Unmanned Surface Vehicle Simulator with Realistic Environmental Disturbances

Abstract

The use of robotics in disaster scenarios has become a reality. However, an Unmanned Surface Vehicle (USV) needs a robust navigation strategy to face unpredictable environmental forces such as waves, wind, and water current. A starting step toward this goal is to have a programming environment with realistic USV models where designers can assess their control strategies under different degrees of environmental disturbances. This paper presents a simulation environment integrated with robotic middleware which models the forces that act on a USV in a disaster scenario. Results show that these environmental forces affect the USV’s trajectories negatively, indicating the need for more research on USV control strategies considering harsh environmental conditions. Evaluation scenarios were presented to highlight specific features of the simulator, including a bridge inspection scenario with fast water current and winds.

High-Level Path Planning for an Autonomous Sailboat Robot Using Q-Learning

“…These experimental scenarios and situations use similar maps as shown above in Figure 5a. Our usv_sim simulator (https://github.com/disaster-robotics-proalertas/usv_sim_lsa) [34] is used in this work to perform the trajectories based on the paths resulting from the experiments. These multiple scenarios were created for the tests, basically, by changing the initial and end points and also the wind direction.…”

AutoTuning Environment for Static Obstacle Avoidance Methods Applied to USVs

“…All these obstacle avoidance methods have a common requirement, they need a simulation environment for their design, development and evaluation. In this aspect, there are very complete approaches [32][33][34][35], where the authors are focused on the development of simulation environments for the validation of USVs under realistic sea conditions (wind, current and waves), considering also the models of inertial and LIDARs sensors available in the GAZEBO framework [36]. On the other hand, most of the works focused on obstacle avoidance methods do not take into account the ambient disturbances that affect the USV and, moreover, they assume known: position, dimension and speed of the obstacles.…”

“…In addition, in [15,22] the external disturbances that affect the USV are also considered by modelling them as constant forces and moments referred to the earth axes. As an alternative to these extremely simplified simulation environments [10,13,21,[23][24][25][26][27][28][29][30][31], or the highly complete environments proposed in [32][33][34][35][36], in this work a new simulation environment based on the simplified modelling of a LIDAR sensor is proposed. As a result, the time of numerical simulations is reduced and the methods of obstacle avoidance are exposed to a higher level of uncertainty present in the environment surrounding the vessel.…”

“…Once the beams that have detected obstacles are known, the measured distance vector D L m is defined by the Equation (14). This LIDAR modelling considers the perspective effects that affect to this kind of sensors [1,21,26,32], since from each beam only the closest distance (d i L min ) is taken, while the later points of intersection with other segments (obstacles) are ignored.…”

“…This modelling, with respect to other works [10,12,13,15,[21][22][23][24][25][26][27][28][29][30][31] that also evaluate avoidance systems for USVs through numerical simulations, is a novel contribution. Because it exposes the avoidance systems to a higher level of the uncertainty present in the environment surrounding the vessel, without reaching the complexity required when a complete modelling of the environment is realized [32][33][34][35][36]. This facilitates its application in autotuning environments based on evolutionary algorithms, due to the large number of simulations that these algorithms must carry out.…”