I was born on 29th January, 1971 in Brazil. I received my B.Sc. degree in Mechanical Engineering with an emphasis on Mechatronics from the University of São Paulo (USP), Brazil, in 1993. I earned my M.Sc. and D.Sc. degrees in Mechanical Engineering from the State University of Campinas (Unicamp), Brazil, in 1997 and 2000, respectively. During my D.Sc. studies, I spent eight months as a guest student at the Institute of Robotics (IfR) at the Swiss Federal Institute of Technology, Zurich (ETHZ), where I conducted research on obstacle avoidance and map-building techniques for indoor mobile robots. From August 2005 to July 2006, I completed a sabbatical at the Autonomous Systems Lab (ASL) at the Swiss Federal Institute of Technology, Lausanne (EPFL), focusing on research related to obstacle avoidance for both indoor and outdoor mobile robots. Between 2001 and 2008, I served as an associate professor at the Pontifical Catholic University of Minas Gerais (PUC Minas), Brazil. During this time, from 2002 to 2005, I also co-headed the Mechatronics Engineering Department and the Robotics and Automation Group (GEAR) at PUC Minas. Since 2008, I have been a professor at the University of São Paulo (EESC-USP). In 2021, I was appointed Co-President of the ETH Alumni Chapter in São Paulo, Brazil. Over the course of my career, I have published more than 100 papers in the fields of mechanical design and mobile robotics in various conferences and journals. My primary research interests include mobile robots for agriculture, inspection robots, design methodologies and tools, mechanical design applied to robotics, and mechatronics. (Source: Lattes Curriculum)
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Today, autonomous MAVs (Mini Aerial Vehicles) are well-known as one of the most important topics in the field of mobile robotics. They have interesting applications on search-and-rescue, aerial surveillance, etc. When a mission involves risk of human lives, the importance and usefulness of this kind of vehicle is highlighted, since it does not compromise human lives. In this scenario, thi…
This project aims to develop a computational model for detecting failures in pipelines in the oil and gas industry, using computer vision and convolutional neural networks (CNNs). The oil industry constantly faces challenges in maintaining its infrastructure, and pipeline failures, such as cracks and corrosion, pose serious environmental and economic risks. Traditional inspection techniqu…
Currently, the control of high-precision robotic systems has been the subject of numerous studies. In this context, Model Predictive Controllers have gained prominence in the automation of autonomous vehicles. The optimization of the control process is commonly the focus of scientific approaches. However, to achieve optimal predictive control, it is equally necessary to have a dynamic mod…
The project proposes the implementation of an autonomous navigation system for ground robots in agricultural fields, specifically, under the canopy of crops. In this regard, for the robot to navigate between crop rows, comprehensive environmental data acquisition is necessary. Therefore, in this project, the Light Detection and Ranging (LiDAR) sensor will be used, as it is a traditional a…
(Only some records are available in English at this moment)
The navigation of autonomous mobile robots can be based on the definition of "safe points" that define a complete path. However, this method called "WayPoint based" - has been shown to be ineffective, slow and dependent on human intervention mainly in unfamiliar and unstructured environments. This occurs because there may be several variables that must be detected such as fallen trees, ho…
Autonomous robot navigation is typically considered a geometric problem, in which the robot must identify the geometry of the environment to plan a collision-free trajectory. However, a purely geometric view of the environment may be insufficient for many problems in robotic navigation. For example, geometry-based navigation methods may cause a robot to avoid an area blocked by plant leav…
Population growth disproportionate to the increase in agricultural production requires the modernization of technologies used in agriculture. In this field, terrestrial robotics stands out for its versatility of applications to increase the sector's productivity. Featuring versatility and accessibility in a single platform, the TerraSentia robot is a solution for under-canopy navigation, …
(Only some records are available in English at this moment)
This project aims to address the challenge of Reinforcement Learning (RL), which requires large volumes of data for its policies to converge to optimal solutions, especially in autonomous navigation tasks. To overcome this limitation, Federated Learning (FL) will be explored, a distributed machine learning paradigm in which multiple clients train local models with their own data, under th…
Robots are essential in performing demanding tasks in outdoor settings like agriculture, construction, and remote exploration. However, their ability to navigate challenging terrains, known as traversability, poses a significant challenge. Traditional methods often produce false positives, particularly in scenarios involving tall grass, muddy areas, and snowy surfaces. Addressing these ch…
The growth in the application and dissemination of artificial intelligence techniques, especially the so-called Neural Networks, also impacts the automation of the agricultural environment. In this context, it is worth highlighting the importance of autonomous robots for rural applications, which can use AI to improve the effectiveness of their results.However, the latent difficulty of ca…
The project focuses on developing a controller for agricultural rovers like theTerrasentia capable of handling uncertainties in challenging environments. To achievethis, the proposed approach involves employing the nonlinear optimal controller Non-linear Model Predictive Controller (NMPC) to generate suitable control actions forprecise path tracking. Additionally, a deep neural network (D…
(Only some records are available in English at this moment)
| 3 / 3 | Completed research grants |
| 4 / 4 | Ongoing scholarships in Brazil |
| 32 / 27 | Completed scholarships in Brazil |
| 1 / 1 | Ongoing scholarships abroad |
| 8 / 8 | Completed scholarships abroad |
| 48 / 43 | All research grants and scholarships |
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