Translation Control of a Fleet Circular Formation of AUVs under Finite Communication Range

International audienceThis work proposes a control algorithm to stabilize a circular formation of AUVs tracking a time-varying center. We also consider the problem of uniform distribution of all the agents along the circle from two approaches: all-to-all and limited communication. We tackle with this communication constraint using a cooperative control which includes the Laplacian matrix of the communication graph (fixed or distance-dependent). The system was implemented in computersimulation, accessible though Web1

Multi - level classification and formulation of an integration framework for estimation/ communication/ computation (EC2) co-design

This report is an overview of the research activities regarding the WP06 (C4E co-design) of the FeedNetBack European project for the first six months of the year 2010 at INRIA. The research team consists of Post Doctoral Fellow Alireza Farhadi and Director Carlos Canudas de Wit. In preparing this report we had: a) A meeting with our industrial partner Ifremer. b) A three days visit at the University of Padova (UNIPD), Italy. c) A short discussion with our industrial partner Videotech. d) Long discussions with Sandro Zampieri and Luca Schenato from UNIPD. We also received some results from our colleagues working in ETH (Swiss).The objective of the FeedNetBack project is to propose a co-design framework, which allows the integration of control-estimation, communication, computation, complexity, and energy in networked control systems. This co-design framework is developed for the following case studies: a) A fleet of Autonomous Underwater Vehicles (AUVs) b) Intelligent camera networks for motion capture c) Surveillance systems using a network of smart cameras. The three case studies have been selected to demonstrate the wide spectrum of possible applications of the FeedNetBack project: From systems with relatively few, highly mobile nodes, communicating over a network subject to communication imperfections; to systems with a very high number of immobile nodes, with high available bandwidth but also high computation requirements (smart camera network for surveillance applications and motion capture). To create such a co-design framework we first need to fully understand the constraints imposed by control, communication, computation, complexity, and energy on the above case studies. This is the first objective of this report. The second objective is to formulate an estimation/ communication/ computation co-design framework which is applicable to the above case studies. To achieve these goals, in Section 1, we study fleet of AUVs; and following our discussions with Ifremer, we identify the interactions between control, communication, computation, etc. in this case study. In Section 2, we study smart networks of cameras for motion capture; and following our discussions with Sandro Zampieri and Luca Schenato, we identify the interactions between different components (control, communication, etc.). In Section 3, we study smart networks of cameras for surveillance applications; and following our discussions with Videotech, Sandro Zampieri and Luca Schenato, we identify the interactions between different components. Then, in Section 4, based on this studies, we formulate an integration framework for estimation/ communication/ computation co-design which is applicable to fleet of AUVs and smart camera network for surveillance applications

Integration of control, communication, computation, com- plexity and energy considerations in a coherent design strategy

This report is an overview of the research activities regarding WP06 (C4E co-design) of the FeedNetBack project. The objective of WP6 of Feed- NetBack is to propose a co-design framework, which allows the integration of control-estimation, communication, computation, complexity, and energy considerations in networked control systems. In this report we outline gen- eral guidelines for co-design and illustrate their applicability to the following case studies: (i) surveillance systems using a network of smart cameras and (ii) eets of Autonomous Underwater Vehicles (AUVs).

Elastic Formation Control Based on Affine Transformations

International audienceThis paper deals with the control of a fleet of nonlinear systems representing AUVs (autonomous underwater vehicles). The purpose is here to design a control law to stabilize the fleet to time-varying formations which are not only circular. A novel framework is proposed to express a general control law for a large class of formations. This is produced by applying a sequence of affine transformations such as translations, rotations and scalings. The paper also includes a cooperative control to distribute the agents along the formation which takes into account the communication constraints. The system was implemented in computer simulation, accessible through Web

Collective motion, sensor networks, and ocean sampling

Author Posting. © IEEE, 2007. This article is posted here by permission of IEEE for personal use, not for redistribution. The definitive version was published in Proceedings of the IEEE 95 (2007): 48-74, doi:10.1109/jproc.2006.887295.This paper addresses the design of mobile sensor networks for optimal data collection. The development is strongly motivated by the application to adaptive ocean sampling for an autonomous ocean observing and prediction system. A performance metric, used to derive optimal paths for the network of mobile sensors, defines the optimal data set as one which minimizes error in a model estimate of the sampled field. Feedback control laws are presented that stably coordinate sensors on structured tracks that have been optimized over a minimal set of parameters. Optimal, closed-loop solutions are computed in a number of low-dimensional cases to illustrate the methodology. Robustness of the performance to the influence of a steady flow field on relatively slow-moving mobile sensors is also explored

Collaborative Estimation of Gradient Direction by a Formation of AUVs

International audienceThis work deals with the source-seeking problem in which the task is to locate the source of some signal using a fleet of AUVs (autonomous underwater vehicles). The present paper proposes a distributed solution in which a group of vehicles uniformly distributed in a ¯xed circular formation, estimates the gradient direction of the signal propagation. The distributed algorithm takes into account the communication constraints and depends on direct signal measurements. Our approach is based on the previous results in formation control to stabilize the °eet in a circular formation with time-varying center and in a collaborative source-seeking algorithm. The results are supported through computer simulations

Collective dynamics and control of a fleet of heterogeneous marine vehicles

Cooperative control enables combinations of sensor data from multiple autonomous underwater vehicles (AUVs) so that multiple AUVs can perform smarter behaviors than a single AUV. In addition, in some situations, a human-driven underwater vehicle (HUV) and a group of AUVs need to collaborate and preform formation behaviors. However, the collective dynamics of a fleet of heterogeneous underwater vehicles are more complex than the non-trivial single vehicle dynamics, resulting in challenges in analyzing the formation behaviors of a fleet of heterogeneous underwater vehicles. The research addressed in this dissertation investigates the collective dynamics and control of a fleet of heterogeneous underwater vehicles, including multi-AUV systems and systems comprised of an HUV and a group of AUVs (human-AUV systems). This investigation requires a mathematical motion model of an underwater vehicle. This dissertation presents a review of a six-degree-of-freedom (6DOF) motion model of a single AUV and proposes a method of identifying all parameters in the model based on computational fluid dynamics (CFD) calculations. Using the method, we build a 6DOF model of the EcoMapper and validate the model by field experiments. Based upon a generic 6DOF AUV model, we study the collective dynamics of a multi-AUV system and develop a method of decomposing the collective dynamics. After the collective dynamics decomposition, we propose a method of achieving orientation control for each AUV and formation control for the multi-AUV system. We extend the results and propose a cooperative control for a human-AUV system so that an HUV and a group of AUVs will form a desired formation while moving along a desired trajectory as a team. For the post-mission stage, we present a method of analyzing AUV survey data and apply this method to AUV measurement data collected from our field experiments carried out in Grand Isle, Louisiana in 2011, where AUVs were used to survey a lagoon, acquire bathymetric data, and measure the concentration of reminiscent crude oil in the water of the lagoon after the BP Deepwater Horizon oil spill in the Gulf of Mexico in 2010.Ph.D