Oregon State University (OSU) researchers have built a framework for autonomous underwater vehicles that enables the machines to plan energy-efficient trajectories through disturbances that are strong and uncertain, such as ocean currents and wind fields.
The framework involves an algorithm that samples alternative paths, as well as comparison metrics that let the vehicle decide when it makes sense to switch paths based on new information collected about environmental disturbances.
The researchers tested the framework on a dataset of currents from the Regional Ocean Modeling System, as well as on a windy lake with an autonomous boat.
The results show the algorithm can plan vehicle paths that are more energy efficient than those planned by existing methods, and it is robust enough to handle environments for which little data is available.
The findings also indicate three of the framework's five comparison metrics can be used to plan more efficient routes compared to planning based on the ocean current forecast alone.
From Oregon State University News
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