Search

This article describes the design and construction journey of a self-developed unmanned surface vehicle (USV). In order to increase the accessibility and lower the barrier of entry we propose a low-cost (under EUR 1000) approach to the vessel construction with great adaptability and customizability. This design prioritizes minimal power consumption as a key objective. It focuses on elucidating the intricacies of both the design and assembly processes involved in creating an economical USV. Utilizing easily accessible components, the boat outlined in this study has been already participated in the 1st Aegean Ro-boat Race 2023 competition and is tailored for entry into similar robotic competitions. Its primary functionalities encompass autonomous sea navigation coupled with sophisticated collision avoidance capabilities. Finally, we studied reinforcement learning strategies for constructing a robust intelligent controller for the task of USV navigation under disturbances and we show some preliminary simulation results we have obtained. [ABSTRACT FROM AUTHOR]

In this work we present a multiagent deep reinforcement learning approach for autonomous driving vehicles that is able to operate in traffic networks with unsignalized intersections. The key aspects of the proposed study are the introduction of route-agents as the main building block of the system, as well as a collision term that allows the cooperation among vehicles and the construction of an efficient reward function. These have the advantage of establishing an enhanced collaborative multiagent deep reinforcement learning scheme that manages to control multiple vehicles and navigate them safely and efficiently-economically to their destination. In addition, it provides the beneficial flexibility to lay down a platform for transfer learning and reusing knowledge from the agents' policies in handling unknown traffic scenarios. We provide several experimental results in simulated road traffic networks of variable complexity and diverse characteristics using the SUMO environment that empirically illustrate the efficiency of the proposed multiagent framework. [ABSTRACT FROM AUTHOR]

In the artificial intelligent and big data technology era, the marine industry among others is inevitably developing in this direction, aiming at becoming autonomous and completing tasks without relying on human involvement while providing safety. The technology of small unmanned surface vehicles (USVs) is relatively mature but with a large development potential and wide research interest expecting significant benefits such as safety and high efficiency in shipping and transportation systems. This article addresses these issues and utilizes an imitation learning algorithm to resolve autonomous navigation for USVs even in complex environmental conditions. We formulate the trajectory modeling as a data-driven imitation learning problem where we employ a state of the art imitation learning algorithm. Experiments are performed in a particular simulated environment tailored to match the specific weather conditions of the local area. The simulation results show the potential of the proposed imitation learning scheme to create advanced intelligent agents for USVs under real-world environmental settings, and USV actuation constraints that allow to predict trajectories with high accuracy and safety. In addition, we evaluated the method's robustness in generating successful trajectories under environmental conditions that differed from those encountered during training, thereby promoting knowledge reusing without the need for retraining. • Formulate the modeling of USV trajectories as an imitation learning problem. • Using generative adversarial imitation learning for USV navigation policies. • Validation under realistic environmental disturbances and actuation constraints. • Simulation results provide evidence for the accurate modeling of USV trajectories. • Transfer learning and knowledge reuse capabilities to handle unknown scenarios. [ABSTRACT FROM AUTHOR]