Model-Free Execution Monitoring in Behavior-Based Robotics

In the near future, autonomous mobile robots are expected to help humans by performing service tasks in many different areas, including personal assistance, transportation, cleaning, mining, or agriculture. In order to manage these tasks in a changing and partially unpredictable environment without the aid of humans, the robot must have the ability to plan its actions and to execute them robustly and safely. The robot must also have the ability to detect when the execution does not proceed as planned and to correctly identify the causes of the failure. An execution monitoring system allows the robot to detect and classify these failures. Most current approaches to execution monitoring in robotics are based on the idea of predicting the outcomes of the robot's actions by using some sort of predictive model and comparing the predicted outcomes with the observed ones. In contrary, this paper explores the use of model-free approaches to execution monitoring, that is, approaches that do not use predictive models. In this paper, we show that pattern recognition techniques can be applied to realize model-free execution monitoring by classifying observed behavioral patterns into normal or faulty execution. We investigate the use of several such techniques and verify their utility in a number of experiments involving the navigation of a mobile robot in indoor environments.

[1]  Shang-Liang Chen,et al.  Orthogonal least squares learning algorithm for radial basis function networks , 1991, IEEE Trans. Neural Networks.

[2]  David W. Aha,et al.  Lazy Learning , 1997, Springer Netherlands.

[3]  Richard A. Johnson,et al.  Applied Multivariate Statistical Analysis , 1983 .

[4]  Ronald C. Arkin,et al.  An Behavior-based Robotics , 1998 .

[5]  Erann Gat,et al.  Path planning and execution monitoring for a planetary rover , 1990, Proceedings., IEEE International Conference on Robotics and Automation.

[6]  Sebastian Thrun,et al.  Real-time fault diagnosis [robot fault diagnosis] , 2004, IEEE Robotics & Automation Magazine.

[7]  Froduald Kabanza,et al.  History checking of temporal fuzzy logic formulas for monitoring behavior-based mobile robots , 2000, Proceedings 12th IEEE Internationals Conference on Tools with Artificial Intelligence. ICTAI 2000.

[8]  David E. Wilkins,et al.  Interactive Execution Monitoring of Agent Teams , 2003, J. Artif. Intell. Res..

[9]  R. James Firby,et al.  An Investigation into Reactive Planning in Complex Domains , 1987, AAAI.

[10]  P. Pandurang Nayak,et al.  A Model-Based Approach to Reactive Self-Configuring Systems , 1996, AAAI/IAAI, Vol. 2.

[11]  Karen L. Myers.  CPEF: A Continuous Planning and Execution Framework , 1999, AI Mag..

[12]  Alessandro Saffiotti,et al.  A Multivalued Logic Approach to Integrating Planning and Control , 1995, Artif. Intell..

[13]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[14]  E. Applebaum.  Fuzzy classification for fault isolation in gas turbine engines , 2001, Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569).

[15]  B. Ripley,et al.  Pattern Recognition , 1968, Nature.

[16]  Milind Tambe,et al.  Monitoring Teams by Overhearing: A Multi-Agent Plan-Recognition Approach , 2002, J. Artif. Intell. Res..

[17]  Reid G. Simmons,et al.  Robust execution monitoring for navigation plans , 1998, Proceedings. 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems. Innovations in Theory, Practice and Applications (Cat. No.98CH36190).

[18]  Joachim Hertzberg,et al.  Learning to Ground Fact Symbols in Behavior-Based Robots , 2002, ECAI.

[19]  Keinosuke Fukunaga,et al.  Leave-One-Out Procedures for Nonparametric Error Estimates , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  Richard Fikes,et al.  Learning and Executing Generalized Robot Plans , 1993, Artif. Intell..

[21]  Manuela M. Veloso,et al.  Rationale-Based Monitoring for Planning in Dynamic Environments , 1998, AIPS.

[22]  Raja Chatila,et al.  Plan execution monitoring and control architecture for mobile robots , 1995, IEEE Trans. Robotics Autom..

[23]  Alessandro Saffiotti,et al.  Model-free execution monitoring in behavior-based mobile robotics , 2003 .

[24]  Ola Pettersson,et al.  Execution monitoring in robotics: A survey , 2005, Robotics Auton. Syst..

[25]  Roger Sauter,et al.  Introduction to Probability and Statistics for Engineers and Scientists , 2005, Technometrics.

[26]  Sebastian Thrun,et al.  A lifelong learning perspective for mobile robot control , 1994, Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'94).

[27]  V. Verma,et al.  Real-time fault detection and situational awareness for rovers: report on the Mars technology program task , 2004, 2004 IEEE Aerospace Conference Proceedings (IEEE Cat. No.04TH8720).

[28]  F.J. Von Zuben,et al.  Fault detection in dynamic systems based on fuzzy diagnosis , 1998, 1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36228).

[29]  Michael J. Swain,et al.  The animate agent architecture , 1998 .

[30]  Amy L. Lansky,et al.  Reactive Reasoning and Planning , 1987, AAAI.

[31]  Alessandro Saffiotti,et al.  Steps towards model-free execution monitoring on mobile robots , 2002 .

[32]  Rolf Isermann,et al.  Trends in the Application of Model Based Fault Detection and Diagnosis of Technical Processes , 1996 .

[33]  Alessandro Saffiotti,et al.  The Saphira architecture: a design for autonomy , 1997, J. Exp. Theor. Artif. Intell..

[34]  Rolf Isermann,et al.  Trends in the Application of Model Based Fault Detection and Diagnosis of Technical Processes , 1996 .

[35]  Reid G. Simmons,et al.  A task description language for robot control , 1998, Proceedings. 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems. Innovations in Theory, Practice and Applications (Cat. No.98CH36190).

[36]  P. Pandurang Nayak,et al.  Back to the Future for Consistency-Based Trajectory Tracking , 2000, AAAI/IAAI.

[37]  Rolf Isermann,et al.  Supervision, fault-detection and fault-diagnosis methods — An introduction , 1997 .

[38]  Janos Gertler,et al.  Fault detection and diagnosis in engineering systems , 1998 .

[39]  Michael J. Swain,et al.  An Architecture for Vision and Action , 1995, IJCAI.

[40]  Reid G. Simmons,et al.  Lessons learned from Xavier , 2000, IEEE Robotics Autom. Mag..

[41]  Richard D. Braatz,et al.  Fault Detection and Diagnosis in Industrial Systems , 2001 .

[42]  Alessandro Saffiotti,et al.  Robots with the Best of Intentions , 1999, Artificial Intelligence Today.

[43]  Malik Ghallab,et al.  On Chronicles: Representation, On-line Recognition and Learning , 1996, KR.