Robotic soccer is a challenging research domain because many different research areas have to be addressed in order to create a successful team of robot players. This paper presents the CS Freiburg team, the winner in the middle size league at RoboCup 1998, 2000 and 2001. The paper focuses on multi-agent coordination for both perception and action. The contributions of this work are new methods for tracking ball and players observed by multiple robots, team coordination methods for strategic team formation and dynamic role assignment, a rich set of basic skills allowing to respond to large range of situations in an appropriate way, an action selection method based on behavior networks as well as a method to learn the skills and their selection. As demonstrated by evaluations of the different methods and by the success of the team, these methods permit the creation of a multi-robot group, which is able to play soccer successfully. In addition, the developed methods promise to advance the state of the art in the multi-robot field.

As people move through their environments, they do not move randomly. Instead, they are often engaged in typical motion patterns, related to specific locations they might be interested in approaching. In this paper we propose a method for adapting the behavior of a mobile robot according to the activities of the people in its surrounding. Our approach uses learned models of people's motion behaviors. Whenever the robot detects a person it computes a probabilistic estimate about which motion pattern the person might be engaged in. During path planning it then uses this belief to improve its navigation behavior. In different practical experiments carried out on a real robot we demonstrate that our approach allows a robot to quickly adapt its navigation plans according to the activities of the persons in its surrounding. We also present experiments illustrating that our approach provides a better behavior than a standard reactive collision avoidance system.

consist of stationary or mobile nodes such as Autonomous Underwater Vehicles (AUVs), which may be classified as propellerdriven vehicles and gliders, that are equipped with a variety of sensors for performing collaborative monitoring tasks. The missions entrusted to the AUVs in this work are critical to human life and property, are bound by severe time and energy constraints, and involve a high degree of inter-vehicular communication. In this work, a task allocation framework for networked AUVs that participate as a team to accomplish critical missions is developed. The team formed as a result of this task allocation framework is the subset of all deployed AUVs that is best suited to accomplish the mission while adhering to the mission constraints. Research specific to this area has been limited, hence a task allocation framework for networked AUVs to accomplish critical missions is proposed.

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