Kelen Cristiane Teixeira Vivaldini

Email: vivaldini@ufscar.br

Address:

Department of Computer
Prof. Dr. Kelen Teixeira Vivaldini
Federal University of São Carlos
Rod. Washington Luís, Km 235
Caixa Postal 676 - 13565-905 São Carlos-SP - Brazil

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Federal University of São Carlos
2015 – actual
Assistant Professor in the Department of Computer
Teach class
Disciplines: Autonomous Robots, Operational Systems, System Engineering,
Guides Graduate, Masters, and Doctoral students;

Flying U2 Team Coordinator
2019.09- actual
Develop strategies for the competitions;
Coordinator of the team of Planning path, Localization, Control, Simulation, Perception and Mapping;
Project Manager;
Systems Engineering Manager;
Path Planning Advisor.

Awards:

2021
First Place: 1st SARC-BARINET Aerospace Competition: Collaborative Unmanned Aerial Vehicles (Awards for the best projects in this field will be granted with backing from industries in Sweden and Brazil.)
First Place: Desafio de Inovação Petrobras - Brazil Open/ RoboCup Brasil
Second Place: Flying Robots Trial League: Desafio Petrobras - Brazil Open/ RoboCup Brasil
2020
Third Place: Flying Robots Trial League: Desafio Petrobras - Brazil Open/ RoboCup Brasil

Project/Research

	UAV Route Planning for Active Disease Classification The harmful effects of diseases on eucalyptus are well-known and cause an estimated millions losses per year. Thus, this research proposes a single framework that combines perception, environment representation, and route planning using an Unmanned Aerial Vehicle. A probabilistic model of an environment that contains diseased eucalyptus, soil, and healthy trees is incrementally built according to the data acquired by the vehicle. Then, the map is used in the estimation of the next point to be explored through the minimization of a certain objective function over the current incomplete model. • Design and experimental implementation of a UAV route planning for active disease classification; • Design and experimental implementation for automatic detection of Ceratocystis wilt in Eucalyptus crops from aerial images. [Related papers:ICRA2016 and Autonomous Robot 43 2019] [More Info]
	Detection of Biological invasions in the cerrado using Deep Learning In this research, we developed a UAV capable of autonomously flying over the interest site and capturing visible spectrum (RGB) images for autonomous detection of areas with the presence of invasive grasses in the Cerrado biome using a Deep Learning Algorithm. [Video]
	Our proposal to the 1st SARC-BARINET was an application case of collaborative unmanned aerial vehicles on the Identification System of Surface Cyanobacterial and Aquatic Macrophytes using Multi-UAV. [Video]
	Descobrindo Computar [Documentation]
	Hexapod-robot for inspection of airframe components oriented by deep learning technique This research presents an integrated robotic solution for the inspection of fastened structural joints by a hexapod crawler robot, equipped with a vision sensor, embedded systems, managed by a deep learning algorithm and coordinated in the cloud, that moves on the surface of an aircraft providing real-time monitoring via mobile devices. [Patent] [Paper]