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Real-world autonomous driving in city traffic must cope with dynamic environments including other agents with uncertain intentions. This poses a challenging decision-making problem, e.g., deciding when to perform a passing maneuver or how to safely merge into traffic. Previous work in the literature has typically approached the problem using ad-hoc(More)
Coverage Path Planning (CPP) is the task of determining a path that passes over all points of an area or volume of interest while avoiding obstacles. This task is integral to many robotic applications , such as vacuum cleaning robots, painter robots, autonomous underwater vehicles creating image mosaics, demining robots, lawn mowers, automated harvesters,(More)
This paper proposes a coverage path planning (CPP) method for inspection of 3D natural structures on the ocean floor charted as 2.5D bathymetric maps. This task is integral to many marine robotics applications, such as microbathymetry mapping and image photo-mosaicing. We consider an autonomous underwater vehicle (AUV) with hovering capabilities imaging the(More)
Coverage path planning is the problem of moving an effector (e.g. a robot, a sensor) over all points in a given region. In marine robotics, a number of applications require to cover a region on the seafloor while navigating above it at a constant depth. This is the case of Autonomous Surface Vehicles, that always navigate at the water surface level, but(More)
—To operate reliably in real-world traffic, an autonomous car must evaluate the consequences of its potential actions by anticipating the uncertain intentions of other traffic participants. This paper presents an integrated behavioral inference and decision-making approach that models vehicle behavior for both our vehicle and nearby vehicles as a discrete(More)
We present a novel survey path planning technique which minimizes the robot's position uncertainty along the planned path while taking into account area coverage performance. The proposed technique especially targets bathymetric mapping applications and respects application constraints such as the desire to survey in parallel tracks and to avoid turns in(More)
This paper reports on a Gaussian belief-space planning formulation for mobile robots that includes random measurement acquisition variables that model whether or not each measurement is actually acquired. We show that maintaining the stochasticity of these variables in the planning formulation leads to a random belief covariance matrix, allowing us to(More)
This paper reports on the inclusion of a probabilistic channel model within a cooperative localization planning framework. Underwater cooperative localization reduces positioning errors by sharing sensor data across a team of underwater vehicles. Relative range constraints between vehicles are measured by the one-way-travel-time of successfully received(More)
This paper proposes a field application of a high-level Reinforcement Learning (RL) control system for solving the action selection problem of an autonomous robot in a cable tracking task. The underwater vehicle ICTINEU<sup>AUV</sup> learns to perform a visual based cable tracking task in a two step learning process. First, a policy is computed by means of(More)