The objective is to show that on-board mission replanning for an AUV sensor coverage mission, based on available energy,\r\nenhances mission success. Autonomous underwater vehicles (AUVs) are tasked to increasingly long deployments, consequently\r\nenergy management issues are timely and relevant. Energy shortages can occur if the AUV unexpectedly travels against stronger\r\ncurrents, is not trimmed for the local water salinity has to get back on course, and so forth. An on-board knowledge-based agent,\r\nbased on a genetic algorithm, was designed and validated to replan a near-optimal AUV survey mission. It considers the measured\r\nAUV energy consumption, attitudes, speed over ground, and known response to proposed missions through on-line dynamics and\r\ncontrol predictions. For the case studied, the replanned mission improves the survey area coverage by a factor of 2 for an energy\r\nbudget, that is, a factor of 2 less than planned. The contribution is a novel on-board cognitive capability in the form of an agent\r\nthat monitors the energy and intelligently replans missions based on energy considerations with evolutionary methods.
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