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This paper describes the PISCES system, an integrated approach for fully autonomous mapping of large areas of the ocean in deep waters. A deep water AUV will use an acoustic navigation system to compute is position with bounded error. The range limitation will be overcome by a moving baseline scheme, with the acoustic sources installed in robotic surface vessels with previously combined trajectories. In order to save power, all systems will have synchronized clocks and implement the One Way Travel Time scheme. The mapping system will be a combination of an off-the-shelf MBES with a new long range bathymetry system, with a source on a moving surface vessel and the receivers on board the AUV. The system is being prepared to participate in round one of the XPRIZE challenge.

Abstract
The concept of underwater docking stations has long been proposed to support the long term deployment of AUVs, but the number of successful solutions is still very disappointing. Hovering type AUVs can navigate arbitrarily slow, simplifying the docking maneuver and the requirements for the receiving structure. This paper describes a docking system that was developed to extend the mission duration of the MARES AUV, a man portable hovering type AUV. Given the wide range of operational scenarios and configurations of this AUV, one of the design requirements was to have a simple modular structure, that could easily be reconfigured to support different vehicle configurations, deployment scenarios and docking maneuvers. The paper provides details of the mechanical aspects, the onboard electronic subsystems, and the general operational procedure, as well as preliminary data from the first trials. © 2017 IEEE.

Abstract
Traditional coverage path planners create lawnmower-type paths in the operating area completely ignoring the uncertainty in the vehicle's position. However, in the presence of significant uncertainty in localization estimates, one can no longer guarantee that the vehicle will cover all the area according to plan. Aiming to bridge this gap, we present a coverage path planning technique for search operations which takes into account the vehicle's position and detection performance uncertainties and tries to minimize this uncertainty along the planned path. The objective is to plan paths, using a localization error model as input, to reduce as much uncertainty as possible and to minimize the extra path length (swath overlap) while satisfying mission feasibility constraints. We introduce an algorithm that calculates what will be the best moments for bringing the vehicle to surface to ensure a bounded position error. We also consider time and energy constraints that may influence the planned trajectory as path overlap is increased to account for uncertainty. Additionally we challenge the assumption frequently seen in coverage algorithms where two observations of the same target are considered independent. © 2017 IEEE.