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• W3134267643 abstract "This article investigates the problem of deploying a swarm of UAVs equipped with gas sensors for industrial remote gas-plume sensing. This setup’s objective is to continuously adjust the swarm formation to maximize the combined perception for the dynamically evolved plume’s cloud, focusing around areas with the highest concentration/intensity. Initially, such a setup is formulated into an optimization problem, the solution of which could be acquired by the maximization of an appropriately defined objective function. Due to the model-free approach, this objective function’s analytical form is not available, prohibiting standard gradient descent methodologies. To this end, a tracking algorithm is developed and studied, which operates in a distributed manner and enables the UAV swarm to build a common consensus dynamically, during the evolution of the leakage phenomenon. The overall performance is tested in a simulative yet realistic environment using ANSYS Fluent suite, considering a simultaneous gas-leak incident at two different points. Aside from the standalone evaluation study, the proposed gas-plume tracking scheme is able to outperform a state-of-the-art alternative algorithm, namely Efficient Global Optimization (EGO), in various simulation setups, deploying a different number of UAVs on the field." @default.
• W3134267643 created "2021-03-15" @default.
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• W3134267643 date "2021-05-01" @default.
• W3134267643 modified "2023-09-25" @default.
• W3134267643 title "Building synergetic consensus for dynamic gas-plume tracking applications using UAV platforms" @default.
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• W3134267643 doi "https://doi.org/10.1016/j.compeleceng.2021.107029" @default.
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