University of Central Florida
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Automation is being increasingly introduced into every man-made system. The thrust to achieve trustworthy autonomous systems, which can attain goals independently in the presence of significant uncertainties and for long periods of time without any human intervention, has always been enticing. Significant progress has been made in the avenues of both software and hardware for meeting these objectives. However, technological challenges still exist and particularly in terms of decision making under uncertainty.
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Abstract:
This project, NSF grant 1329363, is a modular, computationally-distributed multi-robot cyber-physical system (CPS) for assisting young developmentally-delayed children learning to walk. The challenges of assisting young developmentally-delayed children learning to walk are 1) stabilizing medio-lateral body sway, 2) developing gait that exploits energy exchange, and 3) coordinating multiple degrees of freedom.
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Exhaustive state space exploration based verification of cyber-physical system designs remains a challenge despite five decades of active research into formal verification. On the other hand, models of intelligent automotive cyber-physical systems continue to grow in complexity. The testing of intelligent automotive models often uses human subjects, is expensive, and can not be performed unless the system has already been prototyped and is ready for human interaction. We propose the use of machine learning methods to learn stochastic models of human-vehicle interaction.
