Cognitive Coordination for Cooperative Multi-Robot Teamwork

Abstract

Multi-robot teams have potential advantages over a single robot. Robots in a team can serve different functionalities, so a team of robots can be more efficient, robust and reliable than a single robot. In this dissertation, we are in particular interested in human level intelligent multi-robot teams. Social deliberation should be taken into consideration in such a multi-robot system, which requires that the robots are capable of generating long term plans to achieve a global or team goal, rather than just dealing with the problems at hand. Robots in a team have to cope with dynamic environments due to the presence of the others. Thus, a robot cannot foresee what its environment will be because other robots may change the environment. Moreover, multiple robots may interfere with each other. We can say that the need for coordination in a robot team stems from interdependence relationships between the robots. More specifically, one robot performing an activity may influence another robot's activity. In order to achieve good team performance, the robots in a team all need to well coordinate their activities. This dissertation studies the multi-robot teamwork in the context of search and retrieval, which is known as foraging in robotics. In a foraging task, a team of robots is required to search targets of interest in the environment and also deliver them back to a home base. Many practical applications require search and retrieval such as urban search and rescue robots, deep-sea mining robots, and autonomous warehouse robots. Requiring both searching and delivering makes a foraging task more complicated than a pure searching, exploration or coverage task. Foraging robots have to consider not only where to explore but also when to explore. Coordination for a foraging task concerns how to direct the movements of the robots and how to distribute the workload more evenly in a team. In this dissertation, we first proposed an agent-based cognitive robot architecture that is used to bridge the gap between low-level robotic control with high-level cognitive reasoning. Cognitive agents realized by means of the agent programming language GOAL are used to control both real and simulated robots. We carried out an empirical study to investigate the role of communication and its impact on team performance. The results and findings were used to study the multi-robot pathfinding and multi-robot task allocation problems. A novel fully decentralized approach was proposed to deal with the multi-robot pathfinding problem, which also reduces the communication overhead, compared to usual decentralized approaches. An auction-based approach and a prediction approach were proposed to deal with the dynamic foraging task allocation problem. The difference is that the prediction approach performs better with respect to completion time, while the auction-based approach performs better with respect to travel costs. In order to facilitate the identification of interdependence relationships between the agents in the early design phase of a multi-agent system, we developed a formal domain-independent graphical language that reflects the need for coordination in multi-agent teamwork.