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Article

Distributed Adaptive Neural Network Control Applied to a Formation Tracking of a Group of Low-Cost Underwater Drones in Hazardous Environments

1
COSMER Laboratory, University of Toulon, 83130 Toulon, France
2
Hanoi University of Science and Technology, Hanoi 100803, Vietnam
3
IM2NP Laboratory, University of Toulon, 83130 Toulon, France
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(5), 1732; https://doi.org/10.3390/app10051732
Received: 31 January 2020 / Revised: 25 February 2020 / Accepted: 27 February 2020 / Published: 3 March 2020
(This article belongs to the Section Computing and Artificial Intelligence)
This paper addresses a formation tracking problem of multiple low-cost underwater drones by implementing distributed adaptive neural network control (DANNC). It is based on a leader-follower architecture to operate in hazardous environments. First, unknown parameters of underwater vehicle dynamics, which are important requirements for real-world applications, are approximated by a neural network using a radial basis function. More specifically, those parameters are only calculated by local information, which can be obtained by an on-board camera without using an external positioning system. Secondly, a potential function is employed to ensure there is no collision between the underwater drones. We then propose a desired configuration of a group of unmanned underwater vehicles (UUVs) as a time-variant function so that they can quickly change their shape between them to facilitate the crossing in a narrow area. Finally, three UUVs, based on a robot operating system (ROS) platform, are used to emphasize the realistic low-cost aspect of underwater drones. The proposed approach is validated by evaluating in different experimental scenarios. View Full-Text
Keywords: low-cost underwater robotics; distributed adaptive neural network control; collision and obstacle avoidance; robot operating system (ROS); Gazebo low-cost underwater robotics; distributed adaptive neural network control; collision and obstacle avoidance; robot operating system (ROS); Gazebo
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MDPI and ACS Style

Pham, H.A.; Soriano, T.; Ngo, V.H.; Gies, V. Distributed Adaptive Neural Network Control Applied to a Formation Tracking of a Group of Low-Cost Underwater Drones in Hazardous Environments. Appl. Sci. 2020, 10, 1732. https://doi.org/10.3390/app10051732

AMA Style

Pham HA, Soriano T, Ngo VH, Gies V. Distributed Adaptive Neural Network Control Applied to a Formation Tracking of a Group of Low-Cost Underwater Drones in Hazardous Environments. Applied Sciences. 2020; 10(5):1732. https://doi.org/10.3390/app10051732

Chicago/Turabian Style

Pham, Hoang A., Thierry Soriano, Van H. Ngo, and Valentin Gies. 2020. "Distributed Adaptive Neural Network Control Applied to a Formation Tracking of a Group of Low-Cost Underwater Drones in Hazardous Environments" Applied Sciences 10, no. 5: 1732. https://doi.org/10.3390/app10051732

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