Introduction

As robots become more adept at operating in the real world, the high-level issues of collaborative and adversarial planning and learning in real-time situations are becoming more important. An interesting emerging domain that is particularly appropriate for studying these issues is Robotic soccer, as first proposed by [9] and actively pursued within the RoboCup initiative [7, 1]. Although realistic simulation environments exist [10, 11] and are useful, it is important to have some physical robotic agents in order to address the full complexity of the task.

Robotic soccer with real robots is a challenging domain for many reasons. The fast-paced nature of the domain necessitates real-time sensing coupled with quick behaving and decision making. Furthermore, the behaviors and decision making processes can range from the most simple reactive behaviors, such as moving directly towards the ball, to arbitrarily complex reasoning procedures that take into account the actions and perceived strategies of teammates and opponents. Opportunities, and indeed demands, for innovative and novel techniques abound.

One of the advantages of Robotic Soccer is that it enables the direct comparison of different systems: they can be matched against each other in competitions. In particular, the system described here was designed specifically for RoboCup97 in which several robotic teams competed on an ``even playing field.'' [6]. The scientific opportunities involved in this effort are enormous. Our particular scientific focus is on multiagent systems coupled with collaborative and adversarial learning in an environment that requires real-time dynamic planning.

This paper describes the overall architecture of our robotic soccer system. The combination of robust hardware, real-time vision, and intelligent control represented a significant challenge which we were able to successfully meet. The work described in this paper is fully implemented as our CMUnited-97 RoboCup team. CMUnited-97 won the RoboCup-97 small-robot competition at IJCAI-97 in Nagoya, Japan. Our team scored a total of thirteen goals and only suffered one. Figure 1 shows a picture of our robotic agents.

  
Figure 1: The CMUnited-97 robot team that competed in RoboCup-97.

The specific contributions of the CMUnited-97 robot team, as presented in this paper, include:

• The complete design and development of robots with robust navigation and communication hardware.

• Reliable perception through the use and extension of a Kalman-Bucy filter. Sensing through our vision processing algorithm allows for (i) tracking of multiple moving objects; (ii) and prediction of object movement, particularly the ball, even when inevitable sharp trajectory changes occur.

• Multiagent strategic reasoning. Collaboration between robots is achieved through: (i) a flexible role-based approach by which the task space is decomposed and agents are assigned subtasks; (ii) a flexible team structure by which agents are organized in formations, homogeneous agents flexibly switch roles within formations, and agents switch formations dynamically; and (iii) alternative plans allowing for collaboration (e.g. passing or shooting), are controlled by pre-defined metrics for real-time evaluation.