Before autonomous robotics can be used for dangerous or critical missions, performance guarantees should be made available. This paper overviews a software system for the verification of behavior-based controllers in context of chosen hardware and environmental models. Robotic controllers are automatically translated to a process algebra. The system comprising both the robot and the environment are then evaluated by VIPARS, a verification software module in development, and compared to specific performance criteria. The user is returned a probability that the performance criteria will hold in the uncertainty of real-world conditions. Experimental results demonstrate accurate verification for a mission related to the search for a biohazard.

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h i g h l i g h t s• This paper addresses the challenges involved in building a software tool for automatically verifying the behavior of multi-robot waypoint missions using formal methods.• Missions can include uncertainly located obstacles and uncertain environment geometry as well as uncertainty in robot motion.• We leverage a unique approach, VIPARS, to verifying performance guarantees for autonomous behavior-based robot software based on a combination of static analysis and Bayesian networks.• Two approaches to modeling probabilistic localization for verification are presented: a high-level approach and an approach that allows run-time localization code to be embedded within verification.• Verification and experimental validation results are presented for several autonomous robot missions, demonstrating the accuracy of verification and the mission-specific benefit of localization. a b s t r a c tEstablishing a-priori mission performance guarantees is crucial if autonomous robots are to be used with confidence in missions where failure could incur high costs in life and property damage. Automatic mission software verification, in addition to simulation and experimental benchmarking, is a key component of the solution for establishing performance guarantees. This component requires automatically verifying that the software constructed by the mission designer when executed in a partially known environment will adhere to the performance guarantee. In prior work we developed VIPARS, a unique approach to verifying performance guarantees for autonomous behavior-based robot software based on a combination of static analysis and Bayesian networks. While that approach produced fast and accurate verification of single robot missions with robot motion uncertainty, it did not address multiple-robot missions or any form of uncertainty related to environment geometry. This paper addresses the challenges involved in building a software tool for verifying the behavior of a multi-robot waypoint mission that includes uncertainly located obstacles and uncertain environment geometry as well as uncertainty in robot motion. An approach is presented to the problem of a-priori specification of uncertain environments for robot program verification. Two approaches to modeling probabilistic localization for verification are presented: a high-level approach and an approach that allows run-time localization code to be embedded in verification. Verification and experimental validation results are presented for several autonomous robot missions, demonstrating the accuracy of verification and the mission-specific benefit of localization

An approach for the verification of autonomous behavior-based robotic missions has been developed in a collaborative effort between Fordham University and Georgia Tech. This paper addresses the step after verification, how to present this information to users. The verification of robotic missions is inherently probabilistic, opening the possibility of misinterpretation by operators. A human study was performed to test three different displays (numeric, graphic, and symbolic) for summarizing the verification results. The displays varied by format and specificity. Participants made decisions about high-risk robotic missions using a prototype interface. Consistent with previous work, the type of display had no effect. The displays did not reduce the time participants took compared to a control group with no summary, but did improve the accuracy of their decisions. Participants showed a strong preference for more specific data, heavily using the full verification results. Based on these results, a different display paradigm is suggested.

Localization and mapping algorithms can allow a robot to navigate well in an unknown environment. However, whether such algorithms enhance any specific robot mission is currently a matter for empirical validation. In this paper we apply our MissionLab/VIPARS mission design and verification approach to an autonomous robot mission that uses probabilistic localization software.Two approaches to modeling probabilistic localization for verification are presented: a high-level approach, and a samplebased approach which allows run-time code to be embedded in verification. Verification and experimental validation results are presented for two waypoint missions using each method, demonstrating the accuracy of verification, and both are compared with verification of an odometry-only mission, to show the mission-specific benefit of localization.