Approximate multi-agent planning in dynamic and uncertain environments

Abstract

Teams of autonomous mobile robotic agents will play an important role in the future of robotics. Efficient coordination of these agents within large, cooperative teams is an important characteristic of any system utilizing multiple autonomous vehicles. Applications of such a cooperative technology stretch beyond multi-robot systems to include satellite formations, networked systems, traffic flow, and many others. The diversity of capabilities offered by a team, as opposed to an individual, has attracted the attention of both researchers and practitioners in part due to the associated challenges such as the combinatorial nature of joint action selection among interdependent agents. This thesis aims to address the issues of the issues of scalability and adaptability within teams of such inter-dependent agents while planning, coordinating, and learning in a decentralized environment. In doing so, the first focus is the integration of learning and adaptation algorithms into a multi-agent planning architecture to enable online adaptation of planner parameters. A second focus is the development of approximation algorithms to reduce the computational complexity of decentralized multi-agent planning methods. Such a reduction improves problem scalability and ultimately enables much larger robot teams. Finally, we are interested in implementing these algorithms in meaningful, real-world scenarios. As robots and unmanned systems continue to advance technologically, enabling a self-awareness as to their physical state of health will become critical. In this context, the architecture and algorithms developed in this thesis are implemented in both hardware and software flight experiments under a class of cooperative multi-agent systems we call persistent health management scenarios.