Humanoid soccer serves as a suitable benchmark problem for research in artificial intelligence and robotics, and has seen impressive progress in recent years. Thus, many teams participating in the Standard Platform League (SPL) of the RoboCup competition were focusing on improving basic skills for many years. But flexible and fast behavior engineering incorporating team tactics still remains as...

VISION SYSTEM, produced by Newton Research Labs. The authors embedded the vision system in a small robot base to tie for first place in the Clean Up the Tennis Court event at the 1996 Annual AAAI Mobile Robot Competition and Exhibition, held as part of the Thirteenth National Conference on Artificial Intelligence. Of particular interest is that the authors’ entry was the only robot capable of u...

In this paper an evolutionary algorithm is developed to learn a fuzzy knowledge base for the control of a soccer playing micro-robot from any configuration belonging to a grid of initial configurations to hit the ball along the ball to goal line of sight. The knowledge base uses relative co-ordinate system including left and right wheel velocities of the robot. Final path positions allow forwar...

Robotic soccer is a challenging research domain that can be used to explore new problems and to demonstrate new techniques. We participated in RoboCup'98 in order to explore the problems of cooperation in multi-robot-systems and to demonstrate our self-localization techniques based on laser range nders. In this paper we sketch the main technical points of our team, give a description of the pro...

What drives thousands of researchers worldwide to devote their creativity and energy to make robots kick a ball into a goal? The answer lies not only in the fascination of the soccer game, but rather in the quest to advance the fields of artificial intelligence research and robotics. AI researchers started to investigate games early-on. Already in the 1950th, Simon predicted that computers woul...

In this paper, we apply Reinforcement Learning (RL) to a real-world task. While complex problems have been solved by RL in simulated worlds, the costs of obtaining enough training examples often prohibits the use of plain RL in real-world scenarios. We propose three approaches to reduce training expenses for real-world RL. Firstly, we replace the random exploration of the huge search space, whi...

Humanoid soccer robots are increasingly becoming more autonomous as sophisticated approaches are being developed for challenges in vision, motion and team coordination. The stated goal of the RoboCup initiative is to beat the human soccer champion team by 2050 [1]. In order to achieve this goal, it is essential to enable the robot to fully utilize the information extracted from the available se...

Robotic soccer superseded chess as a challenge problem and benchmark for artificial intelligence research and poses many challenges for robotics. The international RoboCup championships grew to the most important robotic competition worldwide. After preliminary competitions, for the first time soccer games with humanoid robots were played in Osaka 2005. This paper describes the mechanical and e...

The SPL robot soccer league focuses its efforts on the development of robot control software for standard humanoid robots. Nevertheless, few interchange of software modules are observed in the league, being the B-Human effort an exception. In addition, a large difference in performance is observed between experienced teams and new teams. This situation makes difficult the incorporation of new t...

Accurate world modeling has great importance for efficient multirobot planning in robot soccer. Visual detection of the robots on the field in addition to all other objects of interest is crucial to achieve this goal. The problem of robot detection gets even harder when robots with only on board sensing capabilities, limited field of view, and restricted processing power are used. This work ext...