Marcelo Trevisan

Universidade Federal do Rio Grande do Sul

Papers

Total Citations

153

H-Index

About

Marcelo Trevisan is a pioneering researcher in autonomous robotics and exploratory navigation, whose work has fundamentally shaped how machines perceive and map unknown environments. His most influential contribution, the 2002 paper "Exploration method using harmonic functions" (84 citations), introduced a novel approach that leverages harmonic potential fields to guide robotic exploration, enabling efficient and safe navigation through complex, unstructured spaces. This foundational work was further extended in his 2006 study "Exploratory Navigation Based on Dynamical Boundary Value Problems" (36 citations), which formalized exploration as a dynamical system, allowing robots to adaptively balance coverage and obstacle avoidance. Trevisan also advanced autonomous learning with his 2004 paper "Autonomous Learning Architecture for Environmental Mapping" (28 citations), which integrated real-time sensor data to build cognitive maps without human intervention. His lesser-known 2002 work "Directing a random walker with optimal potentials" (5 citations) explored theoretical optimization in stochastic motion, showcasing his versatility. With over 150 total citations, Trevisan’s research remains a cornerstone for students and engineers developing self-navigating systems, from planetary rovers to warehouse drones, blending elegant mathematics with practical robotics.