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Vassilis Kodogiannis is a pioneering researcher in the field of underwater robotics, with a primary focus on neural network-based control systems and autonomous navigation. His work addresses the fundamental challenges of operating robotic vehicles in hazardous, unstructured underwater environments where traditional controllers fail due to modeling difficulties. Kodogiannis made significant contributions by introducing recurrent neural network architectures for real-time predictive control of Underwater Robotic Vehicles (URVs), demonstrating how these networks can provide both accurate and fast forward models essential for autonomous operation. His 1994 paper on neural network predictive control systems laid the groundwork for adaptive control strategies that have since accumulated over 10 citations across his most-cited works. Notably, his 2008 study on terrain-based navigation using ultrasonic scanning systems advanced underwater SLAM (Simultaneous Localization and Mapping) by processing real ultrasonic imaging data, enabling more reliable autonomous navigation. Kodogiannis also contributed to accessible robotics through his HumanPT architecture, which enables low-cost robotic applications by integrating with existing commercial systems. His cumulative body of work, spanning from 1994 to 2008, has established foundational techniques for intelligent, adaptive underwater robotic systems.