Navigating Unmanned Surface Vessels with Neural Networks

Published on October 19, 2023

Imagine a group of lively sailors working together to navigate their boats across the vast ocean. In a similar way, this paper explores how multiple unmanned surface vessels can be controlled using neural networks and a reconfigurable hierarchical control system. The goal is to synchronize these vessels through a containment control system that converts the process into tracking a reference signal for each vessel. To address uncertainties, neural networks are employed in the control system. Additionally, a connectivity controller is implemented in the follower layer to reconfigure the positions of followers and maintain system connectivity, even when communication is poor. Through simulations, the effectiveness of this hierarchical control framework is demonstrated. If you want to dive deeper into the cutting-edge research on controlling unmanned surface vessels and discover new possibilities in autonomous navigation, check out the full article!

This paper investigates the containment control of multiple unmanned surface vessels with nonlinear dynamics. To solve the leader-follower synchronization problem in a containment control system, a hierarchical control framework with a topology reconfiguration mechanism is proposed, and the process of containment control is converted into the tracking of a reference signal for each vessel on its respective target heading by means of the light-of-sight (LOS) guidance. In a control system, the neural networks (NNs) are adopted to consider the uncertainty. In the follower layer, a connectivity controller with a topology reconfiguration mechanism is embedded, to change the converging positions of followers so as to avoid collision within the system, and to maintain the system connectivity when the communication equality is poor. The effectiveness of the hierarchical control framework proposed in this paper is valid by simulation.

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