Eyberth Rojas

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In this paper, we perform a comparison between classical PI+D control strategies with a fuzzy modified PI+D control. The fuzzy PI+D controller is a discrete-time version of the conventional PI+D controller, which has constant coefficients of self-tuned control gains. The proposed control strategies were tested using a mathematical model based on a bipedal(More)
This paper shows the results of applying machine learning techniques to the problem of predicting soccer plays in the Small Size League of RoboCup. We have modeled the task as a multi-class classification problem by learning the plays of the STOx’s team. For this, we have created a database of observations for this team’s plays and obtained key features(More)
Plenty of work based on the Rapidly-exploring Random Trees (RRT) algorithm for path planning in real time has been developed recently. This is the most used algorithm by the top research teams in the Small Size League of RoboCup. Nevertheless, we have concluded that other simpler alternatives show better results under these highly dynamic environments. In(More)
In this paper we describe the design and implementation of an educational methodology based on a robotic platform used for the small size league (SSL) challenge of the RoboCup initiative. The methodology is based on three main aspects of the learning process, namely classical conditioning, reinforcement learning and cognitive learning. This is achieved(More)
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