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Multi-Target Observation : An Application of Multi-Robot Learning

In recent years, interest in the area of the performance of multi-robot teams in cooperative tasks has significantly increased. As a result, scientists have delved into a new realm of research and experimentation with multi-robot learning. This paper examines various types of robot learning and the benefits or challenges associated with each type. The Cooperative Multi-Robot Observation of Multiple Moving Targets (CMOMMT) application is presented and viewed as a valuable testing domain in the field of multi-robot teams and cooperative learning. The handgenerated approach to this application (A-CMOMMT) will be used as a control model in our research into generating learning techniques that can improve upon this approach's results. The goal for this particular project is to improve the performance of a previous approach that uses lazy Q-learning and self-organizing maps. This project is designed to generate a learning algorithm that reaches or exceeds the performance of the hand-generated approach. We do this by introducing a component that allows the robots to not only be aware of the nearby targets, but also of the nearby robots and their actions. The ultimate and considerably broader goal of this research is to develop learning techniques that allow for a more generalized application of cooperative robotics to numerous real world problems.