An experience on mechatronics teaching on undergraduate students by means of the Skybot platform: from classroom to robot competition

This paper analyzes the learning experiences and opinions from a group of undergraduate students in a course about Robotics. The contents of this course were taught as a set of seminars. In each seminar, the student learned interdisciplinary knowledge of computer science, control engineering, electronics and other fields related to Robotics. The aim of this course is that the students are able to design and implement their own and custom robotic solution for a series of tests planned by the teachers. These tests measure the behavior and mechatronic features of the students' robots. Finally, the students' robots are confronted with some competitions. In this paper, the low-cost robotic architecture used by the students, the contents of the course, the tests to compare the solutions of students and the opinion of them are amply discussed.

Parallel Computational Intelligence-Based Multi-Camera Surveillance System

In this work, we present a multi-camera surveillance system based on the use of self-organizing neural networks to represent events on video. The system processes several tasks in parallel using GPUs (graphic processor units). It addresses multiple vision tasks at various levels, such as segmentation, representation or characterization, analysis and monitoring of the movement. These features allow the construction of a robust representation of the environment and interpret the behavior of mobile agents in the scene. It is also necessary to integrate the vision module into a global system that operates in a complex environment by receiving images from multiple acquisition devices at video frequency. Offering relevant information to higher level systems, monitoring and making decisions in real time, it must accomplish a set of requirements, such as: time constraints, high availability, robustness, high processing speed and re-configurability. We have built a system able to represent and analyze the motion in video acquired by a multi-camera network and to process multi-source data in parallel on a multi-GPU architecture.

Real time motion estimation using a neural architecture implemented on GPUs

This work describes a neural network based architecture that represents and estimates object motion in videos. This architecture addresses multiple computer vision tasks such as image segmentation, object representation or characterization, motion analysis and tracking. The use of a neural network architecture allows for the simultaneous estimation of global and local motion and the representation of deformable objects. This architecture also avoids the problem of finding corresponding features while tracking moving objects. Due to the parallel nature of neural networks, the architecture has been implemented on GPUs that allows the system to meet a set of requirements such as: time constraints management, robustness, high processing speed and re-configurability. Experiments are presented that demonstrate the validity of our architecture to solve problems of mobile agents tracking and motion analysis.