scholarly journals A Behavior-Based Mission Planner for Cooperative Autonomous Underwater Vehicles†

2012 ◽  
Vol 46 (2) ◽  
pp. 32-44 ◽  
Author(s):  
Laura Sorbi ◽  
Graziano Pio De Capua ◽  
Jean-Guy Fontaine ◽  
Laura Toni
Keyword(s):  
Autonomous Underwater Vehicles ◽  
A Priori ◽  
Underwater Vehicles ◽  
Fixed Target ◽  
Marine Research ◽  
Detection And Tracking ◽  
Mobile Target ◽  
Overall Performance ◽  
Behavior Based ◽  
Unknown Area

AbstractDue to its applications in marine research, oceanographic, and undersea exploration, autonomous underwater vehicles (AUVs) and the related control algorithms recently have been under intense investigation. In this work, we address target detection and tracking issues, proposing a control strategy that is able to benefit from the cooperation among robots within the fleet. In particular, we introduce a behavior-based planner for cooperative AUVs, proposing an algorithm that is able to search and recognize targets in both static and dynamic scenarios. With no a priori information about the surrounding environment, robots cover an unknown area with the goal of finding objects of interest. When a target is found, the AUVs’ goal is to classify (fixed target) or track (mobile target) the target, with no information about target trajectory and with formation constraints. Results demonstrate the good overall performance of the proposed algorithm in both scenarios.

scholarly journals Active SLAM for Autonomous Underwater Exploration

2019 ◽  
Vol 11 (23) ◽  
pp. 2827 ◽  
Author(s):  
Narcís Palomeras ◽  
Marc Carreras ◽  
Juan Andrade-Cetto
Keyword(s):  
Autonomous Underwater Vehicles ◽  
A Priori ◽  
Underwater Vehicles ◽  
View Planning ◽  
Localization And Mapping ◽  
Entropy Reduction ◽  
Graph Slam ◽  
Slam Algorithm ◽  
Iterative Closest Point Algorithm ◽  
Active Localization

Exploration of a complex underwater environment without an a priori map is beyond the state of the art for autonomous underwater vehicles (AUVs). Despite several efforts regarding simultaneous localization and mapping (SLAM) and view planning, there is no exploration framework, tailored to underwater vehicles, that faces exploration combining mapping, active localization, and view planning in a unified way. We propose an exploration framework, based on an active SLAM strategy, that combines three main elements: a view planner, an iterative closest point algorithm (ICP)-based pose-graph SLAM algorithm, and an action selection mechanism that makes use of the joint map and state entropy reduction. To demonstrate the benefits of the active SLAM strategy, several tests were conducted with the Girona 500 AUV, both in simulation and in the real world. The article shows how the proposed framework makes it possible to plan exploratory trajectories that keep the vehicle’s uncertainty bounded; thus, creating more consistent maps.

Cooperative Control of Multiple Autonomous Underwater Vehicles

2013 ◽  
Vol 365-366 ◽  
pp. 905-912
Author(s):  
Bin He ◽  
Da Peng Jiang
Keyword(s):  
Information Exchange ◽  
Cooperative Control ◽  
Distributed Simulation ◽  
Autonomous Underwater Vehicles ◽  
Underwater Vehicles ◽  
Programming Algorithm ◽  
Simulation Environment ◽  
Efficient Communication ◽  
Behavior Based Control ◽  
Behavior Based

The focus of research of AUV is gradually moving towards multiple autonomous underwater vehicles (MAUV) in recent years. This paper describes an investigation into cooperative control of MAUV. Firstly, a distributed control architecture (MOOS) was applied to MAUV system. According to MOOS, functionalities of AUV were organized in a modular manner and a unified information exchange mechanism was used to ensure an efficient communication between different modules. Secondly, a behavior based control strategy was proposed to enable the AUV to cooperate with each other intelligently and adaptively. Interval programming algorithm was applied to make sure that behaviors of each AUV can be coordinated in a timely and optimal manner. Stability of behavior-based control of AUV was analyzed. Finally, a distributed simulation environment was established and a series of simulation were carried out to verify the feasibility of methods mentioned above.

scholarly journals Test Results of the Long Baseline Navigation Solutions under a Large a Priori Position Uncertainty

2022 ◽  
Vol 1215 (1) ◽  
pp. 012006
Author(s):  
V.V. Bogomolov
Keyword(s):  
Autonomous Underwater Vehicles ◽  
A Priori ◽  
Underwater Vehicles ◽  
Posteriori Probability ◽  
Global Maximum ◽  
Test Results ◽  
Final Solution ◽  
Position Uncertainty ◽  
Modified Method ◽  
Long Baseline

Abstract A method is proposed for long baseline navigation of autonomous underwater vehicles (AUV) to be used in the case of a large a priori position uncertainty. The new modified method is based on the iterated Kalman filter (IKF) working with different initial linearization points. The final solution is calculated by clustering and weighting the IKF results. This approach allows position estimates to be determined in accordance with the global maximum of posteriori probability density of coordinates. The test results obtained with the use of three beacons and an underwater vehicle are presented.

scholarly journals Path Planning for Underwater Information Gathering Based on Genetic Algorithms and Data Stochastic Models

2021 ◽  
Vol 9 (11) ◽  
pp. 1183
Author(s):  
Matteo Bresciani ◽  
Francesco Ruscio ◽  
Simone Tani ◽  
Giovanni Peralta ◽  
Andrea Timperi ◽  
...  
Keyword(s):  
Path Planning ◽  
Information Content ◽  
Autonomous Underwater Vehicles ◽  
A Priori ◽  
Posidonia Oceanica ◽  
Underwater Vehicles ◽  
Information Gathering ◽  
Technological Developments ◽  
Path Planner ◽  
Areas Of Interest

Recent technological developments have paved the way to the employment of Autonomous Underwater Vehicles (AUVs) for monitoring and exploration activities of marine environments. Traditionally, in information gathering scenarios for monitoring purposes, AUVs follow predefined paths that are not efficient in terms of information content and energy consumption. Informative Path Planning (IPP) represents a valid alternative, defining the path that maximises the gathered information. This work proposes a Genetic Path Planner (GPP), which consists in an IPP strategy based on a Genetic Algorithm, with the aim of generating a path that simultaneously maximises the information gathered and the coverage of the inspected area. The proposed approach has been tested offline for monitoring and inspection applications of Posidonia Oceanica (PO) in three different geographical areas. The a priori knowledge about the presence of PO, in probabilistic terms, has been modelled utilising a Gaussian Process (GP), trained on real marine data. The GP estimate has then been exploited to retrieve an information content of each position in the areas of interest. A comparison with other two IPP approaches has been carried out to assess the performance of the proposed algorithm.

scholarly journals Deep Reinforcement Learning Controller for 3D Path Following and Collision Avoidance by Autonomous Underwater Vehicles

2021 ◽  
Vol 7 ◽  
Author(s):  
Simen Theie Havenstrøm ◽  
Adil Rasheed ◽  
Omer San
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Collision Avoidance ◽  
Autonomous Agents ◽  
Autonomous Vehicle ◽  
Autonomous Underwater Vehicles ◽  
A Priori ◽  
Path Following ◽  
Underwater Vehicles ◽  
Stability Of Dynamical Systems

Control theory provides engineers with a multitude of tools to design controllers that manipulate the closed-loop behavior and stability of dynamical systems. These methods rely heavily on insights into the mathematical model governing the physical system. However, in complex systems, such as autonomous underwater vehicles performing the dual objective of path following and collision avoidance, decision making becomes nontrivial. We propose a solution using state-of-the-art Deep Reinforcement Learning (DRL) techniques to develop autonomous agents capable of achieving this hybrid objective without having a priori knowledge about the goal or the environment. Our results demonstrate the viability of DRL in path following and avoiding collisions towards achieving human-level decision making in autonomous vehicle systems within extreme obstacle configurations.

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