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This paper proposes and implements a convolutional neural network (CNN) that maps images from a camera to an error signal to guide and control an autonomous underwater vehicle into the entrance of a docking station. The paper proposes to use an external positioning system synchronized with the vehicle to obtain a dataset of images matched with the position and orientation of the vehicle. By using...
This paper proposes a novel approach for constructing a docking path for underwater vehicles, using a new spiral resulting of combining the Fermat and logarithmic spirals. The proposed spiral path has two properties that will help solve some of the challenges of docking autonomous underactuated vehicles (AUVs). The first property is that the spiral path reaches the entrance of the docking station...
This paper proposes a docking maneuver for an underactuated autonomous underwater vehicle (AUV) to dock into a funnel shaped docking station. The novelty of the proposed approach is enabling an underactuated AUV that is unable to control its sway motion, to dock with no crab angle, in the presence of cross-currents. Docking without a crab angle can be beneficial in cases where the geometry of the...
Reinforcement learning (RL) is a form of motor learning that robotic therapy devices could potentially manipulate to promote neurorehabilitation. We developed a system that requires trainees to use RL to learn a predefined target movement. The system provides higher rewards for movements that are more similar to the target movement. We also developed a novel algorithm that rewards trainees of different...
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