RT journal article
T1 AutoTuning Environment for Static Obstacle Avoidance Methods Applied to USVs
A1 Guardeño Ramírez, Rafael
A1 López Sánchez, Manuel Jesús
A1 Sánchez, Jesús
A1 Consegliere Castilla, Agustín
A2 Ingeniería en AutomáticaElectrónica, Arquitectura y Redes de Computadores
K1 unmanned surface vehicles
K1 autonomous navigation
K1 autotuning environment
K1 obstacle avoidance
K1 LIDAR sensor modelling
K1 vessel modelling
K1 course control
K1 velocity control
AB This work is focused on reactive Static Obstacle Avoidance (SOA) methods used to increase the autonomy of Unmanned Surface Vehicles (USVs). Currently, there are multiple approaches to avoid obstacles, which can be applied to different types of USV. In order to assist in the choice of the SOA method for a particular vessel and to accelerate the pretuning process necessary for its implementation, this paper proposes a new AutoTuning Environment for Static Obstacle Avoidance (ATESOA) methods applied to USVs. In this environment, a new simplified modelling of a LIDAR (Laser Imaging Detection and Ranging) sensor is proposed based on numerical simulations. This sensor model provides a realistic environment for the tuning of SOA methods that, due to its low load computation, is used by evolutionary algorithms for the autotuning. In order to analyze the proposed ATESOA, three SOA methods were adapted and implemented to consider the measurements given by the LIDAR model. Furthermore, a mathematical model is proposed and evaluated for using as USV in the simulation enviroment. The results obtained in numerical simulations show how the new ATESOA is able to adjust the SOA methods in scenarios with different obstacle distributions.
PB MDPI
SN 2077-1312
YR 2020
FD 2020-05
LK http://hdl.handle.net/10498/23292
UL http://hdl.handle.net/10498/23292
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 10-may-2026