Artificial Intelligence and Systems Theory: Applied to Cooperative Robots

TL;DR

This paper presents a systems theory and AI-based approach to designing cooperative soccer robots capable of complex tasks like obstacle avoidance, team cooperation, and real-time decision-making, tested within the SocRob project.

Contribution

It introduces a novel integration of systems theory and AI for cooperative robot design, applied specifically to soccer robots in a competitive environment.

Findings

Successful implementation of cooperative sensor fusion and world modeling.

Effective multi-robot task planning and coordination.

Demonstrated real-time cooperative behavior in soccer robots.

Abstract

This paper describes an approach to the design of a population of cooperative robots based on concepts borrowed from Systems Theory and Artificial Intelligence. The research has been developed under the SocRob project, carried out by the Intelligent Systems Laboratory at the Institute for Systems and Robotics - Instituto Superior Tecnico (ISR/IST) in Lisbon. The acronym of the project stands both for "Society of Robots" and "Soccer Robots", the case study where we are testing our population of robots. Designing soccer robots is a very challenging problem, where the robots must act not only to shoot a ball towards the goal, but also to detect and avoid static (walls, stopped robots) and dynamic (moving robots) obstacles. Furthermore, they must cooperate to defeat an opposing team. Our past and current research in soccer robotics includes cooperative sensor fusion for world modeling, object recognition and tracking, robot navigation, multi-robot distributed task planning and coordination, including cooperative reinforcement learning in cooperative and adversarial environments, and behavior-based architectures for real time task execution of cooperating robot teams.