Underwater guidance of distributed autonomous underwater vehicles using one leader

Kim, Jonghoek

doi:10.1002/asjc.2969

Cited by 6 publications

(8 citation statements)

References 62 publications

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“…Reference [39] addressed guidance of distributed robots using one leader. Reference [39] used rendezvous controls in [40] to make all robots rendezvous with the leader. Once they rendezvous, the leader guides all robots towards the goal.…”

Section: Literature Reviewmentioning

confidence: 99%

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Leader-Based Flocking of Multiple Swarm Robots in Underwater Environments

Kim

2023

Sensors

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Considering underwater environments, this paper tackles flocking of multiple swarm robots utilizing one leader. The mission of swarm robots is to reach their goal while not colliding with a priori unknown 3D obstacles. In addition, the communication link among the robots needs to be preserved during the maneuver. Only the leader has sensors for localizing itself while accessing the global goal position. Every robot, except for the leader, can measure the relative position and the ID of its neighboring robots by utilizing proximity sensors such as Ultra-Short BaseLine acoustic positioning (USBL) sensors. Under the proposed flocking controls, multiple robots flock inside a 3D virtual sphere while preserving communication connectivity with the leader. If necessary, all robots rendezvous at the leader to increase connectivity among the robots. The leader herds all robots to reach the goal safely, while the network connectivity is maintained in cluttered underwater environments. To the best of our knowledge, our article is novel in developing underwater flocking controls utilizing one leader, so that a swarm of robots can safely flock to the goal in a priori unknown cluttered environments. MATLAB simulations were utilized to validate the proposed flocking controls in underwater environments with many obstacles.

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Section: Literature Reviewmentioning

confidence: 99%

“…Once they rendezvous, the leader guides all robots towards the goal. In [39,40], all robots rendezvoused with the leader by generating the adjacency matrix. Thereafter, a tree, which is rooted with the leader, is built based on the adjacency matrix.…”

Section: Literature Reviewmentioning

confidence: 99%

Leader-Based Flocking of Multiple Swarm Robots in Underwater Environments

Kim

2023

Sensors

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“…Nowadays, underwater robots are applied in various scenarios, such as underwater exploration with or without human intervention [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 ]. The efficiency of underwater robots can improve significantly, and we can reduce the human intervention needed on robot controls.…”

Section: Introductionmentioning

confidence: 99%

“…the robot moves along a circle with radius D. The robot moves along a half circle with radius D, by setting its velocity vector as (7) for π×D S s. In (7), RotAngle ∈ { π 2 , − π 2 } indicates the rotation direction of the robot, while moving along a half circle with radius D. Here, RotAngle = π 2 implies that the robot moves along a half circle with radius D in the counter-clockwise direction. In addition, RotAngle = − π 2 implies that the robot moves along a half circle with radius D in the clockwise direction.…”

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confidence: 99%

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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“…Modeling is developed to understand collective behaviors in nature better, as well as to effectively control artificial swarms, such as multi-robot systems [8] and unmanned vehicles [9][10][11]. Reynolds proposed the Boid model and ran the first computer simulation of flocking based on three heuristic rules, known as cohesion, separation, and alignment [12].…”

Section: Introductionmentioning

confidence: 99%

An Improved Vicsek Model of Swarms Based on a New Neighbor Strategy Considering View and Distance

Wang,

Zhao,

2023

Applied Sciences

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Collective behaviors in nature and human societies have been intensively studied in recent decades. The Vicsek model is one of the typical models that explain self-ordered particle systems well. In the original Vicsek model, the neighbor strategy takes all its neighbors’ mean directions into account when updating particles’ directions, which leads to a longer convergence time and higher computation cost due to the excess number of neighbors. In this paper, we introduce a new neighbor strategy to the Vicsek model. It defines that each particle will only select a certain number of particles with the farthest distance that fall into its vision sector as its neighbors. In addition, we classify the Vicsek model as the static model and the dynamic model according to whether the features of particles in the model are constant or not. Moreover, we design a new rule to apply the new neighbor strategy to dynamic Vicsek models. The simulation results indicate that our new neighbor strategy can significantly decrease the average number of particles’ neighbors but still be able to further enhance the Vicsek model’s convergence performance. The comparative results found that the static and dynamic model applied with the new neighbor strategy outperforms the models that only apply view restriction or remote neighbor strategy in noiseless and noisy conditions.

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Underwater guidance of distributed autonomous underwater vehicles using one leader

Cited by 6 publications

References 62 publications

Leader-Based Flocking of Multiple Swarm Robots in Underwater Environments

Leader-Based Flocking of Multiple Swarm Robots in Underwater Environments

Camera-Based Net Avoidance Controls of Underwater Robots

An Improved Vicsek Model of Swarms Based on a New Neighbor Strategy Considering View and Distance

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