Introduction

Problem solving in complex domains often involves multiple agents, dynamic environments, and the need for learning from feedback and previous experience. Robotic soccer is an example of such complex tasks for which multiple agents need to collaborate in an adversarial environment to achieve specific objectives. Robotic soccer offers a challenging research domain to investigate a large spectrum of issues of relevance to the development of complete autonomous agents [7, 3].

The fast-paced nature of the domain necessitates real-time sensing coupled with quick behaving and decision making. 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.

We have been pursuing research in the robotic soccer domain within the RoboCup initiative [6], which, in 1997, included a simulator league and small-size and medium-size robot leagues. We have been doing research extensively in the simulator league, developing learning techniques and team strategies in simulation [12, 11]. Many of these team strategies were directly incorporated into the robotic system described here. We eventually hope also to transfer these learning techniques to the real system as we develop a complete Robotic Soccer architecture.

In this paper, we focus on presenting our team of small robotic agents, namely CMUnited-97, as a complete system with action, perception, and cognition capabilities. We developed the physical robots as actuators, a vision processing algorithm to perceive the world, and strategic reasoning for individual and collaborative behaviors.

The team is clearly not a perfect version of our multiple autonomous agents. We have developed previous versions of the team [1], and, as presented in the discussion and conclusion section, we are currently (and will continue) improving the team further. However, we believe that CMUnited-97 represents a major advance in our work and has several interesting contributions which we present in this paper:

• 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.

The combination of robust hardware, real-time vision, and intelligent control code represented a significant challenge which we were able to successfully meet. The work described in this paper is all fully implemented. Figure 1 shows a picture of our robotic agents. For the hardware description of our robots, see [13]. This paper is organized as follows: Section 2 presents the vision processing algorithm. In Section 3, we focus on the agent behaviors ranging from low-level individual behaviors, to coordinated, strategic, multiagent behaviors. Section 4 reports on our experiences using these robots in the RoboCup-97 robot competition and concludes.

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