Leader-Based Flocking of Multiple Swarm Robots in Underwater Environments

Kim, Jonghoek

doi:10.3390/s23115305

Cited by 3 publications

(2 citation statements)

References 52 publications

(123 reference statements)

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“…Before presenting our net avoidance controls, we address the motion model of the robot. This paper considers a spherical underwater robot as our platform [ 34 , 35 , 36 , 37 ]. A spherical robot may move slower than a torpedo-shaped underwater vehicle.…”

Section: Net Avoidance Controlsmentioning

confidence: 99%

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Camera-Based Net Avoidance Controls of Underwater Robots

Kim

2024

Sensors

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Fishing nets are dangerous obstacles for an underwater robot whose aim is to reach a goal in unknown underwater environments. This paper proposes how to make the robot reach its goal, while avoiding fishing nets that are detected using the robot’s camera sensors. For the detection of underwater nets based on camera measurements of the robot, we can use deep neural networks. Passive camera sensors do not provide the distance information between the robot and a net. Camera sensors only provide the bearing angle of a net, with respect to the robot’s camera pose. There may be trailing wires that extend from a net, and the wires can entangle the robot before the robot detects the net. Moreover, light, viewpoint, and sea floor condition can decrease the net detection probability in practice. Therefore, whenever a net is detected by the robot’s camera, we make the robot avoid the detected net by moving away from the net abruptly. For moving away from the net, the robot uses the bounding box for the detected net in the camera image. After the robot moves backward for a certain distance, the robot makes a large circular turn to approach the goal, while avoiding the net. A large circular turn is used, since moving close to a net is too dangerous for the robot. As far as we know, our paper is unique in addressing reactive control laws for approaching the goal, while avoiding fishing nets detected using camera sensors. The effectiveness of the proposed net avoidance controls is verified using simulations.

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Section: Net Avoidance Controlsmentioning

confidence: 99%

“…The motion dynamics of the robot are given in ( 1 ), and the robot’s speed is

m/s. Recall we consider a spherical underwater robot as our platform [ 34 , 35 , 36 , 37 ]. Thanks to the high water pressure resistance of spherical objects, a spherical robot can perform rotational motions with a 0 degree turn radius.…”

Section: Matlab Simulationsmentioning

confidence: 99%

Camera-Based Net Avoidance Controls of Underwater Robots

Kim

2024

Sensors

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Surface Tracking Controls of an Unmanned Underwater Vehicle with Fixed Sonar Ray Measurements in Tunnel-Like Environments

Kim

2024

J Intell Robot Syst

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This paper introduces 3-D surface tracking control of an Unmanned Underwater Vehicle (UUV) in tunnel-like environments. Consider the case where a sonar transducer in the UUV does not rotate, and it only emits fixed sonar ray reporting a simple distance measurement. This reduces the power consumption of the UUV, while reducing the UUV’s size and price. The UUV is controlled to proceed in tunnel-like environments, while maintaining a predefined distance from the tunnel boundaries. For maintaining a predefined distance from tunnel boundaries, the UUV uses fixed sonar rays surrounding it. As far as we know, our article is novel in developing 3-D surface tracking controls of tunnel-like environments utilizing an UUV with fixed sonar rays surrounding it. MATLAB simulations are used for demonstrating the performance of the proposed tracking controls.

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Using Hill Climb Assembler Encoding neural networks to control follower vehicles in an underwater swarm

Praczyk

2024

Applied Soft Computing

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Leader-Based Flocking of Multiple Swarm Robots in Underwater Environments

Cited by 3 publications

References 52 publications

Camera-Based Net Avoidance Controls of Underwater Robots

Camera-Based Net Avoidance Controls of Underwater Robots

Surface Tracking Controls of an Unmanned Underwater Vehicle with Fixed Sonar Ray Measurements in Tunnel-Like Environments

Using Hill Climb Assembler Encoding neural networks to control follower vehicles in an underwater swarm

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