Run-time detection of faults in autonomous mobile robots based on the comparison of simulated and real robot behaviour

This paper presents a novel approach to the run-time detection of faults in autonomous mobile robots, based on simulated predictions of real robot behaviour. We show that although simulation can be used to predict real robot behaviour, drift between simulation and reality occurs over time due to the reality gap. This necessitates periodic reinitialisation of the simulation to reduce false positives. Using a simple obstacle avoidance controller afflicted with partial motor failure, we show that selecting the length of this reinitialisation time period is non-trivial, and that there exists a trade-off between minimising drift and the ability to detect the presence of faults.

[1]  Jonathan Timmis,et al.  Towards Exogenous Fault Detection in Swarm Robotic Systems , 2013, TAROS.

[2]  Phil Husbands,et al.  Evolving Formation Movement for a Homogeneous Multi-Robot System: Teamwork and Role-Allocation with Real Robots , 2007 .

[3]  Francesco Mondada,et al.  The e-puck, a Robot Designed for Education in Engineering , 2009 .

[4]  Rolf Isermann.  Model-based fault-detection and diagnosis - status and applications § , 2004 .

[5]  Alan F. T. Winfield,et al.  On Fault Tolerance and Scalability of Swarm Robotic Systems , 2010, DARS.

[6]  Marco Dorigo,et al.  From Fireflies to Fault-Tolerant Swarms of Robots , 2009, IEEE Transactions on Evolutionary Computation.

[7]  Alan F. T. Winfield,et al.  Safety in numbers: fault-tolerance in robot swarms , 2006, Int. J. Model. Identif. Control..

[8]  VARUN CHANDOLA,et al.  Anomaly detection: A survey , 2009, CSUR.

[9]  Jon Timmis,et al.  A low-cost real-time tracking infrastructure for ground-based robot swarms , 2014 .

[10]  Matthew Studley,et al.  The distributed co-evolution of an embodied simulator and controller for swarm robot behaviours , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[11]  Mauro Birattari,et al.  Automatic Synthesis of Fault Detection Modules for Mobile Robots , 2007, Second NASA/ESA Conference on Adaptive Hardware and Systems (AHS 2007).

[12]  Inman Harvey,et al.  Noise and the Reality Gap: The Use of Simulation in Evolutionary Robotics , 1995, ECAL.

[13]  Wenguo Liu,et al.  Open-hardware e-puck Linux extension board for experimental swarm robotics research , 2011, Microprocess. Microsystems.

[14]  Rolf Isermann,et al.  Model-based fault-detection and diagnosis - status and applications , 2004, Annu. Rev. Control..