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Skill-based exception handling and error recovery for collaborative industrial robots

: Beck, A.B.; Schwartz, A.D.; Fugl, A.R.; Naumann, M.; Kahl, B.

Volltext (PDF; )

D'Haro, L.F.:
FinE-R 2015. Path to Success: Failures in rEal Robots. Online resource : Proceedings of the workshop the Path to Success: Failures in rEal Robots co-located with IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2015), Hamburg, Germany, October 1, 2015
Hamburg, 2015 (CEUR Workshop Proceedings 1484)
Workshop the Path to Success - Failures in rEal Robots (FinE-R) <2015, Hamburg>
International Conference on Intelligent Robots and Systems (IROS) <2015, Hamburg>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IPA ()

Moving robots from their carefully designed and encapsulated work cells into the open, less structured human workspace for collaboration with workers requires robust error detection and recovery strategies. Foreseeing all possible uncertainties and unexpected events and to program in recovery actions at setup time is unfeasible. Online learning of nominal execution behaviour and automatic detection of anomalies using an Extended Markov Model, combined with interactively trained Bayesian networks for mapping anomalies to error causes and recovery actions, enables automatic recovery from previously experienced errors. A three-layered user-friendly model of errors-causes-responses and a simple GUI allows non-expert user to define new recovery activities and error causes when not yet handled anomalies occur.