Learning a probabilistic self-awareness model for robotic systems

Golombek, R., Wrede, S., Hanheide, M. and Heckmann, M. (2010) Learning a probabilistic self-awareness model for robotic systems. In: 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 18-22 Oct. 2010, Taipei.

Full content URL: http://dx.doi.org/10.1109/IROS.2010.5651095

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Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive


In order to address the problem of failure detection in the robotics domain, we present in this contribution a so-called self-awareness model, based on the system's internal data exchange and the inherent dynamics of inter-component communication. The model is strongly data driven and provides an anomaly detector for robotics systems both applicable in-situ at runtime as well as a-posteriori in post-mortem analysis. Current architectures or methods for failure detection in autonomous robots are either implementations of watch dog concepts or are based on excessive amounts of domain-specific error detection code. The approach presented in this contribution provides an avenue for the detection of more subtle anomalies originating from external sources such as the environment itself or system failures such as resource starvation. Additionally, developers are alleviated from explicitly modeling and foreseeing every exceptional situation, instead training the presented probabilistic model with the known normal modes within the specification of the robot system. As we developed and evaluated the self-awareness model on a mobile robot platform featuring an event-driven software architecture, the presented method can easily be applied in other current robotics software architectures. ©2010 IEEE.

Additional Information:cited By (since 1996) 0; Conference of 23rd IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems, IROS 2010; Conference Date: 18 October 2010 through 22 October 2010; Conference Code: 83389
Keywords:Anomaly detector, Autonomous robot, Awareness model, Data driven, Data exchange, Domain specific, Error Detection Code, Event-driven softwares, External sources, Failure detection, In-situ, Mobile robot platforms, Normal modes, Posteriori, Postmortem analysis, Probabilistic models, Resource starvation, Robot system, Robotic systems, Robotics systems, Runtimes, System failures, Detectors, Error detection, Robotics, Software architecture, Systems engineering, Intelligent robots
Subjects:H Engineering > H670 Robotics and Cybernetics
Divisions:College of Science > School of Computer Science
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ID Code:8322
Deposited On:30 Jul 2013 09:21

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