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.
The aim of the present work is to analyze coordination modes for multiple unmanned underwater vehicles (UUV) which are an open-source and low-cost platform based on the Model Driven Architecture approach. The medium-term goal should be to transform an existing remote control system into an autonomous control system capable of multiple missions. Specially, we describe the general re-engineering process in the use-case of a coordination of two UUVs. The abstract model is studied and verified based on Matlab/Simulink. Furthermore, we propose a framework, which is able to support the development towards the real platform thanks to Robot Operating System (ROS) middleware and Gazebo simulator.
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