2 citations · 3 across the 9 of their papers we have counts for
9 papers
From Seedling to Harvest: The GrowingSoy Dataset for Weed Detection in Soy Crops via Instance Segmentation
Raul Steinmetz, Victor A. Kich, Henrique Krever +5
Deep learning, particularly Convolutional Neural Networks (CNNs), has gained significant attention for its effectiveness in computer vision, especially in agricultural tasks. Recen…
Improving Generalization in Aerial and Terrestrial Mobile Robots Control Through Delayed Policy Learning
Ricardo B. Grando, Raul Steinmetz, Victor A. Kich +7
Deep Reinforcement Learning (DRL) has emerged as a promising approach to enhancing motion control and decision-making through a wide range of robotic applications. While prior rese…
Enhanced Low-Dimensional Sensing Mapless Navigation of Terrestrial Mobile Robots Using Double Deep Reinforcement Learning Techniques
Linda Dotto de Moraes, Victor Augusto Kich, Alisson Henrique Kolling +4
In this study, we present two distinct approaches within the realm of Deep Reinforcement Learning (Deep-RL) aimed at enhancing mapless navigation for a ground-based mobile robot. T…
Parallel Distributional Prioritized Deep Reinforcement Learning for Unmanned Aerial Vehicles
Alisson Henrique Kolling, Victor Augusto Kich, Junior Costa de Jesus +4
This work presents a study on parallel and distributional deep reinforcement learning applied to the mapless navigation of UAVs. For this, we developed an approach based on the Sof…
DoCRL: Double Critic Deep Reinforcement Learning for Mapless Navigation of a Hybrid Aerial Underwater Vehicle with Medium Transition
Ricardo B. Grando, Junior C. de Jesus, Victor A. Kich +3
Deep Reinforcement Learning (Deep-RL) techniques for motion control have been continuously used to deal with decision-making problems for a wide variety of robots. Previous works s…
Double Deep Reinforcement Learning Techniques for Low Dimensional Sensing Mapless Navigation of Terrestrial Mobile Robots
Linda Dotto de Moraes, Victor Augusto Kich, Alisson Henrique Kolling +6
In this work, we present two Deep Reinforcement Learning (Deep-RL) approaches to enhance the problem of mapless navigation for a terrestrial mobile robot. Our methodology focus on…