Papers
34
Total Citations
717
H-Index
16
About
Armando Alves Neto is a robotics researcher whose work spans autonomous navigation, SLAM (Simultaneous Localization and Mapping), unmanned aerial and aquatic vehicles, and path planning. He has made significant contributions to the field of hybrid unmanned aerial underwater vehicles (HUAUVs), exploring their dynamics, propulsion configurations, attitude control, and trajectory planning across aerial and aquatic environments — a body of work collectively amassing over 180 citations. His 2022 paper on EKF-LOAM, which fuses LiDAR SLAM with wheel odometry and inertial data for geometrically sparse environments, has rapidly become influential with 107 citations, demonstrating his ability to address real-world robotic localization challenges. Earlier foundational contributions include feasible trajectory generation using Bézier curves, Dubins vehicle path optimization, and multi-UAV navigation in obstacle-laden environments, reflecting a sustained commitment to motion planning. More recently, Alves Neto has turned his attention to deep reinforcement learning for robotic navigation, showcasing his adaptability to emerging AI-driven methodologies. His interdisciplinary expertise — bridging control theory, sensor fusion, vehicle design, and machine learning — positions him as a versatile and impactful figure in modern autonomous systems research.
Research Focus
Key Achievements
Top Papers
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- 6Nonholonomic path planning optimization for Dubins' vehicles45 citations · 2011
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