Certain robot missions need to perform predictably in a physical environment that may have significant uncertainty. One approach is to leverage automatic software verification techniques to establish a performance guarantee. The addition of an environment model and uncertainty in both program and environment, however, means that the state space of a model-checking solution to the problem can be prohibitively large. An approach based on behavior-based controllers in a process-algebra framework that avoids state-space combinatorics is presented here. In this approach, verification of the robot program in the uncertain environment is reduced to a filtering problem for a Bayesian network. Validation results are presented for the verification of a multiple-waypoint and an autonomous exploration robot mission.

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 behavior-based 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.

This paper describes the need and methods required to construct an integrated software verification and mission specification system for use in robotic missions intended for counter-weapons of mass destruction (c-WMD) operations, as part of a 3-year effort for the Defense Threat Reduction Agency. The overall system architecture is described. The principal tool for verification is a process algebra, PARS, based on port automata theory. PARS is introduced, emphasizing its ability to represent probabilistic programs and uncertain and dynamic environments, followed by the analysis of mission properties for an example robotic mission.