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A self-adaptation framework based on functional knowledge for augmented autonomy in robots Print E-mail
Thursday, 11 April 2013
Carlos Hernández, Julita Bermejo-Alonso and Ricardo Sanz
To appear in Integrated Computer-Aided Engineering
IOS Press through http://dx.doi.org/10.3233/ICA-180565

Robot control software endows robots with advanced capabilities for autonomous operation, such as navigation, object recognition or manipulation, in unstructured and dynamic environments. However, there is a steady need for more robust oper- ation, where robots should perform complex tasks by reliably exploiting these novel capabilities. Mission-level resilience is re- quired in the presence of component faults through failure recovery. To address this challenge, a novel self-adaptation framework based on functional knowledge for augmented autonomy is presented. A metacontroller is integrated on top of the robot control system, and it uses an explicit run-time model of the robotís controller and its mission to adapt to operational changes. The model is grounded on a functional ontology that relates the robotís mission with the robotís architecture, and it is generated during the robotís development from its engineering models. Advantages are discussed from both theoretical and practical viewpoints. An application example in a real autonomous mobile robot is provided. In this example, the generic metacontroller uses the robotís functional model to adapt the control architecture to recover from a sensor failure.


A self-adaptation framework based on functional knowledge for augmented autonomy in robots. Carlos Hernández, Julita Bermejo-Alonso and Ricardo Sanz. Integrated Computer-Aided Engineering 2018, IOS Press

Article @ ASlab

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