Abstract
Odour Source Localisation (OSL) is the task of finding the origin of a chemical emitting event by reasoning from observed measurements. Multiple cost functions have been proposed to assist in the decision-making process of cognitive strategies, but it is not yet clear which of these information metrics performs better in the OSL process. Additionally, most of these works have only been validated in simulation or in small controllable conditions such as wind tunnels with the agent starting the search close to the source position, raising additional questions about their performance in real-world scenarios. This work aims to compare the performance of cognitive search strategies between three distinct information metrics: Entropy, Kullback-Leibler (KL) divergence and Free Energy, and at the same time, evaluate their efficiency in a natural water stream with an Autonomous Surface Vehicle (ASV). All three strategies successfully locate the source in most of the trials, with Entropy and KL showing similar performance. Free Energy had a lower success rate than Entropy an KL but generated more efficient travelled paths, at the cost of a higher computational effort.
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Magalhães, H., Baptista, R., Marques, L. (2023). Evaluating Cognitive Odour Source Localisation Strategies in Natural Water Streams. In: Tardioli, D., Matellán, V., Heredia, G., Silva, M.F., Marques, L. (eds) ROBOT2022: Fifth Iberian Robotics Conference. ROBOT 2022. Lecture Notes in Networks and Systems, vol 590. Springer, Cham. https://doi.org/10.1007/978-3-031-21062-4_13
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