A behavior-based intelligent control architecture with application to coordination of multiple underwater vehicles

2000 ◽  
Vol 30 (6) ◽  
pp. 767-784 ◽  
Author(s):  
R. Kumar ◽  
J.A. Stover
Keyword(s):  
Intelligent Control ◽  
Underwater Vehicles ◽  
Control Architecture ◽  
Behavior Based

scholarly journals Combining Dense Structure from Motion and Visual SLAM in a Behavior-Based Robot Control Architecture

10.5772/7240 ◽  
2010 ◽  
Vol 7 (1) ◽  
pp. 7 ◽  
Author(s):  
Geert De Cubber ◽  
Sid Ahmed Berrabah ◽  
Daniela Doroftei ◽  
Yvan Baudoin ◽  
Hichem Sahli
Keyword(s):  
Structure From Motion ◽  
Robot Control ◽  
Control Architecture ◽  
Visual Slam ◽  
Dense Structure ◽  
Behavior Based

Intelligent control of autonomous underwater vehicles groups

2017 ◽  
Author(s):  
Igor Bychkov ◽  
Artem Davydov ◽  
Maksim Kenzin ◽  
Nikolai Maksimkin ◽  
Nadezhda Nagul ◽  
...  
Keyword(s):  
Intelligent Control ◽  
Autonomous Underwater Vehicles ◽  
Underwater Vehicles

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.

A modular and scalable control architecture for navigation of autonomous underwater vehicles

2015 ◽  
Author(s):  
Francisco J. Lastra ◽  
Francisco J. Velasco ◽  
Elias Revestido Herrero ◽  
Jesus A. Trujillo Medina ◽  
Luis M. Vega
Keyword(s):  
Autonomous Underwater Vehicles ◽  
Underwater Vehicles ◽  
Control Architecture
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