follows: An autonomous obstacle avoidance control system for underwater robots includes sensors such as sonar sensors, camera sensors, pressure sensors, and other sensors to collect underwater environmental information.

[0012] A controller uses artificial intelligence algorithms, including deep learning algorithms and obstacle avoidance optimization algorithms, to process sensor-collected data and control robot actions.

[0013] A processor includes components such as the robot's power system and rudder to achieve the robot's movement and attitude adjustment.

[0014] An autonomous obstacle avoidance control method for underwater robots includes the following steps: sensors collect underwater environmental information, including underwater terrain, obstacles, water flow, and other information.

[0015] The collected data is input into the controller, which uses deep learning algorithms to train and establish an obstacle recognition model and integrates it into the controller.

[0016] The robot's path is optimized through an obstacle avoidance optimization algorithm to ensure that the robot can quickly and efficiently complete tasks while avoiding obstacles.

[0017] The controller controls the robot, identifies obstacles that may be encountered during the robot's movement, and plans and controls the robot's actions to achieve autonomous obstacle avoidance.

[0018] In the specific implementation process, the type and number of sensors can be adjusted and changed as needed, such as adding water quality sensors, temperature sensors, etc. to obtain more underwater environmental information. The deep learning algorithm in the controller can also be optimized and adjusted as needed to improve the accuracy and efficiency of obstacle recognition. The obstacle avoidance optimization algorithm can also be adjusted and optimized according to different tasks and underwater environments. The actuator can also be selected and configured according to different robot types and tasks.

[0019] The above specific embodiments are only the preferred embodiments of the present invention and do not limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

Claims

1. An autonomous obstacle avoidance control system and method for an underwater robot, characterized in that... The underwater robot autonomous obstacle avoidance control system includes the following components: a sensor component for detecting obstacles in the surrounding underwater environment and generating sensor data signals; a controller component for receiving sensor data signals and controlling the robot's movement according to a preset obstacle avoidance strategy; and a power drive component for controlling the robot's movement, including a motor (4), a rotating shaft (3), a propeller (5), and a power supply (9).

2. The underwater robot autonomous obstacle avoidance control system according to claim 1, characterized in that... The deep learning algorithm module in the controller uses the convolutional neural network (CNN) algorithm to train the obstacle recognition model; the obstacle avoidance optimization algorithm uses the A* algorithm or the Dijkstra algorithm; the sensor components include a sonar sensor (1), a camera sensor (2), a pressure sensor (6), a controller (7), and a processor (8).

3. The underwater robot autonomous obstacle avoidance control system according to claim 1, characterized in that, The controller component includes the following sub-components: an obstacle avoidance algorithm module, used to analyze sensor data signals and generate robot motion control strategies; a motion control module, used to control robot motion, including adjusting parameters such as speed and direction; and a decision module, used to make decisions based on current environmental information and robot status.

4. The underwater robot autonomous obstacle avoidance control system according to claim 1, characterized in that, The obstacle avoidance strategy may include one or a combination of the following strategies: obstacle distance measurement based on sonar sensors, automatically stopping or changing direction when an obstacle is detected; obstacle recognition based on camera sensors, automatically avoiding obstacles when an obstacle is detected; depth measurement based on pressure sensors, automatically stopping or changing direction when the robot approaches the seabed or other objects; and path planning based on a positioning system, automatically avoiding obstacles on a preset path.

5. An autonomous obstacle avoidance control system and method for an underwater robot, characterized in that... The autonomous obstacle avoidance control method for underwater robots includes the following steps: sensors collect underwater environmental information, including underwater terrain, obstacles, and water currents; the collected data is input into the controller, which uses deep learning algorithms to train and build an obstacle recognition model, which is then integrated into the controller; the robot path is optimized using obstacle avoidance optimization algorithms to ensure that the robot can complete the task quickly and efficiently while avoiding obstacles; the controller controls the robot, identifies obstacles that the robot may encounter during its movement, and plans and controls the robot's actions to achieve autonomous obstacle avoidance; during execution, the controller continuously identifies and plans based on the information collected by the sensors to ensure that the robot can make timely adjustments and reactions when encountering new obstacles or environmental changes; the actuator implements the controller's instructions to complete the robot's actions and posture adjustments to complete the task. In the controller component, artificial intelligence technologies such as deep learning and reinforcement learning can be used to process and analyze sensor data signals, thereby improving the accuracy and efficiency of obstacle avoidance.

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