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.
Highlights
Nowadays, the development of the Unmanned Surface Vehicles (USVs) is an active and growing field of research
We show the results obtained by the new AutoTuning Environment for Static Obstacle Avoidance (ATESOA) applied to the USV model proposed in this work
Four Static Obstacle Avoidance (SOA) methods (LROABRA, potential field (PF), generalized potential fields (GPFs) and VFH+) have been automatically adjusted for the USV formed by the vessel Model (1), which is governed by the Controllers (7)
Summary
The development of the USV is an active and growing field of research. The reason is that the applications of a USV are very broad, covering from environmental control, as well as many scientific and commercial applications, to national security and surveillance issues [1]. Once the state of the vehicle is known, the detection system processes the information received by the surrounding sensors in order to generate a model of the environment in which the USV is located [6,7,8,9,10] Using this model of the environment, the guidance system (composed by algorithms of: obstacle avoidance [8,11,12,13,14,15,16], path following [4,5,17,18] and path planning [3,7,17,19,20]) demands the course and speed setpoints which guide the vessel safely to its goal. On a lesser scale, works such as [10,16,17,29,30,31] are centered on the capacity of a USV to avoid static obstacles
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