Mobile robotic networks; (e.g., fleets of unmanned aerial vehicles) offer expanded capabilities for recognized military uses as well as a wide variety of civilian uses. There are several factors that contribute to their increasing potential and importance. In particular, technological advances have enabled smaller platforms with increased sensing, communication, and processing capabilities. In addition, autonomous operations offer several competitive advantages such as persistent surveillance that exceeds human fatigue limitations or remote operation capabilities without the logistical transport costs for assets and personnel.
Intellectual Merit: Distributed control becomes key to fully realize the potentials of mobile robotic networks. Current distributed control paradigms are mainly model-based and inadequate to handle significant uncertainties, including (1) environmental uncertainties; i.e., unforeseeable elements in unstructured environments where mobile robots operate; (2) dynamic uncertainties; i.e., inaccuracies of the physical dynamics of mobile robots. To bridge the gaps, this project will leverage reinforcement learning, an area of machine learning, and game theory, initially developed in economics, to develop a new data-driven (more specifically, model-free) distributed control framework. The developed framework is model-free, fully distributed, autonomous, and its performance is rigorously provable. The framework will significantly improve the autonomy of mobile robots when they face significant environmental uncertainties and dynamic uncertainties especially in long-term missions.
Broader Impacts: Successful completion of this research will provide engineering guidelines in analysis, synthesis and prototyping of mobile robotic networks which can effectively operate in unstructured environments. The research findings profoundly impact a variety of engineering disciplines, including scientific data collection, homeland security operations and intelligent transportation systems. The proposed research is interdisciplinary and involves interactions among game theory, machine learning, control, robotic motion planning and distributed algorithms. This will lead to educational and training opportunities that cross traditional disciplinary boundaries for high-school, undergraduate and graduate students in STEM. The collaborations with industrial partners stress the potentials to make an impact beyond academia.
