Autonomous underwater vehicle guidance by integrating neural networks and geometric reasoning

This paper presents a method for guiding an autonomous underwater vehicle (AUV) during sea bottom inspection missions. The vehicle is equipped with several sensors (optical, sonar, acoustic) and is able to detect and follow a pipeline placed on the sea bottom. Neural networks and geometric reasoning methods are integrated to perform a real-time identification of pipeline borders in a complex underwater environment. Different scenarios characterized by both obstacles and/or artifacts (due to reflections of artificial light sources used by the vehicle to illuminate the scene) have been considered. Results focus on pipeline detection accuracy and on AUV missions in the absence or presence of down stream and/or obstacles. (C) 1999 John Wiley & Sons, Inc.