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- W3139898358 abstract "This paper presents a new planner based on Evolutionary Algorithms (EAs) to optimize the trajectory of an Autonomous Surface Vehicle (ASV), equipped with a probe, that has to determine the location of a pollutant in lentic water bodies (e.g. reservoirs, dams). To achieve it, our planner 1) exploits the information provided by a simulator that determines the pollutant distribution based on the water currents and 2) is supported by an EA that optimizes the mission duration, the ASV trajectory length and the measurements taken by its probe in highly polluted areas. The current version of the planner also ensures that the trajectories are feasible from the ASV and water body perspective, and solves this constrained multi-objective problem as a mono-objective one that linearly combines the constraint and objective functions. The preliminary results over different scenarios show that the planner can already determine overall good solutions, but that needs to be modified (e.g. using a multi-objective intended EA) to improve them further." @default.
- W3139898358 created "2021-04-13" @default.
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- W3139898358 date "2021-01-01" @default.
- W3139898358 modified "2023-10-03" @default.
- W3139898358 title "EA-Based ASV Trajectory Planner for Pollution Detection in Lentic Waters" @default.
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- W3139898358 doi "https://doi.org/10.1007/978-3-030-72699-7_51" @default.
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