Purposive behavior acquisition for a real robot by vision-based reinforcement learning
Machine Learning - Special issue on robot learning
Robot Motion Planning
The CMUnited-97 Simulator Team
RoboCup-97: Robot Soccer World Cup I
The CMUnited-97 Small Robot Team
RoboCup-97: Robot Soccer World Cup I
The CMUnited-98 Champion Simulator Team
RoboCup-98: Robot Soccer World Cup II
Situation Based Strategic Positioning for Coordinating a Team of Homogeneous Agents
Balancing Reactivity and Social Deliberation in Multi-Agent Systems, From RoboCup to Real-World Applications (selected papers from the ECAI 2000 Workshop and additional contributions)
CM-Dragons'01 - Vision-Based Motion Tracking and Heteregenous Robots
RoboCup 2001: Robot Soccer World Cup V
CS Freiburg: Global View by Cooperative Sensing
RoboCup 2001: Robot Soccer World Cup V
The University of Pennsylvania RoboCup Legged Soccer Team
RoboCup 2001: Robot Soccer World Cup V
The CMUnited-98 Champion Simulator Team
RoboCup-98: Robot Soccer World Cup II
The CMTrio-98 Sony-Legged Robot Team
RoboCup-98: Robot Soccer World Cup II
The CMUnited-99 Champion Simulator Team
RoboCup-99: Robot Soccer World Cup III
Motion Control in Dynamic Multi-Robot Environments
RoboCup-99: Robot Soccer World Cup III
Big Red: The Cornell Small League Robot Soccer Team
RoboCup-99: Robot Soccer World Cup III
Bridging Gap between the Simulation and Robotics with a Global Vision System
RoboCup 2000: Robot Soccer World Cup IV
Emergent cooperation in robocup: a review
RoboCup 2005
Current and future trends and challenges in robot soccer
EUROCAST'05 Proceedings of the 10th international conference on Computer Aided Systems Theory
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In this chapter, we present the main research contributions of our champion CMUnited-98 small robot team. The team is a multiagent robotic system with global perception, and distributed cognition and action. We describe the main features of the hardware design of the physical robots, including differential drive, robust mechanical structure, and a kicking device. We briefly review the CMUnited-98 global vision processing algorithm, which is the same as the one used by the previous champion CMUnited-97 team. We introduce our new robot motion algorithm which reactively generates motion control to account for the target point, the desired robot orientation, and obstacle avoidance. Our robots exhibit successful collision-free motion in the highly dynamic robotic soccer environment. At the strategic and decision-making level, we present the role-based behaviors of the CMUnited-98 robotic agents. Team collaboration is remarkably achieved through a new algorithm that allows for team agents to anticipate possible collaboration opportunities. Robots position themselves strategically in open positions that increase passing opportunities. The chapter terminates with a summary of the results of the RoboCup-98 games in which the CMUnited-98 small robot team scored a total of 25 goals and suffered 6 goals in the 5 games that it played.