AutoTuning Environment for Static Obstacle Avoidance Methods Applied to USVs

Clicks: 191
ID: 117049
2020
Free PDF
Article Quality & Performance Metrics
Overall Quality Improving Quality
0.0 /100
Combines engagement data with AI-assessed academic quality
AI Quality Assessment
Not analyzed
Abstract
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.
Reference Key
guardeño2020journalautotuning Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Rafael Guardeño;Manuel J. López;Jesús Sánchez;Agustín Consegliere;Guardeño, Rafael;López, Manuel J.;Sánchez, Jesús;Consegliere, Agustín;
Journal journal of marine science and engineering
Year 2020
DOI
10.3390/jmse8050300
URL
https://www.mdpi.com/2077-1312/8/5/300
Keywords
autonomous navigation obstacle avoidance unmanned surface vehicles autotuning environment lidar sensor modelling vessel modelling course control velocity control

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.