A learning approach to integration of layers of a hybrid control architecture

Authors:
Matthew Powers;Tucker Balch
Affiliations:
College of Computing, Georgia Institute of Technology, Atlanta, GA;College of Computing, Georgia Institute of Technology, Atlanta, GA
Venue:
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Year:
2009

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Abstract

Hybrid deliberative-reactive control architectures are a popular and effective approach to the control of robotic navigation applications. However, the design of said architectures is difficult, due to the fundamental differences in the design of the reactive and deliberative layers of the architecture. We propose a novel approach to improving system-level performance of said architectures, by improving the deliberative layer's model of the reactive layer's execution of its plans through the use of machine learning techniques. Quantitative and qualitative results from a physics-based simulator are presented.