Výsledky vyhledávání - "Neto, Armando Alves"

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Robust Longitudinal Control for Vehicular Autonomous Platoons Using Deep Reinforcement Learning

Autor: Neto, Armando Alves, Mozelli, Leonardo Amaral

In the last few years, researchers have applied machine learning strategies in the context of vehicular platoons to increase the safety and efficiency of cooperative transportation. Reinforcement Learning methods have been employed in the longitudina

Externí odkaz: http://arxiv.org/abs/2206.01175

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Trajectory Planning for Hybrid Unmanned Aerial Underwater Vehicles with Smooth Media Transition

Autor: Pinheiro, Pedro Miranda, Neto, Armando Alves, Grando, Ricardo Bedin, da Silva, Cesar Bastos, Aoki, Vivian Misaki, Cardoso, Dayana, Horn, Alexandre Campos, Drews-Jr, Paulo Lilles Jorge

In the last decade, a great effort has been employed in the study of Hybrid Unmanned Aerial Underwater Vehicles, robots that can easily fly and dive into the water with different levels of mechanical adaptation. However, most of this literature is co

Externí odkaz: http://arxiv.org/abs/2112.13819

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A Semi-Lagrangian Approach for the Minimal Exposure Path Problem in Wireless Sensor Networks

Autor: Neto, Armando Alves, Campos, Víctor C. da Silva, Macharet, Douglas G.

A critical metric of the coverage quality in Wireless Sensor Networks (WSNs) is the Minimal Exposure Path (MEP), a path through the environment that least exposes an intruder to the sensor detecting nodes. Many approaches have been proposed in the la

Externí odkaz: http://arxiv.org/abs/2108.05868

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Deep Reinforcement Learning for Mapless Navigation of a Hybrid Aerial Underwater Vehicle with Medium Transition

Autor: Grando, Ricardo Bedin, de Jesus, Junior Costa, Kich, Victor Augusto, Kolling, Alisson Henrique, Bortoluzzi, Nicolas Pieper, Pinheiro, Pedro Miranda, Neto, Armando Alves, Drews-Jr, Paulo Lilles Jorge

Since the application of Deep Q-Learning to the continuous action domain in Atari-like games, Deep Reinforcement Learning (Deep-RL) techniques for motion control have been qualitatively enhanced. Nowadays, modern Deep-RL can be successfully applied t

Externí odkaz: http://arxiv.org/abs/2103.12883

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Minimal Exposure Dubins Orienteering Problem

Autor: Macharet, Douglas G., Neto, Armando Alves, Shishika, Daigo

Different applications, such as environmental monitoring and military operations, demand the observation of predefined target locations, and an autonomous mobile robot can assist in these tasks. In this context, the Orienteering Problem (OP) is a wel

Externí odkaz: http://arxiv.org/abs/2010.11932

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