The objective is to show that on-board mission replanning for an AUV sensor coverage mission, based on available energy, enhances mission success. Autonomous underwater vehicles (AUVs) are tasked to increasingly long deployments, consequently energy management issues are timely and relevant. Energy shortages can occur if the AUV unexpectedly travels against stronger currents, is not trimmed for the local water salinity has to get back on course, and so forth. An on-board knowledge-based agent, based on a genetic algorithm, was designed and validated to replan a near-optimal AUV survey mission. It considers the measured AUV energy consumption, attitudes, speed over ground, and known response to proposed missions through on-line dynamics and control predictions. For the case studied, the replanned mission improves the survey area coverage by a factor of 2 for an energy budget, that is, a factor of 2 less than planned. The contribution is a novel on-board cognitive capability in the form of an agent that monitors the energy and intelligently replans missions based on energy considerations with evolutionary methods. 1. Introduction Autonomous underwater vehicles (AUVs) are robots used for underwater tasks that range from surveys, inspection of submerged structures (e.g., pipelines), searching for downed aircraft, tracking oceanographic features, laying undersea cable, undersea mapping, and finding mines, to name a few. Such robots work in an unstructured dynamic environment with unique perception, communication, and decision issues compared to land, air, or space robots. Means for perception and detection of underwater targets include magnetic, optical, electric field, thermal (infrared), hydrodynamic changes (pressure), and sound (acoustic). Sound is unsurpassed, compared to other means, for detection underwater. As an example, the sonar (sound navigation and ranging) is a popular underwater perception sensor that uses sound for detection, classification, and location of underwater targets. Having said that, there are acoustic propagation difficulties in the highly variable, noisy, and reverberant water medium. The ocean is a nonstationary and dynamic environment where the conductivity, temperature, and density of its water varies temporally and spatially and thus affects the propagation of acoustic signals within it. Add to this multi-path, absorptive losses (high attenuation) [1], and low bandwidth for acoustic signal propagation that also occur in nonpredictable ways. Consequently, underwater communication issues stem from the variability and poorness
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