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.

This work is located in a growing sector within the field of renewable energies, wave energy converters (WECs). Specifically, it focuses on one of the point absorber waves (PAWs) of the hybrid platform W2POWER. With the aim of maximizing the mechanical power extracted from the waves by these WECs and reducing their mechanical fatigue, the design of five different model predictive controllers (MPCs) with hard and soft constraints has been carried out. As a contribution of this paper, two of the MPCs have been designed with the addition of an embedded integrator. In order to analyze and compare the MPCs with conventional PI type control, an exhaustive study about performance and robustness is realized through the computer simulations carried out, in which uncertainties in the WEC dynamics and JONSWAP spectrum are considered. The results obtained show how the MPCs with embedded integrator improve power production of the WEC system studied in this work.

This paper is centered on the guidance systems used to increase the autonomy of unmanned surface vehicles (USVs). The new Robust Reactive Static Obstacle Avoidance System (RRSOAS) has been specifically designed for USVs. This algorithm is easily applicable, since previous knowledge of the USV mathematical model and its controllers is not needed. Instead, a new estimated closed-loop model (ECLM) is proposed and used to estimate possible future trajectories. Furthermore, the prediction errors (due to the uncertainty present in the ECLM) are taken into account by modeling the USV’s shape as a time-varying ellipse. Additionally, in order to decrease the computation time, we propose to use a variable prediction horizon and an exponential resolution to discretize the decision space. As environmental model an occupancy probability grid is used, which is updated with the measurements generated by a LIDAR sensor model. Finally, the new RRSOAS is compared with other SOA (static obstacle avoidance) methods. In addition, a robustness study was carried out over a set of random scenarios. The results obtained through numerical simulations indicate that RRSOAS is robust to unknown and congested scenarios in the presence of disturbances, while offering competitive performance with respect to other SOA methods.

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This work is located in a growing sector within the field of renewable energies, wave energy converters (WECs). Specifically, it focuses on one of the point absorbers wave (PAWs) of the hybrid platform W2POWER. With the aim of maximising the mechanical power extracted from the waves by these WECs and reduce their mechanical fatigue, the design of five different model predictive controllers (MPCs) with hard and soft constraints has been carried out. As contribution of this paper, two of the MPCs have been designed with the addition of an embedded integrator. In order to validate the MPCs, an exhaustive study on performance and robustness is realized through simulations carried out in which uncertainties in the WEC dynamics are considered. Furthermore, looking for realistic in these simulations, an identification methodology for PAWs is proposed and validated by means of real time series of a scale prototype.

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