This paper outlines challenges and opportunities in operating underwater robots (so-called AUVs) on a seaweed farm. The need is driven by an emerging aquaculture industry on the Swedish west coast where large-scale seaweed farms are being developed. In this paper, the operational challenges are described and key technologies in using autonomous systems as a core part of the operation are developed and demonstrated. The paper presents a system and methods for operating an AUV in the seaweed farm, including initial localization of the farm based on a prior estimate and dead-reckoning navigation, and the subsequent scanning of the entire farm. Critical data from sidescan sonars for algorithm development are collected from real environments at a test site in the ocean, and the results are demonstrated in a simulated seaweed farm setup.

Driven by worldwide demands for renewable energy source, photovoltaic (PV) inverters, which are the most important part for energy conversion, have seen a considerable amount of innovations in recent years. The trends for PV inverters are toward high efficiency, high reliability, low ground leakage current, low-output ac-current distortion, and reactive power capability. This paper provides an efficiency optimized design of an Auxiliary Resonant Snubber with Coupled-Magnetic Reset Zero-voltage switching (ZVS) inverter for PV application. The main device is Power MOSFETs, which have low conduction loss and could achieve ZVS in all load condition. The auxiliary devices are low current, low cost IGBTs and Diodes, which could achieve zero-current switching (ZCS). To achieve high efficiency, resonant circuit is analyzed to optimally design the resonant components, which is based on the guarantee of full range ZVS, gating delay-time design, and lower power loss in auxiliary circuit. The power loss model is also analyzed to select suitable power device and further improve the efficiency. At last, experiment results of a 5kW single phase inverter in PV system was presented, which has more than 99% CEC efficiency and work in full load condition without cooling fan. Besides, reactive power capability is also demonstrated.

A graph-based particle filter bathymetric simultaneous localization and mapping (BSLAM) method is proposed to solve the oscillation problem of the trajectories estimated by particles when using a low precise vehicle motion model and obtain accurate navigation results for autonomous underwater vehicles (AUVs). A graph-based trajectory update method is proposed to update the trajectories stored in particles before particle weighting to weaken the influence of the low precise odometer model on the particle trajectories. A particle weighting method based on submap matching is proposed to improve the robustness of the particle filter. Besides, a graph-based map generation method is proposed to solve the map selection problem of the particle filtering theory. The performance of the proposed method is demonstrated using a simulated dataset and a field dataset collected from a sea trial. The results show that the proposed method is more accurate and effective compared with a state-of-art particle filter BSLAM method.

Terrain-aided navigation (TAN) is one of the most effective approaches for solving the long-range navigation of autonomous underwater vehicles. However, the positioning accuracy of TAN can be greatly influenced by the terrain information of matching areas. A TAN system might also fail catastrophically when the seabed topography changes. To address these problems, a dynamic path planning method for TAN, which includes environment modelling, offline path planning, and online re-planning, is proposed in this paper. Terrain standard deviation is applied to represent terrain information in environment modelling, and the Mahalanobis distance and roulette approach are introduced in the offline path planning approach. Furthermore, the topographic changes are identified and handled via an online re-planning approach. Simulation experiments are conducted, and comparisons are made with the A ^* algorithm. According to the experimental results, the proposed method improves the positioning accuracy of TAN during the mission, and its ability to deal with seabed topographic changes is also verified.

AUV (autonomous underwater vehicles) are required to have long-term and high-precision positioning capability relative to seabed targets in most deep-sea exploration tasks. However, acoustic positioning error is positively correlated with its operating range and inertial navigation has inevitable accumulated time errors, neither of which provide precise AUV positions. TAN (terrain aided navigation) directly calculates the AUV position to the seabed terrain coordinate system by tracking the seabed topographic characteristics, which can guide the AUV to seabed target accurately. However, the initial TAN positioning error will increase with the AUV operation depth, which causes a large PF (particle filter) initialization error and particle coverage interval, and will affect the convergence and stability of TAN. To solve this problem, we first propose a TAP (terrain aided position) confidence interval model. We then use the confidence interval to constrain the initial particles to a smaller range. Finally, the validity of the algorithm is verified by playback simulation with ship-borne multi-beam sonar sensor measured data. The results show that the TAP confidence interval can reduce the coverage of the initial particle, and can improve the convergence speed and filtering accuracy of the TAN.