Flávio Henrique Teles Vieira
Papers
4
Total Citations
16
H-Index
2
About
Flávio Henrique Teles Vieira is a leading researcher in autonomous robotic navigation, specializing in the integration of deep reinforcement learning, sensor fusion, and real-time collision avoidance. His work centers on developing intelligent, adaptive algorithms that enable mobile robots to navigate complex, dynamic environments safely and efficiently. A key contribution is his pioneering use of Deep Q-Networks (DQN) hybridized with sensor data fusion and localization techniques, such as the Extended Kalman Filter (EKF-DQN), which dramatically accelerates learning curves and improves reward optimization. His most cited paper (2023, 9 citations) introduces an autonomous navigation approach that fuses LiDAR and visual data via a Double DQN, incorporating people detection for robust collision avoidance. More recently, he has advanced the field by addressing data transmission bottlenecks, proposing a method using autoencoders for latent representation and efficient LiDAR data transmission over LoRaWAN networks. With a growing citation impact, Vieira’s research is at the forefront of making autonomous systems more responsive, secure, and practical for real-world industrial applications.
Research Focus
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Top Papers
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