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

• Published in: Applied sciences Vol. 10; no. 5; p. 1732
• Main Authors: Pham, Hoang Anh, Soriano, Thierry, Ngo, Van Hien, Gies, Valentin
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-03-2020; Multidisciplinary digital publishing institute (MDPI)
• Subjects: Automatic; Autonomous underwater vehicles; collision and obstacle avoidance; Communication; Computer Science; distributed adaptive neural network control; Drones; Engineering Sciences; gazebo; Hazardous areas; Low cost; low-cost underwater robotics; Multiagent Systems; Network control; Neural and Evolutionary Computing; Neural networks; Radial basis function; robot operating system (ros); Robotics; Robots; Robust control; Sensors; Underwater vehicles; Vehicles; robot operating system (ROS); low-cost underwater robotics; collision and obstacle avoidance; distributed adaptive neural network control; Gazebo
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app10051732

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.