Super-extensible robot cluster system based on natural spinning and implicit cooperation

Abstract

The invention relates to a super-extensible underwater robot cluster system based on natural spinning and implicit cooperation and a coordination method of the super-extensible underwater robot cluster system, and aims to solve the challenges of a traditional underwater robot cluster system in the aspects of communication, expansibility and adaptability through a simple design and an innovative cooperation mechanism. The system comprises a plurality of autonomous robot monomers, cooperates with adjacent robots through a visual guidance form information interaction mechanism, and transmits state information by using an optical beacon system to realize implicit cooperation and self-organization. According to the method, a coordination mechanism based on consensus initiative is adopted, the robot adjusts behaviors only through local sensing and does not need to depend on global positioning or a complex communication network, the system has high self-healing capacity, and if an individual loses efficacy, other robots can automatically recover cluster stability and task execution. The underwater robot cluster system is low in cost and high in efficiency, and a brand new solution is provided for practical application of large-scale autonomous underwater robot clusters.

Description

Technical Field

[0001] This patent relates to the field of large-scale autonomous underwater robot swarm systems, particularly to the robot coordination and control technology based on natural spin and implicit cooperation mechanisms. Background Art

[0002] Swarm robotics aims to complete complex tasks through the cooperation of a large number of autonomous robots. Inspired by the collective behavior of organisms in nature, such as ants, fish schools, and bird flocks, these organisms achieve highly coordinated collective actions through simple interaction rules. Scientists hope to develop large-scale robot swarm systems applicable to complex environments, such as ocean monitoring and disaster relief, by mimicking the cooperation mechanisms of these organisms. Since the 1990s, swarm robotics has gradually developed into an interdisciplinary research field. Research has shown that collective behavior of organisms, such as ants communicating through pheromones and fish schools adjusting their directions based on neighbor information, provides ideas for the design of robot swarms. The core concept of swarm robotics is self-organization, where robots achieve global coordination through local interaction rules, avoiding global communication and central control. This feature improves the robustness of the swarm system, enabling it to cope with dynamic environments and local failures.

[0003] However, the expansion of the swarm scale has led to a sharp increase in communication requirements, resulting in problems such as communication delays and channel conflicts. In addition, robots face challenges such as occlusion and signal interference in complex environments, and traditional control methods are difficult to adapt to. Therefore, researchers have proposed an implicit cooperation strategy based on stigmergy. This mechanism transmits information through environmental changes rather than direct communication, thereby reducing communication requirements and improving the scalability and robustness of the system. Swarm robotics has broad application prospects in fields such as disaster relief, environmental monitoring, and military. Robot swarms can complete tasks without central control, enhancing the flexibility and efficiency of task execution. Especially in the field of underwater robots, with the increasing demand for large-scale robot groups, swarm robotics is expected to play an important role in ocean monitoring and other applications. Summary of the Invention

[0004] The object of the present invention is to solve the problem of how to achieve a highly scalable robot swarm system in an underwater environment. Specifically, the present invention aims to construct an underwater robot swarm system that is flexible, simple, and does not require complex communication, with strong self-organization and self-healing capabilities, by designing flexible and minimalist robot units and combining vision-based morphological information interaction and stimulus-response-based coordination mechanisms. This system aims to address the communication bottlenecks and environmental challenges faced by traditional swarm robots, providing a more efficient, economical, and easily scalable solution.

[0005] The technical solution of the present invention is based on an innovative robot monomer design, a vision-guided morphological information interaction mechanism, and a stimulus-response-based coordination mechanism, aiming to achieve an efficient, scalable, and economical underwater robot swarm system. The specific technical solution includes the following aspects: Firstly, it is the robot monomer design and mechanical implementation. The robot monomer design in the present invention is inspired by the circular motion pattern of Effrenum voratum (eukaryotic flagellates). It adopts a single drive system that can not only push the robot forward but also achieve steering, with high flexibility and simplicity. The core design of this monomer robot includes: Drive system: A single motor realizes the translation and rotation of the robot through the anti-torque effect, and the power output of the motor is adjustable, enabling the robot to flexibly perform different tasks in the underwater environment.

[0006] Hardware integration design: The hardware integration of the robot takes into account various factors such as cost control, computing power, power consumption, and service life. A highly integrated module is designed, including a computing unit (ESP-32-S3 chip), a sensing system (OV2640 camera), a propulsion system (610 coreless DC motor), and a wireless charging module, etc. Each module ensures the efficient operation of the robot through simple design and precise functional cooperation.

[0007] Sensing system: The sensing system of the robot uses a low-power CMOS camera (OV2640) with a wide-angle field of view of 150°, which can widely capture environmental information, helping to improve the cooperation accuracy within the swarm, especially in multi-robot cooperation tasks.

[0008] Propulsion and movement: The 610 coreless DC motor adopted by the robot provides an efficient and compact drive solution, adapting to the high-speed rotation and flexible control in the underwater environment. The design of the propulsion system fully considers the miniaturization of the robot, reducing the volume and cost, while improving the endurance of the robot.

[0009] Secondly, it is the vision-guided morphological information interaction mechanism. To achieve efficient cooperation in the swarm, the present invention adopts a vision-based morphological information interaction mechanism, which is mainly realized through the following technical means: Optical beacon system: Each robot is equipped with an optical beacon that can convert the state information of the robot into optical signals and transmit them to the surrounding robots. These beacons adopt LED modulation circuits, and the optical signals can effectively propagate in water, ensuring that robots can exchange necessary state information, such as position, motion state, and task progress, etc.

[0010] Efficient Battery Design: The beacons are equipped with efficient batteries to ensure continuous signal transmission during long-term tasks. Each beacon can operate for 15 - 20 hours, reducing the risk of communication interruption due to insufficient battery power.

[0011] Perception and Collaboration: Robots adjust their positions and behaviors in real-time by receiving and processing beacon information from neighboring robots, enabling coordinated operations. Based on the vision system and perception data, robots can accurately perceive the surrounding environment, determine the positions and states of adjacent individuals, and then make corresponding motion decisions.

[0012] Thirdly, it is a coordination mechanism based on stigmergy. To avoid the resource consumption and communication bottlenecks caused by high-frequency communication in traditional cluster systems, the present invention adopts an implicit collaboration strategy based on stigmergy. This mechanism realizes the self-organization and collaboration of the cluster through local perception and reaction among robots without direct communication. The specific technical solutions are as follows: Self-organization and Implicit Collaboration: Robots make adjustment actions by perceiving the state information of surrounding neighbors (such as position, speed, etc.) and combining environmental factors (such as water flow, obstacles, etc.), gradually realizing the self-organization of the cluster. Each robot only needs to perceive the three relative relationship information of the nearest neighbors within a limited field of view, avoiding the dependence on precise distances and global coordinates, and greatly reducing the communication and computational burdens.

[0013] Circular Swimming and State Transition: Referring to the circular swimming pattern of Effrenum voratum, robots determine their forward speed and turning radius according to the relative positions of neighbors. According to the real-time perception results, robots adjust their distances from neighbors and achieve uniform distribution of the formation and self-repair ability by changing the steering angle and moving speed.

[0014] Decentralization and Scalability: This mechanism is decentralized and does not rely on a central control node. Each robot makes independent decisions based on its own perception data and can effectively handle the expansion of the cluster scale. When new robots join or existing individuals are removed, the cluster can quickly adjust and maintain a stable formation and task execution state.

[0015] Finally, it is the self-organization and self-healing ability. The system of the present invention can not only perform efficient collaboration in static tasks but also has strong dynamic adaptability. The specific manifestations are as follows: Self-healing Ability: When some robots in the cluster malfunction or fail, the remaining robots can perceive the change in their states and adjust their behaviors to restore the overall structure and function of the cluster. This self-healing ability ensures the robustness of the cluster and the stable execution of tasks.

[0016] Scalability: As the scale of the cluster changes, the robots can still maintain effective cooperation and coordination when adding or removing individuals, ensuring that the cluster tasks are not affected. Whether underwater or in other complex environments, the cluster system can flexibly adapt to the changing task requirements.

[0017] Through the implementation of the above technical solutions, the underwater robot cluster system of the present invention has significant advantages in terms of performance, scalability, cost control, and ease of operation, and is suitable for application scenarios such as ocean monitoring and underwater exploration that require large-scale cluster cooperation. Brief Description of the Drawings

[0018] Figure 1 is the hardware component of the single robot platform of the present invention; Figure 2 is the result of the perception accuracy of the present invention; Figure 3 is the result of the system self-healing ability of the present invention. Detailed Description of the Invention

[0019] The following further describes the present invention in detail with reference to the drawings and specific embodiments. For the step numbers in the following embodiments, they are only set for the convenience of explanation and illustration, and no limitation is imposed on the order between the steps. The execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0020] The present invention provides a highly scalable underwater robot cluster system based on natural spin and implicit cooperation, and details the components of the system and their working principles. The underwater robot cluster system implementing the present invention is composed of multiple autonomous underwater robot monomers, and completes underwater tasks through cooperation and coordination among them. The following will detail the specific implementation of the present invention from aspects such as the hardware design, motion control, morphological perception and information transmission, and cluster coordination of the robots.

[0021] S1. Hardware design and implementation of the single robot. The single robot is the basic building block of the system of the present invention. Each single robot includes a computing unit, a perception system, a driving system, a propulsion system, and a power management system. The following details the design and implementation of each part. The overall hardware design is as Figure 1 shown.

[0022] The computing unit is responsible for processing the perceptual data acquired by the robot, calculating the motion trajectory, and coordinating the cluster behavior. Each robot is equipped with an ESP-32-S3 chip, which supports dual-mode wireless communication (Wi-Fi and Low Energy Bluetooth LE). This chip integrates an Xtensa 32-bit LX7 dual-core processor with a maximum frequency of up to 240 MHz, providing powerful computing capabilities. It also integrates an internal storage module, including 8M external flash memory and 8M pseudo-static random access memory (PSRAM), which can be used for real-time data processing, image processing, and path planning. This module also supports vector instructions, enabling efficient processing of image recognition and other complex algorithms.

[0023] Perception System

[0024] The perception system of a single robot mainly consists of a camera and optical beacons. Each robot is equipped with an OV2640 CMOS camera with a 150° wide-angle field of view, capable of capturing extensive image information of the surrounding environment. This camera provides static images with a maximum resolution of 1600×1200 pixels and supports automatic exposure and automatic white balance functions to ensure image quality under different lighting conditions.

[0025] The optical beacons convert the status information of the robot into optical signals through an LED modulation circuit and transmit them to neighboring robots. These optical beacons can effectively transmit signals in the underwater environment, helping robots to position and cooperate within the cluster. Each beacon is powered by a battery and can work for 15 to 20 hours, ensuring communication stability during long-term tasks.

[0026] Drive System and Propulsion System. The robot adopts a single drive system, powered by a single motor that not only propels the robot forward but also enables rotation. The robot rotates through the anti-torque effect, providing flexible movement capabilities. The propulsion system uses a 610 coreless DC motor with a rated power of 0.5 watts and can reach a rotational speed of 10,000 revolutions per minute, ensuring that the robot can operate flexibly underwater. The design of this propulsion system focuses on compactness and efficiency while ensuring power performance. The high-speed rotation of the motor not only provides propulsion but also generates a natural rotation effect, enhancing the mobility and stability of the robot.

[0027] Power Management Unit. Each robot is equipped with a 700 mAh battery, which can support the robot to run for more than 4 hours under normal working conditions. The battery is charged through a wireless charging module, eliminating the need for disassembly operations, simplifying the charging process, and improving maintenance efficiency. The wireless charging module can efficiently charge the robot through the principle of magnetic resonance, meeting the needs of simultaneous charging of multiple robots in a cluster environment.

[0028] Mechanical structure design. The outer shell of the robot is made of 3D printed transparent material, ensuring the accuracy, transparency and compressive resistance of the structure. Through optimized design, the robot has low water resistance and good buoyancy, enabling it to move stably underwater. The outer shell design takes into account the hydrodynamic characteristics of water flow and underwater movement, reducing the interference of water flow on the robot and ensuring its stable progress in complex water areas. The center of gravity of the robot coincides as much as possible with the morphological center to ensure the stability of the robot. In the external design, the robot is equipped with two keels (fixed fins) to reduce the impact of waves on the robot and ensure the stable posture of the robot when moving forward in water. The keels also play a protective role to avoid damage to the robot when colliding with external objects.

[0029] S2. Visual guidance and morphological information interaction of the robot cluster. The robot cluster system of the present invention adopts a visual guidance-based morphological information interaction mechanism, enabling the robots to achieve efficient cooperation in cluster tasks. Each robot can perceive the surrounding environment through a camera and optical beacons and coordinate with neighboring robots.

[0030] Visual guidance mechanism. Each robot is equipped with a CMOS camera that can capture the surrounding environment within a range of 150° and extract environmental features and the status information of neighboring robots through image processing algorithms. In actual operation, the robot perceives the beacon information around it through vision, obtains the position and motion state of neighboring robots, and adjusts its own motion trajectory based on the relative position relationship with the surrounding robots.

[0031] During the image processing process, the computing unit of the robot performs HSV color space conversion on the images captured by the camera to enhance color contrast, especially for the recognition and extraction of beacons. By customizing the threshold, the robot can effectively distinguish beacons from the background and accurately extract the information of neighboring robots. Through geometric figure fitting technology, the robot can obtain the position, posture and relative distance of adjacent robots. Based on the real-time acquired image information and the positions of adjacent robots, the robot can automatically judge the motion adjustments it needs to make and execute corresponding behaviors, such as changing the motion direction, adjusting the speed, changing the rotation radius, etc. This mechanism allows the robot to make autonomous decisions in the cluster without the need for global control or excessive communication. The perception accuracy analysis is as Figure 2 shown.

[0032] Optical Beacon and Information Transmission. Each robot is equipped with an optical beacon that converts the robot's status information into optical signals through an LED modulation circuit. These optical signals can effectively propagate in the underwater environment for neighboring robots to receive and utilize for swarm coordination. The optical beacon not only provides information transmission but also supports precise positioning of the robot in the swarm. The beacon is designed with high brightness and long-duration continuous working ability, enabling stable operation in complex underwater environments. The battery design of each beacon supports at least 15 hours of continuous operation, ensuring stability during long-duration mission execution.

[0033] S3. Consensus Initiative-based Coordination Mechanism. The present invention adopts a consensus initiative-based coordination mechanism to achieve implicit collaboration and self-organization among individuals in the swarm. Each robot adjusts its behavior based on local information by sensing the positions and states of neighboring robots without frequent communication.

[0034] Implicit Collaboration and Self-organization. Robots obtain morphological information (such as position, direction, speed, etc.) of surrounding robots through cameras and optical beacons, and adjust their motion states through a local perception and decision-making mechanism. Based on this local perception data, robots can implicitly collaborate with neighboring robots through simple behavior rules and decision-making processes without direct communication. This implicit collaboration mechanism avoids resource consumption caused by high-frequency communication and simplifies the control process of the swarm.

[0035] Dynamic Formation Adjustment and Self-healing Ability. When the number of individuals in the swarm changes (e.g., a robot fails or a new robot joins), the swarm can automatically adjust the relative positions and speeds of each robot through a self-organization mechanism, thereby restoring the overall structure of the swarm. Robots judge and adjust their own positions by observing the position changes of neighboring robots to maintain the stability of the swarm. For example, when a robot fails, other robots can sense the change in the position of that robot and quickly adjust to restore the formation; when a new robot joins, the swarm can automatically adapt and incorporate the new member into the original formation to ensure the continuity of task execution. The results are as Figure 3 shown. The uniform formation process of this system in simulation. In the experiment, agents were sequentially removed and added, but this system could still restore its formation. This demonstrates its self-healing ability when the number increases or decreases. An index is used to evaluate the formation quality, which is designed based on the mean deviation of the distances between adjacent robots.

[0036] System Swarm Coordination and Task Execution. The robot swarm system of the present invention can complete complex swarm tasks, such as surface search and rescue, environmental monitoring, marine resource exploration, etc., without central control. Through self-organization, robots can dynamically adjust the formation and collaboration method according to task requirements and environmental changes to flexibly respond to the changing working environment.

[0037] During the task execution, the robot cluster will flexibly adjust the motion strategy according to the real-time perception data. For example, when the cluster needs to cover a specific area, the robots will adjust their relative positions so that each robot can effectively cover the designated area. Through implicit cooperation and self-healing capabilities, the robots in the cluster can maintain high-efficiency task execution without global control.

[0038] Environmental adaptability. The cluster system can cope with variable water flows, obstacles, and dynamic changes in the complex underwater environment. When sudden changes occur in the environment, the robot cluster will quickly respond according to the perceived information, adjust the motion trajectory, avoid collisions, and maintain the continuity of task execution.

Claims

1. A super-scalable underwater robot swarm system based on natural spin and implicit collaboration, characterized in that, The system includes multiple robot units, and each robot unit includes: A drive system that is powered by a single driver, which is not only used to propel the robot forward but also to make the robot rotate; A computing unit for processing the environmental information sensed by the robot and making motion decisions; A sensing system including a camera and an optical beacon, which obtains environmental information through the camera and transmits morphological information to neighboring robots through the optical beacon; A propulsion system that provides driving force through a coreless DC motor and enables the robot to translate and rotate underwater; A power management unit that provides power support for the robot unit, and the power management unit includes a wireless charging module.

2. The underwater robot swarm system according to claim 1, wherein, The drive system adopts a single-motor drive mode, realizes the translation and rotation of the robot through the anti-torque effect, and controls the motion state of the robot by adjusting the output power of the motor.

3. The underwater robot swarm system according to claim 1, wherein, The sensing system uses a low-power CMOS camera with a field of view angle of at least 150°, which is used to detect the surrounding environment and generate relative distance data to support the collaborative work of robots in the cluster.

4. The underwater robot cluster system according to claim 1, wherein, The optical beacon is an LED modulation circuit that transmits the robot status information through optical signals, and the beacon has a continuous working time of at least 15 hours.

5. The underwater robot cluster system according to claim 1, wherein, The system adopts a morphological information interaction mechanism based on visual guidance. The robot realizes implicit cooperation and self-organization among clusters by obtaining the morphological information transmitted from neighboring robots.

6. The underwater robot swarm system according to claim 1, wherein, The system realizes the self-organization and self-healing of large-scale clusters through visual perception and consensus initiative mechanism without the support of global positioning or external communication.

7. The underwater robot swarm system according to claim 1, wherein The system can be densely deployed underwater or in complex environments, and each robot can adjust its behavior according to local sensing data to avoid collisions between clusters.

8. A coordination method for an underwater robot cluster, adopting the underwater robot cluster system described in claim 1, the method includes: Each robot obtains the morphological information of neighboring robots and generates relative position data based on visual guidance and local sensing; According to the relative position data, adjust the motion state of each robot to achieve implicit cooperation between robots in the cluster; Adjust the underwater motion trajectory of each robot through the drive system to ensure the stability of the cluster and the task execution efficiency.

9. The coordination method of the underwater robot swarm according to claim 11, wherein, ​ 10. The coordination method of the underwater robot swarm according to claim 11, wherein, ​

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