Abstract
Adversarial multi-robot problems, where teams of robots compete with one another, require the development of approaches that span all levels of control and integrate algorithms ranging from low-level robot motion control, through to planning, opponent modeling, and multiagent learning. Small-size robot soccer, a league within the RoboCup initiative, is a prime example of this multi-robot team adversarial environment. In this paper, we describe some of the algorithms and approaches of our robot soccer team, CMDragons'02, developed for RoboCup 2002. Our team represents an integration of many components, several of which that are in themselves state-of-the-art, into a framework designed for fast adaptation and response to the changing environment.
Highlights
RoboCup small-size robot soccer is a game where two teams of five robots each play soccer on a 2.8m x 2.3m field with an orange golf ball [7]
Small-size robot soccer is unique in that teams are allowed to use global vision, via overhead cameras, to augment any local sensors
The dynamic nature of the task and environment means that motion control algorithm must be efficient, low latency, and run at the highest rate possible
Summary
RoboCup small-size robot soccer is a game where two teams of five robots each play soccer on a 2.8m x 2.3m field with an orange golf ball [7]. Small-size robot soccer is unique in that teams are allowed to use global vision, via overhead cameras, to augment any local sensors. Small-size games are highly dynamic, where the ball can reach speeds of 2 to 3m/s and robots can reach speeds of over 1m/s. Controlling a team of robots in such a highly dynamic and competitive environment is a challenging research problem. Successful teams require both good team coordination and good individual robot skills. We address individual robot skills of motion control and path planning for obstacle avoidance through to team coordination and strategy generation. We refer the reader to [4], [3] and [2] for more information
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