Online data-driven anomaly detection in autonomous robots

The use of autonomous robots is appealing for tasks, which are dangerous to humans. Autonomous robots might fail to perform their tasks since they are susceptible to varied sorts of faults such as point and contextual faults. Not all faults can be known in advance, and hence, anomaly detection is required. In this paper, we present an online data-driven anomaly detection approach (ODDAD) for autonomous robots. ODDAD is suitable for the dynamic nature of autonomous robots since it declares a fault based only on data collected online. In addition, it is unsupervised, model free and domain independent. ODDAD proceeds in three steps: data filtering, attributes grouping based on dependency between attributes and outliers detection for each group. Above a calculated threshold, an anomaly is declared. We empirically evaluate ODDAD in different domains: commercial unmanned aerial vehicles (UAVs), a vacuum-cleaning robot, a high-fidelity flight simulator and an electrical power system of a spacecraft. We show the significance and impact of each component of ODDAD . By comparing ODDAD to other state-of-the-art competing anomaly detection algorithms, we show its advantages.

[1]  Meir Kalech,et al.  Online anomaly detection in unmanned vehicles , 2011, AAMAS.

[2]  Javier Ruiz-del-Solar,et al.  RoboCup 2010: Robot Soccer World Cup XIV , 2010, Lecture Notes in Computer Science.

[3]  Louise Travé-Massuyès,et al.  Bridging control and artificial intelligence theories for diagnosis: A survey , 2014, Eng. Appl. Artif. Intell..

[4]  P. Mahalanobis.  On the generalized distance in statistics , 1936 .

[5]  R. Walker,et al.  Sensor Fault Detection for UAVs using a Nonlinear Dynamic Model and the IMM-UKF Algorithm , 2007, 2007 Information, Decision and Control.

[6]  Sahin Yildirim,et al.  Fault detection on robot manipulators using artificial neural networks , 2011 .

[7]  Malik Yousef,et al.  One-Class SVMs for Document Classification , 2002, J. Mach. Learn. Res..

[8]  Eliahu Khalastchi,et al.  Detecting anomalies in unmanned vehicles using the Mahalanobis distance , 2010, 2010 IEEE International Conference on Robotics and Automation.

[9]  Eric Sorton,et al.  Simulated Flight Testing of an Autonomous Unmanned Aerial Vehicle Using FlightGear , 2005 .

[10]  Aleksandar Lazarevic,et al.  Incremental Local Outlier Detection for Data Streams , 2007, 2007 IEEE Symposium on Computational Intelligence and Data Mining.

[11]  R. Mackey,et al.  Anomaly detector fusion processing for advanced military aircraft , 2001, 2001 IEEE Aerospace Conference Proceedings (Cat. No.01TH8542).

[12]  Franz Wotawa,et al.  Improving Robustness of Mobile Robots Using Model-based Reasoning , 2007, J. Intell. Robotic Syst..

[13]  K. Pearson.  Contributions to the Mathematical Theory of Evolution , 1894 .

[14]  Martti Juhola,et al.  Informal identification of outliers in medical data , 2000 .

[15]  Nancy G. Leveson,et al.  An investigation of the Therac-25 accidents , 1993, Computer.

[16]  Gunnar Rätsch,et al.  Constructing Boosting Algorithms from SVMs: An Application to One-Class Classification , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Gaurav S. Sukhatme,et al.  Fault detection and identification in a mobile robot using multiple model estimation and neural network , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[18]  Paul R. Cohen,et al.  Detecting Complex Dependencies in Categorical Data , 1995, AISTATS.

[19]  Patric Jensfelt,et al.  Fault detection for mobile robots using redundant positioning systems , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[20]  Franz Wotawa,et al.  Real-Time Diagnosis and Repair of Faults of Robot Control Software , 2005, RoboCup.

[21]  Gautam Biswas,et al.  A Qualitative Event-Based Approach to Continuous Systems Diagnosis , 2009, IEEE Transactions on Control Systems Technology.

[22]  Latifur Khan,et al.  Facing the reality of data stream classification: coping with scarcity of labeled data , 2012, Knowledge and Information Systems.

[23]  Takafumi Kanamori,et al.  Statistical outlier detection using direct density ratio estimation , 2011, Knowledge and Information Systems.

[24]  Li Li,et al.  Support Vector Machines , 2015 .

[25]  Andreas Christmann,et al.  Support vector machines , 2008, Data Mining and Knowledge Discovery Handbook.

[26]  Gerald Steinbauer,et al.  A Survey about Faults of Robots Used in RoboCup , 2012, RoboCup.

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

[28]  Gerald Steinbauer,et al.  An integrated model-based diagnosis and repair architecture for ROS-based robot systems , 2013, 2013 IEEE International Conference on Robotics and Automation.

[29]  Brian Goldiez,et al.  A Survey of Commercial & Open Source Unmanned Vehicle Simulators , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[30]  Inseok Hwang,et al.  A Survey of Fault Detection, Isolation, and Reconfiguration Methods , 2010, IEEE Transactions on Control Systems Technology.