Smart Robotics for Automation.

In recent years, the demand for efficient automation across various sectors has accelerated significantly [...

Analytical Formalism for Data Representation and Object Detection with 2D LiDAR: Application in Mobile Robotics.

In mobile robotics, LASER scanners have a wide spectrum of indoor and outdoor applications, both in structured and unstructured environments, due to their accuracy and precision. Most works that use this sensor have their own data representation and their own case-specific modeling strategies, and no common formalism is adopted. To address this issue, this manuscript presents an analytical approach for the identification and localization of objects using 2D LiDARs.

A Machine Learning Approach to Robot Localization Using Fiducial Markers in RobotAtFactory 4.0 Competition.

Localization is a crucial skill in mobile robotics because the robot needs to make reasonable navigation decisions to complete its mission. Many approaches exist to implement localization, but artificial intelligence can be an interesting alternative to traditional localization techniques based on model calculations. This work proposes a machine learning approach to solve the localization problem in the RobotAtFactory 4.

Moving robotics competitions virtual: The case study of RoboCupJunior Soccer Simulation (SoccerSim).

For almost 25 years, the goal of the RoboCup has been to build soccer robots capable of winning against the FIFA World Champion of 2050. To foster the participation of the next generation of roboticists, the RoboCupJunior competition takes place in parallel and provides a similar challenge of appropriate difficulty for high school students. RoboCupJunior has three main categories: Soccer, Rescue and OnStage.