Game Strategies for Physical Robot Soccer Players: A Survey

Abstract

Effective team strategies and joint decision-making processes are fundamental in modern robotic applications, where multiple units have to cooperate to achieve a common goal. The research community in artificial intelligence and robotics has launched robotic competitions to promote research and validate new approaches, by providing robust benchmarks to evaluate all the components of a multiagent system—ranging from hardware to high-level strategy learning. Among these competitions <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RoboCup</i> has a prominent role, running one of the first worldwide multirobot competition (in the late 1990s), challenging researchers to develop robotic systems able to compete in the game of soccer. Robotic soccer teams are complex multirobot systems, where each unit shows individual skills, and solid teamwork by exchanging information about their local perceptions and intentions. In this survey, we dive into the techniques developed within the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RoboCup</i> framework by analyzing and commenting on them in detail. We highlight significant trends in the research conducted in the field and to provide commentaries and insights, about challenges and achievements in generating decision-making processes for multirobot adversarial scenarios. As an outcome, we provide an overview a body of work that lies at the intersection of three disciplines: Artificial intelligence, robotics, and games.