Robot Soccer World Cup 

About: Robot Soccer World Cup is an academic conference. The conference publishes majorly in the area(s): Robot & Mobile robot. Over the lifetime, 1444 publications have been published by the conference receiving 17256 citations.

Real-time plane segmentation using RGB-D cameras

Abstract: Real-time 3D perception of the surrounding environment is a crucial precondition for the reliable and safe application of mobile service robots in domestic environments Using a RGB-D camera, we present a system for acquiring and processing 3D (semantic) information at frame rates of up to 30Hz that allows a mobile robot to reliably detect obstacles and segment graspable objects and supporting surfaces as well as the overall scene geometry Using integral images, we compute local surface normals The points are then clustered, segmented, and classified in both normal space and spherical coordinates The system is tested in different setups in a real household environment The results show that the system is capable of reliably detecting obstacles at high frame rates, even in case of obstacles that move fast or do not considerably stick out of the ground The segmentation of all planes in the 3D data even allows for correcting characteristic measurement errors and for reconstructing the original scene geometry in far ranges

The RoboCup Synthetic Agent Challenge 97

Abstract: RoboCup Challenge offers a set of challenges for intelligent agent researchers using a friendly competition in a dynamic, real-time, multi-agent domain. While RoboCup in general envisions longer range challenges over the next few decades, RoboCup Challenge presents three specific challenges for the next two years: (i) learning of individual agents and teams; (ii) multi-agent team planning and plan-execution in service of teamwork; and (iii) opponent modeling. RoboCup Challenge provides a novel opportunity for machine learning, planning, and multi-agent researchers — it not only supplies a concrete domain to evalute their techniques, but also challenges researchers to evolve these techniques to face key constraints fundamental to this domain: real-time, uncertainty, and teamwork.

RoboCup: A Challenge Problem for AI and Robotics

Abstract: RoboCup is an attempt to foster AI and intelligent robotics research by providing a standard problem where wide range of technologies can be integrated and examined. The first RoboCup competition was held at IJCAI-97, Nagoya. In order for a robot team to actually perform a soccer game, various technologies must be incorporated including: design principles of autonomous agents, multi-agent collaboration, strategy acquisition, real-time reasoning, robotics, and sensorfusion. RoboCup is a task for a team of multiple fast-moving robots under a dynamic environment. Although RoboCup's final target is a world cup with real robots, RoboCup offers a software platform for research on the software aspects of RoboCup. This paper describes technical challenges involved in RoboCup, rules, and simulation environment.

The Open Source Traffic Simulation Package SUMO

Abstract: Since the year 2000, the Institute of Transportation Research (IVF) at the German Aerospace Centre (DLR) is developing a microscopic, traffic simulation package. The complete package is offered as open source to establish the software as a common testbed for algorithms and models from traffic research. Since the year 2003 the IVF also works on a virtual traffic management centre and in conjunction with this on traffic management. Several large-scale projects have been done since this time, most importantly INVENT where modern traffic management methods have been evaluated and the online-simulation and prediction of traffic during the world youth day (Weltjugendtag) 2005 in Cologne/Germany. This publication briefly describes the simulation package together with the projects mentioned above to show how SUMO can be used to simulate largescale traffic scenarios. Additionally, it is pointed out how SUMO may be used as a testbed for automatic management algorithms with minor effort in developing extensions.

Co-evolving Soccer Softbot Team Coordination with Genetic Programming

Abstract: In this paper we explain how we applied genetic programming to behavior-based team coordination in the RoboCup Soccer Server domain. Genetic programming is a promising new method for automatically generating functions and algorithms through natural selection. In contrast to other learning methods, genetic programming's automatic programming makes it a natural approach for developing algorithmic robot behaviors. The RoboCup Soccer Server was a very challenging domain for genetic programming, but we were pleased with the results. At the end, genetic programming had produced teams of soccer softbots which had learned to cooperate to play a good game of simulator soccer.