Thierry Soriano

This study presents a relative localization estimation method for a group of low-cost underwater drones (l-UD), which only uses visual feedback provided by an on-board camera and IMU data. It aims to design a distributed controller for a group of robots to reach a specific shape. This controller is based on a leader–follower architecture. The main contribution is to determine the relative position between the l-UD without using digital communication and sonar positioning methods. In addition, the proposed implementation of the EKF to fuse the vision data and the IMU data improves the prediction capability in cases where the robot is out of view of the camera. This approach allows the study and testing of distributed control algorithms for low-cost underwater drones. Finally, three robot operating system (ROS) platform-based BlueROVs are used in an experiment in a near-realistic environment. The experimental validation of the approach has been obtained by investigating different scenarios.

In many engineering applications, the vibration analysis of a structure requires the set up of a large number of sensors. These studies are mostly performed in post processing and based on linear modal analysis. However, many studied devices highlight that modal parameters depend on the vibration level non linearities and are performed with sensors as accelerometers that modify the dynamics of the device. This work proposes a significant evolution of modal testing based on the real time identification of non linear parameters (natural frequencies and damping) tracked with a linear modal basis. This method, called Kinematic-SAMI (for multiSensors Assimilation Modal Identification) is assessed firstly on a numerical case with known non linearities and secondly in the framework of a classical cantilever beam with contactless measurement technique (high speed and high resolution cameras). Finally, the efficiency and the limits of the method are discussed.

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.

This paper's goal is to present a framework for studying multi‐underwater drone coordination, specifically formation control, based on a real‐time object‐oriented paradigms in an open‐source environment (the Operating Robot System Environment). This framework will capture the system's whole development life cycle, from the requirements specification to testing out the simulation and realization models. Based on this specialized framework, we can easily and quickly develop and verify the control algorithms while meeting future input requirements or manageable changes.