Interactive visualization, information sharing, planning and learning for a team of robots

Abstract

Abstract — In this paper we are interested in intelligent real-world multiagent systems that try to cooperatively solve a task. A multiagent (or multi-robot) system is a collection of agents that coexist in an environment and interact with each other. In our case, we are interested in fully cooperative multi-robot systems in which all robots share a common goal. We report in this work on research on the following topics: First, we developed a shared world model which estimates the global positions of objects in order to reduce the uncertainty in the environment. It allows us to fuse observations made by different agents and improve position estimates of each agent. Second, we extended our visualization of a robot soccer game by incorporating sensor data generated by a set of virtual sensors and by enabling remote human interaction. Third, we developed a new POMDP algorithm for agent planning in uncertain environments, in which the agent only receives partial information (through its sensors) regarding the true state of environment. Fourth, we have proposed a multiagent Q-learning technique, that allows a group of agents to learn how to jointly solve a task given the global coordination requirements of the system. I