FC Portugal 3D Simulation Team: Team Description Paper

FC Portugal 3D team is built upon the structure of our previous Simulation league 3D teams and standard platform league team. Our research on high-level soccer coordination methodologies and team plying is mainly focused on the adaptation of previously developed methodologies from our 2D soccer teams [1, 2, 3, 4, 5] to the 3D humanoid environment and on creating new coordination methodologies based on the previously developed ones. In our 2D teams, which participated in RoboCup since 2000 with very good results, we have introduced several concepts and algorithms covering a broad spectrum of the soccer simulation research challenges. From coordination techniques such as Tactics, Formations, Dynamic Positioning and Role Exchange, Situation Based Strategic Positioning and Intelligent Perception to Optimization based low-level skills, Visual Debugging and Coaching, the number of research aspects FC Portugal has been working on is quite extensive [1, 2, 3, 4, 5]. Our researches on developing low-level behaviors are mainly focused on the approaches which can also be applied on the real NAO robot with minimal adaptation. Our experiments on developing our previous standard platform league team are helps us to do our research in this direction. The research-oriented development of our team has been pushing it to be one of the most competitive over the years (World champion in 2000 and Coach Champion in 2002, European champion in 2000 and 2001, Coach 2nd place in 2003 and 2004, European champion in Rescue Simulation and Simulation 3D in 2006, World Champion in Simulation 3D in Bremen 2006 and European champion in 2007,,2012 and 2013). This paper describes some of the main innovations of our 3D simulation league team during the last year. New low-level behaviors have been developed for the simulated humanoid agent, which was based on the controlling of the dynamics of the behaviors, by using the principle of the physical modeling. On top of the lowl-evel behaviors, optimization theqniques were also used in order to tune the parameters. The paper also includes information related to the agent architecture and low-level considerations. The current research is focused on improving these skills by developing a generic skills optimization framework, developing omnidirectional walking and running engine, omnidirectional kick engine and integrating high-level coordination mechanisms. Very good results were already achieved, in 2011, 2012 and 2013, concerning the improvement of low-level skills.