Abstract
Path planning for sailboat robots is a challenging task particularly due to the kinematics and dynamics modelling of such kinds of wind propelled boats. The problem is divided into two layers. The first one is global where a general trajectory composed of waypoints is planned, which can be done automatically based on some variables such as weather conditions or defined by hand using some human–robot interface (a ground-station). In the second local layer, at execution time, the global route should be followed by making the sailboat proceed between each pair of consecutive waypoints. Our proposal in this paper is an algorithm for the global, path generation layer, which has been developed for the N-Boat (The Sailboat Robot project), in order to compute feasible sailing routes between a start and a target point while avoiding dangerous situations such as obstacles and borders. A reinforcement learning approach (Q-Learning) is used based on a reward matrix and a set of actions that changes according to wind directions to account for the dead zone, which is the region against the wind where the sailboat can not gain velocity. Our algorithm generates straight and zigzag paths accounting for wind direction. The path generated also guarantees the sailboat safety and robustness, enabling it to sail for long periods of time, depending only on the start and target points defined for this global planning. The result is the development of a complete path planner algorithm that, together with the local planner solved in previous work, can be used to allow the final developments of an N-Boat making it a fully autonomous sailboat.
Highlights
The number of researches working on the development of navigation methods for autonomous sailing has grown significantly
In order to validate our proposal, we start with a set of experimental scenarios that were designed in order for the algorithm to be used to generate paths to go from a point to another, in a lake-like environment
These multiple scenarios were created for the tests, basically, by changing the initial and end points and the wind direction
Summary
The number of researches working on the development of navigation methods for autonomous sailing has grown significantly. The main motivation for research into autonomous sailboats is the possibility of uninterrupted navigation by using the wind, a physically vast and free source of propulsion. This source is generally allied to solar panels and/or turbines that can be used to recharge internal batteries that feed the computers and actuators necessary for Sensors 2020, 20, 1550; doi:10.3390/s20061550 www.mdpi.com/journal/sensors. It is difficult to use such an approach for sailboats, noting that motorized boats do not need to take into account wind direction This restriction can be incorporated in the Q-Learning model by using a kind of punishment, still keeping the model as a Markovian decision process, enabling using the same approach for developing a path planning for a sailboat
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