Synergistic Performance Enhancement in Heterogeneous Robot Teams

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**This research addresses a fundamental question that can have a transformative impact on the applications of autonomous robot teams: "how can a team of heterogeneous robots synergistically enhance their performance through learning from peers, people, and environment, under real-world conditions?" Despite the advancement of research and development on multi-robot teams, a key challenge still remains as to how to develop effective mechanisms that enable the robots to autonomously generate, adapt and enhance group behaviours in real-life situations, while improving their individual performance concurrently. The goal of the research is to advance autonomy by expanding upon the role that social behaviour can play in a heterogeneous team of robots, under real-world conditions, and through studying and developing the key aspects of group performance enhancement. It attempts to comprehend and implement, through proper mechanisms, the human's capability of learning how to increase their contributions towards teamwork, in uncertain and unstructured scenarios, by enhancing not only their own performance but also their ability to transfer knowledge and experience to others through effective advice and criticism. The premise of the research is that such computational mechanisms can be effective in real-life scenarios mainly through a synergistic integration of all learning layers within a modular, scalable and hierarchical control architecture, which allows for dynamically distributed processing, communication, task allocation and coordination, and failure recovery. Through the utilization of such architecture, a multi-layered approach is proposed to the team learning decomposition problem. The approach will consider three layers of collective (individual), cooperative (task allocation and coordination) and collaborative (advice sharing) learning behaviours, and attempt to develop distributed Markov decision processes as learning problem models for the three layers concurrently. Further, the contribution of human advisors to group performance enhancement, in the form of labelled and unlabeled training data from individuals and/or anonymous crowd, will be considered through an augmenting human advice layer. Finally, the subject of state uncertainty as well as team/task reconfiguration will be explored, and the developed concurrent Markov decision processes will be enhanced with a state estimator, based on a particle filter, to form a robust group performance enhancement mechanism. The objective is to enable the robots to handle situations, such as sensory noise, varying communication bandwidth and intermittent disconnections, and concurrent software and hardware failure, in performing various team tasks, including basic navigation and mapping, collision avoidance, path planning, and object discovery, foraging and herding in an unknown cluttered environment.