Document Type



Computer Engineering | Robotics


Certain robot missions need to perform predictably in a physical environment that may only be poorly characterized in advance. We have previously developed an approach to establishing performance guarantees for behaviorbased controllers in a process-algebra framework. We extend that work here to include random variables, and we show how our prior results can be used to generate a Dynamic Bayesian Network for the coupled system of program and environment model. Verification is reduced to a filtering problem for this network. Finally, we present validation results that demonstrate the effectiveness of the verification of a multiple waypoint robot mission using this approach.

Article Number


Publication Date



Intelligent Robots and Systems (IROS) 2013, Tokyo, Japan

This research was conducted at the Fordham University Robotics and Computer Vision Lab. For more information about graduate programs in Computer Science, see, and the Fordham University Graduate School of Arts and Sciences, see

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Robotics Commons