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<p>Current autonomous vehicle interoperability is limited by vehicle-specific data formats and support systems. Until a standardized approach to autonomous vehicle command and control is adopted, true interoperability will remain elusive. This work explores the applicability of a data model supporting arbitrary vehicles using the Extensible Markup Language (XML).  An exemplar, the Autonomous Vehicle Command Language (AVCL), encapsulates behavior-scripted mission definition, goalbased  mission definition, inter-vehicle communication, and mission results.  Broad applicability is obtained through the development of a behavior set capturing arbitrary vehicle activities, and automated conversion of AVCL to and from vehicle-specific formats. The former uses task-level behaviors suitable for mission scripting and goal decomposition. Translations use the Extensible Stylesheet Language for Transformation, XML data binding, context-free language parsing, and artificial intelligence machine learning and search techniques. Translation capability is demonstrated through mappings of AVCL to and from multiple vehicle-specific formats.  A final demonstration of the power of a common autonomous vehicle data model is provided by the implementation of a hybrid control architecture. The model's vehicle-independence and the ability to generate vehicle-specific data are leveraged in the design of an architecture that provides increased autonomy by augmenting a vehicle's existing controller. The utility of this architecture is demonstrated through implementation on the Naval Postgraduate School's ARIES Unmanned Underwater Vehicle.
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<br>Subjects: Ontology; AUV; UUV; USV; UAV; robotics; autonomy; control architecture; hybrid control; autonomous vehicle behaviors; state-based control; data model; ontology; XML; XSLT; XML data binding; context-free grammar; data translation</div>
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Davis, Duane T.</div>
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