Papers
8
Total Citations
143
H-Index
6
About
A. Miranda Neto is a leading researcher in autonomous robotics, with a primary focus on real-time navigation, perception, and energy efficiency. His most significant contribution is the innovative application of Pearson’s Correlation Coefficient (PCC) to solve critical challenges in autonomous systems. This work, detailed in his highly cited 2013 paper (69 citations), uses PCC to intelligently discard redundant visual and LiDAR data, dramatically reducing computational load and power consumption without sacrificing navigation accuracy. This core idea extends into real-time dynamic power management (19 citations) and collision risk estimation, establishing a novel framework for efficient, long-endurance missions. Neto also made foundational contributions to road detection using monocular vision (19 citations) and path tracking control with Model Predictive Control (MPC) for the VILMA robotic vehicle at UNICAMP. His research, consistently focused on discarding non-deterministic information, has shaped how autonomous platforms manage limited onboard resources, directly impacting the design of more intelligent, self-sufficient robots for unknown environments.
Research Focus
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Top Papers
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- 8A Visual-Perception Layer Applied to Reactive Navigation3 citations · 2012